Lecture Notes in Economics and Mathematical Systems
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Michael Hierzenberger
Price Regulation and Risk The Impact of Regulation System Shifts on Risk Components
Dr. Michael Hierzenberger Pestalozzistraße 6/13 8010 Graz Austria
[email protected] http://www.xing.com/profile/Michael_Hierzenberger
The publication of this book was financially supported by the Karl-Franzens-University Graz and the government of the province of Styria, Austria
ISBN 978-3-642-12046-6 e-ISBN 978-3-642-12047-3 DOI 10.1007/978-3-642-12047-3 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2010926590 # Springer-Verlag Berlin Heidelberg 2010 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover design: WMXDesign GmbH, Heidelberg, Germany printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
... Cause we are the ones that want to play Always want to go But you never want to stay And we are the ones that want to choose Always want to play But you never want to lose ... (from “Aerials” by SOAD)
Dedicated to My Family.
Preface
The present book was written within the scope of my doctoral studies in economics at the University of Graz.1 It has been a special honor of mine to be able to write my dissertation as a visiting doctoral student at the Institute for Corporate Accounting and Auditing at the University of Graz with Professor Dr. Gerwald Mandl, Head of the institute. I am very grateful for this opportunity. Additionally, I am grateful to Professor Dr. Edwin O. Fischer, Head of the Institute for Corporate Finance at the University of Graz, for his willingness to be a committee member. I am also thankful to Professor Dr. Ulrike Leopold-Wildburger from the Institute for Statistics and Operational Research for her encouragement to publish my dissertation and for her willingness to be the third committee member. Along with the goal of writing a dissertation that meets academic standards, it was also my goal to write a dissertation that serves a practical purpose. I am especially thankful to the managing directors of Energie Graz GmbH & Co KG, Mr. Dr. Gert Heigl and Mr. Dr. Rudolf Steiner, as well as to the managing directors of Stromnetz Graz GmbH & Co KG, Mr. DI Gerhard Krampl and Mr. DI Erich Slivniker. I am also thankful to Mr. Mag. Michael Mock, managing director of the Austrian Association of Gas- and District Heating Supply Companies, for his encouragement. However, my deepest gratitude belongs to my parents, Anna and Friedrich Hierzenberger, to whom this dissertation especially is dedicated. Without the insight learned from them, “nothing worthwhile comes easily”, this would not have been brought to completion. Moreover, I am grateful to my relatives, friends and colleagues for their forbearing charity during the time spent on this dissertation. Hopefully, the empirical evidence of this work will be considered in the future price regulation in Austria. Michael Hierzenberger Graz, 2009-12-08
1
This published version of my dissertation is a shorted one of the original and approbated version ¨ sterreich” (German). “Die Bestimmung von Eigenkapitalkosten regulierter Unternehmen in O
vii
Contents
1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Purpose of the Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Structure of the Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2
Capital Market-Based Calculation of the Cost of Equity . . . . . . . . . . . . . . . 5 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Capital Market-Based Calculation of the Cost of Equity . . . . . . . . . . . . . . 5 2.2.1 Capital Asset Pricing Model (CAPM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2.2 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.3 Rate of Return on Equity as a Regulatory Parameter . . . . . . . . . . . . . . . . . 21 2.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.3.2 Goals in Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.3.3 Defining Fair Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.3.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3
Methods of Price Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1 Rate of Return Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.2 RPI-X Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 The Principal Agent Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Rate of Return Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Diversification with Price-Based Regulation . . . . . . . . . . . . . . . . . . . 3.2.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Regulatory Systems and Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Buffering Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2 Regulatory Lag Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.3 The Risk Effect from a Regulatory System Shift . . . . . . . . . . . . . . 3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
29 29 29 30 30 31 37 38 39 39 40 41 44
ix
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Contents
4
Empirical Secondary Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Stigler and Peltzman’s Theory of Regulation . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Methodology: Event Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Estimation Period, Event Window and Postevent Window . . . . 4.3.2 Measuring Abnormal Returns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.3 Hypotheses’ Derivations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.4 Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.5 Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
45 45 46 47 48 49 53 57 60 62
5
The Primary Empirical Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Hypotheses and Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.1 Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.2 Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Empirical Analysis: Structural Break Analysis . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Systematic Risk and Welfare Effect: Verbund . . . . . . . . . . . . . . . . . 5.2.2 Systematic Risk and Welfare Effect: EVN . . . . . . . . . . . . . . . . . . . . . 5.2.3 Systematic Risk and Welfare Effect: DJ600UTIL . . . . . . . . . . . . . 5.2.4 Unsystematic Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.5 Total Risk and Return Averages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.6 Summary of the Structural Break Analysis . . . . . . . . . . . . . . . . . . . . 5.3 Empirical Investigation: Event Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 The Event List . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Event Study: Constant Beta Factor; 3-Day Event Window . . . 5.3.3 Event Study: Dummy Variables from 1 July 2005; 3-Day Event Window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.4 Event Study: Constant Beta Factor; 1-Day Event Window . . . 5.3.5 Event Study: Dummy Variables from 1 July 2005; 1-Day Event Window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.6 Summary of the Event Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
67 67 67 69 81 84 85 86 88 91 91 94 97 101
6
105 112 118 123
Summary of the Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
Chapter 1
Introduction
The market-based fundamental principle of competition is not present for natural monopolies. For this reason, monitoring corporations that possess a natural monopoly is necessary within the scope of price setting, in order to minimize welfare loss due to the lack of effect from competition. Network infrastructures, such as electricity and gas networks, are classic examples of natural monopolies. This monitoring function for the process of price setting is administered by the state or through an institution authorized by the state (a regulatory agency) for this purpose. The interests of the state and of the monopolist are diametrically opposed to one another according to the assumption of a rationally acting subject. The monopolist attempts to maximize their profits by setting prices accordingly. The goal of the state is production and consumption of an amount that maximizes welfare. Within the scope of price regulation, a definition is provided for price settings or levels, the net benefit for the consumer from consuming the goods or services produced and for the owner of the monopoly in the form of present and future returns. Finding a “fair price” should be the highest maxim for this. In practice, finding this price most often takes place as a part of a negotiation process between the monopolist and the regulatory agency, for which the distribution of negotiation power appears to be of central importance. The prices or profit authorized by the regulatory agency must permit the monopolist to cover the variable and fixed costs. Moreover, based on the principle of opportunity costs, the monopolist must be entitled to a return on the capital employed, in order to compensate for investments or to have an incentive for those type of investments. The challenge lies with the regulatory agency to assess fair interest calculations for the monopolist’s capital employed. The rate of the financing costs allowed has a direct influence on the monopolist’s cash flow. Should a business valuation be conducted on the basis of cash flow, discounting the expected cash flow is a must. The equivalence of the finance cost rate allowed by the regulatory agency and the “actual” finance costs rate of the monopolist appears to be ensured only in an ideal situation – from which positive and negative consequences arise for the M. Hierzenberger, Price Regulation and Risk, Lecture Notes in Economics and Mathematical Systems 641, DOI 10.1007/978-3-642-12047-3_1, # Springer-Verlag Berlin Heidelberg 2010
1
2
1 Introduction
company value. Differences in amounts of interest occur, especially for calculating a return on equity, which are due to the diametrically opposing goals of the regulatory agency and the monopolist.
1.1
Purpose of the Dissertation
The problem described above about potentially differing calculations of financing costs is the starting point of this dissertation. It should be shown how one performs a calculation of financing costs, in order to be able to make a claim about “a future orientation”. The present dissertation concentrates on the aspect of considering expected changes in risk, to which the price regulated company is exposed, contingent upon the change of regulation parameters or a shift in the regulatory system. Answering this set of questions will be done using the example of the Austrian electric and gas network industry. Besides the relevance of determining the cost of equity capital for future-based business valuations as discount interest, so the cost of equity capital carries a further, central significance during business valuation for regulated companies as to the prognosis of future cash flow (cash flow as the future profit amount to be discounted). Since a future orientation is essential for both valuating the company as well as establishing the cost of equity, answering this question about how the regulatory system shift changes the company’s cost of equity effected thereby, is the core of this dissertation. Since this set of problems will become more significant in the future in light of efficiency considerations, the principal agent theory should be presented to the reader.
1.2
Structure of the Dissertation
To calculate an appropriate return on equity, the Capital Asset Pricing Model (CAPM) will be explained in Chap. 2. This chapter demonstrates how model parameters should be adjusted, in order to be able to determine more precise projected values. In conjunction with this, this chapter demonstrates how the costs of equity assessed by the regulatory agency have a direct influence on the cash flow rate, within the scope of regulated price settings. This chapter shows premises within which capital cost calculation must move, in order to be able to claim economical correctness when it comes to determining appropriate operating costs. From this, a range for projecting future cash flows should be able to be defined when conducting business valuations based on cash flow. In Chap. 3 an account is given as to what advantages the transition from costbased to price-based regulatory systems offers, both for regulation agencies and for
1.2 Structure of the Dissertation
3
regulated companies. These advantages are derived by applying the principle agent theory. The advantages of this type of change are contrasted with the theoretically possible interpretation of this type of change in the capital market, with the “regulatory lag effect” and the “buffering effect” in Sect. 3.3. Moreover, based on empirical results from the USA, significant criteria are established as to the capital market’s evaluation of a regulatory system as positive or negative. The secondary data analysis from previously published empirical studies on capital market reaction to changes of regulation parameters forms the content of Chap. 4. The methods applied in the primary studies to calculate abnormal returns are explained, in order to analyze the effect of these types of parameter changes on the stock prices of regulated companies in the USA and in Great Britain. By doing this, it should be possible to identify possible significant differences between the different regulatory systems. Simultaneously, international experience is integrated into the present dissertation from the results of Chap. 4. In Chap. 5 an investigation is conducted as to whether international, empirical findings on the effect of regulatory system shifts, as presented in Chap. 4, are also identifiable within the scope of Austrian regulation policies. This takes place in the course of a structural break analysis in Sect. 5.2 with the example of the ¨ sterreichische Elektrizit€atswirtschafts-AG (shorthand: Verbund) and the EVN O AG (shorthand: EVN). It should be determined from this structure fracture analysis whether introducing an incentive regulation for the electricity industry changed the risk and return structure of both securities. Furthermore, an event study is conducted in Sect. 5.3, in order to analyze the stock price reaction of stock from Verbund and from EVN upon the publication of information in the course of the change of regulation parameters for electric and gas network industries. As a part of the event study, different model specifications will be applied to calculate abnormal returns, in order to quantify the influences, based on the models, on the determined rate of abnormal returns. By this, influences based on the models should be able to be considered, especially for the interpretation of the results and general information should be gained such as how sensitive results from event studies react to different model specifications. Chapter 6 serves as the summary of the results from this work as well as the statement about which areas appear meaningful for other works.
Chapter 2
Capital Market-Based Calculation of the Cost of Equity
2.1
Introduction
To conduct cash flow-based business valuations, the projection of future cash flow is necessary. The future cash flow is to be evaluated by means of an appropriate discount interest calculation. For regulated companies, determining an appropriate interest rate has effects on the rate of the discount interest calculation and on the volume of the projected future cash flow. This double meaning from determining the interest rate is the peculiarity for companies whose prices are regulated compared with companies that are active in competitive markets. For planning future profits in price-regulated companies, interest costs for borrowed funds and equity must be considered. As a part of the cash flow-based business valuation, it depends on the valuation model applied whether a return on equity (return required by the investor) or a weighted average cost of capital (WACC) is used as the discount interest calculation. Common to all cash flow-based procedures is that the rate of return is contained in the discount interest calculation. To calculate the return on equity, the capital asset pricing model (CAPM) will be explained more carefully in Sect. 2.2 and the advantages and disadvantages are portrayed. In Sect. 2.3 a normative statement is provided as to how operating costs and capital costs should be made for projecting future cash flow within the scope of a regulatory system, in order to do justice to business requirements. This should enable a founded projection of future cash flow. Section 2.4 serves as the summary of this chapter.
2.2
Capital Market-Based Calculation of the Cost of Equity
By applying capital market-based models to calculate equity costs, risk premiums, which investors require for taking on risks, are derived from capital market data and are not the result of a subjective estimate. For this purpose, reference is most often M. Hierzenberger, Price Regulation and Risk, Lecture Notes in Economics and Mathematical Systems 641, DOI 10.1007/978-3-642-12047-3_2, # Springer-Verlag Berlin Heidelberg 2010
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6
2 Capital Market-Based Calculation of the Cost of Equity
made to CAPM in practice, which is a so-called one-factor model. CAPM defines the investor’s return requirements on the basis of a risk-free interest rate, which is increased according to a risk premium. This risk premium is determined by multiplying the market risk premium with the so-called beta factor, as a measurement for the systematic risk of a security. The Arbitrage Pricing Theory (APT) developed by Ross1 is classified as a multifactor model. With APT, risk premium is established by several factors. APT assumes, in contrast to CAPM, that the risk of a security is contingent on an unknown amount of factors in a linear fashion. These factors can be, e.g., exchange rates, interest levels and index trends in various stock exchanges.2 An exceptional form of APT is presented in the FF3F model by Fama and French. This model defines the investor’s return requirements on the basis of 3 factors and hence is classified among the group of multi-factor models.3 In the following, CAPM is explained, on which basis the consideration of risk factors for assessing a risk premium is presented in detail.
2.2.1
Capital Asset Pricing Model (CAPM)
Building on the portfolio theory from Markowitz4 and the separation theory from Tobin,5 the CAPM was developed primarily by Sharpe (1964).6,7 CAPM defines the investor’s return requirements as follows:8 EðRi Þ ¼ Rf þ bi ½EðRm Þ Rf with: EðRi Þ Rf bi EðRm Þ
1
Expected returns from the risky security i Returns from a risk-free capital investment (risk-free interest rate) Measurement for the systematic risk of security i (beta factor) Expected return from the market portfolios
cf. Ross (1976). cf. Mandl and Rabel (1997), p. 310; Buckley et al. (2000), p. 283 ff. 3 cf. Fama and French (1992). 4 cf. Markowitz (1952). 5 cf. Tobin (1957). 6 cf. Sharpe (1964); along with Sharpe, CAPM goes back to Lintner, Treynor and Mossin as well. 7 cf. Damodaran (2001), p. 164 f. 8 cf. Mandl and Rabel (1997), p. 290; Copeland et al. (2002), p. 265; Fischer (2002), p. 74; Drukarczyk (2001), p. 354. 2
2.2 Capital Market-Based Calculation of the Cost of Equity
7
Security Market Line (SML) 17.5% 15.0%
Rendite
12.5% m 10.0% 7.5% 5.0% 2.5% 0.0% 0
0.5
1 Beta
1.5
2
Fig. 2.1 Security market line. “m” in the figure represents the situation of the market portfolios, which intrinsically has a beta factor of 1
This linear interrelationship between expected volume of the investor’s return requirement and the systematic risk is depicted graphically as the Security Market Line in Fig. 2.1.9 When determining risk premium, CAPM assumes the entire risk of a precarious security decomposes into a systematic and an unsystematic part. The unsystematic risk is not influenced by the capital market, rather it is influenced from factors that are evaluated as specific to a security. These could be, e.g., certain characteristics of the management or the client structure. These factors can be diversified through portfolio formation, which is why the capital market does not compensate for unsystematic risk components.10 Systematic risk components cannot be avoided through diversification, which is why they are compensated from the capital market.11 Systematic components are generally e.g., tax policy measures, economic and interest trends.12 Moreover, the original form of CAPM is based on further restrictive premises:13 l l
9
The planning horizon is one period. All investors are unwilling to take risks (risk aversion).
cf. among others Fischer (2002), p. 75; Mandl and Rabel (1997), p. 290; Spremann (2006), p. 310; Copeland et al. (2002), p. 265; Franke and Hax (2004), p. 353. 10 cf. Mandl and Rabel (1997), p. 290; Fischer (2002), p. 74, 103; Copeland et al. (2002), p. 265; Damodaran (2001), p. 155 ff. 11 cf. Spremann (2006), p. 314 f. 12 cf. Mandl and Rabel (1997), p. 290 f.; Purtscher (2006), p. 108. 13 cf. Mandl and Rabel (1997), p. 291; Ballwieser (2002), p. 738; Fischer (2002), p. 71 f.; Damodaran (2001), p. 164.
8
2 Capital Market-Based Calculation of the Cost of Equity
l
All investors have homogeneous expectations. All risky securities are traded on the capital market and can be divided in any way. Funds can be received or invested without restriction at a risk-free interest rate. There are no limitations, transaction costs or taxes. All information is available to the investor at no charge (information efficiency). The prices of risky securities are not influenced by an investor’s purchase or sales activities.
l
l l l l
Further developments in the original CAPM have nullified several of these premises (partially).14 The original form of CAPM is the basis of the investigations in this work. The planning horizon of one period is especially a problematic assumption for determining capital costs for a business valuation because business valuations very often imply an infinite planning horizon. However, if the cost of equity rate determined in accordance with CAPM is applied for a longer period of time, stationary conditions are implied. The parameters defining returns are to be assumed as constant for the entire period under consideration.15 How individual parameters from CAPM are determined for defining the cost of equity rate is shown in the following.
2.2.1.1
Risk-Free Interest Rate
The risk-free interest rate can only be determined approximately. In practice, long term, fixed-interest bearing securities are offered by debtors with very good solvency (e.g., long term state debentures), for which equivalence in the term and in the planning horizons between the company’s expected holdings and the interest maturity are of special importance for reasons of comparison.16 An orientation to actual rate of return on state bonds with a term of 10–30 years is often recommended, whereas for Austrian securities with a very long term, infrequent disbursement, low liquidity and the increased sensitivity to inflation rates thwart the advantage of the approximated matching maturities. A practicable alternative is to draw on the approx. 10-year government bonds and to apply the returns to long term government bonds, published monthly from the Austrian National Bank.17 Fixing the risk-free interest rate should essentially occur on the basis of the future. Consequently, the historical interest rates would be discarded. However, since estimating future interest rates is only possible with great uncertainty, the 14 cf. Brennan (1971); Black (1972); Merton (1973); Rubinstein (1976); Lucas (1978); Breeden (1979); Hansen and Richard (1987); Overviews on this by: Rudolph (1979) and Copeland and Weston (1988), among others. 15 cf. Fama (1977), p. 7 ff. 16 cf. Ballwieser (2002), p. 737; Purtscher (2006), p. 109. 17 cf. Purtscher (2006), p. 109.
2.2 Capital Market-Based Calculation of the Cost of Equity
9
alternative also exists to use current rates from government bonds. For this, it is assumed that the current returns on these types of securities are the best estimates for future returns.18 Government bonds must provide evidence for cash in any currency that was implemented for the calculation of the expected cash flow of the company to be valuated. Otherwise, there would be a currency exchange risk, which would complicate the comparison of profit with cash flow from alternative transactions.19 Implementation of the above is recommended for small return differences between short and long term, risk-free securities. However, should the yield curve, which displays the interrelation between rate of return and maturity, not show a flat structure, period-specific interest rates can also be applied as an alternative to applying a uniform, risk-free interest rate. This indeed increases the calculation effort for the costs of equity, but it leads to more consistent results.20
2.2.1.2
Market Risk Premium
Market risk premium is calculated as the difference between return from the market portfolio and the risk-free interest rate, on the basis of historical return. The amount of this premium is dependent upon the risk-free interest rate chosen as well as the calculation period; both the risk-free interest rate and the return from the market portfolio are not constant throughout the time period. To avoid inconsistencies when determining market risk premium, the risk-free interest rates that must be used are those that were fixed for the interest of risk-free alternative investments in CAPM.21 Further, it is important to take note that the determination of market risk premium, as the difference between return from the market portfolio and the riskfree interest rate, can only be approximated by applying the appropriate market indices. The decision for or against the use of arithmetic or geometric averages has a significant influence on the rate of the market risk premium determined, bearing in mind that arithmetic averages for return fluctuations end up higher than geometric averages.22 Copeland, Koller and Murrin assume that the actual market risk premiums lies between the geometric and the arithmetic mean.23 Subsequently, the market risk premium is to be checked as to whether the expected future market trends can be described as plausible from this. Provided
18
cf. Ballwieser (2002), p. 738; Busse and Colbe (2002), p. 7; Copeland et al. (2002), p. 266. cf. Ballwieser (2002), p. 737. 20 cf. Daske and Gebhardt (2006), p. 531; Mandl and Rabel (2006), p. 104 f. 21 cf. Purtscher (2006), p. 109. 22 cf. Ballwieser (2002), p. 739; Purtscher (2006), p. 109. 23 cf. Copeland et al. (2002), p. 271. 19
10
2 Capital Market-Based Calculation of the Cost of Equity
that this is not the case, the historical returns thus do not give the best estimate for future trends; an appropriate adjustment of the market risk premium is to be made.24
2.2.1.3
Beta Factor
In CAPM, consideration of company-specific or project-specific systematic risk for defining the required rate of return on equity occurs via the beta factor. This factor measures the change of the (historical) individual rate of return from the security with the change of the (historical) market rate of return.25 The beta factor represents the quotient of the covariance between the rate of return of the security i and the rate of return on the market portfolio m and the variance of the rate of return of the market portfolio:26 bi ¼ with: bi CovðRi ; Rm Þ s2m
CovðRi ; Rm Þ s2m
Beta factor from company i Covariance of security return i and market return m Variance in the market return m
A beta factor of 1, determined according to its structure and in accordance with the method of the least square estimate, means that the rate of return of the security develops proportionally to the market rate of return. A beta factor of >1 means that the rate of return of the security strongly reacts disproportionately to market fluctuations in relationship to the market rate of return, and thus displays stronger price fluctuations than the market portfolio and for this reason, a higher rate of return on equity is required in this scenario as a compensation for taking on an increased risk. Conversely, if the beta factor is less than 1, this leads to a reduction of the required rate of return on equity because price fluctuations for this security are lower in comparison with the market portfolio and thus this security presents a lower risk. A beta factor of 0 presents a risk-free assessment, for which reason the risk-free interest rate corresponds to the return on equity requirements in this scenario.27 24
cf. Maier (2001), p. 299; Daske and Gebhardt (2006), p. 531; It must be especially noted that the data used does not include events such as wars or currency reforms, as long as these events are not expected in the future. 25 When using historical returns to calculate the beta factor, one is bound by the following assumption: ex-ante probability distribution ¼ ex-post probability distribution, stochastically independent of the realization of returns, stationary process of returns generation within one period; cf. among others, Maier (2001), p. 300. 26 cf. Mandl and Rabel (1997), p. 297; Fischer (2002), p. 75. 27 cf. Fischer (2002), p. 74; Mandl and Rabel (1997), p. 297.
2.2 Capital Market-Based Calculation of the Cost of Equity
11
As noted above, measuring the beta factor most often takes place on the basis of historical rate of return. However, since business valuation is oriented toward the future, the representativeness of a beta factor determined on the basis of historical market trends should be reviewed for the future.28 Systematic risk can be decomposed into two fundamental component parts:29 l l
Operating Risk Financial Risk
On the basis of this type of fundamental decomposition of the beta factor based on historical data and only statistically presented, it should be possible to increase the future reference of the beta factor by means of an effective hypothesis.
2.2.1.3.1
Operating Risk
Operating risk contains any systematic risk factors that are shaped predominantly through the industry in which the respective company is active.30 The profit cycle of a company is defined by its belonging to an industry. This cycle can be strongly or less strongly shaped and can correspond to the general market cycle or exhibit an acyclical trend compared with the market index. The amount of the beta factor is influenced by the strength of the cyclicality. Here the company with a stronger cyclicality tends to exhibit a higher beta factor than does a company with a lower cyclicality.31 However, substantial, expected changes of the operating risk can only be taken into account in a simplified manner.32
2.2.1.3.2
Financial Risk
The financial risk is contingent upon the level of debt of the company in question because it is assumed that the risk for the investor increases with increased financing from borrowed funds.33 This effect is weakened from the tax-related
28 cf. Mandl and Rabel (1997), p. 306; Purtscher (2006), p. 111; Knieps (2003), p. 1000; Maier (2001), p. 299. 29 cf. Mandl and Rabel (1997), p. 299. 30 cf. Mandl and Rabel (1997), p. 299. 31 cf. Buckley et al. (2000), p. 311; Spremann (2006), p. 344 f.; Born (1995), p. 151 f.; Nielsen (1992), p. 228 ff.; Mandl and Rabel (1997), p. 306. 32 cf. Mandl and Rabel (1997), p. 306. 33 cf. Fischer (2002), p. 129 f.; Buckley et al. (2000), p. 313 ff.; Drukarczyk (2001), p. 357.
12
2 Capital Market-Based Calculation of the Cost of Equity
consideration of interest on borrowed funds. The interrelation between the level of debt and the indebted or debt-free beta factor is presented formally as follows:34 h i bv ¼ bu 1 þ ð1 sÞ FK bf ð1 sÞ FK EK EK with: bv bu s FK EK bf
Beta factor from the indebted company Beta factor from the debt-free company Corporate tax rate Market value of the borrowed funds Market value of the equity Beta factor of the borrowed funds
Provided that the investor’s rate of return requirement (rðFKÞ) does not correspond to the risk-free interest rate (Rf ), and hence the beta factor of the borrowed funds is greater than 0, then the beta factor for the borrowed funds can be determined from the following equation with an appropriate conversion:35 rðFKÞ ¼ Rf þ bf ½EðRm Þ ir
2.2.1.3.3
Other Influencing Factors
Besides the influence of operating and financial risks on the beta factor, other potential influencing factors are to be accounted.36 The highly condensed information on the effect of influencing factors listed in Table 2.1 can only be understood as a very rough directional indicator because the basic, underlying empirical studies have produced different results and an unequivocal cause-effect interrelation is not demonstrable. Table 2.1 Influencing factors on the beta factor level Influencing factor Characterized by Disbursement behavior High disbursement rates Growth Large growth Company size Bigger company Degree of diversification High degree of diversification Market power Significant market power Liquidity High liquidity a BF beta factor.
34
cf. Mandl and Rabel (1997), p. 299 f.; Fischer (2002), p. 126. cf. Mandl and Rabel (1997), p. 300. 36 cf. Hachmeister (2000), p. 217 ff. 35
Effecta Index for lower BF Index for higher BF Index for lower BF – Index for lower BF Index for lower BF
2.2 Capital Market-Based Calculation of the Cost of Equity
2.2.1.4
13
A CAPM Evaluation
In the following, difficulties with defining model parameters necessary for CAPM are discussed and possible problems when implementing CAPM in empirical papers are explained.
2.2.1.4.1
Efficiency and Definition of Market Portfolio
As outlined above, the market portfolio is formed by a market index, as the sum of all risk-laden investment possibilities. As for the validity of the assumption of a linear relationship between the systematic risk of a security and the average rate of return from the market, the choice of the market portfolio is of utmost importance for an empirical review.37 Roll (1977) and Roll and Ross (1994) have demonstrated that the linear interrelation between systematic risk and the average market rate of return is not a given, if the market portfolio chosen is inefficiently diversified in comparison with a theoretically ascertainable, overall “investment universe”.38 Stambaugh (1982) points out how sensitively CAPM tests react to different definitions of the market portfolio. Oertmann and Zimmermann (1996) examined the effects of different specifications of the market portfolio on the level of the betas determined for stock from credit institutions. They arrived at the following result, which underscores the leverage of the choice of the market portfolio, as shown in Table 2.2.39 Spremann (2006) adds to the reasons why empirical reviews of CAPM evaluate this as inaccurate by including the possibility that investors make irrational Table 2.2 Sensitivity of the beta factor with change in the market portfolio specification
37
Country Switzerland Switzerland Switzerland Germany Germany Germany French French French England England England
Enterprise
Beta MSCI-country UBS 1.095 SBC 0.863 CS 1.279 Deutsche Bank 0.901 Dresdner Bank 0.751 Commerzbank 0.895 Paribas 1.451 Societe Generale 0.999 BNP 0.946 Barclays 1.316 Nat West 1.386 Lloyds Bank 1.125
Beta MSCI-world 0.889 0.703 0.944 0.535 0.479 0.572 1.034 0.751 0.717 0.925 0.972 0.688
cf. Laux (2003), p. 208. The theoretical validity of CAPM for additional consideration of an investment possibility not yet contained in the market index is shown by Spremann (2006), p. 324 ff. 39 cf. Oertmann and Zimmermann (1996), p. 276. 38
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2 Capital Market-Based Calculation of the Cost of Equity
decisions. He grounds this in the restricted possibility of acting rationally or in the fact that investors make portfolios from more complex investment decisions than those assumed by Markowitz’s portfolio theory.40
2.2.1.4.2
Anomalies
Already in the 1980s empirical investigations came to the conclusion that expectations of the rate of return on the basis of CAPM systematically deviate from the actual, observable expectations of the rate of return. Investigations on the interrelationship of company growth potential, measured for instance by price earning ratios and their rate of return on equity, showed that securities with low growth potential exhibit a positive, risk-adjusted rate of return. This effect has been coined as the “value effect” and was recognized by Basu (1977).41 The “book to market effect” was recognized by Stattmann (1980). This anomaly describes the interrelation between the ratios of equity book values to equity market value with equity returns. As long as the ratio between book value and market value of equity is high, a higher rate of return is expected.42 Banz (1981) recognized that small companies exhibit a positive, risk-adjusted rate of return. This effect of market capitalization of a security is called “size effect”.43 Empirical studies also came to the conclusion that temporary anomalies exist. Stocks in January and on certain weekdays, show significant, positive, risk-adjusted rate of returns, as Fama (1991) shows, among others.44 Fama and French have especially examined “size effect” and “value effect” in a detailed manner. For the period between 1963 and 1990, Fama and French established the average monthly rate of return for approx. 1,000 US stocks on the basis of ten categories for company size and ten categories for the beta value determined. The result from Fama and French’s investigation is summarized in Table 2.3.45 As Table 2.3 shows, the average rate of return for companies with similar market capitalization hardly changes on the different beta levels. On the basis of the assumption of CAPM, this could not be the case because CAPM assumes a linear interrelation between rate of return on equity and the beta factor. However, the average rate of return for companies with identical beta factors on their market capitalization changes in a way that the average rate of return drops with increased 40
cf. Spremann (2006), p. 334 ff.; Spremann (2007), p. 456 f. cf. Basu (1977); Reinganum (1981); Sharpe et al. (1993). 42 cf. Spremann (2007), p. 461. 43 cf. among others Hung et al. (2004), p. 89; Spremann (2007), p. 459. 44 An overview on works confirming this effect is given by Spremann (2006), p. 338 f. 45 cf. Fama and French (1992), p. 434; an overview on the works from Fama/French in the 1990s is given by, among others: Spremann (2007), pp 462–464; Spremann (2006), p. 341 ff.; Franke and Hax (2004), p. 357; Ziegler et al. (2007), p. 359 ff.; Wallmeier (2000), p. 32 ff. 41
2.2 Capital Market-Based Calculation of the Cost of Equity Table 2.3 Beta factors according to size categories Average Beta-low 2 3 4 Average 1.3 1.3 1.3 1.4 1.3 Small 1.5 1.7 1.6 1.8 1.6 2 1.3 1.3 1.4 1.4 1.4 3 1.2 1.1 1.3 1.2 1.7 4 1.3 1.3 1.1 1.5 1.1 5 1.3 1.3 1.4 1.4 1.5 6 1.2 1.1 1.5 1.3 1.2 7 1.1 1.0 1.2 1.3 1.1 8 1.1 1.1 1.1 1.4 1.2 9 1.0 1.0 0.9 1.0 1.1 Big 0.9 1.0 0.9 1.1 0.9
5 1.3 1.5 1.7 1.3 1.3 1.4 1.2 1.2 1.3 1.1 0.9
15
6 1.3 1.5 1.6 1.1 1.1 1.2 1.2 1.1 1.0 1.2 0.9
7 1.2 1.4 1.4 1.3 1.4 1.1 1.2 1.2 1.2 0.9 1.0
8 1.2 1.6 1.3 1.4 1.2 1.3 1.0 0.6 1.0 0.8 0.7
9 1.3 1.5 1.3 1.3 1.4 1.2 1.1 1.3 1.0 0.9 0.7
Beta-high 1.1 1.4 1.1 0.8 1.0 1.1 1.0 0.8 0.9 0.6 0.6
market capitalization. It can be derived from this that the beta factor does not provide an explanation for the average rate of return, however, market capitalization appears to have a significant influence on this. The findings from Fama and French were refuted by several authors.46 The basic question, as is formulated by Roll (1977), is whether the validity of CAPM is even possible because the market portfolio can only be approximated for this by implementing a market index as a proxy variable and any empirical CAPM test can only be a test for the market index, regardless of whether this corresponds to the market portfolio.47 However, this point of criticism overlooks the empirically fixed, systematic interrelation between rate of return deviations and certain figures, as presented by Fama and French.48
2.2.1.4.3
Estimate and Specification Problems When Determining Beta
When determining the beta factor based on historical market data, various problems arise which reduce the quality of the beta factor. Determining the beta factor essentially is based on a linear equation, which is also described as a market model: Rit ¼ ai þ bi Rmt þ uit with: Rit Security returns in period t Rmt Returns from the market portfolios in period t ai The constant from the regression line bi Slope of the regression line (beta factor) uit Confounding variable from the regression model for security i in period t 46
cf. Damodaran (2001), p. 173 f. cf. Roll (1977); Damodaran (2001), p. 172; Spremann (2006), p. 331 ff.; Wallmeier (2000), p. 34. 48 cf. Wallmeier (2000), p. 34. 47
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2 Capital Market-Based Calculation of the Cost of Equity
This market model is based on four premises: 1. The expected value of the confounding variable is 0. 2. The variance of the confounding variable is constant over time. 3. The confounding variables from two periods that follow each other are not correlated. 4. The confounding variable has a normal distribution. Violating one or more of these premises as well as additional peculiarities and specification problems can adversely effect the quality of a beta factor determined according to the method presented above. These causes are explained as follows:
Heteroscedasticity The property that the variances of a confounding variable change over time is called heteroscedasticity. This can be evoked by a falsely assumed linear interrelationship or be based on strong time-related trends of the variable.49 The implication is that the beta factor determined is no longer efficient because the regression line determined no longer minimizes the confounding variable.50
Autocorrelation of the Confounding Variable Autocorrelation of the confounding variable is present if the previously observed values of the confounding variable exert a systematic influence on the following observation values. A systematic trend of this type can be caused by not considering a significant parameter in the regression line or can be based on a falsely assumed linear interrelation.51 Additionally, in the case of autocorrelation from observation values from the confounding variable, the beta factor determined is no longer efficient.
