Performance Evaluation, Benchmarks, and Attribution Analysis

Foreword The measurement of portfolio and manager performance is an integral part of the portfolio management process. Measurement is the way investors and investment managers decide if the strategy they developed and the actions they took have led to the attainment of investor objectives. It is the ultimate feedback investors and managers use to decide if their management of the portfolio needs to be changed. A great deal of solid, high-quality work is being done in the development of practical tools for performance measurement, including the measurement of global portfolio performance. So, we are particularly pleased to be presenting this proceedings at this time. The speakers come from all parts of the industry, and they offer readers analyses of numerous current approaches and concepts. The speakers also address the aspects of performance measurement that are the subject of much debate. Aspects such as the appropriateness of benchmarks (particularly the use of manager universes), the identification of what decisions and what actions caused what effects in the portfolio's performance, and the role of quantitative performance evaluation in manager-client relationships are examples. Another area of debate is the proper presentation of performance results. This issue has always been a major concern for AIMR, and since 1987 when work began on the Performance Presentation Standards, we have held a number of programs devoted to both performance measurement and its presentation. This proceedings contains a summary of issues in presentation that have recently been resolved and issues currently under review. In addition, we are pleased to include perspectives on the PPS presented by speakers from the client and manager sides. In-

formation for ordering AIMR's Performance Presentation Standards, 1993 is given at the end of this proceedings. We wish to extend special thanks to Edward P. Rennie, CFA, of Pacific Investment Management Company, who spoke about firm adoption of the PPS and served very ably as the moderator of the seminar. In addition, we are grateful to Jan R. Squires, CFA, of Southwest Missouri State University, for his help in editing this volume and preparing the Overview. He has been a staunch, dedicated supporter of AIMR and the process of continuing education. Finally, we wish to thank all the speakers for their insights and assistance: Keith P. Ambachtsheer, KP.A. Advisory Services; Gordon M. Bagot, The WM Company; Jeffery V. Bailey, CFA, Richards & Tierney; Peter L. Bernstein, Peter L. Bernstein; Charles B. Burkhart, Jr., Investment Counseling; Michael S. Caccese, AIMR; Thomas J. Cowhey, CFA, Bell Atlantic Corporation; J. Paul Dokas, CFA, Bell Atlantic Corporation; D. Don Ezra, Frank Russell Company; Michael J. Flynn, Stratford Advisory Group; Philip Halpern, The Washington State Investment Board; Jack L. Hansen, CFA, The Clifton Group; Robert C. Kuberek, Wilshire Associates; Patricia K Lipton, State of Wisconsin Investment Board; Christopher G. Luck, BARRA; Scott L. Lummer, CFA, Ibbotson Associates; John P. Meier, CFA, Strategic Investment Solutions; Brian D. Singer, CFA, Brinson Partners; Lawrence S. Speidell, CFA, Nicholas-Applegate; Donald W. Trotter, CFA, Atlantic Asset Management Partners; Reza Vishkai, RogersCasey; and Craig B. Wainscott, CFA, Frank Russell Company.

Katrina F. Sherrerd, CFA Senior Vice President Education

vii

Performance Evaluation: An Overview Jan R. Squires, CFA Professor ofRnance Southwest Missouri State University The evaluation of investment managers' performpensated, and fired are important elements in the overall evaluation picture. ance-always a major concern of investment management firms, consultants, sponsors, and The speakers who address these questions come clients-becomes increasingly difficult in today's from nearly all facets of the investment industrycomplex investment environment. The sophistication portfolio management, research, investment analyof markets, instruments, and investment professionsis, and consulting. They discuss an impressive variety of performance topics, from domestic and als; the proliferation of both performance-related data and techniques to analyze those data; and the global benchmarks to implications of investment worldwide increase in potential sources of positive styles, attribution analysis for nontraditional asset performance-all fuel the suspicion that yesterday's programs, and the role of manager universes. The common themes throughout are the unquestioned performance yardsticks may not be appropriate for the challenges of today and tomorrow. These very importance of performance evaluation, the need to factors, ironically, raise the level of competitiveness think critically and carefully about its implementaamong investment managers and reduce opportunition, and the desire to serve clients best by "getting ties for the"easy" achievement of added value. The it right." result is that meaningful performance evaluation is all the more important at the same time that it has - - - - - - - - - - - - - - - - - - - - - become all the more difficult. Benchmark selection In such a high-stakes environment, a fresh look Both equity and fixed-income indexes, whether doat performance evaluation may enable all particimestic, global (inclusive of domestic assets), or interpants in the evaluation process, from the managers national (exclusive of domestic assets), are used for being evaluated to the clients whose welfare is being a variety of purposes, from benchmarking to reserved, to sharpen their understanding of and test search and asset allocation. Scott Lummer addresses their assumptions about the nature of performance several issues that arise in the use of U.S. equity and its measurement. This proceedings is the prodindexes. He contends that capitalization size best uct of an AIMR seminar intended to give participants explains differing levels of stock performance; the just such a renewed perspective on the key ingredichoice between value and growth and the choice of ents of meaningful performance evaluation: index provider are less important factors and should • Which benchmark is most appropriate for evaluatbe so recognized-by clients and managers. John ing a particular manager's performance? A rapidly Meier, describing and comparing the equity indexes growing set of domestic and global benchmarks, for developed and emerging markets, examines hiscoupled with an infinite variety of customized torical returns, volatility, and correlations. The major benchmarks, makes benchmark selection an espenon-U.s. equity indexes exhibit only minor differcially complicated task. ences, but they may not be particularly good proxies, • What are the major contributing factors to a manMeier suggests, for what many global managers are ager's apparent performance? The isolation and meastrying to achieve. As a result, clients and managers urement of the many potential sources of may find advantages in using the regional indexes performance, particularly in a global setting, is a and/or specifically customized global equity benchconstant challenge, and the quest for a proper attrimarks. bution model is ongoing. Donald Trotter reviews the most commonly ac• How should performance results be presented? cepted U.S. fixed-income benchmark providers and The best benchmark decisions and the most sophisproposes guidelines for evaluating their products. ticated attribution models are of little use if perHe argues that index composition is more important formance results are not comparable across a than construction methodology or provider and that variety of managers. many sponsors and clients would be well served by • How should important manager relationships be a customized fixed-income benchmark. Reza Vishkai reflected in, or reflective of, performance evaluation? compares the four major worldwide fixed-income The processes by which managers are hired, comindexes in terms of standard index characteristics. 1

He finds no compelling reason to choose one index over another; rather, the benchmark choice should reflect the investor's unique specifications and constraints. Benchmark selection must also reflect an increasingly global but, at the same time, segmented investment environment. Christopher Luck discusses several issues involved in standard and customized equity benchmarks. In particular, he emphasizes the importance of investment style, the availability of style applications, and the importance of benchmarks reflecting a manager's long-term style bias. Philip Halpern notes that international portfolios pose important challenges for those choosing or developing benchmarks, and he questions whether any of the standard benchmarks are useful proxies in today's investment world. Lawrence Speidell explores whether markets around the world are becoming more homogenous internally and whether they are drawing closer together externally. His findings indicate that the answer to both questions is no: First, small-capitalization stocks are singularly different in nearly all markets, and second, intermarket correlations actually decline in the absence of severe global "shocks." The decisions in benchmark selection, in summary, need to recognize and incorporate the manager's unique strategy and the global market inefficiencies and high global trading costs suggested by research findings.

Attribution Analysis

classes; in the latter, he introduces a Manager Model to test for manager skill. Brian Singer and Gordon Bagot present analyses of the usefulness and drawbacks of attribution analysis specifically in the global context. Singer argues that attribution analysis-especially for global portfolios but also for domestic portfolios-should reflect only those processes and parameters that the manager can control and manage, He elaborates a framework for global attribution analysis that takes into account both currency and market considerations. Bagot provides an overview of the development of performance measurement for global portfolios that highlights current issues and problems in attribution analysis. He joins other speakers in emphasizing that only attribution that recognizes the subtleties of the investment context and the manager's own decisions and constraints is valuable. Jack Hansen discusses the importance of performance measurement for nontraditional assets and establishes a framework for such measurement. That framework focuses on the investment decision, the implementation vehicle, and the selection of the manager or managers most likely to add value in implementing the decision. Michael Flynn and Jeffery Bailey confront the controversial issue of manager universes. Flynn outlines what clients should know in using manager universes and peer groups as effective measurement tools. Unfortunately, he notes, much of the needed information is not readily available. Accordingly, he argues that these tools should be used only in conjunction with other, potentially more reliable tools, such as customized benchmarks or indexes. Bailey presents a critical examination of manager universes detailing four serious problems that compromise their usefulness as measurement tools. He contends that these problems are largely insurmountable and recommends that sponsors and managers devote their attention to developing improved customized benchmarks with well-defined quality characteristics.

Whether its objective is traditional-to measure value added by managers-or more ambitious-to quantify manager skill-attribution analysis is an important facet of the performance measurement process. Peter Bernstein sets the stage for a detailed exploration of attribution analysis by contributing a lively look at the foibles of performance measurement and the false hopes it may raise. Questionable bogeys, uncertain excess returns, inadequate distinctions between luck and skill-these and other issues should keep investment professionals humble as they go about the performance evaluation process. Performance Presentation Focusing on equity attribution, Craig Wainscott points out both the useful and the troublesome asDevelopment of the Performance Presentation pects of performance attribution. If it reflects the Standards (PPS) by AIMR has provided the impetus manager's actual decisions, attribution analysis can for investment organizations and professionals around the world to rethink what clients need in link positive investment results to those decisions order to make performance judgments over time and and provide the client with a basis for ascribing such among managers. Michael Caccese reviews the inresults to the manager's skill. Analyzing fixed-income attribution analysis, Robert Kuberek details creasing industry acceptance of and regulatory intertwo important aspects: the decomposition of manest in the PPS. He also identifies a number of agement return and the assessment of value. In the initiatives that are under way to address still unreformer, he argues for the use of subindex weights as solved issues, such as verification of compliance and the nature of composites. Paul Dokas presents the well as risk factors in describing fixed-income asset 2

views of a plan sponsor that has endorsed and supported the PPS. He believes that, although refinements are needed in the use of composites and the treatment of nontraditional assets, the standards have enhanced and improved the investment industry. Edward Rennie presents an investment manager's view of the PPS. He notes, in particular, that the standards are entirely consistent with his firm's client-service objectives of proactivity and full disclosure.

Manager Relationships To be useful, a performance measurement paradigm must reflect, and be reflected in, the processes through which client-manager relationships are defined-in particular, manager compensation, evaluation, and hiring and firing. Charles Burkhart details several measures of investment firm performance within the context of trends in the investment industry. Especially noteworthy is the comparison and contrast of operating characteristics and compensation levels of U.S. and Canadian firms. Thomas Cowhey sets forth an approach for

evaluating plan sponsors' management of their overall pension funds. The approach enables fiduciaries to focus on plan performance relative to the plan's policy portfolio, appropriate benchmarks, and the relevant costs incurred. Keith Ambachtsheer offers a critical exploration of the nature of the investment management services industry and the elements of a sound manager search strategy. His continuing interest in the economics of investment management is evident in his contrasting of inductive and deductive approaches to hiring and firing managers. Patricia Lipton outlines the process used by the State of Wisconsin Investment Board (SWIB) in conducting a manager search. SWIB requests a variety of performance information from managers, and SWIB's extensive analysis of that information often raises important warning signals about managers and their performance. The final presentation is a thought-provoking look provided by Don Ezra at the best use of a limited budget for manager fees. Both his discussion and the research findings he presents affirm the importance, and costeffectiveness, of active management.

3

u.s. Equity Indexes as Benchmarks Scott L Lummer, CFA Managing Director Ibbotson Associates

In using U.s. equity indexes, especially for benchmarking, clients and managers alike must deal with several key issues. Capitalization size appears to be the most substantial factor in systematically differentiating stock index performance; this factor is followed by the proportions of value and growth stocks. No single index may have the appropriate capitalization and value-growth mix needed to serve as a benchmark for a particular portfolio. Thus, a customized benchmark may be preferable.

Think about how analysts and investors use indexes. Sometimes they use them for research, and on the basis of that research, they frequently make asset allocation decisions. Sometimes they use them for benchmarking. Investors' attitudes toward indexes depend somewhat on the type of investors they are and their personalities. Those who do not worry about details are not focused on analyzing the indexes they use; those who are detail oriented believe that analyzing the various indexes, particularly for use as benchmarks, is very important. This presentation compares the major U.S. equity indexes and discusses how they can be best used in benchmarking. The two aspects of an index that are most important in judging its suitability as a benchmark for a particular portfolio are, first, capitalization and, second, mix of value and growth stocks.

Capitalization The first necessity for using equity indexes for performance measurement and analysis is to pinpoint the capitalization of the stocks in the index. Table 1 contains calculations of long-term U.S. equity returns and risks (volatilities) for deciles of NYSE stocks based on capitalization. The data used for the table are Center for Research in Security Prices (CRSP) data that go back to 1926 and break down equities into ten deciles-with Decile 1 being the 10 percent of stocks with the largest capitalization and Decile 10 being the 10 percent with the smallest. The figures reported are geometric averages for the period. The definitions of large-cap, small-cap, midcap, and so on are not entirely consistent in the industry, but Table 1 indicates how standard indus4

try definitions would likely be applied to the deciles. Table 1 underlines the importance of capitalization for volatility and return. The differences between the largest-cap figures and the smallest-cap figures is huge. The difference in returns is 450 basis points (bps), and Decile 10 volatility is almost 2.5 times Decile 1 volatility. Thus, how a manager will perform in relation to an index will depend significantly on whether the capitalization ranges in the index match those of the manager's portfolio. Figure 1 graphs the historical performance, $1.00 invested at year-end 1925, by standard capitalization category. The smallest-cap decile clearly has a return well above that of the others, but when you look at downs in the market, it also has the biggest fall. The movements of the decile groups are not perfectly correlated; as Figure 2 shows, at times (in this case, the 1984-91 period), some deciles move up and down over a time period while the returns for other deciles are more consistent. Table 2 clarifies these different patterns. Returns are dramatically and consistently different as the deciles go from large cap to small cap. What is interesting is that the groups had almost identical volatility in this period. The result was a small-cap bear market; small caps performed terribly relative to large caps, and the smallest-cap decile did the worst. The patterns for the groups in a bull market for small caps are shown in Figure 3, and the results for the period are given in Table 3. The period is the three and a half years up through June 1994. Some exceptions in the relative patterns occur in such short time periods; Decile 9 is one example. During this period, in general, the smaller the capitalization, the higher the returns.

Table 1. Long-Term U.S. Equity Returns and Volatilities by Capitalization Deciles, 1926-94 Decile Decile 1 (very large cap) Decile 2 (somewhat large cap) Deciles 3-5 (mid cap) Deciles 6-8 (small cap) Decile 9 (micro cap) Decile 10 (quark cap)

Table 2. Returns and Volatilities by Capitalization Deciles, 1984-90

Compounded Return

Volatility

9.3% 10.7 11.4 11.7 12.0 13.8

20.0% 24.2 26.8 31.8 39.6 49.4

Decile

Compounded Return

Decile 1 Decile 2 Deciles 3-5 Deciles 6-8 Decile 9 Decile 10

Volatility

15.2% 14.2 11.6 7.6 1.8 -7.4

19.2% 21.0 20.7 22.0 20.8 20.1

Source: Ibbotson Associates.

Source: Ibbotson Associates.

of the large-cap universe (Decile 2). The important lesson is that deciles have significantly different returns during different time periods. So, capitalization does make a difference in The Indexes performance and performance measurement. This Pure decile data provide better yardsticks of perlesson raises two issues for investors. First, is the by cap size than do indexes because the formance "large-cap" manager really large cap? If not, a true compilation of indexes requires judgments about large-cap index will be the wrong benchmark for that what to include and what to exclude. In addition, the manager. Second, how large cap is the manager; that decile data go all the way back to 1926, whereas the is, how much small cap and mid cap is in the largeu.s. equity indexes go back only to the 1970s; so, cap portfolio? This mix makes a difference to evaluusing decile data directly allows comparison of veryations of manager performance. For instance, in a long-term performance. market like that depicted in Figure 3, a large-cap Although capitalization is important, which curmanager who stuck solely with the very largest rent large-cap index the fund uses is not important. stocks (Decile 1) would be expected to have a 500-bp As Table 4 shows, the three classic indexes for largelower return for the past three and a half years than cap U.s. equities are all highly correlated with each other and with the two largest-cap deciles of the a large-cap manager who stuck with the bottom half Figure 1. Total Returns by Deciles, 1926-94 Year-End 1925= $1.00 $10,000

$1,000

$100

$10

$1

$0 26

86

- - NYSE Decile 1

---. . . . NYSE Deciles 3-5

NYSE Decile 9

• • • NYSE Decile 2

- - - NYSE Deciles 6-8

NYSE Decile 10

91

96

Source: Ibbotson Associates.

5

Figure 2. Total Returns by Deciles, 1984-91 Year-End 1983= $1.00 $5

$4

$3

$2

$1

$0 84

85

86

87

89

88

90

NYSE Decile 1

NYSE Deciles 3-5

NYSE Decile 9

NYSE Decile 2

NYSE Deciles 6-8

NYSE Decile 10

91

Source: Ibbotson Associates.

Figure 3. Total Returns by Deciles, 1991-94 Year-End 1990= $3.00 $3

$2

$1

[ I 1/91 4/91 7/91 10/91

NYSE Decile 1

NYSE Deciles 3-5

NYSE Decile 9

NYSE Decile 2

NYSE Deciles 6-8

NYSE Decile 10

Source: Ibbotson Associates.

6

[

1/92 4/92 7/92 10/92 1/93 4/93 7/93 10/93 1/94 4/94

7/94

Table 3. Returns and Volatilities by Capitalization Deciles, 1991~une 1994 Compounded Return

Decile

Volatility

10.1% 15.5 18.7 20.0 19.4 27.2

Decile 1 Decile 2 Deciles 3-5 Deciles 6-8 Decile 9 Decile 10

11.6% 12.7 13.6 14.9 19.1 33.9

Source: Ibbotson Associates.

market. Table 5 shows that during the longest period of time when all of these indexes were in use-13 1;2 years-the difference in returns from highest to lowest was a mere 60 bps. The S&P 500 and the Russell 1000 indexes have slightly higher returns than Deciles 1 and 2, which is predictable because all three indexes dip outside of Deciles 1 and 2 to some extent.

Table 6. Correlations between Mid-eap Indexes, 1981~

Index

Deciles 3-5

S&P400

Wilshire Mid-Cap

1.00 .97 .99

1.00 .98

1.00

Deciles 3-5 S&P400 Wilshire Mid-Cap

Source: Ibbotson Associates.

fairly high correlations but not 1.00. Does that imperfect correlation make a difference? Examining the returns to the mid-cap indexes, given in Table 7, shows that imperfect correlation does make a difference. The Wilshire Mid-Cap returns have been quite a bit lower than those of the Table 7. Performance of Mid-eap Indexes, 1981~ Index

Table 4. Correlations between Large-Cap Indexes, 1981~

Index Deciles 1 and 2 S&P 500 Wilshire Large-Cap Russell IOOO

Deciles 1 and 2 1.00 1.00 1.00 1.00

Wilshire S&P 500 Large-Cap 1.00 1.00 1.00

1.00 1.00

Russell IOOO

1.00

Source: Ibbotson Associates.

The returns from the Wilshire Large-Cap Index are somewhat surprising, although they may result because the time period included the small-cap bear market of 1984 through 1990. The indexes have almost identical volatilities, and higher volatilities than the top two deciles.

Compounded Return

Volatility

15.1% 16.1 13.6

18.8% 19.0 19.6

Deciles 3-5 S&P400 Wilshire Mid-Cap

Source: Ibbotson Associates.

S&P 400 and Deciles 3-5 but with a little more volatility. The probable reason is that the compilers of the Wilshire Mid-Cap are including lower-capitalization stocks than the compilers of the S&P 400. The differences thus illustrate the effects of judgments in compiling indexes. The small-cap indexes, as Table 8 indicates, are almost as highly correlated with each other and their appropriate decile group as are the large-cap inTable 8. Correlations between Small-eap Indexes, 1981~

Table 5. Performance of Large-Cap Indexes, 1981~ Index

Compounded Return

Volatility

13.0% 13.5 12.9 13.1

16.8% 17.3 17.2 17.3

Deciles 1 and 2 S&P500 Wilshire Large-Cap Russell 1000

Index Deciles 6-8 Wilshire Small-Cap Russell 2000

Deciles 6-8 1.00 .99 .99

Wilshire Small-Cap

Russell 2000

1.00 .99

1.00

Source: Ibbotson Associates.

Source: Ibbotson Associates.

Disciples of one index or another among invest-. ment professionals devote a great deal of debate to differences among the large-cap indexes. Considering the minor differences shown here, this debate appears to be much ado about nothing. True differences show up in the mid-cap indexes. Table 6 shows correlations between the two major mid-cap indexes, the S&P 400 and the Wilshire Mid-eap, and the group of Deciles 3-5. They have

dexes. Returns, at least for this short time period, reflect some differences, as shown in Table 9. The Wilshire Small-Cap Index and the Russell 2000 Index dipped into lower deciles than Deciles 6-8, and in this market, the smallest of the small-cap performed poorly. The choice of small-cap index might not make a difference in the long term, but it might in the short term. The small-cap U.s. equity indexes have a short history, and compositions may change from time to 7

Table 9. Performance of Small-eap Indexes, 1981-94 Compounded Return

Index Deciles 6-8 Wilshire Small-Cap Russell 2000

Volatility

14.2% 13.2 11.5

20.4% 20.3 21.3

Source: Ibbotson Associates.

time in the future. Determining which is the correct index to use is thus difficult.

Choosing a Benchmark by Capitalization

Table 11. Small-eap Returns

One way for an investor to determine which index is the correct one to use as a benchmark is to analyze the correlations between the fund and the various possible indexes. Table 10 gives the correlations of four mutual funds classified as growth and income funds by both Morningstar and Lipper Analytical TClbie 10. Correlations between Funds and Benchmarks, 1988-94 Fund Maxus Mutual Beacon Windsor Mainstay Value

S&P 500

Wilshire 5000

Russell 3000

Customized Benchmark

.71 .77 .85 .86

.77 .82 .87 .89

.76 .80 .86 .89

.89 .87 .89 .91

Source: Ibbotson Associates.

ized benchmarks are compared with the performance of the Wilshire 5000 Index during the recent small-cap bull and bear markets in Table 11. (We performed a similar analysis comparing the S&P 500 and the Russell 3000 with similar results.) Note that the customized benchmarks' returns are much closer to the returns of the four funds than are the returns of the index. The most dramatic difference for the small-cap bull market is for the Mainstay Value Fund, and the second most dramatic is for the Maxus Fund.

Benchmark/Fund

Small-Cap Bear Market (1988-90)

Small-Cap Bull Market (1991-94)

12.6%a

13.4%

11.6 10.7

18.8 15.5

7.8 10.1

18.7 15.4

9.8 10.3

20.5 15.9

-8.4 -13.0

19.6 18.0

Wilshire Small-Cap Mutual Beacon Fund Customized benchmark Windsor Fund Customized benchmark Mainstay Value Fund Customized benchmark Maxus a Fund Customized benchmark

aReturns for the Maxus Fund in the bear market are only for a short subperiod; the Wilshire Small-Cap Index return for the same subperiod is -2.4 percent.

Source: Ibbotson Associates.

Services with the three large-cap indexes and a customized benchmark for the 1988-94 period. Most of The reason the customized benchmarks work so the large-cap growth and income funds Ibbotson well is the small-cap exposure in the four funds. Associates examined had a correlation of at least .9 Because all of these funds had some exposure to with the large-cap indexes. These four funds were small-cap stocks, they all underperformed the Wilthe exceptions, and we wanted to know why. shire 5000 in the small-cap bear market. The customThe customized benchmark we built is much ized benchmarks come much closer than that more naively customized than what a fund would standard benchmark to describing the funds' peractually do. We first determined what proportion of formances and to differentiating their performances. large-cap and small-cap stocks composed each of the four funds. Some funds had much heavier small-cap exposure than others, and some had much heavier Value versus Growth large-cap exposure. The benchmark for each fund After capitalization, the second most important facconsists of the large-cap and small-cap indexes in tor in systematically differentiating stock performthose proportions. ance is whether the stock is growth stock or value In all cases, the customized benchmark raises the stock. Therefore, the three major providers of US. correlation, and in a couple of cases, it raises it draequity indexes all compile subindexes classified as matically. What is the conclusion? Bear in mind that growth or value. Panel A of Table 12 contains returns the benchmark should never predict an individual and volatilities for the six indexes for periods ranging fund perfectly; that would take all the usefulness out from 16 to 19 years (based on when various indexes of the benchmark. But knowing that a simple split were begun). Some growth indexes experienced betbetween large-cap and small-cap composition will ter performance than others, but for the entire time tell you something about the performance of a fund period, growth indexes underperformed value inis fairly useful. dexes. The performances of the funds and their customPanel B of Table 12 uses the S&P-BARRA value 8

Table 12. Returns from Large-Cap Value and Growth Indexes Index

Years

A. Long-term returns 1975-94 S&P SOD-BARRA Value Growth 1978-94 Wilshire 5000 Value Growth 1979-94 Russell 3000 Value Growth

Return

Volatility

16.3% 12.8

16.8% 18.9

15.6 14.3

15.8 20.1

15.6 13.8

16.6 19.8

12.6 15.2

15.2 17.5

13.7 10.4

11.8 13.6

stocks in a manager's portfolio makes a difference in choosing a benchmark for the portfolio. Even if their portfolios are intended to be a blend of value and growth, most managers do have a tilt toward either growth or value. And they tend not to change that tilt much in different time periods; if they have tended toward three-quarters value in the past, they will tend to remain around that point in future markets. For measuring a manager that is tilting one way or another, the fund needs a customized benchmark built on a value-growth basis instead of a broad S&P 500, Wilshire 5000, or Russell 3000 index.

B. Most recent short-term returns S&P 500-BARRA Value Growth S&P SOD-BARRA Value Growth

1988-90

Conclusion

1991-94

Source: Ibbotson Associates.

and growth subindexes to illustrate the patterns of returns and volatilities for the 1988-90 and 1991-94 periods. The 1988-90 period was a bullish market for growth stocks, and the later period was bullish for value. The earlier period was a bad market for small caps in general but good for growth stocks within the small-cap sector and the large-cap sector. Therefore, the balance of growth and value

Differences among the available U.s. equity indexes are minor, and no one index may have the appropriate capitalization and value-growth mixes to be suitable as a benchmark for a particular portfolio. Therefore, customized benchmarks may be preferable. The process of customizing benchmarks requires sponsors to learn about managers' preferred capitalization mix and value-growth tilt. Customized benchmarks based on those factors, in turn, allow sponsors to track and judge performance better than does using the available indexes.

9

Question and Answer Session Scott L. Lummer, CFA Question: Are there generally accepted definitions of large cap, mid cap, and small cap by the index providers or in your translation, as in Table 1, of the deciles into general capitalization terms? Lummer: In all the US. equity indexes, standards for capitalization depend on overall market capitalization. Instead of actual capitalization numbers, the indexes use a specific number or proportion of stocks in the universe to create an index. So, what large cap is, for example, changes from time to time. The capitalization breakpoint is a lot higher now than in 1980, and the breaks between small and mid and large depend on the particular index. We prefer to use the decile data because it has ten breakpoints rather than the usual three-mid cap, large cap, and small cap. I can give you some general definitions for the decile groups. For example, micro cap would be around $150 million market capitalization. Question: How often are the deciles of capitalization recalculated? Lummer: CRSP reweights its indexes every year based on market capitalization. Without rebalancing, you tend to get some drift in an index. Managers do not dump a stock just because it has moved from small cap to large cap, of course. They rebalance sometimes when they stop following that stock and certainly when they sell that stock. At that point, they do not buy a similar cap stock; they will go back to their preferred habitat in the stock universe.

10

Question: Are value and growth properly defined by the various indexes? Lummer: The definitions are somewhat subjective. The S&P 500-BARRA Index has simple definitions of value and growth based solely on P /E. I would prefer the ratio of price to book value (P/B) because it is much more stable than P/E; stocks leave and enter the index much less frequently if a stable measure such as P /B is used. We take what the three providers give us, however; we don't want to inspire yet another company to provide indexes. Question: How do you customize a benchmark by small cap and large cap for a manager who is continuously changing the balance of the portfolio? Lummer: To pick a benchmark that is appropriate to that manager, you need very-long-term data just to see where the manager tends to be. For instance, if the manager's tendency is toward 60 percent large cap and 40 percent small cap, then that will be your customized benchmark. The manager is behaving as a sector rotator in the equity market, so defining the average combination of sectors (in this case, large cap and small cap) is very important for measuring performance correctly. You also have to recognize that the manager is not going to track any benchmark as closely as a manager who does not move the portfolio around much. The customized benchmark is useful, nevertheless, because part of a manager's job is to rotate his or her style. If the manager is 60/40

large/small cap on average but moves more toward the small-cap sector during some period, you would reward or penalize the manager for making that decision depending on how things work out. We have found that most managers do not change the proportions of large- and small-cap sectors much. For them, a customized benchmark is relatively easy to calculate and allows you to examine what they are really doing, which is picking specific stocks to deviate from the index within each of the large-cap and smallcap sectors. Question: If a manager changes from a style that the manager initially stated would be followed, how do you measure this manager? Lummer: If a change in investment policy causes a manager to change proportions of large and small or growth and value in the portfolio, we would immediately change the customized benchmark. Keep in mind, however, that we often customize benchmarks not according to what managers say they will do but according to what the managers are actually doing. Many smallcap managers are trapped in the bodies of large-cap managers; they may say they are large-cap managers, but they look a lot like small-cap managers in terms of returns, composition of portfolios, and volatilities. If a manager announced a change in policy in 1992, we would be looking at the returns and volatilities three years later to see if the policy actually changed.

Non-U.S. Equity Indexes John P. Meier, CFA Director of Quantitative Consulting Strategic Investment Solutions, Inc.

Few differences mark the world or developed market equity indexes, but equity investors in emerging markets should choose carefully between the investable indexes and the global, capitalization-weighted indexes that are currently available. The development of combined and/or customized equity indexes is accelerating to address the difficulties investors confront in finding suitable benchmarks for particular international investment strategies.

With the rapid growth in global equity investing during the past several decades and the emergence of varied global equity asset classes, non-U.s. equity indexes have become increasingly important in asset allocation and performance measurement. Current non-U.S. equity indexes can be compared on the basis of how the different indexes are constructed and other characteristics, the most important of which is the country weights. This presentation will describe and compare developed market and emerging market indexes and describe subindexes and combined indexes that are available. The overview will examine historical returns, historical volatility, and historical correlations between assorted indexes. Because portfolio managers and clients want to know how similar or different these indexes will be in the future (not merely what the past characteristics have been), forecasts of future index risks and correlations are presented.

Index Construction Of the three major international market index providers, Morgan Stanley Capital International (MSCI), which has been providing indexes for the longest time (since about 1970), is the index most often used by people in the U.s. investment community. The second is the Financial Times Actuaries (FT) Index (devised by a consortium of the Financial Times, Goldman, Sachs & Company, and NatWest Securities), which has existed since 1987. The third provider is Salomon Brothers, which formerly produced international indexes jointly with Frank Russell Company. In addition to these major providers, the International Finance Corporation and Bar-

ing Securities produce emerging market indexes and Goldman, Sachs produces a combined developed and emerging market index.

Developed Market Indexes The developed market indexes of the three providers can be compared by capitalization coverage, industry and country coverage, and asset restrictions. As for capitalization coverage, MSCI currently tries to capture 60 percent of market capitalization in its developed market indexes, the FT covers 85 percent of the investable universe, and the Salomon, which aims for full coverage, encompasses 95 percent of total market capitalization. In their industry coverage, the MSCI and FT indexes attempt to replicate the market; Salomon Brothers states that it has no industry constraints, which effectively results in market replication. In their coverage of the developed countries, the FT has 26 countries and the Salomon has 22; the MSCI currently has 22 countries plus South African gold. The biggest differences among the developed market indexes occur in asset restrictions and in the resulting asset coverages. All exclude nondomestic securities and funds, but the FT and Salomon indexes differentiate themselves by including only assets available to nondomestic investors; in this way, they try to capture the opportunity set that is available to an international investor. The MSCI indexes use a sample of large, medium, and small assets, while taking the stocks' liquidity into account. MSCI also avoids restricted shares and those with limited float. The FT indexes restrict assets to those with at least 25 percent free float (which are included at full capitalization) and 11

the FT Europe and Pacific (EurPac) Index, and three subsets of the Salomon Europe and Pacific (EPAC) Index-the EPAC Broad Market Index (BMI), the EPAC Primary Market Index (PMI), and the EPAC Extended Market Index (EMI). The MSCI EAFE, FT EurPac, and the Salomon EPAC BMI have almost the same country coverages and country weights. Because of the float-based construction rules of the Salomon indexes, one might expect Japan to be substantially underweighted in the EPAC, but in fact, it is not. In short, despite some different characteristics and construction rules, if country coverage basically drives the performance of an index, these indexes will have similar performance. The big difference is between the MSCI EAFE CDP-Weighted Index and the other developed market indexes. Conceptually, a CDP-weighted index is an economically justifiable way of underweighting Japan in a benchmark. When investors indicated an unwillingness to place from 40 percent to as much as 60 percent of a portfolio in Japan, CDP weighting in an international benchmark was developed as an alternative approach. Now, CDP-weighted indexes are offered by all the major index providers.

exclude the bottom 5 percent in capitalization; in addition, to be included in the FT indexes, assets must have traded 15 days in each of the preceding two quarters. The Salomon developed market indexes exclude assets of firms with less than US$100 million in capitalization. In addition, instead of looking at total capitalization when including assets, Salomon concentrates on float capitalization. In markets with many cross-holdings, such as Japan, that restriction makes a big difference. The Salomon approach is based on the question: If an investor were trying to buy the entire market, how much would the investor have to invest? Because of the many cross-holdings, an investor would not have to buy the full market capitalization of every issue. One result of this approach is that the Salomon indexes are float/cap weighted, whereas the MSCI indexes are cap weighted and the FT indexes are investable/cap weighted. The MSCI World Index includes about 1,600 assets; the FT World Index, about 2,200; and the Salomon World Index, 6,500. The Salomon World Index is so large primarily because it is trying to include the bottom 30 percent in capitalization-the capitalization range that contains most assets. The specific equity benchmarks that people use most often for investing in the non-North American developed markets are quite similar in country coverage. Table 1 shows the country coverages, by percentage weights, of the MSCI Europe/ Australia/Far East (EAFE) and the EAFE CDP-Weighted indexes,

Emerging Market Indexes As with the developed markets, three organizations provide the major emerging market indexes. The International Finance Corporation (IFC) was the only provider of these indexes until Baring Securities and MSCI began publishing indexes in the early 1990s. Baring tries to differentiate its index by deliberately

Table 1. Developed Market Index Country Weights, June 30, 1994 MSCI

Country

EAFE

Australia 2.6 Austria 0.4 Belgium 1.0 Denmark 0.8 Finland 0.5 France 6.0 Germany 6.2 Hong Kong 3.6 Ireland 0.2 Italy 2.3 46.3 Japan Malaysia 2.2 The Netherlands 3.4 New Zealand 0.4 Norway 0.4 Singapore 1.1 Spain 1.7 Sweden 1.5 Switzerland 4.3 United Kingdom 15.1

Salomon

GDP 2.5 1.5 1.8 1.2 0.8 11.5 14.8 1.0 0.4 8.4 35.0 0.6 2.7 0.4 0.9 0.5 3.9 1.6 2.0 8.4

FT EurPac

BMI

PMI

EMI

2.5 0.2 1.1 0.6 0.4 5.7 5.8 3.6 0.2 2.5 49.1 1.8 3.2 0.3 0.2 0.9 1.7 1.4 3.7 15.4

2.8 0.2 0.8 0.6 0.4 4.8 5.8 3.1 0.2 2.0 47.0 1.4 3.5 0.3 0.2 0.9 1.2 1.4 4.3 19.0

2.8 0.2 0.8 0.6 0.4 4.9 5.8 3.1 0.3 2.0 47.0 1.4 3.5 0.3 0.2 0.9 1.2 1.4 4.4 19.1

2.9 0.2 0.7 0.6 0.4 4.8 5.9 3.1 0.2 2.0 47.1 1.3 3.5 0.3 0.2 0.9 1.2 1.3 4.2 18.6

Source: John P. Meier, based on data from BARRA for the MSCI and FT indexes and from Salomon Brothers.

12

seeking to provide an investable type of emerging market index, which the other two do not stress. Capitalization coverage is about 60 percent for the MSCI and IFC emerging market indexes. Baring does not state that it is trying to capture any degree of market capitalization. As for industry coverage, MSCI attempts to replicate the market for emerging countries and Baring seeks "reasonable sector representation." Emerging market indexes tend to add a new country every month or so; thus, country coverages change rapidly. In mid-1994, as Table 2 shows, the IFC Global Index covered 24 countries, the MSCI Global Index covered 18, and the Baring Index covered 15. The Baring 15 countries and the MSCI 18 countries are subsets of the IFC 24 countries, and the Baring 15 are a subset of the MSCI 18 countries with one exception, Peru. Table 2. Emerging Market Index Country Weights, June 30, 1994 MSCI Country

Global

3.4 Argentina Brazil 9.4 Chile 4.0 1.2 Colombia 1.0 Greece Hungary India 7.0 2.8 Indonesia 0.2 Jordan 12.7 Korea 13.7 Malaysia 13.6 Mexico Nigeria 0.9 Pakistan Peru The Philippines 2.6 Poland 1.1 Portugal Sri Lanka 17.2 Taiwan 7.7 Thailand 1.1 Turkey Venezuela 0.4 Zimbabwe

IFC Free

Global Investable

Baring

4.9 13.3 5.7 1.6 1.4

2.5 8.8 4.0 1.5 0.8 0.1 7.5 2.2 0.3 11.9 14.4 13.4 0.2 0.9 0.4 2.7 0.2 1.1 0.2 16.1 8.6 1.5 0.4 0.1

7.5 17.6 6.1

10.0 4.0 0.2 3.6 19.6 17.4 1.3

2.4 1.5

11.0 1.5 0.5

5.3 12.1 1.9 2.4 1.6 0.1 3.5 2.3 0.2 2.4 24.5 25.1 1.0 0.8 2.7 0.5 1.6 0.1 2.9 5.3 3.2 0.6

1.5

2.1 3.7 14.5 23.4 0.7 1.2 3.1 2.7 7.1 8.0 0.8

Source: John P. Meier, based on data from BARRA for the MSCI indexes, from Baring Securities, and from the IFC.

As with the developed country indexes, the emerging market indexes exclude nondomestic securities and funds, but the emerging market indexes exhibit some differences in other asset restrictions. Each provider uses different country weights, which does make a difference in the return characteristics of the indexes. MSCI has basically the same philosophy for the emerging markets as for the developed markets (a sample of sizes, liquidity considered, re-

stricted and limited-float shares avoided), so if an investor is looking for a combined developed and emerging market index, the MSCI indexes provide a consistent approach. In the MSCI's full emerging market index, if a company has one issue that international investors can buy, MSCI includes all the listed issues for that company no matter whether international investors can actually buy those issues or not. The IFC full index includes only stocks listed on local exchanges, and it covers all classes of stocks regardless of liquidity levels. The Baring index is designed to be an investable index; it includes only companies that have capitalization of more than 1 percent of the Baring emerging markets data base and an average daily trading volume of US$100,OOO. All three providers use capitalization-based weighting schemes for their emerging market indexes. The IFC full index, with 1,270 issues, is the largest, and the IFC is continually increasing the number of issues as it continues to add countries. The MSCI full emerging market index includes approximately 840 issues. The Baring Index, at 288 issues, is the smallest because of its effort to create an investable index. As Table 2 indicates, whereas Baring provides one investable index, MSCI and the IFC subdivide their emerging market indexes into "global" (that is, full) and "free" or "investable" indexes. The table shows the country coverages, by percentage weights, for all five indexes. The Baring and IFC Investable indexes are noticeably different in country coverage from the MSCI Free Index. The philosophy behind the Baring and IFC Investable indexes is to weight a market by the international investor's ability to invest in that market. The MSCI Free Index has that underlying philosophy (issues that cannot be held by foreigners are removed), but the philosophy has had a smaller impact on the characteristics of that index than on the IFC and Baring investable indexes. The different levels of investability create significant differences between some countries' weightings in the IFC Global Index, which uses capitalization weighting and includes issues that are not available to foreign investors, and their corresponding weightings in the IFC Investable Index. For example, Korea goes down from about 12 percent of the IFC Global to 2.4 percent of the IFC Investable because a foreign investor in Korean stocks can hold only 20 percent of the capitalization of any security, so Korea is included at 20 percent of its market capitalization. Taiwan is a large market in the global index, about 16 percent, but drops to 2.9 percent in the investable. In contrast, Malaysia, Mexico, and Brazil receive high weights in the Baring (investable) Index and higher weights in the MSCI and IFC investable indexes than in the corresponding global indexes. 13

Index Sub- and Supersets The broad categories of developed and emerging market indexes are augmented by many index subsets and supersets. In addition to the investable subsets for the emerging markets, MSCI has investable (free) indexes for the developed markets. Moreover, any of the providers will calculate indexes for a specific country or region, so if a portfolio manager wants the Pacific Basin without Japan, for example, that index is obtainable. Customized weighting schemes, such as the GDP weighting that has become popular, are also available, and recently, currencyhedged indexes have become available. Until recently, the international index providers did not create indexes differentiated by capitalization coverage. FT, however, has started to split its index into a large-cap subindex, which is the top 75 percent of capitalization by country, and a medium/ small-cap subindex, which is the bottom 25 percent of capitalization by country. The Salomon Index has always comprised two subindexes based on issuer size-similar to the way in which the Russell 3000 Index comprises the 2000 and 1000 subindexes. The Salomon EPAC Primary Market Index is the top 80 percent of capitalization and covers 1,684 issues; it has about the same number of issues as the MSCI World Index. When a Salomon Brothers non-U.s. index is being compared with other indexes, what has usually been compared is the PMI. The Salomon EPAC Extended Market Index, the bottom 20 percent of capitalization, is a particularly interesting index because it is the only international small-cap index available. The latest creations are combinations of indexes, or supersets. For example, the Goldman Sachs Extended Global Market Index is the FT World Index plus the IFC Investable Index. Some emerging markets are already in the FT World (Mexico, Brazil, Thailand, and Malaysia) but the addition of the IFC Investable includes more emerging market countries and provides a useful, broad international benchmark. Because managers of all kinds of international portfolios, not solely emerging market portfolios, are investing sizable portions of assets in emerging markets, people want a benchmark that has at least some emerging markets in it. MSCI is also making available a combination of its emerging and developed market indexes.

Historical Risk and Return Some marked differences show up in the various indexes' reported returns and risks for similar regions. As the top part of Table 3 reports, from mid1989 to mid-1994, the MSCI EAFE Index reported almost twice the returns of the FT EurPac Index, with 14

Table 3. Historical Risk and Return, Mid-1989to

Mid-1994

Index MSCIEAFE MSCI Europe MSCI Pacific MSCIWorld FT EurPac Salomon EPAC PMI Salomon EPAC EMI Emerging markets MSCIGlobal IFC Global IFC Investable Baring GS Extended ex U.s. S&P 500

Cumulative Return Annualized 5.01'/'0 9.82 2.05 6.44 2.94 1.81 3.02 7.88 7.28 23.67 18.08 3.03 10.33

Total Risk Annualized 20.76'1<, 17.29 35.06 15.63 21.51 27.94 30.52 21.98 21.36 20.86 23.24 17.47 12.77

Note: Calculated in U.s. dollars. Source: John P. Meier.

nearly the same level of risk. Even among the MSCI indexes, regions and combinations showed very different returns and volatilities. For example, the Europe Index returned about 10 percent with about 17 percent volatility; the World returned 6.44 percent with about 16 percent volatility; and the Pacific returned about 2 percent with a much larger volatility than the others, about 35 percent. The returns for the FT EurPac Index were similar to those for the Salomon EPAC PMI, with a difference of about 1 percent, but less similar to those for the MSCI EAFE, with a difference of about 3 percent. The FT EurPac and MSCI EAFE have virtually the same volatility; why the PMI has a much higher volatility is not clear. The middle section of Table 3 shows the large differences in returns between the global (capitalization-weighted) and investable emerging market indexes. The significantly higher returns of the investable indexes arise primarily from differences in country weights; in the investable indexes, certain countries that were overweighted in this period also had much higher returns than some other countries. Whether the investability itself caused these countries' higher returns is not clear. The bottom of Table 3 reports the returns and volatilities for the S&P 500 Index and the Goldman Sachs (GS) Extended Index excluding the United States, which is basically the FT EurPac plus the IFC Investable without the United States. The GS Extended is primarily cap weighted, and adding the IFC Investable to the FT EurPac Index does not make a big difference in return-3.03 percent for the GS Extended versus 2.94 percent for the FT EurPac. One beneficial result, however, of adding emerging markets to an international or developed market index is that the volatility drops quite a bit; the 21.5 percent

MSCI Europe and MSCI Pacific indexes. The correlation between the MSCI Europe and MSCI Pacific is very low, .35, which probably explains the common argument that Pacific Basin mandates should be in a separate asset class from European mandates. The correlations among the EAFE, EurPac, and PMI indexes are very high. Some differences do appear between the full indexes and the small-cap indexes of the developed markets; for example, the correlation between the Salomon EPAC EMI and the MSCI EAFE is lower than the correlation between the PMI and EAFE indexes, but only slightly lower. In short, as long as a portfolio manager is picking among the broad-based developed market indexes, correlations are so high that index choice does not make much difference. This conclusion does not hold with respect to the emerging market indexes. Table 5 shows that, whereas the emerging market global indexes have high correlations with each other, the investable indexes have much lower correlations with the global indexes than with each other. For example, the IFC Global and MSCI Global indexes have a correlation of .97. The IFC Investable and the Baring indexes have a higher correlation with each other, .83, than either has with the MSCI or IFC global indexes. Thus, choice of index actually makes a difference when dealing with emerging markets. Some extremely low correlations between in-

volatility of the FT EurPac goes down to 17.5 percent for the GS Extended, and the investor obtains a slight increase in returns. Figure 1 compares the cumulative index returns (that is, time-weighted cumulations of monthly returns) from 1989 through June 1994 for selected indexes of the major providers with the GS Extended ex U.S. Index and S&P 500 returns. The returns for the GS Extended ex U.s. and the MSCI EAFE are almost the same. The GS Extended ex U.s. fares less well when compared with the emerging market indexes, as Figure 2 shows. Note also the virtually identical returns of the two cap-weighted indexes, the MSCI Global and the IFC Global, the dissimilarities between those returns and the returns of the investable indexes, and the dissimilar returns for the two investable indexes.

Historical Correlations between Indexes Correlations between broad-based developed market indexes are high, but the correlations between regional returns in non-U.s. markets are relatively low. Table 4 presents the correlations between developed markets and illustrates that moving beyond global indexes to regional benchmarks causes dramatic drops in correlations. For instance, the MSCI EAFE Index has a relatively low correlation with the

Figure 1. Cumulative Returns for Selected Equity Indexes 140

120

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12/88

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12/89

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Source: John P. Meier, based on data from Standard & Poor's Corp., International Finance Corp., Morgan Stanley Capital International, and Goldman, Sachs & Co.

15

Figure 2. Cumulative Returns for the Emerging Market Indexes 350 /

I I I I

300

250

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Source: John P. Meier, based on data from International Finance Corp., Baring Securities, Morgan Stanley Capital International, and Goldman, Sachs & Co.

global equity and emerging markets risk models, which are multiple-factor models that forecast the future risks of portfolios based on their exposures to the risk factors in the model. A comparison of the forecasted risks for the MSCI developed market indexes, shown in the top part of Table 7, with the historical risks given in Table 3 shows some differences but not dramatic ones. Note in Table 7 that the risk forecasts for the MSCI EAFE and MSCI GDPWeighted EAFE indexes are similar, but the GDPweighted index has a slightly lower risk because it underweights Japan, which results in a more diversified portfolio and thus lower volatility than the EAFE provides. Table 7 includes the forecasted risk for a currency-hedged EAFE Index, for which earlier data are not available, that is lower than risks for any

dexes illustrate the diversification benefits of emerging market assets. For example, Table 6 shows that for emerging markets, the IFC Investable and Global indexes have a lower correlation with the EAFE and GS Extended ex U.S. indexes than does the S&P 500. So, a good case can be made for considering emerging markets a separate asset class and, as many managers are doing, adding emerging markets to any broad non-U.s. index that is being used.

Future Risks and Correlations The risks (volatilities) of the major indexes for the developed markets are expected to be similar in the future to what they have been in the past. Table 7 reports forecasted volatilities calculated by BARRA's

Table 4. Historical Correlations between Developed Market Indexes, Mid-1989 to Mid-1994 Index MSCIEAFE MSCIEurope MSCI Pacific MSCIWorld FT EurPac Salomon EPAC PMI Salomon EPAC EMI

Source: John P. Meier.

16

MSCI EAFE 1.00 .79 .67 .96 1.00 .95 .90

MSCI Europe 1.00 .35 .84 .77 .57 .58

MSCI Pacific

MSCI World

FT EurPac

1.00 .59 .68 .72 .69

1.00 .95 .87 .82

1.00 .96 .92

Salomon Salomon EPAC PMI EPAC EMI

1.00 .92

1.00

Table 5. Historical Correlations between Emerging Markets, Mid-1989 to Mid-1994 Index MSCI Global MSCI Free IFC Global IFC Investable Baring

MSCI Global

MSCI Free

1.00 .69 .97 .69 .75

1.00 .62 .89 .94

IFC Global

IFC Investable

1.00 .65 .68

1.00 .83

Baring

1.00

Source: John P. Meier.

other index in the table except the full MSCI World Index. For the emerging markets, given in the middle of Table 3 and the bottom of Table 7, the risks are predicted to be slightly higher than in the past and slightly higher, as one might expect, than risks for the Table 6. Selected Historical Correlations, Mid-1989 to Mid-1994

Index MSCIEAFE S&P500 IFCGlobal IFC Investable GS Extended ex U.s.

GS IFC IFC Extended Global Investable ex U.s.

MSCI EAFE

S&P 500

1.00 .48 .37 .42

1.00 .26 .41

1.00 .65

1.00

.91

.54

.50

.53

1.00

Source: John P. Meier.

developed markets. As an asset class, however, the emerging market equities are not especially risky in comparison with small-cap U.S. stocks. 1 As in the past (see Table 3), Baring and the IFC Investable Index are expected to have much higher risks than the global emerging market indexes. The investable indexes tend to be concentrated in certain countries, so they provide less diversification and higher risk 1 See Mr. Lummer's presentation, pp. 4-9.

Table 7. Forecasted Risks Index

Total Risk Annualized

Developed markets MSCI EAFE Europe Pacific World EAFE (GOP weighted) EAFE (currency hedged) FT EurPac

17.98% 16.78 22.47 14.66 17.53 16.13 18.46

Emerging markets MSCIGlobal MSCIFree IFCGlobal IFC Investable Baring

22.88 23.28 22.44 26.27 27.89

Note: Calculated in U.s. dollars. Source: BARRA.

than the global indexes. The forecast is for much higher correlations among the developed market indexes in the future than in the past. One reason is that much of the history for some indexes has been artificially reconstructed because the indexes did not exist previously; forecasts based on that "history" can thus be distorted. Another reason for increasing correlations is the tendency of indexes to gravitate to the same companies. Forecasted correlations between the various MSCI developed market indexes and between those indexes and the FT EurPac Index are given in Table 8 (forecasted correlations for other indexes were not available). These forecasts, which were also calculated by BARRA's risk models, can be compared with the historical data given in Table 4 (except the correlations for the currency-hedged EAFE Index). Table 8 shows that the GDP-weighted and capweighted EAFE indexes are expected to have a reasonably high correlation, .97, and a comparison with Table 4 shows that the Europe and the Pacific Basin index correlations are expected to be much higher than they were historically.

Table 8. Forecasted Correlations between Developed Market Indexes

Index MSCI EAFE Europe Pacific World GOP-Weighted EAFE Currency-Hedged EAFE FTEurPac

MSCI EAFE

MSCI Europe

MSCI Pacific

MSCI World

1.00 .83 .94 .94 .97 .85 1.00

1.00 .59 .86 .88 .69 .80

1.00 .84 .87 .81 .97

1.00 .92 .88 .93

GDP- CurrencyWeighted Hedged EAFE EAFE

1.00 .82 na

1.00 na

FT EurPac

1.00

na = not applicable

Source: BARRA.

17

Forecasted correlations for the various emerging market indexes, also calculated by BARRA's risk models, are given in Table 9. The correlation between the global indexes is expected to be at or near 1.00 in the future, and as a comparison with Table 5 shows, the correlations between the investable indexes are expected to be even higher in the future than they were in the past, primarily because these indexes have added countries rapidly and presently exhibit nearly identical country compositions. Table 9. Forecasted Correlations between Emerging Market Indexes Index

MSCI Global

MSCI Free

MSCIGlobal MSCI Free IFC Global IFC Investable Baring

1.00 .90 1.00 .92 .94

1.00 .91 .97 .97

IFC IFC Global Investable

1.00 .92 .94

1.00 .97

Baring

1.00

SOl/ree: BARRA.

Conclusion In choosing non-U.s. equity indexes/benchmarks, portfolio managers can rest assured that the MSCI EAFE Index, the FT EurPac Index, and the Salomon EPAC PMI are virtually identical. Differences appear among the more regional indexes because of differ-

18

ent country choices, and differences exist among developed market indexes that are customized by weighting schemes or currency hedging. Those differences require the portfolio manager thinking of choosing such an alternative to know what the indexes are truly reflecting. The emerging markets are a vastly different asset class from developed markets. Basically, two types of indexes are available for the emerging marketsthe free or investable indexes and the global, capweighted indexes. Enough differences exist between these two types that investors should be careful in their emerging market index choices. An interesting new development, and one that will be seen more and more, is the combining or customizing of indexes. Portfolio managers are considering the risk and return characteristics of specialized country-weighting schemes for international indexes and what happens if emerging markets are put into developed market indexes. Fund sponsors are considering indexes that measure a manager's added value and reflect a manager's current opportunity set. The MSCI EAFE Index, FT EurPac, and similar indexes are not particularly good proxies for those purposes. Thus, much opportunity remains to customize and fine-tune international equity indexes to meet the needs of increasingly sophisticated global markets.

Question and Answer Session John P. Meier, CFA Question: Is any provider making an effort to distinguish between growth and value in international indexes? Meier: I did some work at BARRA on growth and value by countries, and I have heard that the Financial Times consortium is looking into it. I don't know when the results will come out, but such an approach is the next logical step, so it is only a matter of time before the conclusions are released. Question: Are any indexes going to incorporate the People's Republic of China (PRC)? Meier: The IFC has China in its data base. It has a separate index for the PRC itself, but China is not yet part of the IFC composite index. The IFC has been very aggressive in adding countries, however-at the beginning of 1993, the IFC had 17 countries, and now it has 24-so it will no doubt add China. Question: What should be the compelling factors in benchmark selection for an emerging market portfolio? Meier: Investability is the measure of the opportunity set that your investment manager can deliver to you, but you also want diversification, which is greatest in the global indexes. Managers must choose which is most important; most people have not yet gotten to the point of customizing international indexes. Question: Are the construction rules for the indexes standardized, and if so, how quickly are they passed on to index managers?

Meier: The providers' rules for index construction are fairly well defined and are contained in booklets that all the index providers make available. The providers have committees that examine adding stocks to or subtracting stocks from their indexes to make them more representative, so the providers carry out a constant monitoring process in order to maintain pure indexes. Question: You discussed only the international index providers and their products. Are the indexes compiled by local providers relevant? Meier: The focus of this presentation was dictated more by questions from the user's point of view of cost and practicality than by relevancy. Obtaining all your data and index information from one provider is more convenient and less expensive than going to 30 different local vendors and then coming up with some customized index that is the sum of all the local indexes. Recent research by Gastineau published in the July/ August 1994 Financial Analysts Journal used various countries' futures markets and various local indexes instead of broad-based international indexes to produce returns that are very similar to those of the global indexes on a country-by-country basis. Question: If the MSCI EAFE Index, the FT EurPac Index, and the Salomon EPAC PMI are all so similar, why is the EAFE Index used more frequently than the others (if it is used more frequently)? Meier.