Autocorrelation of Security Returns Provided that the security returns to be estimated themselves exhibit the effect of autocorrelation, the estimate of a beta factor according to the OLS method52 leads to a distorted and inconsistent value determination.53
49
cf. Zimmermann (1997), p. 62. cf. Ulschmid (1994), p. 210. 51 cf. Becker (2000), p. 39. 52 The form known in German as the “method of least squares” for determining regression lines is described as an “OLS method”. 53 cf. Becker (2000), p. 41; Hachmeister (2000), p. 194. 50
2.2 Capital Market-Based Calculation of the Cost of Equity
17
Measurement Errors If securities possess low liquidity or do not react synchronously with the market index to new information relevant to market value, by which the assumption of an efficient capital market is violated, this leads to a distorted and inconsistent beta factor.54
Choice of Market Index The beta factor portrays the upward slope of the regression line, in relationship to the market index used. By choosing the market index, against which the security returns should be recovered according to the OLS method, the amount of the beta factor is influenced. Reference is made here to the explanations at Sect. 2.2.1.4.1.
Estimate Period Lengths Beta factors are not constant over time. If the interval of time for determining beta is extended, this leads to a higher quality of the regression lines. However, this causes an allowance of anachronistic market data for determining beta and contradicts the basic future orientation.55 It has been substantiated empirically that the beta factor sinks when extending the estimate period because strong, short term fluctuations can balance out this factor.56 In practice, an interval of 1 year is defined for determining the beta factor. For cyclical values, at least one cycle should be completely incorporated into the interval.57
Definition of Return Interval The definition of a return interval is also important for determining the beta factor. The return interval defines any period on which the calculation of a security return is based. In a normal scenario, this could be daily, weekly or monthly returns. Frantzmann and Pfennig, among others, substantiated this empirically on the German stock market. On the basis of their investigations, they came to the conclusion that the extent of the return interval has an increasing influence on the beta factor.58
54
cf. Becker (2000), p. 42 f. cf. Mandl and Rabel (1997), p. 297 f. 56 cf. Hachmeister (2000), p. 197. 57 cf. Timmreck (2002), p. 302; Becker (2000), p. 51. 58 cf. Frantzmann (1990), p. 71; Pfennig (1993), p. 17. 55
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2 Capital Market-Based Calculation of the Cost of Equity
2.2.1.4.4
Determining Adjusted Beta Factors
As portrayed in Sect. 2.2.1.4.3, beta factors determined on the basis of historical market data might not meet the theoretical requirements for various reasons. Furthermore, a beta factor determined by the OLS method was exclusively determined by historical data and thus brackets the future orientation. However, should the cost of equity be determined for the future, the beta factor must be reviewed in a continuous manner as to whether or not the data basis can be used as the best estimate for future trends.59 These deficiencies should be corrected by adjusting the historical beta factor. Different procedures can be applied for this type of adjustment, of which the following are explained:60 l l l l
Mean value method The blume procedure The MLPFS procedure Vasicek procedure
Mean Value Method Any adjustment procedure is identified as a mean value method when the procedure is applied to large data providers such as Barra or Bloomberg. This method assumes that beta factors have the intrinsic tendency during the time lapse to converge on the beta factor of the market portfolio, which is 1. For this reason, the following adjustment is made in the mean value method, in order to minimize the effect of underestimation and overestimation: badj ¼ with: badj bhist bM
2 1 b þ bM 3 hist 3
Adjusted beta factor following the mean value method Historical beta factor before adjustment Beta factor from the market portfolios ¼ 1
Pedell (2007) writes about this: “. . . When estimating beta factors, gearing towards past data is particularly problematic because significant structural breaks regarding risk can result for companies whose fees are regulated, precisely from changes in regulation itself. The estimated beta factors are thus to be interpreted carefully and adjusted, if necessary, especially for changes in the regulation mechanism. These type of adjustments require a foundation from theoretical and empirical knowledge about the determination of the risk in regulated comanies. . . .”, Pedell (2007), p. 47. Please note, that this is a translation (German). 60 It is noted in advance that there are no plausible reasons for the inherent preference of one of these procedures compared with other procedures in general. cf. Pfennig (1993), p. 23. 59
2.2 Capital Market-Based Calculation of the Cost of Equity
19
The Blume Procedure The procedure developed by Blume accounts for the determination of the beta factor for one period, whose value arises from the previous period:61 bit ¼ at þ bt bi;t1 with: bit bi;t1
Realized beta factor from security i in period t Realized beta factor from security i in period t 1
The values determined for a and b on the basis of this equation are assumed to be constant. However, this assumption cannot be substantiated empirically.62 Nevertheless, the estimate precision can be increased on the basis of this method.63 The MLPFS Procedure This procedure developed by the investment bank Merril Lynch Pierce Fenner and Smith accounts for the interrelation of the beta factor from two periods by means of the correlation coefficient of the time-dependent beta factor as follows:64 bit ¼ 1 þ rt ðbi;t1 1Þ with: bit bi;t1 rt
Realized beta factor from security i in period t Realized beta factor from security i in period t 1 Correlation coefficient betweenbi;t and bi;t1
If the correlation coefficient takes on a value of 0, this means that there is no interrelation between the beta factor from the time period t and t 1. The best estimate for the beta factor for t is thus displayed in the beta factor from the market portfolio, which is 1. Otherwise, if the correlation coefficient takes on a value unequal to 0, this shows the interrelation between the beta factor of previous periods and the current periods. The Vasicek Procedure This procedure defined by Vasicek on the basis of the Bayes theorem accounts for the degree of imprecision of the beta estimate from previous periods for determining the most efficient beta estimate from the current period. This transpires 61
cf. Blume (1971). cf. Zimmermann (1997), p. 246. 63 cf. Ulschmid (1994), p. 248. 64 cf. Hachmeister (2000), p. 187. 62
20
2 Capital Market-Based Calculation of the Cost of Equity
by considering the security-specific beta factor and the average beta factor as follows:65 bi;tþ1 ¼ with: bi;tþ1 bm;t bi;t Varðbi;t Þ Varðbt Þ
Varðbi;t Þ Varðbt Þ bm;t þ bi;t Varðbi;t Þ þ Varðbt Þ Varðbi;t Þ þ Varðbt Þ
Estimated value of the beta factor for security i in period t þ 1 Average beta factor in period t Realized beta factor in period t Security-specific variance of the beta factor in period t Variance of all beta factor in period t
By weighing the security-specific beta factor and the average beta factor with the respective variance of the factor to be weighted, the estimate precision is improved.66 In accordance with the above equation, security-specific beta factor estimates that are relatively reliable in the previous periods are considered stronger in the weighted average than the average beta factor and vice versa.
2.2.2
Conclusion
The capital market-based calculation of the cost of equity by means of CAPM is convincing because of the model’s plain intelligibility as well as because of the theoretical foundation of the interrelation between the expected rate of return and systematic risk. The theoretical foundation, however, is based on restrictive premises, which would essentially limit the possible applications of this model in practice. CAPM’s parameters are only definable empirically by applying the proxy variables. How sensitive the results from CAPM react to the application of various indices as proxy for the market portfolio was demonstrated above. In practice, CAPM is used readily, not least because of its (apparent) simple, didactic usability. Empirical studies prove that the prognosis quality of capital market rate of returns is low with CAPM. By adjusting the beta factor, one can attempt to increase the prognosis quality. In whatever form this type of adjustment should be made, it is not formulated in general terms, however, and this type of adjustment often lacks a theoretical point of reference. Any adjustments are to be substantiated on an individual basis.
65
cf. Hachmeister (2000), p. 187 f.; Ulschmid (1994), p. 252. cf. Schultz and Zimmermann (1989), p. 201; Zimmermann (1997), p. 249.
66
2.3 Rate of Return on Equity as a Regulatory Parameter
2.3 2.3.1
21
Rate of Return on Equity as a Regulatory Parameter Introduction
In market-based systems, attempts are essentially made to arrive at price formations by means of the effects of supply and demand in markets. For certain goods, however, this type of price formation mechanism is not possible or is only hardly possible, for which reason official regulatory measures become necessary. Besides public goods such as national defense, included in this are goods that have an enormous investment cost, which leads to their being no appeal for companies to become active in that type of market. These type of natural monopolies are, e.g., electric, gas and water networks as well as railway and road infrastructures.67 In the following, possible goals from official regulatory measures are presented, in order to emphasize price regulation related to them. The following shows how fair prices are determined in regulated industries and what meaning that has for the cost of equity in regulated companies. By considering these economical principles, it should be possible to produce a grounded prognosis regarding future revenue for price-regulated companies, in order to derive the necessary cash flow volume within the scope of a cash flow-based business valuation convincingly.
2.3.2
Goals in Regulation
The legislator, i.e., the regulatory agency wants to achieve certain goals by regulating industries. These can be (1) control of market power and guidelines for (2) quality and the extent of the provision of goods and also certain (3) social goals. The first group of goals includes the prevention of abuse from monopoly power and anticompetitive behavior. The second group. Is directly oriented toward the consumer. A basic, sufficient provision of goods should be ensured and minimum qualities of service provision are defined. The third group attempts to protect the interests of socially weaker people (e.g., retirees, the handicapped and the sick) or certain groups (e.g., agricultural communities).68
2.3.3
Defining Fair Prices
In order to avoid overloading the state’s budget, it can be assumed as a rule that price-regulated companies have prices, i.e., profits approved that ensure the 67
cf. K€upper (2002), p. 31; Ko¨nig and Benz (1997), p. 70 ff.; Geradin et al. (2005), p. 25 ff. cf. K€upper (2002), p. 32 f.; taken from: Broomwich and Vass (Broomwich and Vass (2002)), Sp. 1678. 68
22
2 Capital Market-Based Calculation of the Cost of Equity
self-financing of the company in question. In order to attain this, the company must have prices approved that can cover both the operating costs as well as the financing costs.69 Operating costs are to be understood as the variable costs (e.g., material costs, external services etc.). Due to capital consumption, financing costs are effected in the form of depreciation and the cost of interest.
2.3.3.1
Operating Costs
Accounting for operating costs as a prognosis parameter for fair prices, i.e., profits can take place by using actual costs or budget costs. Whereas actual costs can be verified to a great extent upon application, this is not the case for budget costs, which can be verified upon application only to a lesser extent compared with actual costs. Bearing in mind the requirement of legality when setting fair prices, applying actual costs seems to be expedient. However, in addition to legality, the aspect of efficiently providing a service is considered, in which the consideration of objectives in the form of budget costs and target costs seem to be expedient. Much attention must be given to the generally unattainable operationalization of the efficiency concept to attain an equilibrium between legality and efficiency orientation when defining fair prices.70
2.3.3.2
Financing Costs
Due to capital consumption, financing costs are effected in the form of depreciation and the cost of interest.71 The cost of interest can be subdivided into the costs for borrowed funds and the costs for equity. Swoboda defines three principles that should apply for defining fair financing costs:72 Principle 1. “The investor’s expected rate of return from the EVU should be fair for the capital market and it should also correspond to the special risk of the investor”. Principle 2. “The costs should be distributed fairly across the consumer’s various periods”. Principle 3. “The costs that underlie pricing are to be determined in such a way that negative incentives can be avoided”. Principle 1 is derived from the goals in regulation presented above. In order to prevent companies from attaining a monopoly income, the basic principles of pricing in markets with full competition must be considered when setting fair prices. For this reason, investors must be entitled to a rate of return on invested equity 69
cf. K€upper (2002), p. 34; taken from: Broomwich and Vass (2002), Sp. 1679. cf. K€upper (2002), p. 33 f. 71 cf. Knieps (2003), p. 994; Seicht (2001), p. 105 ff. and 115 ff. 72 Swoboda (1990), p. 66 ff. Please note that this is a translation (German). 70
2.3 Rate of Return on Equity as a Regulatory Parameter
23
appropriate to the risk. Setting a higher or lower rate of return should be avoided within the scope of setting prices.73 Principle 2 is justified by the relatively long life of investments in the network infrastructures, compared with other branches of the economy, and the possibility of a very different periodization of cash flow. Besides the life of the investment, the consideration of future expenditures in the form of accruals has a significant influence on the setting of fair prices (e.g., accruals for pension).74 Principle 3 contains the requirement that fair prices should not be set on the basis of (historical) actual costs because this does not provide an incentive for the management of a regulated company to organize service provision in an efficient manner.75 As has already be portrayed above on setting fair operating costs, the consideration of budget costs instead of actual costs is a possibility for providing an incentive to provide services efficiently. As a part of setting fair financing costs, this principle can be realized, for instance, by checking the investment costs for large projects if the investment costs that are classified as too high may not be passed on to the customer as financing costs.76
2.3.3.2.1
Depreciation
When determining fair depreciation, the depreciation period applied and the depreciation method applied must be reviewed. Before this, it must be defined whether historical and initial costs, production costs, current price or the projected replacement costs should be applied for setting the depreciation to be accounted for.77 Historical Costs and Current Prices or Replacement Costs Answering the question about the economically correct accounting of capital consumption in the form of depreciation can only be answered when bearing in mind the set of questions about the fair rate of return for companies that have an intrinsic right of monopoly. This question is answered by means of the target definition for the regulatory system, especially by means of the definition in which form the costs to be approved must be determined. In the 1990s in Austria, the basic objectives of price regulation in the electricity industry were based in finding “economically justified prices”.78 Whether setting “economically justified prices” is to be understood in the sense of the lowest prices possible or whether there is interpretive leeway for considering “actual” profits (excess returns), is 73
cf. Swoboda (1990), p. 67. cf. Swoboda (1990), p. 67 f.; Swoboda (1992), p. 84. 75 cf. Swoboda (1990), p. 68. 76 cf. Swoboda (1990), p. 68. 77 cf. Swoboda (1990), p. 69. 78 cf. Seicht (1996), p. 345; Mayer (2002), p. 197. 74
24
2 Capital Market-Based Calculation of the Cost of Equity
ultimately a political question. It must be noted with certainty, however, that excess returns are not attainable in markets with full competition, but rather the investors only receive interest appropriate to the risk for the capital invested. In this respect, an orientation to pricing premises with full competition seems to be the only correct basis for further investigations.79 Swoboda, representing an orientation to historical costs and production costs, argues his position as follows:80 . . .Investments can be self-financed, externally financed or a combination of both. To the extent that they are externally financed, obvious depreciation of the cost price and of the nominal interest calculated from the respective book values are sufficient to satisfy the claims of the lender. Depreciation of more than 100% of the investment or a higher interest settlement would end in profits for the lender, for which no initial investment accounts. This would stand in contrast to a competition situation. Analogously, this is also valid for the self-financed part of the investment. Investors expect a return from their assets that is appropriate to the risk. This type of expected return is enabled by means of pricing, in which the calculation incorporates depreciation of cost price (which could be used for repayment of principal) and interest, including an appropriate risk premium from each of the book values. That depreciation and a part of the targeted return on equity are not disbursed cannot be used as a counter argument. The depreciation compensated as well as the retained profit can be invested in assets that again justify a fair return. . . .
However, Swoboda grants that under the following condition the application of replacement prices would lead to the same result as does the orientation to historical costs:81 . . .The inflation rate, toward which the nominal interest rate is adjusted, must be exactly the same in the increase of the replacement price, in order to maintain the real interest rate. . . .
If this condition postulated by Swoboda is not fulfilled, an orientation to replacement prices would lead to positive or negative excess returns. To qualify this, it must be mentioned that real capital maintenance is not possible if taxation of collected compensation due to inflation is involved. Nevertheless, this is the case if nominal capital costs are passed on to the customer on the basis of the book value of historical costs and production costs.82 On the contrary, Seicht argues that sheer orientation to replacement price can guarantee the regulated company a long-term maintenance of asset value. He bases this postulate of asset value maintenance on the company’s mandate to supply a good, which he interprets as a service duty. This mandate to supply a good precludes a change of industry or the liquidation of a price-regulated company, for which reason the assurance of asset value maintenance must be the highest goal.83
79
Even the ordinance on charges for system use stipulates for prices to be allowed that these “. . . are to be determined based on costs . . .”, cf. Mayer (2002), p. 197. 80 Swoboda (1996), p. 365. Please note that this is a translation (German). 81 Swoboda (1996), p. 364. Please note that this is a translation (German). 82 cf. Swoboda (1992), p. 83; Swoboda (1996), p. 366. 83 cf. Seicht (1996), p. 351 ff.
2.3 Rate of Return on Equity as a Regulatory Parameter
25
Seicht grants, however, that only a real interest rate is to be used to calculate financing costs when orienting to replacement prices.84
Depreciation Period Principle 2 presented above requires the application of the service life when calculating depreciation, which in the ideal situation corresponds to the technical service life of the fixed assets. Since the technical service life does not only depend on the demands of the assets, but also on the maintenance and replacement policies, applying the service life defined outside of operation seems to be hardly possible.85 It must be ensured that 100% of the depreciation must be passed on to the customer even if the technical and operational service lives differ from each other.86
Depreciation Method Besides linear methods of depreciation, digressive or progressive procedures can also be applied. Bearing in mind Swoboda’s principle 2 presented above, the straight-line methods appear to correspond to this, although the financing costs exhibit a digressive trend and hence the sum total of the capital costs are lower over time because the book value decreases as the basis for setting financing costs. On an international level, straight-line depreciation has prevailed.87
2.3.3.2.2
Financing Costs
To calculate fair financing costs, a calculatory approach can be implemented, which reflects the subjective view, or a market-based approach, which reflects the view of the capital market. Both approaches together provide the answer to specific detailed questions, which are presented in the following.
A Basis for Calculating Interest The basis for determining financing costs is formed by means of a balance sheet view of the assets or capital, corrected for certain adjustments. These adjustments relate on one hand to the assets that are necessary for operation because, in general, only assets that are necessary for operation are considered as a basis for financing costs. A further criterion is formed by whether the assets are interest-bearing. 84
cf. Seicht (1996), p. 355. cf. Swoboda (1990), p. 70. 86 cf. Swoboda (1990), p. 70; Swoboda (1996), p. 372 ff. 87 cf. Swoboda (1990), p. 71. 85
26
2 Capital Market-Based Calculation of the Cost of Equity
Assets that are necessary for operation form the basis for interest calculation only to the extent that no interest-bearing equity items account for them. Noninterest related equity items are understood as contributions to building costs and government grants, for instance.88 Concerning accruals for pension, it is important to consider whether the interest claims from employees eligible for benefits are designated in personnel expenditures or in financial income. As long as these claims are designated in personnel expenditures and hence are a component part of operating costs, accruals for pension may not be calculated into the basis of calculation for interest because this would mean a double calculation of these interest claims. If the latter is the case, the accruals for pension are described as borrowed funds that are eligible for interest.89 For supplier’s accounts payable, interest settlement can arise in the form of guaranteed discounts. As long as services including discounts are activated and flow into the price calculation in the form of depreciation, including supplier’s accounts payable in the basis of interest would mean a double charge of interest from the interest expenditure to the customer.90 The question about applying historical cost items or daily prices when determining depreciation also has effects when defining the basis of calculation for interest. As long as historical costs are used to calculate depreciation, it only seems consistent to apply the balance sheet book value to calculate the capital employed on the basis of historical costs. The same applies in an analogous form for applying current prices or future replacement prices when calculating depreciation.
Calculating Interest Rates – The Calculatory Approach The calculatory approach denotes the concept of determining an appropriate rate of capital costs as summarized in cost accounting under the concept calculatory interest. Methodologically this is reached by raising a risk-free interest rate to a subjectively guaranteed risk surcharge. When using nominal interest rates, the basis for calculating interest is the continued historical costs and production costs. As long as real interest rates are used, the basis for calculating interest is formed by the balance sheet assets evaluated at the replacement price minus the interest-free borrowed funds.91 The basic problem of the calculatory approach, the subjective establishment of a risk surcharge, should be overcome by the capital market-based approach.92
88
cf. Swoboda (1990), p. 71. cf. Busse and Colbe (2002), p. 9. 90 cf. Busse and Colbe (2002), p. 9. 91 cf. Busse and Colbe (2002), p. 4. 92 cf. Swoboda (1996), p. 376. 89
2.3 Rate of Return on Equity as a Regulatory Parameter
27
Calculating Interest Rates – The Capital Market-Based Approach The capital market-based approach defines the interest rate when determining financing costs by taking account of the return requirements from the self-financiers and external investors. The risk-free interest rates serve as the basis for both the return requirements from the self-financier as well as for those of the external investor. These interest rates are increased for specific risk surcharges that are determined on the basis of capital market models. Besides CAPM, as the capital model used most often, there are also other models available.93 Reference is made here to the explanations in Sect. 2.2 of this work.
2.3.4
Conclusion
For projecting future cash flow surplus in price-regulated companies, the regulatory system defining profit or price must be considered. From this, a range can be determined for the amounts of future cash flows. The expected profits from regulated companies correspond with the allowed costs that account for them. These costs can be separated into operating costs and capital costs. Whereas for the operating costs specific assumptions must be made regarding their acceptance in the regulatory system applied, budgeting of the capital costs is possible on the basis of basic economical reflection, as long as it can be expected that the regulatory agencies also are oriented to these principles. The following can be maintained for incoming payments that correspond to capital costs: 1. Depreciation To calculate future cash flow, which serves as the cover for capital consumption in the form of depreciation, it must be taken into consideration whether the depreciation is calculated assuming historical costs or on the basis of replacement values. Allocating compensation due to inflation either to depreciation or to financing costs must be considered when calculating financing costs. The service life underlying the depreciation determination as well as the depreciation method used (straight-line, progressive, digressive) must comply with the principle of “fair distribution to the generations”, unless the regulatory agencies themselves deviate from this principle. 2. Financing costs Interest-bearing assets that are necessary for operation minus non-interest bearing capital items available should apply as the capital basis to calculate financing costs. The rate of capital costs related to this capital basis should be calculated by
93
cf. Swoboda (1996), p. 376 f.
28
2 Capital Market-Based Calculation of the Cost of Equity
applying a capital market model. The calculatory derivation of a rate of capital costs does not have to be performed. A rate determined for financing costs must include a risk surcharge, for which the amount depends on “. . . what risks a price regulation leaves to the power supply company”.94
2.4
Conclusion
In Sect. 2.2 CAPM and the set of problems related to a basic orientation to the past were presented. By adjusting parameters in the model, attempts can be made to impute a higher-value future orientation to the cost of equity rates determined. However, these adjustments take place most often without the theoretical or empirical foundation required by them. The analysis of which economical principles should be considered regarding the projection of future cash flow in regulated companies was presented in Sect. 2.3. This analysis showed that “fair” operating costs and appropriately determined capital costs, which contain a fair risk premium, are to be acknowledged by the regulatory agency and should be significant for regulating “fair” prices.
94
Swoboda (1996), p. 377.
Chapter 3
Methods of Price Regulation
3.1
Introduction
There are essentially two methods for defining fair prices in regulated industries. The first method, known as rate of return regulation, routinely fixes prices in the amount of the actual costs.1,2 The second method, known as RPI-X regulation, defines prices on the basis of a price or profit formula.3 In this case, the price of the current period consists of the price of the previous period subtracted by the mandatory efficiency boosts that take the form of price surcharges and price increases, in order to compensate for the general price escalation.4 In the following, both of these different systems will be defined more carefully. On the basis of the principle agent theory, a statement is made in Sect. 3.2 about which different incentive effects both of these systems have intrinsically for boosting efficiency in providing a service.
3.1.1
Rate of Return Regulation
“Rate of return regulation (abbreviated: ROR Regulation) denotes a regulatory system that fixes prices for products or services for a period on the basis of a 1
Synonyms used for this type of price regulation are also “individual cost audit system”, “costbased price regulation” and “Return regulation”. 2 For a rate of return regulation system, actual costs do not have to be used in practice for fixing prices; budget costs or target costs can also be implemented; cf. Sect. 2.3.3 of this work. For reasons of simplification, implementing actual costs should be assumed, in order to simplify the difference between rate of return regulation systems and RPI-X regulation systems. 3 Synonyms used for this type of price regulation are also “Revenue cap regulation”, “Price cap regulation” and “multiple-period incentive regulation”. 4 The expression “RPI-X regulation” is derived from both of the factors listed. “RPI” stands for the factor related to price increase from inflation (Retail-Price-Index) and “X” for the factor related to efficiency increase.
M. Hierzenberger, Price Regulation and Risk, Lecture Notes in Economics and Mathematical Systems 641, DOI 10.1007/978-3-642-12047-3_3, # Springer-Verlag Berlin Heidelberg 2010
29
30
3 Methods of Price Regulation
regulatory agency’s auditing results on costs reported by price-regulated companies. The definition of these prices is made by a regulatory agency that is authorized by the state for this purpose. It should be assumed that a company’s total costs are the subject of the cost audit. Furthermore, it should be assumed that the company audited possesses hidden information on the composition of their total costs compared with the regulatory agency, which does not know the actual composition of the total costs, but can only make an inference about the actual composition by means of the report issued about this composition by the company. It should not be ruled out that the costs reported correspond to the actual costs. In this type of system, the risk incurred from business activity by a company is compensated only in the form of an appropriate capital return. Profits that go beyond a fair capital return are not permitted.
3.1.2
RPI-X Regulation
The term RPI-X regulation should be understood as a system for defining prices in natural monopolies that fixes prices for products without ever considering the costs that underlie the production of goods or service provision. Thus, it is possible for price-regulated companies to generate profits that go beyond a capital return permitted. Losses are also possible in this type of price regulatory system because the cost-covering principle in ROR regulation is no longer the basis for fixing prices. By isolating price trends from cost trends, an incentive should be created for a company over the course of time to make use of its efficiency potential, in order to generate profits. Price trends can be affected directly by a price-cap regulation or indirectly by a revenue-cap regulation.
3.2
The Principal Agent Theory
The goal of Sect. 3.2 is to highlight reasons for the transition between cost-based and price-based regulatory systems and to illustrate what economical consequences justify this type of transition for regulated companies as well as for regulatory agencies. These statements are made on the basis of the principal agent theory. In Sect. 3.2.1, a principal agent model is outlined5 that describes the situation of an ROR regulation with complete information. In connection therewith, the same section shows how the situation with incomplete information creates incentives for the price-regulated company not to report costs accurately in this type of regulatory system for the purpose of increasing company profit. Following this is the outline of what decisions price-regulated companies come to regarding the use of possible efficiency increase potential, both assuming the existing efficiency potential and 5
An introduction to the principal agent theory is given by Macho-Stadler and Perez-Castrillo (2001), among others.
3.2 The Principal Agent Theory
31
assuming the decision about (new) production technology. All the explanations in Sect. 3.2.1 are based on the assumption of an ROR regulatory system. Section 3.2.2 highlights how the results from Sect. 3.2.1 change when assuming an RPI-X regulatory system. Section 3.2.3 serves as the summary of Sect. 3.2.
3.2.1
Rate of Return Regulation
3.2.1.1
A Solution for Complete Information
The basic function of an ROR regulatory system should be clarified in the following model, which derives from Laffont and Tirole:6 A company that is subject to an ROR regulatory system produces a good and possesses the following total cost function C ¼ ðy eÞ q þ a k e þ e with: C e y q a K e
Total costs Effort/Employment of labor (adjustment for hardship) Efficiency Output Fixed costs Technology Random variable
This function illustrates that the total costs are dependent upon the volume produced, in which the costs/unit are dependent upon the employment of labor used by the company or management. The company’s fixed costs are independent of the volume produced; however, these fixed costs depend on the choice of (production) technology. The parameter e possesses an expected value of 0 and for this reason can be ignored for further explanations. The good produced generates a benefit for consumers of the good in the form of SðqÞ, with S0 > 0 and S00 < 0. This means that the consumer’s total benefit increases with the boost in the volume produced (and consumed); however, the marginal utility decreases with the increase of the volume consumed. It should further be assumed that the management of the company receives a fixed payment in the amount of t from the state or the regulatory agency for their service rendered. In order to be able to pay the t to the management, it is valid to assume that taxation is necessary for this and the increase of t is charged to the consumer in the form of a tax increase. 6
cf. Laffont and Tirole (1986), p. 614 ff.
32
3 Methods of Price Regulation
The management’s benefit is defined in the form UM ¼ Et dðeÞ and dðeÞ represents any disutility from work provided on the management’s side. It should be assumed that this disutility rises with the increase of labor services (d0 ðeÞ > 0) and also that the marginal disutility is increasing (d00 ðeÞ > 0). The consumer’s net benefit from the regulated company’s business activities can be stated as follows: UK ¼ SðqÞ ð1 þ lÞ Eðt þ CÞ In this net benefit function, the factor ð1 þ lÞ should depict the issue presented above that the service of t to the company’s management is related to tax collections and thus, exhibits a disadvantage for the consumer, which increases by means of the increase of t. Thus, the following maximization problem results, as long as e as well as the other parameters of the cost function are observable: maxfSðqÞ ð1 þ lÞEðt þ CÞ þ UM g
ðq;e;tÞ
¼ fSðqÞ ð1 þ lÞ½dðeÞ þ ðy eÞq lUM g With the restriction of the following participation condition: UM 0 This condition formally illustrates that a solution of the maximization problem is only possible on the condition that the company’s management at least targets a benefit of 0 from the business activity. Otherwise, participation for management would not be meaningful for logical reasons. The first-order conditions from the maximization problem presented above are: UM ¼ 0 S0 ðqÞ ¼ ð1 þ lÞðy eÞ d 0 ðeÞ ¼ q The participation condition is binding and the marginal utility from one consumer unit (S0 ðqÞ) corresponds to the company’s marginal costs. Furthermore, the marginal disutility for the management of the labor services corresponds to the marginal utility achieved from it for the consumer, which corresponds to the reduction of marginal costs.
3.2.1.2
Incomplete Information: Cost Report Reimbursement
As presented above, incoming payments that a company receives from fulfilling the production tasks are dependent upon the total costs reported, (CR ), among other
3.2 The Principal Agent Theory
33
things. If the total costs reported deviate from the actual total costs (CT ) and the total costs reported are “acknowledged” by the regulatory agency, this means a profit improvement for the company (CT < CR ), in contrast to a report issued about the actual total costs, and vice versa. It is thus assumed, following Currier (2004),7 the regulatory agency knows the actual cost function of the company, except for one parameter. This parameter is the productivity parameter y. The cost function CT is defined by b and the volume produced q, with y ¼ ðy1 ; :::; ym Þ as the vector. y can also be written in a simplified manner as y ¼ ðyi ; yi Þ. In this case, yi stands for all possible values of y, with the exception of yi . The cost function CT ðy; qÞ hence, corresponds to CT ðyi ; yi ; qÞ. It is further assumed that the regulatory agency is aware of yi and the company would like to maximize utility by reporting yi and is ready to attain this by means of issuing a false report. The parameter k should depict this formally with k > 0. The company reports kyi . Unless k ¼ 1 is applicable, the costs reported correspond to the actual costs. The cost function that is made known to the regulatory agency, thus has the following form: CR ðkyi ; yi ; qÞ. By choosing the parameter k, it should become possible for the company to maximize profits. Both the company and the regulatory agency are aware of the demand function pða; qÞ, with a ¼ ða1 ; :::; an Þ as the vector. The economic consequences of the assumptions and definitions outlined should be demonstrated by the example of an average cost regulatory system.8 The following maximization problem arises for the company:9 max pða; qÞq CT ðy; qÞ on the condition: pða; qÞ ¼ ACR ðkyi ; yi ; qÞ with: ACR ¼
CR q
In order to solve this maximization problem, the following langrange function is to be generated: Lðk; q; lÞ ¼ pða; qÞq CT ðy; qÞ þ l pða; qÞ ACR ðkyi ; yi ; qÞ 7
cf. Currier (2004), p. 51 ff. Currier (2004) shows analogously in his work for a marginal cost regulatory system, cf. Currier (2004), p. 51 f. A marginal cost regulatory system is different by an additional consideration of a payment s to the firm, which is paid by the state or the regulatory agency to cover fixed costs. 9 cf. Currier (2004), p. 53 f. 8
34
3 Methods of Price Regulation
The necessary, first-order conditions are thus: Lk ¼ l ACRkbi yi ¼ 0 Lq ¼ p þ qpq CTq þ lpq ACRq ¼ 0 Ll ¼ pða; qÞ ACR ðkyi ; yi ; qÞ ¼ 0 Since yi 6¼ 0 and ACRkbi 6¼ 0 are the case, l ¼ 0 is applicable. For this reason, the marginal profit corresponds to the marginal costs MR ¼ MCT because: p þ qpq ¼ CTq Hence, it can be summarized that for an average cost regulatory system and a false report, a company achieves a profit and chooses a production volume (q), as if without regulation. Thus, the regulated company has an incentive to notify the regulatory agency of increased costs. 3.2.1.3
Incomplete Information: Efficiency Boosts
As presented above, fixing prices in cost-based regulatory systems occurs on the basis of costs made known. Through this, companies have no incentive to lower costs as long as the regulatory agency acknowledges the costs made known and fixes prices on this basis. The following shows what consequences this can have when realizing possible efficiency boosts potential through pre-existing technology as well as through new production technology. 3.2.1.3.1
A Model with Pre-existing Technology
Following Braeutigam and Panzar (1989),10 assume a company produces two goods: G1 and G2 . G1 describes the product that is offered in a regulated market and G2 describes each product that is offered on an unprotected market. To produce both of these goods, fix costs F are incurred that cannot be directly allocated to a product (e.g. overhead costs). The variable unit costs, however, can be allocated directly to G1 and G2 . The question thus arises, how F should be allocated to both goods. Relative quantities such as revenue, variable unit costs or output volumes act as reference parameters for this type of allocation. This type of allocation function for G1 is presented formally as follows: Relative output volume: f ðG1 ; G2 Þ ¼ ðG1 Gþ1 G2 Þ Relative unit costs: f ðG1 ; G2 Þ ¼ ðc1 cþ1 c2 Þ Relative revenue: f ðG1 ; G2 Þ ¼ R1 ðGR11ÞðGþ1pÞ2 G2 10
cf. Braeutigam and Panzar (1989), p. 373 ff.
3.2 The Principal Agent Theory
35
The function f ðG1 ; G2 Þ takes on a value between 0 and 1 and the following is applicable: f1
@f >0 @G1
f2
@f <0 @G2
Assuming that the regulatory agency allows the company the total cost for producing the price-regulated goods as maximum profit, in order to be able to cover the costs of production, the following is applicable for the company’s regulated market served with R1 ðG1 Þ as revenue from product 1: R1 ðG1 Þ C1 ¼ f ðG1 ; G2 ÞF þ c1 ðG1 Þ The company pursues the goal of maximizing the overall company profit, which is composed of a profit component from the regulated market and a profit component from the unprotected market. The company has to solve the following maximization problem: max R1 ðG1 Þ þ p2 G2 C1 C2 On the (rate of return) condition: R1 ðG1 Þ C1 ¼ f ðG1 ; G2 ÞF þ c1 ðG1 Þ It is further assumed that the company possesses three forms of efficiency potential: direct potential in the protected market (e1 ), direct potential in the unprotected market (e2 ) and potential in the (common) fixed costs (eg ). This potential should be considered to be additional cost components in the respective area for the above maximization problem: max R1 ðG1 Þ þ p2 G2 C1 C2 e1 e2 eg on the condition: f ðG1 ; G2 ÞðF þ eg Þ þ c1 ðG1 Þ þ e1 R1 ðG1 Þ 0 The langrange function is presented as follows: Lðe1 ; e2 ; eg Þ ¼ R1 ðG1 Þ þ p2 G2 C1 C2 e1 e2 eg þ l f ðG1 ; G2 ÞðF þ eg Þ þ c1 ðG1 Þ þ e1 R1 ðG1 Þ
36
3 Methods of Price Regulation
The first-order conditions for e1 and e2 are: Le1 ¼ 1 þ l 0 Le2 ¼ 1 Hence, using efficiency potential in the unprotected market leads to the company’s desired results. The question thus arises how e1 is developed to the maximum. As long as e1 > 0 is the case, the first derivation of L following e1 would be exactly zero. However, this is only possible as shown above if l ¼ 1 is the case. If, however, it were applicable that the condition l ¼ 1 is met, then the first derivation of L following G1 equals f1 F > 0. This is not possible for maximum development.11 Hence, it can be held that: @L=@e1 < 0 l<1 e1 ¼ 0 It can be inferred from this that eg ¼ 0 is also applicable and that a company always strives to produce as efficiently as possible in both the protected and the unprotected areas. In the following, this should be examined assuming the choice of technological alternatives.
3.2.1.3.2
A Model with Choice of Technology
Assuming again that a company produces two goods (G1 and G2 ). G1 is the product from the protected market and G2 from each of the unprotected markets. In contrast to the model presented above for a pre-existing technology, it should be assumed that there is a choice in production technology. The production technology chosen defines the unit costs of G1 and G2 . The company’s total cost function (C) is depicted as follows:12 C ¼ F þ c1 ðG1 ; FÞ þ c2 ðG2 ; FÞ with: @ci <0 @F Increased investments in production technology thus led to lower unit costs of G1 and G2 , but also to an increase of F and vice versa. 11
cf. Braeutigam and Panzar (1989), p. 378. cf. Braeutigam and Panzar, p. 381.
12
3.2 The Principal Agent Theory
37
The maximization problem presented in Sect. 3.2.1.3.1 is now expanded to the following first-order condition: LF ¼ ð1 þ cF1 þ cF2 Þ þ lðcF1 þ f Þ 0 F0 FðLF Þ ¼ 0 The following problem arises at this juncture: The company minimizes the total costs if the first bracket term of LF is equal to 0. This choice of technology is only made if the second bracket term is equal to 0. This shows that only under certain circumstances is a company willing to lower the total costs by an investment or by expenditures for commonly used facilities. A company thus accepts a certain level of inefficiency. Braeutigam and Panzar justify this as follows:13 The profit-maximizing firm operating under a binding rate-of-return constraint may choose an inefficient level of common facilities. It will overinvest (underinvest) in common facilities if, at the margin, the cost reductions such facilities yield for the core service are less than (greater than) the common costs allocated to the core service.
3.2.2
Diversification with Price-Based Regulation
As was shown before, on certain assumptions a company has no incentive to use its efficiency potential or to select certain technologies that are advantageous for consumers. The question thus arises whether a price-based regulatory system influences the decision. Again, let us assume a company that provides two products or services. One product is set up in a regulated market, the second product in a market with competition. The following total cost function is applicable:14 CðG1 ; G2 Þ ¼ F þ c1 ðG1 Þ þ c2 ðG2 Þ If a price-based regulatory system is assumed, the company maximizes profit by abiding by the following condition: p1 p The rate of return condition R1 ðG1 Þ C1 ¼ f ðG1 ; G2 ÞF þ c1 ðG1 Þ is no longer significant. This means that the profit-maximum price is dependent upon the costs and is subject to a cap that is defined by the regulatory agency. 13
cf. Braeutigam and Panzar, p. 382. cf. Braeutigam and Panzar (1989), p. 387 ff.
14
38
3 Methods of Price Regulation
The economical advantages of this type of regulation are:15 A company whose profits are not regulated on the basis of costs have an incentive to increase efficiency as do companies that are in competition. Every unit reduced in cost leads directly to an increase in profit because profit does not develop analogously to costs (any longer). Hence, companies can have no incentive to conceal costs or to issue unclear cost reports because, as already mentioned, costs and profits have no bearing on each other. A price-based regulatory system leads to a reduction in regulation costs. Elaborate, cost-intensive audit procedures are dispensed with because the costs are no longer an object of elaborate audit activities. Finally, a company with price-based regulation can practice a purely marketoriented diversification policy. Diversification is deemed advantageous if company profit can be increased by it. Decisions about technology can be reached independently of distribution functions for “common costs”.
l
l
l
However, disadvantages are also present vis-a`-vis the advantages presented on a price-based regulatory system, contrasting still with the cost-based:16 The question arises how the price should develop over several periods. For this, a certain price formula is used very often. These types of price formulas are major simplifications. It seems questionable whether major simplifications are appropriate in general or appropriate to portray complex interrelationships. Certain markdowns are very often set into the price formula, individual efficiency factors are employed and the general price trend is considered as compensation for inflation. The success of a price-based regulatory system is in essence dependent upon how fixing price caps on companies is administered. As long as the company’s trust develops in a way that price caps are not adjusted in an unusually strong way (downwards) when absorbing “big” profits, the function of this type of system is not endangered when fixing prices. However, this scenario seems to be charged differently if this trust is not given. Efficiency boosts are of low importance in this scenario because the expected profits for future periods are already regarded as extraordinary price adjustments.
l
l
It can be inferred from this that the first-time fixing of a price cap must be conducted “carefully”, in order to minimize or even to exclude the possibility of the probability of needing extraordinary price adjustments.
3.2.3
Conclusion
The goal of Sect. 3.2 was to highlight the differences between cost-based and pricebased regulatory systems based on the principal agent theory. 15
cf. Braeutigam and Panzar (1989), p. 388. cf. Braeutigam and Panzar (1989), p. 389.
16
3.3 Regulatory Systems and Risk
39
This section demonstrated why a cost-based regulatory system gives an incentive for price-regulated companies to report costs incorrectly for the purpose of maximizing company profit. This section showed that issuing a report of higher costs ends in an increase of profit compared with the issuance of an accurate cost report. Based on a comparison of reaching decisions regarding the use of efficiency boost potential and the selection of production technologies, the advantages of a pricebased regulatory system were shown compared with a cost-based regulatory system: Incentive given for optimal efficiency boosts. Incentive given for optimum technology selection for all departments of the company.
l l
The disadvantages of a price-based system were also discussed. First and foremost, fixing a general price formula is a substantial problem when efficiently and practically implementing a price-based regulatory system. The company’s trust in the reliability of a price formula is a critical success factor for the price-based regulatory system.
3.3
Regulatory Systems and Risk
Regulatory agencies create an essential, economic framework for regulated companies by means of regulation. This framework can be manifested in the form of price-defining mechanisms and/or creating hindrances for other companies to enter these industries. The intensity of this price regulation and/or market entrance regulation is of special significance for the economic success of regulated companies. Price regulations influence revenues and entrance regulations influence the intensity of competition and thus, indirectly, again revenues. In the course of time, the intensity of regulation can increase or also decrease. In the event of decrease, deregulation is involved. If regulation intensifies again, this is identified as re-regulation. In Sects. 3.3.1 and 3.3.2, a statement is made about the two contrary theoretical reflections on how measures of deregulation or re-regulation can effect the priceregulated companies when bearing risk.
3.3.1
Buffering Effect
The “buffering effect” is identified by any reflection which derives a reduction of company risk from an intensification of regulatory policies.17 Peltzman (1976) explains this as follows:18 17
cf. Taggart (1985), p. 259. cf. Peltzman (1976), p. 230.
18
40
3 Methods of Price Regulation . . . 7. Finally, I note an implication for the theory of finance. Regulation should reduce conventional measures of owner risk. By buffering the firm against demand and cost changes, the variability of profits (and stock prices) should be lower than otherwise. To the extent that the cost and demand changes are economy-wide, regulation should reduce systematic as well as diversifiable risk. . . .
As long as (re)regulation in industries leads to a reduction of risk, this means that the risk must increase for deregulation measures.19 Swoboda explains concerning this that, for an optimal rate of return regulation, the regulated company assumes no risk and in this respect no risk premium can be applied when calculating a fair cost of equity rate. However, Swoboda qualifies this by saying this reflection is only of a theoretical nature because the distance between price regulation deadlines or the possibility that (political) opinion changes on the type of price regulation, creates de facto risks.20
3.3.2
Regulatory Lag Effect
Assumptions that underlie the “regulatory lag effect” lead to opposite results. It is assumed here that the risk for a price-regulated company must increase when industries are (re)regulated. This is justified in the reflection that the necessary adjustments to cost and profit-related changes from the regulatory system can only take place with a temporary setback. This lessened flexibility would not be present in non-regulated companies, which is why regulated companies exhibit higher risks compared with non-regulated companies.21 The temporary setback when adjusting parameters from a regulatory system to new, economic framework requirements could endanger the economic survival of the regulated company e.g. because of cost increases. This problem was encountered in the USA with the introduction of the “fuel adjustment clauses”. These regulations allow price-regulated companies to pass on certain cost increases to the consumers without requiring a comprehensive price audit. Temporary setbacks also provide stimulus to the management of regulated companies not to go through with certain investments because the investments costs necessary for them are passed on to the customers in the temporary setback,22 which is caused by a comprehensive price audit by the regulatory agency. Joskow and MacAvoy (1975) conducted investigations on the example of the electricity market in the USA and reached the following conclusion, among others:23 19
cf. Fraser and Kannan (1990), p. 68. cf. Swoboda (1996), p. 377. 21 f. Fraser and Kannan (1990), p. 68 f. 22 Joskow and MacAvoy allege, for example, that price audits in the USA last around 12 months and are based on data from the closed fiscal year, from which delays of several years can result in terms of cost allocation to customers, cf. Joskow and MacAvoy (1975), p. 296. 23 cf. Joskow and MacAvoy (1975), p. 297. 20
3.3 Regulatory Systems and Risk
41
. . . Such flexibility [comment: Fuel Adjustment Clauses], whatever the benefits, has been purchased at a potentially high economic cost. The automatic pass through of fuel charges, along with the slower and more cumbersome regulatory review of other costs, can lead to distortions. The choice of generating techniques may therefore be biased toward energy intensive turbines away from capital intensive nuclear capacity. . . .
3.3.3
The Risk Effect from a Regulatory System Shift
The advantages of a price-based regulatory system presented in Sect. 3.2, compared with a cost-based regulatory system, illustrate reasons why in the recent past an increasing change from cost-based to price-based regulatory systems is observable. By the change of fundamental price-defining principles, the question arises how a regulatory system shift can be evaluated by the capital market. Bearing in mind the explanations in Sects. 3.3.1 and 3.3.2, the reaction of the capital market to the regulatory system shift expected does not appear to be unequivocally predictable. In practice, classifying the regulatory system to one of the two models presented (“rate of return regulation” or “RPI-X regulation”) is not always clearly possible because there can be countless mixed forms. The formal description of a regulatory system is not always relevant for the capital market, rather the economic principles resulting from applying a system, in the form of expected cash flow and risks, are significant. For this reason, the following presents the basis on which criteria are used by the capital market to make an evaluation of the regulatory system. Navarro24 conducted an investigation in the beginning of the 1980s in the USA, based on which the capital market, represented by more than 20 institutional capital market participants, uses criteria to evaluate the regulatory system of individual states in the form of a juxtaposition of these regulatory systems. The data necessary for this was gathered through publications and in personal conversations with representatives from the institutions. Navarro came to the conclusion that from among the eight criteria identified, only two exert significant influence on the sequence of the regulatory system (a) the amount of capital return that is permitted and (b) the acceptance of investments in buildings as a component part of interestbearing capital. A further investigation about the capital market’s evaluation of regulatory systems is available from Davidson and Chandy.25 The basis for the investigation was made of evaluations on 46 state regulation systems in the USA by Merrill Lynch, Argus, Value Line, Goldman Sachs and Duff and Phelps. The evaluations took place by classifying a state’s regulatory system in quality categories, while implementing nine evaluative criteria. Quality category 3 is characterized by 24
cf. Navarro (1983). cf. Davidson and Chandy (1983).
25
42 Table 3.1 Evaluation results for regulatory systems in the USA
3 Methods of Price Regulation State Alabama Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Mississippi Missouri
Rank 1.0 2.2 2.0 2.2 2.0 1.8 2.4 3.0 1.6 3.0 2.2 2.2 3.0 1.4 2.0 2.8 1.6 1.0 2.0 1.8 1.4 1.4 1.0
State Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin
Rank 1.0 1.8 2.0 2.4 2.0 3.0 2.4 3.0 1.2 2.2 1.8 2.2 1.4 1.4 1.0 1.2 3.0 3.0 2.4 1.8 2.0 1.2 3.0
“highest quality” and quality category 1 by “lowest quality”. The arithmetic mean of the five evaluations for each state can be seen in Table 3.1:26 Using the evaluations from the five institutions listed as a representation of a capital market evaluation is underscored by the high rank correlation between the individual evaluations as shown in Table 3.2 Davidson and Chandy conducted a multiple regression analysis in which the evaluative criteria they used were defined as independent and classification in a quality categorization was defined as the dependent variable. The result from this analysis can be taken from Table 3.3. To summarize, it can be held from the results of Davidson and Chandy’s study that the regulation-defining mode of the commissioner from the regulatory agency as well as the duration of a regulatory period possesses a strong influence on the classification of a regulatory system in a quality category as shown in Table 3.4. The regulatory definition made by the commissioner of the regulatory agency appointed by the governor,27 as well as the shortest possible regulation period were evaluated as positive for the capital market. 26
cf. Davidson and Chandy (1983), p. 51. Davidson and Chandy justify the significance of this criterion as follows: “. . . The importance of this variable may be attributable to the degree of certainty with which investors can anticipate an appointed commissioner´s attitude toward utility rate increase. Investors may feel better able to anticipate the character of a commissioner who is appointed by the governor of the state than one who is chosen by election. . . .”, Davidson and Chandy (1983), p. 51 f. 27
3.3 Regulatory Systems and Risk Table 3.2 Correlation grade from the evaluation results Spearman Rank Average Merrill Duff and Correlationa rank Lynch Phelps Average rank 1.000 0.894 0.863 Merrill Lynch 1.000 0.739 Duff and Phelps 1.000 Goldman Sachs Value line a All significant at the 0.01 level. Table 3.3 Regression results from Davidson and Chandy Variable Utility commissioner elected (el., 1) or appointed (app., 0) Revenue received as a percentage of revenue requested Fuel adjustment clause (1 or 0) Average regulatory lag (in months) Test year (historical, 1; current or future, 0) Return on Equity Work in progress is in the rate base (1 or 0) Rate Base: original cost (oc, 1) or fair value (fv, 1) Interim rates (1 or 0) (CONSTANT)
43
Argus 0.869 0.757 0.712
R2 0.24 0.31 0.35 0.40 0.43 0.45 0.48 0.49 0.50
Goldman Sachs 0.893 0.709 0.767 1.000
Value line 0.898 0.758 0.858 0.802 1.000
B 0.60 0.01 0.33 0.68 0.24 0.04 0.23 0.31 0.15 1.55
Beta 0.40 0.21 0.19 0.29 0.17 0.16 0.18 0.12 0.12
Table 3.4 Qualitative capital market evaluation of changes from the regulatory system Capital market evaluation Evaluation Nr. Kriterium Positive Negative 1 Return on Equity þ 2 Average regulatory lag þ 3 Interim rates y n 4 Test year (historical, current or future) Future Historical 5 Work in progress is in the rate base y n 6 Normalization of tax benefits by accelerated y n depreciation 7 Fuel adjustment clause y n 8 Rate Base: original cost (oc) – fair value (fv) fv oc 9 Revenue received as a percentage of revenue þ requested 10 Utility commissioner elected (el.) or appointed (app.) app. el.
Also the criteria whether investments in buildings as a component part of the interest-bearing capital are acknowledged, whether “fuel adjustment clauses” are applied and to what extent costs requested by the regulatory agency are approved possess a significant influence on classification in a quality category. In the following, the nine criteria used by Davidson and Chandy are complemented by one more criterion, which was additionally included by Navarro, and the necessary qualitative characteristic involving a positive or negative capital market evaluation is listed:
44
3 Methods of Price Regulation
Accordingly, a change in the regulatory system that is evaluated as positive by the capital market is identified by the following characteristics: l
l l l
l
Orientation of the commissioner from the regulatory commission toward the interests of the regulated company. Shortening the regulation periods. Increasing the capital return. Acceptance of “investments in buildings” as a component part of the interestbearing capital. Direct passing of cost increases to the customer.
3.4
Summary
Chapter 3 provides definitions of “price-based regulatory system” (RPI-X regulation) as well as “cost-based regulatory system” (ROR regulation). Building on these definitions and based on reflections on efficiency, it was shown that the increasingly observable change from cost-based to price-based regulatory systems is taking place. These types of changes in the regulatory system must be contrasted with costs as well, in order to be able to determine an achievable net utility from this type of change. The potentially contradictory effects outlined due to a regulatory system shift (regulatory lag and buffering effect) were reviewed for their empirical contents already at the beginning of the 1980s in the USA. As the results presented in Sect. 3.3.3 show, a change from a cost-based regulatory system to a price-based regulatory system, attributable to the strengthening of the regulatory lag effect, is evaluated by the capital market as negative.
Chapter 4
Empirical Secondary Data Analysis
4.1
Introduction
Chapter 4 examines whether decisions from regulatory agencies on future regulation parameters, contingent on the applied regulatory system (ROR-regulation or RPI-X regulation), have significant differences. Each decision to be reached by the regulatory agency or the complexity of the calculation is defined as the regulation parameter. The economic consequences for price-regulated companies resulting from these decisions by the regulatory agency are identified as regulatory risk. Regulatory risk is a component of unsystematic risk.1 Unsystematic risk is primarily of significance only for investors that are not completely diversified.2 For investors that are completely diversified, the systematic risk component is not relevant for business valuation.3 A significant difference in the variance of abnormal returns between the regulatory systems would nonetheless point out the heteroscedasticity of the confounding variables from the market model, contingent on the regulatory risk.4 In this case, an estimated b factor based on historical data (from periods of application of a regulatory system) would not be an efficient value estimation for future periods (application of another regulatory system).5 An expected regulatory system shift would thus be associated with a change of the b factor, attributable to a structural break, as a measurement for systematic risk, and for this reason, is relevant for business valuation to all investors, independently of their level of diversification.6
1
cf. Robinson and Taylor (1998), p. 337. cf. Baecker et al. (2007), p. 276. 3 cf. Mandl and Rabel (1997), p. 290. 4 cf. Sect. 2.2.1.4.2 of this work. 5 This statement assumes that regulatory risk is the decisive factor for the amount of unsystematic risk and that the remaining non-systematic risk factors exert no decisive influence. 6 cf. Ballwieser (2002), p. 739; Mandl and Rabel (1997), p. 306; Purtscher (2006), p. 111; Ulschmid (1994), p. 210; on the relevance of considering industry peculiarities for business valuation cf. 2
M. Hierzenberger, Price Regulation and Risk, Lecture Notes in Economics and Mathematical Systems 641, DOI 10.1007/978-3-642-12047-3_4, # Springer-Verlag Berlin Heidelberg 2010
45
46
4 Empirical Secondary Data Analysis
The examination of this set of question is based on results from event studies on regulated network industries in Great Britain and in the USA. Consequences for investors from the change of regulation parameters are measured by means of abnormal returns from regulated companies’ stock. The theoretical framework for the present work is structured by the works of Stigler and Petlzman. Chapter 4 is divided as follows to answer the set of questions presented above. The positive theory of regulation from Stigler and Peltzman is presented, on which basis the subsequent investigations in this chapter are built. Section 4.3 presents the theoretical basis for conducting the event studies. In Sect. 4.4, examples from the results of three current event studies are summarized to emphasize the heterogeneity of research studies in this area. Connected to this, the hypotheses tested in this work are derived, which are summarized in descriptive form for the primary studies implemented in the test. Inferences from statistic results are presented. In Sect. 4.5, results from this chapter are interpreted and summarized.
4.2
Stigler and Peltzman’s Theory of Regulation
In the seminal work on a positive theory of regulation “The Theory of Economic Regulation”, Stigler (1971) points out that the configuration of a regulatory system is the result of regulation on supply first and then to demand. Stigler postulates the thesis that the configuration of a regulatory system is dependent on how “strong” the interest groups are that are effected by the regulation. His thesis states that regulation always acts as an advantage for companies (“producer protection”):7 The central tasks of the theory of economic regulation are to explain who will receive the benefits of burdens of regulation, what form regulation will take, and the effects of regulation upon the allocation of resources. Regulation may be actively sought by an industry, or it may be thrust upon it. A central thesis of this paper is that, as a rule, regulation is acquired by the industry and is designed and operated primarily for its benefit.
Peltzman expanded and formalized Stigler’s theory. Consumers were also taken as those who demand regulation in his theory (“producer protection” and “consumer protection”) and the regulatory agencies were no longer defined as the “black box”, but rather as an institution that maximizes its own selfinterest and whose goal is to “stay in office”. In order to reach this goal, the regulatory office requires support from companies and/or consumers. Providing support can be through votes or in monetary form. Drukarczyk and Ernst (2007): “. . . Industry peculiarities in valuation is thus not an academic game, but rather of decisive, practical relevance. Its meaning is, we presume at least, much higher than the value relevance from the so-called tax shields, which are hardly discussed in the scientific literature at length, although their value influence shrivels with increasing knowledge of interdependency. . . .,” Drukarczyk and Ernst (2007), p. VIII. 7 Stigler (1971), p. 3.
4.3 Methodology: Event Studies
47
Peltzman formally outlines the maximization problem from a regulatory agency as follows:8 M ¼ n f ðN nÞ h with: n Number of potential voters in the beneficiary group. f (Net) probability that a beneficiary will grant support. N Total number of potential voters. h (Net) probability that he who is taxed (every non-n) opposes. Peltzman gauges small, homogeneous and capital-empowered interest groups (companies) to have a stronger forcefulness than the interests of large heterogeneous interest groups (consumers).9 He justifies this by costs for acquiring information as well as by costs for group organization required to promote interests within the scope of political processes.10 Peltzman justifies the demand of the company for regulation (among other things) by saying the variability of the company profit is lower for regulated markets than for nonregulated markets because the regulation protects companies before changes occur in demand and cost.11 For this reason, companies attempt “to capture” the regulatory agencies and, because of this, to expect direct cash flows, market entry restrictions for potential new competitors or advantageous pricing. The regulatory agency will select the solution of the regulation question that maximizes their advantage: regulatory equilibrium. If there are changes in the distribution of the power relationship between the interest groups, the regulatory system also is changed, in order to portray a solution involving equilibrium.12 It has become possible from Peltzman’s work to explain regulatory decisions economically, which previously were not explainable by Stigler: the change in regulatory system and the new delineation of regulatory parameters that lead to lower prices, which are to the consumers’ advantage and to the companies’ disadvantage.
4.3
Methodology: Event Studies
Event studies investigate the effects of public announcements on the capital market. On one hand, the efficiency of the capital market can be tested and, on the other, the evaluation of new information about their effect on company values (stock prices) 8
cf. Peltzman (1976), p. 214. cf. Peltzman (1976), p. 212. 10 cf. Peltzman (1976), p. 213. 11 cf. Peltzman (1976), p. 230. 12 Binder and Norton (1999) characterize this as follows: “Unlike the earlier capture (Gray 1940) or public interest theories (Bernstein 1955), the Peltzman model predicts that the regulator will not choose a corner solution (maximize the wealth of one group) unless the other group is completely politically powerless.” Binder and Norton (1999), p. 250. 9
48
4 Empirical Secondary Data Analysis
is measured.13,14 Both questions cannot be examined separately from each other, which is why one speaks of “joint hypothesis” in the context of this type of investigation. The theoretical basis for event studies, which investigate the market reaction to public announcements, is made up of the semiefficient form of capital market efficiency.15 Fama et al. (1969) and Ball and Brown (1968) conducted the first event studies by applying the methodology that is still common today.16 The public announcements examined can either be of an endogenous or an exogenous nature. Endogenous events are those that are brought on from company decisions. By way of example, information about a future product policy or the publication of a scheduled company acquisition or sale are endogenous. Events of an exogenous nature are those that a company cannot directly influence. Among these are, for example, publications on credit worthiness evaluations or companyrelated regulation decisions by governmental authorities (e.g., announcing price audits or the basic change of a regulatory system). The overall stock yield, which is measured within a certain time period before and/or after the public announcement, is subdivided into two components: an “expected stock return”, which is defined based on a benchmark model and an “abnormal return”.17 The abnormal return can either be positive or negative, depending on the stockholder’s change of expected value of the enterprise value, attributable to the public announcement. As a part of an event study, besides the methodology for measuring abnormal return, which is explained in Sect. 4.3.2, the relevant event window is defined.18
4.3.1
Estimation Period, Event Window and Postevent Window19
Before the abnormal return can be determined, a time period must be defined within which the stock price reaction, attributable to the public announcement, takes place and is measured. This time period is called the “event window”.