The EAFE Index is defi-

nitely used more frequently in the United States, but in Europe, the FT Index is used much more than the EAFE. The preferences probably stem from history. The MSCI indexes have been around the longest, so in the United States, the EAFE gained momentum. The Financial Times is a British publication, so in London, managers adopted the FT Index. Question: Are GDP-weighted indexes superior to and more desirable than cap-weighted indexes? Meier: I do not like the GDP indexes. If you want to underweight Japan, then simply underweight it. If you do not like Japan at 50 percent, weight it at 25 percent and leave it at that. Question: In general, how far back in time are data available for the various indexes? Meier: The MSCI developed market data go back to 1970. The Financial Times indexes have live index data from the beginning of 1986 and reconstructed data back to the beginning of 1981. Salomon Brothers has live data from 1989. The MSCI indexes for emerging markets begin in 1988, and IFC goes back to the beginning of 1976. The Baring Securities Index is live from October 1992 and reconstructed back to 1987. Question: From your historical risk and return chart, the United States (the S&P 500) has lower risk and higher returns than international markets. Could you comment? Meier: Most people would agree that international equity, by

19

itself, is a riskier asset class than U.S. equity. When you put the two together, however, you will get a less risky portfolio. Which asset class has the higher returns depends on the time period. I would not conclude that the United States is going to outperform international in the long term solely on the basis of the short time period used for the tables in this presentation. Question: Does "60 percent capitalization coverage" refer to the largest companies in the market? Do the industry choices replicate the market in industry-replicated indexes?

20

Meier: The definitions of percentage capitalization and market replication came from the index providers' books on index construction methodology. The providers say they try to have stocks in their indexes that cover, say, 60 percent of the market capitalization, but they never truly define total market capitalization. They probably pick and choose among all the stocks in a market and examine their characteristics because the index providers are seeking stocks with reasonable liquidity. Typically, although some differences may occur, the stocks that end up in these indexes are the biggest stocks in the relevant countries.

Question: What is the impact of dividends on the various indexes?

Meier: You can look at gross dividends (dividends paid by the companies) or net dividends (dividends realized by investors after taxes) with probably a difference of 50-60 basis points between a total return with gross dividends and a total return with net dividends. When I examined the returns, I looked at gross dividends. The typical gross dividend yield on the EAFE Index is 2.4-2.5 percent. I have been told that investment managers will actually recognize only about 80 percent of that entire 2.5 percent.

u.s. Fixed-Income Indexes Donald W. Trotter, CFA Senior Vice President Atlantic Asset Management Partners, Inc.

Many plan sponsors may be using inappropriate fixed-income benchmarks. The proper choice for most sponsors is a customized index that meets their unique objectives, constraints, and risk and return trade-offs.

Some may think fixed income is not the most exciting of asset classes, but in terms of selecting an index to guide investment managers, plan sponsors have more options and greater opportunities to influence their funds' performance and to manage risk with fixed income than with any other asset class. The objective of this presentation is to provide plan sponsors with guidance to deal with this flexibility and to select the appropriate fixed-income benchmark. The presentation starts with a review of the most widely recognized providers of core domestic fixed-income benchmarks and examines their index construction techniques and performance characteristics. The presentation concludes by proposing a process investors can use to select an index that is consistent with their own objectives. 1

Comparison of the Major Index Providers Lehman Brothers (Lehman), Merrill Lynch & Company (Merrill), and Salomon Brothers (Salomon) are the three most widely recognized providers of fixedincome indexes. Each claims the capability of producing hundreds of different fixed-income indexes. Each of these indexes is derived, however, from the same basic framework or hierarchy, as depicted in Figure 1. At the top of the hierarchy is a market-weighted index that Lehman calls the Aggregate, Salomon calls the Broad, and Merrill calls the Master (hereafter often referred to generically as aggregate indexes). These aggregate indexes can be divided into smaller indexes to measure returns in different sectors of the market. Typically, these divisions are lSome information in the presentation is an update to a study by Frank K. Reilly, G. Wenchi Kao, and David J. Wright, "Alternative Bond Market Indexes," Financial Analysts Journal (May /June 1992):44-58.

along sector and maturity lines. For example, government and corporate bonds are often separated from mortgages and asset-backed securities. Depending on the needs and objectives of the investor, each provider has the ability to create even finer divisions or to develop customized indexes.

Construction Rules The primary rules that each provider currently uses to construct its aggregate index are provided in Table 1. Fundamentally, these rules are very similar; that is, the aggregate indexes are all market weighted and contain only the securities of the U.s. Treasury, U.s. agencies, U.s. corporations, and mortgages and asset-backed securities. They all exclude floating-rate securities, municipal bonds, flower bonds, Eurodollar bonds, and collateralized mortgage obligations (CMOs). All issues are investment grade. As Table 1 points out, some subtle differences in construction techniques exist. First, each provider has different liquidity criteria. For example, Merrill includes all issues with an initial issue size and currenriy outstanding par amount equal to or exceeding $25 million. This criterion is shown as $25/$25 in the table; the first $25 figure indicates initial size, and the second $25 indicates currently outstanding. On the other hand, Salomon has established a $200 million minimum (both original issue and currently outstanding) for including mortgage securities, which the company is raising to $1 billion at year-end 1994. Lehman has established the highest minimum inclusion standards for Treasury, agency, and corporate issues. Table 1 also shows subtle differences in pricing. Salomon prices at 5 p.m. every day, whereas Lehman and Merrill price at 3 p.m. The percentage of bonds that are actually individually priced each day by 21

Figure 1. Traditional Index sector Hierarchy

Agfregate (Broa /Masterl

Government/ Corporate

Mortgage

Treasury

Agency

a GNMA

Corxorate (AA -BBBl

Government

Industrial

Finance

Utility

FHLMC

b

Asset-Backed Securities

c FNMA

Yankee

aGovernment National Mortgage Association. bFederal Home Loan Mortgage Corporation. cFederal National Mortgage Association.

Source: Atlantic Asset Management Partners, based on data from Lehman Brothers, Merrill Lynch, and Salomon Brothers.

traders and those that are priced using matrix-pricing formulas also differentiate the three providers. Lehman indicates it prices 99 percent of the securities individually every day by traders. Salomon and Merrill rely more heavily on matrix pricing for daily returns, although they do price more issues by trader at each month's end. Other subtle differences in index construction

techniques include settlement and reinvestment assumptions. Merrill assumes all bonds entering and exiting the index occur on their actual settlement days and all cash flows are reinvested in the index daily. Salomon and Lehman assume settlement does not occur until the end of the month, which means new issues do not enter their indexes until month end. Salomon assumes that all intermonth cash flows

Table 1. Aggregate Indexes' Construction Rules Lehman

Salomon

Merrill

> One year

> One year

> One year

> $100/$100 > $100/$100 > $100/$100 > $100/$100

> $200/$200 > $50/$25 > $50/$25 > $50/$25

> $25/$25 million > $25/$25 million > $25/$25 million > $25/$25 million

No floaters, municipals, privates, flowers, CMOs Market value 99% bid @3 p.m. First of following month except mortgagesd No intramonth investing None Beginning of next month US$

No floaters, municipals, privates, flowers, CMOs Market value Bid @5:00 p.m. b First of following month

No floaters, municipals, privates, flowers, CMOs Market value [email protected] c Same day except mortgages e

Monthly average of onemonth bills None Beginning of next month US$

Daily at market except f mortgages None Day after settlement US$

Characteristic Maturity Liquidity" Mortgage Treasury Agency Corporation Sector constraints

Weight Pricing Settlement assumption

Reinvestment of cash flows Transaction costs New issues impact on returns Currency

aDollars in millions. b300 issues priced daily by traders; remainder, matrix priced. All issues trader priced at month end. cTreasuries trader priced. Agencies, corporates, and mortgages trader or matrix priced. dMortgages settle last day of month (recently changed from first of following month). eAssume forward settlement. f Assume 30th of month for principal paydowns.

Source: Atlantic Asset Management Partners.

22

are invested at the 30-day T-bill rate, whereas Lehman assumes that all cash flows occur and are reinvested at month end.

Sector Weights Because the construction rules of each index provider are only subtly different, their resulting indexes are also only subtly different. Table 2 contrasts the differences among each of the three aggregate indexes' sector weights. Merrill and Lehman currently have slightly higher Treasury and agency sector weights, whereas Salomon weights mortgages and industrials slightly higher. Notice that the Merrill index has more issues (because they have lower liquidity criteria), but the total market value of all three indexes is essentially the same. Another way of analyzing the aggregate indexes is by rating quality. For example, as of September 30, 1994, 53.9 percent of the Lehman Aggregate Index was AAA+ government bonds, 28.5 percent was mortgages (almost all of which were AAA rated), 2.2 percent was AAA-rated industrials, 3.5 percent was AA-rated industrials, 7.9 percent was A-rated industrials, and 4 percent was BAA-rated securities. Merrill's and Salomon's quality weightings are very similar to Lehman's. All the indexes are extremely high-quality, conservative proxies.

Risk and Return The historical risk and return profiles of the providers' indexes are also very similar. Table 3 shows the annualized return and risk for a 14-year period of Salomon's, Lehman's, and Merrill's government, aggregate, corporate, and mortgage indexes. (Lehman's and Merrill's index returns series commence in 1973, but Salomon's does not begin until 1980; hence, 1980 is the common starting point.) The overall results are as one would expect: lower risk and return on governments, higher risk and return on corporates and mortgages.

Table 3 also provides the correlations of each index or subindex with the Lehman Aggregate Index. The high correlations indicate similarity. Figure 2 graphs the return and risk data from Table 3 to confirm the similarity of the indexes by sector. For example, the three index providers' government indexes congregate at about the same risk-return point. The three mortgage indexes also are at about the same location at the high end of the risk-return spectrum.

Tracking Error Although the long-term returns indicate great similarities among the three index providers, large monthly differences have occurred, particularly in the early 1980s. In the period from January 1980 through December 1989, a 100-basis-point (bp) return difference occurred between the Merrill and Salomon indexes in one month (November 1981), followed by an 89-bp difference between Lehman and Salomon in December 1981, and a 73-bp difference between Lehman and Merrill in January 1982. Subsequently, the differences have tended to be smaller, probably because pricing accuracy has improved over time and the providers have gradually drifted toward similar index construction procedures. Thus far in the 1990s, the largest monthly difference has been only 21 bps-between Merrill and Salomon in March 1994. Table 4 provides the highest, lowest, and mean absolute return differentials (tracking errors) between each provider's aggregate index and sector indexes (government, corporate, and mortgage). The standard deviations of tracking errors are also shown. The tracking errors increase as the table goes from the aggregate indexes to the mortgage indexes, which reflects the degrees of pricing difficulty in the sectors. Because mortgages are the most difficult issues to price accurately, mortgages have the widest tracking errors, followed by corporates.

Table 2. Sector Comparisons: Aggregate Indexes, September 30, 1994 Percent of Index Sector

Lehman

Treasury Agency Industrials Utilities Finance Yankee Mortgage Asset-backed securities Total

47.2% 6.8 5.7 3.2 4.2 3.0 28.5 1.4 100.0

Percent Difference

Salomon

Merrill

Lehman/Salomon

Lehman/Merrill

45.3%5.9 7.1 3.8 3.6 2.8 30.2

49.3% 6.4 5.4 3.7 2.7 3.2 27.4

1.9% 0.9 -1.4 -0.6 0.6 0.2 -1.7 0.1

-2.1% 0.4 0.4 -0.5 1.4 -0.1 1.1 -0.5

-.U 100.0

-.bQ 100.0

Note: Market value ($billions): Lehman, 4.1; Salomon, 4.1; Merrill, 4.0. Number of issues: Lehman, 4,773; Salomon, 4,840; Merrill, 5,469. Source: Atlantic Asset Management Partners, based on data from Lehman Brothers, Merrill Lynch, and Salomon Brothers.

23

Table 3. Return versus Risk and Selected Correlations, 1980-August 1994 Index

Return

Standard Deviation

11.16 11.08 11.16

7.23 7.29 7.34

.998 .997 1.000

10.89 10.89 10.85

6.46 6.48 6.56

.979 .986 .984

11.61 11.62 11.71

8.53 8.54 8.74

.980 .979 .986

11.64 11.49 11.55

9.22 9.29 9.43

.962 .958 .956

exist between these fixed-income indexes, no material factors significantly differentiate the major index providers. Thus, choice of index should not be an issue in performance measurement or asset allocation for U.S. fixed-income portfolios.

Correlation with Lehman Aggregate

Aggregate Salomon Merrill Lehman

Changes in the Indexes over Time

Government Salomon Lehman Merrill

Corporate Merrill Salomon Lehman

Mortgage Salomon Merrill Lehman

Source: Atlantic Asset Management Partners, based on data from Lehman Brothers, Merrill Lynch, and Salomon Brothers.

Figure 3 graphs the monthly tracking error between the Lehman and the Merrill aggregate indexes in the period from December 1991 through August 1994. Performance differences tend to be almost immediately self-correcting. For example, if the Lehman index outperformed one month, it tended to underperform by a similar amount the following month. The fact that noise tends to leave the system very quickly-usually within one or two months-is indicative of pricing error. In summary, although minor construction differences and short-term performance differences do

The cash flows of the Lehman Aggregate Index as of September 30,1994, shown in Figure 4, peaked at 1.5 years, but for the most part, they were evenly distributed between 1 and 10 years. These cash flows produced a duration of about 4.7 years. Investors should be aware, however, that the characteristics of market-weighted fixed-income indexes are not stable over time. For example, the duration of the index (a measure of its exposure to interest rate risk) can vary significantly. Figure 5 shows the dramatic changes that can occur over time in the duration of a market-weighted index, as exemplified by the Salomon Corporate Index. The cause of the changes lay, in part, in the practices of corporate treasurers. In the early 1980s, corporate treasurers were using the shorter-term maturities relatively heavily for their financing needs, a practice that tended to shorten the index duration when rates were high. In addition, duration is a function of the level of interest rates and usually varies inversely with interest rates. When interest rates decline, as they did in the early 1990s, the index duration tends to increase. The result is that the interest rate exposure of the index tends to be greater when interest rates are at a low, which is the opposite of what investors would choose if they had perfect foresight.

Table 4. Monthly Tracking, 1980-August 1994 (basis points) Index Pairs Tracked

Widest Negative Difference

Widest Positive Difference

Mean Absolute Tracking Error

Standard Deviation of Tracking Error

Aggregate LminusM LminusS MminusS

-73 -100

53 89 54

10.5 9.2 10.3

11.5 11.1 11.9

-60 -96 -75

63 98 158

9.5 10.1 10.6

11.0 12.9 15.9

-124 -70 -104

95 143 125

25.1 20.7 23.1

21.6 21.0 21.2

-210 -183 -212

288 234 176

25.3 18.0 22.2

42.3 28.3 34.3

-72

Government LminusM LminusS MminusS

Corporate LminusM LminusS MminusS

Mortgage LminusM LminusS M minusS

Note: L = Lehman, S = Salomon, and M = Merrill Lynch. Source: Atlantic Asset Management Partners, based on data from Lehman Brothers, Merrill Lynch, and Salomon Brothers.

24

Figure 2. Return versus Risk, 1980-August 1994 11.9

LC SC

~

11.7

MC ---.. •

'\..

•

11.5

~

..::: .2


SM

• •

/

t

LG

11.3

MG

•

11.1

t

MM*LM

t·~SB MM

10.9

LA

• 10.7 6.3

7.3

6.8

8.3

7.8

9.3

8.8

Standard Deviation (%) LA = Lehman Aggregate Index LG = Lehman Government Index

LC = Lehman Corporate Index LM = Lehman Mortgage Index

MC = Merrill Corporate Index MM = Merrill Master Index

MG = Merrill Government Index MM* = Merrill Mortgage Index

SB = Salomon Broad Index SG = Salomon Government Index

SC = Salomon Corporate Index SM = Salomon Mortgage Index

Source: Atlantic Asset Management Partners, based on data from Lehman Brothers, Merrill Lynch, and Salomon Brothers.

The sector weights of the aggregate indexes have also been unstable over time. Figure 6 uses the Lehman Aggregate Index to illustrate, for example, that the corporate sector has declined significantly in these indexes since 1976 while, with the success of securitization, the mortgage sector has increased. The risk characteristics of market-weighted indexes can be unstable, and plan sponsors who choose such an index because they like its current duration and sector diversification may be surprised by changes after a few years.

Assessing Manager Performance The fixed-income index that plan sponsors most commonly use to guide construction of their fixed-

income portfolios tends to be either the aggregate or the government!corporate form, both of which are usually deemed representative of core fixed-income portfolios. Managers who have been given either index as a benchmark are usually combined in the same performance measurement universes. Understanding that these indexes are not interchangeable and that the selection of one over the other does affect performance is important. Table 5 highlights the last two years' performance of the Lehman indexes. In 1993, a 131-bp difference existed between the aggregate and the government/corporate in favor of the government/corporate index. As of September 1994, the performance differential had reversed in favor of the aggregate index by 55 bps. Choice of benchmark can make managers look

Figure 3. Monthly Return Differences in Aggregate Indexes: Lehman versus Merrill 15 10 Vl

£0

0... .;!l Vl


5 0 -5

>0

-10 -15 -20 12/91

I

I

3/92

6/92

9/92

12/92

3/93

6/93

9/93

12/93

3/94

6/94 9/94

Source: Atlantic Asset Management Partners, based on data from Lehman Brothers and Merrill Lynch.

25

Table 5. Difference in Total Return for Lehman Aggregate and Government/Corporate Indexes Index

1993

Lehman Aggregate Lehman Government/Corporate Difference

9.75 11.06 -1.31

1994a -1.84 -2.39 0.55

aEight months.

Source: Atlantic Asset Management Partners, based on data from Lehman Brothers.

good or bad. Figure 7 indicates that the government/ corporate return was in the first quartile of core fixed-income manager performance for 1993. Managers who were given this performance bogey by plan sponsors tended to look impressive in 1993 relative to their "core" peers. Their outperformance, in fact, resulted largely from the choice of benchmark by their clients. Figure 7 also shows that the situation was reversed in the first six months of 1994, when the government/corporate performance fell to the third quartile of the core fixed-income manager performance universe. The difference between the aggregate and government/corporate index performance shown in Figure 7 was obviously partially attributable to the fact that the aggregate included mortgages and the government/corporate did not. The performance difference was also a direct result of the difference in the durations of the two indexes. Because the government/ corporate index duration was 5.3 years and the aggregate index duration was 4.8 years, when interest rates fell in 1993, the government/corporate sector had an advantage. The reverse was true in 1994 when interest rates rose. In addition, plan sponsors need to be aware of how their investment guidelines are influencing

judgments about their managers' relative performance. For example, when assessing manager performance, understanding what sectors are not included in the index is important. The impact of a mismatch between portfolio guidelines and the benchmark can be illustrated with the following example. Plan sponsors are increasingly giving active managers a mandate to invest a portion of core fixed-income portfolios in high-yield bonds and a portion in international bonds. If a manager had invested 80 percent of a client's portfolio in the Lehman Aggregate Index, 10 percent in the Lehman International Index, and 10 percent in the Lehman High-Yield Index, that manager would have outperformed the aggregate index by 32 bps since the beginning of 1987. Managers have been given credit for such excess return when the factor that really added the value was a plan sponsor's strategic decisions. All three index providers have the capability of eliminating these apples-to-oranges comparisons by providing customized indexes to meet unique mandates.

Market-Weighted Indexes Is a market-weighted index the appropriate tool for plan sponsors to use in measuring the performance of fixed-income managers? Certainly, the capital asset pricing model argues that the market-weighted indexes are the most efficient. The counterargument is that a market-weighted index reflects the composite decisions of very different investors with different objectives. For example, insurance companies and banks are both huge factors in the fixed-income market, but their risk and return objectives are quite different. Banks tend to buy short-term government instruments, whereas insurance companies tend to buy long-term corporates. Similarly, pension funds have an entirely different

Figure 4. Discounted Cash Flows: Lehman Aggregate Index, September 30, 1994 12 10

~ "'~ 0

~

8 6

..c:

"'ro

U

4 2

0 0

'I.

1/ 2

3/ 4

Years

Note: Duration 4.7 years; convexity 0.2. Source: Lehman Brothers.

26

Figure 5. Duration and Yield Changes in salomon Corporate Bond Index 5.8

15

5.6 -

-

14

5.4 -

-

13

5.2 -

'. -

12

-

11


a 5.0-

b

§

§ 4.8-

8

QJ

v

:P

\:l

10"0

;,::

4 .6 f--

9

4.4 -

8

4.2 -

/

7

f../

5 E!i!11!!Duration

-- Yield

Source: Atlantic Asset Management Partners, based on data from Salomon Brothers.

set of objectives than endowments. Added to these disparities are the differences in objectives of taxable and nontaxable investors and of short-duration and long-duration mutual funds. Why would any plan sponsor want an index that is simply the average of all these different influences? Moreover, the financing tactics and policies of corporate and government treasurers can vastly influence the duration of a market-weighted index. Why would a pension fund, whose risk tolerance and risk objectives do not shift in unison with these borrowers, allow its investment portfolio to be influenced by them? For example, the Lehman Aggregate

Index as of September 30, 1994, was 53.9 percent government debt, 28.5 percent mortgages, 16.1 percent corporate bonds, and 1.4 percent asset-backed securities; it had a duration of 4.7 years. The allocation by credit quality was about 85 percent to AAArated debt and only 4 percent to BBB-rated debt. A pension fund, however, might want a higher allocation to BBB-rated debt in consideration of one of the verities in the capital markets: Investors tend to be compensated for taking credit risk. According to Lehman Brothers data, the difference between the returns of BBBs and AAAs was nearly 200 bps in the 1973-94 period, which is as significant as the differ-

Figure 6. Sector Changes in Lehman Aggregate Index 100 90 80 70 60 50 40 30 20 10

o 12/76

12/78

12/80

12/82

12/84

12/86

12/88

12/90

12/92

9/94

ml Government D Corporate IJl.'I Mortgage-Backed Securities. Asset-Backed Securities

Source: Lehman Brothers.

27

Figure 7. Quartile Ranking Analysis: Core Fixed-Income Manager Universe 20 15

~

-

10

~

l-<

.2
~

5

-

'0 2co

0

-

-5

-

~

-10

~

High (0.05) First Quartile Median Third Quartile Low (0.95) Mean

12/92-12/93 13.64 11.17 9.89 6.65 9.87

12/93-6/94 0.28 -2.27 -3.48 -4.12 -6.00 -3.23

• Lehman Aggregate Index o Lehman Government/Corporate Index December 1992-December 1993

December 1993-June 1994

Index

Value

Rank

Value

Rank

Lehman Aggregate Lehman Government/Corporate

9.75 11.03

53 27

-3.87 -4.33

64 81

Note: Manager universe constructed using Plan Sponsor Network performance measurement software. Source: Atlantic Asset Management Partners, based on data from Plan Sponsor Network.

ence between large- and small-capitalization equity returns. Why would a pension fund orient itself, by the choice of a benchmark, to a market weight of only 4 percent in BBB-rated debt if it is not otherwise restricted?

Optimized Indexes When plan sponsors and investment consultants establish total-fund strategic asset allocation policy, they often use quadratic programs or "optimizers" to help sort through all the asset-class investment alternatives. The optimizer inputs are expected return, volatility of return, and correlation of returns among asset classes. The output is the combination of asset classes that maximizes return at each level of risk (a set of efficient portfolios). The same approach can be used to help sort through the choices that plan sponsors face when selecting a fixed-income benchmark.2 An example will help clarify how an optimizer can be used to establish a benchmark to guide construction of a fund's fixed-income portfolio. Using only four asset classes for simplicity (stocks, US. 2Many organizations market and support optimizers that, based on their analyses of and adjustments to historical returns, develop efficient portfolios. Ibbotson Associates and DeMarche Associates were kind enough to provide access to their models for this presentation.

28

T-bills, long-term (20-year) government bonds, and the Lehman Government/Corporate Index), Ibbotson Associates's model provides the interesting results presented in Figure 8 and Figure 9. Notice that Figure 8 shows that an allocation to the Lehman Government/Corporate Index is not part of any of the portfolios on the efficient frontier; this marketweighted index simply does not increase efficiency when risk is defined as absolute return volatility. T-bills are the low-risk asset and stocks are the highrisk/high-return asset. Basically, as risk tolerance increases, the model substitutes stocks for T-bills. Not all investors define risk as absolute return volatility, however. Many plan sponsors are more concerned with maintaining or improving their funded ratios (the difference between the market values of assets and liabilities). Figure 9 shows the model results when the same inputs are used as for Figure 8 but risk is defined as surplus volatility. (In this example, the plan's liabilities were assumed to have a duration similar to that of the long bond.) Again, the government/corporate index is never on the efficient frontier. Stocks remain the high-risk asset. The long bond, however (rather than T-bills), becomes the low-risk asset because its value is highly correlated with the value of liabilities. The model substitutes stocks for the long bonds as the investor's

Figure 8. Efficient Frontier: Risk Defined as Standard Deviation of Returns 13.79 12.80 12.00 11.20 10.40 9.60 8.80 8.00 7.20 6.40

.S&P 500

G E

D

C

• Ibbotson Associates 20-Year Government Index

- B

• Lehman Government/Corporate Index

5.60 A 4.80

• 90-Day T-Bill

3.71 6.00

3.01

12.00

10.00

8.00

14.00

16.00

18.00

20.45

Returns Standard Deviation (%)

A. Portfolio allocation S&P 500 Lehman Government/ Corporate Ibbotson Associates 20Year Government 90-Day T-bill

A

B

D

E

F

G

0.2%

14.7%

22.4%

29.3%

36.0%

42.4%

48.8%

55.1%

61.4%

67.6%

73.8%

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

9.6 90.2

11.1 74.2

12.0 65.6

12.7 57.9

13.4 50.6

14.1 43.4

14.8 36.4

15.5 29.4

16.2 22.5

16.9 15.6

17.5 8.7

4.9 3.0

6.3 4.3

7.0 5.6

7.6 6.9

8.3 8.2

8.9 9.5

9.5 10.8

10.1 12.1

10.7 13.4

11.3 14.7

11.8 16.0

C

H

K

B. Annual return and risk Expected return Standard deviation

Source: Ibbotson Associates.

appetite for risk and return increases. tive is to endorse using the quadratic programming Similar relationships were obtained using Detechnology that plan sponsors already use to estabMarche's inputs and model and when more complex lish asset allocation policy (coupled with the three sets of asset-class alternatives were used as inputs. major index providers' capabilities of providing cusInvestors' definitions of and attitudes toward tomized indexes) to develop benchmarks that are risk should prescribe their choices of fixed-income more likely to meet each investor's unique objectives benchmarks. In the previous simple example, if the and risk tolerances. investor viewed risk as traditional absolute return volatility, the appropriate portfolio would be some combination of very-short-duration bonds and Conclusion higher-risk equity assets. If the investor viewed risk Who makes the fixed-income index is not a critical in relation to liabilities and measured risk in terms of factor in selecting a fixed-income benchmark. What surplus volatility, a fixed-income portfolio with a duration close to the duration of the plan liabilities is in the index is the critical factor. Currently, marwould be appropriate. As a benchmark to guide ket-weighted fixed-income benchmarks are comfixed-income policy, the market-weighted Lehman monly used by plan sponsors, but such indexes Government/Corporate Index would not make reflect the strategies and decisions of many different investors with different objectives. These combined sense in either case. It would be only a poor compromise between the two risk perspectives. objectives may be very inconsistent with the plan The use of a particular model for Figure 8 and sponsor's objectives. Furthermore, the risk characFigure 9 is not an attempt to endorse any model or teristics (duration, sector weights, and credit quality) of these indexes are not stable. Plan sponsors already the conclusions drawn from it. Rather, the discussion is an effort to make plan sponsors think about their have access to the technology to help them build fixed-income benchmark decisions instead of blindly stable fixed-income benchmarks that meet their accepting the market-weighted indexes. The objecunique objectives. Why not use it?

29

Figure 9. Efficient Frontier: Risk Defined as Standard Deviation of Surplus Volatility 6.55 5.60 4.80 4.00 3.20 2.40 1.60 0.80 -o.OOA

S&P 500

-0.80 -1.60 -2.40

• Lehman Government/Corporate Index • 90-Day T-Bill

-3.53 0.00

2.00

4.00

8.00

6.00

10.00

12.00

14.00

18.89

16.00

Surplus Standard Deviation (%) B

C

0.0%

8.5%

16.9%

25.4%

33.9%

42.4%

50.8%

59.3%

67.8%

76.2%

84.7%

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

100.0 0.0

91.5 0.0

83.1 0.0

74.6 0.0

66.1 0.0

57.6 0.0

49.2 0.0

40.7 0.0

32.2 0.0

23.8 0.0

15.3 0.0

0.0

0.6

1.1

1.7

2.2

2.8

3.3

3.9

4.4

5.0

5.5

0.0

1.6

3.2

4.8

6.4

8.0

9.6

11.2

12.8

14.4

16.0

A A. Portfolio allocation S&P500 Lehman Government/ Corporate Ibbotson Associates 20Year Government 90-Day T-bill B. Annual return and risk Expected surplus Surplus standard deviation

Source: Ibbotson Associates.

30

D

E

F

G

H

K

Question and Answer Session Donald W. Trotter, CFA Question: Do any index providers have plans to include collateralized mortgage obligations in existing indexes or to create a CMO index? Are any plans being made for a private-placement index, a commercial mortgage index, or an after-tax index?

Trotter: The reason CMOs are not included in the indexes is that the pass-through loans from which they are derived-the Ginnie Maes and the Fannie Maesare already included in the indexes. To include CMOs would be double counting. The performance of a CMO index that included all the CMOs would approximate the performance of the underlying residential mortgages, except for spread changes. I am not aware of a commercial mortgage index, but commercial mortgages are a rapidly

developing market, and no doubt, a commercial mortgage index will be developed soon. It will be a very interesting index to follow in terms of return. I doubt that an after-tax index would be feasible. How would you decide whose tax bracket you were measuring? Question: Is software available for personal computers that would allow easy construction of custom benchmarks?

Trotter: A simple custom benchmark is easy to create yourself if you have access to subsector returns from Merrill, Salomon, or Lehman. Then, you simply rebalance your custom index as you choose. For a more sophisticated product, the three providers will do the rebalancing for you.

Question: What percentage of returns of fixed-income securities are attributed to changes in interest rates, credit risk, and other factors?

Trotter: Bond returns are predominately affected by the level of interest rates and changes in the level of interest rates. All other factors pale in comparison. Question: How should an index for life/annuity assets be chosen?

Trotter: The most important factor would be to create an index with similar duration and cash flow characteristics. It should have a significant allocation to corporate debt and mortgages, depending on each insurance company's investment guidelines and objectives.

31

Global Fixed-Income Indexes Reza Vishkai Director, International Research RogersCasey

No compelling evidence exists to support the choice of anyone of the four major global bond indexes over another. That choice should reflect the preferences of, and the regulatory requirements faced by, individual investors.

As global bond markets have increased in number, size, and sophistication during the past several decades, so has the need to understand global fixedincome benchmarks clearly and use them effectively. In assessing the international and global fixed-income indexes, portfolio managers and investors need to know what differentiates the indexes and whether any compelling reasons exist to choose one rather than another as a benchmark. This presentation first introduces and compares the four major worldwide bond indexes according to key index characteristics. The analysis then concentrates on the advantages and disadvantages of the two most popular indexes. Several criteria are important in selecting a global-fixed income index as a benchmark. These include the breadth of coverage (the number of countries in the index), depth of coverage (securities within countries), research coverage, liquidity considerations, and number of issues included in the index. In addition, the capitalization make-up of the index (allocation to different markets), inception date, sources of pricing, treatment of coupon flows, and segmentation of the benchmark by sectors or maturities are criteria for index selection.

Major Global Indexes The four major global index providers are Salomon Brothers, J.P. Morgan Securities, Lehman Brothers, and Merrill Lynch & Company. The index comparisons that follow emphasize the first two indexes, reflecting what appears to be greater institutional acceptance of those indexes compared with the latter two. Selected important aspects of the Lehman Brothers and Merrill Lynch indexes, however, will be highlighted. Salomon Brothers has the longest history in the

32

business of providing international and global fixedincome indexes; its global bond indexes date from the late 1970s, and its World Government Bond Index, which was launched in 1986, includes data from the beginning of 1985. J.P. Morgan, whose index has a year less of data and was introduced in 1989, has been the most aggressive new entrant into the market. A comparison of the global indexes according to ten index characteristics reveals some key differences. For example, as Table 1 shows, the indexes have different pricing sources. Some pricing is done internally; some through major market makers. Uniform pricing is difficult to obtain in the U.s. fixedincome markets-even in the U.s. Treasury markets-so pricing sources can have a significant effect on index performances over time. In markets in which the index providers are not major players, the providers need to rely on other market makers to provide pricing. This situation could provide either a source of error or some biases in pricing. The indexes being compared are all government bond indexes, but they do not include the same types of bonds within each market. The Salomon Brothers index is the broadest because it has far fewer liquidity criteria in determining issue eligibility than the others. The liquidity constraints on the J.P. Morgan index cause that index to be the narrowest in coverage of the four and are an important factor differentiating the indexes. Historically, the number of countries in a fixedincome index has made a difference in index performance. The specific countries included in three of the global fixed-income indexes are thus noted by Xs in Table 2. The Lehman index, with 20 countries, is the broadest of the group. Salomon Brothers has added four countries to its index since the beginning of 1993, the latest being Austria. Clearly, the index

Table 1. Characteristics of Major Global Fixed-Income Indexes Characteristic

J.P. Morgan

Lehman Brothers

Merrill Lynch

Salomon Brothers

History from

12/31/85 daily

1/1/87 most countries

12/31/85 monthly 5/31/90 daily

12/31/84 monthly March 1992 daily

Pricing source

Local market closing prices.

Lehman trading desks and affiliated market makers. Prices taken at close of futures markets, if available; otherwise, at close of cash market.

Merrill Lynch trading desks supplemented by other sources as required.

Salomon Brothers in major markets; "significant market makers" in others.

Treatment of coupon flows

Immediate reinvestment of coupons into the constituent series.

Monthly reinvestment.

Daily reinvestment of coupons into the constituent series.

Reinvestment of intramonth payments into a short-term rate until the end of the month.

New issues

Issues included from the Bonds added when New issues enter index first business day of the on the first business issued but contribute to following month. returns from the next day following settlement. month.

Issues included from the first business day of the following month.

Total capitalization of index (US$ billions)

3,875

5,259

NA

=

4,646

NA

not available.

Sources: WM Company; RogersCasey.

providers compete to expand their country coverage as more markets fit the criteria for inclusion in global fixed-income portfolios. Country allocation can also make a big difference, and allocations are given in Table 3. Salomon Brothers has the highest allocation to Japan, almost 19 percent. In contrast, Lehman Brothers, because of including only the top 40 debt issues, has only 11 percent of its index in Japan. A look at the U.S. allocations of these three index providers shows why

the Japan allocation of the J.P. Morgan index is relatively small: The Morgan index has a higher allocation to the United States than the other two indexes (particularly Salomon Brothers). In the Morgan index, liquidity is a primary criterion for inclusion, and the U.S. market is, of course, the most liquid market. Because illiquid bonds of other countries will be dropped from the Morgan index, a very liquid market like the U.s. market will end up being a relatively larger part of that index. 33

Table 2. Country Inclusion, September 30, 1994 Country

J.P. Morgan X

Australia Austria Belgium Canada Denmark European Currency Unit Finland France Germany Ireland Italy Japan The Netherlands New Zealand Norway Portugal Spain Sweden United Kingdom United States

X X X

X X X X X

X X X X

Lehman Brothers X X X X X X X X X X X X X X X X X X X X

Salomon Brothers X X X X X

X X X X X

X X X X

Note: X denotes country is represented in index. Source: RogersCasey.

Comparison of salomon Brothers and J.P. Morgan Indexes The Salomon World Government Bond Index (WGBI) and J.P. Morgan Global Bond Index (GBI) are most often chosen as benchmarks by portfolio managers and investors, and each index provides certain advantages. The key difference between the two indexes lies in the liquidity criteria. Table 3. Country Allocations, September 30,1994 Country

J.P. Morgan

Australia Austria Belgium Canada Denmark European Currency Unit Finland France Germany Ireland Italy Japan The Netherlands New Zealand Norway Portugal Spain Sweden United Kingdom United States

Source: RogersCasey.

34

1% 0 3 3 2 0 0 7 9 0 4 13 3 0 0 0 3 1 6 45

Lehman Brothers 1.0% 10.0 3.0 3.0 1.0 1.0 0.2 7.0 10.0 0.4 7.0 11.0 3.0 0.2 0.3 0.2 2.0 1.0 6.0 41.0

Salomon Brothers 1% 1 2 3 1 0 0 6 11 0 7 19 3 0 0 0 2 1 5 37

The Salomon WGBI is the oldest and has the broadest coverage of government issues within the markets it covers, although not necessarily the broadest country coverage. It is typically chosen because of its transparency: Investors and portfolio managers know what goes into the index because it is essentially all inclusive. Broad coverage means that not all the bonds in the index can be purchased by investors, however, and this lack of liquidity could be a concern. Should an institution use an index as a benchmark that includes bonds the institution cannot purchase? The J.P. Morgan index, on the other hand, is liquidity driven. An institutional investor can purchase any of the bonds in the Morgan GBI; so, the argument goes, this index is a true representation of the market. The emphasis on liquidity also ensures accurate pricing. Because liquid securities are defined by the existence of a two-way market for those securities, the Morgan GBI uses less matrix pricing and less pricing off the yield curve than the Salomon WGBI. The existence of liquidity criteria also introduces a concern about the J.P. Morgan index, however, just as the absence of liquidity criteria causes concern about the Salomon WGBI. On average, J.P. Morgan represents about 60 percent of the universe of government bonds. Therefore, the Morgan index might not represent the efficient portfolio. In addition, the liquidity of the bonds in the Morgan index may result in a distortion of prices because the bonds command a liquidity premium. Most importantly, a liquidity-driven index is a dynamic index; the composition of the index will change from month to month with changes in the bonds' liquidity. Because bonds that have been issued most recently typically have higher liquidity than older issues, new issues are constantly replacing older ones in a liquidity-driven index. The emphasis on liquidity could thus result in index distortions when an issuer is very active within a certain maturity of the market. For example, if an issuer becomes very active in short-term issuance, the Morgan index could become overexposed to the short-term sector of the market. Used as a benchmark, then, the index becomes a moving target.

Index Performance The performances of the Salomon Brothers and J.P. Morgan indexes have differed substantially but not consistently in favor of one or the other. Figure 1 shows the performance of the indexes on the basis of rolling two-year periods. Performance diverged by 50 basis points (bps) to almost 200 bps in some two-year periods. These differences are substantial if a fund is, for example, using one of the indexes as

Figure 1. Salomon WGBI versus J.P. Morgan GBI: Annualized Returns, Two-Year Rolling Periods 18 16 14 12 J.P. Morgan GBI

~

10

~

8

:::....

P<::

6 4 2

....:. Salomon WGBI

o 3/88 9/88 3/89 9/89

3/90 9/90

I I 3/91 9/91 3/92 9/92 3/93 9/93 3/94 9/94

Source: RogersCasey, based on data from Salomon Brothers and J.P. Morgan.

a benchmark to measure active management. The causes of divergences in performance are not clear, but possible explanations include differences in country compositions and/ or differences in allocations to the large markets. The high-yield Euro-

pean markets-Belgium, Italy, Spain, and Swedenwere in the J.P. Morgan GBl from its inception, whereas the Salomon Brothers WGBI did not add those fixed-income markets until late 1992. So, the run those markets had and their subsequent poor

Figure 2. World Bond Index Allocations: Salomon WGBI versus J.P. Morgan GBI 100

80

20

0 1/91 [ill

g

Other Italy

1/92 ~

•

Canada France

1/93 [ill

•

1/94

United Kingdom Germany

10/94

0

•

Japan United States

Note: Salomon WGBI is the first bar and J.P. Morgan GBI is the second bar for each date. Source: RogersCasey, based on data from Salomon Brothers and J.P. Morgan.

35

performance made a difference in the two indexes' performance. The allocations by the two indexes to the large markets have also been different enough to cause divergence in index performance. Figure 2 shows the indexes' compositions at the beginnings of 1991, 1992, 1993, and 1994 and as of October 1994. Based on the data, Japan would have a large impact on the performance of the Salomon index, and the United States would have a large impact on the Morgan index. Divergences in performance also arise because Salomon's broad exposure to the markets and J.P. Morgan's liquidity-driven exposure cause significant differences in duration. Figure 3 shows the duration contribution of the various countries to each index; the contribution is simply the weight of the country in the index multiplied by the country's duration.

The CUSUM Procedure RogersCasey's cumulative sum (CUSUM) procedure incorporates statistical quality-control methodology in the analysis of active-manager performance versus benchmarks. The CUSUM procedure can also be applied to comparing the performance of two indexes to identify similarities and differences. Figure 4 applies this methodology to

compare the performance of the Salomon WCBI (treated as the benchmark) and the J.P. Morgan CBI (treated as the active portfolio). The four sections of Figure 4 highlight the sometimes substantial differences in performance between the two indexes. The"annualized tracking error" chart presents a measure of variance in performance between two series of returns; the starting point was set at 3 percent, which is average, or maybe even a bit low, for active-manager variance versus a benchmark. In this methodology, as more information (monthly returns) becomes available, the model includes that information in calculating the tracking error. The more recent returns are weighted more heavily in determining tracking error, but all past data are included in the calculation. The chart shows the tracking error declining, converging to the 1 percent level that is the empirically observed variance-the residual standard deviation based on historical returnsof the two indexes. The"excess return" chart in Figure 4 consists of two return measures. The line chart represents the cumulative arithmetic sum of the portfolio's (J.P. Morgan's) excess returns over the benchmark (Salomon). The bars depict the quarterly differences in performance between the two indexes (subtracting Salomon's return from Morgan's). Again, the quar-

Figure 3. Duration Contribution Allocation: Salomon WGBI versus J.P. Morgan GBI 100

~ ~

0

'"§

60

E ~

0

u ~

.9 ~ !-<

40

;:l

0

20

0 1/91

[J]

Other

B

Italy

1/92

iil

•

Canada France

1/93

[J]

•

1/94 United Kingdom Germany

10/94

0

•

Note: Salomon WGBI is the first bar and J.P. Morgan GBI is the second bar for each date. Source: RogersCasey, based on data from Salomon Brothers and J.P. Morgan.

36

Japan United States

Finally, the "Page's procedure" chart in Figure 4 offers a statistical measure of consistency of performance versus a benchmark by plotting the sequence of likelihood ratios. The numbers on the y-axis give three figures-for example, the top one is 24, 16, and II, which means that a "good" manager would cross that line once every 24 months, a "bad" manager would cross it once every 16 months, and a "very bad" manager would cross it once every 11 months. The chart shows one statistical alarm signal raised in September 1992, which for all practical purposes could be noise. Figure 5 applies the same methodology as shown in Figure 4 to the two providers' U.s. bond indexes' performance. Because the U.s. bond market

ter-by-quarter performance shows some material difference in performance between the two indexes. The CUSUM plot in Figure 4 basically divides the excess-return chart by the tracking error; in effect, it represents the information ratio. In a comparison with an index, a "good" active portfolio manager should have a positively sloped information ratio; that is, given the risks the manager has taken in shifting from the benchmark, the manager has been successful in generating consistent excess returns. The protractor on the left side of the chart is a visual aid to help determine the slope of the line. For the most recent period, the slope is significantly negative; that is, the J.P. Morgan index in that period significantly underperformed the Salomon index.

Figure 4. Perfonnance Differences: J.P. Morgan GBI versus Salomon WGBI Annualized Tracking Error

Excess Return

6,--------------------,

2r-------------------, 12-Month Moving Average

~

0

:::...

.a

~

'"'" u

-1

Q)

x

~

-2 -3

0"--

3/86

1....-.

3/89

--1.

3/92

------'

-4

3/86

3/95

3/89

3/92

3/95

Page's Procedure: Information Ratio

CUSUM Plot: Information Ratio a -1.5 -1.0 o

-D.5

.2

0

OJ

p:::

~

24/16/11

]

36/22/15

~;:l

-D.5

48/27/18 60/32/21 72/37/23

1.0

84/41/25 x 3/92

1.5 3/86 aGood

3/89

3/92

3/95

3/86

3/89

3/92

3/95

= 0.5; bad = 0; very bad = 0.5.

Source: RogersCasey, based on data from J.P. Morgan and Salomon Brothers.

37

Figure 5. Performance Differences: J.P. Morgan U.S. Government Bond Index versus Salomon Brothers U.S. Government Bond Index Excess Return

Annualized Tracking Error 0.75

6

0.5

~....

§

4 -

.8

>Il

~

bO

c<

Q)

u

E!:::

0

'"'" u

~

«l

0.25

c< ....

g

x

2

>Il

-0.25 12-Month Moving Average -0.5

0

-0.75 3/86

3/89

3/92

3/86

3/95

3/89

3/92

3/95

Page's Procedure: Information Ratio

CUSUM Plot: Information Ratio a -1.5 -1.0 o

'.0

02

-0.5

g 36/22/15

0

~

24/16/11

:-5

0

]

::i 48/27/18

-0.5

60/32/21

x 9/93

72/37/23 84/41/25 f - - - - - - - - - - - - - - - I J - - -

1.0

x

3/93

1.5 3/86 "Good = 0.5; bad

3/89

3/92

3/95

3/86

3/89

3/92

3/95

= 0; very bad = 0.5.

Source: RogersCasey, based on data from J.P. Morgan and Salomon Brothers.

is the most liquid in the world, one would expect the Performance Comparison of Three Indexes differences between the two indexes to be minimal. Figure 5 demonstrates, however, that differences beA comparison of risk-return profiles shows that diftween the two indexes exist even in a such a liquid ferences in the indexes can have substantial impacts market. on risk and return. Although the scale is a bit deceivA ratio of the durations of the two U.S. bond ing because it magnifies the difference, Figure 7 indexes, shown in Figure 6, explains some of the shows that the Salomon WGBI did relatively better divergence in performance between the two. The J.P. than the J.P. Morgan GBI and Lehman Global Bond Morgan U.s. bond index has only about 80 percent Index (GBI) in the 1987-94 period. The Salomon inof the duration of the Salomon U.S. bond index. In a dex has also had higher volatility than the other two bull market, therefore, the Salomon index is likely to for the same time period. These results are highly outperform; in a bear market, the Morgan index is period dependent, however; in other periods, the likely to outperform. relative return and risk positions of these indexes have been different. A comparative performance report for the Salo38

Figure 6. Ratio of Bond Index Durations: J.P. Morgan U.S. versus Salomon U.S. 1.5

Figure 7. Risk-Retum Profiles of Salomon WGBI, J.P. Morgan GBI, and Lehman GBI, 1987 through September 30, 1994 10.0 r - - - - - - - - - - - - - - - - - ,

1.25 o

E 1.0 ......

Salomon r......\~~,,_:::::-'<'-::::,-----,,----,------,-----------i , , . . . \ ......... '\......., ......... \ - . / - - - " " - . . . ........ -./'"AV ........

0.75

,,/_-....I

Morgan

9.8

'-

9.6

'-

.2

'-

~ ...l:::

Salomon WGBI •

• Lehman GBI

9.2 0.5 l------..J1_ _..J...1_.l..-I----1I_---'-I_ _...I..-I----..JIL.....---l 1/86 2/87 3/88 4/89 5/90 6/91

7/92 8/93 9/94

Source: RogersCasey, based on data from J.P. Morgan and Salomon Brothers.

• J.P. Morgan GBI 9.0

I

6.0

I

6.2

6.4

I

6.6

6.8

7.0

Risk (Standard Deviation, %)

Source: RogersCasey, based on data from Salomon Brothers, J.P. mon WGBI, J.P. Morgan GBI, and Lehman GBI is Morgan, and Lehman Brothers. presented in Table 4. The last row of the table is the Piper Global Fixed Income Composite, which reports the performance of the median manager in this Conclusion and a Note for Canadian Investors category. Two of the columns illustrate particularly Over the long term, no compelling evidence has well the difference a choice of index can make. First, accumulated to select one benchmark rather than another. An investor who prefers a broad index will for the five years ending September 1994, the Piper lean toward the Salomon index; an investor who composite has a return of 10.8 percent; the Salomon believes an index should reflect liquid secl!:rities will WGBI is close to that figure, but the Morgan GBI and prefer the J.P. Morgan. Liquidity is subjectively deLehman GBI are below the Piper return. Essentially, fined; it is often driven by investor preferences. If an the comparison indicates that the median managers, investor wants to focus on liquid bonds, the Morgan when measured by the Salomon index, barely earned index for bonds might be the best index for that their fees. When compared with the Lehman or Morinvestor, but it is not inherently better than the other gan indexes, the same median results appear to be indexes. dramatically more favorable to the active managers. In Canada, investors must limit their non-CanaSecond, the three-year column shows the Piper comdian investments to 20 percent of their total portfoposite at 8.9 percent, which is good when compared lios. Therefore, the main issue for Canadians is not with the Lehman and the Morgan indexes but disapwhich index provider to choose but what internapointing when compared with the Salomon index. tional asset classes to choose. International equities, Table 4 suggests that, at least in the short run, the especially emerging market equities, have substanindex used can make a difference in both benchmarktial diversification benefits and much better return ing and asset allocation. potential than global fixed-income securities. Given the 20 percent limit and the range of international options, does the investor want to include any global fixed income in the portfolio?

39

~

o

4.4% 5.0 4.5 5.6

18.4% 13.8 16.0 23.3 8.0

4.3% 6.8 6.7

Year Ending 12/89

Note: Returns for periods greater than one year are annualized. Source: RogersCasey.

J.P. Morgan GBI LehmanGBI Piper Global Fixed Income Composite

Salomon WGBI

Index

Year Ending 12/88

Year Ending 12/87

13.1

12.0% 12.7 12.7

Year Ending 12/90

16.9

15.5 15.3

15.8%

Year Ending 12/91

Table 4. Comparative Perfonnance Report: Global Bond Indexes

7.1

5.5% 4.5 4.5

Year Ending 12/92

16.7

12.3 12.3

13.3%

Year Ending 12/93

10.8

10.2% 9.7 9.9

5 Years Ending 9/94

8.9

8.5 8.6

9.5%

3 Years Ending 9/94

5.7

5.4% 4.6 4.6

2 Years Ending 9/94

-1.2

1.8% 1.0 1.0

Year Ending 9/94

Question and Answer Session Reza Vishkai Question: What is the impact of currency on global bond performance, and are hedged indexes available? Vishkai: In global or international fixed-income investing, the choice between hedging and not hedging is much more clearcut than in international equity portfolios. In equity portfolios, hedging has historically made only a small difference in portfolio volatility. In fixed-income portfolios, hedging lowers volatility-substantially. Historically, the volatility of international bonds that are hedged has been in line with, even somewhat lower than, the volatility of U.S. fixed-income securities. To hedge or not to hedge depends on what the investor wants the international fixed-income assets to accomplish for the investment program. All four index providers make available hedged

variations of their indexes. Question: What are the differences among the emerging market debt indexes-in terms of coverage of markets or instruments, for example? Vishkai: Salomon Brothers and J.P. Morgan are the two major providers of emerging market debt indexes. These indexes are somewhat strange because emerging market debt is concentrated in one sector of the market, Brady bonds, so the indexes are dominated by Brady bonds. 1 The debt is also very dynamic; for example, Brazil has suddenly replaced Mexico as the largest component of emerging market debt indexes because of Brazil's massive issuance of Brady bonds. Another issue arises because mutual funds often have diversification criteria that do not allow them to make certain

investment choices-for example, to reach the maximum Brazil weight in the index. To address this issue, Salomon Brothers has developed an index that arbitrarily assigns certain weights to the large markets and certain weights to the small markets. No good index exists for the emerging debt markets, however, because of the anomalies in the markets; all existing indexes are flawed to some extent. 1 Editor's Note: These bonds are available to developing countries under the Brady plan, which offers debtor nations some relief from their debt through partial debt forgiveness, lowered interest rates, and extended maturities. In return, the countries agree to reduce their inflation rates, public-sector borrowing, and trade deficits. Brady bonds are Euro-issues, not custodied in the country of issuance, and the majority of the bonds are denominated in U.s. dollars. The cost of trading Brady bonds is low in comparison with most emerging market debt.

41

Style in Indexes and Benchmarks Christopher G. Luck Director, Sponsor Services BARRA

That equity market subsegments can be identified by such characteristics as size and as value versus growth suggests that investment style differences should be an important consideration in both standard and customized benchmarks. To be appropriate, a customized benchmark should be consistent with a manager's long-term style bias and should reflect the joint inputs of sponsors and managers.

Benchmarks are a way of life for plan sponsors and managers. The number and variety of available benchmarks is almost limitless, from well-known standard indexes to esoteric combinations of indexes. Choosing an appropriate benchmark is a daunting task and involves consideration of many issues, the most important of which is investment style-that is, which benchmark best fits a manager, best explains a manager's long-term style bias. This presentation discusses, from an equity perspective, several issues related to standard and customized benchmarks and benchmark combinations. The discussion emphasizes the importance of style in the market and the availability of style applications in benchmarks.

The Uses of Benchmarks The benchmark portfolio, or "normal portfolio," is the passive investment alternative for both sponsors and managers. A benchmark serves a number of purposes for sponsors and managers, and these users' multiple needs help explain the quantity and variety of benchmarks and suggest that quantity and variety will increase. Benchmarks are important tools for reflecting investment philosophies, different index weighting schemes, and sources of risk.

Sponsor and Manager Needs A sponsor always has at least an implicit choice between passive and active strategies. Sponsors can index a large-capitalization segment of a portfolio, for example, if they think that active management will not, on average, add value in the large-cap arena. Sponsors need benchmarks that reflect the consistent, long-term structure and style biases of the 42

aggregate fund so that the sponsors can monitor the bets of the individual managers toward or away from those biases. Sponsors do not want to hire managers who essentially deliver the same product; managers are hired to manage different slices of the pie. Benchmarks also help, therefore, in calculating correlations between managers, in assessing changes in manager actions over time, and in ensuring that managers are delivering the style profiles for which they were hired. Benchmarks are also useful to sponsors in identifying manager decisions that tend to cancel each other-for example, two managers buying and selling a stock at essentially the same time. Managers want benchmarks that fairly represent their styles and will provide a way to differentiate the sponsor's performance responsibility from the manager's performance responsibility. If the sponsor hires a growth manager, for example, whether the bet on growth does well or poorly should be the sponsor's responsibility; the sponsor selected that style bias and that type of manager. The manager is not responsible for the portion of the fund's return that is benchmarked to a growth index. The manager is responsible for beating that benchmark. Therefore, a good benchmark is one that contributes to differentiating the performance of managers and sponsors based on their respective responsibilities.

Investment Philosophies, Weighting, and Risk At the implementation level, benchmarks are important tools for reflecting different investment philosophies, the effects of different index weighting schemes, and the sources of aggregate portfolio risk. Different investment philosophies require different universes for benchmarks-Iarge-cap versus

sors comes from the fact that managers in the U.s. small-cap universes, for example, or growth or value equity market tend to be underexposed to utilities; market segments. In the United States and in other few managers actually hold utilities in the same procountries, the weight of experience and evidence portion that utility stocks represent in the market. An argues that enough unique universes exist to warexample of an intentional style gap is the choice by a rant benchmarks with correspondingly unique charnumber of funds to be smaller cap than the overall acteristics. target because the sponsors believe in the existence Also, managers use a variety of weighting techof a small-cap premium. niques, and benchmarks can be helpful in isolating Second, as a sponsor decides to invest more and the resulting effects. An equal-weighted index will more on a passive basis, the difference between the portray a market much differently from a capmanaged portfolio and the benchmark will disapweighted index, especially in the United States. Empear. Managers are expected to be different from pirical tests have found that managers do not tend to their benchmarks; that is why active managers are be cap weighted, so they will often have a smaller hired. If they are to add value, they dearly have to be size orientation than what appears to be a similar different. Managers must make active bets, however, cap-weighted index. For example, during the last 20 to realize actual benefits from being different, and years, an equal-weighted S&P 500 portfolio would those bets create the second source of risk identified have outperformed a cap-weighted S&P 500 portfoin Figure l-"active risk," the contribution the manlio by almost 3.5 percent (total return) per year. Managers make to the fund (their added value net of their agers and sponsors alike would be ecstatic with such benchmarks). an annual outperformance. Hence, the issue of the weighting scheme used by the manager relative to the benchmark weighting scheme may have a draThe Importance of Style matic impact on how performance is judged. Benchmarks can also be used to delineate the Style-for example, capitalization bias and value structure of risk in an aggregate portfolio. For examversus growth-is very important, but only if the ple, a plan sponsor might have as an overall target market in question is differentiable into distinct style for a U.S. equity fund a benchmark like the Russell subsegments. If market segments do not exist, style3000 Index or the Wilshire 5000 Index. The sponsor based approaches are inappropriate. Thus, identifyhands out pieces of the fund to various managers, but ing possible style segments within a given market is then finds that the style of the aggregate managed a critical task in the use of benchmarks. The equity portfolios does not add up to the overall target. Figmarkets in Canada and the United States illustrate ure 1 suggests two sources of the difference. the importance of style in markets with clear style subsegments. Figure 1. Structure of Aggregate Risk for an Equity Portfolio

Canada

Aggregate Benchmark

00(

..

Active Risk

Aggregate Managed Portfolio

Source: BARRA.

First, the sponsor may have created, intentionally or unintentionally, style gaps in allocating the portfolio among several managers; that is, some styles represented in the target may not be represented in the chosen managers. These style gaps give rise to what is termed in Figure 1 "misfit risk." An example of an inadvertent style gap for some spon-

Growth and value, as well as size, have played out dramatically in the Canadian market. Figure 2 presents historical cumulative returns from Canada during a 12-year period divided into largecap / growth, large-cap / value, and small-cap market segments. Returns are for the stocks in the Toronto Stock Exchange 300 and the next 200 largest-cap Canadian stocks. The largest 200 of the 500 stocks formed the large-cap sample, which was then stratified into value (high dividend yield, low price to book) and growth (low dividend yield, high price to book). Figure 2 shows that the large-cap /value segment has dominated small cap in the Canadian market, and both those segments have outperformed largecap / growth. The part that value and growth played in the differential returns appears to have been more important than that played by the large-small split in the Canadian market. Remember that large/value and large/growth still add up to "large," and the large-cap segment has actually had a return similar 43

Figure 2. Historical Cumulative Returns to Styles: Canada 300

250

-

200

-

.2 150

-

.. Large/Value

..

~ CfJ

....~ (JJ

>:<: (JJ

.~ ~

;:; 100

-

Ei;::l

....... --.

Small

U

50 Large / Growth

o

'.0;

,'"

-50 12/81

12/82

12/83 12/84

12/85

12/86

12/87 12/88

12/89

12/90

12/91

12/92

12/93

6/94

Note: Data as of December each year. Source: BARRA.

to (with slight outperformance) small-cap stocks' return in Canada during this time period.

United States The results of a similar analysis for the United States equity market are presented in Figure 3. This study covered 14 years and broke the market down into six subsegments based on combinations of size, value, and growth. The large-cap universe is the S&P 500, the mid-cap universe is the next largest 85 percent in capitalization of the total market after subtracting the S&P 500, and the small-cap universe is made up of the remaining 15 percent after subtracting the S&P 500. The value-growth split is based on price-to-market ratios, so 50 percent of the capitalization is in each group. The exact splits are somewhat arbitrary, but the findings are quite robust to different definitions. The largest cumulative return differences are defined by value and growth; the value subsegments were clearly the highest returning of the series for this period, and the growth subsegments clearly the lowest. The value-growth differential is greatest in the small-cap segment, in which the cumulative return to value was about 700 percent and to growth was only about 300 percent. The differential between large/value (700 percent) and large/growth (600

44

percent) is not nearly so pronounced. Since 1980, in fact, the value segments have had very similar cumulative performance, irrespective of capitalization, whereas the growth segments have had very dissimilar cumulative performance by capitalization categories. The implication of these performance findings is that the preferred domains, habitats, and styles of managers may have more of an impact on their performance than even their individual stock selections. Disagreement exists as to whether the valuegrowth bias is more or less important than the largesmall bias. A simple regression of the returns to large-cap /value and the returns to largecap / growth against the full S&P 500 Index during this time period generates statistically significant alphas, at least in the U.S. market. Regressing smallcap returns against the S&P 500 Index generates approximately the same magnitude of alphas, but they are not statistically significant. These tests are time dependent, however, so which difference has the truly dominant effect is unclear.

Style Allocation If at least some styles are important, as Figures 2 and 3 suggest, then style allocation is important to plan sponsors. Sponsors will want to hire the style or

Figure 3. Historical Cumulative Returns to Styles: United States 800 700 600

~ '"....

500

~

.2 400 (l)

~ (l)

;> .~

300

"3

8;:l

U

200 100

a -100 12/79 12/80 12/81 12/82 12/83 12/84

- - Large/Value Large/Growth

12/85

12/86 12/87

12/88

------- Mid/Value - - - Mid/Growth

12/89

12/90

12/91

12/92 12/93 12/94

Small/Value Small/Growth

Note: Data as of December each year. Source: BARRA.

styles that will perform the best in the coming year. Table 1 reveals what the sponsors should have been hiring by imposing perfect foresight on the U.s. style allocation decision. The sponsor has six choicescombinations of value and growth and size-and Table 1 indicates the optimal style choice, based on annual returns, that could have been made each year from 1980 through 1993. Correctly anticipating the style with the highest performance each year would have earned the sponsor an average annual return of nearly 29 percent. Compared with holding each of the six style indexes or the S&P 500 for the entire period, style allocation would have added 10-12 percent in average annual return. Of course, few sponsors could have implemented the style strategy consistently. In addition, the frequent reallocations between value and growth and between small and large would have entailed high, and costly, turnover rates among managers. Nonetheless, Table 1 indicates the opportunity that apparently lies in understanding the performance of style subsegments.

Benchmark Characteristics The goal of benchmark specification is to replicate a

manager's investment style without impinging on the manager's potential to add value. A benchmark is usually a commercially available index, but what the managers are expected to deliver and the complexity of the pension fund may dictate a combination of indexes or a customized benchmark. No matter which of these forms is used, a cardinal rule for benchmarks is that they be specified ex ante. All of the standard indexes are specified ex ante. The practice of using the median manager as part of a benchmark violates this rule and always raises questions of fairness because the median manager is known only ex post. Ex ante specification deflects criticisms because the benchmark's performance is not known prior to its selection or construction. A second desirable benchmark characteristic is understandable construction. A standard index consists of published data, and its construction criteria are published. Customized benchmarks should be handled similarly; their construction should be simple, clear, and open. For purposes of ex post monitoring, a benchmark should be investable, and its stability should match the expected strategy of the manager. A benchmark that turns over 100 percent every quarter is useless, but if a manager has 50 percent turnover every quar-

45

Table 1. Perfect Foresight Test, 1980-93 A. Returns to perfect style allocation

Year

Style Allocation

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993

Small/growth Small/value Small/value Small/value Mid/value Large/growth Large/value Large/growth Small/value Large/growth Large / growth Small/growth Small/value Small/value

Annual Return 57.92% 15.03 36.52 44.62 11.59 33.30 21.68 6.50 25.15 36.38 0.18 55.94 32.32 23.37

B. Average annualized returns S&P 500 Large/value Large / growth Mid/value Mid/growth Small/value Small/growth

16.21% 16.56 15.58 17.18 15.17 17.95 12.97

Source: BARRA.

ter, a benchmark with a commensurate turnover rate may be required.

Customizing Benchmarks

broken down by value-growth splits. These specialized commercial indexes go a long way toward addressing the need for indexes that reflect desired style biases. As the standard indexes have become more specialized, an approach that has become popular is to combine standard indexes into a specialized benchmark. For example, a combination index might be value oriented but with bits chosen from largecap/value, mid-cap/value, and small-cap/value indexes. The ultimate extension would be to recognize even further refinements of style in devising a manager's benchmark. For example, a manager might be a large-cap/value manager but one who concentrates on P /E rather than price to book or has a different weighting technique than most large-cap managers. With all these possibilities available, a key question in customizing benchmarks becomes: How much detail is required? Managers will argue that each manager is sufficiently different from everybody else to warrant a very detailed approach. Sponsors in the United States, who are looking at 17,000 registered investment advisors, will argue that not everyone is different from everyone else, but they will recognize that managers can be grouped by style in some meaningful fashion. The answer to the question is that the complexity of the fund largely determines the detail of the benchmark. For example, a simple pension fund with three or four managers needs benchmarks of relatively limited complexity. A fund with 30-50 managers needs a much higher level of detail in its benchmarks and could probably justify a customized-benchmark approach. The construction of customized benchmarks can be complicated by differences in attitude between sponsors and managers. Sponsors are often much more motivated than their managers to generate constructed benchmarks. Managers often cooperate only because the sponsor has (even if a third-party vendor is constructing the benchmark) the ultimate control. The best customized benchmarks, however, reflect the willing and cooperative input of both sponsors and managers, and both parties have incentives to generate and use the best benchmarks available.

Constructing customized benchmarks is in many respects an art rather than a science. The science exists in the form of the systems, the technology, and the quantitative skill brought to bear on the construction process. Art enters the process because, in real life, the markets are messy. Systematic models impose some order on that messiness, but disorganization is inherent. The aim of the construction process is typically to generate a product that will differentiate between the returns attributable to a style and the returns that reflect value added by a manager. The standard indexes cannot capture that distinction perfectly (because they are, by definition, standard), but customizing can be expensive. So, sponsors face a trade-off. Some sponsors will decide that a custom_ ized benchmark will capture enough of the sources of performance that the costs of construction are Conclusion worthwhile; other sponsors will decide that customBenchmarks are important to sponsors and managized construction is too complicated or labor inteners for a number of reasons, and good benchmarks sive or costly. In addition, sponsors may be able to are characterized by several common characteristics. find what they need from the providers of the standFor example, the existence of market subsegments ard benchmarks, which are also providing increasdefined by such characteristics as value versus ingly complex and sophisticated subindexes. growth and size suggests that investment styles Sponsors can now buy a large-cap, or a mid-cap, or should be an important consideration in both standa small-cap index; they can buy a standard index 46

ard and customized benchmarks. Construction of customized benchmarks presents its own set of chal-

lenges, and the best such products reflect the joint efforts of sponsors and managers.

47

Question and Answer Session Christopher G. Luck Question: How acceptable to plan sponsors are normal portfolios as benchmarks? Luck: Some insist on normal portfolios; some hate them. The trend in the last six years has been for sponsors to push the idea of normal portfolios because they are the ones imposing discipline on the managers. Recently, managers have begun coming to consulting firms to have customized benchmarks built for their processes. So, a little shift has occurred in who is driving the process. Still, a relatively small group of investment professionals is keen on normal portfoliosmainly, the large pension fund sponsors, who have very complex pension fund structures. Question: Does anyone provide indexes split along lines other than growth and value? Luck: Growth versus value is not the only way of looking at the world; this split has meaning because empirical studies appear to show that it drives differential

48

performance. BARRA has looked at other factors as potential drivers. For example, instead of looking at growth and value based on price to book or PIE, we decided to examine the different results from strategies based on price momentum-that is, buying highmomentum and low-momentum companies. This particular split did not result in as large a differential performance as the valuegrowth split did. Another way to approach differences would be in terms of growth, core, and value. The core segment might be arbitrarily defined as no growth and no value. This core segment would be the stocks that have growth and value characteristics that are very similar to the overall market, which implies that growth and value stocks are only those with more extreme characteristics. Question: Do you use the same formula for defining growth and value stocks in the United States and in Canada? Luck:

No. In the United States,

BARRA uses a book-to-price screen to define the split between value and growth. In Canada, the straight book-to-price screen is quite sector dependent and seems to exclude too many high-yielding companies from the value category. So, in Canada, we use a formula of two-thirds book to price and one-third dividend yield. We use the formula to rank all stocks; then the stocks in the top half of the ranking are classed as value and stocks in the bottom half as growth. The classification is rebalanced twice a year. Question: Which indexes do the best job of style segmentation and why? Luck: Size and value versus growth are the two most important variables separating style in the market. Hence, indexes that capture that segmentation are important. Managers' strategies tend to be far more complex than simply concentrating on these two variables, however, so the question is often one of the level of complexity the sponsor desires.

International Equity Benchmarks and Manager Choice Philip Halpern Chief Investment Officer The Washington State Investment Board

Current standard benchmarks are weak proxies for the rapidly changing and expanding global investment world. Assigning a benchmark (and performing attribution analysis) that does not reflect a manager's global investment strategy, that ignores global market inefficiencies, and that trivializes high global trading costs is irrelevant and misleading.

Pension funds and other sponsors are investing heavily in international equities, but they often have difficulty identifying and analyzing international investment managers' styles. This difficulty, in turn, can lead to problems in selecting appropriate benchmarks, to the misleading conclusion that more managers are better than few, and to paying unnecessary management fees. This presentation discusses issues that are important in structuring an international portfolio as they affect choosing or developing benchmarks. It introduces research findings from several years ago on exploitable inefficiencies in world equity markets during a time of crisis. Finally, the discussion presents preliminary research findings on the excess costs resulting from hiring multiple managers.

The International Equity Portfolio Like most institutional investors, the Washington State Investment Board structures its $26 billion portfolio within the major asset classes along the lines of specific benchmarks, including some international benchmarks. The use of benchmarks for the overseas markets is still in its infancy. The construction of international benchmarks is messier and more contentious than it is in the United States or Canada. The issues of multiple currencies, cross-holdings, availability of shares, and liquidity of markets remain to be overcome, and the quality of data, although improving steadily, is still a problem. For a user, this messiness can be quite frustrating. The benchmarks used by most U.S. plan sponsors for international investing are the MSCI EAFE indexes. Several studies have presented statistically

significant evidence, however, that the EAFE indexes are not efficient, and few users seem to like them. Managers often take enormous bets away from the indexes-with decidedly mixed results. Tracking errors are almost always explained by the bet in Japan, and in nearly all cases, the manager's exposure to Japan is less than the weight of the index. One might wonder whether the accepted index truly reflects the neutral investable index as defined by the manager investing abroad. This benchmark fuzziness has obviously not discouraged international investment. Barings, which annually analyzes cross-border investments, estimates that nearly $160 billion of cross-border equity flows occurred in 1993 alone. 1 This amount equates ~o an average annual compound growth of about 37 percent during the past five years. About 44 percent of these cross-border flows emanated from the United States and Canada. The reasons for the increase in flows are well known, including the reduction in capital constraints, increase in world trade, broadening of the capital base by corporations, and trend toward securitization of assets. Despite the great amounts of investment, trading internationally still contains many problems for those used to the North American markets. These problems increase the costs of having the wrong exposure because of the expense of making a correction through trading activity. Using live data for five years, Perold and Sirri estimated the total round-trip cost of trading overseas shares to be more than 200 1 Michael J. Howell and Angela Cozzini, "Cross-Border Equity Flows: Hot or Cold?" The GT Guide to World Equity Markets 1994-1995 (Euromoney Publications, 1994):12-23.

49

basis points (bps).2 This expense is magnified as the size of a portfolio increases. For example, the Washington State Investment Board (WSIB) has $1 billion currently invested in international equities-an exposure that is targeted to increase to $3 billion over time. Because the WSIB already owns 10-12 percent, and sometimes more, of the total trading volumes of some of the issues in its active portfolios. Tripling the exposure as we move toward the $3 billion target has obvious implications. In addition, getting into and out of the small issues is not always easy. The rapid growth of capital entering the small countries has only compounded the problems by magnifying a mismatch between share supply and demand. Most overseas markets are still developing their public-market infrastructures. Even in Europe, at least until the European Union's Investment Service Directive comes into effect in 1995, most countries require local shares to be traded in the local market by locally registered exchange members. The capital requirement to become exchange members can be prohibitively high for many potential players; consequently, a few local players can dramatically affect share pricing. Many non-North American investments are in companies that have majority shareholders or majority control in which nondomestic investors cannot share. Finally, although the situation is improving, voting restrictions, limited insider-trading laws, and market manipulation outside North America continue to make governance problematic and difficult for the investor. For these reasons, sponsors must expend significant energy when developing a structure for overseas investments. The relevant benchmarks for performance measurement should logically flow from the goals of the international program. The greatest benefit of benchmarks may not lie in performance measurement, however, but in instilling a discipline that helps sponsors in their construction of international portfolios through encouraging broad exposure and reduction of the waste that results from portfolio turnover.

Efficiency in International Markets Benchmarks are meaningful only to the extent that markets are efficient; the more individual portfolio managers operate in inefficient markets, the less meaningful benchmarks become. Although the world is moving toward easy and cheap capital flows among markets, the markets will continue to be segregated to some degree for some 2 See Andre F. Perold and Erik R. Sirri, "The Cost of International Equity Trading:' Harvard Business School paper, forthcoming (Fall 1995).

50

time. Interest rate differentials-nominal and realillustrate this segregation most poignantly. For example, can anyone explain why long-term Australian bond rates are 300 bps higher than those of Germany? By most measures, inflation differences between the two countries are small. In a perfectly homogeneous world, these differences would be arbitraged away, but they are not. The world's local stock markets do not move together. Varying nonsystematic risk characteristics are undoubtedly part, but not all, of the reason. According to most attribution analyses of managers in WSIB's most recent request-for-proposal (RFP) process, 40-70 percent of return in excess of the benchmarks can be accounted for by country selection. Currency selection accounts for, on average, a quarter to a third of excess return, and stock selection the remainder. In almost all cases, stock selection versus the index is the variable with the least impact on differences in manager returns. In these RFPs, many managers exhibited negative excess returns for either currencies or stock selection but had positive returns overall because of country selection. Although no attribution technique is perfect, in almost every case with a myriad of attribution characteristics, country selection tends to dominate. The questions, then, are: Can researchers prove that a statistically significant level of managers, in aggregate, add value, and if so, when? To help answer these questions, this presentation reviews a previous study that posed the question this way: Are stock markets inefficient enough to be consistently exploitable by money managers?3 That research explored the questions in a number of ways, always focusing on country selection as the primary decision. The time period was 1988 through 1990. During the Iraqi invasion of Kuwait from the beginning of August 1990 to the end of 1990, the world capital markets were extremely volatile, so the study treated this period separately. The study applied both cross-sectional and timeseries techniques to measure managers with EAFE mandates. The study first hypothesized that active country bets away from the EAFE Index exposures might be correlated with certain total return components. MSCI's EAFE Index was assumed to approximate a true manager benchmark, although arguments against this assumption could certainly be made. Each month, managers would decide what country exposures to take; these country bets would be determined by anticipated returns of the markets in local terms, anticipated value added by specific stocks in those markets, and anticipated currency 3 Philip Halpern, "Investing Abroad: A Review of Capital Market Integration and Manager Performance:' The Journal ofPortfolio Management (Winter 1993).

movements against the U.s. dollar. in explaining preinvasion country bets in general but much less significant in explaining the direction of In the two and a half years prior to the invasion, changes in those bets. If transaction costs were the a manager's country decisions appear to have been reason managers' portfolios differed from the optifavorably affected by all three criteria at a statistically mal portfolio, one would expect directional moves significant level. During the crisis period, however, for the exposure weights to be more positively sigthe only variable that seemed to add value was the nificant than the study found. Therefore, portfolio manager's ability to realize excess return within the exposure weights that deviated from the EAFE Index local stock market. That is, a manager earned money must have been caused by some factor other than by picking stocks, not so much by country allocation trading costs and active bets. The conclusion as to or currencies. that cause is that EAFE was not the managers' norNext, the study hypothesized that these decimal portfolio. Thus, mandating an EAFE benchmark sions were biased by managers managing against a does not seem to matter if the intention is risk control benchmark that was not truly the EAFE Index. All and performance measurement. the variables were normalized against the EAFE In this connection, a third conclusion is that typiweights, but if the true benchmark portfolio was not cal attribution analysis may not be appropriate in known, the managers' decisions could be tested only measuring sources of value because managers' norby examining the directional moves of the portfolio. mal portfolios may not be the stated benchmark That is, managers would move every month into a portfolios. Directional moves may be a better way of country they believed would have excess returns, establishing the information content of a manager's and although biases might still exist, those movedecisions. ments in and out of the countries should reflect the The fourth conclusion is that bottom-up stock managers' true beliefs of what was going on in the apparently does not lead to good overall picking countries. country allocations in aggregate. The result of the The answers were somewhat different from the decision to go into countries because of identifiable first set of answers. Basically, during the preinvasion nonsystematic bets was actually perverse in many period, the local market returns across countries cases. In other words, when managers won, their were significantly positive and the other variables winning was often credited to luck. were not significant. The hypothesis that managers Finally, currency forecasts are apparently not will move into countries because they identify good embedded in buying stocks in countries. The analystocks seems not to hold, and the currency effect sis suggests that currency should be viewed sepaseems to be neutral. During the crisis period, country rately-perhaps as a separate asset class. selection was somewhat less than statistically significant, but it was high and added some value, while the remaining factors do not seem to have mattered. Multiple International Managers and Excess A subordinate part of the study was to discover Trading Costs whether managers make good decisions in terms of currencies. That is, are the currency markets efficient The WSIB is trying to decide how many managers to or do they contain exploitable inefficiencies? The hire for its international portfolio. The argument for independent variable tested was the return from hiring more than we have, with different styles, rests active currency bets-not bets embedded in stock on the benefits provided by diversification-lower selection, but actual, active decisions to hedge or not volatility, less exposure to manager business risk, to hedge. During the preinvasion period, currency and so on. But to achieve diversification through a bets definitely added value to the managers' posimultiple-manager structure will create higher tradtions, which was not the case during the volatile ing costs and management fees. For example, as the invasion period. number of managers increases, the probability that This study led to a number of conclusions. First, one is buying exactly what another one is selling the managers did add value through country deciincreases. The sponsor ends up at the same place but sions during normal times and times of crisis, which pays trading costs to get there. In the international suggests that markets are not efficient relative to one markets, where an investor may be paying 200 bps another; at least, they were not during the periods round-trip, such a situation makes even less sense tested. Second, even though all the managers said than in domestic markets. EAFE was their benchmark, it is unlikely that the Therefore, we have developed a framework for managers really used EAFE as the normal portfolio analyzing the probability and cost of managers buyduring the time of the study. The argument behind ing and selling identical stocks in a country. Figure 1 this conclusion can be summarized as follows: Local depicts the manager trades (MY) for two managers, market return comparisons were highly significant i and j, in country q. The shaded area would consti-

51

Figure 1. Excess Trading Costs with Two Managers MT(i,q)

MT(j,q)

t

Excess Trades

count value for manager n. Ignoring relative differences in price appreciation for simplicity, a manager's expected trade in any one country is assumed to be a random variable MT(n,q), which is normally distributed with E[MT(n,q)] = O. A positive variance is a purchase and negative variance is a sell. In the aggregate portfolio, sales will offset purchases so that MT(n) will equal zero for each manager. The expected monthly sales, when sales occur, can be expressed as

Source: The Washington State Investment Board.

tute excess trades from the sponsor's perspective. The framework is intended to generate a bivariate normal distribution of that excess trading, using the dollar size of the trades to differentiate the excess costs incurred when one manager is buying and another manager is selling the same asset. Assume an investor hires N managers to construct a portfolio around a benchmark that includes Q countries. In the long run, the amount of monthly trades executed by the managers reflects two characteristics: the sale and purchase opportunity set for each country in the portfolio and the individual managers' portfolio turnover philosophies. For simplicity, the model also assumes the probability of trading to be equal for each country within individual manager portfolios. Decisions are made monthly. Turnover percentage, TO(n), may differ among managers but is expected to be similar for each country within each manager portfolio. The term Port(n,q) represents the portfolio amount of manager n in country q; Port(n,Q) represents the total portfolio ac-

and the monthly purchases can be defined as

The model then calculates the expected excess trades between each pair of managers. Table 1 presents the results of the analysis of excess trading costs for a test case. The case involved a $360 million portfolio equally divided among managers; no appreciation or cash inflows or outflows were assumed. Each manager, whether a regional manager or EAFE manager, was assumed to invest in the same country against similar benchmarks. (As argued earlier, different normal portfolios would result in different country exposures and trading patterns. For simplicity, this test assumed similar normal portfolios across managers. Unless normal portfolios are extremely different, the impact on results would be minimal.) Portfolio decisions were assumed to be made monthly. Beginning manager 4

Details of the model are available from the author.

Table 1. Effect of Number of Managers in a Portfolio on Trading Costs: Test Case Assumptions: Total trades Total trading cost Trading cost (country allocation)a

$259 million $5,184 thousand, or 144 bps $2,592 thousand, or 72 bps Number of Managers 2

3

4

5

Expected sales/purchase in each country for each manager (thousands)

$1,350

$900

$675

$540

Standard deviation of sales/purchase in each country for each manager (thousands)

$2,000

$1,330

$1,000

$800

Total excess trading cost Dollars (thousands) Basis points Percent of total trades

$332 9.2 6.3%

$631 17.5 12.2%

$891 $1,188 24.7 33.0 17.1% 22.9%

aFifty percent of all trading is assumed to take place in order to make country allocations; fifty percent is assumed to take place for individual security bets.

Source: The Washington State Investment Board.

52

portfolios were equally weighted among countries, should evaluate the diversification benefit versus this waste. although this assumption is not necessary and does not affect the results dramatically. The turnover was assumed to be 72 percent a year (6 percent a month), which is on the low side of the normal range for most Conclusion active managers. Half of that turnover was related Benchmarks should help sponsors attain the ultisolely to country selection, and the rest to individual mate purpose of investing-to make money. Therestocks. The next assumption for the test case is key: fore, forcing the world into a benchmark paradigm Based on anecdotal observations, international manthat does not reflect real-world opportunities is simagers' moves in and out of the markets apparently reflect almost no correlation on a directional basis. ply silly. The investment world is dynamic; it is Therefore, a.1O correlation of manager country-allochanging and expanding, and the current standard benchmarks are weak proxies for that world. The cation trading decisions was assumed. Finally, all numbers were reported on an annual basis. greater the volatility and changes in the real world, the less meaning benchmarks have-as investment Table 1 shows the excess trading costs for two, goals or as measurement tools. three, four, and five managers. (In the model, the number of countries is irrelevant. The amount of Understanding what a portfolio manager is doing and what is the manager's normal position is trading into and out of any pair of countries is offset difficult. Performing any sort of attribution analysis, by the number of cross-trading opportunities.) As shown, total excess trading costs for two managers even with appropriate benchmarks, is tenuous, were found to be 9.2 bps. That is, a sponsor with two therefore, but assigning a benchmark that does not reflect a manager's investment strategy is irrelevant EAFE managers or two regional managers, as the two managers conducted some trades that canceled at best and misleading at worst. each other out, would be overpaying by 9 percent. If Understanding the key issues in global investthe number of managers with similar mandates is ing, potential inefficiencies in global markets, and increased to five, the excess trading costs increase to the impact of high global trading costs (particularly in connection with using multiple global managers) 33 bps. can help the sponsor stay on track in the difficult A sponsor trying to devise a structure for investtasks of assessing performance in global markets. ing the funds earmarked for international equity

53

Question and Answer Session Philip Halpern Lawrence S. Speidell, CFA Question: Could you briefly outline where the 200-bp roundtrip trading cost comes from for international securities? Halpern: From Perold and Sirri. They looked at the commission, the bid-ask spread, the turnover taxes, the market impact, and the implementation shortfall, which is their concept of the costs of not being able to trade when somebody wants to trade. Question: Please explain your comment that performance benchmarks become less relevant through time if the markets are inefficient. Halpern: If the client cannot define the universe by which the manager is investing, then to try to characterize that universe via a published benchmark or a normal benchmark, which is then misspecified, will not tell you what is really going on in terms of the opportunities.

The volatility of return that the manager will realize will be much greater than the benchmark, and the information content will be much less. Question: Are international small stocks primarily a local, retail investor market? Speidel!: The international markets are no different from the US. market in that regard. Certainly, those who are brave enough to move away from the large, comfortable stocks and pioneer in the small-cap stocks, where the opportunities may be greater, have to deal with liquidity problems and information problems. I think the reward is worth the risk, but the problems do exist. Question: Are institutional investors entering the international small-cap market? Speidel!: I do not have much company right now, but I think

the trend is in that direction. Question: How do you identify small-cap international managers? Speidel!: Finding good international managers is difficult. I would start with where the sponsor wants to be in terms of the world and try to find managers whose exposures closely mirror the sponsor's objective. The manager's experience in those areas is important because the international markets are both riskier and more complex than the domestic market. The manager's investment results are important, but results can be indicative of only one or two individual bets. More important than basing a decision on historical performance is determining that a manager has the infrastructure, experience, and skill to manage in the global markets and to sustain good returns.

67

Market Homogenization and Diversification Benefits Lawrence S. Speidell, CFA Director, Systematic and Global Management and Research Nicholas-Applegate

The diversification benefits long associated with small-capitalization and global investing appear to be intact. Correlations between small- and large-cap stocks and between U.s. markets and developed and developing markets indicate that markets are not becoming more homogenous.

Conventional wisdom holds that equity markets, in becoming more globalized, are also becoming more alike, with fewer barriers and inefficiencies. If this is truly the case, investors must rethink the diversification potential of global investing activities. This presentation discusses two questions related to this issue, one explicit and one implicit. The explicit question to be explored is: Are the markets around the world becoming more alike, more homogeneous, than in the past? The implicit question that follows from the first is: If so, what are the implications for investors in their search for real diversification? The presentation addresses these issues on two levels-first, whether markets are homogeneous within themselves, and second, whether markets around the world are drawing closer together. Are global markets becoming one basket; is trading 24 hours around the clock causing all markets to act the same way?

Internal Homogeneity The first issue for the global investor is whether the non-U.s. equity markets are homogeneous within themselves. This issue has been studied previously, with the conclusion that an investor needs fewer stocks in non-U.s. markets than in the u.s. market in order to achieve a portfolio that tracks the local market. In other words, non-U.s. stocks have a higher correlation with their local markets than do U.S. stocks with theirs. This answer needs to be amended, however, with respect to stock size. Small stocks move independently in most markets. Figure 1 is a comparison of the MSCI EAFE Index and the Salomon Brothers 54

EurPac Extended Markets Index (EMI) for small stocks for the period of June 1989 through September 1994. (All calculations in the figures are in U.S. dollars.) Salomon Brothers uses an 80 percent/20 percent rule to define large and small capitalization; thus, the bottom 20 percent of stocks in market capitalization are categorized as small cap in each market. Salomon also mandates that in order to move to large-cap status, a small-cap stock must move at least 5 percent (top 75 percent cap) into the large-cap region. Figure 1 shows that, globally, the small stocks do behave differently from the large stocks. Figure 2 supports a similar conclusion with data for four of the non-U.S. developed markets; in the international arena, large cap is distinct from small cap just as in the U.s. market. Figure 2 also reveals similarities and differences in stocks' relative performance among national markets. In the United Kingdom, the relative performance graph shows that small stocks relative to large stocks bottomed in about the second half of 1992; this development was followed by strong improvement for the small-cap stocks. In Germany, however, relative small-stock performance was weak from 1989 to 1992 and has been flat since then. In France, small stocks were weak through the end of 1992 and have been strong since then. Figure 3 suggests some coincidence of the economic cycles in these markets, as defined by industrial production, with the capitalization performance cycles. For example, the u.K. market bottomed in terms of industrial production at about the same time small stocks picked up. In the U.s. market, small stocks have performed better since industrial pro-

Figure 1. Performance Comparison of the MSCI EAFE Index and EurPac EMllndex 6/89=100 200

180

160 -

140

.

.'

120

. ... /\.......... ""'/'. '.'

'"\

:.~

'\

.. '..

.

,\;:<'~~,

'

100

80

60 6/89

12/89

12/90

12/91

12/92

12/93

9/94

MSCIEAF EurPacEMI EMI Relative to MSCI Source: Nicholas-Applegate.

duction bottomed at the end of 1990. This behavior may reflect improved competitive conditions for small companies when markets are growing. The difference between large- and small-cap returns is also evident in emerging markets. Figure 4 contains graphs for four Latin American and four Asian countries. Note that small-cap stocks have underperformed in Argentina and Mexico (the relative lines decline). The underperformance was slight in Brazil, where small-cap stocks have actually performed well since early 1992. In such Asian countries as India and Malaysia, small stocks have performed well. Another way of examining return based on size is to calculate the information coefficient (the rank correlation of the size factor for all stocks). The information coefficients for four of the countries included in Figure 4 (Brazil, Chile, India, Thailand) are given in Figure 5. The dotted line is the monthly rank correlation of size (at the beginning of the month) with return. The solid line is a seven-month average. When the lines are above zero, large stocks are outperforming small stocks. These graphs show similar cycles in the relative performance of small and large stocks. Another perspective on the size aspect of market

homogeneity is the relative valuation of small stocks versus large stocks. Figure 6 shows median price-tobook-value ratios (P IBV) as of late 1994 in the U.s. market, four other developed markets, and the emerging markets in aggregate for deciles based on capitalization size. The lines are based on data from the Worldscope data base. An upward-sloping line indicates that small stocks were selling at a significantly lower P IBV than larger stocks-the case, for example, as of late 1994 for the United States, France, Germany, and the United Kingdom. For Japan, the line is flat or slightly downward sloping, indicating equivalent or somewhat higher valuations for small stocks. The shape of the emerging markets graph suggests that small stocks are valued differently in these markets from the way they are valued in developed markets. Figure 7 provides the same graphic information for five of the emerging markets in the same time period. In Thailand, for example, the largest two deciles of stocks are at 4 times P IBV, whereas the smaller deciles are at 1 times P IBV. The spikes in the smallest decile in Thailand and the middle deciles in Chile and Taiwan indicate high-P IBV outliers. 55

Figure 2. Perfonnance Comparison of Large-Cap and Small-eap Stocks in Four DeveloPed Markets MSCI Germany/EMI Germany

MSCI United Kingdom/EMI United Kingdom 6/89=100 200

6/89=100 200

180

180 MSCI

160

160

140

140

120

120 EMI/MSCI

100

.

'. ,",

.....

80

..... : •

.....

100

.

.........

....

EMI

.-

EMI/MSCI

.

80 60

60 6/ff3

12/ff3

12/90

12/91

12/92

12/93

6/ff3 12/ff3

9/94

12/90

MSCI France/EMI France 6/89=100 200

9/94

...... EMI/MSCI

:

160 120

..:\./....: :".

.........

#

. :':

140

100

80

80

60 I

12/ff3

Source: Nicholas-Applegate.

','

. '

.:

:::

100

120

6/ff3

12/93

MSCI Japan/EMI Japan

140

60

12/92

12/91

6/89=100 160

180

56

'.

0"

....

..

.... :" :.:". ' ... ' .".

; ..... ".

....

MSCI

40 6/ff3 12/ff3

12/90

12/91

12/92

12/93

9/94

Figure 3. Industrial Production of the Major Economies 12/88=100

'F...coe

115

:>

co

110

~0

105


E

!J

France '.... 'United State

'.3

7'

.:::"'>«;/

:::0 100

'.0 u ;:l

"0

2 p...

~

Een

Uni'ted Kingdom

95 90

;:l

"0

E

85 12/88

I

12/89

12/90

I

I

12/91

12/92

I

12/93 12/94

Source: Nicholas-Applegate.

Peru is a fascinating market for a variety of reasons. At only $8 billion market capitalization, it is a very small market, but the country has high GNP growth, high potential demand for consumer durables, and improving political conditions. Figure 8 shows a scatter chart of stocks by P IBV and market capitalization as of November 1:94. The l?attern is similar to those in Figure 6 and FIgure 7, wIth stocks above US$100 million capitalization selling at very high P IBVs and stocks below that level selling at low P IBVs. Small-cap stocks seem to be viewed differently in Peru from the way they are viewed in most other markets examined. So far, the analysis has demonstrated that small stocks offer diversification potential within markets. Markets are not so homogenous as to permit global investors the luxury of owning the largest stocks as proxies for the markets as a whole.

Diversification across Markets The second important issue is the degree of homogeneity across markets. Investors try to avoid volatility by diversifying among markets that ~re e~pected to move independently. One problem wIth thIS strategy is that the developed markets may now be moving closely together because of global events. For example, Figure 9 shows five years of quar~erly returns (log returns) for Germany versus the Umted Stateswith and without certain global shocks. 1 Without the global events, the slope is neg~tive and re.al di.versification is possible. The outlymg data pomts m the lower left-hand portion of the graph represent two IFor an extended analysis of rolling five-year correlations between countries, see Lawrence S. Speidel! and Ross Sappenfield, "Global Diversification in a Shrinking World," The Journal of Portfolio Management (Fal!1992): 57--67.

global shocks (the fourth quarter of 1987 was the global stock market crash, and the thi~d quarter of 1990 was the Iraqi invasion of KuwaIt) that completely disrupt the slope and destroy diversification potential. Figure 10 and Figure 11 provide similar an~lyses for developed and emerging markets, respectIvely. The figures show long-term rolling five-year correlations with and without the invasion of Kuwait and the crash of 1987. Figure 10 shows that correlations between developed countries and the United States are much higher when the effects of global shocks are included than when those effects are excluded. Not only does the reduction in diversification potential caused by global shocks need to be considered; asset allocation decisions should also reflect the effect of shocks. Figure 11 shows that correlations be~een the United States and emerging market countnes have been generally lower than correlations between the United States and developed countries. Furthermore, correlations with the emerging markets actually fall in some cases (India and Argentina) during global shocks. . The conclusions seem to be that U.s. mvestors risk increasing homogeneity when investing in the developed countries because of global shocks; this risk is reduced when investors diversify into emerging markets. A study reported in an April 1994 Street Journal article reinforces the argument that mvestors ought to worry about reduced diversification benefits. The study results indicated that when the volatility of global markets rises, the correlations between markets also rise. 2 Recent data on global correlations, shown in Figure 12 and Figure 13, do not indicate any long-term trend of rising global market homogeneity or global integration. The period from mid-1992 through mid1994 was characterized by the absence of global shocks or major global events, and in fact, correlations between markets appear to have dropped during that period. Figure 12 shows th.at rolling 12-quarter correlations between the Umted ~tates and the major developed markets have declmed. These declining correlations are most likely the result of the differences in industrial production trends shown in Figure 3 and of truly independent central bank actions in the various countries. For example, the Bundesbank in Germany has had to cope with inflationary pressures from reunification, and t~e Ministry of Finance in Japan has faced a collaps~ m financial and property markets. These correlatIon declines could reverse, of course, if global economies become more synchronized.

w.all

2"Global Diversification has its Downside," Wall Street Jour-

nal (April 14, 1994).

57

Figure 4. Perfonnance Comparison of Investable and Small-eap Stocks in Individual Emerging Markets Brazil IFC/Brazil Small Cap

Argentina IFC/Argentina Small Cap 12/88=100

12/88=100

1400

600

1200 -

500

1000 400

IFC Investable

800 300 600 -

Small Cap/IFC ;.

200

400

0".'

:.::...

,

.....:.:'

.... Small Cap

';'

..... Small Cap

200 100

o

:'

" '., .. ,., .. , .. :

12/88

12/89

12/90

.,

'.

100

'"

0 •

.

Small Cap/IFC'··· .... . ·········1.·.: .. ····

9/94

12/93

12/92

12/91

0'----....1.----'------'------'-_ _--'-_---' 12/88 12/90 12/89 12/92 12/93 9/94 12/91

Chile IFC/Chile Small Cap

Mexico IFC/Mexico Small Cap 12/88=100

12/88=100 1400

1200

1200

1000

1000 -

IFC Investable

800 800 600 600 -

Small Cap

200 100 , :::.:::

".

12/89

.

12/90

Source: Nicholas-Applegate.

58

12/91

12/92

12/93

.".

'".

200 -

Small Cap/IFC

O'--_ _-'-_ _-'-_ _---'-_ _--L_ _.....l.-_ _- ' 12/88

Small Cap

400 -

,,'

400

9/94

.... Small Cap/IFC

100 0 12/88

12/89

12/90

12/91

12/92

12/93

9/94

Figure 4. Perfonnance Comparison of Investable and Small-eap Stocks in Individual Emerging Markets (Continued)

12/88=100

India IFC/India Small Cap

Korea IFC/Korea Small Cap 12/88= 100

300

200

.'.'

250

160 :

.'

200 -

0"'

. IFC Investable

Small Cap

150 -

100

120 100

........

....

....

Small Cap/IFC

.....

Small Cap/IFC

80

.

:...... . ....

". "

0

••

Small Cap IFC Investable

50

40 -

OL-_ _--'----_ _--l.-_ _----L_ _----L_ _--'----_----'

o

12/88

12/88

12/89

12/'Kl

12/91

12/92

12/93

9/94

Malaysia IFC/Malaysia Small Cap

12/89

12/88=100

12/88= 100

600

600

500

500

400

400 -

12/'Kl

12/91

12/92 12/93 9/94

Thailand IFC/Thailand Small Cap

.'. Small Cap

300 IFC Investable

200 -

.... Small Cap

200

.... .. ,

100

...... .:.0...

or;-'-;"

100

Small Cap/IFC

o 12/88

....

.. '.

300 -

..

IFC Investable '

......

Small Cap/IFC

o 12/89

12/'Kl

12/91

12/92

12/93

9/94

12/88

12/89

12/'Kl

12/91

12/92

12/93 9/94

Source: Nicholas-Applegate.

59

FigureS. Infonnation Coefficients: Relative Return Perfonnance versus Size Brazil

Chile 0.6

0.6

.

0.4

. <::

"" ""

"

0.2

0.4

""

0.2

0 ." ""

-0.2 -0.4

"" ". ""

".

-0.6

"... .

0

,

"

-0.2

""

-0.4

-0.8 87

86

88

89

91

90

92

86

94

93

87

88

90

89

India

91

92

93

94

91

92

93

94

Thailand 0.6

0.4

,

".

""

0.2

, ""

."

0.4

."

.: :.

0.2

0

0

'.

-0.2

. '.

"" ""

"" ." ""

-0.2

""

"" ""

.'

88

89

"" ".

-0.4 -

-0.4 86

87

88

89

90

91

92

94

93

86

87

90

Beginning-of-Month Rank Correlation of Size with Return Seven-Month Average

Source: Nicholas-Applegate.

Figure 6. Median PIBVs for Emerging Markets and Selected Developed Markets, Late 1994 6

5 4

3

2

•• ?ermany -...:. • •

.. . .. .. ..

Japan

...

.;.-.-:-"

------------

.' ..-"--

~.'

United States -:-.:--

.

....:..::':":'---

France

United Kingdom

o 2

3

4

5

6

7

Capitalization Deciles (small to large)

Source: Nicholas-Applegate, based on data from Worldscope.

60

8

9

10

Figure 7. Median PJBVs for selected Emerging Markets, Late 1994 10

9

Chile

'.

8 7

§ >~

'p..

6

5 4

.. .. . .. ,

3 -

2

_

..

~-

-; _ - . : _#

.'--

.-.;.~

Mexico Thailand

0 3

4 7 6 5 Capitalization Deciles (small to large)

8

9

10

Source: Nicholas-Applegate.

Figure 8. PJBV versus Market Capitalization for Peru, November 1994 10 , - - - - - - - - - - - - - : - - - - - - - - ,

9 8 -

7 -

~ - 6 ~ 5 -

'p..

+ +

.. ' .

4 3 21 ;.. . .

+

.. + • :.:+t+ ••" . •• !. +

+

I

++

+

I

[

100

1,000

0'-------'-------'------'-------' 1

10

10,000

Market Capitalization (millions of US. dollars)

Note: Log return. Source: Nicholas-Applegate.

61

Figure 9. Five Years of Quarterly Returns: Germany versus the United States, Third Quarter 1986-First Quarter 1991 0.3

0.2

§

•

0.1

3rd Quarter 1986

~

0::

•

~

'"....

.EOJ

4 h Quarter 1989

Excluding Global Events

•

•

•

4 th Quarter 1990

0

~

ro

1st Quarter 1989 •

Ei....

OJ

C)

3rd Quarter 1989

1st Quarter 1991.

-0.1

1st Quarter 1987 •

-0.2 4th Quarter 1987

3rd Quarter 1990

•

-0.3 -0.3

•

-D.2

o

-0.1

u.s. Returns (%) Note: Rolling five-year correlations. Source: Speidell and Sappenfield, "Global Diversification in a Shrinking World," p. 61.

62

0.1

0.2

Figure 10. Rolling Fiv~Year Correlations of Developed Country Markets with the United States Canada

France

1.0 , . . - - - - - - - - - - - - - - - - - - - ,

1.0 , - - - - - - - - - - - - - - - - - - - - - - ,

.......

-;;:

~o

0.5

0.5

u

c o '.c

~

0 1---------------------1

o

-0.5 Ll----LI---L---L-L---L---'------'------L---'------'----'----....l-...l..--'----.L-J

-0.5

1::.... o

U

~nn~~w~~~M~~~~~~~

Hong Kong

Germany 1.0 , . . - - - - - - - - - - - - - - - - - - - ,

-;;: .~

S

-;;:

.~ u

0.5

0

u

c

c0

0

aJ.... ....

0.5

S

.~

1.0

0

..

0

,

~ aJ .... ....

0

0

u

U

-0.5

-0.5 ~nn~~w~~~M~~~~~~~

I


0.5

'0

S
0

I

I

I

I

I

I

I

I

I

I

I

.,

0.5

0

U

c .S

aJ.... ....

I

-;;:

U

~

I

1.0

-;;:

.~ u

I

United Kingdom

Japan 1.0

S
I

~nn~~W~~~M~~~~~~~

c 0

.'

'.c

~

0

........
0

0

0

U

U

-0.5

I

I

I

I

Including Al! Quarters Excluding 4th Quarter 1987 and 3rd Quarter 1990

Note: Trailing five-year correlations. Source: Speidel! and Sappenfield, "Global Diversification in a Shrinking World," p. 62.

63

Figure 11. Rolling Five-Year CoITeIations of Emerging Markets with the United States Argentina

Chile 1.0

1.0

'E
TJ

'E

0.5

"d

::s

S

0

U

l:: 0

'';::

l:: 0

'. ..........

'';::

0


]

u

U

..$

0.5

0

0

0

-D.5

81

82

83

84

85

86

87

88

89

90

-D.5

91

81

82

83

84

85

'E
0.5

'0


::s

u

U

l:: 0

l:: 0

:=

........
~ u

0

0

-D.5 82

83

84

85

86

87

88

89

90

81

82

83

84

86

88

89

90

91

88

89

90

91

Thailand

'E
0.5

'0

::s

u

U

0.5


0

0

l:: 0

l:: 0

'';::

'';::

]....

0

0

0

0

u

U

-D.5

-D.5 81

82

83

84

85

86

87

88

89

90

91

81

82

83

84

Including All Quarters Excluding 4th Quarter 1987 and 3rd Quarter 1990

Note: Trailing five-year correlations. Source: Speidell and Sappenfield, "Global Diversification in a Shrinking World," p. 63.

64

85

1.0


........
87

0

Korea

..$

91

0.5

91

1.0

::s

90

-D.5 81

'E
89

0

u

'0

88

~

0

..$

87

1.0

'E

::s

86

India

Greece 1.0

.~ u

.,

85

86

87

Figure 12. Rolling 12-Quarter Correlations of S&P 500 Index with Large-Cap and Small-eap Indexes of Selected Developed Markets United Kingdom

Germany

1.0 0.6

~
c ....

.2 Q)

P:::

0.2 0 -0.2

1.0 MSCI/S&P500

I;--..

El~lI;S~P;O~ . -. .

§
....c

.2 Q)

P:::

-D.6

MSCI/S&P50

0.6 0.2 0 -D.2

EMI/S&P500

-0.6

-1.0 6/92

I

I

12/92

12/93

-1.0 6/92

9/94

France

1.0 , - - - - - - - - - - - - - - , 0.6 0.2

1.0

r--.--=-------,. MSCI/S&P50

.

EMI/S&P500 •

o

-0.2

~
c ....

.2 Q)

P:::

0.6

12/92

. . .

.

12/93

Japan

~500

EMI/S&P500' •

.

0.2 0 -D.2

-0.6

-D.6

-1.0 '--_ _...L-_ _--'I'--_--' 6/92 12/92 12/93 9/94

-1.0 6/92

9/94

I

I

12/92

12/93

9/94

Source: Nicholas-Applegate.

A second revelation in Figure 12 may be more long lasting-the indication that small-cap stocks in the non-US. developed countries (the EMI indexes) provide much better diversification than large-cap stocks. For these markets, the correlations of small stocks with the S&P 500 are lower (by .2) than the correlations of the broad market indexes as a whole with the S&P 500. The situation is similar in several developing countries. Figure 13 shows the correlations for the United States and three Latin American and three developing Asian countries for the same time period as Figure 12. All the correlations-large cap and small cap-are low and have dropped during the 1992-94 period. In most cases, differences between small- and large-cap behavior exist.

opening questions. First, the findings with respect to small-cap stocks are that the markets are not becoming more alike or more homogenous: In nearly all markets, when compared with large-cap stocks, small-cap stocks exhibit valuation and correlation differences that continue to make them attractive for both return and diversification purposes. Second, evidence suggests that the markets have not drawn together. Although correlations between the United States and other markets have increased during global shocks, correlations since 1992, in an environment of independent monetary policies and economic activity, have dropped. The diversification benefits long touted for global investing activities appear to remain intact, and certain strategies, such as diversifying into small stocks in each foreign market, may provide additional benefits.

Conclusion The data shown here provide some answers to the

65

Figure 13. Rolling 12-Quarter Correlations of S&P 500 Index with Large-Cap and Small-eap Indexes of Selected Emerging Markets Chile

Brazil 1.0

1.0

0.5

0.5

§ ... <JJ

~

IFC/S&P500

<JJ

~

0

0

.8

.8 Q)

0::

§ ...

-0.5 -1.0

6/92

I

I

12/92

12/93

Small Cap/S&P500

Q)

.. .

Small Cap/S&P500

IFC/S&P500

0::

-0.5 -1.0

9/94

6/92

12/92

1.0 IFC/S&P500

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0

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..•... ,..., .•

~

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-

-0.5

Small Cap/S&P50

0 -0.5

I

-1.0 6/92

12/92

12/93

6/92

9/94

Malaysia 1.0

IFC/S&P500

0::

~

.8

Small Cap/S&P500

0 IFC/S&P500

Q)

0::

-0.5

-0.5

-1.0 6/92

-1.0 12/92

12/93

Source: Nicholas-Applegate.

66

9/94

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Small Cap/S&P500

Q)

12/93

0.5

§ ...

0

.8

12/92

Philippines 1.0

0.5 ~

.

0::

Small Cap/S&P500

-1.0

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• .IFC/S&P500

Q)

Q)

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.

.8

.8 0::

0.5

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9/94

Thailand

Mexico 1.0 0.5

12/93

9/94

[

6/92

12/92

12/93

9/94

Question and Answer Session Philip Halpern Lawrence S. Speidell, CFA Question: Could you briefly outline where the 200-bp roundtrip trading cost comes from for international securities? Halpern: From Perold and Sirri. They looked at the commission, the bid-ask spread, the turnover taxes, the market impact, and the implementation shortfall, which is their concept of the costs of not being able to trade when somebody wants to trade. Question: Please explain your comment that performance benchmarks become less relevant through time if the markets are inefficient. Halpern: If the client cannot define the universe by which the manager is investing, then to try to characterize that universe via a published benchmark or a normal benchmark, which is then misspecified, will not tell you what is really going on in terms of the opportunities.

The volatility of return that the manager will realize will be much greater than the benchmark, and the information content will be much less. Question: Are international small stocks primarily a local, retail investor market? Speidel!: The international markets are no different from the US. market in that regard. Certainly, those who are brave enough to move away from the large, comfortable stocks and pioneer in the small-cap stocks, where the opportunities may be greater, have to deal with liquidity problems and information problems. I think the reward is worth the risk, but the problems do exist. Question: Are institutional investors entering the international small-cap market? Speidel!: I do not have much company right now, but I think

the trend is in that direction. Question: How do you identify small-cap international managers? Speidel!: Finding good international managers is difficult. I would start with where the sponsor wants to be in terms of the world and try to find managers whose exposures closely mirror the sponsor's objective. The manager's experience in those areas is important because the international markets are both riskier and more complex than the domestic market. The manager's investment results are important, but results can be indicative of only one or two individual bets. More important than basing a decision on historical performance is determining that a manager has the infrastructure, experience, and skill to manage in the global markets and to sustain good returns.

67

Measuring the Performance of Performance Measurement Peter L Bernstein President Peter L Bernstein, Inc.

Modern performance measurement is characterized by sophisticated trappings, but reliance on the results still requires a cautionary note. Questionable bogeys and the blurring of luck and skill are but two examples of issues that must be addressed in assessing the performance of performance measurement.

formance usually rest. Second, I will consider the Many years ago, sophisticated performance measquestion of how much sense can be made out of the urement was not even a figment of people's imaginations. The only benchmark that investors or whole business of performance measurement. managers used was the poorly conceived conglom_ eration of 30 stocks known as the Dow Jones IndusThe Slippery Slopes of Bogeyland trial Average. Beyond that benchmark, performance measurement was carried out almost entirely at Choosing a bogey involves unavoidable questions of cocktail parties, dinner parties, bridge games, and benchmark selection and style determination. golf courses, where individuals boasted or moaned Benchmark characteristics and style elements thus to one another about what their investment advisors wield great influence on the performance of perwere doing. This lively channel of communication formance measurement. occurred continuously rather than quarterly, and it ignored adjustments for risk, which only made matBenchmarks and Bogey Risk ters worse. Managers who could keep their heads One major difficulty that managers face can be when everyone around them was losing theirs were summarized with the expression "bogey risk." The rare indeed. bogey is the target at which managers take aim in Today, we can fine-tune our measurements of forming their portfolios. Change the bogey, and manwhat a manager is doing in ways that are nothing agers transform their positions to minimize their short of miraculous. The current issue is not how to tracking errors on the new bogey and to outperform upgrade our software to carry out this task in an even it. The bogey is the hinge of fate for portfolio managmore miraculous fashion; rather, the pressing coners. cern is whether the performance of performance Selection of a benchmark is the investor's remeasurement is quite what we would like to believe sponsibility. Managers may participate in choosing it is. a benchmark on an advisory basis, but the "bogey Performance measurement certainly serves as an buck" stops at the investor's desk. Bogey risk means incentive for managers to try to do their best. No that the investor might miss the opportunity to select manager likes to be caught in the bottom quartile or a better performing bogey-or a bogey with a prefconsistently underperforming the normal portfolio. erable risk profile. What should an investor do, for Nevertheless, the relationship between the effort example, when after choosing the S&P 500 Index as managers expend and their performance is a long the normal portfolio, he or she sees the Wilshire 5000 way from linear. Index significantly outperforming the S&P 500? With This presentation will address performance some 1,000 indexes available today to serve as readymeasurement in two ways. First, I will briefly discuss made benchmarks, the odds are high that whatever technical concerns that relate to the choice and track benchmark is selected will be outperformed by some record of the bogeys on which judgments about perother benchmark that could just as well have been

68

selected. Which is better: a stable of managers who outperform bogeys that underperform or a stable of managers who underperform bogeys that outperform? Simply phrasing the question in this way points out the difficulties involved in the benchmark selection process. The difficulties are most important when the performance of the fund as a whole is considered, not merely such individual parts as large-capitalization equity or fixed income or currency overlay. On those occasions when I sit on the investor's side of the table, my interest is less in the performance of the individual managers-as long as they live up to their style commitments-than it is in how the portfolio's overall asset mix compares with alternative asset mixes. Managers work hard to design an asset mix that could be invested passively as an alternative to the actual portfolio. Even when active managers are doing better than their passive counterparts, however, how can the investor ever feel confident that the appropriate master mix has been chosen for the portfolio as a whole? Moreover, the difficulty extends beyond the possibility that another asset mix would have been more appropriate. With a different asset mix, the investor might have had a different set of active managers who, unlike the set the investor actually had, might not have outperformed their passive counterparts. In short, even performance measurements we can rely on leave us in an unremitting state of uncertainty about what performance measurements mean for this critically important part of the investment process. Zero Mostel captured this uncertainty during the McCarthy era of 40 years ago when he sang a little ditty containing the line "who will investigate the man who'll investigate the man who'll investigate me?" I often wonder whether we should not be searching for the bogey to be the bogey for the bogey for the bogey we are actually using.