13
cf. Binder (1998), p. 111. Bowman (1983) cites applications in other areas: the definition of equilibrium models and their improvement by identifying variables that explain the market behavior; cf. Bowman (1983), p. 562. 15 cf. Cox and Portes (1998), p. 282. 16 cf. MacKinlay (1997), p. 13 f. 17 cf. Cox and Portes (1998), p. 286 f. 18 Moreover, other procedural steps are to be established; presenting them would exceed the limits of this work. Reference is made here to Bowman (1983). 19 cf. MacKinlay (1997), p. 19 f. 14
4.3 Methodology: Event Studies
49
estimation period
event window
post-event window
>t4
t0
t1
t2
t3
t4
Fig. 4.1 Time axis of an event study
Additionally, the period from which the necessary, historical capital market data is extracted should also be defined, in order to determine the expected return or the relevant regression coefficient (the “estimation period”). This is depicted graphically in Fig. 4.1 for the measurement method described in the following under Sect. 4.3.2.1. Here the period t0–t1 is any period from which the necessary, historical capital market data is extracted, in order to determine the expected return for the “event window”. When conducting a regression analysis to measure abnormal return, as depicted under Sect. 3.2.2 in Chap. 3, the “estimation period” is adjusted by extending it beyond the event window. The event window t1–t3 contains point t2, which is when the public announcement took place. Within this event window, the capital market reaction to the publication is expected. Time period t3–t4 is called the “postevent window” and it portrays the period after the public announcement.
4.3.2
Measuring Abnormal Returns
To calculate abnormal returns, one must compare expected stock returns with observed stock returns. The common procedure for measuring abnormal returns is presented in the following Sects. 4.3.2.1 and 4.3.2.2.
4.3.2.1
Residuals from Observed and Expected Returns
The abnormal return of a stock at time t is determined as follows: ARit ¼ Rit EðRit Þ For this, ARit depicts the abnormal return of stock i at time t; Rit is the observed return at time t and EðRit Þ the expected return at time t.
50
4 Empirical Secondary Data Analysis
The expected return EðRit Þ can be defined by means of statistic models as mean adjusted return, as market adjusted return, as market and risk adjusted return or by the market model.20 Should economic models be used as a benchmark model, the one-factor Capital Asset Pricing Model (CAPM) or the multifactor models (Arbitrage Pricing Theory; Fama-French-3-Factor Model) can be applied.21 Cable and Holland rate the market model and the CAPM models as the preferred models, which prevail among the remaining models cited.22 If the exact timing of the public announcement and the respective pricing factors in the stock price are not precisely definable, it is appropriate to extend the event window. In this scenario, the adjustment necessary to determine abnormal returns appears as follows:23 CARit ¼
y X
ARit
t¼x
with: CARit Accumulated abnormal returns from stock i at time t. ARit Abnormal returns from stock i at time t. X Number of days/weeks/months before time t. Y Number of days/weeks/months after time t. If the average abnormal return is determined for a company at n > 1, the following calculation is to be made for the abnormal return:24 AARt ¼
n X ARit n¼1
with: AARt n
n
Average abnormal returns from the portfolio at time t. Number of shares.
The average CARt for a portfolio of stock at time t is determined as follows:25 CAARt ¼
n X CARit n¼1
with: CAARt
20
Accumulated average abnormal returns at time t.
cf. Brown and Warner (1980), p. 207 ff. cf. Binder (1998), p. 117 ff. 22 cf. Cable and Holland (1999), p. 339. 23 cf. MacKinlay (1997), p. 21. 24 cf. Binder (1998), p. 113; MacKinlay (1997), p. 24. 25 cf. Binder (1998), p. 113; MacKinlay (1997), p. 24. 21
n
4.3 Methodology: Event Studies
4.3.2.2
51
Using Dummy Variables for Regression Analyzes
Alternatively to determining abnormal returns as the difference between observed and expected returns as presented in Sect. 4.3.2.1, the effect of public announcements on stock prices can be examined by using one or more dummy variables over the course of a regression analysis. The dummy variable assumes the value 0 for the time periods before and after the public announcement and 1 for the time period during the public announcement. This is presented formally for a stock with the use of the market model as follows:26 Rit ¼ ai þ bi Rmt þ gi Dt þ uit with: Rit Returns from stock i at time t. ai Constant from the regression equation. bi b factor (slope of the regression equation). Rmt Market returns at time t. gi Coefficient of the dummy variable (abnormal return) for event i. Dt Dummy variable (0 or 1). uit Confounding variable. According to Izan (1978), the abnormal portfolio returns for one portfolio of stock is determined as follows:27 Rpt ¼ ap þ bp Rmt þ
A X
gpa Dat þ upt
a¼1
with: Rpt Returns from portfolio p at time t. Rmt Market returns at time t. gpa Coefficient from the dummy variable for event a. Dat Dummy variable for event a (0 or 1). upt Confounding variable. The regression models presented can be expanded to include additional dummy 0 variables – dummy variables such as (Ds ) are especially used for the b factor (bi ) 0 and for the constants of the regression equation (ai ), in order to measure the changes of the systematic risk or the constant:28 0
0
Rit ¼ ai þ ai Ds þ bi Rmt þ bi Ds Rmt þ
n X k¼1
26
cf. Binder (1998), p. 123 f; Teets (1992), p. 278. cf. Binder (1998), p. 124. 28 cf. The groundbreaking work on this by Binder (1985a). 27
DIk gk þ uit
52
4 Empirical Secondary Data Analysis
with: 0 ai 0 bi Ds DIk gk
Change in the constant ai if: Ds ¼ 1. Change in the constant bi if: Ds ¼ 1. Dummy variable with value (time lapse before event) or 1 (time lapse after event). Dummy variable with value 0 or 1 (at time of the public announcement). Coefficient of the regression equation to calculate abnormal returns.
As long as no significant changes in the systematic risk, attributable to the events studied, can be established, the abnormal returns determined by a regression analysis can be directly compared with the abnormal returns that are determined from the method applied in 4.3.2.1.29 However, if systematic risk cannot be assumed to be constant, the measurement method using regression analysis and a changeable b factor is to be preferred because, in this case, the estimate of the abnormal returns is more precise for the regression analysis.30 The goal of this chapter is to verify whether the mean value and the variance of abnormal returns are contingent on the regulatory systems applied or not. Answering this question is based on the statistic review of the hypotheses formulated under 4.4.1. Then, the (partially contradictory) results from three events studies are presented in a compressed, qualitative form, in order to emphasize the relevance of the set of questions and the heterogeneity of the state of research:31 Robinson and Taylor (1998). Within the scope of their work in “The Effects of Regulation and Regulatory Risk in the UK Electricity Distribution Industry”, Robinson and Taylor (1998) examined the effects of regulatory decisions on stock prices between 1992 and 1995 and the regulatory risk of companies effected thereby. The results published in their work are nearly identical to those from the work of Paleari and Redondi (2005), which is why the more extensive work of Paleari and Redondi (2005) is incorporated into the present work for the quantitative analysis. Robinson and Taylor used a market model with a constant b factor in the event study as a benchmark model. An ARCH process was implemented to model the regulatory risk. Robinson and Taylor were not able to identify any evidence for the validity of the capture theory, however, they established an increase of the regulatory risk for approx. 2/3 of the events studied (Norton (1988)). The event study from Norton (1988) “Regulation, the OPEC Oil Supply Shock, and Wealth Effects for Electric Utilities” examined the effects of the oil price shock from 16 October 1973 on stock from electricity providers effected by this in the USA, using a market model with a constant b factor. Norton tested the buffering 29
cf. Dewan and Ren (2007), p. 9. cf. Dewan and Ren (2007), p. 9. 31 Besides the study results presented here in a compressed, qualitative form, which are not integrated into the quantitative analysis of this work, reference is made to works listed in Appendix 2 that are also not included in the database of this work, in order to give the reader an overview of the current literature related to the set of questions thematized here. The claim to completeness is not made. Special notice should be given to the works of Norton (1985), Davidson et al. (1997), Morana and Sawkins (2000) and Buckland and Fraser (2002). 30
4.3 Methodology: Event Studies
53
hypothesis from Peltzman and the regulatory lag hypothesis, which contrary to Peltzman’s hypothesis, predicted an increase of risk through intensified regulation because necessary price increases, attributable to cost increases, can only be carried out by regulated companies in a delayed manner.32 For this, he classified the regulatory systems from the states in the USA to three categories and derived three portfolios from this in which he classifies the 21 companies examined: unregulated (6 companies), weakly regulated (4 companies) and strongly regulated (11 companies). Norton comes to the following conclusion:33 The primary results of this study are that shareholder wealth is endogenous to regulation and that we cannot reject the existence of a regulation-induced buffering effect. . . . Dnes et al. (1998).
In the event study “The Regulation of the United Kingdom Electricity Industry: An Event Study of Price-Capping Measures”, Dnes et al. examined the stock price reactions of ten events, attributable to changes in regulatory parameters from the beginning to the middle of the 1990s. The events studied are included in their completeness in data samples from Paleari and Redondi (2005), with the exception of one event. Three models were used as benchmark models: mean adjusted abnormal returns, market adjusted returns and the market model. During the period from 1991 to 1994, “total abnormal returns” were determined at 60.7%, of which only 0.7% are omitted from the “total abnormal regulatory returns”.34 They interpret this result to be an indication of the validity of the capture theory:35 The lesson therefore of this event study [. . .] is that where the regulatory framework is applied to a starting point that differs substantially from equilibrium [. . .], transparent objective hands off regulation is not possible. . . .
4.3.3
Hypotheses’ Derivations
Regulation on profitability is defined as a strongly formed regulatory system and the multiple-period incentive regulation is defined as a weakly formed regulatory system. This definition is based on the thought that a company does not have to undertake economic risks for an ideal-typical regulation on profitability because, by fixing prices and the regular cost audits associated with them, it is guaranteed that the total costs, including a fair capital return, can be passed on to the customer.36
32
cf. Norton (1988), p. 224; Sect. 3.3 of the present work. Norton (1988), p. 232. 34 cf. Dnes et al. (1998), p. 219. 35 Dnes et al. (1998), p. 221. 36 cf. Borrmann and Finsinger (1999), p. 343; Swoboda (1996), p. 377. 33
54
4 Empirical Secondary Data Analysis
Table 4.1 Regulatory systems – differences for determining revenue Regulation on profitability Determining revenue Actual costs increase Actual costs are constant Actual costs decrease
Individual cost audit Price increase No price adjustment (a) Cost audit = prices decrease (b) No cost audit = no price adjustment
Multiple-period incentive regulation Price formula: RPI-X Price trend with RPI-X Price trend with RPI-X Price trend with RPI-X
This is a description of the significant difference between both systems, qualified by a temporary delay for adjusting revenues to costs during a multiple-period incentive regulation.37 In addition, this classification is strengthened by the reflection that a “captured” regulatory agency always can approve the same revenues within the scope of regulation on profitability as they are defined by an incentive regulation formula, but for higher revenues as well, as summarized in Table 4.1. Assuming a semiefficient capital market, the publication moves from new information (that is relevant to business valuation) via establishing regulatory parameters to a positive or negative stock price reaction. Measuring this change of value takes place by determining an abnormal return. These abnormal returns result from a change of the expectancy of the investors regarding the expected cash flow of the company and/or from the change of the discount interest rate for future cash flow.38 The condition of this change in investors’ expectation for generating positive or negative abnormal returns is presented formally by using a cash flowbased equity approach:39 ARt ¼
EðCFt Þ EðCFt1 Þ ðRf þbt RMRP Þ ðRf þbt1 RMRP Þ ða þ bt1 RMRP Þ EðCFt1 Þ |fflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflffl} ðRf þbt1 RMRP Þ expected returns
|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} observed returns
with: ARt EðCFt Þ EðCFt1 Þ
37
Abnormal returns. Continuously expected future cash flow at time t (after public announcement). Continuously expected future cash flow at time t 1 (before public announcement).
cf. Borrmann and Finsinger (1999), p. 415. cf. on this Mandl and Rabel (1997) “. . . Market value-defining quantities are thus the expected cash flow [. . .] on one hand and the return requirements from the investor operating in the capital market on the other. . . .,” Mandl and Rabel (1997), p. 18 f. 39 The simplifying assumption of constant cash flow, a constant, risk-free interest rate, a constant market premium and a ¼ Rf are at the basis of the formalization. Furthermore, constant, unreal, complete self-financing is assumed. “Flow to equity” is to be slated as “cashflow”. Reference is made to Mandl and Rabel (1997), p. 367 ff. for a detailed presentation of the “equity approach.” 38
4.3 Methodology: Event Studies
55
9 8
positive
7 value of new information: +3
Value
6 5
normal
4 value of new information: –3
3 2
negative
1 0 t-9 t-8 t-7 t-6 t-5 t-4 t-3 t-2 t-1
t t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8 t+9 Time
Fig. 4.2 Value trend with a public announcement
Rf bt bt1 RMRP a
Risk-free interest rate. b factor at time t (after public announcement). b factor until time t 1 (before public announcement). Constant market risk premium. Constant from the market model.
As long as the assumption of a semiefficient capital market is valid, this is depicted graphically with an information value of +/ 3 as shown in Fig. 4.2. This simplified illustration shows how the stock price (value) changes at the time of a public announcement (t). The stock price increases by three units for positive changes in expectation (“positive”) or decreases three units for negative changes in expectation (“negative”). The change of the value from +/ three units illustrates the positive/negative abnormal returns to be measured. Stigler and Peltzman’s theory states that small homogenous and capital-empowered interest groups (companies) tend to prevail over large heterogeneous interest groups (consumers) regarding the setting of regulatory parameters. That is why companies strive toward a stronger degree of regulation, in order to be able to achieve economic advantages over weaker regulatory systems. As long as the (“captured”) regulatory agency acts to the benefit of the regulated company, it must have been valid that the definition of the regulatory parameters tends to generate positive abnormal returns (mAR ) because they work to the advantage of companies and thus increase the value of the companies. Hypothesis 1 states:40
40
In this work, the 2-sided T-test is generally used to validate hypotheses by mean values or the Levene Test is used to validate hypotheses by spread measures. For forming hypotheses cf. Bortz (1999), Chap. 4. To validate various hypotheses, interrelated hypotheses and for the introduction in
56
4 Empirical Secondary Data Analysis
H0: mAR ¼ 0. H1: mAR 6¼ 0. Since, as Peltzman reasons, stronger regulatory systems reduce the variability of commercial profits, the stock price volatility during this type of system must be lesser than during weaker systems (“buffering hypothesis”). Assuming time constant b factors that are independent of the regulatory system applied, the variance of abnormal returns during stronger regulatory systems (s2 AR;RoR ) must be lesser than during weaker systems (s2 AR;PC ). Hypothesis 2 states: H0: s2 AR;RoR ¼ s2 AR;PC . H1: s2 AR;RoR 6¼ s2 AR;PC . The interrelationship between the total risk and the systematic and unsystematic risk of a stock is presented formally as follows:41 VarðRi Þ ¼ b2i VarðRm Þ þ Varðui Þ with: VarðRi Þ b2i VarðRm Þ Varðui Þ
Total risk of security i. Systematic risk of security i. Unsystematic risk of security i.
The assumption that b factors from regulated companies are time constant and are independent of the characteristic of the regulatory system applied must be assessed critically.42 For this reason, no abnormal returns that are based on a shift in regulatory system were incorporated into the database for this study.43 Furthermore, it must be assumed, based on the reflections in hypothesis 2, that the intensity of the “capture level” of a regulatory agency by a company for multiple-period incentive regulation models is less than for regulations on profitability. The average of abnormal returns with regulation on profitability (mAR;RoR )
variance-analytical methods cf. Bortz (1999), Chaps. 5 and 6 as well as part II. Reference is made to Backhaus et al. (2000), p. 1–69 for detailed explanations on conducting regression analyzes. 41 cf. Fischer (2002), p. 102. 42 cf. among others, Binder and Norton (1999), Buckland and Fraser (2001) and Grout and Zalewska (2006). 43 Grout and Zalewska (2006) investigated the change announced from the RPI-X system into a “profit sharing system” in the electricity network industry in Great Britain in the 1990s. This type of system is identified by the feature that a company shares possible profits or losses from an RPI-X regulation with the consumers. It hence can be identified as a form combining a profitability regulation and an RPI-X regulation. While implementing the CAPM and the Fama French 3-factor model, Grout and Zalewska determined that the announced change led to a significant reduction in systematic risk, stating that: “. . . Taking the single-factor and three-factor evidence together, the paper shows that the impact on risk of regulatory changes specifically designed to impact on risk is both significant and consistent with theory,” Grout and Zalewska (2006), p. 180.
4.3 Methodology: Event Studies
57
must be higher than the average for a multiple-period incentive regulation model (mAR;PC ). Hypothesis 3 states: H0: mAR;RoR ¼ mAR;PC . H1: mAR;RoR 6¼ mAR;PC . Since the abnormal returns examined were determined with various measurement methods (“residuals” according to Sect. 4.3.2.1 and “regression” according to Sect. 4.3.2.2 of this work), the review is additionally made whether this exerts a significant influence on the abnormal returns determined. The hypotheses to be tested state: Hypothesis 4a: H0: s2 AR;Residuen ¼ s2 AR;Regression H1: s2 AR;Residuen 6¼ s2 AR;Regression Hypothesis 4b: H0: mAR;Residuen ¼ mAR;Regression H1: mAR;Residuen 6¼ mAR;Regression
4.3.4
Database
Examining how the capital market reacts, when regulatory parameters are changed or a regulatory system is significantly changed, is a classic application of event studies. Already in 1981, Schwert conducted this type of study for the first time.44 In the course of his examination of 20 regulatory system changes, Binder (1985b) acknowledged in the end that event studies in this field must take note of three peculiarities i.e., their results are only able to be interpreted in a limited manner:45 The timing of the public announcement cannot always be defined unambiguously (“slowness of regulatory decisions”) The change of a regulatory system does not have to have identical economic consequences for all regulated companies (“winner–loser”) Changes in a regulatory system involve an entire industry, which is why abnormal returns cannot be grounded exclusively through them since simultaneous industryspecific shocks that were not the object of study can also produce abnormal returns
l
l
l
Despite the problems noted by Binder about the reliability of the results from event studies, conducting event studies should not essentially be criticized or rejected. When conducting event studies, the problem areas mentioned should be considered, in order to improve the reliability of the primary results. For this reason, the following studies placed value on incorporating primary results from various industries, countries, time periods and from a variety of authors, in order to minimize systematic bias. 44
cf. Schwert (1981). cf. Binder (1985b), p. 167 f.
45
58
4 Empirical Secondary Data Analysis
Furthermore, 100 publications were identified through an extensive research of the literature, which are found in Appendix 2 of this work. Of these 100 publications, every work that corresponded to the following criteria was included in the database of this work: l l l l
l
l
l
l l
Event study Industry examined: infrastructure that is bound to the network Detailed information about the regulated events (date, description) Details about the abnormal returns determined (Event window, benchmark model, estimation period, significance, measurement method) Information on the abnormal return determined per event – no restriction to significant abnormal returns alone For abnormal returns from portfolios: information on the number of companies included and on the weighing method Only studies with the primary goal of examining the capital market reaction for changes announced in price, profits and/or cost base (“changes of regulatory parameters”) A one-factor market model was used as a benchmark model If several studies examine the same events, the study that takes a more extensive data sample into account is incorporated (as long as methodological deficiencies do not oppose this)
The database for this study, given in Table 4.2, is made of six event studies, excluding publications that did not correspond to the above criteria.46 The descriptive summary of the study results included regarding the amount of abnormal returns, separate from the predominant regulatory system, is given in Table 4.3.47 Since the samples illustrate only around 30 cases, reviewing the normal distribution of the overall sample as well as the two partial samples took place in accordance with Kolmogorov–Smirnov and Shapiro–Wilk.48 The normal distribution hypothesis is retained (cf. Table 4.4) for both samples as the basis for conducting the review. The Histogram of the overall sample as well as the two partial samples are given in Figs. 4.3–4.5. The abnormal returns taken from the event studies, distributed over time, grouped according to regulatory system and measurement method (the study group), are as follows. In order to examine whether the measurement method applied in the primary studies exerted a significant influence on the results of this work, an examination of
46
Reference is made to Appendix 1 of this work for a detailed presentation of the events examined in the primary studies. 47 The results from the primary studies were integrated in this work in a balanced manner. 48 In this work, logarithmized returns are not computed analogously as done methodologically in the primary studies. cf. Strong (1992), p. 535 for applying logarithmized returns.
Electricity 1991–1993 ROR
Electricity 1992–1995 PC
US
UK
Johnson, Niles, 1998 and Suydam Paleari and 2005 Redondi
Water
Gas
1970–1995
1990–1994 PC
1977–1978 ROR
ROR
66 (ROR: 31; PC: 35)/71 (ROR: 31; PC: 40)
14/18
f
180
10
42
12
21/22e
6/6
67
39
10
6/6
1/1
18/18
Events assumed/ nb total events
Market model Market model
Market model Market model Market model
Market model
Model
Methodc
gg
gg
gg
gg
Portfolio weightd
01 Jan. 76–31 Regression (fb) gg Dec. 80 with dummy 01 Dec. Regression (cb) gg 89–30 with dummy Sep. 94
01 Jan. 81–31 Regression (cb) Aug. 85 with Dummy 01 Jan. 71–31 CAAR (cb) Jan. 74 (ew: 3/0) 01 Jan. 91–03 CAAR (cb) Feb. 91 (ew: 2/+2) 300 days CAAR (fb) (ew: 1/+1)
Estimation period
Bi-weekly
Monthly
Daily
Daily
Monthly
Weekly
Returns
0.01:13 0.05:7 0.1:6
0.05:5 0.1:3
0.01:4 0.05:1 0.01:9 0.05:1 0.1:1 0
0.1:1
0.1:1
Significance
b
ROR Rate of return regulation; PC Price cap regulation (or RPI-X regulation). n Number of companies (size of portfolio). c (cb) ¼ constant b – determining the abnormal returns by assuming a constant b factor; (fb) ¼ floating b – determining the abnormal returns by considering the changes of systematic risk; EW event window. d gg in equilibrium. e One event was not included in the database (a statistical outlier). f Four events were not included in the database because they do not exhibit any changes in the content of regulatory parameters.
a
Totals
UK
Sawkins
1995
US
Chen and Sanger 1985
Electricity 1970
1981–1985 ROR
US
Cable TV
1980
US
Clarke
Regulatory systema
1992
Period of event
Prager
Country Industry
Year
Author
Table 4.2 Database – event studies included
4.3 Methodology: Event Studies 59
60
4 Empirical Secondary Data Analysis
Table 4.3 Descriptive statistics
Abnormal return
Descriptive statistics Regime RoR Mean 95% Confidence interval Lower bound for mean Upper bound 5% Trimmed mean Median Variance Std. deviation Minimum Maximum Range Interquartile range Skewness Kurtosis PC
Mean 95% Confidence interval for mean 5% Trimmed mean Median Variance Std. deviation Minimum Maximum Range Interquartile range Skewness Kurtosis
Lower bound Upper bound
Statistic 0.002149 0.0009145 0.004847 0.001958 0.001400 0.000 0.0190726 0.0380 0.0310 0.0690 0.0326 0.172 0.849
Std. error 0.0034255
0.002520 0.010228 0.015268 0.002481 0.000700 0.001 0.0371114 0.0628 0.0712 0.1340 0.0579 0.008 0.902
0.0062730
0.421 0.821
0.398 0.778
Table 4.4 Test of normal distribution – partial sample ROR and PC Tests of Normality Shapiro–Wilk Regime Kolmogorov–Smirnova Statistic df Sig. Statistic df Sig. 0.970 31 0.522 Abnormal Return RoR 0.093 31 0.200* 0.972 35 0.505 PC 0.085 35 0.200* a Lilliefors significance correction. * Lower bound of the true significance.
the normal distribution of the partial samples formed by the measurement method used took place (cf. Table 4.5 and Fig. 4.6).
4.3.5
Empirical Results
The test results from the hypotheses derived above are presented quantitatively in the following. The interpretation of these results occurs together with the summary in Sect. 4.5 of this work (cf. Tables 4.6–4.8).
4.3 Methodology: Event Studies
61
Histogram 12
Frequency
10
8
6
4
2
0 0.10 0.09 0.08 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0.00 –0.01 –0.02 –0.03 –0.04 –0.05 –0.06 –0.07 –0.08 –0.09 –0.10
Abnormal Return
Fig. 4.3 Frequency distribution of the overall sample
Histogram
Frequency
6
4
2
0
Fig. 4.4 Frequency distribution of the partial sample, ROR
0.10 0.09 0.08 0.07 0.06
0.05 0.04 0.03 0.02 0.01 0.00
–0.01 –0.02 –0.03 –0.04 –0.05 –0.06 –0.07 –0.08
–0.09 –0.10
Abnormal Return
62
4 Empirical Secondary Data Analysis
Histogram 5
Frequency
4
3
2
1
0 0.10 0.09
0.08 0.07 0.06 0.05
0.04 0.03
0.02 0.01 0.00 –0.01 –0.02 –0.03
–0.04
–0.05 –0.06 –0.07 –0.08 –0.09 –0.10
Abnormal Return
Fig. 4.5 Frequency distribution of the partial sample, PC
4.3.5.1
Hypothesis 1 H0: mAR ¼ 0. H1: mAR 6¼ 0.
4.3.5.2
Hypotheses 2 and 3 Hypothesis 2: H0: s2 AR;Residuen ¼ s2 AR;Regression H1: s2 AR;Residuen 6¼ s2 AR;Regression
4.3.5.3
Hypotheses 4a and 4b Hypothesis 4a: H0: s2 AR;Residuen ¼ s2 AR;Regression H1: s2 AR;Residuen 6¼ s2 AR;Regression
4.4
Hypothesis 3: H0: mAR;RoR ¼ mAR;PC H1: mAR;RoR 6¼ mAR;PC
Hypothesis 4b: H0: mAR;Residuen ¼ mAR;Regression H1: mAR;Residuen 6¼ mAR;Regression
Summary
The mean value of abnormal returns for change (cf. Table 4.8) in regulatory parameters is not significantly different from regulations on profitability (mean value: 0.215%) and from multiple-period incentive regulatory systems (mean value: 0.252%). The null hypothesis, stating the mean value = 0, could not be
4.4 Summary
63 Regime PC
RoR 0.0600 0.0300 0.0000 –0.0300
0.0600 0.0300 0.0000 –0.0300 –0.0600
2
Abnormal Return
1
–0.0600
0.0600 0.0300 0.0000 –0.0300
3
–0.0600 01.Jan. 01.Jan. 01.Jan. 01.Jan. 01.Jan. 01.Jan. 1970 1975 1980 1985 1990 1995
01.Jan. 01.Jan. 01.Jan. 01.Jan. 01.Jan. 01.Jan. 1970 1975 1980 1985 1990 1995
Eventdate
Fig. 4.6 Distribution over time of the abnormal returns. This figure clarifies the fact that multipleperiod incentive regulation models (PC) compared with profitability regulation (ROR) is a relatively new form of price regulation, cf. on this also Borrmann and Finsinger (1999), p. 415. Study group 1 establishes the abnormal returns as in Sect. 5.3.2.1 in Chap. 5, study group 2 (with a constant b factor) and study group 3 (with changed b factor) as outlined in Sect. 5.3.2.2 in Chap. 5
Table 4.5 Test on normal distribution – measurement methods Tests of Normality Study group Kolmogorov–Smirnova Statistic df Sig. Abnormal return 1 0.073 32 0.200* 2 0.255 6 0.200* 3 0.136 28 0.196 a Lilliefors significance correction. * Lower bound of the true significance.
Shapiro–Wilk Statistic df Sig. 0.981 32 0.826 0.866 6 0.210 0.977 28 0.783
Table 4.6 Test results from hypothesis 1 One-sample test Test value = 0 t df Sig. (2-tailed) Mean difference 95% Confidence interval of the difference Lower Upper Abnormal return 0.089 65 0.930 0.0003268 0.007023 0.007676
Abnormal return Equal variances assumed 13.003 Equal variances not assumed
0.001
Sig. Mean Std. error 95% Confidence interval of (2-tailed) difference difference the difference Lower Upper 0.630 64 0.531 0.005 0.007 0.019 0.010 0.653 52.055 0.516 0.005 0.007 0.019 0.010
t-test for equality of means
Mean Std. error 95% confidence interval of difference difference the difference Lower Upper 0.005 0.007 0.019 0.010 0.005 0.007 0.019 0.010
t-test for equality of means Sig. (2-tailed)
0.630 64 0.531 0.653 52.055 0.516
Independent samples test Levene’s test for equality of variances F Sig. t df
Table 4.8 Test result from hypotheses 4a and 4b
0.001
Independent samples test Levene’s test for equality of variances F Sig. t df
Abnormal return Equal variances assumed 13.003 Equal variances not assumed
Table 4.7 Test result from hypotheses 2 and 3
64 4 Empirical Secondary Data Analysis
4.4 Summary
65
discarded for the overall sample. Stigler’s thesis that regulation always acts as an advantage for a regulated company cannot be supported. A significant difference was established in the variance of abnormal returns attributed to regulatory risks between both regulatory systems. The spread of abnormal returns proved to be larger for multiple-period incentive regulation models (standard deviation: 0.037) than for regulation on profitability (standard deviation: 0.019). Assuming a constant b factor, a significant increase (reduction) in variance in the confounding variables of the market model, attributable to a regulatory system shift, means a structural break in the regression model. A b factor estimated on the basis of historical data is no longer efficient because it no longer minimizes the future abnormal risks (confounding variables) expected from the regulatory risk. This aligns with the results from Binder and Norton (1999), Buckland and Fraser (2001) and Grout and Zalewska (2006), which established significant changes of systematic risks in regulatory system shifts. The primary studies included in this work implemented different measurement methods for abnormal returns. No significant distortions in the results came from this. To summarize, it can thus be held that a shift in regulatory system is relevant to investors for business valuation. A b factor estimated on the basis of data from periods from one regulatory system is no longer efficient for periods when using another regulatory system. This is valid as long as the difference of variance from unsystematic risk established in this work, attributed to regulatory risk, is not offset by other factors of unsystematic risk. If this is not the case, a higher risk for investors of price-regulated companies results for multiple-period incentive regulations compared with regulations on profitability, which is to be satisfied appropriately, especially in due consideration of the results from Binder and Norton (1999); Buckland and Fraser (2001) and Grout and Zalewska (2006).
Chapter 5
The Primary Empirical Study
Building on the results from Chap. 4, this chapter analyzes how risk from the companies investigated is effected by the regulatory system shift in the Austrian electric power supply industry. For this purpose, the database used for this investigation and the hypotheses to be tested are presented in Sect. 5.1. In Sect. 5.2, the inference-statistical results are presented and interpreted for the structural break analysis. The results of the events study are presented an interpreted in Sect. 5.3.