Style Differences and Elusive Bogeys Bogeys are elusive creatures; they refuse to stand still. As a consequence, what you see is not necessarily what you get. This observation came to me recently when I was using Zephyr's Style Advisor, which is based on William Sharpe's algorithm for analyzing and defining manager styles. Zephyr produces a graph that shows a manager's movement through time toward or away from quadrants based on combinations of capitalization size and growth versus value. For comparison, the graph also shows the S&P 500, or some other chosen index, throughout the same time period. To my surprise, the style graph showed the S&P 500 moving. Although the S&P 500 was still clearly, solidly in the large-cap I growth quadrant, it was just as clearly drifting toward the

small-cap I growth quadrant. What does this drift do to the performance measurement of a large-cap I growth manager who has been assigned the S&P 500 as the normal portfolio? If the manager feels compelled to reduce the average cap size of the portfolio in order to minimize tracking error, then the investor is losing coverage in the large-cap area. A solution might be to seek out an index that will stay with its initial size orientation; one of those provided by Frank Russell Company, for example, might be appropriate, but the monthly rebalancing of those indexes gives them the flavor of actively managed portfolios. In a masterful paper on this subject prepared five years ago, Russell Fogler pointed out a variation on the bogey risk theme. 1 Consider the case in which a managed portfolio outperforms its normal portfolio by, say, 400 basis points in a particular time period. This achievement has an odd and not necessarily welcome by-product: The outperformance raises the PIE and cap size of the managed portfolio relative to the normal portfolio. If the normal portfolio is not rebalanced at the end ofeach time period, subsequent performance ofthe managed portfolio may appear to result from factor bets on size and valuation, whereas in fact, the manager is simply waiting for specific selections to reach maturity. How can subsequent performance be explained? Was it the result of the manager's factor bets, or was it the manager's ability (or inability) in security selection? Suppressing these spurious factor bets by using the Russell method of rebalancing the normal portfolio after each performance measurement merely replaces one distortion with a different, butequally serious, one. Rebalancing the bogey to match the current bets on size and valuation will wipe out any information on the factor bets, but over time, information on factor bets is just as important as information on security selection skills. The bottom line is that performance measurement withoutbogeys makes no sense but performance measurement with bogeys makes only a little bit of sense most of the time. Instead of poring over the details of performance measurement data, we would do better to shift our attention to identifying and tracking a manager's style, which is an important and feasible objective and one that rests on firmer foundations than does measuring the manager's performance relative to a bogey. 1

See H. Russell Fogler, "Normal Style Indexes-An Alternative to Manager Universes?" in Performance Measurement: Setting the Standards, Interpreting the Numbers (Charlottesville, Va: The Institute of Chartered Financial Analysts and the Financial Analysts Federation, 1989):97-104.

69

Back to Basics Even if we could get around all the problems involved in the choice of normal portfolios, I would like to raise two less technical but more profound issues about performance measurement. First, nobody has succeeded in clearing up the messy boundaries between luck and skill in the interpretation of performance measurement data. This problem may be an old chestnut, but it deserves revisiting. Second, given the inherent difficulties in the performance measurement process, why is that process in such great demand?

Skillful or Lucky? Capital market data are infested with noisewide standard deviations and unstable covariances. In addition, the markets are subject to waves of fads and fashions that bubble up, and then returns regress to a mean that refuses to stand still so that the mean itself is a kind of floating crap game. Most of the time, the cacophonous noise generated by these swirling and inconstant forces blanks out the decisions that portfolio managers make in the quiet of their offices. The result is that we need decades of data before we can satisfy ourselves with any conventional degree of certainty that a manager has truly outperformed or underperformed a benchmark (assuming, in addition, that the chosen bogey was appropriate in the first place). Consider the following example. Since 1976, the mutual funds included in the Morningstar growth category have outperformed the S&P 500 by an average of 170 basis points a year, with a standard deviation of annual return that was 120 bps below the S&P 500 and an R2 of 0.92. The mutual funds' year-over-year returns exceeded the index returns in 57 percent of the months. How certain can we be that this performance is the result of skill rather than luck? Probability analysis indicates that we can be 89 percent certain that the result is attributable to skill. This percentage of certainty is below conventional thresholds, which call for us to be 95 percent certain before we can confidently distinguish between luck and skill. We would need another 16 years of performance results of this caliber to become 95 percent certain. If the funds' excess returns were only 50 bps a year less, we would need an additional 50 years of results'before we would reach 95 percent certainty. Moreover, a manager may chalk up a sufficiently impressive string of excess returns to persuade us that the results came from skill rather than luck, but that news is history. For example, the Fidelity Magellan Fund passes the confidence test with flying colors; the fund's excess performance over the S&P

70

500 during the past 11 years is sufficient to make us 99 percent certain that the results are attributable to skill. Indeed, the existence of outliers like Magellan is a key factor in sustaining our beliefs in and preferences for active rather than passive management. The problem is that investors had no way of knowing back in 1983 that Peter Lynch should be chosen to manage their money; they had no way of knowing what Magellan's track record would be from 1983 forward. So, even if an investor decided to buy Magellan shares in 1983, how can that investor seriously claim to know now that the decision was a matter of skill rather than luck? If such a question about our own performance is difficult for us to answer, how confident can we be, even when we pick winning managers, about our performance measuring skills? As a practical matter, we have no choice but to settle for mere hunches about how managers are doing. Nevertheless, investors and managers alike continue to use performance measurement, and for time periods that are much shorter than the decades needed to evaluate active managers. A hard number appearing on a printed page or computer screenespecially a number carried out to several decimal places-has enormously persuasive powers. We want the number so badly that we accept the perception of accuracy and reality as reality itself.

Why Performance Measurement? In calm and quite moments, we admit to concerns about performance measurement data; advisors warn investors to play down these data in hiring and firing managers, for example. The interesting question is why we in the investment communitysophisticated managers, analysts, and investors a11insist on putting so much weight on measurements that we recognize are flawed. One answer is suggested in an anecdote about University of Chicago economist Frank Knight. As he was walking through the campus, Knight noted a plaque on which was inscribed a quotation from the famous physicist Lord Kelvin: "That which cannot be measured cannot be known." Knight pondered the inscription for a moment and then muttered to his companion, "Oh, well, if you can't measure it, go ahead and measure it anyway." The view that something is better than nothing is one factor behind our dependence on performance measurement to evaluate portfolio management skill. But that view is only part of the answer. The primary reason we go on measuring something that cannot be measured in defiance of our training in statistics and hypothesis testing lies in the type of performance measurement that went on at the cocktail parties and golf games. This reason is seldom discussed, and it is more implicit than explicit. The

primary justification for performance measurement percent is in itself an attraction; it means that many is what can be called lottery risk-that is, the risk of managers will produce excess returns each year.) not winning the lottery. The decision to choose active management is disarmLottery risk must be distinguished from the risk ingly simple: Nobody likes to pass up a chance to find the Big One. Some managers are certain to outinvolved in dropping a small amount of money on a gamble with very unfavorable odds. Lottery risk is a perform the index-if only as a result of the law of potent force that induces people to take positions averages-and investors who choose to index their with odds that they know are unfavorable. When portfolios pass up the opportunity to have their lottery risk is involved, the pay-offs are so big that, money managed by one of those outperforming despite the bad odds, the gamble is irresistible. Peomanagers. Passive investors are walking a sure road ple believe that somebody will win, and that someto regret. body might be them. These beliefs are sufficient to motivate the gamble almost without regard to the Where We Stand odds; if they do not play, they cannot win. In the The modern investment world is, after all is said and language of decision theory, the motivation comes under the heading of regret. The notion is that we done, not so different from the world of the 1950s. will be more disappointed by missing out on a great The trappings of today's sophisticated performance but uncertain opportunity than we would be cheered measurement-quartiles, deciles, percentiles, factor by winning from a less attractive but also less uncerbets, R2s, style coefficients, betas, alphas, standard deviations-make the measurement process and retain opportunity. Better unsafe than sorry. Lottery risk explains why investors hang on in a sults all the more exciting. Nevertheless, the quesmarket they know is overvalued; as long as the martionable character of the bogeys we are forced to use, ket can sustain its upward momentum, the payoff is the uncertain meaning of excess performance, the blurred boundary between luck and skill, and the there. irresistible delights of taking lottery risks mean that Lottery risk also explains why so many investors choose active over passive management in the face we have not progressed as far as we would like. We of odds that exceed 50 percent only on occasion and have learned a great deal since the old days, but how seldom by very much. (The way those odds hug 50 much more do we truly know?

71

Question and Answer session Peter L. Bemstein Question: What are the implications of the drift toward small cap that you found in the S&P 500? Bernstein: How much of a difference this movement has had as a practical matter I don't know. The interesting point is that we think of a benchmark as set in cement and then measure everything else off it, but the benchmark itself is dynamic and in motion. Beating the target is more complicated than you thought because the target is moving. Question: Could you comment on performance-based fees in light of the luck versus skill dilemma? Bernstein: I have thought performance-based fees are by and large a good thing, but if performance has a large overlay of luck, they are not fair. It is not fair either to give a manager a bonus or take a bonus away from a manager if the results are noisy. The people who are using performance fees should think about the

72

possibility that we do not have enough data to know whether results are coming from skill or luck. Whatever else is done, good performance fees are not based on a quarter's performance. Enough of a time span is needed that at least there is a presumption that you have enough data to make a judgment. Question: When managers have a large portfolio, much of what they are doing must be indexing, so would one way of judging their skill be to limit active managers to, say, 20 stocks? Bernstein: Theoretically, you could follow such an approach. If a manager purports to have a style and purports to develop an alpha, you should try to get as much of that alpha as possible. Implementation will be hard, however. The problem is that when a portfolio is not very diversified and something begins to go down, the manager can't afford to have it in the portfolio. So, managers are continuously shedding the bad bets. The turnover is fero-

cious. Transaction costs can add up. Also, you will need a lot of active managers if each one will have only 20 stocks, which can be expensive because you will never have the advantage of fee breaks.

Question: Because all the studies conclude that asset allocation, not active management, is the primary determinant of portfolio returns, would one be naive simply to use all passive indexing?

Bernstein: Some people do exactly that, but your use of the term "naive" confirms the point that I was making. Passive management is unusual; people want active management because if they do what you suggest, they will be passing up the opportunity that the active manager will outperform. The passive business is not a negligible part of institutional business, but active management dominates. What you are proposing may be wise, but it is not as interesting as active management.

Attribution Analysis for Equities Craig B. Wainscott, CFA Manager, Plan Sponsor Products Frank Russell Company

Performance attribution has a mixed reputation, but properly conducted, attribution can assure clients that apparent positive results do indeed stem from manager skill rather than luck. Attribution that consistently ties added value to manager decisions over time is an important source of management information.

Performance attribution is a methodology to quantify the success of or value added by a strategy. In that sense, it is not much different from traditional performance measurement, in which one set of returns is subtracted from another and the result is the value added. Performance attribution goes one step further, however, to identify the sources of value, and if used properly, it can thus distinguish between skill and luck. When the sources of added (or lost) value have been identified, the sponsor or client can decide where to focus attention. If things are going well in one area but not so well in another area, the sponsor can spend more time on the area where performance is below expectations. Performance attribution is defined in many ways. The aim of this presentation is to describe a powerful and broadly accepted approach to performance attribution and also to provide some warnings about its careful and proper use.

Approaches to Equity Performance Attribution

turn, this approach focuses on the sources of added value relative to a benchmark and examines the different segments that contributed to the return. It separates the return by primary sources and can be based on the most detailed, accurate data available. The return decomposition approach has a number of benefits. First, it is fairly easy to interpret because the numbers add up: Here is the index, here is the portfolio return, and the attribution effects explain the difference. It is also fairly easy to calculate; the math is no more complicated than adding, subtracting, and multiplying. It is flexible; as long as the necessary underlying rates of return and cash flows are provided, the data can be examined and used in any number of ways. Finally, the data used for return decomposition are familiar-standard rates of returns, cash flows, and weights in either sectors or asset classes. One drawback is that, although the numbers all add up nicely when the method is applied for a single period, that additive property is lost when compounding over time is undertaken. For users who are comfortable with the geometric nature of returns, this issue is not important. But for those who are new to complex performance analysis, the aesthetics of an additive solution can make the difference between enlightenment and frustration. Another issue is the "interaction effect," which is a cross-product that must be dealt with in this type of performance attribution. Volume of data can also be a challenge; each time the attribution analysis is cut finer, the amount of data required to do the analysis compounds.

The two main approaches to performance attribution are factor models and return decomposition. The factor model approach typically takes the list of securities in an equity portfolio at a point in time and, on a security-by-security basis, tries to attribute performance to various factors (beta and size, for example); the approach is then to sum those factor effects at the portfolio level and create a report of which factors were responsible for what amounts of return. Few systems use the actual return that the portfolio achieved over time; they focus on the returns of specific securities at a particular time. Return decomposition takes the opposite ap- Dissecting the Net Management Effect proach by asking how the actual rate of return on a The ultimate aim of performance attribution is to portfolio was achieved. Starting with that actual re73

describe the net management effect-the difference between the portfolio return and the benchmark. The point is not to explain the total portfolio return. If the portfolio achieves 12 percent and the benchmark 10 percent, where did the 2 percent come from? What are the activities that actually added value? What was the net management effect? The net management effect-positive or negative-for each equity portfolio can be dissected into the value added by two or three pieces, depending on whether the portfolio is purely domestic or global: (1) allocation, which for a traditional equity portfolio is the industry groups or economic sectors, (2) security selection, which is the stocks picked, and for a global portfolio, (3) currency management. Keep in mind in relation to these three pieces that to attempt performance attribution without some idea about the style or the intention of the manager being measured is dangerous and potentially useless. Attribution is a tool that helps interpret the results of decisions, but one must be certain that the results come from intentional decisions, not simply luck. To illustrate the concept of performance attribution, Table 1 contains a typical attribution report for a U.s. small-capitalization equity portfolio. This type of presentation clearly shows differences in returns and the sources of those differences. It uses portfolio and benchmark weights and returns by industry group to show allocation, selection, and total effects. The basic formulas for the allocation, security selection, and interaction effects are as follows. These formulas are for a single period only, howevertypically one month-and ignore the effects of compounding; thus, the formulas will not directly generate the results shown in Table 1. The benchmark returns in the formulas are local currency returns:

Allocation effect = (Portfolio weight - Benchmark weight) x (Benchmark segment return - Benchmark total return);

Security selection effect = (Portfolio segment return Benchmark segment return) x (Benchmark weight); Interaction effect = (Portfolio weight - Benchmark weight) x (Portfolio segment return - Benchmark segment return)

Allocation Effect To analyze the effect of allocation, performance attribution compares the weight of the portfolio in a sector or industry with the benchmark weight (the average weight during the measurement period is used) and compares the return to that sector in the benchmark with the total return of the benchmark. For example, focus on the allocation effect for the "health care" industry group in Table 1. The weight for this group is 16.6 percent in the portfolio and only 13.5 percent in the benchmark, so the portfolio manager overweighted the health care group. The return in the benchmark for health care was 85.2 percent versus a total return for the benchmark of 46.5 percent. The portfolio manager thus overweighted an industry group that significantly outperformed the benchmark as a whole. The result is a positive allocation effect of 1.78 percent. A similar calculation can be made for each sector for the portfolio, and the results add up to the total effect of allocation on this portfolio of 5.15 percent; that is, the total value added from allocation to this portfolio's performance is just over 5 percent.

Security selection Effect Security selection is the other effect typically examined in attribution analysis for U.s. equity port-

Table 1. Equity Performance Attribution: Allocation, Security Selection, and Interaction Effects: One Year Ending December 31 Portfolio Industry Technology Health care Consumer discretionary Consumer staples Integrated oils Other energy Materials and processing Producer durables Autos and transportation Financial services Utilities Other Total aNet management effect.

Source: Craig B. Wainscott.

74

Attribution Effects

Benchmark

Weight

Return

Weight

Return

Allocation

Selection

16.8% 16.6 31.0 5.0

46.80% 133.73 37.37 44.67

16.1% 13.5 17.3 3.1 0.0 5.3 14.2 7.0 0.0 15.2 6.9 1.3 100.0%

52.56% 85.20 51.79 28.50 8.92 -3.83 29.84 29.42 41.06 65.27 28.59 56.08 46.46%

0.26% 1.78 0.85 -0.37

-1.07% 6.09 -2.63 0.70

-0.51% 0.10 -2.33 0.24

3.61 2.66 -5.13

-3.35 -1.62 4.66

5.00 1.65 1.77 12.64%

-2.54 1.62 -1.03 --4.77%

0.6 3.9 0.3 8.9 16.4 ~

100.0%

45.15 51.31 -25.04 94.30 51.12 201.01 59.48%

2.99 1.49 1.32 -0.79 -2.32 -0.08 5.15%

+

Interaction

+

Total -1.32% 7.98 --4.11 0.58 3.25 2.53 0.84 1.67 0.95 0.65 13.02%a

folios. In performance attribution for security selection, allocation to the sector does not matter; what matters is how well the stocks perform in that sector compared with the benchmark. The size of the negative or positive effect, however, does depend on the sector weight. Note from Table 1 that this portfolio manager achieved a return of about 133.7 percent for the health care group, which was weighted at 16.6 percent in the portfolio. This sector's return for the benchmark was about 85.2 percent. Therefore, the portfolio performed considerably better with its health care stocks than the benchmark did. The resulting security selection effect was nearly 6.1 percent. Indeed, for the total portfolio, security selection contributed a positive 12.64 percent; that is, this manager added more value from security selection (nearly 13 percent) by picking better stocks than the benchmark within each industry group than the manager added from allocation (about 5 percent) by picking better industry groups.

Interaction Effect The interaction effect is a residual when the allocation and selection effects are calculated in their purest forms. It is not merely a leftover, however, with no information content. The interaction effect shows the impact of the allocation and selection effects working together. For example, recall from Table 1 that the health care segment had a positive allocation effect of 1.78 percent and a positive selection effect of 6.09 percent. This manager apparently had confidence in being able to pick superior stocks in this sector and backed that confidence by overweighting it. These two positive results combined to create an interaction effect of 0.1 percent. When the interaction effect is buried, where it is placed should depend on the manager's approach. If a manager is truly approaching the portfolio composition in a top-down fashion, focusing on picking industry groups or countries, the interaction effect is usually combined into the allocation effect. For this manager, picking stocks is either not a focus or is

implemented passively. If a manager is focusing on stock picking, the allocation effect can be considered something of a residual. Most or all of the attribution for the cross-product should be in the security selection because that is what the manager is trying to do well. If the interaction effect is not explicitly identified in a report, it is more than likely combined into the selection effect. Asking where it is included is a fair question; the answer gives some insight into the manager's focus.

Compounded Effects Attribution effects for a single period can be compounded for multiple periods. Table 1 illustrated an annual period, and Table 2 provides an example of compounding for four years. The table includes the interaction effect as well as the allocation and selection effects, the actual annualized returns for the benchmark and the portfolio, and the net management effects. These results are straightforward and easy to interpret; they represent a tradeoff, however, between complete precision and ease of understanding. Compounding the attribution effects generates precise results but does not produce totals that are simply additions of the period-by-period effects; the figures could be left in the compound/untotaled (and more precise) form, but Frank Russell Company has found that explaining performance attribution to clients is easier if the effects add up than if they are absolutely precise. The size of the difference in the two approaches is typically small (often no more than 10 basis points), and that level of detail is less important than the conclusions clients should reach. In the Frank Russell Company approach, therefore, the effects are calculated on a monthly basis and compounded; then, the differences are rounded off and smoothed so that the effects appear to be additive again.

Currency Effect Allocation, selection, and interaction effects apply to both domestic and global portfolios. Global or inter-

Table 2. Equity Performance Attribution: Total Effects for Four Years Annualized Returns 3 6 December Months Months Russell 2000 (modified) Allocation Selection Interaction Actual return Net management effect

Calendar Years

1 Year

2 Years

3 Years

4 Years

19XX

19XX

19XX

19XX 19XX

14.0

6.0 1.8 4.5 0.5 12.8

14.6 3.6 8.7 0.1 27.0

46.5 5.1 12.6 -4.8 59.5

8.5 2.3 10.2 -7.3 13.7

11.2 2.0 11.0 -5.4 18.8

14.2 2.3 7.8 -3.7 20.7

-19.6 0.6 8.1 -8.0 -18.9

16.7 1.4 12.7 -1.3 29.5

24.0 3.01 -2.5 2.1 26.7

-8.4 1.9 22.0 -D.l 15.4

5.9

6.8

12.4

13.0

5.2

7.6

6.4

0.7

12.8

2.6

23.8

8.1 1.1 5.2 -D.4

5.2

Source: Craig B. Wainscott.

75

national portfolios, however, are also subject to the attributions to skill simply because of poor benchmark definition. effects of currency moves. Because they are important measures of global or international managers' Inappropriate benchmarks are common with decisions, currency effects on the portfolio in comglobal and international equity portfolios because parison with currency effects on the benchmark managers tend to have biases for or against certain should be isolated in performance attribution analycountries. The objective of attribution analysis, howsis. ever, is to measure the impact of decisions, so the Sponsors and managers should note that much benchmark should reflect the options among which research has been and continues to be performed in the manager can choose. For example, if the portfolio this area-using single- and multiple-currency is allowed to include emerging markets, the chosen frameworks. Brian Singer, for example, has develglobal benchmark should include them. oped a framework for global attribution that separates currency effects from market and interest rate effects. 1 Conclusion

Benchmarks Because performance attribution is based on differences between a portfolio's return and a benchmark return-and not in terms of total returns alone but on industry-by-industry and stock-by-stock baseshaving the right benchmark is critical to the analysis. An inappropriate benchmark, such as a large-cap benchmark for a small-cap portfolio or a broad market benchmark for a growth portfolio, can result in

1 See Mr. Singer's presentation, pp. 86-90.

76

Performance attribution is an important source of management information. If a client can define the types of decisions the portfolio managers are making and where they are trying to add value, and if the client can see that value is added as a result of those decisions, the information generated by attribution analysis can reassure the client that the good results are coming from skill rather than luck. The client will be even more reassured if the results of the performance attribution analysis show consistency over time.

Question and Answer Session Craig B. Wainscott, CFA Question: Is attribution analysis used to monitor managers more than it is used to select managers?

Wainscott: Yes, but not by a large margin. Attribution is a great tool for use in selecting a manager, and it should be used for that purpose. The reason it is not used is generally a lack of data. Manager data are usually available from the trustee or a consultant, but when numerous prospective managers are being screened, all the data on industrylevel cash flows, weights, and returns that are needed to conduct the attribution analysis often are not available. Some large consulting firms or research houses will provide that type of data, however, and many analysts are actively doing performance attribution as part of manager searches. Question: Is attribution analysis available for market-neutral strategies?

Wainscott: Market-neutral strategies introduce problems with benchmarks and data. Correct attribution is very difficult when derivatives are involved, and most market-neutral strategies are option-based strategies. Question: In attribution analysis, does risk matter?

Wainscott: Risk matters. Risk can be considered in terms of the volatility of returns; we know we should never look at returns without looking at the risk experience through time. We also need to

look at risk in terms of factor exposures; large allocations to cap size, for example, or other factors that might affect the portfolio, need to be incorporated when measuring either performance attribution or a manager on an ongoing basis. With market risk, if a manager hikes up the portfolio beta, the manager may be able to construct a portfolio that is, in a sense, industry neutral. The manager or client might imagine that the manager is getting rewarded for some skill when, in fact, all the high beta means is that the manager has simply taken on more market risk. Question: What groupings other than economic sectors are most important in attributing performance?

Wainscott: The most important two are cap size and economic sector. At Frank Russell Company, we regularly analyze attribution for those two factors, and for some managers, we also carry out performance attribution on the bases of price-to-book ratios, price-to-earnings ratios, dividend yields, and growth in earnings per share. Question: As the number of time periods being joined together increases, does the interaction effect become a huge factor, perhaps even larger than the other two effects?

Wainscott: The interaction effect can be large, but it is not so much a function of time and how much you compound as of the ac-

tual results of a strategy, which is an additional reason why knowing the underlying strategy is important. For example, the interaction effect often will become noticeable when a strategy focused heavily on an industry that had a huge cash flow and a sizable return. Such a strategy causes some of the numbers (weights and returns) to loom very large and can cause a large interaction effect. In such a case, looking closely at the individual components and becoming familiar with the math in order to understand the interaction effect is important. Question: In calculating the value added from security selection, why not multiply the return difference by the actual weight instead of the benchmark weight, which would cause the interaction effect to disappear?

Wainscott: If you use the actual weight, you are automatically burying the interaction effect in the selection effect. Question: Some people can trade only at high trading costs and others can trade at low cost. How do differences in trading costs show up in attribution analysis?

Wainscott: Trading costs are simply part of the return achieved by the portfolio, so they will all go into the security selection effect. Low costs will raise returns for the stocks in an industry group or portfolio.

77

Attribution Analysis for Fixed Income Robert C. Kuberek Vice President and Principal Wilshire Associates Incorporated

Fixed-income attribution extends from the same general framework as attribution for other asset classes. The specific Manager Model discussed here is potentially a more powerful test of manager skill than the traditional t-test.

Attribution analysis poses the same questions for both equity and fixed-income portfolios: How much value has the manager's investment process added, and what decisions appear to have created that value? Is the added value significant, and does the process consistently add value over time? This presentation uses a general conceptual framework to consider how fixed-income attribution fits into the overall process of performance measurement. Within the context of this attribution framework, the presentation discusses two particular aspects of fixed-income attribution analysis: (1) the decomposition of management returns in fixed-income portfolios, including the pros and cons of alternative descriptions of risk, and (2) alternative approaches to assessing the significance and consistency of added value. The framework described is general, in that it encompasses a variety of common techniques for performance decomposition, including variance analysis (e.g., sector weights and selection) and multifactor techniques. The framework also has the advantages of requiring no cross-product or interaction terms and of having all attributed returns except selection rely on large market cross-sections of returns, which improves the quality of the results. The framework separates currency effects from all other effects to allow these activities to be evaluated independently. Finally, because benchmark currency weights show up explicitly, the framework easily accommodates hedged or partially hedged benchmarks.

Sources of Value and Manager Returns Manager returns can be decomposed according to the three sources of value: timing, issue selection, and trading. The first source, marketwide timing,

78

includes currency management and asset-class decisions. Timing refers to the process of altering a portfolio's exposure to marketwide influences such as interest rates, exchange rates, or spreads. Changing the duration or corporate bond exposure of a portfolio is an example of timing. Within asset classes, timing refers to how the manager decides to deploy those assets among risk categories, such as duration and issue quality. Selection of specific issues is the second source of value added to the investment process. Issue selection refers to the process of choosing security issues that, in the aggregate, provide the overall risk exposures desired in the portfolio but that, individually, will outperform their asset-class and risk categories. Thus, the contribution of selection may depend on the specific description of risk used in the attribution analysis. The third source of value is trading, which comprises two components. The first is trading within the performance interval. This component is actually timing that is not captured as marketwide timing because it occurs during performance periods. An example is daily trading to alter portfolio duration based on the monthly analysis of performance. Performance differences arise when the portfolio is lengthened or shortened strategically during the month prior to market moves. The second component of trading is execution, which refers to the ability of the portfolio manager to obtain prices that are better (or worse) than average at a given time. If traders can take advantage of their inventories or knowledge of other traders' inventories or intentions to carry out trades on the right side of the bid-ask spread, they can add value through execution.

stocks, and 20 percent to U.S. bonds. The portfolio is fully diversified, so no selection effect exists. The total buy-and-hold return difference from the benchmark is -2.2 percent. Most of this underperformance results from the underweighting of U.s. stocks, which outperformed U.K. stocks.

Currency Returns The portfolio's currency return can be computed as (1)

where wph is the portfolio weight for currency h, wbh is the benchmark weight for currency h, and xh is the hedge return for currency h. (The appendix to this presentation summarizes the definitions of terms and contains the complete decomposition of returns.) Formula 1 sums, for all currencies, the hedged return for each currency weighted by the difference between the portfolio weight and the benchmark weight for that currency. Conceptually, this formula includes any hedges in the fund. (Hedging effects can be separated, but that process is not part of this calculation.)

Return to Risk Factors The analyst can probe further by examining attribution by risk categories within the asset classes, which can be carried out through this formula: (3)

where bpij is the portfolio exposure to risk factor j for asset class i, bbij is the benchmark exposure to risk factor j for asset class i, Qij is the excess return to risk factor j, and wpi is the portfolio weight for asset class

i.

Asset-elass Returns

The advantage of this approach for active investment managers is that it allows them to attribute effects among risk categories independently of portfolio weights. In other words, the b's could be subindex weights, with Q denoting the excess return on the subindex, which would allow a relatively simple attribution scheme by asset class and then within asset class by subindexes. For example, the asset class for fixed income could be the Lehman Brothers Aggregate; the subindexes could be corporate bonds, government bonds, and mortgages; and the b's would simply be subindex weights. An alternative strategy is to use a risk model to describe performance within an asset class; in this case, the b's are the exposures of the portfolio and of the benchmark to the risk categories in whatever form those categories are defined in the model. Risk categories could be duration, term structure, or sector exposures within a fixed-income class. The only caveat is that risk within asset classes be defined in such a way that the returns to the j risk factors average to the actual excess return of the asset class.

Asset classes in this context could be represented by, for example, the S&P 500 Index for U.s. equities, the EAFE Index for international securities, or the Lehman Brothers Aggregate Index for u.s. bonds. The portfolio's asset-class return is given by

-

(2)

I./wpi - wbi)Hi,

where wpi is the portfolio weight for asset class i, wbi is the benchmark weight for asset class i, and Hi is the excess return on asset class i. The difference between the portfolio weight and the benchmark weight is multiplied by the excess return for an asset class, and the results are summed for all asset classes to determine value added to the portfolio through asset-class decisions. Table 1 contains a numerical example to show how this scheme works. In the example, the benchmark is 50 percent U.s. stocks, 30 percent u.K. stocks, and 20 percent U.s. bonds. The portfolio has allocations of 10 percent to U.s. stocks, 70 percent to U.K. Table 1. Portfolio Asset-elass Returns

Asset Class

Currency

Portfolio weight

United States

United Kingdom

U.s. Stocks

U.K. Stocks

0.5

0.5

0.1

0.7

U.s. Bonds 0.2

Benchmark weight

0.7

(U

(l5

(U

Q2

Difference

-0.2

+0.2

-0.4

+0.4

0.0

±3J2%

±.5...Q%

-2.0%

+0.6

-2.0

-0.8

Return Attribution

Q.,Q% 0.0

Total Attribution

±l..Q% 0.0

-2.2

Source: Wilshire Associates.

79

If that condition holds for subindexes, then this ap-

proach will reveal the total difference between the portfolio return and the benchmark return in a consistent way. Risk factors are discussed further in a later section.

Issue selection The portfolio's selection returns are given by

where R p is the return of the buy-and-hold portfolio, in the base currency, during the relevant performance attribution interval and all other variables are as previously defined. Historically, the relevant interval has been monthly or quarterly, but investment managers, particularly those who are using attribution as a diagnostic tool, are moving increasingly toward daily attribution analysis.

Attribution to Risk Factors in Detail The part of the attribution that is concerned with the description of risk within the asset class is essential to effective fixed-income attribution. The approach was given in Formula 3, and it is enhanced by using either subindex weights or a risk model to portray the risk. Using subindex weights was described previously, but an example of risk modeling will make a comparison of the two approaches clear.

Risk Model Example One risk model used by Wilshire Associates for fixed-income attribution describes two types of risk in fixed-income portfolios: term-structure risk and spread risk. Term-structure risk is captured in the model by three types of yield-curve movements that, together, seem to explain variations in the returns of broadly diversified Treasury portfolios-parallel yield-curve shifts, twists, and changes in curvature. Figure 1 shows these three interest rate shifts. Empirically, the three interest rate shifts account for roughly 95 percent of the variance in Treasury returns, across maturities. The first type of shift, a parallel yield-curve movement, accounts for approximately 85 percent of the variance. Simple twists account for another 7 percent (after parallel shifts) of the total variance, and changes in curvature (intermediate shifts) account for an incremental 3 percent. Together, the three shifts also explain more than 99 percent of the variance in the return of the Treasury index over time, which makes this three-factor model a very useful description of risk for attribution analysis of broadly diversified portfolios. Because these risk factors are associated with actual yield-curve movements, a duration measure 80

is also implied in each factor. Thus, the b's for these factors in Formulas 3 and 4 are the durations of the portfolio with respect to each of the three yield-curve shifts. The second type of risk factor in the domestic fixed-income portfolio is spread risk. Particularly in high-grade fixed-income portfolios, returns attributable to changes in spread are associated with such attributes as sector, quality, coupon, and prepayment risk. Empirically, aside from mortgages, for which prepayment risk is obviously significant, the most important of these risk factors seems to be sector. Quality is statistically insignificant in all but the Baa and lower quality ratings; that is, quality does not appear to be a systematic component of return once the sector effect is removed. The b's for the spread-risk measurement are simply the effective duration contributions in the portfolio from each of the categories. For example, a portfolio with a duration for corporate bonds of ten years, a duration for government bonds of ten years, and half its assets in corporate bonds will have a five-year duration contribution from the corporate sector. Thus, all else being equal, if corporate spreads widen by 10 basis points, the portfolio will return approximately -0.5 percent.

Subindexes versus Risk Models The primary advantage of using subindex returns to describe the asset class's risk is that this approach is easy to explain. Almost all investors can understand performance as it relates to portfolio weights and to the performance of market averages or indexes. The subindex approach, however, also involves substantial disadvantages. Using subindexes to attribute return may ignore the value added by a manager's intentional variations from the subindex, such as duration and term-structure bets. The use of subindexes also implicitly assumes that the characteristics of the subindexes are constant over time. For example, suppose a particular manager's skill is in timing growth versus value. If the attribution analysis uses subindexes whose growth/value characteristics change over time, then the portfolio's changing sector orientation will be partly a reflection of this drift in characteristics rather than of the portfolio manager's changing view of the sectors per se. An important advantage in using the risk model rather than the subindex approach is flexibility. First, the subindex approach can be viewed as a special case of the risk model, one in which the risk factors are the subindexes. Second, describing the returns generated by a subindex in terms of the attributes of the securities in the subindex (such as duration or style) may be more plausible than describing them

Figure 1. Yield-Curve Movements Parallel Shift a 3.0

8

.............

Yield Curve after Shift ......

7

2.5

f-

2.0

r-

1.5

!-

6

5

~

Beginning Yield Curve

4

5 G'i

3

Movement

1.0

2

0.5

1

o

o

5

15

10

20

o

30

25

!-

o

I

I

I

I

I

5

10

15

20

25

30

20

25

30

Maturity (years)

Years to Spot

Simple Twist a 3.0

8 Yield Curve after Shift .... .........

7

..

6

5

.

2.5

~-~----...

,

4

~

2.0

$

1.5

~

Beginning Yield Curve

G'i

3

Movement

1.0

2

0.5

1

o

o

5

10

15

20

25

o

30

o

5

10

15 Years to Spot

Maturity (years)

Intermediate Shift a 7

3.0 , - - - - - - - - - - - - - - - - - - - . . . . ,

Yield Curve after Shift ..........................

6

2.5

5 Beginning Yield Curve

4

2.0

$

1.5

~

:2 ~

~

3

G'i

2

Movement

1.0

0.5

o

o

5

10

15

20

25

30

Maturity (years)

oo

5

10

15

20

25

30

Years to Spot

aDuration measures.

Source: Wilshire Associates.

by the identity of the securities in the subindex. Thus, if the portfolio manager owns securities that are similar to the securities in a subindex but are not in the subindex, attribution analysis based on the risk model will correctly account for this similarity. A disadvantage-particularly in the case of fixed income-is that the risk model approach is subject to various sources of measurement error. For example,

duration is a key component of most fixed-income risk models, but measuring duration in fixed-income portfolios can be quite difficult, especially with the derivative products and exotic instruments that now exist. When using subindexes, only the return of the subindex must be calculated, and the quality and consistency of subindex returns lead to easier and more readily understood calculations. 81

The use of subindex weights is most appropriate when the manager being evaluated is responsible for asset allocation and/ or when the benchmark is stable and the manager focuses exclusively on security selection. The risk model approach is more useful when the manager is not responsible for asset allocation but focuses on a specialized portfolio within a given asset class and/or when the manager is attempting to add value through varying durations, yield curves, sectors, or other risk attributes.

sis, because it is a labor-intensive process, is frequently characterized by small samples. So, the t-statistic can easily be skewed by one or two large observations. Small samples can also lead to the t-statistic being very weak in its ability to reveal actual manager skill. Furthermore, the attributed returns may not satisfy a key underlying assumption of the valid use of t-statistics, that of normally distributed data. Because of these potential limitations, alternative models for value assessment have been developed.

The Manager Model

Assessing Value The formulas are useful tools for measuring the impact of deviations from the benchmark. The second, and most difficult, part of attribution analysis for fixed income, however, is the interpretation of the attribution results. Two issues are particularly important: Is the value that is being added significant? Is the value being added on a consistent basis? These questions can be addressed through two approaches, a modified risk model based on t-statistics and a Manager Model.

The Manager Model addresses the process by which the manager decides whether to take risk in a particular risk category, such as duration, and how much risk to take. The previous definition of attributed returns can be rewritten simply as the difference in a risk factor between the portfolio and the benchmark multiplied by the return to that risk factor. For example, if the risk factor is duration, the difference in duration between the portfolio and the benchmark, Opj, multiplied by the return to duration, Qj, can be written as

The ~istic Approach Formula 3 described the value added within an asset class; that is, it can identify whether the manager is adding value in a significant and/ or consistent way with respect to the risk categories defined for an asset class. Formula 3 can be modified slightly to focus narrowly on the value being added in each j risk category, rp (5)

The basic calculation in Formula 5, as in Formula 3, is the difference between the portfolio's exposure and the benchmark's exposure times the excess return to the risk category. The resulting attributed return indicates the value in basis points that the manager has added during the performance period. For example, Qij could be the market return to duration; bpij, the portfolio duration; and bbij, the benchmark duration. Then, rj is the value added by the portfolio manager through the duration bet. One common way to assess the significance and consistency of this added value is to calculate a tstatistic based on an average of those attributed returns (an average value added by that risk category) and a standard deviation of that average, hypothesizing that E(rj ) equals zero. A relatively large t-statistic-greater than 2, for example-would support the conclusion that the value being added is significant. This judgment should be tempered, however, by an awareness of potential problems. Attribution analy82

(6)

where attributed returns reflect the portfolio's exposure to a given risk factor (in this case, duration) and the return to that risk factor. (Loosely speaking, Q is the negative of the change in the overall level of interest rates during the performance period.) If the portfolio duration is longer than the benchmark's (0 is positive) and the return to duration Q is positive, then the product is positive and the manager has added value. Consider the following description of manager behavior:

-

-

Opj= Cpj+ Uj'

(7)

where Q. is the return to the duration factor, Cj is the skill coefficient for the manager, Qj is the (negative of the) interest rate change, and Uj is an error term. This model describes a situation in which the manager with at least some skill in calling interest rate changes will set the duration of the portfolio relative to (plus or minus) the benchmark, with partially correct anticipation of the next period's interest rate movement and with some error. Formula 7 resembles a regression equation; in fact, if a time series of such attributed returns is available, the skill coefficient can be estimated by regressing the duration differences between the portfolio and the benchmark on the subsequent periods' returns to duration-or on the subsequent

periods' interest rate changes. If the manager has skill, the skill coefficient c will be greater than zero and the associated t-statistic should be relatively large, perhaps larger than 2. If the manager has no skill for this risk category, then c will not be significantly different from zero. Figure 2 and Figure 3 illustrate manager duration bets in the skill and no-skill cases, respectively. In each figure, the vertical axis shows the difference in duration between the portfolio and the benchmark, so 1.5 years means the portfolio duration is longer than the benchmark duration by 1.5 years and -1.5 years means the portfolio is shorter than the benchmark by 1.5 years. The horizontal axis measures the actual return to duration, or the negative of the interest rate change. Figure 2. Manager Bets: Manager with Skill 1.5

c;; ....

'" c
c .9

1.0

+

0.5

~ ....

0 ;:l Cl .~ QJ -1.0 ~

-1.5 -0.8

+ -0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

Return (%)

Figure 3. Manager Bets: Manager with No Skill c;; ....

1.0

+ +

'" c-

0.5

.§

0


c

+

+

+ +

'...." 8 -0.5

+


:5

.!9 -1.0

+


~

-1.5 -0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

Return (%)

Source: Wilshire Associates.

duration difference and the return to duration. If the Manager Model, or something like it, accurately describes manager behavior, then the attributed return is the product of two random variables, and therefore, attributed returns may be skewed. Simulations can be designed to compare the Manager Model with the t-statistic approach. Simulations constructed so that the manager has skill indicate that, in more than 80 percent of the cases, the t-statistic associated with the skill coefficient in the Manager Model is larger than the t-statistic associated with the average value added in the t-statistic approach. Thus, the Manager Model seems to have a stronger ability to uncover manager skill when skill exists than does the simple t-statistic approach (at least in simulation).

Source: Wilshire Associates.

Figure 2 shows that a manager with skill tends to be able to anticipate subsequent interest rate movements, and this ability is stronger as the magnitude of the subsequent interest rate movement increases; the forecast error, however, indicates the presence also of substantial noise in the process. When the manager has no skill, as illustrated in Figure 3, the pattern of durations in the portfolio relative to subsequent interest rate movements is random. In either case, attributed returns are probably not normally distributed; Formula 6 showed that the attributed return is the product of the portfolio's

Conclusion Fixed-income attribution fits into the same general framework as attribution for other asset classes; only the specific descriptions of risk differ. In measuring return attributable to risk factors, the use of subindex weights may give a better view of the total fund, but specific risk factors from the risk model approach are needed to gain a sense of detailed attribution. For interpreting the results of attribution, the t-statistic is easy to use and explain, but simulations suggest that the Manager Model, with only a few additional assumptions needed, is potentially a more powerful test of manager skill and is less prone to sample error.

83

Appendix Rp Wph Wbh Xh

Wpi Wbi

Hi bpij bbij Qij

Definitions the buy-and-hold portfolio return in the base currency the portfolio weight in currency h the benchmark weight in currency h the hedge return to currency h (loosely, the currency return) == the portfolio weight in asset class i the benchmark weight in asset class i the hedged return to asset class i (loosely, the local currency return) == the portfolio exposure (e.g., weight) within asset class i to risk factor j the benchmark exposure to risk factor j the return to factor j in asset class i ==

Return decomposition Currency ~

Asset class

Li (Wpi - Wbi) Hi

Risk factor

Li Wpi Lj (bpij - bbij) Qij

Selection

R p - LhwphXh - LiWpiHi - LiWpi L

~

Total return

84

-

~

~

R p - LhwbhXh -

LiWbiHi

Question and Answer Session Robert C. Kuberek Question: Considering that most fixed-income securities are matrix priced, how reliable would fixed-income attribution be? Kuberek: The answer depends, first, on the premise that matrix pricing predominates; some providers claim that their indexes are largely trader priced. So, the data source being used may be a factor. Second, almost all of the numbers for the value-added calculations given here were actually derived from large market crosssections rather than from the returns of individual securities. Only the selection factor-and, to some extent, the execution factor-depends on individual security prices. Question: Why is fixed-income attribution more compli-

cated than equity attribution? Kuberek: I don't know that it is. But measuring the duration of many types of fixed-income securities--collateralized mortgage obligations, for exampleis difficult. So, duration's importance as an element of attribution may make fixedincome attribution appear to be difficult. Question: Can the formulas be used with asset classes other than fixed income? Kuberek: The framework presented here is very general and can be used for equities and balanced-fund management as well as fixed-income securities. Question: With high portfolio turnover, would the return attributed to execution grow large

enough to make the attribution meaningless? If so, how would you handle this problem? Kuberek: I have yet to see a portfolio for which one-month buy-and-hold attribution does not provide at least some useful information. Question: Have you seen any studies on the accuracy of mortgage models and/or attribution? Kuberek: A study we have carried out strongly suggests that a significant amount of the variance in mortgage returns across coupons is explained by unexpected changes in prepayment behavior. Therefore, an attribution model for mortgages should factor in such prepayment risk.

85

A Framework for Global Attribution Analysis Brian D. Singer, CFA SeniorAssetAllocation Analyst Brinson Partners, Inc.

Attribution analysis in any context should apply only to what is manageable and controllable in asset management. Effective global attribution analysis must incorporate global market, interest rate, and currency considerations.

Basing attribution analysis on a manager's decisions cost of hedging, which reflects the difference in shortis important because the analysis should reflect the term interest rates between the non-U.S. country and processes and parameters relevant to the manager. the United States. If the short-term non-US. interest For example, if a hypothetical Russian note has a rate is 7 percent and the US. interest rate is 3 percent, yield of roughly 500 percent, can US. investors actuthe cost of hedging is -4 percent and the return on the ally obtain a 500 percent yield on that note? No, they portfolio is -2 percent. The portfolio has a 100 percent cannot; the ruble exposure can be hedged, dependallocation to the non-U.S. asset, which has a local ing on the availability of a forward market, but it currency return of zero. The exchange rate return on cannot be eliminated. Elimination of the ruble expothe 50 percent that was unhedged is also a percent. sure is beyond investors' or managers' control. AttriThe remaining 50 percent cost -4 percent to hedge, bution can apply only to what is manageable and so the return to the active portfolio is 50 percent of the hedge cost, or -2 percent. controllable by the manager. This presentation will This portfolio underperformed the benchmark. provide a framework for global attribution analyThe portfolio had a return of -2 percent, and the sis-a conceptual basis for taking global currency and market considerations into account. It will adbenchmark had a return of a percent. What is the dress both global portfolio analysis and attribution source of the underperformance? The only two decisions made were a market decision and a currency analysis. decision. Which decision caused the underperforAn exercise in attribution analysis will set the mance? Before answering this question, consider a stage for the framework. Consider two assets-a U.S. global performance attribution model. asset and a non-U.S. asset-in a benchmark portfolio. The US. asset provides a return of a percent. The non-U.S. asset provides a local currency return of a percent and an exchange rate return of a percent; Global Attribution thus, the non-US. asset also provides an unhedged The framework for global attribution analysis rests US. dollar return of a percent. If the benchmark is on several essential aspects of investment theory, the equally weighted and unhedged, what is its return? primary truism being that assets are distinguished The U.S. asset return is a percent, and the non-U.S. by their performance relative to cash. In other words, asset return is a percent, so weighted 50/50, the the local currency risk premium is the measure of benchmark return is a percent. market performance. Investors are accustomed to Now, consider an active portfolio with two pothinking in such terms in domestic markets, but in tential decisions: a market decision and a currency looking outside the United States-or wherever their decision. Assume that the portfolio is 100 percent currency base is-they tend to drop that convention invested in the non-U.S. asset, but half of the foreign and look at local currency returns. In a risk-premium currency exposure is hedged back to the dollar. In context, if the cash return is not considered part of other words, the non-U.s. asset is overweighted by the market return, then it must be considered part of 50 percent but the currency allocation is neutral. the currency performance; that is, currency performWhat is the return on that portfolio? ance must be defined as changes in the exchange rate To answer this question requires knowing the plus the local cash return.

86

Unhedged Returns A hypothetical example may help explain these concepts. The investor's first investment objective in this example is to find the best country market return. Table 1 shows a hypothetical global market with local currency market and exchange rate returns in four markets: Germany, the United Kingdom, Japan, and the United States. In this example, the investor is dollar based, and returns are assumed to be continuously compounded.

Table 1. A Global Market with Eurocurrency Rate

Country

Local Currency Exchange Rate 3-Month EuroMarket Return, Return, currency Rate, R; E$,; C;

Germany United Kingdom Japan United States

7,0% 10,5 9.5 8,0

1.0% -3,0 -1.0 0,0

5,0% 11.0 8.5 7.5

man return back to the U.S, dollar, giving a local currency return plus a forward return, For this analysis, Table 1 also outlines the three-month Eurocurrency rates in the four local markets. The currency-hedged return equals the local currency market return plus the difference in cash returns-in the German market case, a 7 percent return to Germany and the difference in cash returns between the mark and the dollar. By hedging from the German mark to the dollar, the manager gives up the 5 percent cash return in Germany but gets the 7.5 percent return in the United States for a 2.5 percent cash return increment and a total return of 9.5 percent; that is, Dollar-hedged market return = Ri + F$,i

= Ri+(C$-Ci), so Dollar-hedged German return = 7.00 + (7.50 - 5.(0) = 9.50.

Source: Brian D. Singer,

A first glance at Table 1 might suggest that the United Kingdom is the best market because it has the highest local currency market return. Exchange rate returns, however, range from 1 percent for the German mark to -3 percent for sterling, with the yen returning -1 percent (and the dollar obviously providing no exchange rate return to a dollar-based investor). Thus, sterling might appear to be the worst currency to be exposed to and the German mark is the best. Therefore, an investor's initial reaction might be to invest in the United Kingdom and hedge currency exposure into marks. Computing an unhedged market return for this example is fairly simple. For example, Germany's 7 percent local currency market return (in German marks, OM) plus the 1 percent exchange rate return provides an 8 percent unhedged market return; that is, 1

Unhedged market return = Ri + E$,i, so Unhedged German return = 7.00 + 1.00 = 8.00.

The Effect of Hedging Suppose the manager decides to hedge the Ger1 The variables used in this and the following section are defined as follows: Ri Local currency market return E$,i Exchange rate return F$,i Forward premium!discount between the US, dollar and currency i Fj,i Forward premium!discount between currency j and currency i C$ u.s. cash return Ci Country i cash return

A third strategy is to cross-hedge; in this example, one would cross-hedge German mark exposure into yen. The analysis is exactly the same as the analyses for unhedged and hedged returns: The local currency return to Germany is 7 percent; by cross-hedging into yen, the investor loses the 5 percent German cash return and gains the 8.5 percent Japanese cash return. Hedging into yen causes exposure to the yen, so the -1 percent exchange rate return of the yen must be included. In this crosshedge, the investor would end up with a return of 9.5 percent:

Cross-hedged market return

=

Ri + Fj,i + E$,j

Ri + (Cj - Ci) + E$,j, so German mark cross-hedged into yen

= =

7.00 + (8.50 - 5.00) + (-1.00) 9.50. Table 2 shows the unhedged, hedged, and crosshedged returns just illustrated for all combinations of these four markets in the four currencies; these 16 alternatives reflect the full set of securities or portfolios this investor can hold. Note, first, that regardless of currency, Germany provides the best market return and the United Kingdom the worst. This result is somewhat perplexing, because Germany has the lowest local currency market return and the United Kingdom the highest. Second, regardless of market strategy, hedging into sterling provides the highest return currency exposure, even though sterling de87

Table 2. Dollar Returns from all Strategy Alternatives Hedged into: Country Germany United Kingdom Japan United States

Dollars

Marks

Sterling

Yen

9.5% 7.0 8.5 8.0

8.0% 5.5 7.0 6.5

10.0% 7.5 9.0 8.5

9.5% 7.0 8.5 8.0

Source: Brian D. Singer.

preciated by 3 percent and the German mark appreciated by 1 percent. Therefore, investing in the German market and hedging into sterling would provide the best possible return among all 16 alternatives. Conversely, this investor's first reactioninvesting in the United Kingdom and hedging into marks-would be the worst alternative. The conclusion is that traditional analysis, in which the market performance is gauged by the local currency market return and the currency performance by the exchange rate return, and in which simply adding the market and currency returns gives the dollar return to the asset, does not adequately explain the performance of a portfolio in which the manager has applied currency hedging. That is, the traditional analysis does not take into account aspects that managers of a global portfolio can control. So, the question is: What is the appropriate framework for portfolios that include currency hedging?

A Unified Approach A framework focused on variables that investors can control would define the market return to be the local risk premium, not the local currency market return, and would define the currency return to be the cash return expressed (for U.s. investors) in U.s. dollars. The formulas delineating these variables would be: Unhedged Hedged Cross-hedged

(Ri - Ci) + (C + E$,i) (Ri-Ci) + (C$ + E$,$) (Ri - Ci) + (Cj + E$,j)

appreciation in the German mark, which together provide a dollar return to German mark cash of 6 percent. The manager can compute the risk premium and cash return in this manner for each individual market. In deciding which markets and currencies to invest in, investors should pick the market with the highest risk premium and the currency with the highest cash return in dollar terms. (Investors should consider the covariances of risk premiums and cash dollar returns in order to maximize riskadjusted returns.) On that basis, in this example, one would invest in Germany and hedge into sterling; that is, invest in the market with the lowest expected local currency market return and hedge into the currency that is expected to depreciate the most. This conclusion may seem counterintuitive, but the framework incorporates all of the variables that affect hedging, including short-term interest rates, which are implicitly involved in any hedge decision. A manager should break down the decision according to risk premium and cash dollar return, not local currency market and exchange rate returns. Table 3. Ranking Market and Currency Alternatives Country

Risk Premium,

Ri-C;

Cash Return in Dollars, C; + E$.i

Germany United Kingdom Japan United States

2.0% -0.5 1.0 0.5

6.0% 8.0 7.5 7.5

Source: Brian D. Singer.

These formulas define unhedged, hedged, or cross-hedged returns as the total of the risk pre- Performance Attribution mium of the invested market plus the cash returns The unified framework can now be used to focus on in U.S. dollars resulting from the hedge. The formuthe specific needs of performance attribution for las simply rearrange the same variables used preglobal portfolios. Security selection is obviously a viously. key element in any performance attribution apTherefore, instead of looking at all 16 combinaproach, but issues relating to market allocation and tions of market or currency or at local currency marcurrency allocation are particularly important in ket returns and exchange rate returns in the global global performance attribution. market, the manager can consider the market in The dollar return to a global or non-U.S. portfoterms of variables a manager can control. As Table 3 lio is simply the sum of the market-weighted risk shows, the risk premium, Ri - C, is 2 percent. The cash premiums to which the portfolio is exposed and the return for Germany in dollar terms, Ci + E$,i, is the 5 percent German mark cash return plus the 1 percent currency-weighted cash returns in dollar terms. 88

That is, Portfolio return (in dollars)

Market-weighted Currency-weighted risk premium + cash return (in dollars).

Currency exposure is achieved in three ways: holding a risky asset, such as stocks or bonds; holding cash in the currency; or hedging into the currency. How currency exposure is achieved does not matter; attribution is not to the hedge decision, but to the currency exposure relative to the benchmark.

The Market Allocation Decision

(

Active currency _ Passive currency ) weight weight

x (

J

Currency cash returns _ Index cash returns (in dollars) (in dollars) .

The conventional analysis focuses on exchange rate returns, ignoring any potential impact of the interest rate differentials that determine forward currency hedging returns, rather than on cash dollar returns. Again, how the currency exposure is achieved is not a concern; the concern is how the ultimate currency exposure compares with that of the index.