5.1
Hypotheses and Database
As presented already in Sect. 4.4.1 in Chap. 4, the total risk of a security is made up of the systematic and the unsystematic risk. This relation is presented formally as follows: VarðRi Þ ¼ b2i VarðRm Þ þ Varðui Þ The investigations in this chapter serve to answer the set of questions about whether there was a change in total risk from introducing the incentive regulation. To the extent that this is the case, this should also clarify whether this is substantiated by a change in systematic and / or unsystematic risk. Moreover, further investigations are conducted that are explained in detail in Sect. 5.1.1.
5.1.1
Hypotheses
Building on the results from Chap. 4, the following hypotheses are tested throughout the course of the primary study to be conducted in this chapter:
M. Hierzenberger, Price Regulation and Risk, Lecture Notes in Economics and Mathematical Systems 641, DOI 10.1007/978-3-642-12047-3_5, # Springer-Verlag Berlin Heidelberg 2010
67
68
5 The Primary Empirical Study
(1) Hypothesis on systematic risk As was presented in Chap. 4, Binder and Norton (1999), Buckland and Fraser (2001) and Grout and Zalewska (2006) established significant changes of systematic risk for regulatory system shifts. Building on these results, the following hypothesis is tested as to the difference of the beta factor in the Austrian power operator before introducing the incentive regulation (bi;RoR ) and after introducing the incentive regulation (bi;PC ). Hypothesis 5 states: H0 : bi;RoR ¼ bi;PC H1 : bi;RoR 6¼ bi;PC (2) Hypothesis on the welfare effect The change in the constants of the market model (a0i ), upon introducing an incentive regulation, is defined as the welfare effect. It is examined whether introducing the incentive regulation on electricity led to a significant change in the constants of the market model for the regulated companies investigated. Hypothesis 6 states: H0 : a0i ¼ 0 H1 : a0i 6¼ 0 (3) Hypothesis on unsystematic risk As was presented in Chap. 4, a significant difference in unsystematic risk, attributable to regulatory risk, between incentive regulatory models and individual cost audit systems was able to be established. In this chapter, the investigation, based on the data available, is not restricted to regulatory risk, but rather includes the total unsystematic risk. The unsystematic risk can be studied by observing the correlation of security returns and market returns. It should then be examined whether the correlation between security returns and market returns were changed by introducing the incentive regulation on electricity. This takes place by comparing the correlation coefficient between security returns and market returns before the introduction of the incentive regulation (rRoR;ri ;Rm ) with the same correlation coefficient after the introduction of the incentive regulation (rPC;ri ;Rm ). Hypothesis 7 states: H0 : rRoR;ri ;Rm ¼ rPC;ri ;Rm H1 : rRoR;ri ;Rm 6¼ rPC;ri ;Rm (4) Hypotheses on total risk and returns Based on the hypotheses explained in (1) and (3) of this investigation, the hypothesis follows that total risk, measured by the variance of the security returns of the regulated company, differs before the introduction of the incentive regulation (s2 Ri ;RoR ) with the one from after the introduction of the incentive regulation (s2 Ri ;PC ). Hypothesis 8 states:
5.1 Hypotheses and Database
69
H0 : s2 Ri ;RoR ¼ s2 Ri ;PC H1 : s2 Ri ;RoR 6¼ s2 Ri ;PC Furthermore, the hypothesis that the mean value of the current returns after introduction of the incentive regulation on electricity (mR;i;PC ) differs from the one before the introduction (mR;i;RoR ) is verified. Hypothesis 9 states: H0 : mR;i;RoR ¼ mR;i;PC H1 : mR;i;RoR 6¼ mR;i;PC (5) Testing the significance of abnormal returns measured Whether the events to be investigated led to a capital market reaction in the form of abnormal returns for company i and model m (rAR;i;m ) takes place by a review with the help of a two sided t-test. The abnormal returns on the company level are used for this purpose. Reference is made here to the detailed presentation of the models, parameters used and results in Sect. 5.3. Hypothesis 10 states (individual for each company examined) for event i with model m: H0 : rAR;i;m ¼ 0 H1 : rAR;i;m 6¼ 0
5.1.2
Database
The consequences from the changes in the Austrian regulatory policy for the ¨ sterreielectric and gas network industries are investigated from examples of the O chische Elektrizit€atswirtschafts-AG (shorthand: Verbund; ISIN:1 AT0000746409) and the EVN AG (shorthand: EVN; ISIN: AT0000741053). Besides offering power supply systems, both corporations operate in other business areas. The investigations within the scope of the structural break analysis (Sect. 5.2 of this work) and the event studies (Sect. 5.3 of this work) are based on stock returns that include trends in other business areas beyond the electricity and / or gas network industry. The percentage of the network industry to the total business of the corporation is taken in the following segment reports from the fiscal years 2003 to 2008 (cf. Tables 5.1–5.3).2 1
International Securities Identification Number (ISIN). May it be noted for the segment report issued from EVN that the segmentation changed in the 2006 fiscal year, which is why segment results for the time period 2002–2005 and 2005–2008 are presented in this work. The 2005 fiscal year was adjusted in the 2006 fiscal year as a year of comparison for the new structure. To the extent the electricity network figures and the gas network figures for Verbund and EVN were reported separately, they are presented separately in the present work. The remaining areas (e.g. water, long-distance heating, electricity generation, etc) are summarized under “Sum other”.
2
88 0 88 351
385
75 0 75 310
20 0 20 80
100
20 0 20 80
117 0 117 563
527
70 0 70 456
17 0 17 83
100
13 0 13 87
95 0 95 651
806
68 0 68 738
2,878
275 0 275 2,604
2006
100
8 0 8 92
100
10 0 10 90
%
916
62 0 62 854
3,038
285 0 285 2,753
2007
808
– – – –
1
0 0 0 1
100
12 0 12 88
%
Total n.a. – n.a. – 439 100 681 100 746 100 cf. Data taken from the 2002 to 2008 Annual Reports, available online at: http://www.verbund.at (25 Mar. 2009)
n.a. n.a. n.a. n.a.
322
76 0 76 246
2,135
255 0 255 1,880
2005
82 0 82 726
– – – –
100
28 0 28 72
100
9 0 9 91
%
13 0 13 87
n.a. n.a. n.a. n.a.
331
Total
Cash flow – in Mio. € Electricity network Gas network Sum network Sum other
93 0 93 238
3,078
100
EBIT – in Mio. € Electricity entwork Gas network Sum network Sum other
2,478
Total
100
266 0 266 2,812
10 0 10 90
2,072
2004
%
Table 5.1 Segment report for Verbund 2002–2008 2002 % 2003 External sales – in Mio. € Electricity network 265 13 237 Gas network 0 0 0 Sum network 265 13 237 Sum other 1,807 87 2,241
100
10 0 10 90
100
7 0 7 93
100
9 0 9 91
%
934
102 0 102 832
1,139
88 0 88 1,051
3,745
308 0 308 3,437
2008
100
11 0 11 89
100
8 0 8 92
100
8 0 8 92
%
70 5 The Primary Empirical Study
5.1 Hypotheses and Database
71
Table 5.2 Segment report EVN 2002–2005 2002 % 2003 External sales – in Mio. € Electricity 557 50 634 Gas 417 37 317 Sum electricity/gas 974 87 951 Sum other 140 13 131 Total EBIT – in Mio. € Electricity Gas Sum electricity/gas Sum other
%
2004
%
2005
%
59 29 88 12
679 271 950 258
56 22 79 21
996 276 1272 337
62 17 79 21
1,114
100
1,082
100
1,207
100
1,610
100
94 36 130 2
74 28 101 1
111 6 105 3
109 6 103 3
50 54 103 11
43 47 90 10
55 29 83 48
42 22 63 37
Total 128 100 103 100 115 100 131 100 Data taken from the 2002 to 2008 Annual Reports, available online at: http://www.evn.at (25 Mar. 2009). “Electricity” contains both the electricity distribution (network), electricity generation, the business and the marketing of electricity Table 5.3 Segment report EVN 2005 – 2008 2005 % External sales – in Mio. € Sum network (electricity/gas) 426 26 Sum other 1,184 74 Total EBIT – in Mio. € Sum network (electricity/gas) Sum other
2006
%
2007
%
2008
%
467 1,182
28 72
448 1,785
20 80
475 1,922
20 80
1,610
100
1,649
100
2,233
100
2,397
100
78 53
60 40
13 129
9 91
38 135
22 78
58 97
37 63
Total 131 100 142 100 172 100 155 100 Data taken from the 2002 to 2008 Annual Reports, available online at: http://www.evn.at (25 Mar. 2009)
The segment report of both companies show relatively constant, external sales in the network business for the period from 2002 to 2008. Profits from other areas are registered as increases from approx. €1.8b in 2002 to approx. 3.4b in 2008 for Verbund and from approx. €0.1b in 2002 to approx. 1.9b in 2008 for EVN. It is to be noted along with the data listed from Verbund that this company is active primarily as a power transmission network operator, which is why the introduction of the incentive regulation on electricity had no direct influence on the trend of net profits because they are subject to an ROR regulatory system. The incentive regulation on electricity was introduced only to fix the rates of distribution system operators. Verbund holds stakes in distribution system operators in Austria, which is why the introduction of incentive regulation on electricity is significant indirectly.3 3
Verbund holds stake in Steweag-Steg GmbH (stakes: 34.57%) and in KELAG - K€arntner Elektrizit€ats-Aktiengesellschaft (35.12%) during the entire period of the investigation 01/2003 to 06/2008. As of the 2005 fiscal year, Verbund also holds stake in Energie Klagenfurt GmbH (49%). The three stakes cited that Verbund holds are distribution operators.Moreover, it should be
72
5 The Primary Empirical Study Energy Price History (Delivery follows next year) 450
Index January2004 = 100
400 350 300 250 200 150 100
01 .2 02 .20 04 03 .20 04 0 04 .2 04 05 .20 04 06 .20 04 07 .20 04 0 08 .2 04 09 .20 04 10 .20 04 11 .20 04 12 .20 04 01 .20 04 02 .20 05 03 .20 05 04 .20 05 05 .20 05 06 .20 05 07 .20 05 08 .20 05 09 .20 05 0 10 .2 05 11 .20 05 12 .20 05 01 .20 05 0 02 .2 06 03 .20 06 04 .20 06 05 .20 06 06 .20 06 0 07 .2 06 08 .20 06 09 .20 06 10 .20 06 11 .20 06 12 .20 06 01 .20 06 02 .20 07 03 .20 07 0 04 .2 07 05 .20 07 06 .20 07 07 .20 07 08 .20 07 09 .20 07 10 .20 07 11 .20 07 12 .20 07 0 01 .2 07 02 .20 08 03 .20 08 04 .20 08 05 .20 08 0 06 .2 08 07 .20 08 08 .20 08 09 .20 08 0 10 .2 08 11 .20 08 12 .20 08 0 08
50
Strompreisindex EEX- Phelix Futures Base Frontyear
Erdölpreisindex Rohölpreis Brent Light
Kohlepreisindex ARA Coal Year Futures
Erdgaspreisindex auf Basis Rotterammer Indezes
Fig. 5.1 Price trend in the energy markets from 2004 to 2008 (At this point a special thanks goes to Dr. Stefan Altenhofer for providing Fig. 5.1)
The relative percentage of the electric power supply presented in the segment reports from the company as a whole arises from both an expansion in business activities,4 the trend of energy prices and the profit increase related to those prices in the energy business, as illustrated in Fig. 5.1. The stock market trends of both companies for the period of 2002 to 2008 are presented in Tables 5.4 and 5.5.
assumed that introducing an incentive regulation is a signal of the shift of negotiating power between the regulatory agency and the price-regulated company, according to the reflections from Stigler/Peltzman and Binder, as outlined in Sect. 5.2 of this work. On the basis of the explanations in Sect. 3.3.3 in Chap. 3 of this work, it is assumed that the shift of negotiating power follows because of the strengthening of the regulatory agency’s position, which also influences the results from the individual cost audit as a part of the ROR regulatory system. 4 By way of example, EVN AG made acquisitions in 2005 in Southeastern Europe; the 2005 Annual Report cites the following on this:: “With the acquisition of 67.0% of the shares to both southeastern Bulgarian electricity distribution companies ERP Plovdiv and ERP Stara Zagora, EVN has taken over the operative governance in both companies in January 2005. Overall, ERP Plovdiv and ERP Stara Zagora supply electricity to approximately 1.5 million end clients, and thus around 33% of the Bulgarian electricity customers. For this, both companies operate a medium and low-voltage network totaling 56,800 km. Electricity sales from both Bulgarian suppliers jointly came to around 6.597 GWh in 2004, of which ERP Plovdiv accounted for around 3.622 GWh and ERP Stara Zagora accounted for around 2.974 GWh. By taking over the majority of both companies, the EVN group thus practically tripled their previous client base in electricity and doubled their electricity sales to end clients”.cf. Annual Report EVN AG 2005, p. 58.
5.1 Hypotheses and Database
73
Table 5.4 Stock trend of Verbund 2002–2008 Verbund 2002 2003 2004 2005 2006 2007 2008 High (in €) 9.71 9.26 16.56 30.13 41.60 49.95 59.30 Low (in €) 7.01 7.75 9.29 16.42 30.05 31.21 29.74 Last (in €) 8.11 9.26 16.39 30.13 40.42 47.88 32.56 Capitalisation 2,500.8 2,853.9 5,051.4 9,286.1 12,457.4 14,756.6 10,035.0 (on 31.12. in Mio. €) Data taken from the 2002 to 2008 Annual Reports, available online at: http://www.verbund.at (25 Mar. 2009) Table 5.5 Stock trend of EVN 2002–2008 EVN 2002 2003 2004 2005 2006 2007 2008 High (in €) 12.11 11.13 11.84 19.63 24.75 23.87 23.38 Low (in €) 10.25 8.85 9.03 10.23 16.30 20.38 14.39 Last (in €) 43.98 9.06 10.38 18.75 20.90 22.63 14.99 Capitalisation 1,653.0 1,361.0 1,697.0 3,066.0 3,417.0 3,700.0 2,451.0 (on 31.12. in Mio. €) Data taken from the 2002 to 2008 Annual Reports, available online at: http://www.evn.at (25 Mar. 2009)
The structure of the stockholders from Verbund and for EVN is presented as follows for 31 December 2008 in Fig. 5.2.5 The majority stake of both Verbund and EVN is held by public authorities or other strategic domestic and foreign energy supply companies. The relatively small proportion in free float resulting from this, with the consequence of relatively smaller trade volumes in comparison to a purely public company, should remain disregarded for the investigation and be assumed as sufficient because of the relatively high market capitalization of both companies (cf. Tables 5.4 and 5.5). Verbund and EVN are listed on the Vienna stock exchange. In addition to this, Verbund is listed on the stock exchanges in Frankfurt, Berlin, Stuttgart, Hamburg and London. The following is the stock market trends of both companies as well as the benchmark for the trend in the “Austrian Traded Index” (shorthand: ATX) of the Vienna stock exchange, for the period of 1 January 2003 to 30 June 2008 (cf. Fig. 5.3). Since the level of indebtedness of a company is a relevant parameter for its beta factor established empirically, the following outlines the trend of the level of indebtedness for Verbund and EVN, as reported in the quarterly report from both
5
Only the ownership structure as of 31 Dec. 2008 is cited in this work because the percentage of the free float changed only slightly over the entire time period of the investigation (01/2003–06/2008). Verbund reports free float percentage of 15.7% in the 2002 Annual Report. For EVN, the free float percentage at the end of the 2002/2003 fiscal year (the calendar year deviating from the fiscal year) was around 34%, which lowered to around 14% in October 2005 because Energie Baden W€ urttemberg AG increased their percentage held up to that point, cf. (2005) Annual Report EVN AG, p. 32.
74
5 The Primary Empirical Study
Stockholder structure VERBUND free float < 24%
Austrian Republic 51% TIWAG (Province Tyrol) > 5%
Wiener Stadtwerke (Province Vienna) > 10% EVN AG > 10%
Stockholder structure EVN free float < 14%
Energie Baden Württemberg AG > 35%
NÖ LandesBeteiligungsholding GmbH (Province Lower Austria) 51%
Fig. 5.2 Stockholder structure for Verbund and EVN (cf. Annual Reports from Verbund and EVN for the 2008 fiscal year)
companies in accordance with IFRS for the period between December 2002 and September 2008 (cf. Fig. 5.4).6 During this period, both companies demonstrate a relatively constant or slightly falling tendency in the level of indebtedness. For this reason, the changes in the
6
The values presented are book values and should serve as an appropriate estimation for the market values, especially because of the valuation in accordance with IFRS. The interrelation between level of indebtedness and the beta factor was explained in Sect. 2.2.1.3.2 in Chap. 2 of this work.
5.1 Hypotheses and Database
75 Market trend: ATX / Verbund / EVN
Index: 1.1.2003 = 100 %
800.0% 700.0% 600.0% 500.0% 400.0% 300.0% 200.0% 100.0%
01 .0 1. 01 03 .0 4 01 .03 .0 7 01 .03 .1 0. 01 03 .0 1 01 .04 .0 4. 01 04 .07 . 01 04 .1 0. 01 04 .01 . 01 05 .0 4. 01 05 .0 7 01 .05 .1 0 01 .05 .0 1 01 .06 .0 4. 01 06 .07 . 01 06 .1 0. 01 06 .0 1 01 .07 .0 4 01 .07 .0 7.0 01 7 .1 0 01 .07 .01 . 01 08 .0 4.0 01 8 .0 7. 08
0.0%
Time ATX
Verbund
EVN
Fig. 5.3 Market trend from ATX, Verbund and EVN(01/2003 – 06/2008) (Stock prices were made available from Verbund’s Investor Relations Department. The prices relayed were compared with those from the database http://de.finance.yahoo.com/ (14 Feb. 2009). No deviations were established. Stock splits and dividend disbursements were taken into consideration) Level of indebtedness: Verbund and EVN 90.0% Level of indebtedness
80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 06.08
09.08
03.08
12.07
09.07
06.07
03.07
09.06
12.06
06.06
03.06
12.05
06.05
09.05
03.05
12.04
09.04
06.04
03.04
12.03
06.03
09.03
03.03
12.02
0.0%
Quarterly report Verbund
EVN
Fig. 5.4 Level of indebtedness for Verbund and EVN (12/2002–09/2008)
level of indebtedness should be disregarded for the investigations in this chapter and the hypotheses tests should occur on the basis of the beta factor that was established empirically.7 7
A lowering of the level of indebtedness tends to lead to a lower risk for investors. Since the investigations of this work involve increasing risk amounts, not considering this effect should contribute to an addition, if small, strengthening of potentially significant results. As to the
76
5 The Primary Empirical Study
The definition of a proxy size for the market trend is also necessary for the empirical study implementing a market model. For this, three indices are used: the “ATX”, the “Dow Jones Stoxx 600” and the “Dow Jones Stoxx Global 1800”. The “Dow Jones Stoxx 600 Utilities” is used as a benchmark or a control portfolio for Verbund and EVN. In the following, the configuration of the indices listed is explained.8 (1) Austrian Traded Index (ATX) The ATX, as the most significant stock index from the Vienna Stock Exchange, is composed of the 20 biggest corporations on the Vienna Stock Exchange. According to Ultimo 2008, the ATX is made of the weights of 20 values displayed in Table 5.6, with a weight factor of approx. 10.1% for Verbund.9 (2) Dow Jones Stoxx 600 (DJ600) The DJ600 is a stock market exchange indices in which the 600 largest European corporations are included.10 Table 5.6 Composition of the ATX on 31 December 2008 Code Unternehmen EBS ERSTE GROUP BANK AG TKA TELEKOM AUSTRIA AG OMV OMV AG VER VERBUNDGESELLSCHAFT AG VOE VOESTALPINE AG VIG VIENNA INSURANCE GROUP RIB RAIFFEISEN INT. BANK-HLDG AG ICL INTERCELL AG WIE WIENERBERGER AG PST OESTERR. POST AG ANDR ANDRITZ AG MMK MAYR-MELNHOF KARTON AG FLU FLUGHAFEN WIEN AG STR STRABAG SE BWIN BWIN INT. ENTERT. AG RHI RHI AG SBO SCHOELLER-BLECKMANN AG PAL PALFINGER AG ZAG ZUMTOBEL AG AUA AUSTRIAN AIRLINES AG Sum
ISIN AT0000652011 AT0000720008 AT0000743059 AT0000746409 AT0000937503 AT0000908504 AT0000606306 AT0000612601 AT0000831706 AT0000APOST4 AT0000730007 AT0000938204 AT0000911805 AT000000STR1 AT0000767553 AT0000676903 AT0000946652 AT0000758305 AT0000837307 AT0000620158
Weight (%) 15.9 14.7 11.6 10.1 7.7 6.4 6.2 4.3 4.1 3.5 2.9 2.3 2.1 1.9 1.8 1.3 1.1 0.8 0.8 0.7 100.0
interrelation between level of indebtedness and the beta factor, reference is made to Sect. 2.2.1.3.2 in Chap. 2 of this work. 8 Price indices were used (from Laspeyres). Data from the Dow Jones Indices were taken from http://www.stoxx.com/ (14 Feb. 2009). 9 Data was taken from:http://www.indices.cc/static/cms/sites/indices/media/de/pdf/download/ ultimo/2008/atx_2008.pdf (04 Mar. 2009). 10 cf. http://www.stoxx.com/indices/types/benchmark.html (04 Mar. 2009).
5.1 Hypotheses and Database
77
(3) Dow Jones Stoxx Global 1800 (DJ1800) The DJ1800 is a global stock market exchange indices and includes the 600 largest European, American and Asian corporations.11 (4) Dow Jones Stoxx 600 Utilities (DJ600UTIL) The DJ600UTIL is a subindex of the DJ600 and is made up of the corporations from the utility sectors contained in the DJ600. This index contains 33 companies, including Verbund with a weight of around 0.7%. Besides Verbund, the following 32 values are contained in this index with the weights listed in Table 5.7.12 Table 5.7 Composition of the DJ600UTIL on 31 December 2008
11
Country DE FR DE ES IT GB GB GB FR FR FI PT ES GB GB ES IT IT GB ES ES ES GB ES AT GB IT GR CH PT GB CH IT Sum
Enterprise Weight (%) E.ON 16.1 GDF SUEZ 13.5 RWE 8.9 IBERDROLA 7.6 ENEL 6.9 NATIONAL GRID 6.6 CENTRICA 6.2 SCOTTISH and SOUTHERN ENERGY 4.4 EDF 3.4 VEOLIA ENVIRONNEMENT 2.8 FORTUM 2.4 EDP ENERGIAS DE PORTUGAL 2.1 UNION FENOSA 2.1 INTERNATIONAL POWER 1.7 UNITED UTILITIES GRP 1.6 RED ELECTRICA CORPORATION 1.3 TERNA 1.2 SNAM RETE GAS 1.2 SEVERN TRENT 1.1 IBERDROLA RENOVABLES 1.0 ENAGAS 0.9 GAS NATURAL SDG 0.9 DRAX GRP 0.8 ENDESA 0.8 VERBUND 0.7 PENNON GRP 0.7 A2A 0.7 PUBLIC POWER CORPORATION 0.6 ALPIQ HOLDING REG 0.4 EDP RENOVAVEIS 0.4 NORTHUMBRIAN WATER GRP 0.4 BKW FMB ENERGIE 0.3 HERA 0.2 100.0
cf. http://www.stoxx.com/indices/types/benchmark.html (04 Mar. 2009). Data was taken from:http://www.stoxx.com/indices/download.html?symbol=SX6P
12
78
5 The Primary Empirical Study Market trend: Verbund / EVN / ATX / DJ600UTIL / DJ600 / DJ1800
Index: 1.1.2003 = 100 %
800.0% 700.0% 600.0% 500.0% 400.0% 300.0% 200.0% 100.0% 01 .0 1. 01 03 .0 4. 01 03 .0 7 01 .03 .1 0. 01 03 .0 1 01 .04 .0 4. 01 04 .07 . 01 04 .1 0. 01 04 .0 1 01 .05 .0 4. 01 05 .07 . 01 05 .1 0. 01 05 .0 1 01 .06 .0 4. 01 06 .07 . 01 06 .1 0. 01 06 .0 1 01 .07 .0 4. 01 07 .07 . 01 07 .1 0. 01 07 .01 . 01 08 .0 4. 01 08 .07 .0 8
0.0%
Time Verbund
EVN
DJ600UTIL
ATX
DJ600
DJ1800
Fig. 5.5 Market trend for Verbund, EVN, ATX, DJ600UTIL, DJ600 and DJ1800 (01/2003–06/ 2008)
The market trends between 1 January 2003 and 30 June 2008 from the stocks and indices relevant to the empirical investigation of this chapter are displayed in the graph (cf. Fig. 5.5). The following displays the descriptive statistics from the six stock market quotes, separated according to time periods:13 l
l
01 Jan. 2003 – 30 Jun. 2005: time period before introducing the incentive regulation on electricity 01 Jul. 2005 – 30 Jun. 2008: time period after introducing the incentive regulation on electricity
The time period of the investigation from 01 January 2003 to 30 June 2008 was chosen because the regulatory agency E-Control took over the business activity for electricity in 2001 and for natural gas in 2002, thus ensuring that the basic conditions before the founding of E-Control are not portrayed in the market performances of Verbund and EVN.14 The end of the time frame on 30 June 2008 was selected so as not to incorporate into this investigation the trends in the internal capital markets after the middle or end of 2008 (keywords “financial market crisis” and “real economy crisis”). The descriptive statistics from the database can be seen in Tables 5.8–5.10. For additional investigation of the data, the data compiled was examined at its extreme values. The extreme values determined on the basis of a box plot analysis can be seen in Fig. 5.6 and the respective extreme value tables (cf. Table 5.11).
13
cf. the event list (event A1 “introduction of the incentive regulation”) in Table 5.57, Sect. 6.3.1 of this work. 14 cf. Section 4.2 in Chap. 4 of the present work.
Table 5.9 Descriptive statistics from the database (time period of 01/2003 to 06/2005) Descriptive Statistics 01/2003–06/2005 N Range Minimum Maximum Mean Std. Statistic Statistic Statistic Statistic Statistic Standardfehler Statistic Verbund 620 0.094 0.042 0.052 0.00173 0.000455 0.011331 EVN 620 0.104 0.044 0.060 0.00067 0.000529 0.013160 DJ600UTIL 620 0.087 0.044 0.043 0.00077 0.000372 0.009267 ATX 620 0.070 0.040 0.030 0.00161 0.000338 0.008427 DJ600 620 0.099 0.041 0.058 0.00057 0.000399 0.009936 DJ1800 620 0.084 0.036 0.048 0.00039 0.000368 0.009152 Valid N (listwise) 620
Table 5.8 Descriptive statistics from the database (time period of 01/2003 to 06/2008) Descriptive Statistics 01/2003–06/2008 N Range Minimum Maximum Mean Std. Statistic Statistic Statistic Statistic Statistic Standardfehler Statistic Verbund 1,360 0.195 0.109 0.085 0.00158 0.000436 0.016083 EVN 1,360 0.168 0.081 0.087 0.00068 0.000407 0.015011 DJ600UTIL 1,360 0.133 0.071 0.062 0.00064 0.000259 0.009556 ATX 1,360 0.130 0.075 0.055 0.00097 0.000304 0.011228 DJ600 1,360 0.115 0.057 0.058 0.00029 0.000271 0.009979 DJ1800 1,360 0.084 0.036 0.048 0.00015 0.000231 0.008503 Valid N (listwise) 1,360
Variance Statistic 0.000128 0.000173 0.000086 0.000071 0.000099 0.000084
Variance Statistic 0.000259 0.000225 0.000091 0.000126 0.000100 0.000072
Skewness Statistic Std. Error 0.355 0.098 0.480 0.098 0.144 0.098 0.460 0.098 0.104 0.098 0.200 0.098
Skewness Statistic Std. Error 0.381 0.066 0.335 0.066 0.268 0.066 0.660 0.066 0.144 0.066 0.036 0.066
Kurtosis Statistic Std. Error 2.121 0.196 2.436 0.196 3.097 0.196 2.100 0.196 3.643 0.196 2.555 0.196
Kurtosis Statistic Std. Error 4.081 0.133 4.103 0.133 5.525 0.133 4.069 0.133 3.682 0.133 2.450 0.133
5.1 Hypotheses and Database 79
Table 5.10 Descriptive statistics from the database (time period from 07/2005 to 06/2008) Descriptive Statistics 07/2005–06/2008 N Range Minimum Maximum Mean Std. Statistic Statistic Statistic Statistic Statistic Standardfehler Statistic Verbund 740 0.195 0.109 0.085 0.00145 0.000705 0.019185 EVN 740 0.168 0.081 0.087 0.00068 0.000603 0.016411 DJ1600UTIL 740 0.133 0.071 0.062 0.00053 0.000360 0.009796 ATX 740 0.130 0.075 0.055 0.00044 0.000482 0.013105 DJ600 740 0.110 0.057 0.052 0.00006 0.000368 0.010015 DJ1800 740 0.068 0.031 0.037 0.00005 0.000291 0.007920 Valid N (listwise) 740 Variance Statistic 0.000368 0.000269 0.000096 0.000172 0.000100 0.000063
Skewness Statistic Std. Error 0.461 0.090 0.264 0.090 0.352 0.090 0.591 0.090 0.346 0.090 0.203 0.090
Kurtosis Statistic Std. Error 2.911 0.179 4.288 0.179 7.142 0.179 3.203 0.179 3.713 0.179 2.005 0.179
80 5 The Primary Empirical Study
5.2 Empirical Analysis: Structural Break Analysis
81
Boxplot 01/2003 - 06/2008 0.10
0.05
0.00
–0.05
–0.10
–0.15 Verbund
EVN
DJ600UTIL
ATX
DJ600
DJ1800
Fig. 5.6 Graphic representation of the extreme values of the database
The extreme values were not eliminated from the database for the remaining course of the investigation because possible causes of incorrect data compilation were precluded for the extreme values.