The market allocation decision involves overweighting the markets that will add to portfolio performance relative to the benchmark. Outperforming Conclusion the benchmark requires having an active weight in Analysis of markets and currencies must be framed excess of the passive weight in countries where the in terms of variables that managers can control. A US. market risk premium is in excess of the index risk investor cannot avoid the currency exposure in the premium. For example, if Germany has the highest Russian note, but if a forward market exists, the inrisk premium in the global market-higher than that vestor can hedge the currency exposure. Of course, of the index-the manager should overweight Gerhedging ruble exposure would give up an astronomimany. When the active market weight exceeds the cally high short-term interest rate in Russia to get a passive market weight and the market risk premium relatively low cash return in the United States. Thus, exceeds the index risk premium, the investment will even though the Russian bond's yield is very high, its expected risk premium would be quite low; in fact, contribute positively to the performance of the portone would expect the risk premium to be much closer folio; that is, the contribution of a market allocation to a similar-risk asset in the United States. decision to total portfolio performance is Exchange rates alone do not define currency risks and returns. The price of currency conversionActive market _ Passive market) relative short-term interest rates-is central not only weight weight ( to the currency decision but also to the market decision. Risk premiums, not local currency market rex (p~SSiVe m~ket _ Index. risk). turns, define market risks and returns; cash returns nskprerruum prerruum in U.S. dollars, not exchange rate returns, define The conventional analysis portrays essentially currency risks and returns. Currency strategy, therethe same computation, but the "market risk prefore, is probably more appropriately viewed as global cash management because that is what a curmium" would be replaced by "local currency market rency manager using forward contracts can actually return" and the "index risk premium" would be the control. "index local currency market return." One question remains unanswered: In the portfolio example given earlier, was the portfolio's underperformance caused by a market decision or a The Currency Allocation Decision currency decision? Think of the problem in the most In currency allocation, a manager should overbasic terms: The portfolio was 100 percent allocated weight currencies that will contribute positively to to the non-U.S. asset, with 50 percent of that amount the performance of the portfolio-that is, those curhedged; the currency exposure was neutral to the rencies that have high dollar returns relative to the benchmark. The benchmark had 50 percent dollar benchmark. For example, Table 2 showed sterling exposure and 50 percent non-U.S.-currency expowith a cash return of 11 percent, even though it sure. Having exactly the same exposure as the benchdepreciated by 3 percent. Because the cash return in mark does not add value; thus, the currency dollars to sterling is so high (8 percent), hedging into allocation did not add or detract from portfolio persterling would be quite beneficial. The contribution formance. The investor is 100 percent exposed to the of a currency allocation decision to total portfolio non-US. market relative to benchmark exposure that performance is is 50/50 in U.S. and non-U.S markets. The risk pre-

89

mium of the non-U.s. market is -7 percent, and the risk premium of the U.s. market is -3 percent. In that example, the portfolio was allocated to the non-U.s.

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market and, therefore, to the wrong market. The underperformance resulted from a market decision, not a currency decision.

Question and Answer Session Gordon M. Bagot Brian D. Singer, CFA Question: How would attribution analysis address the issue of allocation of cash to each asset class? Bagot: In a way, it is not for the measurer to say how the cash should be allocated. The allocation depends on what the manager is working toward. For example, for an equity manager to be holding cash, the manager must have decided to be in cash rather than the assigned asset class, equities. So, the cash investment should be judged according to the value it added relative to taking an equity position. Question: Please comment on the fact that your attribution model appears to advocate speculation in currencies.

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Singer: The model is opinionless on the advisability of speculation in currencies. From a policy perspective, trying to decide whether to split global portfolio decisions between a market allocator and an overlay manager requires some important considerations. If you are splitting the decision, you need to understand that the relevant variables are not local currency market returns and exchange rate returns; rather, they are risk premiums and cash dollar returns. You must have an appropriate framework for global analysis. The framework presented does not advocate speculation or active management; it is a means for determining strategy ex ante and for attribution analysis of the results

ex post.

Question: Does using arithmetic models for long-term attribution analysis present a problem? Bagot: The problem with longterm attribution is that you are trying to analyze chain-linked returns. If you determine the geometric average over five years and then do the attribution annually and try to compute five years' worth of individual attribution numbers, you end up with a set of numbers that does not add to the five-year total return. So, in any long-term analysis, you are actually forced to try to move from additive to multiplicative contributions to return. As a consequence, in practical attribution analysis, a certain amount of fudging goes on to make sure the numbers add up in what seems to be an intuitively correct way.

Problems and Issues in Global Attribution Analysis Gordon M. Bagot Director of Research and Consultancy The WM Company

Attribution analysis that explains performance based on a manager's decisions is valuable. Attribution analysis that is not based on a manager's decisions and does not recognize global investing realities and constraints is valueless.

Current methods of analyzing portfolio performance, including attribution analysis, are easy to criticize. A brief overview of the development of performance measurement should help put into perspective the problems and troublesome issues in current attribution analysis, particularly for a globally diversified portfolio.

Development of Performance Measurement

faster and better analysis. Attribution analysis had to take into account not only multiple asset classes in a single currency but also multiple currencies in global portfolios. Today's equity portfolios in a single currency are analyzed according to industrial or economic sectors, individual stock contributions, impact of dealing (usually the deal price versus the end-ofday price), and impact of timing of transactions within a defined period (e.g., one month or one quarter). Multiple asset classes and currencies add considerations of asset and currency mix and selection to the process. All these developments are reflected in the emergence and growth of the multifactor models that are now available for performance analysis. Attribution analysis now must also address a broader range of securities than before. Mixed portfolios of at least two asset types with contributions from the initial asset-mix position and changes to that asset mix are now quantified. The introduction of nondomestic securities has also widened the analysis. In addition to greater detail of analysis in the standard asset types-equities, bonds, and cash-the types of assets to be analyzed now include real estate, forward currency contracts, derivative securities, commodities, and works of art. The asset type that causes-and will continue to cause-the greatest attribution problem is derivatives. Real estate and works of art also pose particular problems for attribution analysis.

In the early days of its application, performance measurement concentrated on calculating returns. A principal concern was how to move from what was then a money-weighted, or internal, rate of return for a fund to a statistical return that could be used to compare fund performance with that of other funds. The standards for calculating total weighted return (TWR) were set during the late 1960s and early 1970s in the United States and the United Kingdom. Once those methods came to be accepted as robust, further computation of numbers depended on the quality and composition of the data that were available and on the timeliness of data. The data used in calculating TWR tended to be monthly or quarterly valuations and cash flows, which were subtotaled by principal asset class-domestic equities, domestic bonds, real estate, cash, and perhaps a miscellaneous asset category. Time scales were not an issue, and the pace of performance analysis was much slower than today. Analysts spent the first six months of the year collecting and checking data and the next three months computing Problems in Attribution Analysis and checking returns of individual funds and the fund universe. Final publication of performance took In essence, what is required for good attribution place in the fourth quarter. analysis in performance measurement is good data, Demand arose, however, for the development of but problems exist. Three types of problems arise in 91

attribution analysis: problems that underlie all such analysis, problems that involve the multiple assets and multiple markets (including special problems of derivative securities) that are particular to analyzing global investments, and problems that are endemic to specific analytical techniques.

Basic Problems Problems that are characteristic of all data-dependent analyses involve inadequate and unfocused data. When interest in performance attribution began, the data were limited and their quality was suspect. Now, for any detailed analysis, voluminous data can be provided at the transaction level, but coping with this amount of detail requires vast computing power and attention to relevance. The volume and accuracy of the data are very important if correct attribution is to follow, but using the data to compute numbers that are not relevant and do not reflect the way a portfolio is managed is a great danger.

Problems with Multiple Assets and Markets The complex modern portfolio, particularly global portfolios and those containing derivative securities and infrequently priced assets, poses difficulties and dangers in attribution analysis. Global portfolios. Practical issues arise when attribution must encompass international markets. Data can become available very slowly in some markets, and data that are even two days late can create performance measurement problems, particularly in marking to market an internationally diversified portfolio containing derivatives. Currency differences were formerly a problem, but the problem has been substantially resolved through the creation by The WM Company and Reuters of a data base of London closing spot rates, which all the major index compilers are now using. If the custodian uses New York exchange rates rather than the new London rates, however, differences will exist in the data. Attribution analysis that uses an index may throw out spurious---even false--eonclusions. Many of today's multinational companies have listings on multiple exchanges, which raises a question about the appropriate listed price to use in analyzing portfolio performance against an index. What price is appropriate for the portfolio versus the peer group, and more to the point, what price has the peer group been using? Offshore registration also introduces complications. When measuring companies with global businesses that are registered in Bermuda or Luxembourg, for example, in what country or geographical area should the analyst group them? For instance, consider a company like the Jardine Group 92

of Companies, which is registered in Bermuda, is now listed in Singapore (after being listed in Hong Kong), and carries out its business principally (but not entirely) throughout the Far East. In which country should that company be classified? In some markets, the amount of stock available to nondomestic investors is limited, which has created premiums on the foreign-held component in a few markets. Comparison with a peer group from that domestic market will thus cause problems, particularly when trades in the domestic stock may be regular but trades in the foreign-traded stock that carries the premium may be sporadic. In this situation, meaningful attribution analysis must cope with what are essentially dual classes of the same stock. Many European stocks have several different lines of equity. For performance measurement to be meaningful, plan sponsors, their custodians, and their money managers must be using the same stock. For example, a manager once held a line of stock of an Italian company and the custodian was, unknowingly, holding a different line. The price movements of the stocks were very close, however, so no one suspected the problem-until the two prices started deviating when prospects of a corporate capital restructuring arose. In this case, performance analysis and attribution alerted analysts to the problem because the analysis produced such unexpected results. The manager had bought one stock but the custodian had settled the trade with another stock. Attribution analysis should be simple, but the addition of foreign assets destroys that simplicity. To illustrate the practical problems, Table 1 presents an analysis of holdings in principal countries of The WM Company universe of ERISA funds. The universe consisted of more than 30 portfolios at the time of the analysis (December 31, 1993). Together, The WM Company portfolios had 1,710 different stocks spread among 41 countries; 910 of the stocks were in the Financial Times Actuaries World (FTAW) EuroPacific Index. That index comprised 1,467 stocks, so these 30 funds did not own approximately 500 of the index stocks. In only three countries-Japan, the United Kingdom, and France-did the average number of holdings by the 30 funds exceed 10 stocks. The question is whether the index is an appropriate benchmark for this portfolio for purposes of attribution analysis. For example, in the case of Japanese holdings, only 7 percent of the index companies were represented in the portfolios. This small a percentage cannot represent the Japanese market. Moreover, the stocks actually held were not the largest index constituents. So, market attribution based on comparing the portfolios with the index may not actually make sense. Attributions to country selection based on com-

Table 1. Analysis of The WM Company Universe of ERISA Funds: Holdings in Principal Countries, December 31, 1993

Country Japan United Kingdom France Germany Hong Kong Switzerland Netherlands All other countries (34) Total

Number of Different Stocks in WM Universe

Number of Such Stocks in FTAW Euro-Pacific Index

379 283 115 91 63 52 55 672 1,710

242 171 74 45 41 29 23 285 910

Number of Constituents ofFTAW Euro-Pacific Index 469 215 99 59 55 49 26 495 1,467

Average Number of Holdings 34 23 11 8 7 5 5 49 142

Source: The WM Company.

paring the portfolios with the index also may not make sense. The "all other countries" category in Table 1 shows 49 holdings, on average, in 34 countries-fewer than 1.5 stocks per country. The impact on the portfolios of holding nonindex constituents-that is, the overall value added or lost relative to the Euro-Pacific benchmark-should be identified and calculated. In global portfolios, not only is correctly allocating cash and derivatives to each asset class important, but the analyst must also consider what to do about foreign currency exposure. For example, portfolios have been known to hedge their exposures to a currency because of their real estate holdings. How are these holdings to be allocated? Uncovered currency trading is also an issue for global portfolios. Imagine a U.S.-dollar-denominated portfolio that holds neither sterling nor French franc securities and shorts sterling for French francs. Does attribution analysis place the gain or loss in sterling, French francs, or U.S. dollars? Special problems of derivatives. Attribution analysis must also cope with all types of derivatives and overlays, including the notional cash backing those derivatives and the notional income deriving from the notional, but invested, cash. The challenge when derivatives must be included in attribution analysis has two aspects: (1) identifying the specific additional return or loss resulting from the derivatives portfolio (which is particularly a problem for funds using a separate overlay manager) and (2) identifying the exposure changes because of the derivatives in the portfolio and the gains and losses that will ensue if the factors change for the better or for the worse. Table 2 presents an example of an attribution problem arising from the presence of derivatives-in this case, sale of a call option. The table shows a total portfolio valued at 1,000 (the values can refer to any

currency) split between equities of 650 and cash of 350. Halfway through the measurement period, an option is written for which the portfolio receives 80 cash. The proper way to analyze this transaction is to show a transfer of equities from the "free" portfolio to the"options" portfolio at the then market price of 320. So, the options portfolio picks up half the equity line and uses it as the underlying security for the outstanding option. At the end of the period, the equity is worth 315-in both the options portfolio and, obviously, the free portfolio. The free equities portfolio has produced a return of -3.1 percent. The return on the options portfolio is 20.8 percent, and the return on total equities is 1.6 percent. Writing this particular option was a good decision: The total portfolio has a return of 1 percent, but if that option had not been in place, the return would have been -2 percent (final equity market value of 630 versus the initial equity market value of 650 would have produced a loss of 20 to the total portfolio). From an attribution point of view, however, a transfer (notional, not an accounting transaction) is needed from the free portfolio to the options portfolio. This approach allows correct returns to be calculated, followed by attribution based upon the money actually and notionally used, but most accounting systems cannot handle such transfers. Index futures pose another attribution problem. An evaluation of the performance of index futures should consider not only the variation margin of profit or loss that has been generated but also how much capital is at risk. What is the full net economic exposure? In addition, nondomestic futures incur currency exposure when variation margin is paid or received. How can the attribution method cope with this situation? Asset weights can differ; depending on the exposures being calculated, they can differ greatly. When tactical asset allocation is used or currency overlays

93

Table 2. Example of a Derivatives Problem: Selling a Call

Portfolio Total portfolio Total equities Equities (free) Options portfolio Equities Options Cash

Initial Market Value 1,000 650 650

350

Net Investment 0 -80 -320 240 320 -80 +80

Final Market Value 1,010 580 315 265 315 -50 430

Capital Gain/Loss 10 10 -15 25 -5 30

Mean Market Value 1,000 610 490 120 160 -40 390

Total Percent Return 1.0% 1.6 -3.1 20.8 -3.1 -75.0a 0.0

aNot applicable.

Source: The WM Company.

are in place, only currency and country contributions should be nonzero, but the calculation generates cross-terms that can appear to reflect selection contribution even though no selection is possible. Valuation estimations. In the cases of real estate and works of art, the immediate problem is valuation on a regular and appropriate basis. If the possibility of valuation estimation errors is forgotten, attribution and other analysis will lead to unreal, and thus unacceptable, solutions.

Analytical Problems Attribution analysis must be undertaken in a way that reflects how a portfolio is being managed. The fact that a manager holds only large-capitalization stocks, for instance, and does not hedge but has another party handle the foreign exchange deals must be reflected in the attribution. If the analysis does not reflect the reality of the portfolio, inappropriate attribution follows. Using a median of a peer group for comparison is foolhardy. A portfolio should be analyzed against an index or a weighted average of constituents. To illustrate, in The WM Company's U.K. universe of pension funds (about 1,300 in 1993),50 funds were at the median return level. The asset-mix range of those 50 funds was almost as wide as the other 99 percent of funds in the universe. None of the 50 funds had median returns in any asset-class categories, so any attribution analysis using the median as a benchmark would have been a meaningless exercise. As in the case of an index, an average of the peer group that is weighted by market capitalization should be used. Major problems of comparison arise when longterm performance numbers are generated. Not all attributable aspects of performance contribute in every time period, so the problem is how to link short-term numbers. Other practical aspects of attribution analysis should be considered. As noted previously, prices in the index or benchmark should be the same as prices in the portfolio. Classification of securities by eco94

nomic or industrial sectors should be the same for the benchmark as in the portfolio. Similar comments apply to currency and country. For example, at one time, managers in the United Kingdom were using Extel London 5 p.m. foreign exchange rates, but the MSCI indexes had a different set of rates for ERISA funds. During one quarter, the Extel foreign exchange rates indicated that one manager had added 50 basis points (bps) to performance from currency, but if the MSCI rates were used, they indicated that the manager had lost 100 bps. At the moment, standardized definitions of growth and yield companies and oflarge- and smallcap companies are lacking, which can create flaws in defining peer groups for attribution analysis. This problem is not specific to global analysis; it affects regional and country analysis also because consultants and managers have slightly different definitions of size and style. As more fundamental data become available, standardized definitions should be used in attribution analysis. Finally, the use of total weighted return produces problems. TWR calculations are a compromise, an attempt to produce an accurate and meaningful return figure in a cost-effective way when all the data may not be available. Because large cash flows tend to distort measurement methods, analysts moved to using day-dated (daily) cash flows in TWR calculations, but daily TWR calculation is not the solution. For example, consider a portfolio of $1 that doubles to $2 in the first period; its performance for this period is 100 percent. Now, suppose the portfolio receives $1 million in new money but its performance during the second period is flat. Performance of this portfolio for the two periods as measured by the TWR formula, whether calculated daily or not, would still be 100 percent even though most people would argue that the return is, by and large, zero.

Analyzing the Team in Attribution Analysis

ranged from +10 to -15 bps. In most cases, that variation was attributable to the individual who settled the foreign exchange, who was not necessarily the investment manager. Before attribution analysis is even attempted, therefore, those doing the measuring must identify who is responsible for what part of the investment process.

Attribution analysis needs to take into account that all the parties involved with a portfolio or fundsponsor, consultant, manager, trader, and custodian-have contributed to its performance. The sponsor is in overall control and is ultimately responsible for the fund. The consultant gives advice or provides information that the plan sponsor uses to _ make decisions. Conclusion The manager is the key contributor to the portAttribution analysis is valuable if it truly explains folio's performance. He or she delegates trade flow performance. For that purpose, the analysis must be to the trader, who then also contributes to portfolio able to identify the manager's decisions: What did results. Poor trades detract from performance, and good trades add value, but the manager receives the the manager do and why? Attribution analysis that is not based on the manager's brief-what the mancredit or the blame. ager was supposed to do, the constraints placed on The custodian is responsible for foreign exchange settlements. Poor foreign exchange trades the manager's actions, and the discretion allowed the directly detract from portfolio performance, but manager-is valueless. Viewed in this manner, attriagain, the manager usually gets the blame. bution analysis may be best carried out by managers The WM Company studies of foreign exchange themselves; ultimately, only the people who bought or sold the shares know why they did so. Nonmanperformance have revealed some considerable losses on individual accounts from foreign exchange tradagers will be doing the measuring, however, and managers thus need to label what transactions they ing; for example, 10 bps a quarter is not unusual, and in one case, more than 100 bps were lost in one made and why they made them for eventual proper assessment. quarter because of poor foreign exchange dealing. In 1993, The WM Company developed a detailed analyIn addition, attribution analysis that does not recognize the constraints and the practical aspects of sis of 53 portfolios dealing through 26 different custodial banks, inc! uding internal and external global investing is also valueless. The greater the custodians. The weighted-average foreign exchange asset types in the portfolio and the more numerous the markets, the greater the prospect of invalid contribution of all these trades was zero, but the overall variation from the 5th to the 95th percentile analysis.

95

Question and Answer Session Gordon M. Bagot Brian D. Singer, CFA Question: How would attribution analysis address the issue of allocation of cash to each asset class? Bagot: In a way, it is not for the measurer to say how the cash should be allocated. The allocation depends on what the manager is working toward. For example, for an equity manager to be holding cash, the manager must have decided to be in cash rather than the assigned asset class, equities. So, the cash investment should be judged according to the value it added relative to taking an equity position. Question: Please comment on the fact that your attribution model appears to advocate speculation in currencies.

96

Singer: The model is opinionless on the advisability of speculation in currencies. From a policy perspective, trying to decide whether to split global portfolio decisions between a market allocator and an overlay manager requires some important considerations. If you are splitting the decision, you need to understand that the relevant variables are not local currency market returns and exchange rate returns; rather, they are risk premiums and cash dollar returns. You must have an appropriate framework for global analysis. The framework presented does not advocate speculation or active management; it is a means for determining strategy ex ante and for attribution analysis of the results

ex post.

Question: Does using arithmetic models for long-term attribution analysis present a problem? Bagot: The problem with longterm attribution is that you are trying to analyze chain-linked returns. If you determine the geometric average over five years and then do the attribution annually and try to compute five years' worth of individual attribution numbers, you end up with a set of numbers that does not add to the five-year total return. So, in any long-term analysis, you are actually forced to try to move from additive to multiplicative contributions to return. As a consequence, in practical attribution analysis, a certain amount of fudging goes on to make sure the numbers add up in what seems to be an intuitively correct way.

Evaluating Nontraditional Strategies Jack L. Hansen, CFA Director ofEquity Investments The Clifton Group

Performance measurement of nontraditional programs poses unique challenges in both application and interpretation. Separation of the measurement process into three levels-investment decision, implementation vehicle, and manager actions-is helpful for sponsors, consultants, and managers.

Performance measurement is important for all investment programs but may be especially important for nontraditional-in particular, derivative securities-programs. Although such programs are relatively new, they are becoming increasingly common (a trend that will continue despite recent, well-publicized instances of their misuse), and the programs present some unique problems in performance measurement. This presentation first discusses the importance of measuring the performance of derivatives programs and examines factors that make the measurement process difficult. The discussion then establishes a framework for derivatives program measurement and provides examples of the framework's application. The analysis reflects primarily the viewpoint of a portfolio manager involved not only in management of a portfolio but also in helping clients formulate appropriate benchmarks for evaluation purposes.

Performance Measurement of Derivatives Programs Derivatives programs are generally stand-alone programs, usually in an overlay context, that are evaluated against their own risk and return objectives. A derivatives program that composes a portion of an asset management program in which a manager might temporarily gain exposure through a futures position until actual securities can be purchased is not-the type of program discussed in this presentation; such a derivatives position is more likely to be viewed as a tool for a traditional manager than as a separate program. In general, examples of separate derivatives programs are overlays (such as option overwriting), hedging, asset allocation, and currency hedging.

Performance measurement of derivatives programs is important because it offers potential solutions to a variety of client and manager problems. The performance measurement is challenging because of the newness and complexity of the securities involved.

Importance of Derivatives Performance Measurement Performance measurement has several important and useful roles to play in a derivative securities program. First, performance measurement of derivatives is helpful in educating users. Working through the benchmarking process helps users learn about reasonable expectations and measurements. The benchmarks used in performance measurement also facilitate investment comparisons. Benchmarks that can be used for multiple comparisons are useful to fund sponsors that, because of time constraints, can work with only a limited number of programs as they evaluate the appropriateness of alternative investment choices. A related benefit lies in the comfort factor that benchmarks for derivatives programs provide to fund sponsors. This factor is especially important when returns are negative, which occurs in certain periods for any active program. If the benchmark for the program has produced negative returns, the sponsor is forewarned that a negative return is likely from the derivatives manager. Derivatives program benchmarks allow objective evaluation of manager ability. Using a previously agreed upon benchmark in a quantitative comparison is preferable for the manager and the client to subjective analysis and conclusions drawn from undocumented expectations. With benchmarks, differing and/ or unrealistic expectations among interested parties are deemphasized. Because

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a performance measurement system for derivatives programs must be created, instituting the system allows managers to be involved in the process (which is not true when the customary benchmarks are used for the measurement of traditional assets). Preparing the benchmark early in a program's life allows a manager to be proactive. Performance measurement of derivatives programs can satisfy an area that has traditionally had a consultancy gap. Without performance benchmarks for derivatives programs, consultants are less likely to introduce programs incorporating the use of nontraditional securities. In the same manner, fund sponsors are justifiably hesitant to adopt a program for which no understandable or objective measurement is possible. Because of the importance of measuring derivatives' performance, some consultants have recently begun to specialize in derivatives, and some traditional consultants are committing resources to this area. Moreover, performance measurement of a derivatives program helps solidify the relationships and understanding of objectives for the program among the client, the client's consultant, and the manager. By using benchmarks, the client and the client's consultant can base investment decisions on facts that emerge from the process of creating the benchmark rather than on conclusions drawn from undocumented expectations. Derivatives performance measurement is also important in satisfying the AIMR Performance Presentation Standards. The standards strongly recommend the use of performance benchmarks and the discussion of those benchmarks in advance with the client. The standards state that a benchmark should parallel the risk and return objectives of a client account. Therefore, discussion in advance with clients to determine what the measurement will include is particularly important.

Challenges in Derivatives Performance Measurement The first challenge in creating a performance measurement process for a derivatives program is deciding to what extent clients, consultants, and/or managers should be involved in creation of the benchmark and the measurement system. Playing a role in the creation and ongoing process may be particularly important for clients, for whom the world of derivatives is often new territory. Creating benchmarks enables clients to determine what can be expected from a derivatives program. In particular, they can assess whether their risk and return expectations are realistic. Moreover, they can participate in continually monitoring the program to gain an understanding of the current results and to formu-

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late pertinent questions if the decisions and/ or results differ from their expectations. Finally, clients can perform periodic evaluations of the process. Consultants need to participate in the performance measurement of derivatives programs because one of their jobs is to assist clients in evaluating investment alternatives to achieve funds' specific risk and return objectives. Consultants can also play an important role by raising issues that fund sponsors are likely to overlook and that managers may deemphasize. Ultimately, managers must take the lead in benchmark creation for derivatives programs because, among the parties involved, they are closest to the investment process. Managers are more familiar than the other parties with derivative securities and are thus usually better qualified to suggest effective benchmark characteristics. They should not, however, simply create a benchmark and deliver it to clients; instead, managers should work with clients and their consultants to formulate a reasonable benchmark. Derivatives performance measurement presents several additional challenges. The universe of derivative securities is constantly expanding in terms of contract availability and types. These developments create new, and often unfamiliar, investment alternatives for fund sponsors. Although derivatives use has grown rapidly, knowledge about and experience in derivatives management generally-and in derivatives performance measurement specifically-is developing only gradually. Derivatives programs introduce challenges both in the measurement of risk, which is often nonlinear, and in the measurement of return, which is usually incremental to the overlayed portfolio returns. Measuring risk is particularly difficult in the case of options that exhibit nonlinear payoffs. How can this risk be measured in a traditional performance framework? How should the utility associated with different investors be assessed? The existence of multiple sources of return in derivatives programs, as in traditional programs, makes the benchmarking process difficult and complicated. An additional problem arises because derivatives programs tend to be trade intensive. Obtaining good information on trade prices and market liquidity is difficult. Unlike stocks, which may trade in their same forms for a long time, derivative securities have limited lives. As time passes, the benchmark for a derivatives program may need to adjust regularly to passive positions. The need to trade makes price information such as bid-ask spreads all the more important. Moreover, because of frequent trading activity, commission costs are a consideration. Defining a "normal" portfolio for a derivatives

program is quite challenging. The large universe of derivatives, each with a unique return path related to time, makes formulating a normal portfolio of derivative securities difficult. Program objectives also vary through time and among managers, which for the most part precludes a peer group for many overlay programs. Finally, maintenance of a customized or normal portfolio as the benchmark involves potentially substantial costs, which must be weighed against the benefits of the customizing.

A Framework for Derivatives Program Measurement Measurement of derivatives programs can be approached through a simple framework composed of measuring the investment decision, the implementation vehicle, and the value added by the manager. The investment decision relates to the reason for undertaking a derivatives program, including expectations for its impact on risk and return. The implementation vehicle, which addresses how the expectations will be realized, is the method chosen to achieve the desired result. Once the investment decision has been made and implementation vehicle chosen, a manager must be selected to deliver the desired program results. Table 1 compares these three components for a traditional equity investment program and a nontraditional hedge program. The traditional program, in this example, involves an investment decision to commit money to U.s. equities, presumably in light of a bullish assessment of the performance potential of u.s. equities. The implementation vehicle may be a growth style, a value style, a corel active approach, an indexed structure, or a strategy using small- or mid-capitalization securities. The "manager value

added" component involves manager selection to implement a chosen style or strategy and comparison of manager results with some appropriate equity performance benchmark. This familiar framework can be applied to a nontraditional program as itemized in the last column of Table 1. The investment decision is to reduce the risk of the equity portfolio. Clients who are concerned about high market valuations with corresponding downside risk might want to address the risk through an overlay program. The implementation vehicle in this case might be a synthetic cash position by means of a futures overlay. Alternatives might be an optionsbased hedge strategy of purchasing puts to establish a protective floor, undertaking dynamic asset allocation that would combine selling calls and futures and buying puts, or pursuing some combination strategy, such as a collar. Managers can be selected to implement the chosen strategies, and the results can be compared with an appropriate derivatives benchmark. Although measurement at all three component levels is important, the investment decision and choice of implementation vehicle have the largest impact on the results of any program. For example, depending on future market movements, the investment decision to hedge can meaningfully alter portfolio results. Similarly, the implementation vehicle-for example, puts versus futures-will create significantly different results during a span of market moves. In many circumstances, a manager will deliver outcomes that are very close to the expectation associated with the investment and implementation decisions. Manager results will vary most in conjunction with management flexibility vis-a-vis the defined normal portfolio. Although manager results are important, the primary benefit of performance measurement is meaningful evaluation of the overall derivatives investment program.

Table 1. The Performance Measurement Framework Traditional Equity Investment Program

Nontraditional Hedge Program

Investment decision

Buy US. equities

Reduce risk of U.S. equity holdings

Implementation vehicle

Equity structure Growth style

Hedge structure Move to cash via futures overlay

Components

Value style Core / active approach

Hedge with options Protective floor

Indexed structure Small/mid-cap approach

Dynamic asset allocation Combination

Manager value added

Results versus equity performance benchmark

Results versus derivatives performance benchmark

Source: The Clifton Group.

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Applying the Measurement Framework For an illustration of the framework, consider a client who expects to make a large plan distribution in four months and wants to avoid a meaningful decline in fund value in the interim. The client is concerned that a large market decline will create problems for the upcoming distribution. The measurement framework might be applied to this scenario in the following manner. The portfolio is a $40 million balanced fund with 57 percent in equities and 43 percent in fixed income. The client is unwilling to liquidate the fund because timing of the distribution is somewhat uncertain and, in fact, may not take place at all. In addition, the client cannot afford more than a 5 percent decline if the anticipated distribution needs are to be met, and the client wants to maintain exposure to the market to avoid missing the risk premium that should accrue to portfolio holdings. The investment decision is to follow an overlay strategy by hedging the portfolio against the 5 percent price decline. The chosen implementation vehicle is a no-premium cost collar-a strategy that appeals to investment committees because it addresses the risk with no out-of-pocket costs. The collar has a -5 percent price floor that fits with the client's objective for controlling downside risk. In exchange for this risk control, the balanced fund has a total return cap of +5.2 percent, which consists of an estimated 2.3 percent dividend and interest return during the fourmonth period and a 2.9 percent price cap. To implement the strategy, the client chooses a managed hedge program using exchange-traded options.

Evaluating Results of the Program In evaluating this hedge program, questions to ask at the investment decision level relate to the riskiness of the market and consequences of a market decline. For evaluating the implementation vehicle, questions concern the potential outcomes of the alternative risk management structures. Clearly, different choices from many potential structures can be evaluated. For the final category, the evaluation considers value added by the manager-that is, how the manager performed versus the predetermined benchmark. Investment decision. The asset-class components of the balanced portfolio went up during the time the hedge was in place. The S&P 500 Index four-month total return was 7.0 percent, the T-bill return was 1.0 percent, and the Lehman Brothers Intermediate Government Corporate Index return was 4.5 percent. An indexed balanced portfolio of the same proportions as the client's portfolio, 57 percent equities and 43 percent fixed income, would have 100

gone up 5.9 percent (that is, [7.0 x 0.57] + [4.5 x 0.43] = 5.9). The hedged portfolio, which had the 5.2 percent total return cap, returned 5.2 percent. Therefore, the investment decision was associated with a reduction in return, or opportunity cost, of 70 basis points (bps). Clearly, the capital market returns indicate that the feared market decline did not materialize. If the investment decision to hedge had been based on a market call, the evaluation would conclude that this investment decision detracted value from the fund. The investment decision was based on a qualitative aspect also, however-inability to follow through with the distribution plan if the fund value were to fall significantly before the distribution date. Was the decision to hedge a good one? Considering the client's risk-control needs, the answer is probably yes. Implementation vehicle. In evaluating the implementation vehicle, the fund must keep in mind the objective, namely, to minimize or eliminate the risk of more than a 5 percent decline in portfolio value during the coming four months. Several investment vehicles could have been selected. One approach would be to liquidate the portfolio and hold cash-either on an outright basis or, as in this example, on a synthetic basis. The synthetic liquidation choice could be implemented by selling Treasury bond or note futures in addition to S&P 500 futures to create a riskless or nearly riskless position. Either selection would fulfill the risk-reduction objective, but both would also eliminate any possibility of capital appreciation. Alternatively, the client could purchase a put option to protect against the downside, which would address risk reduction and leave open the potential for capital appreciation; in this particular example, structured hedge providers quoted a 3.9 percent total return cap for the -5.0 percent price floor. If the market were to be flat, however, this approach would underperform the first choice. The vehicle that was ultimately chosen, a no-premium cost collar, addressed the risk and left some appreciation potential in place. The drawback to the choice of the no-premium cost collar is some underperformance versus the cash portfolio or the synthetic cash portfolio if the balanced portfolio is fla t or down on a total return basis. None of these choices is inherently right or wrong. The best decision depends on a number of different factors, such as expected cost, cash flows, and definition of risk. The decision must reflect the needs and preferences of the fund sponsor. Table 2 compares the results of using the three alternative implementation vehicles. Considering the capital market returns noted for the four-month period, the client experienced a 70-bp underperfor-

Table 2. Managed Protection Program: Implementation Vehicle Returns

Investment Decision Sell stocks/bonds; hold cash Purchase protective floor No-premium cost collar

Hedged Portfolio Total Return

Unhedged Portfolio Total Return

1.0% 4.9 5.2

5.9% 5.9 5.9

Difference -4.9% -1.0 -D.?

Source: The Clifton Group.

mance with the no-premium cost collar but would have experienced a 490-bp underperformance with the balanced portfolio liquidation and a 100-bp underperformance with the put vehicle. Thus, the client's selection of an implementation vehicle was very good in light of the market environment. Manager value added. The client selected a program in which the manager was to use exchangetraded options to create the implementation vehicle payoff. Unfortunately, no balanced portfolio options that would enable managers to hedge the portfolio directly are available on any of the option exchanges. Structured hedges are available, however, from some large brokerage houses and banks. Structured hedge price quotes can be used for inputs, such as correlations, which can then be used to approximate costs for exchange-traded options and create a normal portfolio benchmark. The benchmark can then serve as a comparative measure of the managed program's actual performance. The normal portfolio is not a perfect measure when multiple-asset portfolios must be hedged, but for the most part, a normal portfolio will give a good approximation of the results that are available for a passive program in the exchange-traded options market. Considering the prices available at the inception of the hedge, the exchange-traded options were to create a total return cap of 5.2 percent using a -5.0 percent price floor. The manager's estimate of a total return cap of 5.2 percent will thus serve as the primary benchmark to evaluate the manager's actual performance, and the structured alternative will serve as an additional benchmark in evaluating the actual manager results. The value added by the manager is calculated as follows: The unhedged portfolio return would have been 5.9 percent; the managed program earned 5.0 percent total return. According to the normal portfolio that was created from exchange-traded hedge costs, the program should have earned 5.2 percent, so the manager "lost" 20 bps of value in creating the no-premium cost collar. When the actual total return of 5.0 percent for the managed program is compared with the 3.9 percent for the structured alternative, the manager is deemed to have added 110 bps versus the structured alternative. One caveat is in order: The example provides no details about what was lost on

the calls and puts to create the 90-bps drag versus the unhedged market portfolio or the 20-bps drag versus the hedged normal portfolio. Nonetheless, this fund sponsor should have been pleased that the manager came close to the benchmark and that the managed approach was selected instead of the structured alternative. Many variables differ between exchange-traded options and structured programs. A structured program's cost or payoff includes the fee for the underwriter or investment bank; a managed program's cost or payoff is quoted separately from the management fee. Generally, questions about credit quality, liquidity, flexibility, and cost favor the managed program, whereas factors such as relationship structure and certainty of outcome favor the structured alternative. A fund sponsor's decision to choose a managed program instead of a structured program is not unlike other "build versus buy" decisions. The market clearly was not risky during the hedge period, but the market's riskiness was not the key concern. The important issue is how the client protected against an unacceptable outcome-in this case, a large market decline. The client needed to hedge, and choosing a collar as an implementation vehicle was a good decision. The manager modestly underperformed a normal or customized benchmark but provided a better return than some alternatives that were available, such as a structured hedge.

Other Applications In addition to the collar used in the preceding example, the three-component performance measurement framework can be applied to option overwriting and asset allocation. Option overwriting may be selected as an investment decision to enhance return and/ or reduce risk. A fund sponsor choosing option overwriting for the next five years, for example, would be basing the decision on some expectation for the market to be favorable for overwriting-that is, not volatile and perhaps on a downward trend for the selected program. After identifying a favorable option overwrite environment, the fund sponsor must then decide the best way to capture the favorable option overwrite returns. Choices include the use of index or individual options, an active or passive approach, or some 101

integral part of the total investment program. In this combination of these methods. To evaluate the vehiexample, option losses consistent with the model cle decision, the sponsor will compare how well the signal actually lead to the program outperforming program captures the benefits of the good overwriting environment with how well alternative implethe benchmark because some level of equity loss is avoided. At the manager-value-added level, the mentation vehicles would have done. In this type of evaluation would consider the results in terms of the program, the value added by the manager is often measured simply on a plus or minus basis: Did the vehicle selected and relative to some appropriate derivatives performance benchmark. manager make money? Normal portfolios that are meaningful and understandable can also be created. In the past, howSummary ever, some overwrite index benchmarks have been Performance measurement for programs composed difficult to use because of their complexity. Alternaof nontraditional investment vehicles is difficult betively, static measures may be used to attempt to cause these vehicles, primarily derivative securities, adjust for risk in the measurement process. This introduce complexities that are not found in more method uses a security market line approach to detraditional programs. The measurement process can termine what return should have been generated for be separated into three components: the investment the incurred risk. decision, which is a driving factor and one that fund Asset allocation may also be evaluated using the performance measurement framework. Asset allocasponsors and consultants, with the help of managers, must spend the most time evaluating; the implemention choices fall into three categories: tactical (shorttation vehicle, which addresses how to fulfill the term asset allocation adjustments), strategic (long-term or infrequent changes), and static (rebalinvestment decision in the most advantageous way; and the "manager value added," which determines ancing to a target mix). At the investment-decision the manager or structure that will provide the best level, a client may determine that one of these asset result. allocation overlay approaches may add value. At the The use of derivatives has been increasing and implementation vehicle level, the decision involves will continue to increase. Derivatives programs are which method of implementing the asset allocation will be the most profitable and efficient-a swap, the being improved through serious discussion of the risks and benefits of these instruments and through cash markets, futures, or options. Making this choice sound quantitative performance measurement. Perrequires care because some asset allocation managformance measurement is important for the investers have integrated the use ofoptions in their models. For example, a manager who is bearish on U.S. equity ment industry at all levels but especially for the derivatives area. Involving clients in the performmarkets at current levels but who would become ance measurement process helps educate clients, inmore bullish if the market were to fall 10 percent creases the likelihood that they will be satisfied with might sell a put option on the market. If the market falls, that manager will have contingent exposure, the programs they choose, and may encourage clients and consultants to add derivatives programs and measuring the option-only effect in implemenwith different or expanded applications. tation would not be appropriate because it is an

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Question and Answer Session Jack L. Hansen, CFA Question: In the case of enhanced index strategies using derivatives or perhaps swaps, is it acceptable to use an index-say, the S&P SOO-as the benchmark regardless of the complexity of the strategy at risk? Hansen: Whether using cash management and futures or cash management and swaps, the approach to enhanced indexing that the question addresses introduces risks that are not present when buying an index. Managing a portfolio of high-grade commercial paper and futures, for example, involves in some sense a doubling of risk. The portfolio has

assumed equity market risk through the futures position and the credit risk associated with the commercial paper position. If the commercial paper position is constrained to high-grade and relatively short-term maturities, that risk is generally acceptable to most fund sponsors in exchange for the incremental return that should accrue to that position. A portfolio of S&P 500 futures and high-grade cash should earn higher returns than the S&P 500 because the former is taking on more risk through time. This result has generally held, although in some recent periods, holding cash and futures actually lowered

return while raising risk. The markets are not completely efficient, and many supply and demand flows are at work that can create periodic mispricings. Part of the excess return associated with synthetic programs probably should be identified as arising from additional risk. Perhaps the benchmark for such enhanced index programs should not be the S&P 500 but the S&P 500 total return plus some spread over the riskless rate in order to offset the additional risk borne by the fund sponsor in holding the high-grade credit exposure.

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Universes and Peer Groups: Construction and Use Michael J. Flynn Manager StratfordAdvisory Group, Inc.

Universes and peer groups can be useful tools to measure manager performance if users understand the underlying construction methodology and assumptions. Because much of that information is not readily available, universes and peer groups should be used only in conjunction with other measurement tools.

Universes and peer groups are widely used performance measurement tools today. Thus, understanding how they are constructed and how they differ is important. Unfortunately, obtaining information on universes and peer groups is not easy, which makes effective application of these tools difficult. The purpose of this presentation is to discuss what must be known to apply universes and peer groups-in conjunction with other measurement tools-to measure investment manager performance.

Defining a Universe and Peer Group

to appropriate application of universes and peer groups. Generally, a performance universe is a broad collection of managers that are all investing in a similar area of a market. Broad equity, fixed-income, and balanced-fund universes may each encompass well over 1,000 different managers or managed portfolios. Because selection criteria for such universes are often not narrowly defined, the broad universes can be inappropriate for measuring a portfolio that has investment objectives that are narrowly defined and differ from the objectives of the universe. "Style" peer groups have become popular in the last four to nine years. Peer groups are more narrowly defined than universes, are generally smaller (have fewer managers), and are composed of managers focusing on a specific section of a market and/ or using a particular investment style. They are subsets of the broad universes. Examples of style peer groups include large-capitalization/value, small-capitalization/growth, and emerging market peer groups, all of which are subsets of a broad equity universe. Definitions of style peer groups are often ambiguous, but an understanding of a peer group's definition is important for proper application.

Universes and peer groups are simply rankings of investment manager performance. The term "peer group" is generally applied to a group of managers defined by a specific asset class or investment style; the term"fund universe" is more commonly defined as a broad group of managers or funds. In either case, managers' returns are ranked from the best (top 1 percent or top quartile) to the worst (99th percentile or bottom quartile) of returns. Manager returns and rankings are calculated for all relevant time periods-for example, "five years ended September 1994," or "six months ended June 1994," or perhaps, rolling calendar periods. Presentations in which the managers rank in the Benefits of Using Universes and Peer Groups bottom quartile or lower percentiles are rarely, if ever, seen. The potential exists for investment manUniverses and peer groups are popular tools today agers to manipulate the actual universe or peer for a number of reasons. First, they provide an acgroup, the construction methodology, or the period tively managed benchmark alternative to a passive index benchmark. An actively managed benchmark of measurement to make performance appear supereveals how managers that are applying similar acrior. Understanding how universes are created and tive decision processes are performing in the market. how a manager's performance compares is the key

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Second, clearly defined and effectively constructed universes and peer groups can be used to measure almost any well-understood investment style or type of investment management. A third advantage of universes and peer groups is that portfolio-specific investment restrictions or parameters can be incorporated. A peer group can be developed that excludes portfolios investing in certain geographical areas, for example, or certain types of companies; for instance, a South Africa-free or a socially responsible peer group is possible. In addition, although they are not simple in construction, universes and peer groups are conceptually easy to explain and to understand. Using morecomplicated performance measurement methods often intimidates and/or frustrates clients. The use of universes is easy to understand because it involves making comparisons between a client's portfolio and a group of managers or portfolios that are managed in the same way the client's portfolio is managed. Clients can look at a chart of universe or peer group performance and readily determine where their portfolio performance ranks in comparison with similar portfolio managers.

Construction Methodology Understanding the methodology used in construction of universes or peer groups is the key to their use as performance evaluation tools. Construction from initial manager identification and selection through definition of the percentile rankings involves four primary steps. The first step is to identify a pool of investment managers. The next step is to apply the selection criteria for the universe or the peer group to that pool of managers. This step is highly subjective and highlights the importance of understanding how managers are added to or deleted from the universe or peer group. Calculating rates of return is the third step in construction methodology. This step should be straightforward, but it involves subtleties that must be recognized. The final step is to develop the percentiles, or rankings, of returns. Users of universes or peer groups for performance measurement purposes must be particularly careful to understand how this step is carried out.

The Initial Pool of Investment Managers The type of investment managers included in an initial pool from which a particular universe or peer group is chosen is important. The initial manager pool may be composed solely of bank or trust department managers, of insurance company managers, of independent (registered) money managers, or of managers of separate accounts, and so forth. No

group is inherently right or wrong; the appropriate choice depends on the portfolio a client wishes to measure. If a portfolio holds 30 percent of assets in hedge funds and 20 percent in venture-capital funds, comparing it with a universe made up of bank and trust managers may not be appropriate. In addition to the issue of the type of managers included in the pool, the way in which managers are selected may also affect the construction of universes or peer groups. The author (or publisher of the universe or peer group) may be selecting only managers that the author actively tracks. A consultant is probably tracking managers the consultant thinks are "good" managers, so that consultant's universe might display an upward performance bias. Moreover, managers may pay a fee to be included in the data bases from which universes are created. This situation is not intrinsically right or wrong, but it can bias a group. The pool may not include a diversified group of managers. In addition, the managers of a universe may be chosen simply because they are the only managers to provide performance numbers. Clients often want performance reports very quickly after the close of a measurement period. As a result, consultants may rush to construct peer groups and universes within this time frame. The problem is that the managers with good performance to report make their performance results available before those with poor performance. Those who have had a bad quarter may be slow to provide results. Consultants thus often run two sets of universes-a preliminary and a final set-and the difference between the two sets can be significant. The preliminary universe is generally much more difficult to beat than the final one. Universes and peer groups can also be created from an "open" data base, a data base that attempts to include a comprehensive representation of managers without any selection criteria or fee for inclusion. An example of an open data base is Morningstar's mutual fund data base. Morningstar includes almost all publicly available mutual funds and applies few selection or screening criteria for inclusion. Although an open data base such as Morningstar may appear to be a good solution to the bias problem, the Morningstar system has limitations because it is composed entirely of mutual funds. A comparison of a fund investing in vehicles other than mutual funds with a universe composed entirely of mutual funds may be inappropriate. Does the portfolio being evaluated include separate accounts, commingled funds, mutual funds, partnerships, real estate investment trusts, and/or guaranteed investment contracts (GICs)? If so, the pool from which the universe or peer group is selected should include them. For 105

example, in a market with negative equity returns, GICs earning 6.5-7.0 percent will push up the performance of a peer group or universe. A portfolio that does not hold GICs will rank poorly in that period. The same GICs will negatively bias the universe at a time when equity returns are 15-20 percent. What is included or excluded from a universe can have a significant influence on manager rankings.

Applying selection Criteria The next area of universe construction that can create bias problems is in application of the selection criteria. How are managers screened and selected to fit into specific peer groups or universes? In constructing a broad equity universe, for example, the approach may appear simple, but it can be quite complicated. Questions that arise include: Should the universe contain only equity funds, or should it also include sector funds, gold funds, or convertibles? Are convertibles equity or debt? Should managers that have international exposure be included? One important aspect of developing a universe is determination of the relative weightings of various types of funds within the universe. For example, what percentages of an equity universe should be small-cap stocks, mid-cap stocks, or large-cap stocks? What percentages of the fixed-income universe are composed of short-, intermediate-, or long-term bond managers? What, if any, are the international equity and fixed-income allocations? Making a decision about relative weightings is not easy. For example, the designer of a broad equity universe may decide to use market weights to set the percentages of small-cap, large-cap, and mid-cap, but what are the market weights? Is the market 56 percent large-cap stocks, or 75 percent? The market weights must be defined in order for the universe to be of value. If the comparison portfolio is invested differently from the universe weightings, any comparisons will be difficult. Another area of concern is the development of balanced universes. Should a company use balanced fund managers only, or should the company use a combination of equity and fixed-income managers to create balanced funds? Balanced fund managers may have an allocation anywhere from 25 percent to 75 percent equity-or 0 percent to 100 percent, for that matter. If the allocations to asset classes in the universe and the allocations in the portfolio being measured differ widely, their performance numbers will be quite different. Although the practice of combining equity managers with fixed-income managers gives a universe author control over a balanced universe's equity and fixed-income allocations, critics note that this approach does not actually compare

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balanced managers with balanced managers. The next question involves the construction of style peer groups; that is, how are style peer groups defined? How is a growth manager distinguished from a value manager? What are the cutoffs between large, medium, and small capitalization? In a domestic equity peer group or a domestic equity universe, should managers that hold American Depositary Receipts be included, or those that are allowed to make a 5 percent allocation to international equities? What does the universe or peer group do about funds whose styles or investment managers change? Suppose, for example, a client has established a peer group definition of large-cap / growth managers that clearly defines which managers fall in the group and which do not. The current peer group is composed of all managers that satisfy today's definition and will include these managers' results for one, two, or more years; however, do these managers fit the large-cap / growth definition for the whole period? Comparison of today's universe with that of ten years ago may show that today's large-cap/growth managers used to be large-cap/value managers or maybe mid-cap managers. Failure to recognize style drift-ehanges in management style over time-creates a bias toward whatever style characteristics are currently exhibited.

Calculating Rates of Return Several problems can arise from the ways in which universes or peer groups calculate rates of return. The first problem is survivorship bias, which refers to the fact that today's universe or peer group contains only managers that have been competent enough to "survive." Because of the calculation methodology, managers that have gone out of business cannot and are not reporting performance. So, the universe is a group of survivors, not a fair representation of the true universe. The second issue involves how many managers in the universe or peer group have returns for a measurement period. If the measurement period is one year, the likelihood is that all (or almost all) managers will have performance that can be reported for the period. As the time span increases, however, from one year to five years to ten years, fewer and fewer managers will have been in business for the entire period. The universe that started out with 1,000 managers may quickly drop to 200; the peer group that started with 200 managers may end with 20 or 10. The result is that users of the universe/peer group are no longer getting broad representation of an investment style. A third issue related to calculating rates of return is whether performance numbers are presented before or after fees.

Percentiles and Ranking Returns Finally, problems caused by methods of forming percentiles and ranking returns can afflict a user. The mathematical process is simple, so the construction of percentiles and rankings would appear to be straightforward, but the user must be cautious. Some providers link median returns for time periods for which a universe return is not available-for example, linking a 4-year and a O.5-year return to provide a 4.5-year return. This approach can generate inconsistent performance results that should not be used and performance rankings that have little credibility. Additionally, the user must know whether performance rankings have been calculated from percentiles, deciles, or quartiles. Providers of universes and peer groups often provide data only by quartiles25th, 50th, and so on. Because quartile return distributions are not necessarily distributed equally, rankings based on quartiles may differ markedly from rankings based on actual individual percentiles.

Conclusion Despite problems that can result from using peer groups or universes, they are useful tools for evaluating investment performance if the user understands how they are constructed. Unfortunately, much of the necessary information is proprietary and not readily available. In addition, universes and peer groups should be used only in conjunction with other measurement tools. Knowing the managers and funds being evaluated is important. What are their distinguishing characteristics? Is the universe or peer group an appropriate benchmark for comparison? Are the portfolio and the universe or peer group operating in the same investable universe? Keeping these questions in mind will help clients and other users focus on the attributes needed to apply universes and peer groups effectively as measurement tools.

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Question and Answer Session JefferyV. Bailey, CFA Michael J. Flynn Question: What is the appropriate time frame to rebalance a manager's custom benchmark? Bailey: A custom benchmark for an equity manager should be rebalanced quarterly or semiannually. The characteristics of stocks can change over time. For an extreme example, IBM used to be a growth stock, but it certainly is not today. If you do not rebalance frequently, the characteristics of the benchmark will change from what you were trying to capture initially. Question: Who pays for custom benchmark work, and how do the costs compare with those of manager universes? Bailey: Manager universes are somewhat like derivatives: They are by-products of systems that are primarily designed to generate other products. For instance, bank trust departments collect a great deal of data, from which investment manager universes can be constructed; so, a universe built on that data base is essentially free. This kind of benchmark becomes something of a loss leader for the provider. At Richards & Tierney, we believe managers should produce and pay for their own custom benchmarks. This system puts the responsibility and accountability for quality benchmarks where they belong-in the hands of managers. Custom benchmarks are expensive to produce because they take time and thought, but the issue is whether you get what you pay for. Most people pay nothing for manager universes, and I suspect they get results in accordance. 114

Flynn: Custom benchmark work is expensive and time consuming, which helps explain why universes and peer groups are the more widely used approaches. Whether the benefits are worth the cost and effort comes down to whether an appropriate universe or peer group is available and whether you are willing to go the extra mile to use custom benchmarks. The use of custom benchmarks generally requires hiring an investment consultant and paying significant fees to develop and regularly adjust the benchmarks. All of the fees being paid ultimately affect the investment performance (the bottom line) of the fund being evaluated. So, the question is whether any incremental value that is added is worth the expense. Question: Are consultants relying on universes to assess performance because using universes is easy? Flynn: The consulting process involves determining clients' needs and applying what is most appropriate for them. Using universes or peer groups is not necessarily the easiest approach, nor does it involve less work than some other approaches, but it is easily understood by clients. A consultant using a universe/peer group must first spend a lot of time ensuring that the universe/peer group applies appropriately to the fund being measured. In some cases, client portfolios do not compare directly with available peer groups or universes, and the consultant and client must consider whether to devise custom universes, peer

groups, or benchmarks. Again, an expense is associated with such customization, and given alternative performance measurement tools and a client's objectives, the expense may not be cost-effective. Question: Because most manager searches start with a requirement that managers be in the top quartile of performance to be considered, what responsibility do consultants have to understand the problems in using manager universes and to consider such issues as survivorship bias before eliminating managers from further consideration? Bailey: The basic issue is that manager universes do not provide a valid comparison tool in the first place. Manager universes are completely invalid. For a manager organization to say it is in the "top quartile" means nothing to me. Consultants and fund managers should not be doing something with nothing. Question: Are the Morningstar reports meaningful in evaluating fund managers? Bailey: Some of the research Morningstar does is wonderfulfor example, some sophisticated risk analysis. Morningstar's comparisons among peer groups, however-reports on how a fund did last quarter or last year versus its so-called competitors-are not meaningful. Morningstar has the data and is capable of combining various style portfolios to produce a custom benchmark, which would be useful, but this work has a long way to go. Flynn:

If you want a quick re-

view of a mutual fund's performance versus that of its peer group, Morningstar is useful. We break out Morningstar's return data to create some custom benchmarks and peer groups, which we apply and use. So, Morningstar is a good start. I agree, however, that it needs to go much farther. If you are simply applying Morningstar information as it is presented, without refinement or extension, you are going to get in trouble. Question: Do universes in which the vendor does its own analysis of managers' styles vary significantly in quality or veracity from those in which the managers designate their own styles via a surveyor other techniques? Bailey: Good research on the quality of these universes is lacking. The organizations that have the data do not publish anything. Flynn: Although you need to understand what makes up the peer groups and universes, the organizations that create universes generally do not provide this information. If you are trying to compare apples with apples and you have no idea whether you have apples in both sets, you cannot make a valid comparison. Question: Mr. Flynn, do you agree with the claim that survivorship bias pushes median returns significantly upward and that this problem significantly compromises the integrity of a universe as a performance measurement tool? Flynn: Several types of survivorship bias exist that can compromise the integrity of performance universes. I believe the type of bias you are referring to is the inability of performance universes (because of the calculation methodology) to capture the per-

formance of managers that underperform and eventually go out of business. This type of bias does exist and it can upwardly skew returns. The frequency at which it occurs is low, however, and the impact it has on the overall universe is small. The bias is not significant enough to invalidate the use of universes. Question: Mr. Bailey, other than superiority in market timing and transaction costs, what information can be gained by constructing a peer group or benchmark that nearly mirrors a managed portfolio? Bailey: The goal of benchmark building is not to mirror a manager's portfolio. Rather, the goal is to create an investable portfolio that mirrors a manager's investment style. The distinction may seem subtle, but it is crucial. A manager adds value by selecting the best-performing stocks and avoiding the worst-performing stocks from his or her benchmark. Question: Which is the more appropriate benchmark for a manager: a universe of separate accounts or a composite? Flynn: The type of universe that is most appropriate depends on the type of portfolio you are trying to evaluate. To make the best comparison, the universe's objectives, restrictions, structure, and so forth, should be similar to the portfolio you are measuring. If I were attempting to measure the performance of a separate account, I would prefer to use a universe created from separate accounts. The opposite would be true if I were measuring the performance of a composite fund return. Question: For mutual funds, would a direct comparison with a

benchmark be more appropriate to evaluate performance than using a comparison of a given fund's returns with the universe of mutual fund returns? Bailey: I believe so. In an investment performance context, mutual funds are no different from other investment managers. They display certain investment styles, and their results can typically be best evaluated by comparison with custom benchmarks. Flynn: In an ideal world, I would prefer to use both a benchmark and a peer group. For a mutual fund, I would prefer to use a peer group of other comparably managed mutual funds. A peer group would provide an actively managed comparison that could be tailored to the management style of the fund you are evaluating. A disadvantage would be the amount of time and effort needed to develop the peer group and the possibility of survivorship bias. Custom benchmarks can also be tailored to match the fund's investment style. Custom benchmarks are even more time consuming and costly to develop, however, than peer groups. For the purpose of evaluating the results of a single mutual fund, the costs might be prohibitive. Question: Why do almost all managers show a universe comparison in their marketing materials if it is of no benefit to facilitate manager-elient dialogue? Bailey: Convenience, availability, and naive appeal are at the heart of the popularity manager universes are enjoying. Plan sponsors and managers should be more aggressive in pointing out the flaws of peer comparisons and the advantages of custom benchmarks. 115

Flynn: I do not agree with the statement that performance universes do not facilitate managerclient dialogue. As far as the use of universes versus passive benchmark (standard indexes) by investment managers in marketing presentations is concerned, managers will always attempt to "put their best foot forward" by comparing themselves with whichever (reasonable) benchmark or universe makes them look best. Sometimes, a manager may look best when compared with a passive index, and sometimes, with an active universe. Managers may find custom benchmarks difficult to use in marketing. Explaining a custom benchmark to prospective clients in a marketing presentation is difficult and often beyond the scope of the presentation. In addition, prospective clients are normally attempting to compare prospective managers, which is not facilitated by custom benchmarks designed to evaluate specific investment managers. Question: The median for individual manager returns compounded over a period of several years is significantly different from the median taken one year at a time, and few managers consistently beat the index over time. Is the use of median managers fair? Flynn: The compounding of median manager returns over several periods is not the correct methodology for calculating universes' distributions over long periods. This process will upwardly bias returns. The more correct

116

method is to use the median manager return over the full time period. Linked median returns that are different from median returns calculated over the same period do not necessarily indicate that universes have significant survivorship bias. Rather, most of the variation can be explained by the effect of compounding returns and the volatility of the median manager returns relative to the performance of the entire universe. Question: What analytical tools do consultants use to analyze a manager's style? Flynn: Investment consultants use a number of quantitative and qualitative techniques to evaluate the style of an investment manager. Perhaps the most popular quantitative method in use today is a "style analyzer"-a form of constrained multiple regression in which manager returns are regressed against several different style indexes. The results of the regression indicate the level to which a manager's investment performance is attributable to each of the indexes. Although quantitative techniques are helpful, Stratford Advisory Group emphasizes qualitative techniques that place more weight on meeting with investment managers, understanding their investment processes, and evaluating the holdings in their portfolios than on quantitative tools. Ours is the more costly and time-consuming approach, but we think it significantly enhances our ability to as-

sess a manager's investment style accurately. Question: The AIMR Performance Presentation Standards state that you cannot take a manager's returns out of your composites if, for example, that portfolio manager is fired for poor performance. Why isn't the same idea applied to peer groups and universes to eliminate survivorship bias? Bailey: That sort of analysis would be interesting, but it would entail more effort than the universe providers would be willing to undertake. Flynn: To include managers who no longer report performance data would be impossible because universes are built on performance data from the most recent reporting period-generally the most recent calendar quarter. If a manager is no longer in business or no longer providing performance figures, the manager cannot be included in the universe. Question: Why is survivorship bias a problem if the purpose of performance reviews is to find superior performance? Bailey: Because survivorship bias distorts one's definition of "superior." The limited evidence indicates that, over time, survivorship bias can shift the "median" return more than one quartile from what it would be in a universe that is not subject to survivorship bias.