5.2
Empirical Analysis: Structural Break Analysis
This section investigates whether introducing the incentive regulation on electricity made a change in the systematic risk for Verbund and EVN. The amount of the systematic risk, measured by the beta factor in the market model, is influenced by various parameter stipulations. Reference is made here to the explanations in 2.2.1.4.3 of this work. In order to take account of the influence of the choice of market index, the investigations take place by applying three different indices for each hypothesis test: the ATX, the DJ600 and the DJ1800. The Index DJ600UTIL serves as the control portfolio. The following regression model is used for the investigation: 0
0
Rit ¼ ai þ ai Ds þ bi Rmt þ bi Ds Rmt þ uit
82
5 The Primary Empirical Study
Table 5.11 Extreme values from the database, separated according to time periods 01/2003–06/ 2005 and 07/2005–06/2008 Extreme Values 01/2003–06/2005 (RoR) Extreme Values 07/2005–06/2008 (PC) Nr. Value Nr. Value Verbund Major 1 235 0.052 1 636 0.085 2 264 0.048 2 652 0.072 3 620 0.045 3 648 0.057 4 574 0.045 4 635 0.056 5 86 0.038 5 214 0.054 Least 1 102 0.042 1 224 0.109 2 425 0.035 2 206 0.082 3 467 0.033 3 632 0.077 4 532 0.030 4 231 0.072 5 144 0.028 5 407 0.060 EVN Major 1 613 0.060 1 158 0.087 2 42 0.055 2 130 0.086 3 347 0.052 3 214 0.078 4 254 0.051 4 225 0.069 5 519 0.050 5 51 0.056 Least 1 99 0.044 1 222 0.081 2 72 0.041 2 412 0.061 3 340 0.036 3 634 0.057 4 341 0.034 4 236 0.057 5 90 0.034 5 224 0.057 DJ600UTIL Major 1 50 0.043 1 635 0.062 2 56 0.041 2 162 0.044 3 64 0.034 3 47 0.035 4 1 0.033 4 648 0.032 5 51 0.030 5 712 0.024 Least 1 49 0.044 1 632 0.071 2 57 0.037 2 634 0.049 3 532 0.032 3 412 0.031 4 38 0.030 4 220 0.030 5 62 0.028 5 626 0.027 ATX Major 1 343 0.030 1 635 0.055 2 103 0.028 2 223 0.048 3 83 0.027 3 225 0.040 4 305 0.026 4 599 0.039 5 596 0.025 5 551 0.037 Least 1 406 0.040 1 222 0.075 2 336 0.034 2 671 0.052 3 570 0.031 3 527 0.051 4 303 0.028 4 412 0.049 5 102 0.027 5 632 0.048 DJ600 Major 1 50 0.058 1 635 0.052 2 51 0.039 2 672 0.036 3 1 0.038 3 648 0.033 4 67 0.034 4 680 0.033 5 56 0.033 5 675 0.033 Least 1 57 0.041 1 632 0.057 2 62 0.034 2 671 0.046 3 17 0.033 3 527 0.036 (continued)
5.2 Empirical Analysis: Structural Break Analysis
83
Table 5.11 (continued) Extreme Values 01/2003–06/2005 (RoR) Extreme Values 07/2005–06/2008 (PC) Nr. Value Nr. Value 4 49 0.032 4 643 0.032 5 94 0.032 5 524 0.031 DJ1800 Major 1 50 0.048 1 680 0.037 2 1 0.039 2 672 0.032 3 52 0.035 3 599 0.026 4 64 0.034 4 702 0.025 5 126 0.030 5 667 0.024 Least 1 57 0.036 1 671 0.031 2 62 0.031 2 412 0.030 3 94 0.028 3 738 0.029 4 397 0.026 4 725 0.026 5 47 0.026 5 621 0.024 The case numbers cited in the table indicate the case number within the respective partial sample “01/2003-06/2005” and “07/2005-06/2008”. Table 5.12 Structure of models from the structural break analysis
with: Rit ai bi Rmt 0 ai 0 bi Ds 1 uit
Model structure Dependent variable Verbund EVN DJ600UTIL
ATX Model 1a Model 2a Model 3a
Market index DJ600 Model 1b Model 2b Model 3b
DJ1800 Model 1c Model 2c Model 3c
Observed returns from stock i at time t Constant from the regression model from company i Increase of the regression equation (beta factor) from company i Market returns at time t Change of the constant ai if: Ds ¼ 1 Change of the constant bi if: Ds ¼ 1 Dummy variable with value 0 (time period 01/2003 – 06/2005) or (time period 07/2005 – 06/2008) Confounding variable
In total, nine models are used in calculations for the structural break analysis, as displayed above (cf. Table 5.12). The hypotheses mentioned above in 5.1 are tested in the following section with the results from the nine regression analysis, with the additional correlation analysis and with the results from the descriptive statistics (cf. Table 5.13).15 15
When the results from the structural break analysis (Sects. 5.2.1–5.2.6) were verbally explained, the alpha probability of error (p) for the distortion of H0 at p < 0.1% were described as extremely significant. Events with 0.9% p 0.1% are described as highly significant and those with 5.0% p > 0.9% as significant. Defining p takes place in Sect. 5.2 on a two-sided basis. Events with 10.0% p > 5.0% are described as distinctive, in order to underscore the events that would be rated as significant at the 5% level with a one-sided significant test, but would have to be rated as not significant with a two-sided test.
84
5 The Primary Empirical Study
Table 5.13 Summary of the hypotheses from the structural break analysis Hypotheses H0: H1: bi;RoR 6¼ bi;PC Hypothesis 5: bi;RoR ¼ bi;PC a0i 6¼ 0 Hypothesis 6: a0i ¼ 0 rRoR;ri ;Rm 6¼ rPC;ri ;Rm Hypothesis 7: rRoR;ri ;Rm ¼ rPC;ri ;Rm Hypothesis 8: s2 Ri ;RoR ¼ s2 Ri ;PC s2 Ri ;RoR 6¼ s2 Ri ;PC mR;i;RoR 6¼ mR;i;PC Hypothesis 9: mR;i;RoR ¼ mR;i;PC Table 5.14 Model summary for Verbund – ATX (1a)
Model
R
Model summary Adj. R2 R2
1a
0.481
0.231
Table 5.15 Coefficient model for Verbund – ATX (1a) Coefficients Model Unstandardized coefficients
1a
(Constant) ATX Dummy_(Constant) Dummy_ATX
B 0.001 0.520 0.000 0.219
Standard Error 0.001 0.067 0.001 0.078
0.229
Standardized coefficients Beta 0.363 0.007 0.132
5.2.1
Systematic Risk and Welfare Effect: Verbund
5.2.1.1
Model 1a: Verbund – ATX
Section 5.2.1–5.2.3 5.2.1–5.2.3 5.2.4 5.2.5 5.2.5
Standard error of the estimate 0.014120
t
Sig.
1.551 7.719 0.292 2.801
0.121 0.000 0.770 0.005
A highly significant increase in the beta factor was determined for Verbund by introducing the incentive regulation on electricity when using the ATX as proxy for the market trend (p ¼ 0.5%). A significant change in the constant from the market model was not observed (p ¼ 77.0%), for which reason a welfare effect cannot be assumed based on the introduction of the incentive regulation (cf. Tables 5.14 and 5.15). 5.2.1.2
Model 1b: Verbund – DJ600
When applying the DJ600 as proxy for the market return, an extremely significant increase of the beta factor (p < 0.1%) was established. A welfare effect could not be observed (p ¼ 80.7%) (cf. Tables 5.16 and 5.17). 5.2.1.3
Model 1c: Verbund- DJ1800
When implementing the DJ1800, an extremely significant increase (p < 0.1%) of the beta factor was established. A welfare effect could not be observed (p ¼ 81.8%) (cf. Tables 5.18 and 5.19).
5.2 Empirical Analysis: Structural Break Analysis Table 5.16 Model summary for Verbund – DJ600 (1b)
85
Model
R
Model summary Adj. R2 R2
1b
0.358
0.128
Table 5.17 Coefficient model for Verbund – DJ600 (1b) Coefficients Model Unstandardized coefficients
1b
(Constant) DJ600 Dummy_(Constant) Dummy_DJ600
Table 5.18 Model summary for Verbund – DJ1800 (1c)
B 0.002 0.219 0.000 0.534
Standard error 0.001 0.061 0.001 0.082
1c
(Constant) DJ1800 Dummy_(Constant) Dummy_DJ1800
0.126
Standardized coefficients Beta 0.136 0.006 0.245
Model
R
Model summary Adj. R2 R2
1c
0.236
0.056
Table 5.19 Coefficient model for Verbund – DJ1800 (1c) Coefficients Model Unstandardized coefficients B 0.002 0.147 0.000 0.483
Standard error 0.001 0.069 0.001 0.100
Standard error of the estimate 0.015034
t
Sig.
2.657 3.604 0.244 6.504
0.008 0.000 0.807 0.000
Standard error of the estimate 0.015647
0.053
Standardized coefficients Beta 0.078 0.006 0.175
5.2.2
Systematic Risk and Welfare Effect: EVN
5.2.2.1
Model 2a: EVN – ATX
t
Sig.
2.664 2.138 0.230 4.828
0.008 0.033 0.818 0.000
A highly significant increase of the beta factor could be observed when implementing the ATX for EVN (p ¼ 0.3%). A welfare effect could not be established (p ¼ 62.1%) (cf. Tables 5.20 and 5.21). 5.2.2.2
Model 2b: EVN – DJ600
When implementing the DJ600 as the market index, the beta factor from EVN exhibits an extremely significant boost (p < 0.1%) due to the implementation of the incentive regulation. A welfare effect could not be established when implementing the ATX as the market index (p ¼ 90.9%) (cf. Tables 5.22 and 5.23).
86
5 The Primary Empirical Study
Table 5.20 Model summary for EVN – ATX (2a)
Model
R
Model summary Adj. R2 R2
2a
0.420
0.177
Table 5.21 Coefficient model for EVN – ATX (2a) Coefficients Model Unstandardized coefficients
2a
(Constant) ATX Dummy_(Constant) Dummy_ATX
Table 5.22 Model summary for EVN – DJ600 (2b)
B 0.000 0.390 0.000 0.221
Standard error 0.001 0.065 0.001 0.075
2b
(Constant) DJ600 Dummy_(Constant) Dummy_DJ600
5.2.2.3
0.175
Standardized coefficients Beta 0.292 0.012 0.142
Model
R
Model summary Adj. R2 R2
2b
0.300
0.090
Table 5.23 Coefficient model for EVN – DJ600 (2b) Coefficients Model Unstandardized coefficients B 0.001 0.189 0.000 0.396
Standard error 0.001 0.058 0.001 0.078
Standard error of the estimate 0.013637
t
Sig.
0.076 5.997 0.495 2.928
0.939 0.000 0.621 0.003
0.088
Standardized coefficients Beta 0.126 0.003 0.195
Standard error of the estimate 0.014336
t
Sig.
0.975 3.264 0.114 5.051
0.330 0.001 0.909 0.000
Model 2c: EVN – DJ1800
Even for an approximation of the market index with DJ1800, an extremely significant increase of the beta factor (p < 0.1%) could be established for the EVN stock, for which again no welfare effect could be established from the introduction of the incentive regulation on electricity (p ¼ 91.8%) (cf. Tables 5.24 and 5.25).
5.2.3
Systematic Risk and Welfare Effect: DJ600UTIL
5.2.3.1
Model 3a: DJ600UTIL – ATX
Neither a significant change of the beta factor (p ¼ 14.1%) nor a welfare effect (p ¼ 75.9%) could be established for the DJ600 when applying the ATX as a proxy variable for the market index (cf. Tables 5.26 and 5.27).
5.2 Empirical Analysis: Structural Break Analysis Table 5.24 Model summary for EVN – DJ1800 (2c)
87
Model
R
Model summary Adj. R2 R2
2c
0.207
0.043
Table 5.25 Coefficient model for EVN – DJ1800 (2c) Coefficients Model Unstandardized coefficients
2c
(Constant) DJ1800 Dummy_(Constant) Dummy_DJ1800
Table 5.26 Model summary for DJ600UTIL – ATX (3a)
B 0.001 0.115 0.000 0.404
Standard error 0.001 0.065 0.001 0.094
(Constant) ATX Dummy_(Constant) Dummy_ATX
5.2.3.2
Standardized coefficients Beta 0.065 0.003 0.157
Model
R
Model summary Adj. R2 R2
3a
0.478
0.228
Table 5.27 Coefficient model for DJ600UTIL – ATX (3a) Coefficients Model Unstandardized coefficients
3a
0.041
B 0.000 0.355 0.000 0.068
Standard error 0.000 0.040 0.000 0.047
0.227
Standardized coefficients Beta 0.417 0.007 0.069
Standard error of the estimate 0.014701
t
Sig.
1.059 1.782 0.104 4.295
0.290 0.075 0.918 0.000
Standard error of the estimate 0.008403
t
Sig.
0.581 8.864 0.307 1.472
0.561 0.000 0.759 0.141
Model 3b: DJ600UTIL – DJ600
When approximating market returns through the DJ600, a significant reduction of the beta factor could be observed (p ¼ 4.1%) and again no welfare effect could be established (p ¼ 62.5%) (cf. Tables 5.28 and 5.29).
5.2.3.3
Model 3c: DJ600UTIL – DJ1800
When implementing the DJ1800 as the market index, neither a change in the beta factor (p ¼ 96.1%) nor a welfare effect could be proven (p ¼ 90.4%) (cf. Tables 5.30 and 5.31).
88
5 The Primary Empirical Study
Table 5.28 Model summary for DJ600UTIL – DJ600 (3b)
Model
R
Model summary Adj. R2 R2
3b
0.772
0.595
Table 5.29 Coefficient model for DJ600UTIL – DJ600 (3b) Coefficients Model Unstandardized coefficients B 3b
(Constant) DJ600 Dummy_(Constant) Dummy_DJ600
Table 5.30 Model summary for DJ600UTIL – DJ1800 (3c)
0.000 0.776 0.000 0.068
Standard error 0.000 0.025 0.000 0.033
0.594
Standardized coefficients Beta 0.810 0.008 0.053
Model
R
Model summary Adj. R2 R2
3c
0.596
0.355
t
Sig.
1.338 31.505 0.489 2.050
0.181 0.000 0.625 0.041
Standard error of the estimate 0.007682
0.354
Table 5.31 Coefficient model for DJ600UTIL – DJ1800 (3c) Coefficients Model Unstandardized coefficients Standardized coefficients B Standard error Beta 3c (Constant) 0.001 0.000 DJ1800 0.671 0.034 0.597 Dummy_(Constant) 0.000 0.000 0.003 Dummy_DJ1800 0.002 0.049 0.001
5.2.4
Standard error of the estimate 0.006086
t
Sig.
1.653 19.887 0.120 0.049
0.099 0.000 0.904 0.961
Unsystematic Risk
As to systematic risk, measured in the form of a correlation coefficient between stock return or index return and the market return, the following results, contingent upon the market return implemented, could be determined for the entire period of investigation from 01/2003 to 06/2008 (cf. Table 5.32). The correlation coefficient between stock return and market return for the time periods 01/2003 – 06/2005 (ROR regulation) and 07/2005 – 06/2008 (RPI-X regulation) can be seen in Tables 5.33 and 5.34. Investigating whether the differences for the correlation coefficients for the time periods 01/2003 – 06/2005 and 06/2005 – 06/2008 are significant, hence to test hypothesis 7 as outlined in 5.1, takes place by applying the Fisher-Z-Transformation
5.2 Empirical Analysis: Structural Break Analysis
89
Table 5.32 Correlation coefficients as the measure for unsystematic risk (time period from 01/2003 to 06/2008) Correlations 01/2003–06/2008 ATX DJ600 DJ1800 0.318** 0.198** Verbund Pearson correlation 0.476** Sig. (2-tailed) 0.000 0.000 0.000 N 1,360 1,360 1,360 0.270** 0.173** EVN Pearson correlation 0.413** Sig. (2-tailed) 0.000 0.000 0.000 N 1,360 1,360 1,360 0.771** 0.596** DJ1600UTIL Pearson correlation 0.477** Sig. (2-tailed) 0.000 0.000 0.000 N 1,360 1,360 1,360 **Sig. 0.01 (2-tailed)
Table 5.33 Correlation coefficients as the measure for unsystematic risk (time period from 01/2003 to 06/2005) Correlations 01/2003–06/2005 ATX DJ600 DJ1800 0.192** 0.119** Verbund Pearson correlation 0.387** Sig. (2-tailed) 0.000 0.000 0.003 N 620 620 620 0.143** 0.080* EVN Pearson correlation 0.250** Sig. (2-tailed) 0.000 0.000 0.046 N 620 620 620 0.832** 0.663** DJ1600UTIL Pearson correlation 0.323** Sig. (2-tailed) 0.000 0.000 0.000 N 620 620 620 **Sig. 0.01 (2-tailed) *Sig. 0.05 (2-tailed)
Table 5.34 Correlation coefficients as the measure for unsystematic risk (time period from 07/2005 to 06/2008) Correlations 07/2005–06/2008 ATX DJ600 DJ1800 0.393** 0.260** Verbund Pearson correlation 0.505** Sig. (2-tailed) 0.000 0.000 0.000 N 740 740 740 0.357** 0.250** EVN Pearson correlation 0.488** Sig. (2-tailed) 0.000 0.000 0.000 N 740 740 740 0.723** 0.541** DJ1600UTIL Pearson correlation 0.567** Sig. (2-tailed) 0.000 0.000 0.000 N 740 740 740 **Sig. 0.01 (2-tailed)
90
5 The Primary Empirical Study
of the correlation coefficients. By doing this, the negatively and positively skewed distribution of the correlation coefficient is transformed into the approximated normal distribution of the Z value, with z as the relevant statistical test regarding the significance of the difference established between two correlation coefficients:16 z¼
Z 1 Z2 sðZ1 Z2 Þ
whereas: Zi ¼
sðZ1 Z2 Þ
1 1 þ ri ln 2 1 ri
rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 1 þ ¼ n1 3 n2 3
with: z Statistical test (Fisher0 s z-value) Zi Z-value for correlation coefficient i ri Correlation coefficient i ni Sample size i According to Fisher, this leads to the following z values for the correlation coefficients determined empirically and displayed above (cf. Table 5.35). The critical value for the two-sided test of hypothesis 7 comes to the 5% level 1.96. All z-values lie outside of the acceptance region of H0, which is why this is disregarded. Both the stock from Verbund and the stock from EVN exhibit a significant reduction in the unsystematic risk because the correlation coefficient for the time period 07/2005 – 06/2008 is significantly higher than for the time period 01/2003 – 06/2005, independently of the market index implemented. The trend of the control portfolio DJ600UTIL exhibits an opposite trend when implementing the DJ600 and the DJ1800 as the market index. Only when implementing the ATX as the market index does the control portfolio also move toward a reduction of the unsystematic risk. Table 5.35 Fisher´s z-values of the differences between correlation coefficients
16
cf. Bortz (1999), p. 209 ff.
Fisher´s z-Wert ATX DJ600 DJ1800
Verbund 2.77 4.02 2.67
EVN 5.14 4.32 3.21
DJ600UTIL 5.79 5.14 3.46
5.2 Empirical Analysis: Structural Break Analysis Table 5.36 Averages and standard deviations per stock/index Descriptive statistics Regime N Mean Verbund RoR 620 0.00173 PC 740 0.00145 EVN RoR 620 0.00067 PC 740 0.00068 ATX RoR 620 0.00161 PC 740 0.00044 DJ600UTIL RoR 620 0.00077 PC 740 0.00053 DJ600 RoR 620 0.00057 PC 740 0.00006 DJ1800 RoR 620 0.00039 PC 740 0.00005
5.2.5
91
Std. 0.011331 0.019185 0.013160 0.016411 0.008427 0.013105 0.009267 0.009796 0.009936 0.010015 0.009152 0.007920
Std. error 0.000455 0.000705 0.000529 0.000603 0.000338 0.000482 0.000372 0.000360 0.000399 0.000368 0.000368 0.000291
Total Risk and Return Averages
To verify hypothesis 8, the standard deviations, as a measurement of the total risk, are used for each security, i.e. index for the two time periods examined (cf. Table 5.36). Based on the variance for each stock or index associated with these standard deviations, the Levene test was conducted for variance equality (cf. Table 5.37). A significant increase of the total risk was established for stocks from Verbund and from EVN. The DJ1800 exhibits a significant reduction of the total risk. For the control portfolio DJ600UTIL and DJ600, no significant changes in the total risk could be established, for which reason the H0 cannot be disregarded for these two indices alone. To verify hypothesis 9 as to significant differences in the mean values of daily returns between both time periods examined, a T-test was applied to each stock, i.e. each index (cf. Table 5.37). With the exception of the ATX, there is no H0 that can be disregarded for any stock or any index. With ATX, a significant increase of the daily returns was ascertainable.
5.2.6
Summary of the Structural Break Analysis
As a part of the structural break analysis, the following hypotheses were tested in Sect. 5.2 (cf. Table 5.38). As for hypothesis 5, it is to be held that for the time period of 07/2005 – 06/2008 for Verbund as well as for EVN, a highly significant boost of the beta factor, independent of the market index used, can at least be established. For the control portfolio, only when implementing the DJ600 as a market index could a significant reduction of the systematic risk be established; in the remaining cases, the control
Table 5.37 Results from the Levene test of variance equality and t-tests for mean value equality Independent samples test Levene’s Test t-Test for equality of means for equality of variances F Sig. t df Sig. (2-tailed) Mean Std. error 95% Confidence interval of difference difference the difference Lower Upper Verbund Equal variances assumed 95.989 0.000 0.324 1,358 0.746 0.000284 0.000876 0.001435 0.002002 Equal variances not assumed 0.338 1,228.271 0.736 0.000284 0.000839 0.001363 0.001930 EVN Equal variances assumed 12.677 0.000 0.016 1,358 0.987 0.000013 0.000818 0.001617 0.001590 Equal variances not assumed 0.017 1,355.450 0.987 0.000013 0.000802 0.001587 0.001560 ATX Equal variances assumed 64.208 0.000 1.912 1,358 0.056 0.001168 0.000611 0.000030 0.002366 Equal variances not assumed 1.983 1,277.067 0.048 0.001168 0.000589 0.000013 0.002323 DJ600UTIL Equal variances assumed 0.419 0.518 0.466 1,358 0.641 0.000243 0.000520 0.000778 0.001264 Equal variances not assumed 0.469 1,338.138 0.639 0.000243 0.000518 0.000773 0.001259 DJ600 Equal variances assumed 0.124 0.725 0.951 1,358 0.342 0.000517 0.000543 0.000549 0.001583 Equal variances not assumed 0.952 1,320.131 0.341 0.000517 0.000543 0.000548 0.001582 DJ1800 Equal variances assumed 8.829 0.003 0.943 1,358 0.346 0.000437 0.000463 0.000471 0.001345 Equal variances not assumed 0.932 1,232.920 0.352 0.000437 0.000469 0.000483 0.001357
92 5 The Primary Empirical Study
5.2 Empirical Analysis: Structural Break Analysis
93
Table 5.38 Summary of the hypotheses tested as a part of the structural break analysis H0 H1 Section bi;RoR 6¼ bi;PC 5.2.1–5.2.3 Hypothesis 5: bi;RoR ¼ bi;PC a0i 6¼ 0 5.2.1–5.2.3 Hypothesis 6: a0i ¼ 0 rRoR;ri ;Rm 6¼ rPC;ri ;Rm 5.2.4 Hypothesis 7: rRoR;ri ;Rm ¼ rPC;ri ;Rm 5.2.5 Hypothesis 8: s2 Ri ;RoR ¼ s2 Ri ;PC s2 Ri ;RoR 6¼ s2 Ri ;PC mR;i;RoR 6¼ mR;i;PC 5.2.5 Hypothesis 9: mR;i;RoR ¼ mR;i;PC Table 5.39 Summary of the results from hypothesis 5
Table 5.40 Summary of the results from hypothesis 6
Table 5.41 Summary of the results from hypothesis 7
Probability of error for a rejected H0: ATX DJ600 DJ1800 Average
Verbund
EVN
DJ600UTIL
0.005 <0.001 <0.001 <0.002
0.003 <0.001 <0.001 <0.002
0.141 0.041 0.961 0.381
Probability of error for a rejected H0: ATX DJ600 DJ1800 Average
Verbund
EVN
DJ600UTIL
0.770 0.807 0.818 0.798
0.621 0.909 0.918 0.816
0.759 0.625 0.904 0.763
Probability of error for a rejected H0: ATX DJ600 DJ1800
Verbund
EVN
DJ600UTIL
<0.050 <0.050 <0.050
<0.050 <0.050 <0.050
<0.050 <0.050 <0.050
portfolio does not exhibit significant or distinctive changes (cf. Table 5.39). This points to an increase in the systematic risk for Verbund and for EVN due to the introduction of the incentive regulation on electricity in Austria. As for hypothesis 6, it appears that introducing the incentive regulation on electricity produced no welfare effect for the stockholders from Verbund or from EVN. The H0 cannot be disregarded for the two companies examined or for the control portfolio (cf. Table 5.40). The correlation coefficient between stock return and index return, as a measure for systematic risk, significantly changed for Verbund, EVN and for the control portfolio DJ600UTIL. The H0 from hypothesis 7 can be disregarded for Verbund, EVN and DJ600UTIL (cf. Table 5.41). It is to be noted for this result, though, that whereas for Verbund and for EVN, a reduction in unsystematic risk occurred (significant increase of the correlation coefficient in all cases), the correlation coefficient between DJ600UTIL and DJ600 or DJ1800 reduced significantly. Only the correlation coefficient between DJ600UTIL and ATX exhibits a significant increase between both time periods. Since the change in unsystematic risk from the control portfolio exhibits an increase in two of the three cases, the stock
94
5 The Primary Empirical Study
Table 5.42 Summary of the results from hypothesis 8
Probability of error for a rejected H0: Verbund EVN DJ600UTIL ATX DJ600 DJ1800
<0.001 <0.001 0.518 <0.001 0.725 0.003
Table 5.43 Summary of the results from hypothesis 9
Probability of error for a rejected H0: Verbund EVN DJ600UTIL ATX DJ600 DJ1800
0.736 0.987 0.641 0.048 0.342 0.352
from Verbund and from EVN exhibits a reduction in each case; this is interpreted as a reduction in the systematic risk due to the introduction of the incentive regulation of electricity for Verbund and for EVN. Testing hypothesis 8 yielded the conclusion that variances of returns from the time period 07/2005 – 06/2008 for Verbund and for EVN are higher than those in the time period from 01/2003 – 06/2005. The return variances from the control portfolio DJ600UTIL and from DJ600 do not change in a significant manner between the two time periods. In contrast to Verbund and EVN, a highly significant reduction of variances was established for the index DJ1800. The variance from ATX exhibits a parallel trend to that of Verbund and EVN. These are interpreted as evidence for the boost in total risk for Verbund and EVN due to the introduction of the incentive regulation on electricity (cf. Table 5.42). The average returns exhibit no significant change for Verbund, EVN, the control portfolio, the indices DJ600 and DJ1800. For the ATX, one significant change was established, lying slightly below the alpha probability of error limit of 0.05 at 0.048 (cf. Table 5.43). In summary, it can thus be held that introducing the incentive regulation on electricity in July 2005 led to structural breaks in the market models examined for Verbund and EVN. Significant increases in the beta factor, significant reductions of unsystematic risk and significant increases in the total risk were established. Support for this interpretation of the results lies in the fact that these observations cannot be made for the control portfolio DJ600UTIL.
5.3
Empirical Investigation: Event Study
In Chap. 4 of this work, a secondary analysis was conducted of current event studies, to which the primary empirical results were applied. Based on the practice described in Chap. 4 for conducting event studies, this section contains the outline of the event
5.3 Empirical Investigation: Event Study
95
studies conducted in this work as to the consequences of the Austrian regulatory policies for investors in the electricity and gas network industry. The economic consequences attributed to the regulatory events are quantified by determining the abnormal returns. This is the first of two goals related to the event studies. To calculate abnormal returns, the relevant event window and the measuring method to be applied are to be set. Moreover, model-specific parameter settings are to be made. From the numerous possible model specifications, there is a danger in event studies that abnormal returns determined are, to a certain, unknown percentage, contingent upon the model. For this reason, answering the question as to what extent different model specifications influence the amount of abnormal returns is the second goal related to the event studies. By applying different model specifications, the differences resulting from them should be determined for the abnormal return that is contingent upon the event. The influence on the abnormal return calculated that is contingent on the model is thus able to be factored in more comprehensibly when interpreting the results. The general steps explained in Sects. 4.3 and 4.4 in Chap. 4 about conducting an event study and the general critique of event studies is not repeated at this point, only the definitions set especially for the primary study of this work are presented: 1. Identifying the events to be investigated and the definition of the company to be investigated (stock returns) 2. Establishing the event window 3. Defining the measurement methods for abnormal returns 1. Identifying the events to be investigated and the definition of the company to be investigated (stock returns) For the event study in this work, the event list was compiled based on the annual report published by the regulatory agency E-Control for the fiscal years 2003 to 2007. The events incorporated in this list corresponded to one of the following criteria: (a) Change in charges for the use of system services from Verbund and / or EVN (or an announcement by the Department of Commerce as the owner of the regulatory agency E-Control) (b) Events that are directly linked with the strategic structures of the regulatory agency (E-Control staff or E-Control business management) (c) Events involving court decisions on ordinances from the regulatory agency (ordinances on charges for use of the electrical network or for use of the gas network) The database from Sect. 5.1 is used for the event study. 2. Establishing the event window On the basis of the event time lapse published by the regulatory agency for the events corresponding to the above criteria, two event windows were defined:
96
5 The Primary Empirical Study
(a) 3-day period The event time lapse corresponding to the annual report from the regulatory agency is defined as t ¼ 0. Based on this time lapse, the event window is established by beginning with “t 1 day” and ending with “t +1 day”. (b) 1-day period On the basis of a 3-day period from “t 1 day” to “t + 1 day”, that day is defined as the event window, in which the highest or lowest daily returns are determined. Determining the 1-day time lapse occurs for each company in isolation from the others, by which different event windows can be defined for Verbund and EVN. By considering both of these event windows, it should be clarified within the scope of the event study how sensitive the events from the event studies react to other definitions in the event window. 3. Defining the measurement methods for abnormal returns To calculate abnormal returns, the measuring method is selected with the aid of the market model and direct measurement by a dummy variable. This practice follows the recommendations of Dewan and Ren (2007). Two market models are implemented for the measurement: (a) With a constant beta factor (b) With a dummy variable for the constant and the beta factor from the market model By determining the abnormal return with and without the dummy variable for the constant and the beta factor from the market model, the consequences from different specifications in the market model should be established. The following indices are implemented as proxy variables for market returns from the market model, analogously to the procedure in the structural break analysis in Sect. 5.2 of this work: (a) ATX (b) DJ600 (c) DJ1800 Hence, each of the two market models with the respective three different specifications on market returns is computed both for Verbund and for EVN. Bearing in mind the two different event windows for each event i, this comes to a total of 24 models to be calculated (2 event windows x 2 companies x 2 market models x 3 market indices ¼ 24 models). By comparing the abnormal returns determined from each model, from each event window and from each market index, the results from the event study can be scrutinized critically and outlined for future event studies as to how sensitive abnormal returns can react to model specifications.
5.3 Empirical Investigation: Event Study
97
Table 5.44 Model structure of the event study Model structure of the event study Constant market model
EWa ¼ 3 days EW ¼ 1 day EVN EW ¼ 3 days EW ¼ 1 day a EW ¼ event window Verbund
ATX 4.A.1 6.A.1 4.B.1 6.B.1
DJ600 4.A.2 6.A.2 4.B.2 6.B.2
DJ1800 4.A.3 6.A.3 4.B.3 6.B.3
Market model with dummy variables for the constant and the beta factor ATX DJ600 DJ1800 5.A.1 5.A.2 5.A.3 7.A.1 7.A.2 7.A.3 5.B.1 5.B.2 5.B.3 7.B.1 7.B.2 7.B.3
Testing the hypotheses with the aid of the two-sided T-tests takes place with consideration of the explanations above at point (1) through (3), and consequently for the model specifications given in Table 5.44.17
5.3.1
The Event List
The events outlined in Table 5.45 are those that are analyzed within the scope of the event study. Events that are relevant to gas system network operators (G1–G6) are analyzed exclusively for EVN. Events related to the electricity network industry are examined either only for Verbund, only for EVN or for both companies. The relevance of the events for each company is indicated in columns VER or EVN. “1” indicates the relevance of an event for the company-specific event studies and “0” stands for not considered in the company-specific event studies. General events (E1–E3), which concern both the electricity and the gas network industries, are incorporated in the event studies for both companies. The event list includes a total of 25 events; 18 events are assumed to be relevant to Verbund; 22 events are assumed to be relevant for EVN. Since events S6 and G3 intrinsically have the same event time (26 May 2004), these are summarized for the event study. For this reason, the number of event-specific coefficients decreases for EVN from 22 to 21. Fifteen events of the total twenty-five events are considered for the event study on Verbund and EVN. For models with 1-day event windows, events S4 (event time: 16 October 2003) and S5 (event time: 20 October 2003) are summarized for the event studies on EVN because the highest stock returns within both 3-day event windows were established for the same day (17 October 2003; days of no trade were the 18 and 19 October 2003).