Manager Universes: The Solution or the Problem? JefferyV. Bailey, CFA Managing Partner Richards & Tierney, Inc.

Manager universes, although convenient, intuitively appealing, and widely used, are seriously flawed. Managers and consultants should devote their efforts to constructing improved customized benchmarks based on the criteria of lack of ambiguity, investability, measurability, appropriateness, and specification in advance.

Manager universes are probably the most ubiquitous of performance evaluation tools. A manager universe is a collection of individual manager portfolio returns (and perhaps portfolio characteristics) over specified time periods. The distribution of manager returns relative to individual managers or a pension fund is a widely used method to evaluate performance-primarily because manager universes are convenient, the data are readily available, and making comparisons among managers with similar characteristics has an almost intuitive, naive appeal. Few plan sponsors' investment staffs are willing to report to their investment boards without some reference to peer performance. Manager universes do not warrant such widespread acceptance, however, because they are not valid performance evaluation tools. Specifically, manager universes have four serious problems: conceptual shortcomings, survivorship bias, manager style bias, and failure to pass quality tests to assure that they do the job they are purported to do. Indeed, sponsors and managers should be devoting their energies to incorporating benchmark quality criteria in improved customized benchmarks.

Conceptual Shortcomings A benchmark should, first of all, be a passive representation of a manager's investment process; that is, it should incorporate the financial characteristics of the manager's portfolio in the absence of active management and should capture the manager's area of expertise-the manager's "fishing hole," the pool from which the manager selects securities-and the weights the manager attaches to those particular 108

securities. Within the context of the common stock market, for example, large-capitalization/low-P/E stocks might be thought of as the large benchmark pool for a large-cap/value manager, with the manager's portfolio a small subset of that pool. On a conceptual and intuitive level, for a benchmark to be considered appropriate, the manager's portfolio should reside within that benchmark.

Benchmark Characteristics A benchmark should be considered valid only if it has the following properties: It is unambiguous. The name and the weights of the securities that compose the benchmark should be known. The S&P 500 Index, for example, is an unambiguous benchmark. Analysts can itemize all the securities and their weights in the S&P 500. It is investable. A valid benchmark should represent a passive alternative to a money manager. If for some reason the money manager were to run out of good ideas, passive indexing to the benchmark should be possible. Therefore, investors should be able to buy the securities that are in the benchmark. It is measurable. Performance measurement obviously requires periodic and precise calculation of performance for both active and benchmark portfolios; analysts should be able to calculate the benchmark's performance on a regular basis. It is appropriate. A benchmark must reflect the manager's style. For example, T-bills have the first three properties: They are unambiguous, investable, and measurable. For a common stock manager, however, T-bills are not an appropriate benchmark. It reflects current investment opinions. If a manager's performance is being compared with that of a

particular benchmark, he or she should be knowledgeable about, have an opinion about, the securities that make up the benchmark. The manager certainly has knowledge and opinions about his or her active holdings, and the same should be true regarding the benchmark's holdings. It is specified in advance. Before the start of an evaluation period, the composition of the benchmark (that is, securities in the benchmark and their weights) should be specified and that composition should be available to all interested parties. Manager universes violate all of the principles for valid benchmarks except one: Manager universes are measurable. First, manager universes are certainly ambiguous. Investment managers have little knowledge about the constituents of manager universes and have no knowledge of the composition of their portfolios. Such information is never disseminated. Second, manager universes are uninvestable. An investment manager cannot buy the median manager's portfolio, for example, or the portfolio of any other particular manager in the universe. Manager universes are often inappropriate. Many elements of manager universes are not reflective of a particular manager's style because the universes tend to incorporate a whole group of managers. Manager universes do not-and really cannotreflect consistently anyone manager's current investment opinions. The other managers reflected in the universe may not favor certain stocks that the manager being evaluated is able or willing to consider, or they may favor securities prohibited by the manager's mandate. The final deficiency, and one that underlies all of the other deficiencies, is the fact that manager universes are not specified in advance. Before the start of an evaluation period, investment managers have no idea who the median manager or the first-quartile manager is. These performance benchmarks are determined only after the fact.

Benchmark Functions Another way of evaluating the conceptual shortcomings of manager universes is to consider the functions a benchmark portfolio should fulfill in the manager-elient relationship. Performance evaluation is, of course, one of the key functions. Comparing how a manager has done relative to a particular bogey is important. Additionally, benchmark portfolios should communicate the manager's investment style. They should be the basis for manager-elient discussions of objectives and performance and thus should help define the relationship between managers and clients. Benchmarks are also appropriate standards

for calculating performance fees. An interesting current use of benchmarks is as input for structuring multiple-manager funds. The benchmarks allow clients to consider how various managers fit together in an investment program. Finally, benchmarks provide investment managers themselves with valuable feedback and can be tools for control in decision making, quantifying past successes and failures, and identifying strengths and weaknesses. Again, manager universes do not perform these functions of valid benchmarks. They are crude performance measurement tools. Their use focuses solely on the returns of a manager versus a particular universe. They do not promote understanding of why a particular set of performance results occurred; thus, performance attribution is impossible when using manager universes as benchmarks. Manager universes are poor conveyors of a manager's style. A client can say a manager is a growth manager as specified by the particular universe that the manager is using, but this simple statement does not communicate much about the many subtleties in a manager's investment process. For example, a growth manager may favor certain industries, exclude various types of stocks, or weight portfolio holdings in a particular manner. Such style details cannot be captured in the statement that a manager "belongs in a growth universe." Manager universes are of little benefit in facilitating manager-elient dialogue. The manager either did or did not outperform the benchmark. Because a benchmark's composition is unknown, discussions about why the manager beat or did not beat the benchmark and what sort of changes or strategies the manager may have for the future are not likely to take place. Manager universes are useless as standards for calculating performance fees. Because manager universes do not represent an investable alternative, most managers would be foolhardy to bet their revenue streams on their performance relative to manager universes. They are better off comparing their active bets with more specific passive benchmarks, which can indicate the value of those bets. Manager universes are of little help in assessing and controlling risk in multiple-manager investment programs. Combining a median manager from a manager universe, for which the risk characteristics cannot be determined, with other managers does not enable any meaningful measurement of a program's risk. At least in terms of risk control, manager universes have no role to play in the process of developing successful multiple-manager programs. Manager universes cannot be a tool for improving an investment manager's internal decision-making process through feedback. Comparing a manager's 109

performance with the performance of a manager universe does not invite analysis. Investment managers cannot highlight strengths and weaknesses; they can only conclude whether they performed poorly or well.

Table 1. Reported versus Linked Quarterly Returns

for Median Managers

Years

Survivorship Bias

1 2 3 4 5 6 7 8 9 10

Published SEI Median

Linked Quarterly SEI Median

Linked Quarterly MedianSEI Rank

13.9% 5.9 17.8 16.5 13.3 14.9 15.1 14.7 12.9 14.8

13.1% 6.1 16.9 15.3 11.8 13.6 13.4 13.3 11.1 12.4

57 47 62 69 68 70 73 73 84 84

The providers of manager universes have long been aware of, but have largely ignored, the issue of survivorship bias. Managers who perform poorly tend to drop out of universes, and the result is to bias the performance of the median managers upward. Over many years, the impact of survivorship bias can be highly significant. In fact, the magnitude Source: Bleiberg, "Pension Fund Perspective." of survivorship bias appears nearly to equal the should conduct more research with respect to the amount of value-added return one would expect survivorship bias issue. _ from superior equity managers-one or two percentage points a year. As a result, survivorship bias can skew perceptions of who is a good manager and who Manager Style Misspecification is a poor manager. As noted earlier, manager universes are often critiResearch in this area has been limited because the cized as being poor conveyors of manager style. In owners of the data, those who compile manager unirecent years, the providers of manager universes verses and have access to and understanding of surhave addressed manager style by creating subunivivorship bias, do not publish this information. Table verses relative to the performance of various invest1 shows the results of a 1986 study by Bleiberg of the ment styles-for example, large-capitalization/ performance of the SEI Corporation manager unigrowth managers or large-cap / value managers. The 1 verse for equity managers. He examined reported purpose is to provide bogeys that more closely remedian manager performance for various multiplesemble investment managers' intentions and characyear periods using quarterly data. For example, the teristics than a general-manager-universe bogey. SEI reported performance numbers indicating that, The definitions that go into creating subuniduring a ten-year period, the median equity manager verses are imprecise, however, and the methodology achieved 14.8 percent annual return. Bleiberg linked underlying them is suspect. Theoretically, comparthe information on the median managers' performing a large-cap/growth manager with a largeance on a quarter-by-quarter basis for comparison cap / growth universe makes sense, and universe with the SEI published figures. Although statisticians suppliers and users might argue that such partitionmight question whether this method is an appropriing adequately isolates managers' areas of expertise. ate way to compare medians, nonetheless Bleiberg The evidence that it helps in evaluating manager came to some compelling conclusions. performance, however, is scant. Linking the quarterly median manager performFor example, a Richards & Tierney mutual fund ances together, as Table 1 reports, produced a tenclient recently asked us to analyze its portfolio relayear return of 12.4 percent. The published SEI tive to the portfolios of what the client considered to performance was 14.8 percent, which indicates that be peers; this manager classified itself as a largethe bias upward was 240 basis points for that time cap / growth manager and wanted to publish its perspan. Some survivorship bias is evident during virformance relative to peer organizations. Figure 1 tually all multiple-year periods, and the median SEI shows a graph, as of a particular date, of this mutual rank slowly deteriorates as the time period lengthfund portfolio manager in what we call style space. ens; for the ten-year period, the quarterly linked The diamonds indicate the manager's position over median SEI manager is actually in the 84th percentile time relative to four style portfolios: largerelative to the published median manager, which cap / growth, large-cap / value, small-cap / growth, indicates severe survivorship bias. Bleiberg's concluand small-cap/value. The S&P 500 is shown in the sions suggest that providers of manager universes shaded area, the individual managers (at one point in time) that composed the manager's supposed peer group are indicated by the circles, and the average of lSteven D. Bleiberg, "Pension Fund Perspective: The Nature the peer group managers is denoted by the triangle. of the Universe," Financial Analysts Journal (Marchi April 1986):13Figure 1 shows that the dispersion is large. Most 14. 110

Figure 1. Style Coordinates for a Larg&CapiGrowth Manager and Its Peers 2.0 Large-Cap/Growth 1.0 f-

<>O~

Large-Cap /Value

S&P500

....

0

.8u

>2

0

~

0

OJ

0 00

N

en 0

-1.0

f-

0

Small-Cap/Value

0

0

b. 0 0

0 0

0

e Small-Cap/Growth I

-2.0 -2.0

I

o

-1.0

1.0

2.0

Value-to-Growth Factor o

Peers

<> Manager

b.

Peers' Average

Source: Richards & Tierney.

of the organizations that the manager considered to be its peers lie, in fact, quite far from the style space in which the manager's portfolio lies. In short, the peer group that our client manager wished to be compared with was quite different from the client; most of the individual peer managers-and the average peer manager-would be characterized as small-cap / growth rather than large-cap / growth. Such distances can represent significant performance differences over time. This evidence is anecdotal, but it illustrates poor specification of peer groups and the potential presence of style misspecification. International manager universes are probably worse than domestic ones. The diversity of manager mandates in the international area is wide and hinders the creation of peer groups based on similar style. The usual alternative is to compare an equity manager with the MSCI EAFE Index or another international market index. The EAFE Index is largely irrelevant as a benchmark for many international managers, however, because it inadequately represents their investment tendencies. For example, most international managers maintain a permanent underweight in Japan by using the EAFE Index as a benchmark. Clients, consultants, and managers should devote their energies to constructing better, customized international benchmarks rather than promoting the flawed techniques of universe or EAFE Index comparisons. The conceptual weaknesses associated with manager universes and the presence of survivorship bias and style misspecifications in manager universes suggest that manager universes may not accomplish their intended purposes.

Quality Tests Given the problems associated with using manager universes, a set of criteria is needed that can measure the quality of a benchmark in an objective way-a set of objective measurements that would indicate whether a benchmark is successful. The first five criteria suggested here relate to the composition of the benchmark portfolio itself, and the others relate to the benchmark composition relative to the actual portfolio under review. Coverage. An analysis of benchmark coverage would involve simply discovering whether the stocks in the actual portfolio or the managed portfolio are also contained in the benchmark. Turnover. Benchmark turnover refers to the percentage rate of change in the composition of the benchmark. It relates to investability: Is the turnover rate reasonable in light of trading realities? Positive active positions. This criterion calls for considering whether the manager that is to be compared with the benchmark holds favored stocks in disproportionately higher weights than those same stocks are held in the benchmark. If the manager likes a stock, the manager will typically be weighting it more heavily than does the benchmark. Investable position sizes. This criterion relates to evaluating whether the benchmark weights can be duplicated by a particular manager. If a certain stock makes up 3 percent of the benchmark, can this manager under review actually hold 3 percent of that stock in its portfolio? This criterion is particularly relevant to multibillion dollar managers. Similarity of portfolio characteristics. Do the portfolio and benchmark share similar characteristics, particularly with respect to style? Figure 1 illustrated one way of, and the importance of, analyzing the similarity in composition of a benchmark and a specific portfolio. The next three criteria are more complex than the first five in that they relate to comparing the returns of a managed portfolio with a benchmark's returns. Observed active risk. Analyzing this criterion involves examining the amount of variability of portfolio returns relative to the benchmark. Examination of the median manager benchmark should show whether it was successful in reducing the amount of active risk in the past compared with an alternative such as the S&P 500. For example, if a largecap / growth universe explains a manager's performance well, the manager's past performance should reflect less active risk relative to that benchmark than to the S&P 500.

Correlation of managed portfolio returns versus the market with benchmark style returns versus the market. If the benchmark is appropriate, the returns of the benchmark in excess of market returns should be 111

highly positively correlated with excess portfolio returns. In other words, if a benchmark is a good reflection of a manager's investment style, the manager's portfolio should perform well at the same time the benchmark performs well relative to the market.

Correlation of benchmark style returns versus the market with the managed portfolio returns versus the market. This correlation should be low. How success-

relatively unrelated to the returns of the manager under review. This experiment involves only one manager for one time period, of course, and the results may not be true in every period. The point is that those who provide manager universes should publish information that supports the validity of the median manager as a benchmark for evaluating performance- that is, demonstrates an acceptable correlation between the returns of median managers and those of individual managers.

ful the manager's style is in a particular market should have no bearing on how the manager adds value to the benchmark. That is, whether the market is good or bad for, say, large-cap/growth stocks should not affect a manager's performance versus the large-cap / growth benchmark. Conclusion Now, consider applying these objective tests to the median manager of a manager universe, perhaps Although manager universes may be convenient and the most widely used manager performance benchappealing, comparing performance with manager mark. The first five benchmark quality criteria canuniverses does not make sense. Manager universes not be applied to a median-manager benchmark are hopelessly inadequate tools for conducting perbecause analysts never observe the composition of a formance evaluation or manager risk control. median manager's portfolio. This problem gets to the Managers and plan sponsors argue for the use of heart of the inadequacy of manager universes: They manager universes for comparisons because the are ambiguous and uninvestable benchmarks that available asset-list benchmarks are so poor. This reare specified after the evaluation period. sponse is wrong. Instead, managers and their conThe three remaining benchmark quality tests sultants should focus their energies and efforts on deal only with returns, and therefore, they can be constructing improved customized benchmarks. applied to, for example, the median manager from a With today's technology, constructing custom manager universe. Table 2 uses the quarterly perbenchmarks for individual managers makes more formance of a managed large-cap / growth portfolio, sense than accepting general manager universes. Inof the median manager from a large-cap / growth deed, custom benchmarks are increasingly in use. universe, and of the S&P 500 to apply the three tests. Even on the international level, where custom benchThe time period was from the first quarter of 1985 marks have not yet been widely applied, benchmarkthrough the second quarter of 1990. The test of obbuilding technology is becoming available. served active risk appears in the column for annual What characteristics, then, would the ideal peer standard deviation. The managed portfolio's pergroup in a custom benchmark possess relative to a formance relative to the S&P 500 had an annual manager's portfolio? The ideal peer group, in comstandard deviation (observed active risk) of roughly parison with the manager under review, would 5.7 percent. The manager's performance relative to • be selected from the same sectors and industhat of the median manager from the largetries (and only those sectors and industries); cap / growth universe had an annual standard devia• be selected from (and only from) the same tion of almost 8.1 percent. In other words, noise is market-capitalization groups; introduced in the process simply by using this me• be selected from (and only from) stocks with dian manager's performance as the benchmark. This similar common factor exposures; scenario is not reassuring in terms of the quality of • incorporate the same legal or policy restricthe benchmark. tions on security holdings; The desired large correlation between perform• apply similar weightings to portfolio securiance of the large-cap / growth median manager verand ties; sus the market performance and performance of the specific portfolio manager versus the market per• exhibit similar total portfolio characteristics. These characteristics are grounded in the same formance is not present; the actual correlation is only 0.26. On the other hand, the correlation between the approach as would apply to constructing a customized asset-list benchmark. The implication is that manager's performance versus the large-cap / growth median manager and the median manager versus the manager universes are poor substitutes for carefully market should be near zero but, at -0.45, is not only designed benchmarks that are created specifically for the unique investment process of individual managnegative but fairly high. The indication is that the returns of the median manager in the universe are ers.

112

VJ

,...,...

1.00 0.26 0.72

Note: The market is the S&P 500. Source: Richards & Tierney.

aRate of return divided by standard deviation.

Managed versus market Benchmark versus market Managed versus benchmark

Managed versus Market

1.00 -0.45

1.00

1.10

2.87% 1.76

8.09

5.67% 5.89

Annual Standard Deviation Annual Rate of Return

Performance Statistics

Benchmark versus Market

Managed versus Benchmark

Correlations of Excess Returns

0.14

0.51 0.30

Informati Ratioa

Table 2. Benchmark Quality Results: Managed Portfolio, Median Manager, and Market, First Quarter 1985 through Second Quarter

Question and Answer Session JefferyV. Bailey, CFA Michael J. Flynn Question: What is the appropriate time frame to rebalance a manager's custom benchmark? Bailey: A custom benchmark for an equity manager should be rebalanced quarterly or semiannually. The characteristics of stocks can change over time. For an extreme example, IBM used to be a growth stock, but it certainly is not today. If you do not rebalance frequently, the characteristics of the benchmark will change from what you were trying to capture initially. Question: Who pays for custom benchmark work, and how do the costs compare with those of manager universes? Bailey: Manager universes are somewhat like derivatives: They are by-products of systems that are primarily designed to generate other products. For instance, bank trust departments collect a great deal of data, from which investment manager universes can be constructed; so, a universe built on that data base is essentially free. This kind of benchmark becomes something of a loss leader for the provider. At Richards & Tierney, we believe managers should produce and pay for their own custom benchmarks. This system puts the responsibility and accountability for quality benchmarks where they belong-in the hands of managers. Custom benchmarks are expensive to produce because they take time and thought, but the issue is whether you get what you pay for. Most people pay nothing for manager universes, and I suspect they get results in accordance. 114

Flynn: Custom benchmark work is expensive and time consuming, which helps explain why universes and peer groups are the more widely used approaches. Whether the benefits are worth the cost and effort comes down to whether an appropriate universe or peer group is available and whether you are willing to go the extra mile to use custom benchmarks. The use of custom benchmarks generally requires hiring an investment consultant and paying significant fees to develop and regularly adjust the benchmarks. All of the fees being paid ultimately affect the investment performance (the bottom line) of the fund being evaluated. So, the question is whether any incremental value that is added is worth the expense. Question: Are consultants relying on universes to assess performance because using universes is easy? Flynn: The consulting process involves determining clients' needs and applying what is most appropriate for them. Using universes or peer groups is not necessarily the easiest approach, nor does it involve less work than some other approaches, but it is easily understood by clients. A consultant using a universe/peer group must first spend a lot of time ensuring that the universe/peer group applies appropriately to the fund being measured. In some cases, client portfolios do not compare directly with available peer groups or universes, and the consultant and client must consider whether to devise custom universes, peer

groups, or benchmarks. Again, an expense is associated with such customization, and given alternative performance measurement tools and a client's objectives, the expense may not be cost-effective. Question: Because most manager searches start with a requirement that managers be in the top quartile of performance to be considered, what responsibility do consultants have to understand the problems in using manager universes and to consider such issues as survivorship bias before eliminating managers from further consideration? Bailey: The basic issue is that manager universes do not provide a valid comparison tool in the first place. Manager universes are completely invalid. For a manager organization to say it is in the "top quartile" means nothing to me. Consultants and fund managers should not be doing something with nothing. Question: Are the Morningstar reports meaningful in evaluating fund managers? Bailey: Some of the research Morningstar does is wonderfulfor example, some sophisticated risk analysis. Morningstar's comparisons among peer groups, however-reports on how a fund did last quarter or last year versus its so-called competitors-are not meaningful. Morningstar has the data and is capable of combining various style portfolios to produce a custom benchmark, which would be useful, but this work has a long way to go. Flynn:

If you want a quick re-

view of a mutual fund's performance versus that of its peer group, Morningstar is useful. We break out Morningstar's return data to create some custom benchmarks and peer groups, which we apply and use. So, Morningstar is a good start. I agree, however, that it needs to go much farther. If you are simply applying Morningstar information as it is presented, without refinement or extension, you are going to get in trouble. Question: Do universes in which the vendor does its own analysis of managers' styles vary significantly in quality or veracity from those in which the managers designate their own styles via a surveyor other techniques? Bailey: Good research on the quality of these universes is lacking. The organizations that have the data do not publish anything. Flynn: Although you need to understand what makes up the peer groups and universes, the organizations that create universes generally do not provide this information. If you are trying to compare apples with apples and you have no idea whether you have apples in both sets, you cannot make a valid comparison. Question: Mr. Flynn, do you agree with the claim that survivorship bias pushes median returns significantly upward and that this problem significantly compromises the integrity of a universe as a performance measurement tool? Flynn: Several types of survivorship bias exist that can compromise the integrity of performance universes. I believe the type of bias you are referring to is the inability of performance universes (because of the calculation methodology) to capture the per-

formance of managers that underperform and eventually go out of business. This type of bias does exist and it can upwardly skew returns. The frequency at which it occurs is low, however, and the impact it has on the overall universe is small. The bias is not significant enough to invalidate the use of universes. Question: Mr. Bailey, other than superiority in market timing and transaction costs, what information can be gained by constructing a peer group or benchmark that nearly mirrors a managed portfolio? Bailey: The goal of benchmark building is not to mirror a manager's portfolio. Rather, the goal is to create an investable portfolio that mirrors a manager's investment style. The distinction may seem subtle, but it is crucial. A manager adds value by selecting the best-performing stocks and avoiding the worst-performing stocks from his or her benchmark. Question: Which is the more appropriate benchmark for a manager: a universe of separate accounts or a composite? Flynn: The type of universe that is most appropriate depends on the type of portfolio you are trying to evaluate. To make the best comparison, the universe's objectives, restrictions, structure, and so forth, should be similar to the portfolio you are measuring. If I were attempting to measure the performance of a separate account, I would prefer to use a universe created from separate accounts. The opposite would be true if I were measuring the performance of a composite fund return. Question: For mutual funds, would a direct comparison with a

benchmark be more appropriate to evaluate performance than using a comparison of a given fund's returns with the universe of mutual fund returns? Bailey: I believe so. In an investment performance context, mutual funds are no different from other investment managers. They display certain investment styles, and their results can typically be best evaluated by comparison with custom benchmarks. Flynn: In an ideal world, I would prefer to use both a benchmark and a peer group. For a mutual fund, I would prefer to use a peer group of other comparably managed mutual funds. A peer group would provide an actively managed comparison that could be tailored to the management style of the fund you are evaluating. A disadvantage would be the amount of time and effort needed to develop the peer group and the possibility of survivorship bias. Custom benchmarks can also be tailored to match the fund's investment style. Custom benchmarks are even more time consuming and costly to develop, however, than peer groups. For the purpose of evaluating the results of a single mutual fund, the costs might be prohibitive. Question: Why do almost all managers show a universe comparison in their marketing materials if it is of no benefit to facilitate manager-elient dialogue? Bailey: Convenience, availability, and naive appeal are at the heart of the popularity manager universes are enjoying. Plan sponsors and managers should be more aggressive in pointing out the flaws of peer comparisons and the advantages of custom benchmarks. 115

Flynn: I do not agree with the statement that performance universes do not facilitate managerclient dialogue. As far as the use of universes versus passive benchmark (standard indexes) by investment managers in marketing presentations is concerned, managers will always attempt to "put their best foot forward" by comparing themselves with whichever (reasonable) benchmark or universe makes them look best. Sometimes, a manager may look best when compared with a passive index, and sometimes, with an active universe. Managers may find custom benchmarks difficult to use in marketing. Explaining a custom benchmark to prospective clients in a marketing presentation is difficult and often beyond the scope of the presentation. In addition, prospective clients are normally attempting to compare prospective managers, which is not facilitated by custom benchmarks designed to evaluate specific investment managers. Question: The median for individual manager returns compounded over a period of several years is significantly different from the median taken one year at a time, and few managers consistently beat the index over time. Is the use of median managers fair? Flynn: The compounding of median manager returns over several periods is not the correct methodology for calculating universes' distributions over long periods. This process will upwardly bias returns. The more correct

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method is to use the median manager return over the full time period. Linked median returns that are different from median returns calculated over the same period do not necessarily indicate that universes have significant survivorship bias. Rather, most of the variation can be explained by the effect of compounding returns and the volatility of the median manager returns relative to the performance of the entire universe. Question: What analytical tools do consultants use to analyze a manager's style? Flynn: Investment consultants use a number of quantitative and qualitative techniques to evaluate the style of an investment manager. Perhaps the most popular quantitative method in use today is a "style analyzer"-a form of constrained multiple regression in which manager returns are regressed against several different style indexes. The results of the regression indicate the level to which a manager's investment performance is attributable to each of the indexes. Although quantitative techniques are helpful, Stratford Advisory Group emphasizes qualitative techniques that place more weight on meeting with investment managers, understanding their investment processes, and evaluating the holdings in their portfolios than on quantitative tools. Ours is the more costly and time-consuming approach, but we think it significantly enhances our ability to as-

sess a manager's investment style accurately. Question: The AIMR Performance Presentation Standards state that you cannot take a manager's returns out of your composites if, for example, that portfolio manager is fired for poor performance. Why isn't the same idea applied to peer groups and universes to eliminate survivorship bias? Bailey: That sort of analysis would be interesting, but it would entail more effort than the universe providers would be willing to undertake. Flynn: To include managers who no longer report performance data would be impossible because universes are built on performance data from the most recent reporting period-generally the most recent calendar quarter. If a manager is no longer in business or no longer providing performance figures, the manager cannot be included in the universe. Question: Why is survivorship bias a problem if the purpose of performance reviews is to find superior performance? Bailey: Because survivorship bias distorts one's definition of "superior." The limited evidence indicates that, over time, survivorship bias can shift the "median" return more than one quartile from what it would be in a universe that is not subject to survivorship bias.

AIMR Performance Presentation Standards: An Update Michael S. Caccese Senior Vice President, General Counsel, and Secretary AIMR J. Paul Dokas, CFA Director, Trust Investment Management Bell Atlantic Edward P. Rennie, CFA \!'ice President Pacific Investment Management Company

Development of the AIMR Performance Presentation Standards has matured to the point of global recognition and acceptance of the standards. Controversy still surrounds several aspects of the standards, however, and initiatives are underway to address unresolved issues. Practitioners report that the standards have enhanced and improved the investment industry. Compliance with the standards does not detract from a company's operation in any way.

AIMR became involved in developing and setting industry standards for performance reporting because potential investment manager clients needed a mechanism for comparing managers' performance results. That development process has now matured to the point at which the AIMR Performance Presentation Standards (PPS) are becoming globally recognized. 1 Numerous issues remain to be addressed, however, and a variety of initiatives are underway to enhance and fine-tune the standards. This presentation first provides an overview of industry acceptance of and regulatory interest in the standards. It then addresses a number of issues that are in various stages of incorporation into the standards. These sections are followed by comments on the PPS from the plan sponsor and investment manager points of view.

Overview and Current Issues

Michael S. Caccese

serve as a foundation for a discussion of current PPS development activity. First, the standards are guiding ethical principles that are intended to achieve full disclosure and fair representation of performance presentation; they are not performance measurement standards. Even though some performance measurement requirements are included in the standards, those requirements are minimal and not unique to the PPS. Second, the standards are intended to ensure uniformity in reporting performance so that results are directly comparable among investment managers-that is, so that clients and others will be "comparing apples with apples." Third, the standards require firmwide compliance, not composite compliance. This requirement avoids the "cherry picking" of accounts and composites. Finally, AIMR intends that the standards be user friendly and user responsive; the AIMR Performance Presentation Hotline (804/980-3604 and fax 804/980-8789) are two manifestations of that intent.

A brief review of the nature of the standards will IThe standards as of 1993 are available in Performance Presentation Standards (Charlottesville, Va.: AIMR, 1993).

Industry Acceptance The standards have been accepted far beyond 117

the expectations of AIMR and its predecessor organizations, the Financial Analysts Federation and the Institute for Chartered Financial Analysts. Performance is increasingly being collected and reported in composites, and more and more firms are claiming compliance with the standards. Such firms include the Mobius Group, EFFRON, Wilshire Associates, Nelson's Publications, and the Money Market Directory. Numerous surveys measuring the acceptance of the standards have been conducted since late 1993. Greenwich Associates reports increasing familiarity among different groups with the standards: Familiarity with the standards is indicated by 61 percent of endowments, 44 percent of public funds, 36 percent of corporate funds, 85 percent of funds with more than $1 billion in assets, and 85 percent of funds with less than $100 million in assets. 2 Familiarity with the standards has increased during the past three years among these entities as a group from 28 percent to 45 percent. The Greenwich survey reports that public funds require the highest level of compliance. Three-quarters of public funds claiming familiarity with the standards require manager compliance. These statistics, which are confirmed by the Spaulding Group, show that sponsors of medium-size pools of assets are making the most demands for compliance with the standards, and despite the significant financial cost, investment management firms are imposing firm compliance with the standards. The Spaulding Group surveyed 550 managers about compliance with the standards in December 1993.3 Of the 27 percent who responded, 75 percent are in compliance and 20 percent plan to be in compliance. Of the firms in compliance, 80 percent comply to gain a marketing advantage, 61 percent comply because they are AIMR members, and 38 percent comply because of client pressure. The International Performance Forum reports that 48 out of 70 firms claim compliance with the standards. The Institute for Private Investors reports 45 out of 50 investment advisors for private investors claiming compliance. Pension plan sponsors and major fund managers are some of the main proponents of the standards. AIMR maintains a list of more than 300 fund sponsors and investment managers that endorse the standards (endorsement means that they require potential managers to present performance in compliance with the standards), including General Motors, IBM, AT&T, 2Greenwich Associates, Seismic Shift in Pension Planning (1994). 3 The Spaulding Group, Performance Measurement Survey (December 1993).

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Fidelity Research and Management Corporation, Templeton Worldwide, and J.P. Morgan & Company.

RegUlatory Interest The SEC staff is also displaying an increased interest in the standards. At a recent conference sponsored by the National Society of Compliance Professionals, the associate director of the SEC Division of Investment Management publicly stated to more than 150 compliance professionals that the SEC staff has no present intention of recommending to the commission performance presentation standards for u.S.-registered investment managers. The SEC looks to AIMR to fill this void and encourages AIMR to continue to expand its efforts to address performance presentation issues faced by the investment management profession. AIMR held a day-long training session on the standards for the SEC's New York regional office enforcement staff in 1994. The enforcement staff is interested in understanding the PPS to determine whether firms are being truthful when they claim compliance with the standards. If a firm claims compliance but is not in compliance, that firm, whether it is or is not registered with the SEC, violates the antifraud provisions of the U.s. Investment Advisors Act. AIMR is working with the SEC's Washington, D.c., staff to schedule a similar training session. In Canada, AIMR has met with every provincial securities commission to inform them of the PPS and to offer assistance in training their staffs about the standards. As a practical matter, a firm is less likely to be charged in Canada with violating securities laws for improper representation of compliance than would be the case under SEC jurisdiction because the provincial securities commissions' inspection programs of Canadian investment advisors are typically not as broad as the SEC's program.

Current Issues To refine, expand, or enhance the PPS, a number of issues have been recently resolved; others are in various stages of study. The primary topics of recent attention by the AIMR Performance Presentation Standards Implementation Committee (PPSIC), which is responsible for interpreting the standards and recommending changes when needed, are international compliance, taxable accounts, leverage/derivative securities, and venture and private placements. The two main topics currently being addressed by the PPSIC are verification and wrapfee programs. International compliance. The standards became effective on a global basis on January 1, 1994. The main issue the PPSIC international subcommit-

tee faced was how a multinational money management firm could claim compliance, which must be done on a firmwide basis. Consider the example of a global investment management firm with Japanese accounts. Certain Japanese accounts cannot be in compliance with the PPS because they are required by Japanese law to use book rather than market values. The international subcommittee, whose membership spanned eight countries, addressed this problem by redefining "the firm." According to the standards, a firm can be defined either as a separate legal entity (a separately incorporated and SEC-registered office in New York for a Swiss-based firm, for example) or a division that holds itself out to the public as a separate entity. The international subcommittee broadened the definition of a firm to include all accounts that trade in the same base currency. Taxable accounts. Another major issue recently addressed is the reporting of taxable accounts. The main concerns relate to the appropriate tax rate to be used for performance reporting and to the unfairness of penalizing a manager for trades performed for tax reasons. The reporting of gross-of-tax performance is currently recommended. The main advantage of a manager presenting gross-of-tax results is that prospective clients with taxable assets can then estimate after-tax results based on tax rates that are appropriate to their individual circumstances. Gross-of-tax results also avoid the complexities of presenting after-tax performance in a meaningful and comparable manner-eomplexities that arise because of current limitations in accounting and performance systems. The main disadvantage of reporting gross-of-tax performance is that it does not show how successful a manager has been in applying an investment style to a specific client. Different investment strategies will have different after-tax returns to investors that, over time, can significantly lower the clients' returns because of the compounding effect. For example, even if pretax performance is the same, an income-oriented investment style will have lower after-tax performance than a capitalpreservation style if capital gains taxes are less than income taxes. To address these problems, a PPSIC subcommittee on taxable portfolios issued a detailed report soliciting comments on its proposed approach to reporting performance on an after-tax basis. If aftertax performance is to be presented, some minimum requirements apply. First, the maximum federal tax rate for the type of client is to be used. Second, if a composite holds both taxable and after-tax portfolios, taxable securities must be adjusted to an aftertax basis rather than tax-exempt securities being

grossed up to a taxable equivalent. Third, taxes must be subtracted from the account no matter how and from what source they are paid. The subcommittee hopes to receive comments about the report from the profession and hopes that the report will raise the interest of systems vendors in addressing the needs of taxable portfolios. In addition, AIMR has been in contact with numerous organizations that provide benchmarks to determine interest in establishing after-tax benchmarks. Morningstar appears to be at the forefront in this area because of its work related to mutual funds. Leverage/derivative securities. The subcommittee dealing with performance reporting for leverage and/or derivative securities issued a clarifying report in April 1994. The requirements and recommendations for disclosure depend on the degree of discretion a manager has to leverage the portfolio through the use of derivatives or through margin. If a manager has full discretion, the manager is recommended to present both leveraged and unleveraged returns to current clients. The manager is required to report to potential clients the unleveraged return, disclose whether results are leveraged (and if so, the amount of leverage), and include the leveraged performance as supplemental material. The amount of firm assets to be included in the required statistical disclosure-that is, the percentage of firm assets under management-is the unleveraged amount. The leveraged amount of assets must be reported separately. If the manager does not have full discretion-if a client requires a manager to leverage by a stated percentage-the portfolio return must be calculated on the fully leveraged position and total firm assets must be based on the fully leveraged amount of assets. Venture and private placements. The subcommittee dealing with venture-capital investments and private placements released a report in April 1994 that describes how the PPS apply to these alternative investments. The report provides a detailed description and rationale for the presentation requirements. For applying the standards to these investments, the report draws a distinction between two levels of investment management: One level comprises general partners and fund-raisers, and the other level, intermediaries and investment advisors (which includes funds of funds). The report requires general partners and fundraisers to report a cumulative internal rate of return net of fees, expenses, and carry costs to the limited partners since inception of the fund. Intermediaries and advisors must also report an IRR net of the same fees applicable to general partners but gross of investment advisory fees, unless a regulatory authority 119

requires a net-of-fees performance. Composites must be defined by vintage year-year of a fund's formation and first takedown of capital. Verification. Verification of compliance is the most frequently raised concern about the standards. The purpose of adding verification to the standards was to create a mechanism to assure the client who relies on the standards that the performance numbers are truly presented in accordance with the standards. The PPSIC created a subcommittee, which includes three representatives selected by the American Institute of Certified Public Accountants (AICPA), to address verification. The verification subcommittee expects to publish a report clarifying numerous issues and uncertainties surrounding verification. The report has been approved, in principle, by the PPSIC and the Investment Companies Committee of the AICPA. AIMR recognizes that the standards created a cottage industry in compliance verification that now begs for the imposition of some consistency in scope of verifications and some kind of oversight-perhaps a body for "verifying the verifiers." The verification subcommittee report will thus include fairly detailed guidelines as to the minimum steps a verifier must follow when carrying out verification. It should level the playing field among verifiers and assist managers and their clients in standardizing the steps verifiers must follow. The report will address the scope of Level I and Level II verifications. Level I verification, which applies to the firm, will not change. It will continue to require that the verifier attest that all fee-paying discretionary accounts are included in one or more composites and that the composites are appropriately created. Level II verification, which applies to an individual composite and is similar to an audit that examines both the investment management process and the measurement of performance, will be modified to require only an abbreviated Level I approach. In the modified Level II verification, the verifier will be required to verify only that the composite that is receiving the Level II verification is appropriately created, that no other fee-paying discretionary accounts exist that should be included in the composite, and that all other fee-paying discretionary accounts are included in at least one composite. The verifier will no longer be required to take the additional step of determining that all manager composites are appropriately created. Firmwide compliance will continue to be required, but the subcommittee report will recommend that a Level I verification be requested only if a manager or a client wants the assurance that all composites are appropriately created.

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Finally, the report will provide model requestfor-proposal (RFP) questions that plan sponsors and consultants will be able to use to determine whether a manager is supplying performance numbers that are verified to be compliant with the standards. Wrap-fee programs. The second most controversial issue currently being addressed is performance reporting in relation to wrap-fee programs. A PPSIC wrap-fee subcommittee that includes one representative of the Investment Management Consultants Association and two SEC staff observers is drafting a report that addresses this controversy. The standards currently state that wrap-fee performance may be reported gross of fees only if transaction costs are deducted from the performance. Estimated transaction costs are not permitted. If the transaction costs cannot be determined, then the manager is required to report wrap-fee performance net of all fees, including the total wrap fee. The problem with the standard as currently stated is that if a manager has 90 percent of accounts in compliance, for example, and 10 percent in wrap-fee programs that are not in compliance, the entire firm is considered not in compliance. The wrap-fee subcommittee is likely to recommend that wrap-fee performance be reported on a "pure" gross-of-fees basis, whereby transaction costs are not deducted from performance as long as performance is also shown on a net-of-all-fees basis (that is, after deducting the total wrap fee). Other specific questions this subcommittee is addressing are: Should wrap-fee accounts and nonwrap-fee accounts be included in the same composite? Must gross-of-fees performance be shown at all times together with net-of-fees performance? Can non-wrap-fee accounts that are converted to wrapfee accounts be linked? Other issues. AIMR is working with the SEC to deal with reporting gross-of-fees performance of mutual funds that are included in a composite that contains separate accounts. In the controversial answer to Question #33 of the AIMR PPS Questions and Answers pamphlet, AIMR took the position that mutual fund performance must be reported net of fees at all times. This requirement, given only after discussion with SEC staff, created an outcry in the industry. AIMR then discussed the issue further with the SEC staff, which is reluctant to alter its position. AIMR will continue its discussions with the SEC staff on this and other issues concerning the presentation of mutual fund performance and advisor-subadvisor relationships. In addition, the SEC has contacted AIMR to clarify the standard that recommends gross-of-fees performance "unless otherwise required by the SEC." The SEC permits gross-of-fee performance reporting

only in a one-on-one presentation. AIMR will address this issue in an upcoming question and answer article in the AIMR Newsletter.

The Future Several concerns raised by the profession remain to be addressed. Some in the industry contend that verification needs further clarification, that compliance is too expensive, and that the standards are too institutionally focused. Others observe that limited enforcement capability and the firmwide compliance requirement both work to hinder acceptance of the standards. Some believe that equal-weighted composites should be used instead of dollar-weighted composites. AIMR recognizes that controversy still surrounds the standards, and the controversy will likely continue. The Bank Administration Institute performance measurement calculations issued in 1969 took nine years to implement and are still, after 25 years, somewhat controversial. AIMR intends to continue to focus on the concerns noted here and other issues as they arise. AIMR also intends to continue educational efforts with plan sponsors and consultants. Plans are being made for meetings with public and corporate plan sponsors in major cities to increase their familiarity with and understanding of the standards. AIMR will also continue its dialogues with the Investment Counselors Association and the American Bankers Association to address small-firm and bank compliance issues. In short, AIMR is committed to continuous improvement in its educational efforts regarding the standards, for the betterment of the profession and the good of its clients.

The Plan Sponsor Perspective J. Paul Dokas, CFA Bell Atlantic is among the more than 300 endorsers of the AIMR Performance Presentation Standards. We consider the standards a positive step in strengthening the integrity and credibility of the industry, and we encourage their continued development and implementation.

Benefits for the Sponsor Implementation of the standards has provided a number of benefits to plan sponsors and their ultimate clients. The primary benefit is a basic standard of comparison. Instead of spending a great deal of time going through details to find comparable performance numbers (e.g., gross return to gross return) when evaluating products, if the investment managers are complying with the standards, sponsors can easily make apples-to-apples comparisons and de-

cide whether further analysis is necessary. The standards provide a much greater level of transparency to performance results than existed previously. Some of the most important issues addressed by the PPS requirements and recommended, or guideline, disclosures concern the development of performance composites by an investment firm. Application of the PPS requirements limits a manager's ability to disclose performance results selectively. For example, a manager cannot"cherry-pick" accounts to show as a representative account one that may have performed better than other accounts. The PPS requirements call for all discretionary client accounts to be included in at least one of the firm's composites. In addition, the PPS requirements outline the methods for handling the inclusion of new accounts and the elimination of terminated accounts from the firm's composites to minimize the impact of such activity on the composites. The PPS also address the issue of simulated or model performance results. Saying that all backtests are fraudulent is too strong, but simulations can be very misleading. Sponsors want to be able to distinguish what information represents actual, realized returns and what represents simulated results. The PPS requirements state that a firm's composites must include only actual assets under management. Model results can be presented as supplementary information, but the model results must be identified as such and must not be linked to actual results. Another PPS requirement concerning the calculation of a firm's performance composite involves the inclusion of cash and cash equivalents in composite returns. A common problem without such standards is the reporting of an account's performance that does not reflect results for all the assets under management in that account. For example, the performance of an equity account might show the equity-only results, but the account might have held some level of cash, and during rising markets, that cash position would be a drag on performance; so, by showing equity-only results for that account, performance is made to look better than it actually is. The investment industry is characterized by a great deal of personnel movement, and long track records can be made up of a manager's performance history at several different firms-which often becomes an issue in disclosing performance results. Can a portfolio manager's investment results be moved from firm to firm? According to the standards, if an individual has been the principal involved in developing a performance record at another firm, that record can be displayed as supplemental information with the appropriate disclosures. The new firm, however, cannot link its performance history with that of another firm. The 121

guiding principle is that performance is the record of updating the standards and is working toward imthe firm, not of the individual. provements in a number of areas. The two aspects that have received much attention recently and conThe current PPS requirements call for managers to market-weight or capitalization-weight individtinue to be prospects for future enhancement are ual accounts when calculating a performance comventure capital and real estate. Common problems in posite. Depending on the specific facts in each both these areas include not only valuation methodsituation, one or the other mayor may not be the ologies but leverage and structural issues. These asappropriate choice, and much debate centers on pects are more challenging for nontraditional whether equal weighting or market-value weighting securities, from the standpoint of presentation stanis more appropriate. In a market-value-weighting dards, than for portfolios composed of traditional situation, for example, one very large account that securities. Because most of the nontraditional assets performs much better than the rest of the accounts in are illiquid and pricing is based on appraisals, an the composite will positively skew the composite interesting enhancement to the guidelines for these result. The PPS guidelines recommend, but do not securities would involve the disclosure of some type require, that an equal-weighted composite be calcuof metrics that would allow an investor to evaluate lated. Making the equal-weighted composite calcuthe quality of the valuations. lation a requirement would improve the standards. Another recommendation, or guideline, in the PPS involves the calculation of some type of disper- The Investment Manager Perspective Edward P. Rennie, CFA sion statistic for the composite return calculations. Typically, an analyst will want to examine the standInvestment managers for Pacific Investment Manard deviation or variance of individual account reagement Company (PIMCO) have always had a simturns around the composite return. For example, if ple objective: to be proactive with clients on any the composite return was 10 percent, a plan sponsor major issue. PIMCO's adherence to that objective is will be interested in knowing whether the range of illustrated in its attitude toward using and disclosing returns for accounts included in the composite was 0 its use of derivative securities and in its approach to percent to 20 percent or 9 percent to 11 percent. AIMR's Performance Presentation Standards. Requiring the calculation of this type of internal risk measure would strengthen the PPS. Derivatives One major benefit of the standards is that they PIMCO has used derivatives in a wide variety of help make transparent the impact of fees on returns. strategies for many years. The strategies are based on Fees are becoming much more important in the 1990s sound investment principles. A $4.5 billion equity than they were in the 1980s as total rates of return strategy using only derivatives has worked very regress toward lower absolute levels in line with well. In early 1994, however, when all the bombs long-term averages. With performance results disstarted falling on derivatives, PIMCO faced a choice: played on a gross-of-fee basis, as recommended, and Should we maintain a low profile and hope the dethe required disclosures of the manager's fee schedrivatives crisis goes away, or should we develop an ule, plan sponsors and investors can evaluate the information base for coverage with our clients? We impact of fees on net results better than in the past. chose the latter course because of our goal to be The standards also benefit sponsors by recomproactive. mending that, for comparative purposes, investment That course of action required much time and managers provide return information on a bencheffort. Our first step was to assemble a mailing to mark that is comparable in style or risk with the clients, which consisted of important written and composite. Clearly, this area leaves a great deal of graphic material. Because we wanted to explain the discretion to managers, but the guideline is meaningfirm's thoughts and actions fully, the materials deful because the spirit of the standards is to ensure the scribed why and how PIMCO uses derivatives, what comparison of like things-a small-cap equity acderivatives are used, the risks, and how the risks are count with a small-cap equity index, for example, or controlled. The mailings were then augmented by a long-duration bond account with a long-duration face-to-face visits with clients to give them the opporbond index. tunity to ask questions. PIMCO suffered little repercussion from clients Future Improvements about the performance of our portfolios with derivaAlthough the benefits of the standards are many, tives; therefore, we believe that although we can some areas exist in which improvement could ennever dispel all client fears, our efforts to communihance the quality of the standards. As Michael Caccate proactively helped make clients comfortable cese discussed, AIMR has recognized the need for with the firm's strategies. 122

PPS Strategy Pimco adopted a similarly proactive stance when the Performance Presentation Standards were proposed. PIMCO believed that clients would be asking whether we would use the standards. Therefore, we set out to analyze for ourselves the appropriateness of the standards and what would be involved in implementing them. We believed that presentation standards would be good for the industry. In addition, we knew that PIMCO's historically sound performance results would look good under any reasonable set of reporting standards. When the standards were issued, PIMCO further decided that they did not impose any serious constraints or limitations on the firm. Therefore, the firm made a commitment to adopt the standards. Our implementation process was straightforward; we could easily identify what needed to be done. We assembled small teams of portfolio managers, account managers, and programmers, and the implementation was carried out in a very timely manner; the process was neither terribly time consuming nor costly. In truth, the project was not con-

sidered a major undertaking, simply one of the important tasks that are required from time to time to remain a state-of-the-art firm in the investment industry. Once compliance is built into mechanized systems, compliance is automatic and completely painless. PIMCO's use of the standards is now routine. Compliance with the PPS benefits the profession. The investment management business today is extremely challenging, and many serious issues demand investment professionals' attention. Compliance with the PPS is one of the easiest issues: To be in compliance is the only way to do business. For example, the lifeblood of this industry from a marketing standpoint is the famous (or infamous) RFP. Every RFP PIMCO now receives asks if our performance numbers conform to the AIMR standards. Who would want to answer negatively to that question? Full disclosure is part of PIMCO's straightforward approach to investment management. Meeting the requirements set by the standards is part of the way PIMCO does business.

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Question and Answer Session Michael S. Caccese J. Paul Dokas, CFA Edward P. Rennie, CFA Question: In ongoing firm composites, how should one handle changes in account styles?

Caccese: Once a composite is created, the manager is required to ensure that the composite remains appropriately designed and includes accounts that should be in it. Therefore, a manager has an ongoing responsibility to monitor composites. If an account changes style, you can take that account out and put it in a different composite. The standards also give you the ability, when appropriate and depending on styles, to have the same account in two different composites. Question: Can an account that receives a significant cash inflow be excluded from a compositefor example, if a client with a $100 million account invests another $50 million in the account and several months are needed to invest the cash?

Caccese: The standards do not require you to include an account in a composite right away. You have the flexibility to take time to shape that account so that it is consistent with the desired style. For example, if a manager receives an account that is holding stocks that the manager does not usually hold, the standards give the manager time to sell those stocks and reshape the portfolio so that it is consistent with the manager's style. Only then is the manager required to add the account into the appropriate composite. Whatever procedure you use, you must use it consistently each time the issue comes up. 124

Question: What has been the response to the PPS recommendations on reporting portfolio risk statistics, and what is AIMR doing to promote this aspect of the standards?

Caccese: AIMR is encouraging the reporting of portfolio risk; we do not know how many firms are following the recommendations. The SEC has approached AIMR about developing some type of risk-based performance measurements that are responsive to portfolio changes, and AIMR is considering assembling a group of members to address the issues surrounding risk-based performance measurement. Question: Does AIMR maintain a list of recommended software systems for performance reporting?

caccese: AIMR maintains a list of managers that profess to have software that complies with the standards. AIMR does not verify the accuracy of the claims. Question: Is AIMR aware of any firms that claim compliance but in reality do not comply? If so, what actions does AIMR take?

Caccese: Six written complaints alleging false compliance claims on the part of firms that employ AIMR members have been filed with AIMR. All six claims were filed by competitors. An informal investigation of two of the claims revealed that no violations had occurred. We are still gathering information about the other four complaints.

Question: What is the danger that investment management is going to become overregulated by the standards?

Dokas: We began from a position of virtually no guidance or oversight, so we have a long way to go before we are in jeopardy of becoming overregulated. Rennie: I don't see regulation as a real problem. Although the PPS are a form of regulation, they are not at all onerous or illogical. In addition, with AIMR and others continuing to use their skills and experience to make enlightened improvements to investment management standards in general, overregulation is far down on my list of things to worry about. Question: What are the qualifications to become a verifier of AIMR standards as an independent third party?

caccese:

The standards list no requirements to become a verifier, which has raised concern about "fly-by-night verifiers." A client seeking to hire a verifier does not know the verifiers' skills or capabilities, track records, or verification processes. The guidelines for minimum steps in verification that the verification subcommittee is developing will give some comfort to both managers and clients that everybody who is carrying out verification is at least starting from the same base. Question: AIMR recommends reporting results gross of fees,

and the SEC staff recommends net of fees; could you clarify the issues involved?

Caccese: The SEC staff took the position in the Clover Capital No Action Letter in the late 1980s that gross-of-fees performance may be misleading because the client never actually receives that outcome. Philosophically, the writers of the PPS believed that the gross-of-fees basis is the more appropriate way of complying with the standards. As a result of much negotiation by the Investment Company Institute, the Securities Industry Association, the Institute of Chartered Financial Analysts,

and the Financial Analysts Federation, the SEC created an exception: If a manager presents performance in a one-on-one presentation, the manager can do so on a gross-of-fees basis as long as the manager includes its fee table with the presentation, along with other required disclosures. If the presentation is not on a one-on-one basis, however-for example, if it is in a general advertisement-it must be reported on a net-of-fees basis. Question: Are mutual funds addressed within the AIMR standards? Can they be included in composites (gross or

net)? Can a mutual fund alone make up a composite (gross or net)?