17
Descriptions of the models implemented are cited in the respective cells.
26 May 2004 Gas
29 Oct. 2005
28 Mar. 2006 Gas
21 Dec. 2006 Gas
28 Feb. 2003
08 Apr. 2003 Electric
09 Oct. 2003
G3
G4
G5
G6
S1
S2
S3
Electric
Electric
Gas
06 Nov. 2003 Gas
G2
Table 5.45 Event list Event Date System network G1 14 May 2003 Gas Annual Report E-Control 2003, p. 83.
On 1 June 2003, the EVN gas network charges had to lower. An average household had to pay around 40.00 € p.a. less. Published on 15 May 2003 in the Gazette of the Wiener Zeitung (Vienna Newspaper). The new EU gas guidelines arrange a general, mandatory regulated network access, the introduction of regulatory agencies, the separation of business and network into independent legal units (“legal unbundling”) and a complete opening of the market until 1 July 2007. Publication of the Ordinance on Charges for Use of the Gas Network in the Gazette of the Wiener Zeitung. Publication of the 2005 Ordinance on Charges for Use of the Gas Network in the Gazette of the Wiener Zeitung. The gas network charges decreased around 10%. The energy providers in Vorarlberg (14%) and in Upper Austria (13.2%) must reduce their network system charges the most; the fall in Vienna is less (6.9%). The charges remain unchanged in Tirol. Publication of the 2006 Ordinance on Charges for Use of the Gas Network (1st Amendment) in the Gazette of the Wiener Zeitung. The charges for use of the gas system network are decreased on 1 January 2007 to an average of 4.5% in all federal states. Only in Tirol is there no decrease in the system network charge. Published on 28 October 2006 in the Gazette of the Wiener Zeitung. Publication of the Ordinance on Charges for Use of the Electric Network in the Gazette of the Wiener Zeitung (No. 41). Minister of the Economy, Martin Bartenstein, announces a decrease of the high electricity network charge for 2004, at the latest. The E-Control Commission (ECK) publishes the new ordinance on network charges for electricity. Thus, on 1 November the network charges are reduced on average 4.2% for household customers and 2.4% for industrial customers. This is compensated by an increase in the energy price for several companies. Published in the Gazette of the Wiener Zeitung (No. 194). Annual Report E-Control 2003, p. 83. Annual Report E-Control 2003, p. 83. Annual Report E-Control 2003, p. 87, 101.
Annual Report E-Control 2006, p. 127. Annual Report E-Control 2006, p. 107.
Annual Report E-Control 2004, p. 110. Annual Report E-Control 2005, p. 126.
Annual Report E-Control 2003, p. 88.
Source
Description
1
1
1
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
VERa EVNa
98 5 The Primary Empirical Study
26 May 2004 Electric
20 Nov. 2004 Electric
02 Dec. 2004 Electric
17 Dec. 2004 Electric
08 Jan. 2005
01 Feb. 2005
01 Apr. 2005 Electric
S6
S7
S8
S9
S10
S11
S12
Electric
Electric
Electric
20 Oct. 2003
S5
Electric
16 Oct. 2003
S4
“Legal Unbundling”, the economic unbundling of production, distribution and network, should be converted into a national right by 1 July 2004, according to Walter Boltz from E-Control at a conference on this subject. Unbundling the network operation is the basic prerequisite for the operation of the energy internal market. The current regulation, in which organization and bookeeping are separated, is not sufficient. EVN files suit at the Austrian Constitutional Court against the lowering of the charge for use of the electricity network enacted by E-Control in November. Its investments in the network became impossible with the charge reduction prescribed. The electric companies Bewag and Wienstrom also file grievances. The changes scheduled for 1 July 2004 in the electricity industry and the organization law for unbundling were approved in the parliamentary economic committee. The Austrian Constitutional Court rejected the suit against the ordinance on network charges from E-Control. As owner of the electric provider Bewag, the regional government of Burgenland also filed suit. The K€arntner Elektricity AG (Kelag) files a lawsuit against the lowering of the system network charge announced by E-Control at the Austrian Constitutional Court. The E -Control Commission postponed the system network charge reduction for electricity in Salzburg, K€arnten, Burgenland and for Verbund -Großkundengesellschaft APG to February 2005. A decision from the Austrian Constitutional Court is expected on the ordinance on network use charges from 2003. The Austrian Constitutional Court rejected the petition to revoke the 2003 Ordinance on Charges for Use of the Electricity Network. In Salzburg, K€arnten and Burgenland as well as for Verbund-Austrian Power Grid AG (APG) in the high voltage network, charges for the electric system network lower from 9 to 20%. Charges for the electric system network are lowered 8-10% in Vienna, Lower Austria, Tirol and Vorarlberg. Annual Report E-Control 2005, p. 103.
Annual Report E-Control 2005, p. 102. Annual Report E-Control 2005, p. 102.
Annual Report E-Control 2004, p. 95.
Annual Report E-Control 2004, p. 95.
Annual Report E-Control 2004, p. 94.
Annual Report E-Control 2004, p. 91.
Annual Report E-Control 2003, p. 87.
Annual Report E-Control 2003, p. 87.
0
1
1
1
1
1
1
1
1
(continued)
1
0
1
0
1
1
1
1
1
5.3 Empirical Investigation: Event Study 99
Description
Source
¨ (The Association of Austrian Electricity Companies) Annual Report E-Control E-Control and VEO agree to an “incentive regulatory system” for establishing electric 2005, p. 104. system network charges. S13 10 Dec. 2005 Electric Publication of the 2006 Ordinance on Charges for Use of the Electric Annual Report E-Control System Network in the Gazette of the Wiener Zeitung. On 1 January 2005, p. 125. 2006, the network charges are lowered on average around 3% when the new ordinance on charges for network use in Austria comes into effect. S14 19 Sep. 2007 Electric The new draft of the third legislative package was presented by the EU Annual Report E-Control Commission in Brussels. The main point is the separation of 2007, p. 111. proprietary rights on production and system network (Ownership Unbundling). S15 18 Dec. 2007 Electric Publication of the Ordinance on Charges for Use of the Electricity Annual Report E-Control Network in the Gazette of the Wiener Zeitung. 2007, p. 113. E1 04 Jun. 2003 Electric/ The European Parliament enacted the liberalization of the electricity and Annual Report E-Control Gas gas markets in the EU. Furthermore, EU guidelines arrange for the 2003, p. 84. corporate separation of system network and operation in 2007, at the latest. E2 25 Jan. 2006 Electric/ Walter Boltz, Managing Director of the regulatory agency E-Control, was Annual Report E-Control Gas appointed by the Ministry of the Economy for five more years until 2006, p. 101. 2011. E3 20 Apr. 2006 Electric/ By the decree from the federal government on 20 April, the previous Annual Report E-Control Gas members of the ECK are appointed for five more years. 2006, p. 103. a 1 ¼ relevant event, which is why it is incorporated in the event study; 0 ¼ not incorporated in the event study
Table 5.45 (continued) Event Date System network A1 14 Jul. 2005 Electric 1
1
0
1 1
1
1
1
1
1
1 1
1
1
VERa EVNa
100 5 The Primary Empirical Study
5.3 Empirical Investigation: Event Study
5.3.2
101
Event Study: Constant Beta Factor; 3-Day Event Window
In Sect. 5.3.2, calculation of the event-specific abnormal returns for Verbund and EVN took place by applying a constant market model with a 3-day event window. Changes in the constant from the market model (the welfare effect) or in the systematic risk (change of the beta factor) were thus taken into account. The market model applied in this section is presented formally as follows: Rit ¼ ai þ bi Rmt þ Dk;t gk þ uit with: Rit ai bi Rmt Dk;t gk uit
Observed return from stock i at time t Constant from the market model Slope of the regression line (beta factor) Return from the market portfolio m at time t Dummy variable for event k with value 1 for time lapse of the event window or otherwise 0 Coefficient of the regression equation for calculating abnormal return for event k Confounding variable in the regression equation
Event-specific abnormal returns are determined for Verbund in Sects. 5.3.2.1 (model 4.A.1 with ATX as the market portfolio), 5.3.2.2 (model 4.A.2 with DJ600 as the market portfolio) and 5.3.2.3 (model 4.A.3 with DJ1800 as the market portfolio). For EVN, abnormal returns are calculated in Sects. 5.3.2.4 (model 4.B.1 with ATX as the market portfolio), 5.3.2.5 (model 4.B.2 with DJ600 as the market portfolio) and 5.3.2.6 (model 4.B.3 with DJ1800 as the market portfolio).18
5.3.2.1
Model 4.A.1: Verbund – ATX
¨ 19 about Only for event A1 (Agreement between the regulatory agency and VEO 20 introducing the incentive regulation on electricity; AR ¼ 1.6%; p ¼ 5.1%) 18
When the results from the event study (Sect. 6.3.2–6.3.5) were verbally explained, the alpha probability of error (p) for the distortion of H0 at p < 0.1% were described as extremely significant. Events with 0.9% p 0.1% are described as highly significant and those with 5.0% p > 0.9% as significant. Defining p takes place in Sect. 6.2 on a two-sided basis. Events with 10.0% p > 5.0% are described as distinctive, in order to underscore the events that would be rated as significant at the 5% level with a one-sided significant test, but would have to be rated as not significant with a two-sided test. 19 ¨ ; www.veoe.at ) is the The Association of Austrian Electricity Companies (shorthand: VEO lobbyist from electricity network operators, electricity generation and electricity business in Austria. 20 AR ¼ abnormal returns.
102
5 The Primary Empirical Study
Table 5.46 Model summary for Verbund – ATX (4.A.1)
Model
R
Model summary Adj. R2 R2
4.A.1
0.484
0.235
Table 5.47 Coefficient model for Verbund – ATX (4.A.1) Coefficients Model Unstandardized coefficients B 4.A.1
(Constant) ATX S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 A1 S13 S14 S15 E1 E2 E3
0.001 0.688 0.007 0.005 0.005 0.004 0.003 0.010 0.008 0.010 0.003 0.003 0.003 0.016 0.002 0.013 0.001 0.004 0.001 0.012
Standard error 0.000 0.034 0.008 0.008 0.008 0.009 0.009 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008
Standard error of the estimate 0.014170
0.224
Standardized coefficients Beta 0.480 0.021 0.013 0.015 0.011 0.009 0.028 0.023 0.030 0.010 0.009 0.010 0.047 0.007 0.038 0.004 0.011 0.002 0.034
t
Sig.
2.669 20.032 0.869 0.554 0.624 0.451 0.361 1.162 0.947 1.261 0.421 0.373 0.411 1.951 0.294 1.595 0.164 0.478 0.077 1.440
0.008 0.000 0.385 0.579 0.533 0.652 0.718 0.245 0.344 0.208 0.674 0.710 0.681 0.051 0.769 0.111 0.870 0.633 0.938 0.150
could a distinctive abnormal return be established. The other events display an alpha probability of error of at least 11.1% (event S14) (cf. Tables 5.46 and 5.47).
5.3.2.2
Model 4.A.2: Verbund – DJ600
For model 4.A.2, only event A1 (AR ¼ 1.7%; p ¼ 5.5%) could be identified as distinctive. The other events display an alpha probability of error of at least 23.8% (event S6) (cf. Tables 5.48 and 5.49).
5.3.2.3
Model 4.A.3: Verbund – DJ1800
Also for model 4.A.3, only event A1 (AR ¼ 1,7%; p ¼ 6,0%) exhibits a distinctive abnormal return. The other events exhibit an alpha probability of error of at least 29.3% (event E3) (cf. Tables 5.50 and 5.51).
5.3 Empirical Investigation: Event Study Table 5.48 Model summary for Verbund – DJ600 (4.A.2)
103
Model
R
Model summary Adj. R2 R2
4.A.2
0.329
0.109
0.096
Table 5.49 Coefficient model for Verbund – DJ600 (4.A.2) Coefficients Model Unstandardized coefficients Standardized coefficients B Standard error Beta 4.A.2 (Constant) 0.002 0.000 DJ600 0.520 0.042 0.323 S1 0.008 0.009 0.022 S2 0.006 0.009 0.019 S3 0.006 0.009 0.016 S4 0.003 0.009 0.009 S5 0.001 0.009 0.003 S6 0.010 0.009 0.030 S7 0.010 0.009 0.029 S8 0.007 0.009 0.021 S9 0.005 0.009 0.013 S10 0.002 0.009 0.005 S11 0.003 0.009 0.007 A1 0.017 0.009 0.050 S13 0.001 0.009 0.002 S14 0.010 0.009 0.028 S15 0.000 0.009 0.001 E1 0.002 0.009 0.007 E2 0.002 0.009 0.006 E3 0.009 0.009 0.026
5.3.2.4
Standard error of the estimate 0.015292
t
Sig.
3.667 12.453 0.859 0.731 0.626 0.319 0.106 1.182 1.122 0.824 0.517 0.195 0.286 1.921 0.062 1.097 0.051 0.261 0.250 1.019
0.000 0.000 0.390 0.465 0.531 0.749 0.916 0.238 0.262 0.410 0.606 0.845 0.775 0.055 0.951 0.273 0.959 0.794 0.802 0.308
Model 4.B.1: EVN – ATX
For event S6_G3 (S6: scheduled change in ELWOG is approved in the parliamentary economic committee; G3: publication of the amendment on the Ordinance on Charges for Use of the Gas Network in the Gazette of the Wiener Zeitung), a significant event could be identified with abnormal returns in the amount of 1.9% (p ¼ 1.4%) (cf. Tables 5.52 and 5.53). The abnormal returns from event S10 (the Constitutional Court rejected the petition to revoke the Ordinance on Charges for Use of the Electricity Network) in the amount of 1.5% (p ¼ 5.2%) and G1 (Reduction of the charge on the gas system network for EVN) in the amount of 1.5% (p ¼ 6.5%) are underscored as distinctive. For event A1 (p ¼ 64.4%), the H0 for model 4.B.1, in contrast to models 4.A.1 to 4.A.3, cannot be disregarded.
104
5 The Primary Empirical Study
Table 5.50 Model summary for Verbund – DJ1800 (4.A.3)
Model
R
Model summary Adj. R2 R2
4.A.3
0.214
0.046
0.032
Table 5.51 Coefficient model for Verbund – DJ1800 (4.A.3) Coefficients Model Unstandardized coefficients Standardized coefficients B Standard error Beta 4.A.3 (Constant) 0.002 0.000 DJ1800 0.381 0.051 0.201 S1 0.004 0.009 0.011 S2 0.003 0.009 0.008 S3 0.004 0.009 0.011 S4 0.002 0.010 0.007 S5 0.001 0.010 0.004 S6 0.009 0.009 0.027 S7 0.009 0.009 0.028 S8 0.006 0.009 0.018 S9 0.005 0.009 0.014 S10 0.001 0.009 0.004 S11 0.004 0.009 0.011 A1 0.017 0.009 0.050 S13 0.001 0.009 0.003 S14 0.006 0.009 0.018 S15 0.002 0.009 0.005 E1 0.004 0.009 0.013 E2 0.003 0.009 0.008 E3 0.010 0.009 0.028
5.3.2.5
Standard error of the estimate 0.015822
t
Sig.
3.699 7.518 0.419 0.292 0.407 0.237 0.127 0.996 1.032 0.683 0.531 0.140 0.394 1.883 0.104 0.684 0.177 0.469 0.311 1.051
0.000 0.000 0.675 0.770 0.684 0.813 0.899 0.319 0.302 0.495 0.595 0.889 0.693 0.060 0.917 0.494 0.860 0.639 0.755 0.293
Model 4.B.2: EVN – DJ600
For model 4.B.2, events S6_G3 (AR ¼ 1.9%; p ¼ 2.6%), G1 (AR ¼ 1.5%; p ¼ 6.5%) and S10 (AR ¼ 1.4%; p ¼ 8.9%) exhibit a similarly significant, i.e. distinctive stock price reaction as does model 4.B.1. The other events exhibit an alpha probability of error of at least 11.2% (event S7) (cf. Tables 5.54 and 5.55).
5.3.2.6
Model 4.B.3: EVN – DJ1800
For model 4.B.3, events S6_G3 (AR ¼ 2.0%; p ¼ 2.2%) and G1 (AR ¼ 1.4%; p ¼ 9.0%) exhibit similarly significant, i.e. distinctive stock price reactions as do models 4.B.1 and 4.B.2. Event S10 (AR ¼ 1.4%; p ¼ 10.5%) is no longer distinctive, in contrast to models 4.B.1 and 4.B.2. Moreover, for model 4.B.3,
5.3 Empirical Investigation: Event Study Table 5.52 Model summary for EVN – ATX (4.B.1)
105
Model
R
Model summary Adj. R2 R2
4.B.1
0.435
0.189
Table 5.53 Coefficient model for EVN – ATX (4.B.1) Coefficients Model Unstandardized coefficients B 4.B.1
(Constant) ATX G1 G2 G4 G5 G6 S1 S2 S3 S4 S5 S6_G3 S7 S8 S10 S12 A1 S13 S15 E1 E2 E3
0.000 0.558 0.015 0.002 0.015 0.004 0.006 0.012 0.005 0.004 0.001 0.002 0.019 0.012 0.006 0.015 0.012 0.004 0.002 0.005 0.007 0.011 0.010
Standard error 0.000 0.033 0.008 0.008 0.010 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008
Standard error of the estimate 0.013630
0.176
Standardized coefficients Beta 0.417 0.046 0.006 0.038 0.012 0.019 0.036 0.016 0.012 0.003 0.008 0.060 0.036 0.020 0.048 0.038 0.011 0.005 0.016 0.022 0.036 0.030
t
Sig.
0.376 16.877 1.847 0.237 1.527 0.505 0.791 1.481 0.655 0.497 0.100 0.291 2.454 1.466 0.800 1.942 1.539 0.463 0.217 0.653 0.890 1.449 1.217
0.707 0.000 0.065 0.812 0.127 0.614 0.429 0.139 0.512 0.619 0.921 0.771 0.014 0.143 0.424 0.052 0.124 0.644 0.828 0.514 0.374 0.148 0.224
event S1 (Publication of the Ordinance on Charges for Use of the Electric System Network in the Wiener Zeitung) show, as a distinctive event, an abnormal return in the amount of 1.4% (p ¼ 9.4%) (cf. Tables 5.56 and 5.57).
5.3.3
Event Study: Dummy Variables from 1 July 2005; 3-Day Event Window
In Sect. 5.3.3, event-specific abnormal returns are calculated for Verbund and EVN by applying a market model with dummy variables for the constant, the beta factor
106
5 The Primary Empirical Study
Table 5.54 Model summary for EVN – DJ600 (4.B.2)
Model
R
Model summary Adj. R2 R2
4.B.2
0.300
0.090
Table 5.55 Coefficient model for EVN – DJ600 (4.B.2) Coefficients Model Unstandardized coefficients B 4.B.2
(Constant) DJ600 G1 G2 G4 G5 G6 S1 S2 S3 S4 S5 S6_G3 S7 S8 S10 S12 A1 S13 S15 E1 E2 E3
Standard error 0.000 0.039 0.008 0.008 0.010 0.008 0.008 0.008 0.008 0.008 0.009 0.009 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008
0.001 0.410 0.015 0.001 0.011 0.004 0.006 0.011 0.007 0.004 0.000 0.004 0.019 0.013 0.009 0.014 0.010 0.004 0.001 0.007 0.008 0.009 0.007
Standard error of the estimate 0.014439
0.075
Standardized coefficients Beta 0.273 0.048 0.003 0.028 0.011 0.018 0.036 0.021 0.013 0.000 0.013 0.058 0.041 0.028 0.044 0.030 0.014 0.002 0.020 0.026 0.028 0.023
t
Sig.
1.384 10.402 1.849 0.123 1.070 0.432 0.671 1.362 0.798 0.504 0.008 0.473 2.229 1.588 1.056 1.703 1.158 0.531 0.082 0.779 0.995 1.089 0.878
0.167 0.000 0.065 0.902 0.285 0.666 0.502 0.173 0.425 0.614 0.994 0.636 0.026 0.112 0.291 0.089 0.247 0.596 0.935 0.436 0.320 0.276 0.380
from the market model and a 3-day event window. The market model applied is presented formally in this section as follows: 0
0
Rit ¼ ai þ ai Ds þ bi Rmt þ bi Ds Rmt þ Dk;t gk þ uit with: Rit ai Ds 0 ai bi Rmt 0 bi
Observed return from stock i at time t Constant from the market model Dummy variable with value 0 (01/2003–06/2005) or 1 (07/2005–06/2008) Change in the constant ai if: Ds ¼ 1 Slope of the regression line (beta factor) Return from the market portfolio m at time t Change in the constant bi if: Ds ¼ 1
5.3 Empirical Investigation: Event Study Table 5.56 Model summary for EVN – DJ1800 (4.B.3)
107
Model
R
Model summary Adj. R2 R2
4.B.3
0.217
0.047
Table 5.57 Coefficient model for EVN – DJ1800 (4.B.3) Coefficients Model Unstandardized coefficients B 4.B.3
(Constant) DJ1800 G1 G2 G4 G5 G6 S1 S2 S3 S4 S5 S6_G3 S7 S8 S10 S12 A1 S13 S15 E1 E2 E3
0.001 0.312 0.014 0.000 0.010 0.005 0.006 0.014 0.004 0.003 0.000 0.004 0.020 0.013 0.010 0.014 0.010 0.005 0.001 0.005 0.007 0.009 0.008
Standard error 0.000 0.047 0.009 0.009 0.010 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009
Standard error of the estimate 0.014773
0.031
Standardized coefficients Beta 0.177 0.045 0.001 0.027 0.016 0.018 0.045 0.011 0.009 0.001 0.012 0.061 0.040 0.030 0.043 0.032 0.015 0.003 0.015 0.021 0.027 0.024
t
Sig.
1.498 6.590 1.697 0.033 1.000 0.602 0.667 1.677 0.427 0.328 0.052 0.439 2.301 1.512 1.124 1.622 1.203 0.548 0.121 0.577 0.782 1.008 0.916
0.134 0.000 0.090 0.973 0.317 0.547 0.505 0.094 0.669 0.743 0.959 0.660 0.022 0.131 0.261 0.105 0.229 0.584 0.904 0.564 0.434 0.314 0.360
Dk;t Dummy variable for event k with value 1 for time lapse of the event window or otherwise 0 gk Coefficient of the regression equation for calculating abnormal return for event k uit Confounding variable in the regression equation The event-specific abnormal returns are determined for Verbund in Sects. 5.3.3.1 (Model 5.A.1 with ATX as the market portfolio), 5.3.3.2 (Model 5.A.2 with DJ600 as the market portfolio) and 5.3.3.3 (Model 5.A.3 with DJ1800 as the market portfolio). For EVN, abnormal returns are calculated in Sects. 5.3.3.4 (model 5.B.1 with ATX as the market portfolio), 5.3.3.5 (model 5.B.2 with DJ600 as the market portfolio) and 5.3.3.6 (model 5.B.3 with DJ1800 as the market portfolio).
108
5.3.3.1
5 The Primary Empirical Study
Model 5.A.1: Verbund – ATX
Only for event A1 (AR ¼ 1.6%; p ¼ 4.7%) could a significant abnormal return be determined. For event S14 (Ownership unbundling for power transmission operators; AR ¼ 1.4%; p ¼ 8.6%), a distinctive abnormal return was established. The other events exhibit an alpha probability of error of at least 17.9% (event E3) (cf. Tables 5.58 and 5.59).
5.3.3.2
Model 5.A.2: Verbund – DJ600
For model 5.A.2, only for event A1 (AR ¼ 1.8%; p ¼ 4.3%) could a distinctive abnormal return be identified. The other events exhibit an alpha probability of error of at least 15.2% (event S14) for model 5.A.2 (cf. Tables 5.60 and 5.61). Table 5.58 Model summary for Verbund – ATX (5.A.1)
Model
R
Model summary Adj. R2 R2
5.A.1
0.489
0.239
Table 5.59 Coefficient model for Verbund – ATX (5.A.1) Coefficients Model Unstandardized coefficients B 5.A.1
(Constant) ATX Dummy_(Constant) Dummy_ATX S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 A1 S13 S14 S15 E1 E2 E3
0.001 0.530 0.000 0.214 0.006 0.004 0.005 0.003 0.003 0.009 0.008 0.009 0.004 0.003 0.004 0.016 0.002 0.014 0.001 0.004 0.000 0.011
Standard error 0.001 0.068 0.001 0.079 0.008 0.008 0.008 0.009 0.009 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008
Standard error of the estimate 0.014139
0.227
Standardized coefficients Beta 0.370 0.007 0.129 0.018 0.012 0.013 0.010 0.008 0.027 0.023 0.026 0.011 0.008 0.012 0.047 0.007 0.041 0.004 0.010 0.001 0.032
t
Sig.
1.711 7.820 0.275 2.720 0.738 0.509 0.553 0.390 0.301 1.130 0.972 1.094 0.473 0.346 0.491 1.986 0.294 1.718 0.170 0.429 0.051 1.345
0.087 0.000 0.783 0.007 0.461 0.611 0.581 0.696 0.764 0.259 0.331 0.274 0.637 0.729 0.623 0.047 0.769 0.086 0.865 0.668 0.959 0.179
5.3 Empirical Investigation: Event Study Table 5.60 Model summary for Verbund – DJ600 (5.A.2)
109
Model
R
Model summary Adj. R2 R2
5.A.2
0.368
0.136
Table 5.61 Coefficient model for Verbund – DJ600 (5.A.2) Coefficients Model Unstandardized coefficients B 5.A.2
5.3.3.3
(Constant) DJ600 Dummy_(Constant) Dummy_DJ600 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 A1 S13 S14 S15 E1 E2 E3
0.002 0.226 0.000 0.536 0.005 0.005 0.004 0.003 0.001 0.010 0.009 0.006 0.004 0.001 0.004 0.018 0.001 0.013 0.002 0.003 0.003 0.009
Standard error 0.001 0.061 0.001 0.083 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009
Standard error of the estimate 0.015069
0.122
Standardized coefficients Beta 0.140 0.006 0.246 0.015 0.015 0.013 0.008 0.002 0.029 0.025 0.018 0.013 0.004 0.012 0.051 0.002 0.037 0.007 0.008 0.008 0.027
t
Sig.
2.737 3.690 0.231 6.483 0.595 0.570 0.501 0.281 0.056 1.121 0.993 0.707 0.510 0.164 0.469 2.023 0.068 1.432 0.270 0.315 0.306 1.051
0.006 0.000 0.818 0.000 0.552 0.569 0.617 0.779 0.955 0.262 0.321 0.480 0.610 0.870 0.639 0.043 0.946 0.152 0.787 0.753 0.760 0.293
Model 5.A.3: Verbund – DJ1800
For model 5.A.3, only event A1 (AR ¼ 1.8%; p ¼ 4.4%) exhibits a significantly different abnormal return of 0. The other events exhibit an alpha probability of error of at least 30.0% (event E3) (cf. Tables 5.62 and 5.63).
5.3.3.4
Model 5.B.1: EVN – ATX
For event S6_G3, a significant abnormal return in the amount of 2.0% could be identified with an alpha probability of error of 1.2%. The abnormal returns from event S10 (AR ¼ 1.5%; p ¼ 5.3%), G1 (AR ¼ 1.4; p ¼ 8.2%) and S1
110
5 The Primary Empirical Study
Table 5.62 Model summary for Verbund – DJ1800 (5.A.3)
Model
R
Model summary Adj. R2 R2
5.A.3
0.249
0.062
Table 5.63 Coefficient model for Verbund – DJ1800 (5.A.3) Coefficients Model Unstandardized coefficients B 5.A.3
(Constant) DJ1800 Dummy_(Constant) Dummy_DJ1800 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 A1 S13 S14 S15 E1 E2 E3
0.002 0.150 0.000 0.489 0.004 0.003 0.004 0.002 0.001 0.009 0.008 0.006 0.005 0.001 0.005 0.018 0.001 0.008 0.000 0.004 0.003 0.009
Standard error 0.001 0.069 0.001 0.101 0.009 0.009 0.009 0.010 0.010 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009
Standard error of the estimate 0.015696
0.047
Standardized coefficients Beta 0.079 0.005 0.177 0.010 0.010 0.010 0.007 0.002 0.027 0.024 0.017 0.013 0.004 0.013 0.053 0.004 0.023 0.001 0.010 0.009 0.028
t
Sig.
2.702 2.169 0.196 4.857 0.391 0.372 0.394 0.239 0.060 1.014 0.921 0.626 0.500 0.134 0.509 2.016 0.140 0.877 0.024 0.393 0.324 1.038
0.007 0.030 0.845 0.000 0.696 0.710 0.694 0.811 0.953 0.311 0.357 0.532 0.617 0.893 0.611 0.044 0.889 0.381 0.981 0.694 0.746 0.300
(AR ¼ 1.3%; p ¼ 10.0%) are to be underscored as distinctive. The other events exhibit an alpha probability of error of at least 16.1% (event S12) (cf. Tables 5.64 and 5.65).
5.3.3.5
Model 5.B.2: EVN – DJ600
For model 5.B.2, events S6_G3 (AR ¼ 1.9%; p ¼ 2.0%), G1 (AR ¼ 1.4%; p ¼ 9.5%) and S10 (AR ¼ 1.4%; p ¼ 8.6%) exhibit a similarly significant, i.e. distinctive stock price reaction as in model 5.B.1. Event S1 (AR ¼ 1.3%; p ¼ 10.6%) narrowly exceeds the limit of 10.0% (cf. Tables 5.66 and 5.67).
5.3 Empirical Investigation: Event Study Table 5.64 Model summary for EVN – ATX (5.B.1)
111
Model
R
Model summary Adj. R2 R2
5.B.1
0.441
0.195
Table 5.65 Coefficient model for EVN – ATX (5.B.1) Coefficients Model Unstandardized coefficients B 5.B.1
5.3.3.6
(Constant) ATX Dummy_(Constant) Dummy_ATX G1 G2 G4 G5 G6 S1 S2 S3 S4 S5 S6_G3 S7 S8 S10 S12 A1 S13 S15 E1 E2 E3
0.000 0.396 0.001 0.220 0.014 0.002 0.016 0.004 0.007 0.013 0.005 0.003 0.000 0.003 0.020 0.012 0.008 0.015 0.011 0.004 0.002 0.005 0.008 0.010 0.009
Standard error 0.001 0.065 0.001 0.076 0.008 0.008 0.010 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008
Standard error of the estimate 0.013591
0.180
Standardized coefficients Beta 0.296 0.019 0.141 0.043 0.005 0.041 0.013 0.021 0.041 0.014 0.010 0.000 0.009 0.062 0.037 0.025 0.048 0.035 0.013 0.005 0.015 0.024 0.032 0.027
t
Sig.
0.137 6.071 0.742 2.906 1.738 0.218 1.666 0.539 0.846 1.647 0.583 0.397 0.017 0.339 2.516 1.518 1.002 1.940 1.403 0.518 0.198 0.629 0.968 1.289 1.091
0.891 0.000 0.458 0.004 0.082 0.827 0.096 0.590 0.398 0.100 0.560 0.692 0.986 0.735 0.012 0.129 0.317 0.053 0.161 0.605 0.843 0.530 0.333 0.198 0.275
Model 5.B.3: EVN – DJ1800
For model 5.B.3, events S6_G3 (AR ¼ 2.0%; p ¼ 2.0%) and S10 (AR ¼ 1.4%; p ¼ 9.8%) exhibit a similarly significant, i.e. distinctive stock price reaction as in models 4.B.1 and 4.B.2 as well as in 5.B.1 and 5.B.2. In contrast with model 5.B.2, model 5.B.3 also shows event S1 (AR ¼ 1.5%; p ¼ 8.1%) as a distinctive event. Event G1 (p ¼ 11.8%) is shown as non-distinctive, in contrast with models 5.B.1 and 5.B.2 (cf. Tables 5.68 and 5.69).