Caccese: Mutual funds, commingled funds, or unit trusts may be treated as separate composites or be combined with other portfolios or assets of similar strategies. The performance of portfolios invested in one commingled fund, mutual fund, or unit trust should be represented by the performance of the fund or unit trust. For portfolios invested in more than one fund or unit trust, a total return must also be calculated and performance included in a multiple-asset composite.

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Measuring More than Performance Numbers Charles B. Burkhart, Jr. President Investment Counseling, Inc.

Several qualitative and quantitative operating measures are available to assess the health of an investment management firm. Studies of operating performance, compensation levels, and other indicators reveal both similarities and differences between US. and Canadian firms.

Some numbers, although not directly related to indexes or investment performance, are nonetheless important to and indicative of the overall health of an investment management firm. These critical numbers assess firm health both quantitatively (margins, compensation, and growth in revenues, assets, and expenses) and qualitatively (business and succession planning). Both aspects of firm health are vitally important to three constituencies: fund sponsors, who should go beyond performance measurement and performance attribution if they wish to do a good job of analyzing managers; funds that are planning to start in-house asset management companies; and the professionals in money management firms who want a framework for rating and measuring themselves that goes beyond investment performance. Measurement of firm performance is discussed here in the context of trends in the investment management industry. The presentation provides the results of studies of operating performance and compensation levels in the United States and Canada, and outlines important qualitative dimensions to evaluating investment firms' likelihood of future success.

Industry Trends Major current developments in the investment management business include accelerating consolidation and commoditization, the aging of independent firms, the decision to pursue multiple markets with multiple products versus pursuit of a niche strategy, and the emphasis on relationships versus products. Consolidation of the investment management business in North America is a fact of life, and with it comes increasing commoditization in all three major business segments-mutual fund, institutional,

126

and private client. The trends are much more rapid in the United States than in Canada but are accelerating in the latter market. The trends are also more rapid for mutual funds than for firms serving the institutional market or private clients, such as bank trust departments. The mutual fund business is highly commoditized; price competition is increasing, and size is becoming a critical factor in competition. A recent Goldman, Sachs and Company report speculates that no mutual fund under $10 billion in assets will be able to survive long term. 1 This assessment may be extreme, but competition, especially on price, is intense and increasing for funds that are trying to establish brokerage relationships on the bases of wrap-fee or other managed-account structures. The institutional business is not changing quite as much as the mutual fund business because the former is highly fragmented in the United States (and somewhat so in Canada). For example, probably 18,000 registered investment advisors exist in the United States, and only about 400 of them manage more than $1 billion in assets. A $500 million firm netting $2.5 million in fees and generating $1 million or more in compensation for its founder does not yet feel the impact of competitive pressure or necessary reinvestment. Most have not yet sensed they are in the midst of a revolution. A reduction in the number of managers institutional investors employ is, however, increasing the commoditization of the institutional business. Many managers are complaining that the sponsors with whom they work are paring down their manager lineups from, say, 40 to 20 managers, and they want to know if they will be among those cut or among 1 The Changing Economics of the Mutual Fund Industry, Goldman, Sachs and Company Industry Resource Group (May 1994):3.

those chosen to do more business with the sponsors. viving and can continue to do so. Doing business is becoming increasingly difficult and expensive for Either way, these managers are placed in a more these small firms, however. Historically, they have competitive mode by this kind of sponsor strategy. not had to reinvest in their businesses because they The private-client business is the most amorworked primarily with defined-benefit plans. Now, phous segment of the investment management busithey will have to do so to finance, for example, their ness and the most difficult about which to acquire mutual fund or defined-contribution-plan strategies. competitive information. This segment will probably Data show that serving mutual funds and definednot feel the effects of consolidation as quickly as the contribution plans requires more reinvestment than other two business segments because so many facpursuing the defined-benefit business. Moreover, tions exist in the business-from large bank trust the greater capital intensity of the mutual fund and departments to the brokerage side of the business to defined-contribution businesses suggests that the firms with divisions specializing in private clients. scale of reinvestment may be daunting for the niche The approaches of such firms as Trust Company of firms. The key issue for these firms is to decide where the West and Sanford C. Bernstein are completely they want to direct their businesses and focus their different from those of Northern Trust or U.s. Trust. business strategies. Their cultures, account minimums, and compensaAnother response to competitive forces is "instition practices diverge widely. tutional relationship trending," which refers to Another aspect of today's investment managemoves by a number of u.s. and Canadian firms to ment business is the aging of the independent firms. build more comprehensive and secure relationships Many investment management firms, especially in with their clients than has traditionally been the case. the United States, were started in the 1960s and 1970s Even at the potential cost of lower realized fees, firms by principals who were then in their 20s to 40s but will aggressively pursue market share, especially in who are now in their 50s, 60s, or 70s. They may not the markets with the slowest growth. Institutional have capitalized their firms, and they cannot sell to relationship trending is different from simply offertheir younger employees because the latter simply ing a stable of disconnected products. Firms pursucannot afford to buy in. Therefore, the owners of ing this approach are integrating their marketing, hundreds of American firms are facing questions investment management, and client services. For any about what to do for the second generation. Should company that plans to grow significantly-eertainly they work out "sweat equity" arrangements with on the institutional side-this approach of compreemployees? Should they sell their firms to the emhensive, professionally served relationships is absoployees at a discount? Should they sell outside to lutely vital. strategic or financial buyers? One result of increasing consolidation, commoditization, and competition is an increase in the Operating Performance of Investment number and scope of multiproduct/multimarket Management Firms firms. Investment management firms of all sizes are searching for ways to deliver more products to more Some guidelines for measuring an investment manmarkets from the same resource base in order to agement firm's financial health are provided by an offset the cyclicalities experienced by all firms. For examination of average firm performance. Table 1 example, a firm working with defined-benefit penshows key benchmarks in the form of 1992-93 aversion plans may seek to enter the defined-contribuage profit margins and growth in pretax earnings, tion or private-client and wrap markets. revenues, assets, and expenses for a universe of 25 of Many firms trying to expand in this way are the most dominant institutional firms in the United managing a few billion dollars in assets and subStates. Dominance was determined by net new busiscribe to the idea that expansion equals success in the ness, perceived reputation, size, and clout, and Table investment management business. This strategy is 1 shows the firms segmented into percentiles. The not appropriate for and should not be embraced by benchmarks used in Table 1 reflect what should be all firms, however. Many firms should think about key concerns from a sponsor's perspective; in an retrenching rather than expanding. Even some mulenvironment of shrinking market share and growing tibillion dollar firms should concentrate on refocuscompetition, measures of growth and efficiency ining their people, their current lines of distribution, dicate the degree of manager competence and the and their product lines. Rather than aspiring to grow presence or absence of good business planning. from $5 billion to $10 billion or higher, these firms The performance numbers relating to earnings should, given the industry trends, concentrate on are very impressive for this universe of top firms. Pretax earnings growth was more than 30 percent for managing what they have efficiently and effectively. Well-managed niche or boutique firms are surthe top half of the universe and never lower than 13 127

Table 1. Key Performance Benchmarks by Percentile Ranking: Universe of Dominant 25 U.S. Institutional Firms, 1992-93 Measure Pretax earnings growth Revenue growth Asset growth Expense growth Average profit margin

1st Percentile

25th Percentile

50th Percentile

75th Percentile

100th Percentile

Average

Median

50.0% 58.8 135.5 74.8 73.1

46.8% 33.3 32.0 30.8 61.0

30.8% 24.3 19.1 22.0 50.0

23.8% 19.6 12.2 16.8 33.7

13.5% 14.3 3.3 0.1 23.5

31.0% 26.3 28.8 24.7 47.1

30.8% 22.3 22.9 21.4 45.0

Source: Investment Counseling.

percent. Profit margins were strong: The normalized 1992-93 average profit margin for the midpoint of this universe (the 50th percentile) was 50 percent, and the lowest profit margin was 23 percent. A similar study of 13 Canadian investment management companies recently completed by Investment Counseling (ICI) found an average profit margin of about 33 percent. Table 1 indicates a revenue growth of about 24 percent a year for the midpoint of the universe; revenue growth for the midpoint of the Canadian firms was about 10 percent a year. Expense growth was notably higher for the U.S. firms, reflecting a more competitive and sophisticated infrastructure. Asset growth was far more dispersed than revenue growth for these firms. The midpoint was about 19 percent; for the Canadian firms, it was 5 percent. An important point is that these dominant firms have a median asset growth of 22.9 percent. ICI believes that medium-sized firms (generally defined as $5$20 billion in managed assets) that cannot grow at 10 percent a year (as measured by net new business and additional contributions) will probably be restructured or out of business within 15 years. Firms with extended poor performance in their only product offering have already eroded beyond critical mass.

Efficiency A key concern from the sponsor's point of view is the efficiency of an investment management organization. With that concern in mind, Table 2 presents various employee and productivity data gathered from the lCI study of Canadian firms. The largest firm was about US$14 billion and the smallest, about US$500 million in assets under management. The table summarizes numbers of employees and high, low, and average revenue and asset productivity ratios by functional areas. The "investment professionals" are primarily portfolio managers; the ratio of total professionals to total employees averages 66 percent. This ratio is lower in U.S. firms because they typically have more support staff than Canadian firms. As would be expected, the number of portfolio managers, marketing professionals, and secretarial!clerical employees in a firm is closely

128

correlated with size of assets under management. Revenues and assets per employee, however, do not increase uniformly as size increases. These ratios are two of the most important for measuring a firm's likelihood of future success, but the idea that as a firm grows in size an increasingly larger portion of each dollar flows to the bottom line is something of a myth. When a firm is growing largely because of additions to existing business, expenses will not keep pace with revenues and the firm will benefit from economies of scale. In today's multibusiness paradigm, however, a firm that has served only definedbenefit plans may need to add services and products for the mutual fund or private-client markets. In that case, growth in expenses may well parallel or even exceed revenue growth. Table 1 reinforces this conclusion; for the large, dominant firms reported in the table, average growth in expenses was 24.7 percent, in assets was 28.8 percent, and in revenues, 26.3 percent.

Delegation When an investment management company is showing poor margins or poor growth, or has stagnated for some time, one way to determine possible causes is to analyze how the five or six key people at the firm spend their time. Table 3, also from the Canadian study, shows the average reported amounts of time spent in various tasks. The table shows the chairs and the president/chief executive officers spending almost a quarter of their time on marketing/ client services. This focus would be expected for small firms (a few billion or less in managed assets), but as firms try to realize economies of scale in serving big businesses, this responsibility should be given to others. If a firm cannot develop some kind of marketing or client-service effort that will remove some of the load from the key founders of the company, that firm is not going to grow. Most of the other numbers are as one might expect, except that chief investment officers also are spending nearly a quarter of their time on marketing/client services. The important mix of roles performed by chief financial or chief operating officers is interesting, in that such positions were rare in the

Table 2. Employee Ratios and Organizational Structure: Universe of 13 Canadian Firms, 1992-93 Criteria

Average

Median

High

Low

Number of:

Investment professionals Marketing professionals Total professionals Total employees

13 3 20 36

14 5 21 29

27 16 50 114

6 1 9 12

$ 575 1,726 341 206

$ 365 1,356 234 179

$1,220 4,044 712 349

$241 895 160 120

321 1,235 194 127

316 863 209 131

509 4,265 297 194

92 308 61 46

Revenue ($thousands) per:

Investment professionals Marketing professionals Total professionals Total employees Assets ($millions) per:

Investment professionals Marketing professionals Total professionals Total employees

Note: This chart reflects 8 of 12 firms that supplied both employee and financial information. Source: Investment Counseling.

investment management business until five or six years ago. As firms began to understand themselves as businesses that needed to be run as businesses, the number of, importance of, and demand for those chief financial officers increased dramatically. Table 3 also suggests that the chairs are not figureheads at most of these Canadian firms; they are active in three or four parts of the business. These firms are relatively large, and as part of the growth process, their officers need to determine what the key people do best and where the key people should spend their time. When growth occurs, the officers need to recognize infrastructure changes to become more efficient, or the firm will flounder; that the chairs continue to undertake multiple tasks indicates that this process of delegation may not have occurred. Hiring is a related problem. In small firms or poorly run large firms, new employees often find that their jobs are only loosely described and that their responsibilities overlap with others. Sometimes, firms try to hire (and retain) key people to assume chief financial or chief investment officer positions but the firms do not have a model in place by which they can explain the new employees' roles.

Client Services and Marketing A striking aspect of the U.s. study of 25 dominant institutional firms involves differences between firms that separate and those that combine client services and marketing responsibilities: Of the universe in that study, 71 percent worked through a combined client-service/ sales/ marketing approach. Table 4 shows that new business growth and additional contributions accounted for slightly more than 42 percent of overall asset growth between 1992 and

1993 for firms with separate departments versus about 21 percent for firms that combine the two areas. Fund performance fueled approximately 7 percent of the asset growth in both approaches; plan terminations and fund withdrawals accounted for about 12 percent negative growth in the separate functional approach and about 3 percent negative growth in the combined case. The combined sales and service approach seems to be the most effective approach in terms of retaining business. The large split in favor of a combined approach reflects the reality that separate departments are a luxury most small firms cannot afford. As firms become bigger, they tend to create client-service or product liaison groups that use their skills very specifically to service all different facets of their clientele's needs. The client-service people, as one might expect, are often not paid the formula-based incentives that the marketing people are paid. As suggested by Table 4, separating service and marketing responsibilities has proved to be a tremendous catalyst for asset growth for firms that can take advantage of their economies of scale; the overall change in asset growth from 1992 to 1993 was about 14 percentage points higher for firms with separate marketing and client-service responsibilities than for firms with a combined function. Should portfolio managers have a minor or a significant role in client service and marketing? Table 5 illustrates portfolio managers' involvement in the marketing process of the 25 firms in the U.s. study. "Low involvement" in the study meant little client contact, rare attendance at meetings, and a hands-off stance in general with respect to clients. "High involvement" meant frequent participation in 129

accounts and additional contributions than those in which portfolio managers are not as involved. Overall, the asset-change figures for both groups are similar. The active involvement of portfolio managers has been a catalyst for many firms, but the extent to which that involvement occurs and is beneficial depends a great deal on firm size. To be successful, portfolio manager involvement must be combined with a separate client-service unit. Many client-service managers today at leading firms are former portfolio managers who hold CFA designations. They are not sales people; they are strong, technically sound investment managers. Data such as those presented in Tables 4 and 5 should also persuade investment management firm personnel to think flexibly about their career paths. Portfolio managers may well become involved at some point in selling and servicing. Depending on the nature of a firm's clientele, the firm may retain combined roles in the future, but increased segregation and specialization within a given role seems likely. Separation of responsibilities addresses client-service issues more effectively than a combined approach, and client service is the most important response to the three Cs-eonsolidation, commoditization, and competition-occurring in the industry.

Table 3. Average Percent of Key Executives' Time Allocated by Responsibility: Universe of 13 Canadian Finns, 1992-93 Chair Management of company Investment policy / asset allocation Portfolio management Marketing/client services Administration Operations

38%

31 5 22

2 2

Presidentlchiefexecutive officer Management of company Investment policy/ asset allocation Portfolio management Marketing/client services Administration Operations

34 23

14 24

4 3

Chiefjinanciallchiefoperating officer Management of company Investment policy / asset allocation Portfolio management Marketing/client services Administration Operations

22 1

1 14 34 28

Chief investment officer Management of company Investment policy / asset allocation Portfolio management Marketing/ client services Administration Operations

10 38

33

20

o o

Director of marketing Management of company Investment policy / asset allocation Portfolio management Marketing/client services Administration Operations

4

8 3

Compensation

72 7 7

Note: Numbers will not equal 100 percent because they represent averages in each category. Source: Investment Counseling.

both the initial and final stages of the marketing process. High-involvement managers will see clients and prospects whenever possible. Table 5 shows that firms in which portfolio managers are highly involved in the marketing process generated more new

Whenever compensation figures are offered, identifying the context of those figures with respect to firm size and type is critical. Table 6 reports a study of portfolio manager compensation at 40 small, independent boutiques in the United States. The top panel is sorted by quartiles of total compensation; the bottom panel, by quartiles of average assets under management. The firms manage anywhere between $100 million and $3-$4 billion; most are under $1 billion. Average total compensation in the first quartile is $318,000 and declines to $45,000 in the fourth quartile. The largest rift in portfolio manager pay is

Table 4. Contributions to Asset Growth from Separate versus Combined Market and Client-Service Responsibilities: Top 25 Institutional U.S. Firms, 1992-93

Asset Change New accounts Additional contributions Performance Terminations Withdrawals Total change

Source: Investment Counseling.

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Separate Marketing and Client-Service Responsibility 33.4% 9.2 7.1

Combined Marketing and Client-Service Responsibility

-6.1

18.7% 2.4 6.9 -2.4

::5A

::!l2

38.2%

24.7%

Table 5. Contributions to Change in Assets from Low versus High Involvement of Portfolio Managers in Marketing Process: Top 25 Institutional U.S. Firms, 1992-93 Asset Change New accounts Additional contributions Performance Terminations Withdrawals Total change

Low Involvement

High Involvement

18.8% 3.6 8.8 -3.2 -1.1 26.9%

27.1% 5.4 5.7 -3.9 ::aA 30.9%

Source: Investment Counseling.

between firms managing on either side of $1 billion in assets. A range of firms by size will always show this kind of fragmentation, and compensation levels of in-house fund managers are often lower than even this group of relatively small firms, which are probably the lowest in the U.s. investment management community. The real problem for in-house fund managers, however, is the lack of opportunity. Even in these boutique firms, salary represents at least 2/3 of compensation in every quartile, and the ratio would be higher for strictly in-house managers. In either case, little bonus or equity opportunity exists. Salary as a percentage of total compensation should always be relatively small for the highest paid people in the business. Among the best firms in the business, salary percentage is perhaps 10-20 percent. Average compensation in these 40 small firms, however, would not be expected to reach that kind of percentage; Table 6 confirms that expectation and indicates a direct correlation between compensation and asset size. Table 7 reports a study of the compensation of senior portfolio managers on the domestic equity side in 12 large U.s. investment management firms. Three firms in this study had senior equity portfolio managers receiving cash compensation of more than

$1 million. When those three are excluded, average and median cash compensation is approximately $500,000 and is almost evenly split between base salary and bonus. A comparison of the average total compensation given in Table 7, $1.036 million, with the $318,000 average of the first quartile in Table 6 illustrates how compensation figures can easily be inappropriately compared. As total compensation increases for these firms, salary becomes a consistently smaller percentage of the total, and the highend portfolio managers' total cash compensation is so high that it would almost have to include some ownership or dividends. This observation points to a difficulty in any compensation study-determining the amount of real bonus, whether it is earned on a subjective or formula basis (rather than against a benchmark), and how much of it is dividend or ownership share. In examining salary numbers, using the correct universe is important. As the bottom panel of Table 7 shows, the salary of the lowest paid person in these 12 dominant U.S. firms is nevertheless higher than the first-quintile average shown in Table 6. The important aspect of measuring salaries and total compensation is to compare a firm with its reasonable competition. If the firm is not competing against bank trust departments for personnel, comparing the firm's salaries with bank trust salaries makes no sense. Table 8 shows compensation data for sales and marketing personnel for the same 40 U.s. boutiques reported in Table 6. As shown in the bottom panel, small asset size typically means low compensation. Average total compensation shows the biggest increase as firms cross the $1 billion level of assets. Salaries ranged up to $250,000, but more than 90 percent were $100,000 or less. The more highly paid a staff member, the smaller the percentage of total pay from salary. To relate these compensation levels to productivity, lei estimates that U.S. marketers should produce three times their pay in total produc-

Table 6. Portfolio Manager Compensation: Universe of 40 Small U.S. Firms, 1992-93 (U.S dollars in thousands) Quartile

Average Salary

Average Total Compensation

Range of Total Compensation

Salary as a Percent of Total Compensation

A. Sorted by quartiles of total compensation

W

~~

~W

Q2 Q3 Q4

94 61 40

135 81 45

$177-$1,301 108-165 60-106 25-59

66% 70 76 90

B. Sorted by quartiles ofassets under management Ql ($2,279) $137 $215 Q2 ($632) 56 103 73 102 Q3 ($345) 73 Q4 ($139) 64

$40--$1,301 25-255 40--178 40--120

76% 67 74 88

Source: Investment Counseling.

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Table 7. Compensation Composition: Senior Portfolio Managers Managing Domestic Equity: Universe of 12 Large U.S. Firms, 1992-93 (U.S. dollars in thousands) A. Total sample

Base Salary

Level Average Median Low High

$255 220 146 450

Base Salary as Percent of Cash Compensation 38.4% 33.3 6.7 94.1

Subjective Bonus $332 261 25 900

Subjective Bonus as Percent of Cash Compensation 46.46% 54.3 5.9 66.7

Total Cash Compensation $1,036 581 380 3,000

B. By percentile

Components Base salary Total cash compensation Base salary (percent of total cash compensation)

100th Percentile

75th Percentile

$ 450 3,000

$ 325 1,675

15.0%

19.4%

50th Percentile $220 581 37.9%

25th Percentile $200 494

1st Percentile $146 380

40.5%

38.4%

Source: Investment Counseling.

tion annually to justify these subjective bonus levels. This figure represents a benchmark for the better performing marketers in the United States, but lCI has not been able to formulate a similar benchmark in Canada. Compensation figures for Canadian marketing personnel are much lower than in the United States, reflecting the lack of industry recognition of marketing professionals in Canada. Recent Canadian studies show that some marketing people-not support personnel but full-fledged marketing professionals-are paid a base salary of only US$30,OOOUS$40,000 and receive subjective bonuses of no more than US$5,OOO-US$1O,000. Most of the sales/marketing people included in Table 8 were paid by formula-based incentive programs, which currently prevail in 70-80 percent of the U.S. industry. This percentage is declining, however, as subjective issues and team performance become

increasingly important. Depending on the size of the business, formula-based compensation can provide huge commissions to the marketers. Therefore, medium-size and large firms (those of $5-$10 billion and above) are finding that justifying the formulabased approach is difficult because the sales task is increasingly complex, sophisticated, and service oriented. Portfolio managers are often involved, for example, but if they are receiving subjective bonuses, they are paid nothing for their marketing efforts. One way to overcome these problems is to institute a marketing compensation pool, perhaps 25-30 percent of new business revenue. The person who actually closes the sale would receive a guaranteed percentage or a range of the total percentage, 15 percent for example, and the rest of the people in the group associated with the sale would divide the other 10-15 percent. This approach is designed to engender loyalty among all those contributing to the

Table 8. SaleslMarketing Compensation: Universe of 40 Small U.S. Firms, 1992-93 (U.S. dollars in thousands) Quartile

Average Salary

Average Total Compensation

A. Sorted by quartiles of total compensation Ql $103 $256 Q2 92 128 ~

00

W

C

$176-$440 95-174 69-93 23-61

B. Sorted by quartiles ofassets under management Ql ($2,279) $86 $173 66 104 Q2 ($632) 54 106 Q3 ($345) 76 Q4 ($139) 76

$23-$440 30-235 40-208 40-150

~ ~

Source: Investment Counseling.

132

Range of Total Compensation

Salary as Percent of Total Compensation 42% 75 87 94 66% 74 59 100

marketing effort and to focus the attention of all parties on the relationship between the marketing corps and the new business. Fairness in rewarding client-service and marketing people should be an issue for sponsors; unfair compensation to marketers will result in high turnover and a serious threat to the progress and survival of the management firm. Marketing has the highest rate of turnover in the business. One key to retaining marketing people is the level of compensation potential, and the other is the existence of long-term opportunity with a firm. Providing long-term opportunity is the area where many firms fall short. Compensation for senior client-service professionals in the 12 large US. firms is reported in Table 9. If these figures are to be compared with those for small firms, the context must be kept clear. In the small firms, people tend to occupy dual roles-they are marketers and client-service personnel-because those firms can rarely afford to split the tasks. Therefore, this analysis considers only senior client-service professionals in the large-firm group, for whom compensation generally includes a subjective (rarely formula-based) bonus. Their average total cash compensation, $476,000, was significantly greater than that of the high end of the first quartile of sales/marketing personnel in Table 8, which was $256,000. This difference reflects the substantial pay differential by asset size and firm economics.

Qualitative Evaluation Investment management firms should consider some important qualitative factors in their selfevaluations, and sponsors should also examine these factors in comparing firms. The worth of these firms cannot be judged by using some kind of discounted cash flow analysis based solely on the numbers and rules of thumb often found in leading investment management publications, such as three to four times revenue and six to seven times pretax earnings. Second-generation motivation. This factor pertains to whether the business is organized for and has

provided for succession or is a proprietorship. A proprietorship has an entirely different set of efficiency ratios, often has a whole different idea about compensation (the founder or the key people take it all), and, just as often, has no idea what the firm is going to look like two or three years from now. Any firm and any fund sponsor trying to figure out whether the firm will have low professional turnover and be modestly successful in the next few years must determine whether the important next generation of professionals who are running the firm now are being given the opportunity to develop their abilities and a stake in the firm. Second-generation motivation is certainly lacking in many US. and Canadian firms today.

Understanding of the client and consultant perspectives. Investment managers often do not understand the client and consultant perspectives. Some managers pay external parties a great deal of money to determine the consultant or the client perspectives on their firms. This task could probably be more easily and efficiently achieved, however, by the inhouse marketing crew. The "star" factor. Having a star in a firm is, at first, a catalyst for excitement and success, but then it can become a debilitating influence. Stars have precipitated the rise of many a prominent boutique in the United States, but if they did not provide for second-generation motivation, the stars also more than likely caused their firms' fall. Momentum. This factor includes asset, revenue, and earnings growth; professional development; product diversification as it affects performance; and infrastructure capacity. Sponsors are obviously interested in the firm's ability to sustain reinvestment, but those 52 percent or 60 percent pretax profit margins and that firm value of two to four times revenue are not sufficient. Sponsors want to know where management firms are going in terms of their asset, revenue, and expense growth. Operating performance and the momentum depicted by trends in performance are as important as investment performance. To discover the directions of

Table 9. Compensation Composition: Senior Client-Service Professionals, Universe of 12 Large U.S. Firms, 1992-93 (U.S. dollars in thousands)

Level

Base Salary

Average Median Low High

$196 200 150 225

Base Salary as Percent of Cash Compensation

49.5% 33.3 28.3 100.0

Subjective Bonus

$280 300 0 519

Subjective Bonus as Percent of Cash Total Cash Compensation Compensation

50.5% 66.7 0.0 71.7

$476 450 225 724

Source: Investment Counseling.

133

growth, sponsors need to study at least three years cycle, and it is important to sponsors, to those thinkof operating data; that is a long enough time for most ing of buying or selling a firm, to employees, and to measures to indicate a firm's direction. potential employees. Where is the firm going? Has it Professional development of the people in a firm hit the top of the growth curve and is now either is part of building for the next generation. Sponsors tumbling or drifting down, or is it still growing? Is want to know who they are hiring-not just today, senior management relatively young? but for tomorrow's needs as well. Considering this sort of timing is a major element One aspect of the study of the 25 dominant instiwhen a sponsor or individual is considering a move. tutional firms in the United States was to determine A firm may have great numbers today, but simply whether asset-class and product diversification had applying a few screens may show that the numbers anything to do with asset growth, revenue growth, will not look nearly as good in a year. and margins. The study came to a very distinct conThe same approach is useful for firms in analyzclusion: Firms that concentrate on a narrow set of ing whether the time has come to acquire or to sell. asset classes (two or fewer asset classes) and, at the The current merger and acquisition market is frensame time, have broad product offerings within the zied; as of November, the year 1994 (78 transactions) classes (more than two products in each asset class) had surpassed 1993 (66 transactions) as the busiest have by far the best efficiency ratios. Part of the M&A market in the industry's history. Probably 30reason is that the larger, more mature firms have 40 buyers exist for every seller; so, the market is achieved higher margins than most small competing overwhelmingly a seller's market. firms. At the same time, those large firms have recognized the increasing competition for existing products like defined-benefit plans and recognized Conclusion the importance of growth; they realize that a 40 percent profit margin on $40 billion in assets is prefFrom the fund sponsor who is interviewing or meeterable to a 60 percent profit margin on $10 billion. ing the leading officers of an investment manageThis finding indicates that firms can become too ment firm to the young employees looking up at diverse and too extended-to the detriment of their those officers, the firm's opportunities and its potenprofit margins. tial predicaments are important elements of valuMomentum may depend on the firm's infraation. How is the firm now coping with industry structure capacity. Excess capacity, when a firm is competitiveness, and more importantly, is it poised managing $1 billion and could easily manage $2 or to sustain or achieve success in the future? Under$3 billion with no professional hires, is a good sign. standing industry trends, developing a knowledge Firms that are hard-pressed to manage their $1 bilof operating performance and compensation levels, lion in assets should be devalued in any valuation. and recognizing important qualitative evaluation Timing. This factor pertains to evaluating factors should contribute to answering these queswhere the investment management firm is in its life tions.

134

Question and Answer Session Charles B. Burkhart, Jr. Question: Why do firms need to grow their assets by 10 percent a year? Burkhart: The 10 percent growth number is applicable to only the small and medium-sized firms, firms that are typically $20 or $30 billion or under, which is the huge majority of firms. The number is based on our expectations of market potential for many of these firms, on how tough it will be to string together 12,14, or 16 percent compoundgrowth performance, and on how difficult the institutional business is going to be in terms of the three Cs and actually maintaining one's business. I think 10 percent-5 percent from new accounts and additional contributions and 5 percent from fund performanceis a moderate benchmark for determining whether a firm will survive and grow materially beyond its current asset level or whether it will start shrinking. Many firms in the United States and Canada are shrinking. Question: Why did you give a figure of 5 percent asset growth

for Canadian firms? Burkhart: The Canadian 5 percent was based on a 13-firm sample representing the average asset growth for those firms in the last two years. The number needed for future growth may be somewhat higher or lower than 5 percent but is, in any case, lower than the benchmark for US. firms. The Canadian market is mature but less competitive than the US. market, and the Canadian firms have not been as active as U.S. firms in marketing or client service. The ones that will be the leaders in the current consolidation will grow at rates much higher than 5 percent, and the mediocre firms will grow at lower rates. Question: When two firms combine, what is the percentage of assets or percentage of client accounts lost in the merger? Burkhart: The answer depends on the circumstances. The investment management business is a people business; if the people don't come, the assets rarely follow. So, if the merger is in

friendly circumstances-in our business, it almost has to be-and if it is a combination of two whole businesses, 80-90 percent of assets may transfer into the new entity. Some 10-20 percent of the assets may go elsewhere. If some part of one of the firms is not merging, those assets will stay with the people who are not going over to the newly merged entity. Question: Are the profit margins currently being achieved sustainable in a more competitive world? Burkhart: No, indeed. Margins are going down. We have been studying them for six years, and the days of easily generating 4050 percent normalized pretax margins are over. Everything I discussed is pushing margins down for most firms. Some firms will take advantage of the competitive factors and be able to keep their margins up, even increase them, but the vast majority will experience declining margins and revenue growth.

135

Evaluation of Major Decisions Affecting the Pension Plan ThomasJ. Cowhey, CFA Executive Director, Trust Asset Management Bell Atlantic Corporation

Fiduciaries have an ongoing responsibility to evaluate the nature of plan returns and the quality of investment decisions. Assessment of plan performance is enhanced by focusing on the plan's policy portfolio, the benchmarks used, and the costs incurred.

Pension plan sponsors have fiduciary responsibilities to manage and monitor plans. In response, this presentation presents an approach for evaluating plan sponsors' overall management of pension funds. The focus of the approach is on plan performance relative to the plan's policy portfolio, individual asset-class benchmarks, and management costs.

Plan Performance and Policy Portfolios Suppose an independent auditor has been retained to conduct an audit of a plan sponsor's management of its overall pension fund. The audit begins with a meeting between the auditor and the sponsor in which the sponsor outlines the plan's investment process. The sponsor first talks about setting investment objectives and how the asset allocation policy is established. The sponsor then describes the investment strategy for each asset class, including what percentage of the fund is actively (or passively) managed, what percentage of the fund is internally (or externally) managed, what investment styles are used in the equity asset classes, and what the investment guidelines are for the various managers. Finally, the sponsor describes the monitoring of the pension fund's management. As the discussion concludes, the plan sponsor displays a performance chart that shows how well the fund has done versus other funds in a peer universe during the past one, three, and five years. This chart, Figure 1, shows that the plan (return indicated by solid circles) has outperformed the median fund in the universe (indicated by the cross-bars in each box) in all periods. The sponsor indicates that the individual asset classes also achieved good results. The auditor's initial conclusions might be that

136

this fund has performed well, that it appears to have a reasonable investment approach, and that appropriate controls seem to be in place. Suppose, however, that the auditor decides to probe further. The auditor recalls some analysis done by a firm called Cost Effectiveness Measurement showing that, during a four-year period, when pension funds' actual returns were regressed against the returns of the funds' policy portfolios-that is, passive portfolios constructed to reflect the funds' asset allocation policies-on average, about 35 percent of the variance in the pension funds' returns could be explained by policy differences. 1 Therefore, although the plan sponsor seems to compare well with the peer universe, the auditor asks the sponsor to construct a policy portfolio for the fund and measure actual returns against that policy portfolio to obtain a clear picture of the impact of the fund's asset allocation on the results. Comparison of the fund's performance with the policy portfolio still indicates favorable results for the fund, but this approach finds a lower implementation return (excess return over the policy portfolio) than did the comparison with the median plan. Therefore, the auditor decides that more can be learned about the source of the returns by investigating how other pension funds have performed in relation to their policy portfolios. In this comparison, as Table 1 shows, during the 1991-93 period, the implementation returns for all funds were found to be, on average, about 140 basis points (bps) a year more than their policy portfolios. Therefore, although this plan sponsor has outper1 "The Economics of Pension Fund Management," The Ambachtsheer Letter (October 5, 1994).

Figure 1. Total Fund Investment Performance: Example 13 12

11 10 9

~ 8

~ 7

~

6

1: ~

3

2 1

o -1 -2 1 Year

3 Year

5 Year

Source: Frank Russell Company.

formed the policy portfolio, the outperformance was mix around the policy mix. All of the value added in implementation return appears to have come from consistent only with the average of other funds for in-asset-class investment decision making, which apthe period. pears to have generated the return at fairly high At this point, the auditor decides to examine the levels of statistical confidence. sources of performance for the peer group to see why or how the typical fund managed to average 140 bps a year in excess return over its policy index. Table 1 Plan Performance and Benchmarks shows the return broken down into two components. The auditor's analysis so far indicates that the plan One component is the portion that can be attribsponsor is adding value primarily by generating a utable to varying the asset allocation mix around the return within asset classes in excess of the benchpolicy portfolio objectives. For example, if the policy marks for those classes. Indeed, the sponsor organiobjective for large U.S. equities was 40 percent, the zation states that the fund is performing well versus fund managers might have allowed the fund mix the benchmarks used in the various asset classes. The during some period to go from 35 percent to 45 sponsor's U.S. equity portfolio uses the S&P 500 percent and back again. This component of impleIndex as the benchmark, and the auditor confirms mentation return measures how much return those that the sponsor has outperformed that benchmark variations generated for the fund. The second comfairly well during the past two or three years. The ponent, in-asset-class return, focuses on fund return international equity portfolio uses the MSCI EAFE relative to the benchmark return for the asset classIndex as its benchmark, which it appears to have that is, how much return was earned from the deoutperformed reasonably well during the past couployment of active management in the asset class. ple of years. The figures in Table 1 suggest that these funds Given the importance of the benchmarks in asgenerated little, if any, return by varying the asset sessing the sponsor's apparent superior performance, however, the auditor raises the following Table 1. Fund Implementation Returns questions: How difficult is outperforming these 1991 1992 Return 1993 benchmarks, and how good are the benchmarks? In asset class

Varying asset allocation mix Total

1.6% (4.8)

1.1% (5.5)

1.9% (8.1)

-D.4

0.3

-D.3

lli)

G:§)

1.2 (3.3)

1.4 (6.9)

(-2.0) 1.6 (7.1)

Note: Figures in parentheses are t-statistics. Source: Cost Effectiveness Measurement.

Domestic Benchmark Misspecification One way of examining the appropriateness of domestic equity benchmarks is shown in Table 2. The table lists 13 BARRA risk factors and uses as a base index the exposures to those factors of an approximately 1,200-stock universe. It then shows the exposure to those factors for the S&P 500 and a 137

Table 2. Benchmark Misspecification with the S&P SOOlndex

Risk Factor Success Variability in markets Growth Earnings variation Earnings / price Book/price Yield Size Small capitalization Trading activity Labor intensity Foreign income Financial leverage

S&P 500 -0.06 -0.13 -0.11 -0.05 0.02 -0.02 0.08 0.34 0.00 -0.01 -0.03 0.15 0.04

Institutional Investment Manager Composite 0.12 0.26 0.23 0.19 0.04 0.06 -0.28 -0.36 0.08 0.23 0.17 0.02 0.01

Source: Harris Bank.

Harris Bank composite of its U.S. institutional manager universe in relation to the BARRA index. For example, the figures for the size variable indicate that the capitalization of the S&P 500 is 34/lOOth of a standard deviation above the market-weighted average of all stocks in the universe. This 0.34 statistic means that the S&P 500 has a "largeness" bias relative to the entire universe. In contrast, the size figure for the institutional investment manager composite is 36/l00th of a standard deviation below the BARRA universe. Based on the composite results, U.s. equity investment managers appear to have a considerable "smallness" bias relative to the S&P 500. Plan sponsors, by hiring small-cap and large-cap managers and managing their portfolios with index funds, are able to adjust for these size biases. Then the question becomes: What benchmark does the institutional plan sponsor's portfolio look like? For this comparison, the auditor begins with market capitalizations. The S&P 500 is about $6 billion in terms of average market capitalization, whereas the Russell 3000 Index, a much broader universe than the S&P 500 and one that encompasses more of the overall stock market, is approximately $1.5 billion in average market capitalization. The median market capitalization for the typical pension fund in the Frank Russell Company client universe is $1.51 billion, which means that the median fund looks a lot more like the Russell 3000 than the S&P 500. What are the implications of these size variances over time? Figure 2 charts the performance of the Russell universe and the Russell 3000 in relation to the S&P 500 for rolling one-year periods during the 1985-94 time frame. The solid line charts the performance of the Russell universe, and the dotted line charts the performance of the Russell 3000. Outper138

formance of the S&P 500 by the Russell universe (Russell 3000 Index) is indicated when the solid (dotted) line is above the zero-excess-return horizontal line; underperformance is indicated when a return line falls below the zero line. The figure reveals several quarters when the Russell 3000 and the Russell universe of managers were outperforming the S&P 500 but also several periods when they were underperforming it by large amounts. The figure also shows that the Russell 3000 is highly correlated with the Russell universe; regressing the universe against the Russell 3000 explains 75 percent of the former's return variation. These findings have important implications for performance attribution. If a plan sponsor is using only the S&P 500 as a benchmark and if the actual portfolio has risk characteristics that more closely resemble the Russell 3000, the analysis of implementation return will not be accurate and faulty decisions may be made about management results. Using the correct benchmark-in this case, the Russell 3000leads to an interpretation of added value that is quite different from using the S&P 500, and a true judgment can be made about management decision making. Therefore, using the appropriate benchmark is critical when evaluating the success or appropriateness of active versus passive management.

International Benchmark Misspecification Benchmark selection involves important specification issues in the global arena as well as the domestic. As Table 3 indicates, one benchmark misspecification (illustrated by the EAFE Index in this case) is a weighting of Japan in the benchmark that is different from the weighting given by most portfolio managers. Japan is a large market with a large market capitalization. It represents more than 48 percent of the market capitalization of the cap-weighted EAFE Index, but a typical current manager has about 34 percent exposure to Japan. In addition, the typical active manager seeking to outperform the EAFE Index has about a 6 percent exposure to non-EAFE countries. If a sponsor does not take these kinds of differences into consideration, Table 3. Benchmark Misspecification: Country Weights for the EAFE Index and for the Median Manager Country

EAFE

Median

Japan United Kingdom Germany France Switzerland Non-EAFE

48.5% 14.5 6.0 5.4 4.5 0.0

34.0% 12.9 5.0 6.4 4.0 5.8

Source: Frank Russell Company.

Figure 2. Performance of the Russell 3000 Index and the Frank Russell Company Universe versus the S&P 500: Rolling One-Year Periods 0.06 0.04

~

0.02

l::

.... ;:l OJ

P:::

0

. - 3000 . . Russell

S&P500

if> if>

OJ

u

x

..

-0.02

I:l.I

-0.04 -0.06

Frank Russell Company Universe '----------'---'-----'-_'------"---_'------"---_'------'--_'----'--_'------"---_'------"---_'---.-.L_L---l

Ql Q3 Ql Q3 Ql Q3 Ql Q3 Ql Q3 Ql Q3 Ql Q3 Ql Q3 Ql Q3 Ql 85

86

87

88

89

90

91

92

93

94

Source: Frank Russell Company.

the sponsor's analysis of performance can be misleading.

Plan Performance and Costs The audit process to this point has analyzed the plan's policy portfolio, the extent to which the plan is generating implementation returns, and the potential need to make adjustments for both domestic and global benchmark deficiencies. An important remaining audit element is cost. How much money is the plan sponsor spending in pursuit of these returns? Is the fund a high- or low-cost operator? To answer these questions, the auditor first needs to gather detailed cost information: how much money the sponsor pays for staff, investment managers, trustee fees, consulting fees, and so on. These costs can be totaled and divided by the average assets under management to obtain an expense ratio. The auditor in our example follows this process and finds that the plan, which is a large plan, is in the top 20 percent in terms of management costs. On an absolute basis, it appears to be a high-cost plan, but the auditor decides to examine these costs further. Table 4 provides an analysis of factors that have been determined to have a significant impact on plan costs. It shows that operating costs relate negatively to the size of the plan, which makes sense because the larger the plan, the more economies are realized in terms of investment management fees and the spread of fixed costs across a large asset base. Costs go down with each incremental increase in plan size. Therefore, a $36 billion fund that appears to be much more cost efficient than a $100 million fund mayor may not be efficient once adjustments are made to reflect the impact of the size factor.

Table 4 also shows that plan costs vary directly with the size of the plan's allocations to equities. The higher the proportion of a plan in stocks, the higher the costs. Cost analyses should adjust for this relationship. If one plan decides to run an asset allocation of 60 percent stocks and another plan chooses to run a stock allocation of 40 percent, the plan with the larger stock exposure should not be penalized for having higher costs if the costs are commensurate with the typical costs for other plans following a high-equity policy. Differences in costs associated with differing asset allocation decisions are even more pronounced for real estate and nontraditional investments. Real estate, venture capital, and oil and gas limited partnerships are all higher-cost investment pursuits. Table 4. Fund Operating Costs by Characteristic (bps) Fund Characteristic

1990

1991

1992

1993

92 (13)

79 (10)

76 (10)

(10)

-22 (-12)

-20 (-11)

-19 (-12)

(-11)

Stock proportion

32 (4)

38 (5)

33 (4)

40 (6)

Real estate and nontraditional investments proportion

113 (6)

107 (5)

105 (5)

127 (7)

Canadian

-18 (-7)

-14 (-5)

-12 (-5)

-9 (-5)

Constant Size of plan (log)

72

-19

Nole: The numbers in parentheses are I-statistics. Source: Cost Effectiveness Measurement.

139

Cost analysis limited to the overall expense ratio Table 5. Fund Implementation Returns versus Operating Costs would penalize funds that pursue such investments even though the investments may, in fact, be contrib1990 1991 1991-93 1992 1993 uting higher implementation returns than other inConstant 1.7 0.62 0.96 0.71 0.72 vestments and, in the long run, will boost the fund's (8.2) (2.6) (4.7) (3.7) (4.1) overall expected return and diversification characteristics. Cost -0.03 0.01 0.0 0.03 0.06 (-2.0) (0.3) (-0.2) (4.2) (1.9) When the plan sponsor's costs are analyzed in this type of approach, what the auditor thought to be a very Note: The numbers in parentheses are t-statistics. high-cost pension fund actually appears to be a relaSource: Cost Effectiveness Measurement. tively low-cost fund. Adjustments for plan size and differences in asset allocation provide a completely earlier, however, and if the reported returns were different understanding of a fund's cost structure. adjusted for those deficiencies, the cost-return relaWith this information, the auditor can analyze tionship might be different. Therefore, further work the fund's costs relative to its investment decision needs to be done to refine these results. Nevertheless, making: How much return should be expected from the findings for the peer group and the analytical this fund in light of the amount of risk being taken process of adjusting a fund's results for benchmark beyond the policy portfolio's risk? Are the expenses deficiencies, strategies, and implementation decifor an active equity manager and/or to pursue an sions allow an auditor to reach more supportable implementation return by following certain assetperformance conclusions than otherwise. class strategies appropriate? Table 5 shows an analy_ sis of implementation returns versus operating costs during the 1990-93 period for the same universe of Conclusion funds used for Table 4. The coefficient on the cost Plan sponsors have a fiduciary responsibility to variable is 0.03 and positive, which means that for this period, for every 10 bps of expenditures, the monitor their own decision-making performance in universe was able to generate 30 bps of incremental light of the plan's performance. They must continually evaluate the nature of the fund's returns and the return. In short, during this period, the typical pension created value through its investment decision quality of investment decisions. They must ask themmaking. selves the tough questions about costs and results, These implementation returns were no doubt and they need to seek answers through the types of influenced by the benchmark deficiencies identified analysis discussed here.

140

Question and Answer Session Thomas J. Cowhey, CFA Question: How does Bell Atlantic decide on the amounts of assets to allocate to each manager? Cowhey: As an example, for the U.S. equity asset classes, we first decide how much residual risk we can take in the US. equity portfolio-that is, how much variation of return we can tolerate around our benchmark portfolio for US. equities, the Russell 3000. Then we allocate money between an index fund and a few active managers through an iterative process that achieves the optimal allocation-defined in terms of expected return relative to the amount of acceptable risk taken per unit of cost to be spent to pursue that expected return.

We follow this process for each asset class so that we are optimal in aggregate. The process is dynamic, however, and we monitor the relationships of active to passive to ensure that they remain optimal. A time may come, for example, when active fixedincome management offers a higher return per unit of risk and per unit of cost than other active portions of the portfolio, and we may want to rebalance the portfolio to take advantage of such an opportunity. Question: How does Bell Atlantic decide between internal and external management? Cowhey:

We have not reached

a final conclusion on this issue. We began the type of analysis reported here a few years ago, and we are constantly seeking to improve in cost-effectiveness. We have used index funds and negotiations with investment managers to drive our investment management costs down. These actions and the competitiveness in the industry have helped us achieve significant cost benefits without the need to take the next step to internal management. We might decide to take that step in the future, but before doing so, we would need to assess the benefit side of the issue and ensure that we can put the administrative tools, people, and resources in place to be successful.

141

Hiring and Firing an Investment Manager Keith P. Ambachtsheer President KP.A. Advisory SeNices Ltd.

Hiring and firing investment managers have traditionally been carried out by an inductive approach. A deductive approach, however, may be preferable. A deductive approach draws on a different set of manager search questions to identify and hire successful managers-and to fire unsuccessful ones.

Finding, hiring, and keeping active investment managers that have better than a 50-50 chance of adding value requires more than knowledge of individual investment management firms and their historical performance. It requires a priori beliefs about the characteristics of successful investment management firms and a sound manager search strategy.

The Market for Investment Management Services A retail market and a wholesale market exist for investment management services. The retail market includes mutual funds, 401(k) plans in the United States, group Registered Retirement Savings Plans in Canada, and high-net-worth individuals. In the wholesale market, the primary purchasers of investment management services are defined-benefit pension plans. The wholesale market has been dominant throughout the past 20 years and is the primary subject of this presentation. Aggregate defined-benefit assets in the United States total approximately US$3 trillion, and the corresponding Canadian asset base is approximately C$300 billion, which supports the usual United States-Canada ratio rule of 10 to 1 in almost anything measured.

Pension Funds as Businesses The focus of K.P.A. Advisory Services since its founding in 1984 has been on understanding the motivations and the economics of pension fund management. A useful approach to refining that understanding has been the development of a business

142

paradigm for considering the fundamental issues that surround pension fund management. Thinking of fund management as a business makes certain issues clear. For example, considering pension fund management as a business suggests that two fundamental strategic issues confront the business. One concerns the funding of pension plans, and the other concerns investment policy. Ideally, the two should be jointly determined. What is more germane to this discussion is the business issue that follows the determination of investment policy: How should that investment policy be implemented? Fundamentally, two implementation choices exist. One focuses on the legal necessity to have and to invest pension assets; it involves a "satisficing" posture. The fund wants to get the job done by keeping a low profile and fulfilling its fiduciary responsibility while spending as little money as possible. The approach is to generate a return in the most reliable, dependable, cost-effective way possible. The policy return is, in this strategy, simply a function of policy risk and cost. The alternative choice is to go beyond the legal minimum and view the assets as an opportunity to make money. This approach seeks to generate additional return, with the implication that additional risk and costs will be incurred. Most pension funds opt for the alternative of seeking incremental returns. The challenge then becomes how to implement the decision, which triggers a need to develop information about the costs and benefits of the decision. The fund needs to keep track of its policy return, incremental return, cost to implement the policy, and additional expenses incurred to produce the additional return.

A Business Information System for Pension Funds The essence of the business issue is incremental returns-the notion of producing value for the fund. One way to examine value production is to look at actual returns against calculated policy returns, the ?ifference being the value added by actively managmg the fund. The value added can then be examined in light of the operating costs needed to achieve the returns. Figure 1 reports profiles by implementation returns and incremental operating costs for 76 pension funds from 1991 through 1993 in the Cost Effectiveness Measurement (CEM) data base that have a three-year continuous history.1 The funds, nearly all corporate or public, were mostly multibillion dollar funds with multiple external managers whose average mandates were in the US$100-US$300 million range. Figure 1. Implementation Retums versus Incremental Operating Costs for Funds with Three-Year Histories, 1991-93 S

5

Q)

]

co

4

c cco

3

0

;:l

~ Cfl

E ;:l

0 High Value Added/ High Cost

2-

The high-value-added/low-cost funds averaged 1.2 percent in implementation returns and -6 basis points (bps) in incremental operating costs, and the high-value-added/high-cost funds averaged 1.7 percent in returns and +7 bps in costs. The low-valueadded/low-cost funds returned -1.0 percent on average, with average incremental costs of -8 bps, and the low-value-added/high-cost funds averaged -0.7 percent returns and +9 bps in costs. Figure 1 supports the business decision to pursue incremental returns: Active management produced significant incremental returns for these funds during this three-year period. Extra expenses produced extra returns. Note, however, that how much value is added by active management obviously depends on the benchmarks used for comparison. For example, if the fund uses a broad investment strategy that mcludes small-capitalization stocks and then uses the S&P 500 Index as the only benchmark, measured incremental returns will vary from one scenario to the next depending on how small-cap stocks perform. Thus, these findings must be interpreted with caution. A matrix such as Figure 1 allows a fund to evaluate its business decision to pursue active management by considering whether the incremental returns are sufficient to justify the incremental expenses. If that question is answered ~ffirmatively,a successful manager search strategy IS necessary.

~c O O 0 .~ 0 ~----U-O~On«O)-C,+\f(}d..}-------~ -M----Se--h--------------

~ ~

l5..

..§

-1 -2

0

0

-3 L -_ _------.J -40

00

Low Value Added/ 0 Low Cost 0 00

-20

0

0 0

0

0

-----L

o

anager

0 Low Value Added/ High Cost

----L_ _~

20

40

Incremental Operating Cost (basis points)

Source: Cost Effectiveness Measurement.

The value added by a fund is captured on the vertical axis of Figure 1. The full operating costs of running a fund-both direct investment management and governance and administrative costsare captured on the horizontal. CEM has generated benchmark costs based on fund size and asset mix that are used to determine whether a fund's actual costs are higher or lower than benchmark costs. A fund's ~ncremental operating costs are primarily determmed by the degree to which the fund uses external and/ or active (high-cost) versus internal and/ or passive (low-cost) portfolio management. 1

For a more-detailed exposition of these findings, see Keith P: Ambachtsheer, "The Economics of Pension Fund Management," Fmanczal Analysts Journal (November I December 1994):21-31.

arc Strategy

How do fund spon;furs go about assuring themselves beforehand that the managers they hire have the prospects of adding value? Sponsors can follow either an inductive or a deductive approach to the task.

The Traditional Inductive Approach Most search processes still use an inductive approach; that is, they go from the specific to the general. The process begins with the sponsor finding all the high-performance managers, putting them on a list, and asking them a series of screening questions to narrow the list. These "requests for information" are very detailed and include questions about the people in the firm, the continuity of its employees' tenure, the capital structure of the firm, how the firm manages money (what is the firm's style), and whether the firm presents performance in line with the AIMR Performance Presentation Standards. The decision to fire is also generally straightforward. The standard rule is that a fund manager should be fired if the manager produces four years of poor investment results. 143

These traditional approaches to initiating and terminating investment management relationships should be questioned. The premise on which they are based may be wrong. Table 1 addresses this issue. Consider three possible scenarios for active managers. In each scenario, 1,000 active managers exist. In the first scenario, only good managers and bad managers exist and half of the group are good, half are bad; in the second scenario, only average managers exist; and in the third scenario, most managers (800) are average, but a few (100) are good, and a few (100) are bad. In Scenario I, the good managers have a .9 probability each year of beating their benchmarks (that is, having a positive-alpha, + a, year); the bad managers have a .1 probability of beating their benchmarks. In Scenario 2, all managers have a .5 probability of beating their benchmarks in any given year. In Scenario 3, the distinction between the good and the bad is no longer as severe as in Scenario 1; a good manager has a .6 probability of beating the benchmark in any given year, the average manager has a .5 probability, and the bad manager has a .4 probability. Thus, the probabilities of over- and underperformance are no longer massive, merely marginal. If Scenario 3 reflects reality, as most parties in the investment business doubtless believe, what are the implications for a search process? To help answer that question, Table 1 shows the probability, in all three scenarios, of active managers beating their benchmarks for five consecutive years. In Scenario I, 295 of the 500 good managers would be expected to have five good years in a row; none of the bad managers would be expected to meet that expectation. In Scenario 2, the probability is that 31 of the 1,000 managers will have five successive good years, despite the fact that the individual-manager chances are only .5 each year. In Scenario 3, 8 of the 100 good managers, 25 of the 800 average managers, and none of the 100 bad managers would be expected to have five successive years of positive alphas.

If Scenario 3 represents reality, how can the inductive approach be effective? A screen based on all the high-performing managers for a typical five-year period would identify 33 managers, 8 of which would be truly good and 25 of which would be average. The sponsor would interview many average managers that appear to be good, which would be defeating the purpose of the screen. So, perhaps the whole search strategy deserves rethinking.

The Deductive Approach A deductive approach may offer better prospects for success than the traditional inductive approach. It would begin by outlining the attributes of successful active management-what characteristics a manager would have to possess to be one of those in Scenario 3 with a .6 probability of positive excess return each year-and proceed to look for managers with those characteristics. Defining successful active management. What is known about successful active management that could be used in such a search strategy? A 1979 article by Ambachtsheer and Farrell identified four attributes of successful active management. 2 The first is an understanding that the process of active management can be decomposed into information processing, portfolio rebalancing, and learning components. The second attribute of successful active management is predictive ability about some aspect of the investment universe-be it markets, sectors, or individual securities. Third, that predictive ability must go beyond information content; it must be sufficient to overcome the cost of transaction. So, the active- management challenge involves balancing potentially low-quality forecasts against the certainty of transaction costs in adversarial markets and trying to squeeze out some net positive return-the essence of successful portfolio rebalanc2 Keith P. Ambachtsheer and James L. Farrell, Jr., "Can Active Management Add Value?" Financial Analysts Journal (November/December 1979):39-47.

Table 1. Probability of a Manager Beating the Benchmark Fund Manager Scenarios

Probability of One+ Year

Probability of Five+ Years

Actual Number of Managers

E(Numberof Five+ Years)

Scenario 1: Only good and bad managers exist. Good Bad

.9 .1

.590 .000

500 500

295

Scenario 2: Only average managers exist. Average

.5

.031

1,000

31

.6 .5

.078 .031 .001

100 800 100

8 25

Scenario 3: Good, average, and bad managers exist. Good Average Bad Source: K.P.A. Advisory Services.

144

.4

a

a

ing. Fourth, every portfolio management process needs explicit feedback on the accuracy of the predictions going into the process and on the efficacy of the rebalancing, and people need to learn from this feedback. Ambachtsheer and Farrell, using the Wells Fargo dividend discount model and Value Line momentum inputs, structured portfolios that beat the S&P 500 by 500 bps a year after transaction costs. The question is, of course, can such results be replicated in the "real world," where portfolios can be assembled and managed only through trading in adversarial securities markets? In the securities markets, the only way investment results are achieved is through a manager assembling an initial portfolio and then making portfolio changes by selling to and buying from other parties. If everyone is trying to do the same thingproduce a positive alpha after transaction costs-a profoundly adversarial situation arises. One party can win only if the other one loses. Every successful portfolio adjustment means that someone is making an unsuccessful portfolio adjustment. Treynor has suggested the analogy of a poker game: 3 Everyone is playing a hand (their portfolios); they are playing against other people, who are also trying to play their hands (their portfolios). Portfolio managers know their own portfolios, research, and judgments, but they do not know those of other players. Because in this game when one wins the other loses, ferreting out that information about one's adversary while concealing one's own is crucial to successful active management. If a manager knows more about his or her opponent than the opponent knows about the manager, on average, in their trades, the knowledgeable manager is likely to win and the opponent likely to lose. In about 500 B.C., the Chinese philosopher Suntzu (in The Art of War) described three possible wartime situations: knowing neither the enemy nor one's self, knowing one's self but not one's enemy, and knowing one's enemy as one knows one's self. Only the third kind of portfolio manager has a chance of being successful. Knowing one's self is fine, but it is not enough. A manager who does not know the opposition is a.5 (average) manager. Knowing when to play and when not to play is key, and this wisdom can come only from knowing one's opponents: knowing why one manager is losing when another particular manager is winning. Implications for the manager search. These insights into the game of active management have important implications for active-management search processes. As Perold and Salomon have noted, even if a manager's investment process is 3 Jack 1. Treynor, "The Economics of the Dealer Function," Financial Analysts Journal (November/December 1987):27-34.

successful, it will experience a decay function. 4 Every successful wealth-generating process has a size-related curve that, at some point, will peak and begin to deteriorate. That is, a successful active manager cannot enter the market with increasingly large blocks and maintain a constant probability of success. Figure 2 illustrates Perold and Salomon's point. Figure 2. Wealth-Maximizing Block Size with Linear Spreads 5.5 Ul

5.0

0.-

on '+-<

0


"0

il)

....

"0

4.5 Paper Excess Return 4.0 -

c

3.5 -

6

3.0 -

;:l

"0 il) "0 "0

-<

il)

..8 ~

Bid-Ask Spread

Wealth

2.5 2.0 1.5 1.0 0

2

4

6

8

10

12

Block Size (millions of dollars)

Source: K.P.A. Advisory Services, based on Perold and Salomon, "The Right Amount of Assets Under Management."

Figure 2 depicts the relationship between value added by the manager and trading block size. Excess return on paper is assumed to be 400 bps, and the 45-degree line assumes a linear cost function. 5 When the block size is small, say $1 million, the manager can generate the difference between 400 bps of excess return and approximately 100 bps in transaction costs to achieve approximately a 300-bp net spread. As the assets under management grow and the manager's trading blocks increase in size, however, the transaction costs start to go up. Perold and Salomon suggest that, at some point, transaction costs will equal the 400-bp paper excess return. At that point, the game is up; the paper profits can no longer be achieved in the market. So, a practical issue in evaluating active portfolio management is the nature of the decay function: What does that curve look like for the active manager's investment process? For an active-manage4 Andre F. Perold and Robert S. Salomon, Jr., "The Right Amount of Assets Under Management," Financial Analysts Journal (May /June 1991):31-39. 5 Perold and Salomon based their numbers on a detailed three-dimensional study by Thomas F. Loeb ("Trading Cost: The Critical Link Between Investment Information and Results," The Financial Analysts Journal [May/June 1983]:39-44), which examined the question of size versus spreads versus the liquidity of the stock. The Loeb study generated the econometrically derived relationships based on real market activity.

145

ment process to be successful, the manager must find Performance-based fees are becoming increasthe size at which total wealth derived from running ingly common for funds in the high-valueadded/high-cost quadrant of Figure 1. 6 Sponsors that process is maximized. pay high fees only for high performance; if they do Implications for compensation arrangements. Innot receive high performance, they pay low fees, sights gained from the deductive approach also rewhich is a good business arrangement. Those sponlate directly to manager compensation. Assume that sors and managers who do not like performanceFund Manager A is running a pension fund that based fees need to deal with the issue of the decay owns financial assets, and Active Manager B owns function. Buyers and sellers of investment processes an investment management process that generates must recognize that a cost curve like that depicted in (on paper) 400 bps a year. How do these two parties Figure 2 underlies each process. The challenge is to contract to create a win-win economic relationship? make the shape of the curve and its parameters exA's first instinct might be to buy B, but that may not plicit. Even if they fall short of pinpointing the exact be feasible. An alternative approach for A is to round optimal asset size for wealth creation, both parties up some other owners of fund assets and, collecneed to make the attempt-eonsider together how tively, either buy Bor lease B's process on a long-term and at what point the size of the assets under manbasis. This behavior is rational on the part of those agement will begin to hamper the wealth-creation with the assets. process. What should B, with a winning process but no assets to manage, do? In the actual market of invest_ ment management services, the strategy of most sup- The Right Questions pliers of the processes is to accumulate an What might be the content of a questionnaire deever-increasing pool of assets for their processes signed to identify successful active managers by a from as diversified a portfolio of clients as they can deductive process? A useful starting point is the construct. The insights drawn from Perold and Saloextraordinary questions generated by the conceptual mon's decay function suggest a problem with such a framework developed during the 1970s.7 The quesstrategy, however, and with the compensation apare deemed extraordinary because they were tions proach that commonly accompanies such a stratnot obvious then, and they are still not obvious toegy-asset-based fees. day. These questions ask investment managers the Asset-based fees create a financial incentive for following: suppliers of the processes to become too large. To • How do you define the universe in which the supplier of the process earning asset-based fees, active management will take place? each new client means new revenues, but the decay • What is your valuation methodology, and is function indicates that as B grows larger, B will it based on a single valuation philosophy or become less and less successful in using the suppossome optimal combination of different edly successful process to generate excess returns. methodologies? Moreover, unfortunately for A, once the results of the decay function become evident, termination of a • What is the predictive accuracy of the process? How is the quality of predictions monifund manager can take a long time, perhaps four or tored? five years. Therefore, what is needed is a way of aligning • How do you rebalance a portfolio? the economic interests of the supplier of the capital • What is the nature and magnitude of transand the provider of the process before the hiring action costs? Can inappropriate assumptakes place. This need is the rationale for performtions about such costs be adjusted? How? ance-based fees. Whatever faults exist in perform• What is the information feedback system? ance-based fees, the fees at least motivate providers The 1980s generated even more profound quesof the process to think about Perold and Salomon's tions that need to be incorporated into fund manager curve. If process providers' future revenues and interviews: wealth are going to be linked to maximizing the • If you win, who is losing? Why are you amount of wealth that their processes can generate, winning and they losing? they will be encouraged to analyze the optimal size of their processes. Performance-based fees create the 6 See Ambachtsheer, "The Economics of Pension Fund Manright incentives. Their practical problems-the gamagement," p. 29. ing potential and the difficulty of defining and measuring the benchmarks-are real concerns that 7 For a more detailed exposition of the 1970s framework, see require careful thought, but the economic rationale Ambachtsheer and Farrell, "Can Active Management Add Value?" for performance-based fees is sound. 146

•

•

•

•

What are the profit-portfolio size curves for each of your active processes? What is their shape and where do they peak? Have you thought about and can you describe those curves? What trading strategy minimizes implementation shortfall for each active process; that is, have you thought through whether you should play Treynor's poker game and how you should play it? In a stock portfolio, do you play with large or small trading blocks? Do you use alternative trading mechanisms? If so, how are the sizes of your opportunities and your opportunity costs affected? What is your information feedback and what is your research and development activity for each active process? How much of your revenues are you, as a firm, spending on R&D to improve your process and keep it up-to-date? How does the buyer of the process, the party with the assets, contract with you, the supplier of the process, in a win-win situation? What about problems with asset-based fees that occur because they motivate the man-

ager to continue to increase assets under management?

-eo-n-c-'-u-s-io-n---------------The deductive approach to finding successful managers for the future is certainly different from the traditional inductive approach. Even sponsors following an inductive approach can apply the lessons and questions of the deductive process, however, in their interviews of their final candidates. Manager search finalists ideally should be asked the "extraordinary" questions about process and policies before they are asked about their performance. If the sponsor has carried out a careful search and applied insights and questions from the deductive approach, those final candidates are also likely to have good performance by traditional measures. Finally, what about firing? The principles of sound hiring lead directly to the principles of sound firing. If the sponsor has used the deductive principles to establish an investment management team, the.sponsor need not wait through some arbitrary penod of bad. performance to terminate a manager. The sponsor fIres a manager when key hiring criteria no longer hold.

147

Question and Answer Session Keith P. Ambachtsheer Question: How does a manager apply the concept of knowing yourself and knowing your enemy? Ambachtsheer: I have sat in on some interviews in which management organizations put forth the idea that winning requires being different from the crowd. Everyone is a contrarian. I think the issue is more profound. Legitimate questions to ask any active manager are why the manager is winning and who is losing. A fund manager should be able to explain that his or her process wins systematically because, for example, some behavior by other market participants that the manager can identify and exploit leads to systematic losses by those participants. Question: A three-year period is generally considered meaningful for measurement, so how would you settle performance-based fees in years one and two: not settle until year three, settle on a tentative basis each year, or pay only the basic fee, without a performance element, in those years? Ambachtsheer: The key is to identify some strategy at the start that avoids the temptation to grow assets for growth's sake. One of these arrange-

148

ments that I negotiated used an asset-based fee through the first one-year period; then, a fee arrangement based on quarterly performance was instituted, with calculations always based on the previous four quarters. Question: What is your opinion of flat-fee arrangements as an alternative to either asset- or performance-based fees? How common are such arrangements? Ambachtsheer: I do not think they are common, but they have conceptual appeal. In the context of my deductive model and the respective economic interests of the owner of the assets and the owner of the process, the two parties may well settle on some agreement to limit the total assets being managed. Once that decision is made, the questions become how to split the revenues from the wealthcreation process and who bears what risk. The owner of the assets must keep in mind that the process provider incurs some fixed costs in running the business; it is not in the interest of the asset owner to create a payoff pattern in which the process provider may exhaust his or her capital because of a couple of bad years. In a win-win approach, all these issues come out on the table.

Question: How much lower should the base fee be if a performance element is included? Ambachtsheer: The fee scale should keep the provider of the process operational even if a dry spell occurs in the process's success. The key determinant of how low the base fee should be is the operating costs of running the process. The base fee should also include a modest profit margin. The goal should be a symmetrical payoff pattern rather than an asymmetrical one; that is, where a floor exists below the expected fee revenue stream, an equidistant ceiling should be in place above the expected revenue stream. Question: How many U.S. managers are too big? Ambachtsheer: Perold and Salomon focused on individual stock selection as the basis of active management. Using Loeb's spreads versus liquidity data, their research indicated that the cost curves started to peak at perhaps $2 billion in terms of optimal asset size. That finding suggests that either the transaction cost assumptions are faulty or many active managers are running processes that are considerably larger than their optimal size.

Manager Search Patricia K. Lipton Executive Director State of Wisconsin Investment Board

The more systematic and detailed the search undertaken by a sponsor or agency for a fund manager, the greater the likelihood of a mutually beneficial long-term association. The State of Wisconsin Investment Board gathers and analyzes a variety of performance information during the search process.

The search for a fund manager is one of the most important activities a plan sponsor or agency undertakes. The more systematic and exhaustive that search, the greater the likelihood of a fruitful, longterm association with the selected manager. This presentation details the process used by the State of Wisconsin Investment Board (SWIB). The description begins with an overview of SWIB, then discusses the variety of performance information SWIB requests of managers during the search process and SWIB's analytical approach to that information.

The State of Wisconsin Investment Board

ment activities and investment decision making. A majority of SWIB's assets are managed internally, but we use outside managers for the LBO fund investments and for venture-capital funds. We began investing in these asset classes in the early to mid1980s. The manager searches that are the focus of this presentation actually began when we started the international investing program in early 1989. The international program was a departure from our usual method of operating because we decided to do it on a team basis, using both outside managers and internal staff. The search process described in this presentation draws largely on the searches we have conducted for international asset managers. When we do a search, we send out a fairly standard request for proposal (RFP). The internal panel that conducts interviews after we have sorted the proposals received is composed of staff members who are active in the relevant asset class, a consultant, a quantitative research analyst who works closely with me on performance activities, and me. SWIB directs the entire manager search process internally; we do not have a general consultant to whom we transfer control of manager searches. We do use an outside consultant for portions of the exercise and have found that we can work effectively with a consultant who has skills that complement and supplement our own skills. For example, we work with a consultant in conducting much of the quantitative analysis of performance data, and the consultant participates as a panelist in the manager reviews.

As of the end of September 1994, SWIB had $36 billion under management: 85 percent ($30 billion) in retirement funds, 14 percent in a money-market-type fund, and 1 percent in various small funds. The $30 billion in the retirement funds is divided between an equity-only portfolio and a more balanced portfolio. Considering these two portfolios together, roughly 66 percent of the total $30 billion assets (about $20 billion) is in equities-52 percent ($15.5 billion) in domestic equities, 10 percent ($3 billion) in international equities, 3 percent (about $1 billion) in leveraged buyouts (LBOs). About 1 percent ($340 million) is in emerging market equities, and 3 percent (about $1 billion) is in real estate. Fixed-income assets compose about 29 percent of the portfolio in total; about 16 percent ($4.7 billion) is in the public bond market. Private placements constitute about 9 percent ($3 billion). The international bond markets account for 4 percent. Cash is a residual for SWIB and, at the end of September, was selection Criteria about 2 percent. Important manager selection criteria include the Approximately 80 people work at the investmanager's performance data, investment style and ment board; 45 are professionals involved in invest-

149

philosophy, resources and any unique capabilities, and investment experience.

Performance Data SWIB thinks that the review of performance data is an important component of manager evaluation, but performance data have only a limited weight in our decisions. Given the short time span covered by the data that we have typically analyzed for international managers, we believe past performance is an unreliable predictor of future performance-particularly in the case of searches for emerging market managers. The weight we give performance data depends on the quality and quantity of data that are available and has ranged between 10 percent and 20 percent of our overall decision criteria; the remaining weight is roughly evenly distributed among the three other criteria. We scrutinize the data carefully, however, because we think we obtain more information than simply the numbers from that scrutiny; for example, the data may support or contradict assertions made by the manager about style, timing, or rationale for performance.

Investment Style and Philosophy Manager investment style and philosophy are important factors in our decision making. We always have a clear idea of the type of manager we are seeking when we prepare the RFP; we want an investment philosophy and process that are consistent with or complementary to our existing manager composite. Our style of internal management has largely been the bottom-up approach. In selecting managers, we have been looking to see whether they would be similar to or complement this approach. Thus, our RFP asks managers a number of questions designed to determine whether a manager's style fits that mandate. We look for a clear statement of philosophy from the manager and a process that is capable of implementing the philosophy. If the philosophy has changed since the manager first developed or implemented it, we want to know the rationale for the change. If some aspect of the manager's philosophy or process was not working, we want to know about the perceived problem and the steps the manager took to solve that problem.

Resources and Unique Capabilities The depth and breadth of manager resources, the technology, and other potential sources of adding value are important. We look carefully at the manager's internal research and support resources, the type and content of data bases and models, and any unique capabilities that might give the manager a competitive advantage over other managers in the

150

same asset class. For example, for investing in the emerging markets, we look for managers with knowledge of the language and the ethos of a country or region and those who show that they are willing to spend a lot of time there. We also evaluate the manager's access to outside contacts and sources of information through the manager's reputation,long-term relationships, and established position in the industry. This factor has been especially important to us in our searches for external managers involved in the international markets. The fact that a manager has established contacts in the relevant countries gives us a degree of confidence that the manager knows more than the ordinary outsider would about what is going on in those markets. Because we manage the international assets through a team of outside and inside managers, we also expect to be able to use the external manager's resource base for our internal managers. For example, an outside manager may have nonfinancial information about a country and the ability to give us insights into that country's culture and customs that would supplement the information we can obtain from data bases.

Experience We want a management firm and individual portfolio managers with sustained successful experience in the targeted asset class. We prefer a firm that has a process in place that has produced results and managers who have established track records. A history of results achieved by a manager in the current environment with current staff and support are more meaningful than results achieved by that manager in prior settings. The synergy generated by a good working relationship among a firm's managers can supply substantial value. The absence of that synergy can have a detrimental effect on a firm's chances of being hired by us. We also assess whether the investment process is driven or dominated by one key individual or whether the firm uses a team approach. We are not predisposed to one method or the other, but if one individual is making the key decisions, we must be comfortable with his or her depth of understanding and ability to exercise judgment.

Evaluating Performance Data Although performance may receive a relatively small weighting in SWIB's overall approach to manager selection, the complex nature of performance data and of performance measurement dictate a thorough and thoughtful approach to this criterion. Important elements in our evaluation of data are

analyses of performance relative to benchmarks, risk and return analysis, style analysis, and attribution analysis. SWIB's RFP asks managers to disclose the methodology they use to calculate composite returns. We look specifically for presentations that are unlikely to have much predictive value-the use of any simulated returns, the aggregation of multifirm results, or the aggregation of asset classes drawn together from portfolios that may have different mandates. We ask whether-and, if so, why-any portfolio results have been excluded from a composite. We also ask, to test whether the manager's interpretation of standards is the same as our own, whether performance calculations and presentations are in compliance with the AIMR Performance Presentation Standards. 1 Our consultant helps us verify and check such assertions, and we have found that some people say they are in compliance, but upon examination, we do not agree with their assessment of compliance requirements.

Analyzing Performance Relative to Benchmarks In our initial RFP, we typically ask for quarterly returns of the relevant mandate for five or more years or since inception. We follow up with requests for all relevant portfolio returns (monthly, if possible) and market valuations in order to reconstruct and verify the composite calculations. We perform the calculations ourselves with the help of an outside consultant. We ask for corresponding benchmark returns for the same time periods, and we ask for the manager's justification for using the chosen benchmark. In our analyses, we use benchmarks that we consider reasonable in light of the mandate for the portfolio. When a manager's benchmark produces a significantly lower return than other relevant benchmarks, we look closely at the reasons the manager had for using a particular benchmark. In order to conduct our own attribution analysis, we also request sector or country returns and weights-the raw data as well as the tabulated results. SWIB performance comparisons begin with returns for one year and longer relative to the benchmarks, but our focus whenever possible is on returns for at least three years. We believe judgments about performance based on fewer than three years are questionable. When we conducted our emerging market manager search, however, we were constrained by the availability of performance numbers because most managers, particularly those involved in the emerging debt markets, had been active in those markets for only a short time. 1 See the presentation by Mr. Caccese, Mr. Dokas, and Mr. Rennie, pp. 117-25.

In addition to looking at simple full-period excess returns, we conduct tests of statistical significance of quarterly returns in excess of the benchmark returns. These basic calculations of t-statistics for excess returns and for volatility of returns test for statistical significance at the .05 level-that is, with 95 percent confidence. Achieving a 95 percent confidence level is unusual for investment returns for limited time periods, however; so, we translate the calculations into the confidence levels that actually result for the managers, which are typically in the 55-85 percent range but occasionally reach the 90 percent level. In other words, we consider: If stating that excess returns were earned with 95 percent confidence is not possible, with what lower level of confidence can the statement be made? We then compare the results among the manager candidates.

Risk and Return Analysis We examine the standard deviations of portfolio returns within the composite for each quarter to determine how consistently the manager's process was applied across portfolios in the composite. Portfolio returns within the same composite should exhibit very similar returns and standard deviations; if they do not, we like to know what is causing differences within the composite. Portfolios with specific constraints or unique funding activity could look somewhat different from others in the composite. Once a realistic group of manager candidates is identified, we plot the managers by return and volatility for comparison with each other and our relevant current composite. The composite used for this process will be specific to the asset class in which we are considering adding a manager. We analyze returns and standard deviations for the maximum relevant time period. Figure 1 illustrates this process with return and volatility plots for 12 managers and the SWIB composite for a roughly five-year period. This process provides a first look at the risk-return profile of each manager in terms of our existing base of assets. We next recalculate the return and standard deviation of our composite as if it contained each manager under review at selected funding levels. Figure 2 illustrates the risk-reward impact with a reallocation of 20 percent of existing assets to each prospective manager. We realize that this analysis is retrospective rather than forward looking, but we find it to be an interesting and useful factor in our evaluation and decision-making process; without any comments being made, it gives the panel a picture of what would happen with each manager. For example, based on Figures 1 and 2 and all other factors being equal, Manager I would be preferable to J, and K would be preferable to L. 151

Figure 1. Risk-Return Analysis, Third Quarter 1989-Second Quarter 1994 13

Manager I

12

•

I-

ManagerJ

•

ManagerB

•

ManagerC

•

~anager F

SWIB. Manager D. C omposlte • ·ManagerH 10

I-

Manager K

•

ManaierL 9 10

ManagerG

ManagerE

•

•

Manager A

I

I

I

I

1

I

I

12

14

16

18

20

22

24

•

26

Standard Deviation (%)

Source: AG Risk Management.

Style Analysis In addition to statements of investment style and philosophy, SWIB's RFP asks managers for "snapshots" of their portfolios at different points in time that will show historical country or sector distributions and various portfolio characteristics. We then try to ascertain whether the historical record suggests that the manager has been true to the expressed style. Managers are questioned further on these style

and process issues during face-to-face interviews. We also conduct regression analyses of the historical returns to establish the style exhibited by each manager's return behavior. We then construct a style map containing all manager candidates and our existing composite for a visual view of each manager's style results relative to our own. Figure 3 provides an example of style results categorized by large versus small capitalization and value versus growth for

Figure 2. Risk-Return Analysis with 20 Percent Reallocation to Alternative Managers, Third Quarter 1989-Second Quarter 1994 11.0

SWIB + I

•

SWIB + B

•

• SWIB + J

E

.a

10.5 -

SWIB + F

•

OJ

~

SWIB

-

-SWIB+C

SWIB+ D

Compo~te _ SWIB + H

-

SWIB+A SWIB+E

-

SWIB+K SWIB+G I

10.0 11

12

-

I

I

13

14

Standard Deviation (%)

Source: AG Risk Management.

152

15

Figure 3. Style Analysis, Third Quarter 1989-Second Quarter 1994 Small/ Growth

Man:ger E

Small/ Value

Manager A

•

ManagerB

•

Manager I

•

Manager F

•

ManagerC

•

ManagerD

•

•

ManagerG

• •

ManagerK

•

ManagerH

"

SWIB Composite Large/ Growth

ManagerJ

ManagerL

•

LarNe / Va ue

Source: AG Risk Management.

a five-year period. As with the risk-reward impact analysis, the style impact of adding each manager candidate to the relevant existing composite is then calculated for various funding levels. Figure 4 shows the style impact for an assumed 20 percent reallocation to each prospective manager. For example, if analysis of our equity composite led us to conclude that we had too strong a growth bias relative to objectives, then all other factors being equal, we would prefer Managers K and L rather than C and D.

Attribution Analysis A final part of SWIB's RFP asks managers whether they conduct any attribution analysis of their returns; if so, the nature of the analysis; and the results of the analysis. We prefer monthly rather than quarterly data for accuracy, and we look for dynamic portfolio weights. We also prefer absolute attribution to relative attribution because we can compare the absolute attribution results with benchmarks of our own choosing. The benchmarks that we use must stand up to public scrutiny and be readily understood by the public at large. SWIB deals with people who are not investment professionals but public employees who are beneficiaries-teachers, for example-and legislators who have some control over the SWIB operating

budget. Therefore, even if our intellectual preference might be for carefully crafted, customized benchmarks, we choose benchmarks that are very familiar to people, such as the S&P 500 Index. From the attribution analyses obtained from the managers, we can make our own analyses and compare our results with those provided by the managers. Different methods of calculation yield different results, and any substantial differences are subject to review and further discussion with the manager. The purpose of this analysis is to validate or invalidate manager claims concerning style and investment process. For example, a bottom-up, stockpicking manager should show value added through security selection (or should at least be able to discuss clearly why value was not added in this way). On the other hand, a lack of value added through security selection would be expected and acceptable from a top-down manager. Because opinions vary considerably on this issue, we are also interested in a manager's current opinion and practice regarding the usefulness of attribution analysis. We do not ask the question directly in the original RFP, but we generally discuss the issue with the managers selected for face-to-face interviews. 153

Figure 4. Style Analysis with 20 Percent Reallocation to Alternative Managers, Third Quarter 1989-Second Quarter 1994 Small/ Growth

Small/ Value

SWIB + E

•

SWIB+A

•

SWIB+ B

•

.SWIB + I SWIB + F

•

•

SWIB+C. SWIB+D. /

SWIB+H

Large/ Growth

• SWIB + J

SWIB+G

Pr~

SWIB Composite

·SWIB+K .SWIB + L

LarRe/ Va ue

Source: AG Risk Management.

The Benefits of Performance Evaluation The work devoted to performance evaluation may seem to require too much effort for criteria that carry a small weight in the selection decision, but the steps outlined can sometimes send up warning signals. For example, we have been provided some very creative performance results accompanied by affirmations of compliance with AIMR standards. We take issue with such statements and factor such discrepancies into our decision-making process. The requests for performance data also allow us to determine whether the managers are capable of providing such information. We gain insight into managers when we look carefully at their performance and how they use their support systems to

154

present that performance to us. A manager's support systems are an essential element in the investment process, and we want to know the state of the manager's information base and the uses that are and can be made of it. Analyzing performance in selecting external managers also provides us with suitable criteria for hiring and firing our internal managers. To be honest with ourselves, we must be willing to use the same scrutiny on our own managers that we apply to the managers that we hire externally. So, when we conduct this type of external search analysis, we put a great deal of thought into it. We know we must live by the standards that we develop. These criteria are a precondition for managing money in-house.

Question and Answer Session Patricia K. Lipton Question: How do you select the managers to whom you send RFPs? Lipton: We use standard directories such as Nelson's Directory of Investment Managers and other listings. From time to time, we have asked a consultant to look through the list of names we have and recommend additions because sometimes the directories are incomplete. In the search for an emerging market manager, for example, we failed to send out an RFP to one manager who has been active in that investment class for some time because the manager wasn't listed in any of the key directories. I expect that manager quickly arranged to be listed. Question: Why do you choose to conduct the search process from within rather than hire a consultant for every aspect? Lipton: Doing things internally is part of our culture. In addition, we have some expertise because we have people on staff who actively manage assets in different markets. Question: Is standard deviation the best risk measure to use in evaluating managers? Do you use other measures of risk? Lipton: Volatility is important, and we do rely a lot on standard deviation. We also assess the risk and loss experience of managers. Question: What is the extent of your attribution analysis? Lipton: Because our searches have focused on international markets, we have focused on secu-

rity selection versus country selection. We go through the usual procedures to determine how much return is attributable to stock selection and now much to country selection to determine whether a manager really is a bottom-up manager or a top-down manager. We have not gotten into industry weightings. Question: Do you apply the same experience and other standards and criteria in evaluating people for your internal staff as you apply to outside managers? Lipton: In internal hiring, we are not usually in a position to choose a manager for the "portfolio manager" level. Weare not competitive from a salary standpoint for managers with truly strong track records-unless they happen to be in Madison, Wisconsin, for some other reason. We tend to attract students graduating from the business program at the University of Wisconsin and, occasionally, other universities. We do "grow our own," however, and tend to promote people from within to become portfolio managers. The aspect in which we need to scrutinize ourselves relative to outside managers is at the other end of the process-the firing end. We must be sure that our managers are performing well, and we determine their competency by evaluating their processes, how they go about doing their jobs. We also have their track records and the results of their decisions. Question: Does uncompetitive compensation cause morale or turnover problems?

Lipton: We can capitalize on the Madison effect-that is, the value added to lifestyles from living in Madison, notwithstanding the winter. Also, we believe we offer a tremendous learning environment, particularly for someone just graduating from school. We do virtually every kind of investing in-house, and people do learn. The problem is that some leave for higher salaries. Many, however, stay with us because they enjoy their jobs and find the salaries adequate. Question: From a historical perspective and for the future, what do you expect the average life to be of a manager for your fund? Lipton: Long. The same expectations would hold for internal staff as well as external managers, and internally, the staff certainly expect to be long-lived. For external managers, we certainly don't want to churn managers. The approach we take of making performance a relatively small part of the decision-making process is designed to minimize rapid hiring and firing. If a manager has a good explanation, we do not want to fire the manager. Churning is a waste of time for all concerned. In the hiring process, we generally say we will give managers three years to get up and running with our account before we think about withholding additional funds (as a probationary move) or firing them. Since we have had the international program (five years), we have fired only one manager. We have had some other managers on probation in our international program, but they are still with us.

155

Question: What is the purpose of your short-list interview?

technology and portfolio analytical systems they use and the nature of their firms' operations?

Lipton: That is really to make the final selections. Depending on the number of applications that make our initial cut, we interview, at most, 12 managers for one assignment. In the emerging market search, which was a fixedincome search, we had only about 12 applicants and we were able to narrow that number down to 4 for interviewing.

Lipton: We are looking for the important qualities in the managers we interview that reveal the thumbprint of an organizationthe aspects that permeate the way a company will function. Competitive advantage might be the intellectual honesty and integrity of the people and the support they give the managers.

Question: You ask managers what could give them a competitive advantage. Does this question encompass, for example, the

Question: Do you plot alternative managers on two-dimensional style charts to determine consistency in style using several

156

time periods? Lipton: Up to this point, the data have limited us to running the analysis for the longest time period covered by all manager candidates. Question: To evaluate the riskreturn profile of a retroactively added manager, how do you determine correlations? Lipton: We actually blend each manager's returns with our composite returns, period by period, rather than building and working from a correlation matrix.

The Best Way to Spend 30 Basis Points D. Don Ezra Managing Director, Consulting Frank Russell Company

How to operate on a limited budget is a top-down exercise that concentrates sponsor attention on analyzing the costs and benefits of active management. Analyses reported here affirm the value of active management and suggest that the cost of active management is justified by very little expected excess return.

The best question that I have been asked by a client in a long time is: "Our fund has 30 basis points to spend; what is the best way for us to spend it?" The question has no concrete answer, but this presentation will suggest-for sponsors and investment managers alike-an approach to answering it. The question is such a good one coming from a sponsor because it reflects a consideration of the tough choices that can be made only at the top management level of a fund. Sponsors and their fund managers must deal with all kinds of issues in fund investment. A major issue is asset allocation policy. After this policy is set, a team of managers must be hired to place the assets in the markets to earn a return. Most of the time, each manager has a mandate to be active, in the hope that the investments will earn an even higher return than the market provides on average. The sponsor generally pays whatever is necessary to hire the best managers it can find; managers' fees generally do not appear high relative to the size of the fund or to the amount of value the sponsor hopes the managers can add. This situation calls to mind the pension-funding process that prevailed in the 1970s. The actuary would come up with a required contribution, and a check payable to the pension fund would be drawn. Not until the recession of the early 1980s, when sponsors found they were canceling capital projects in favor of their pension funds, did pension funds stop getting preferential treatment. The sponsors realized that there was absolutely no reason the pension fund should get its money before the rest of the sponsor's operations. So, chief financial officers took control of the funding decision. Actuaries were asked to provide the most realistic extremes within which the contribution should lie, and the actual contribution chosen within that range reflected the pension fund's

competition with alternative uses for the capital. For the first time in pension fund history, the pension fund was treated like the business it is. All funds are businesses, whether they be pension funds, endowments, or foundations. Not much significant progress has been made since that change, however, in applying other business principles to investment funds. A few sponsors have begun to apply the principles of total quality management to the way in which they run their funds. Many have cut their in-house staffs as part of downsizing. But the biggest expenditures in running the fundsmanager fees-have not generally been subjected to the same budgeting pressure as other costs. Rather, fees have been considered on a bottom-up basis: "We want these managers; what does it cost to hire them?" The question "What should we do if our budget is limited to 30 basis points?" comes from the other direction; it is a top-down question. It limits the budget and, in so doing, reveals much about the organization's mindset. My associates say that there is no virtue in coming up with an arbitrary budget. A better principle is to focus on costs and benefits; as long as the marginal benefits outweigh the marginal costs, incurring those marginal costs is worthwhile. True enough, but costs are certain, and benefits are uncertain. So, in practice, the comparison is not quite straightforward. In addition, a fact of business today is that budgets are often being cut simply to show that no part of the organization is going unscathed. No one gets preferential treatment. Thus, one might generalize the 30-basispoint question to: "What would I do on a limited budget?" This presentation discusses what the tough choices are and how a sponsor might go about an157

swering the questions posed. It begins with some general background and then develops two lines of thought that can lead to good answers.

Background

which costs less than active management of international equities, which costs less than active management of any form of privately traded or illiquid assets. The fund sponsor can obtain good numbers on all of these costs. Having gotten those numbers, the sponsor's next step is to estimate the value expected to be added from active management in each asset class. This value is much more difficult to quantify than the cost side. No amount of statistical analysis of historical data will provide a forecast that is reliable over any usable time horizon. Ultimately, the sponsor is left with using educated intuition. Sometimes, the sponsor's experience provides additional help by allowing the sponsor to estimate the expected tracking error a particular manager or a particular strategy will involve-that is, how far away from the relevant index, on average, that particular return is likely to be. The sponsor knows that the tracking error for index funds is virtually zero and that for various forms of active management, it is some percentage points each year. Conceptually, particularly if the sponsor has an engineering frame of mind, the answer to what portions of the fund should receive active management is now simple. The sponsor enters all these inputs into an optimizer and lets that model provide some guidance. What will the sponsor find?

A fund has two sources of wealth: new money contributed and capital market returns. The fund could take no capital market risk, investing in Treasury bills or in dedicated fixed-income portfolios, but virtually no fund does so because minimizing capital market risk makes the need for external contributions too great. Thus, the fund is forced to invest in risky asset classes. Asset allocation policy, therefore, does not matter to the arguments I am developing; all that is necessary is a recognition that risk in investing is unavoidable. When the fund takes this risk, it picks up whatever rewards the market gives for the risk taken. Theoretically, the fund has no need for further decisions. For example, the fund could simply use passive portfolios to capture the market return in each asset class. This approach is an easy way to live: The fund simply rebalances its asset allocation to fit its policy whenever the markets move the allocation away from that policy. And the cost to implement such an approach is very small, perhaps a few basis points. Suppose, however, that the fund wants to achieve more than the market return. The fund then Costs and Added Value: Optimizer Results hires one or more active managers, giving them one If the sponsor's passive inputs were roughly at an of two mandates: Select securities that are different equilibrium, superimposing active expectations of from the index, or make timing decisions that move reward and risk will have two effects. First, the opthe fund consciously away from the mix repretimizer will almost always tell any sponsor to be senting the asset allocation policy. Now, the fund has active rather than passive. An amazingly small introduced meaningful costs into the process of tryamount of expected extra return net of fees is enough ing to achieve higher returns. to justify being active. For an expectation on the order of 50 basis points a year, the optimizer will go Costs and Added Value: Analysis strongly and completely to the active corner, with no passive remainder whatever. Suppose the fund has 30 basis points to spend on fees The second effect is that the sponsor may be but that introducing this active management apsurprised to see where the optimizer recommends proach for the entire fund would cost more than 30 spending the active-management money. A simple basis points. In this case, or in any case of similar cost example can demonstrate this result. Table 1 shows relative to fund size, the fund sponsor is in the posiexpected returns from active management for two tion of any manager who has tough allocation deciasset classes. The chosen asset allocation policy is to sions to make because the current structure of the business is too expensive. The fund sponsor has to Table 1. Expected Returns from Active Management consider how much each activity costs and where the Gross Excess Net Excess fund is most likely to get value for the money that is Asset Expected Management Expected about to be spent. Return Class Fee Return All the conventional wisdom about obtaining 3.00% 0.60% 2.40% A value is well known, and it does serve as a useful 1.50 B 0.20 1.30 starting point: Fixed-income active management Note: Annual basis. costs less than tactical asset allocation, which costs less than active management of domestic equities, Source: Frank Russell Company. 158

split the fund 50/50 between these two classes. Asset Comparing the Size of Rewards and Risks Class A is expected to net 240 basis points (bps) a The concept of relating added value or rewards to year; Asset Class B, 130 bps. In the absence of a costs leads directly to a second line of thought. Asset budget, if the sponsor superimposes these expected allocation risk in some form is unavoidable, but takreturns on the expected passive returns, the oping active management risk is avoidable. Therefore, timizer will favor Class A because active managethe sponsor wants to compare the relative sizes of ment in that class adds much more to the expected these risks to see which one provides the greater return of the asset class than is the case in Class B. So, reward. That is, in the long term, is the fund likely to if the 50/50 allocation reflected the sponsor's risk make more money simply by betting that equities tolerance, then after making the customary changes will outperform fixed income or by betting that the to the risk inputs to reflect active management, the sponsor can find good active managers? optimizer will likely tilt the fund strongly toward The intuitive answer is that equities will outperAsset Class A. form bonds with more reliability than anybody's Using a budget constraint changes the answer. skill at picking active managers. If this intuition is Suppose the excess return inputs are those from Taaccurate, a fund operating under budget constraints ble 1 and the sponsor has a budget of 30 bps to spend should abandon active management entirely and inon expenses. As Table 2 shows, the budget forces the crease market risk by increasing equity exposure by, optimizer to recommend using active management say, 20 percent. The fund will make more money than where the ratio ofthe gross expected return to active-manby adding active management and will not have to agementfees is greatest-in this case, Asset Class B. The pay 30 bps for the increased return. Frank Russell Company has developed a comTable 2. Optimal ActiveJPassive Allocation for 30 bps parative approach that attempts to go beyond the intuitive answer. The first maxim of the approach is Fee on Net Excess always to have a neutral benchmark portfolio, such Asset Total Active Total Expected Return as an index fund, with which to compare the impact Class Allocation Exposure Fund on Total Fund of decisions. For asset allocation decisions, our neuA 50.0% 33.3% 0.20% 0.80% tral portfolio replicates the average allocation of all B 50.0 50.0 0.10 0.65 U.S. pension funds; this approach provides a neutral Total 0.30% 1.45°/" amount of market risk and a neutral amount of marNote: Annual basis. ket reward. Source: Frank Russell Company. This portfolio reflects an asset allocation that is roughly the average of what U.s. pension funds hold, sponsor should pick as much as possible of Asset and each asset class is represented by an index that Class B to manage actively. All 50 percentage points roughly replicates the market exposure of available of exposure in B should be active, which costs 10 bps holdings in that asset class. The actual neutral porton the total fund (from Table 1,50 percentage points folio is 50 percent U.s. equities (Russell 3000 Index), times 20 bps management fee for Asset Class B). That 5 percent non-U.s. equities (MSCI EAFE Index), 30 step adds 65 bps to net excess expected return (50 percent U.S. fixed income (Lehman Brothers Aggrepercentage points times 130 bps expected return) for gate Index), 5 percent real estate (National Council of Real Estate Investment Fiduciaries Index), and 10 Asset Class B. The 20 bps remaining to be spent on percent cash (90-day T-bills). the total fund is used to obtain 33.3 percentage points Figure 1 compares the returns of the neutral of active-management exposure for Asset Class A. with U.S. T-bills during the 20 quarters that portfolio That step adds 80 bps to expected return for Asset ended June 30,1994. The figure shows only that the Class A. This approach has added the maximum neutral portfolio generally outperformed T-bills, possible, 145 bps, to excess expected return. which is not a surprise. Figure 2 gives a better idea In summary, the budget constraint forces the of the reward for taking market risk by showing the optimizer to say that Asset Class B is preferable to excess return of the neutral portfolio over T-bills. The Asset Class A from the perspective of cost versus shaded horizontal line indicates that the average size added value and that Asset Class A is where the of the reward per quarter was 93 bps. budget constraint should be operable. Without the What was the unavoidable (market) risk inbudget constraint, the clear choice based on net exvolved in running this hypothetical portfolio? Whencess expected return was Asset Class A. In short, an ever the excess return was different from explicit budget constraint changes the nature of the zero-whether plus or minus-the portfolio must optimization problem and gives an answer the user have taken some risk relative to T-bills. Whether the might not have expected. excess return was +100 bps or -100 bps, the portfolio 159

Figure 1. Retums of T-Bills and Neutral Portfolio, July 1, 1989, through June 30, 1994

Figure 3. Ex Post Risk of Neutral Portfolio, July 1, 1989, through June 30, 1994

10

12

6

10

2

§

o

E .2 6 ~

-2 -6

8

4

L.. •·· •••••·············=1•.................................

2

>-

OL..l--..-

-10

-..J.....

..........

'---~

.....---..

Quarters

Quarters

D Neutral Portfolio

III T-Bills

~'--

Source: Frank Russell Company.

took enough risk to be 100 bps away from T-bills; if the excess was +200 or -200, the portfolio took even more risk. In other words, the average size of the departure from zero, whether positive or negative, is a reasonable measure after the fact of the risk that was taken. Figure 2. Excess Retum of Neutral Portfolio over T-Bills, July 1, 1989, through June 30,1994

Source: Frank Russell Company.

complicated by holding real estate; so, we did not have problems with asset-class gains and losses relative to an index in which one could argue about the valuation of illiquid assets. Also, we looked at the effect of all the departures from the neutral market portfolio that this particular client took: the effect of an asset allocation policy that was different from the neutral policy, the effect of not rebalancing back to policy, and the effect of not holding indexed assetclass portfolios. Figure 4. Excess Gross Retums from Active Management, JUly 1, 1989, through June 30,1994

8 4

2.5 2.0 1.5

~

-8

1.0

i=: .... .2 0.5
P<:

-12 Quarters

Source: Frank Russell Company.

Figure 3 depicts the application of this measure; it squares, averages, and takes the square root of the quarterly excess returns shown in Figure 2 (rather like a standard deviation calculation). The average size of the resulting risk measure is 402 bps. That is, the neutral portfolio took unavoidable risk of 402 bps a quarter to earn an average reward of 93 bps a quarter; the ratio of reward to market risk in the five-year period (93 divided by 402) was, therefore, 23 percent. We performed similar calculations for all those clients for whom we had good information during that same five-year period. The results shown in Figure 4 are for a client portfolio that is reasonably typical of this group. The client portfolio was not

160

0 -0.5 -1.0 -1.5

Quarters

Source: Frank Russell Company.

Figure 4 shows this client's sequence of gains and losses, and the risk-to-reward relationship, created solely by active management within the asset classes. The bars are the pluses and minuses relative to being indexed within the asset classes in total. The average risk was 98 bps a quarter; the average reward, before fees, was 33 bps a quarter; the ratio of reward to risk was 34 percent. We do not have good data yet for the amount of fees paid by this client, but the fees have probably averaged about 48 bps a year, or 12 bps a quarter. Therefore, net average reward would be about 21 bps a quarter, resulting in a ratio of net

Figure 5. Adding Risks with No Correlation A. Correlation = 0 \ \ \ \ \

Effective Total Risk (412 bps)

\

Neutral Portfolio (400 bps)

\ \ \ \ \ \

\ \

Active Management (l00 bps)

B. Correlation = +1

Neutral Portfolio (400 bps)

{: [ } [ Effective Total Risk {[ (500 bps) Active Management [ (l00 bps) [

C. Correlation = -1

Neutral Portfolio (400 bps)

I} I I I

{ -L}

Effective Total Risk (300 bps) Active Management (l00 bps)

Source: Frank Russell Company.

reward to avoidable risk of 22 percent. That reward is virtually identical with the reward for taking unavoidable risk, 23 percent. At this stage of analyzing this approach, because we obtained similar results for each of the first six clients we studied, our main conclusion is that we should discard the intuitive notion that increasing equity exposure is the simple answer to making more money in the long term.

Combining Unavoidable and Avoidable Risk The comparison of rewards earned to risks taken has one other interesting extension. The unavoidable risk (market portfolio risk) and the avoidable risk (the amount of gain or loss from active management) are almost always completely uncorrelated and, therefore, do not add up arithmetically. The mathematics of adding risks is to take the sum of the squares of each of the underlying risks, add the covariance term, and take the square root of the total. But when the underlying risks exhibit no correlation, the covariance term drops out, and what is left is the sum of the squares. This interesting result is illustrated in Part A of Figure 5 for the client portfolio discussed in Figure 4. If market risk is roughly 400 bps and avoidable risk is another 100 bps a quarter, the two risks combine essentially as a right triangle, and the total fund risk

is 412 bps. The finding shown in Part A contrasts with the total risk obtained when correlations are either perfectly positive or perfectly negative. The former case is represented in Part B of Figure 5; market risk of 400 bps and avoidable risk of 100 bps would combine to produce total fund risk of 500 bps. Part C shows the second case; combining the two risks if they exhibit perfect negative correlation would result in effective total risk of 300 bps. The results for all six portfolios we have studied are similar to the finding in Part A of Figure 5. The implication is that one can add a large amount of active-management risk without much effect on total fund volatility. Therefore, a sponsor can spend almost all of the budget for total fund risk in asset allocation and can think of active-management risk almost as a separate and very minor issue. This conclusion is surprising and important. Frank Russell Company will be exploring it in greater detail because it suggests that the focus in active management should be almost exclusively on expected reward. If a fund expects any reward for active management, the fund should pursue it, because the risk that is traditionally associated with active management appears to be negligible at the total fund level. 161

Conclusion The process outlined to decide how to spend 30 bps has two primary implications, one general and one specific. First, asking yourself what you would change if your budget for manager fees were cut will focus your mind on issues that you may never have considered, issues such as how much expectation of excess return from active management (very little)

162

justifies departing from passivity and how to compare expected returns with costs and risks. The second implication, which may well be country specific and period specific but is based on findings gleaned from much research, is that, during the past five years in the United States, cutting back on active management and increasing capital market risk simply would not have paid off.

Question and Answer Session D. Don Ezra Question: Is risk tolerance different for avoidable and unavoidable risk?

Ezra:

Different committees will view that question in different ways. The risk in using active management is purely the risk of not making what you expect, of not earning the higher return. Active management adds little to total fund volatility; I don't know anyone who has a 400-bp volatility a quarter who can tell the difference between 400 and 412 bps.

Question: Could your analysis and findings reflect some kind of benchmark misspecification?

Ezra: Two misspecification factors are quite possible, which is why I cautioned that the results are for a particular time period in the United States. The more obvious of the two factors is that, in the period studied, the EAFE Index was the benchmark for nonU.s. equities, so the performance of the client fund may have been helped by being underexposed to Japan. Second, we have not

checked, but the client fund may have had a factor bet in U.s. equities. The benchmark for U.s. equities was the Russell 3000 Index; even relative to the 3000, the fund may have had a small-capitalization exposure. If these two bets are permanent characteristics of the fund rather than temporary characteristics during the period considered, then it would be more appropriate to say that the benchmarks were inaccurately chosen than to say that active management added value.

163

Self-Evaluation Examination I.

According to Lummer, which one of the following issues is most important in evaluating the usefulness of U.s. equity indexes? a. The influence of capitalization size. The value versus growth conundrum. b. The choice of index provider. c. d. The method of return calculation.

2.

Which of the following statements regarding the most frequently used U.S. equity indexes is true? a. The large-cap indexes are highly correlated with each other. The mid-cap indexes exhibit meaningb. ful differences. Both of the above. c. Neither of the above. d.

3.

Manager universes may not be valid performance evaluation tools because of various problems. Which of the following is not a problem with manager universes? a. They cannot be measured. They are ambiguous. b. c. They are not investable. d. They cannot be specified in advance.

4.

Managers who perform poorly tend to drop out of manager universes, with the result that median manager performance tends to be biased upward. This effect is termed: a. Style bias. Survivorship bias. b. Quality bias. c. d. Mean-regression bias.

5.

According to Bailey, which of the following quality criteria should be applied in evaluating the composition of a benchmark portfolio? a. Coverage. Turnover. b. Positive active positions. c. d. All of the above.

6.

164

Which of the following benefits does Flynn attribute to the use of universes and peer groups? a. Flexibility in measuring manager style. b. Incorporation of portfolio-specific parameters. Both. c. Neither. d.

7.

Arrange in proper order of execution the four steps in the process of constructing universes and peer groups: I. Use selection criteria. II. Calculate rates of return. III. Identify pool of investment managers. IV. Develop percentile rankings. II, I, IV, III. a. III, I, II, IV. b. I, II, III, IV. c. III, IV, II, I. d.

8.

U.s. fixed-income indexes tend to be subdivided by: a. Capitalization. b. Sector. c. Value versus growth. d. Collateral.

9.

Trotter observes that the three major U.S. fixedincome indexes are most similar to each other with respect to: a. Liquidity. b. Settlement and reinvestment assumptions. c. Risk and return characteristics. d. Pricing and sector allocations.

10.

According to Vishkai, major differences among the global fixed-income indexes pertain to: I. Liquidity. II. Pricing sources. III. Country allocations. a. I only. b. II only. III only. c. d. II and III only. I, II, and III. e.

II.

The major non-U.s. equity indexes for developed markets capture approximately what range of capitalization in their respective universes? a. Under 25 percent. b. About 30-50 percent. c. About 50-60 percent. d. About 60-95 percent.

12.

Meier observes that the biggest differences among the developed market equity indexes occur in: a. Asset coverage. b. Industry coverage.

c. d.

Country coverage. Capitalization coverage.

13.

The major emerging market equity indexes differ least in their: a. Asset restrictions. b. Country weights. c. Capitalization-based weighting schemes. d. Levels of investability.

14.

Speidell presents evidence of differences between large- and small-cap returns in: a. U.s. markets but not in other developed or emerging markets. U.S. and other developed markets but b. not in emerging markets. c. U.S., other developed, and emerging markets. d. Emerging markets but not in U.s. or other developed markets.

15.

Halpern's research suggests that a sponsor with five managers trading in four countries might incur excess trading costs of: a. No more than 9 bps. b. Approximately 15 bps. c. Approximately 33 bps. At least 50 bps. d.

16.

Singer argues that the dollar return to a global portfolio should be evaluated in terms of: a. Local risk premium and dollar cash return. b. Local risk premium and local currency return. c. Local currency return and exchange rate return. d. Dollar cash return and exchange rate return.

17.

"Misfit risk" describes a situation in which: a. Aggregate benchmark positions are different from aggregate managed positions. b. Some styles represented in a benchmark are not represented in managers chosen by a sponsor. c. Equal-weighted indexes perform differently from capitalization-weighted indexes. d. Correlations between managers' returns are not an accurate reflection of their relative performance.

18.

Research on Canadian stock returns indicates that: I. Large-cap/value stocks have dominated small-cap / value stocks. II. Large-cap/value stocks have dominated large-cap / growth stocks. III. Small-cap/value stocks have dominated large-cap / growth stocks. a. I only. b. II only. c. III only. d. I, II, and III.

19.

A typical manager search conducted by Lipton's organization would attach what importance, measured by proportion of overall decision criteria, to performance data? a. Less than 5 percent. b. 5-10 percent. c. 10-25 percent. d. 25-50 percent. e. 50-75 percent. f. More than 75 percent.

20.

Which one of the following conclusions summarizes the historical correlations among nonU.s. equity indexes? a. Correlations among developed market indexes and among emerging market indexes are relatively high. b. Correlations among developed market indexes and among emerging market indexes are relatively low. c. Correlations among developed market indexes are relatively high but among emerging market indexes are relatively low. d. Correlations among developed market indexes are relatively low and emerging market indexes are essentially uncorrelated.

21.

The results of a recent survey suggest that the cost per firm of establishing compliance with the AIMR Performance Presentation Standards (PPS) is approximately: a. $10,000 or less. b. $100,000. c. $500,000. d. $1,000,000 or more.

22.

The PPS currently require wrap-fee performance to be reported gross of fees: a. In all circumstances. b. Only if transaction costs cannot be determined. c. Only if transaction costs are deducted

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d.

23.

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from performance. If transaction costs are estimated rather than actual.

Which one of the following conclusions is consistent with risk-factor exposures provided by Cowhey? Relative to the entire fund universe: a. The S&P 500 Index and the U.s. institutional investment manager universe both exhibit a "largeness" bias. b. The S&P 500 and the U.s. institutional investment manager universe both exhibit a "smallness" bias. c. The S&P 500 exhibits a largeness bias and the U.S. institutional investment manager universe exhibits a smallness bias. d. The S&P 500 exhibits a smallness bias and the U.s. institutional investment manager universe exhibits a largeness bias.

24.

According to Hansen, in performance measurement for a nontraditional program, dynamic asset allocation would be evaluated as part of the: a. Investment decision. b. Implementation vehicle. c. Manager value added. d. Market value added.

25.

The deductive manager search advocated by Ambachtsheer begins by: a. Identifying selected high-performance active managers. b. Outlining the attributes of successful active management. c. Requesting performance information from a broad group of managers. d. Defining appropriate benchmark characteristics.

Self-Evaluation Answers I.

a.

Lummer believes that capitalization size is most important in evaluating the usefulness of US. equity indexes.

12.

a.

According to Meier, the biggest differences among the developed market equity indexes occur in asset coverage.

2.

c.

Among the most frequently used US. equity indexes, the large-cap indexes are highly correlated with each other but the mid-cap indexes exhibit meaningful differences.

13.

c.

The major emerging market equity indexes differ least in their capitalizationbased weighting schemes.

14.

c.

Manager universes may not be valid performance evaluation tools because they are ambiguous, they are not investable, and they cannot be specified in advance.

Speidell presents evidence of differences between large- and small-cap returns in the U.S. market, other developed markets, and emerging markets.

15.

c.

The dropping out of managers who perform poorly from manager universes is termed survivorship bias.

Halpern found that a sponsor with five managers trading in four countries might incur excess trading costs of about 33 bps.

16.

a.

Singer argues that the dollar return to a global portfolio should be evaluated in terms of the local risk premium and the dollar cash returns.

17.

b.

"Misfit risk" describes a situation in which some styles represented in a benchmark are not represented in managers chosen by a sponsor.

18.

d.

Research on Canadian stock returns indicates that large-cap/value stocks have dominated both small-cap/value stocks and large-cap / growth stocks and that small-cap/value stocks have dominated large-cap / growth stocks.

19.

c.

Lipton's organization typically gives a weight of 10-25 percent to performance data in overall decision criteria.

20.

c.

Historically, correlations among developed market indexes have been relatively high but among emerging market indexes, relatively low.

2I.

b.

The survey suggested that the typical cost per firm of establishing compliance with the PPS is approximately $100,000.

22.

c.

The PPS currently require wrap-fee performance to be reported gross of fees only if transaction costs are deducted from performance.

23.

c.

Cowhey states that, relative to the entire fund universe, the S&P 500 exhibits a largeness bias and the US. institutional in-

3.

4.

5.

6.

7.

8.

a.

b.

d.

c.

b.

b.

Bailey states that evaluation of the composition of a benchmark portfolio should consider all three quality criteria-eoverage, turnover, and positive active positions. Flynn ascribes two benefits to manager universes and peer groups-flexibility in measuring manager style and incorporation of portfolio-specific parameters.

The proper order of steps in the process of constructing universes and peer groups is (III) identification of a pool of investment managers, (I) use of selection criteria, (II) calculation of rates of return, and (IV) development of percentile rankings. US. fixed-income indexes tend to be sub-

divided by sector. 9.

10.

1I.

c.

e.

d.

Trotter observes that the three major US. fixed-income indexes are most similar to each other with respect to risk and return characteristics. Vishkai cites three major differences among the global fixed-income indexes: liquidity, pricing sources, and country allocations. The major non-US. equity indexes for developed markets have about 60-95 percent capitalization coverage in their universes.

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vestment manager universe exhibits a smallness bias. 24.

b.

In performance measurement for a nontra-

ditional program, dynamic asset allocation would be evaluated as part of evaluating the performance of the implementation vehicle.

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25.

b.

The deductive manager search advocated by Ambachtsheer begins by outlining the attributes of successful active management.