112
5 The Primary Empirical Study
Table 5.66 Model summary for EVN – DJ600 (5.B.2)
Model
R
Model summary Adj. R2 R2
5.B.2
0.328
0.107
Table 5.67 Coefficient model for EVN – DJ600 (5.B.2) Coefficients Model Unstandardized coefficients B 5.B.2
5.3.4
(Constant) DJ600 Dummy_(Constant) Dummy_DJ600 G1 G2 G4 G5 G6 S1 S2 S3 S4 S5 S6_G3 S7 S8 S10 S12 A1 S13 S15 E1 E2 E3
0.000 0.190 0.000 0.402 0.014 0.000 0.013 0.003 0.006 0.013 0.005 0.003 0.000 0.004 0.019 0.013 0.010 0.014 0.009 0.005 0.001 0.008 0.008 0.009 0.007
Standard error 0.001 0.058 0.001 0.079 0.008 0.008 0.010 0.008 0.008 0.008 0.008 0.008 0.009 0.009 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008
Standard error of the estimate 0.014309
0.091
Standardized coefficients Beta 0.126 0.009 0.198 0.043 0.001 0.034 0.010 0.017 0.042 0.017 0.010 0.001 0.014 0.060 0.039 0.031 0.044 0.030 0.016 0.003 0.024 0.026 0.027 0.023
t
Sig.
0.731 3.267 0.350 5.116 1.671 0.032 1.321 0.371 0.675 1.617 0.641 0.374 0.047 0.497 2.335 1.515 1.194 1.718 1.147 0.617 0.109 0.936 0.991 1.027 0.870
0.465 0.001 0.727 0.000 0.095 0.974 0.187 0.711 0.500 0.106 0.522 0.709 0.962 0.619 0.020 0.130 0.233 0.086 0.252 0.537 0.913 0.349 0.322 0.305 0.384
Event Study: Constant Beta Factor; 1-Day Event Window
Event-specific abnormal returns are calculated in Sect. 5.3.4 for Verbund and EVN by applying a constant market model with a 1-day event window. Changes in the constant from the market model (welfare effect) or in the systematic risk (change in the beta factor) are hence not considered. As in Sect. 5.3.2, the market model is applied formally, however, with a 1-day event window in this section: Rit ¼ ai þ bi Rmt þ Dk;t gk þ uit
5.3 Empirical Investigation: Event Study Table 5.68 Model summary for EVN – DJ1800 (5.B.3)
113
Model
R
Model summary Adj. R2 R2
5.B.3
0.246
0.061
Table 5.69 Coefficient model for EVN – DJ1800 (5.B.3) Coefficients Model Unstandardized coefficients B 5.B.3
(Constant) DJ1800 Dummy_(Constant) Dummy_DJ1800 G1 G2 G4 G5 G6 S1 S2 S3 S4 S5 S6_G3 S7 S8 S10 S12 A1 S13 S15 E1 E2 E3
0.000 0.119 0.000 0.412 0.013 0.000 0.014 0.005 0.006 0.015 0.004 0.002 0.001 0.004 0.020 0.012 0.010 0.014 0.010 0.006 0.002 0.006 0.008 0.008 0.007
Standard error 0.001 0.065 0.001 0.094 0.009 0.008 0.010 0.008 0.008 0.008 0.008 0.008 0.009 0.009 0.008 0.009 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008
Standard error of the estimate 0.014679
0.044
Standardized coefficients Beta 0.067 0.011 0.160 0.042 0.001 0.036 0.016 0.018 0.046 0.012 0.008 0.002 0.014 0.062 0.038 0.032 0.044 0.030 0.018 0.005 0.018 0.024 0.026 0.023
t
Sig.
0.780 10.836 0.390 4.374 1.562 0.037 1.335 0.619 0.663 1.744 0.469 0.285 0.074 0.481 2.333 1.444 1.215 1.655 1.146 0.685 0.177 0.696 0.887 0.973 0.873
0.435 0.067 0.697 0.000 0.118 0.971 0.182 0.536 0.507 0.081 0.639 0.775 0.941 0.630 0.020 0.149 0.225 0.098 0.252 0.494 0.859 0.487 0.375 0.331 0.383
with: Rit Observed return from stock i at time t ai Constant from the market model bi Slope of the regression line (beta factor) Rmt Return from the market portfolio m at time t Dk;t Dummy variable for event k with value 1 for time lapse of the event window or otherwise 0 gk Coefficient of the regression equation for calculating abnormal return for event k uit Confounding variable in the regression equation
114
5 The Primary Empirical Study
The event-specific abnormal return is determined for Verbund in Sects. 5.3.4.1 (model 6.A.1 with ATX as the market portfolio), 5.3.4.2 (model 6.A.2 with DJ600 as the market portfolio) and 5.3.4.3 (model 6.A.3 with DJ1800 as the market portfolio). For EVN, abnormal returns are calculated in Sects. 5.3.4.4 (model 6.B.1 with ATX as the market portfolio), 5.3.4.5 (model 6.B.2 with DJ600 as the market portfolio) and 5.3.4.6 (model 6.B.3 with DJ1800 as the market portfolio).
5.3.4.1
Model 6.A.1: Verbund – ATX
Model 6.A.1 shows event A1 (AR ¼ 3.7%; p ¼ 1.0%) as significant. Events S6 (AR ¼ 2.5%; p ¼ 8.0%) and E3 (Members from ECK are appointed for another term; AR ¼ 2.8%; p ¼ 5.1%) are thus to be underscored as distinctive. The other events do not display any distinctive abnormal returns (cf. Tables 5.70 and 5.71). Table 5.70 Model summary for Verbund – ATX (6.A.1)
Model
R
Model summary Adj. R2 R2
6.A.1
0.490
0.240
Table 5.71 Coefficient model for Verbund – ATX (6.A.1) Coefficients Model Unstandardized coefficients B 6.A.1
(Constant) ATX S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 A1 S13 S14 S15 E1 E2 E3
0.001 0.684 0.021 0.017 0.012 0.014 0.002 0.025 0.009 0.014 0.009 0.000 0.015 0.037 0.008 0.019 0.011 0.007 0.002 0.028
Standard error 0.000 0.034 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014
Standard error of the estimate 0.014118
0.229
Standardized coefficients Beta 0.477 0.035 0.029 0.020 0.023 0.004 0.042 0.015 0.024 0.014 0.000 0.025 0.062 0.013 0.031 0.019 0.011 0.003 0.046
t
Sig.
2.661 19.981 1.489 1.222 0.857 0.974 0.168 1.752 0.643 0.988 0.605 0.010 1.038 2.589 0.548 1.319 0.805 0.481 0.135 1.952
0.008 0.000 0.137 0.222 0.392 0.330 0.867 0.080 0.521 0.323 0.546 0.992 0.300 0.010 0.584 0.187 0.421 0.631 0.893 0.051
5.3 Empirical Investigation: Event Study
5.3.4.2
115
Model 6.A.2: Verbund – DJ600
Model 6.A.2 only shows event A1 (AR ¼ 3.4%; p ¼ 2.4%) as significant. Event S6 (AR ¼ 2.3%; p ¼ 12.4%) is shown as non-distinctive, in contrast with model 6.A.1. For the other events, an alpha probability of error was determined to be at least 16.3% (event S3) (cf. Tables 5.72 and 5.73).
5.3.4.3
Model 6.A.3: Verbund – DJ1800
Also for model 6.A.3, only event A1 (AR ¼ 3.3%; p ¼ 3.6%) is shown as significant. The other events exhibit an alpha probability of error of at least 20.0% (event S14) (cf. Tables 5.74 and 5.75).
Table 5.72 Model summary for Verbund – DJ600 (6.A.2)
Model
R
Model summary Adj. R2 R2
6.A.2
0.337
0.113
Standard error of the estimate 0.015250
0.101
Table 5.73 Coefficient model for Verbund – DJ600 (6.A.2) Coefficients Model Unstandardized coefficients Standardized coefficients B Standard error Beta 6.A.2 (Constant) 0.002 0.000 DJ600 0.511 0.042 0.317 S1 0.020 0.015 0.034 S2 0.013 0.015 0.022 S3 0.021 0.015 0.036 S4 0.009 0.015 0.014 S5 0.002 0.015 0.004 S6 0.023 0.015 0.040 S7 0.009 0.015 0.015 S8 0.011 0.015 0.018 S9 0.011 0.015 0.019 S10 0.004 0.015 0.007 S11 0.013 0.015 0.023 A1 0.034 0.015 0.058 S13 0.001 0.015 0.002 S14 0.020 0.015 0.034 S15 0.016 0.015 0.027 E1 0.000 0.015 0.000 E2 0.007 0.015 0.011 E3 0.019 0.015 0.031
t
Sig.
3.675 12.266 1.308 0.855 1.395 0.563 0.157 1.538 0.600 0.710 0.750 0.276 0.883 2.253 0.068 1.311 1.037 0.009 0.434 1.213
0.000 0.000 0.191 0.393 0.163 0.574 0.875 0.124 0.549 0.478 0.454 0.782 0.377 0.024 0.946 0.190 0.300 0.993 0.664 0.225
116
5 The Primary Empirical Study
Table 5.74 Model summary for Verbund – DJ1800 (6.A.3)
Model
R
Model summary Adj. R2 R2
6.A.3
0.228
0.052
0.039
Table 5.75 Coefficient model for Verbund – DJ1800 (6.A.3) Coefficients Model Unstandardized coefficients Standardized coefficients B Standard error Beta 6.A.3 (Constant) 0.002 0.000 DJ1800 0.372 0.051 0.197 S1 0.020 0.016 0.034 S2 0.019 0.016 0.031 S3 0.016 0.016 0.027 S4 0.012 0.016 0.020 S5 0.000 0.016 0.000 S6 0.020 0.016 0.033 S7 0.011 0.016 0.018 S8 0.010 0.016 0.018 S9 0.016 0.016 0.027 S10 0.005 0.016 0.008 S11 0.014 0.016 0.024 A1 0.033 0.016 0.056 S13 0.002 0.016 0.004 S14 0.020 0.016 0.034 S15 0.019 0.016 0.033 E1 0.005 0.016 0.009 E2 0.007 0.016 0.012 E3 0.018 0.016 0.030
5.3.4.4
Standard error of the estimate 0.015769
t
Sig.
3.770 7.353 1.274 1.173 1.022 0.768 0.001 1.243 0.674 0.660 0.999 0.291 0.899 2.099 0.135 1.284 1.224 0.325 0.464 1.133
0.000 0.000 0.203 0.241 0.307 0.443 0.999 0.214 0.501 0.509 0.318 0.771 0.369 0.036 0.892 0.200 0.221 0.745 0.643 0.258
Model 6.B.1: EVN – ATX
Event S1 (AR ¼ 5.1%; p < 0.001%) proved to be extremely significant for model 6.B.1 as well. Moreover, this model shows events G1 (AR ¼ 3.3%; p ¼ 1.3%), S6_G3 (AR ¼ 4.7%; p ¼ 0.001%) and S10 (AR ¼ 3.2%; p ¼ 1.8%) as significant. Event E2 (Walter Boltz is appointed as directing manager of E-Control GmbH for another term; AR ¼ 2.6%; p ¼ 5.4%) is to be underscored as distinctive. The other events exhibit an alpha probability of error of at least 11.9% (event S12) (cf. Tables 5.76 and 5.77).
5.3.4.5
Model 6.B.2: EVN – DJ600
Event S1 (AR ¼ 5.4%; p < 0.001%) proved to be extremely significant for model 6.B.2 as well. Moreover, this model shows event S6_G3 (AR ¼ 4.8%; p ¼ 0.001%) as highly significant and event G1 (AR ¼ 3.6%; p ¼ 1.2%) as significant. Events
5.3 Empirical Investigation: Event Study Table 5.76 Model summary for EVN – ATX (6.B.1)
117
Model
R
Model summary Adj. R2 R2
6.B.1
0.461
0.212
Table 5.77 Coefficient model for EVN – ATX (6.B.1) Coefficients Model Unstandardized coefficients B 6.B.1
(Constant) ATX G1 G2 G4 G5 G6 S1 S2 S3 S4_S5 S6_G3 S7 S8 S10 S12 A1 S13 S15 E1 E2 E3
0.000 0.571 0.033 0.007 0.015 0.012 0.007 0.051 0.004 0.012 0.008 0.047 0.020 0.015 0.032 0.021 0.009 0.001 0.001 0.019 0.026 0.015
Standard error 0.000 0.034 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013
Standard error of the estimate 0.013460
0.199
Standardized coefficients Beta 0.422 0.061 0.013 0.027 0.021 0.013 0.093 0.007 0.022 0.016 0.086 0.037 0.028 0.059 0.039 0.016 0.002 0.001 0.035 0.048 0.027
t
Sig.
0.248 17.011 2.478 0.541 1.097 0.866 0.536 3.783 0.264 0.897 0.629 3.478 1.498 1.120 2.374 1.559 0.659 0.062 0.045 1.411 1.930 1.095
0.804 0.000 0.013 0.588 0.273 0.387 0.592 0.000 0.792 0.370 0.529 0.001 0.134 0.263 0.018 0.119 0.510 0.951 0.964 0.158 0.054 0.274
S10 (AR ¼ 2.7%; p ¼ 5.8%) and S7 (AR ¼ 2.5%; p ¼ 7.7%) are to be underscored as distinctive (cf. Tables 5.78 and 5.79). Event E1 (Resolution of the European Parliament involving the complete liberalization of the electricity and gas markets and the resolution on the corporate unbundling of network operators; AR ¼ 2.3%; p ¼ 11.3%) is to be assessed as nondistinctive. The other events exhibit an alpha probability of error of at least 18.0% (event S8).
5.3.4.6
Model 6.B.3: EVN – DJ1800
As for models 6.B.1 and 6.B.2, model 6.B.3 also shows event S1 (AR ¼ 5.7%; p < 0.001%) to be extremely significant. Moreover, this model shows event S6_G3
118
5 The Primary Empirical Study
Table 5.78 Model summary for EVN – DJ600 (6.B.2)
Model
R
Model summary Adj. R2 R2
6.B.2
0.332
0.110
Table 5.79 Coefficient model for EVN – DJ600 (6.B.2) Coefficients Model Unstandardized coefficients B 6.B.2
(Constant) DJ600 G1 G2 G4 G5 G6 S1 S2 S3 S4_S5 S6_G3 S7 S8 S10 S12 A1 S13 S15 E1 E2 E3
0.000 0.417 0.036 0.004 0.014 0.011 0.007 0.054 0.007 0.009 0.009 0.048 0.025 0.019 0.027 0.015 0.007 0.004 0.009 0.023 0.022 0.016
Standard error 0.000 0.040 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014
Standard error of the estimate 0.014303
0.096
Standardized coefficients Beta 0.274 0.066 0.007 0.025 0.020 0.013 0.099 0.013 0.016 0.017 0.087 0.046 0.035 0.050 0.028 0.013 0.007 0.017 0.042 0.040 0.029
t
Sig.
1.157 10.425 2.527 0.284 0.955 0.760 0.495 3.778 0.486 0.599 0.659 3.323 1.769 1.340 1.896 1.053 0.484 0.254 0.655 1.586 1.532 1.106
0.248 0.000 0.012 0.776 0.340 0.448 0.620 0.000 0.627 0.549 0.510 0.001 0.077 0.180 0.058 0.292 0.628 0.800 0.513 0.113 0.126 0.269
(AR ¼ 4.9%; p ¼ 0.001%) as highly significant and event G1 (AR ¼ 3.5%; p = 1.6%) as significant. Events S10 (AR ¼ 2.7%; p ¼ 6.6%) and S7 (AR ¼ 2.5%; p ¼ 8.3%) are to be underscored as distinctive. Event E1 (AR ¼ 2.3%; p ¼ 11.1%) is to be assessed as non-distinctive, as it was for model 6.B.2. The other events exhibit an alpha probability of error of at least 14.1% (event E2) (cf. Tables 5.80 and 5.81).
5.3.5
Event Study: Dummy Variables from 1 July 2005; 1-Day Event Window
In Sect. 5.3.5, event-specific abnormal returns are calculated for Verbund and EVN by applying a market model with dummy variables for the constant, the beta factor
5.3 Empirical Investigation: Event Study Table 5.80 Model summary for EVN – DJ1800 (6.B.3)
119
Model
R
Model summary Adj. R2 R2
6.B.3
0.263
0.069
Table 5.81 Coefficient model for EVN – DJ1800 (6.B.3) Coefficients Model Unstandardized coefficients B 6.B.3
(Constant) DJ1800 G1 G2 G4 G5 G6 S1 S2 S3 S4_S5 S6_G3 S7 S8 S10 S12 A1 S13 S15 E1 E2 E3
Standard error 0.000 0.048 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015
0.001 0.331 0.035 0.002 0.016 0.011 0.008 0.057 0.003 0.006 0.007 0.049 0.025 0.021 0.027 0.013 0.006 0.005 0.009 0.023 0.022 0.015
Standard error of the estimate 0.014630
0.054
Standardized coefficients Beta 0.185 0.065 0.003 0.030 0.020 0.014 0.105 0.006 0.011 0.012 0.090 0.047 0.038 0.049 0.024 0.011 0.009 0.016 0.043 0.040 0.028
t
Sig.
1.295 6.838 2.404 0.103 1.112 0.735 0.536 3.910 0.226 0.423 0.458 3.359 1.735 1.418 1.841 0.908 0.408 0.317 0.586 1.593 1.472 1.049
0.196 0.000 0.016 0.918 0.266 0.463 0.592 0.000 0.822 0.673 0.647 0.001 0.083 0.156 0.066 0.364 0.683 0.751 0.558 0.111 0.141 0.294
from the market model and a 1-day event window. The market model is implemented formally as in Sect. 5.3.2, only with differing definitions of the event window: 0
0
Rit ¼ ai þ ai Ds þ bi Rmt þ bi Ds Rmt þ Dk;t gk þ uit with: Rit ai Ds 0 ai bi Rmt 0 bi
Observed return from stock i at time t Constant from the market model Dummy variable with value 0 (01/2003 – 06/2005) or 1 (07/2005 – 06/2008) Change in the constant ai if: Ds ¼ 1 Slope of the regression line (beta factor) Return from the market portfolio m at time t Change in the constant bi if: Ds ¼ 1
120
Dk;t gk uit
5 The Primary Empirical Study
Dummy variable for event k with value 1 for time lapse of the event window or otherwise 0 Coefficient of the regression equation for calculating abnormal return for event k Confounding variable in the regression equation
Event-specific abnormal returns for Verbund are determined in Sects. 5.3.5.1 (model 7.A.1 with ATX as the market portfolio), 5.3.5.2 (model 7.A.2 with DJ600 as the market portfolio) and 5.3.5.3 (model 5.A.3 with DJ1800 as the market portfolio). For EVN, abnormal returns are calculated in Sects. 5.3.5.4 (model 7.B.1 with ATX as the market portfolio), 5.3.5.5 (model 7.B.2 with DJ600 as the market portfolio) and 5.3.5.6 (model 7.B.3 with DJ1800 as the market portfolio).
5.3.5.1
Model 7.A.1: Verbund – ATX
Model 7.A.1 shows event A1 (AR ¼ 3.7%; p ¼ 0.9%) as highly significant. Events S6 (Ar ¼ 2.4%; p ¼ 9.3%) and E3 (AR ¼ 2.6%; p ¼ 6.6%) are additionally to be underscored as distinctive. The other events exhibit an alpha probability of error of at least 16.2% (event a S1) and are hence not distinctive (cf. Tables 5.82 and 5.83).
5.3.5.2
Model 7.A.2: Verbund – DJ600
Model 7.A.2 shows event A1 (AR ¼ 3.5%; p ¼ 1.9%) as significant. The other events are non-distinctive and exhibit an alpha probability of error of at least 14.1% (event S6) (cf. Tables 5.84 and 5.85).
5.3.5.3
Model 7.A.3: Verbund – DJ1800
Also for model 7.A.3, only event A1 (AR ¼ 3.4%; p ¼ 3.1%) is shown as significant. The other events exhibit an alpha probability of error of at least 18.9% (event S6) (cf. Tables 5.86 and 5.87).
5.3.5.4
Model 7.B.1: EVN – ATX
For events S1 (AR ¼ 5.3%; p < 0.1%) and S6_G3 (AR ¼ 4.9%; p < 0.1%), extremely significant abnormal returns were detected. Additionally, this model
5.3 Empirical Investigation: Event Study Table 5.82 Model summary for Verbund – ATX (7.A.1)
121
Model
R
Model summary Adj. R2 R2
7.A.1
0.495
0.245
Table 5.83 Coefficient model for Verbund – ATX (7.A.1) Coefficients Model Unstandardized coefficients B 7.A.1
(Constant) ATX Dummy_(Constant) Dummy_ATX S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 A1 S13 S14 S15 E1 E2 E3
0.001 0.527 0.000 0.212 0.020 0.018 0.012 0.013 0.002 0.024 0.009 0.013 0.010 0.002 0.015 0.037 0.008 0.018 0.011 0.006 0.004 0.026
Standard error 0.001 0.068 0.001 0.078 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014
Standard error of the estimate 0.014088
0.233
Standardized coefficients Beta 0.368 0.005 0.127 0.033 0.030 0.020 0.022 0.004 0.040 0.016 0.023 0.017 0.003 0.025 0.063 0.014 0.031 0.018 0.010 0.007 0.044
t
Sig.
1.767 7.803 0.222 2.701 1.398 1.280 0.838 0.908 0.173 1.683 0.666 0.950 0.699 0.137 1.055 2.635 0.573 1.309 0.745 0.428 0.274 1.843
0.077 0.000 0.824 0.007 0.162 0.201 0.402 0.364 0.862 0.093 0.505 0.342 0.485 0.891 0.292 0.009 0.567 0.191 0.456 0.669 0.784 0.066
shows events G1 (AR ¼ 3.3%; p ¼ 1.3%) and S10 (AR ¼ 3.1%; p ¼ 2.3%) as significant. Event E2 (AR ¼ 2.3%; p ¼ 8.1%) is to be underscored as distinctive. The other events exhibit an alpha probability of error of at least 11.6% (event S7) (cf. Tables 5.88 and 5.89).
5.3.5.5
Model 7.B.2: EVN – DJ600
Events S1 (AR ¼ 5.5%; p < 0.1%) and S6_G3 (AR ¼ 5.0%; p < 0.1%) proved to be extremely significant. Additionally, this model determines significant abnormal returns for event G1 (AR ¼ 3.5%; p ¼ 1.3%). Events S10 (AR ¼ 2.7%; p ¼ 5.6%) and S7 (AR ¼ 2.4%; p ¼ 9.6%) are to be underscored as distinctive.
122
5 The Primary Empirical Study
Table 5.84 Model summary for Verbund – DJ600 (7.A.2)
Model
R
Model summary Adj. R2 R2
7.A.2
0.375
0.140
Table 5.85 Coefficient model for Verbund – DJ600 (7.A.2) Coefficients Model Unstandardized coefficients B 7.A.2
(Constant) DJ600 Dummy_(Constant) Dummy_DJ600 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 A1 S13 S14 S15 E1 E2 E3
0.002 0.217 0.000 0.535 0.018 0.018 0.016 0.010 0.002 0.022 0.009 0.012 0.013 0.006 0.014 0.035 0.001 0.018 0.012 0.002 0.008 0.020
Standard error 0.001 0.061 0.001 0.083 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015
Standard error of the estimate 0.015028
0.127
Standardized coefficients Beta 0.134 0.007 0.245 0.030 0.030 0.027 0.016 0.004 0.037 0.016 0.020 0.021 0.010 0.024 0.060 0.002 0.031 0.020 0.004 0.014 0.034
t
Sig.
2.802 3.536 0.266 6.475 1.180 1.190 1.045 0.642 0.139 1.472 0.627 0.778 0.839 0.394 0.945 2.356 0.075 1.218 0.770 0.162 0.557 1.347
0.005 0.000 0.790 0.000 0.238 0.234 0.296 0.521 0.889 0.141 0.531 0.437 0.402 0.694 0.345 0.019 0.940 0.223 0.441 0.871 0.578 0.178
The other events exhibit an alpha probability of error of at least 12.4% (event S8) (cf. Tables 5.90 and 5.91).
5.3.5.6
Model 7.B.3: EVN – DJ1800
As for models 7.B.1 und 7.B.2, so model 7.B.3 shows event S1 (AR ¼ 5.7%; p < 0.1%) to be extremely significant. For event S6_G3 (AR ¼ 5.1%; p ¼ 0.1%), a highly significant abnormal return was determined and G1 (AR ¼ 3.5%; p ¼ 1.7%) is shown as significant. Event S10 (AR ¼ 2.7%; p ¼ 6.3%) is to be underscored as distinctive and S7 (AR ¼ 2.3%; p ¼ 10.8%) is hardly to be assessed as distinctive any longer. The other events exhibit an alpha probability of error of at least 11.8% (event S8) (cf. Tables 5.92 and 5.93).
5.3 Empirical Investigation: Event Study Table 5.86 Model summary for Verbund – DJ1800 (7.A.3)
123
Model
R
Model summary Adj. R2 R2
7.A.3
0.261
0.068
Table 5.87 Coefficient model for Verbund – DJ1800 (7.A.3) Coefficients Model Unstandardized coefficients B 7.A.3
5.3.6
(Constant) DJ1800 Dummy_(Constant) Dummy_DJ1800 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 A1 S13 S14 S15 E1 E2 E3
0.002 0.145 0.000 0.479 0.018 0.020 0.013 0.011 0.001 0.021 0.010 0.012 0.014 0.006 0.015 0.034 0.003 0.018 0.016 0.005 0.009 0.020
Standard error 0.001 0.069 0.001 0.101 0.016 0.016 0.016 0.016 0.016 0.016 0.016 0.016 0.016 0.016 0.016 0.016 0.016 0.016 0.016 0.016 0.016 0.016
Standard error of the estimate 0.015648
0.053
Standardized coefficients Beta 0.077 0.007 0.174 0.030 0.034 0.023 0.019 0.002 0.035 0.017 0.020 0.024 0.010 0.024 0.057 0.005 0.030 0.026 0.008 0.015 0.034
t
Sig.
2.799 2.096 0.251 4.761 1.129 1.300 0.857 0.710 0.072 1.314 0.639 0.741 0.916 0.393 0.927 2.154 0.195 1.145 0.989 0.298 0.557 1.285
0.005 0.036 0.802 0.000 0.259 0.194 0.392 0.478 0.943 0.189 0.523 0.459 0.360 0.695 0.354 0.031 0.845 0.252 0.323 0.766 0.578 0.199
Summary of the Event Study
The goal of the event study conducted was to gain model-related, theoretical knowledge about how sensitively the event study results react to different model specifications as well as to identify every event in the event list that led to a significant abnormal return. In regards to the set of questions about the sensitivity of the model-specific rate of abnormal returns, it must be held that the parameter set appears to have different levels of strong influence on the results. The decision about what index should be implemented (for the market index), appears to be of relatively little importance for the event study conducted as to the rate of abnormal return determined. Following from this, mean value differences from 0.00% up to a max. of 0.13% (cf. Table 5.94) between the models occur for
124
5 The Primary Empirical Study
Table 5.88 Model summary for EVN – ATX (7.B.1)
Model
R
Model summary Adj. R2 R2
7.B.1
0.469
0.220
Table 5.89 Coefficient model for EVN – ATX (7.B.1) Coefficients Model Unstandardized coefficients B 7.B.1
(Constant) ATX Dummy_(Constant) Dummy_ATX G1 G2 G4 G5 G6 S1 S2 S3 S4_S5 S6_G3 S7 S8 S10 S12 A1 S13 S15 E1 E2 E3
0.000 0.385 0.001 0.258 0.033 0.006 0.015 0.012 0.007 0.053 0.005 0.011 0.009 0.049 0.021 0.018 0.031 0.018 0.010 0.001 0.001 0.020 0.023 0.014
Standard error 0.001 0.065 0.001 0.076 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013
Standard error of the estimate 0.013402
0.206
Standardized coefficients Beta 0.284 0.019 0.162 0.061 0.011 0.028 0.022 0.014 0.096 0.008 0.020 0.016 0.089 0.039 0.034 0.056 0.033 0.018 0.002 0.002 0.036 0.043 0.025
t
Sig.
0.153 5.960 0.766 3.419 2.492 0.429 1.151 0.886 0.554 3.913 0.341 0.793 0.659 3.620 1.574 1.358 2.282 1.349 0.731 0.071 0.066 1.473 1.748 1.033
0.879 0.000 0.444 0.001 0.013 0.668 0.250 0.376 0.579 0.000 0.733 0.428 0.510 0.000 0.116 0.175 0.023 0.177 0.465 0.943 0.948 0.141 0.081 0.302
Verbund. For EVN, mean value differences for abnormal returns are 0.01% up to a max. of 0.18% (cf. Table 5.95), attributable to the choice of market index. Whether structural breaks, in the form of dummy variables for constants and beta factors in the market model, should be considered or not appears to be of secondary importance after comparing the mean value differences arising from them. Differences in the mean value of abnormal returns follow from this for Verbund at 0.01% up to a max. of 0.17% (cf. Table 5.94) and at 0.00% up to a max of 0.15% for those from EVN (cf. Table 5.95). The largest mean value differences arise from the event window setting. The differences between the mean values of the abnormal returns, attributable to the event windows used, reach from 0.40% to 0.64% (cf. Table 5.94) for Verbund and from 0.14% to 0.37% (cf. Table 5.95) for EVN. The largest difference between
5.3 Empirical Investigation: Event Study Table 5.90 Model summary for EVN – DJ600 (7.B.2)
125
Model
R
Model summary Adj. R2 R2
7.B.2
0.361
0.131
Table 5.91 Coefficient model for EVN – DJ600 (7.B.2) Coefficients Model Unstandardized coefficients B 7.B.2
(Constant) DJ600 Dummy_(Constant) Dummy_DJ600 G1 G2 G4 G5 G6 S1 S2 S3 S4_S5 S6_G3 S7 S8 S10 S12 A1 S13 S15 E1 E2 E3
0.000 0.189 0.000 0.433 0.035 0.003 0.014 0.010 0.006 0.055 0.007 0.009 0.010 0.050 0.024 0.022 0.027 0.014 0.008 0.004 0.009 0.021 0.020 0.014
Standard error 0.001 0.057 0.001 0.079 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014
Standard error of the estimate 0.014150
0.115
Standardized coefficients Beta 0.125 0.006 0.206 0.065 0.005 0.026 0.018 0.012 0.100 0.013 0.016 0.018 0.091 0.043 0.040 0.050 0.026 0.015 0.007 0.017 0.039 0.037 0.026
t
Sig.
0.720 3.293 0.241 5.468 2.484 0.199 0.996 0.691 0.443 3.850 0.513 0.603 0.704 3.503 1.668 1.538 1.913 0.985 0.566 0.264 0.667 1.517 1.440 1.004
0.472 0.001 0.810 0.000 0.013 0.842 0.319 0.490 0.658 0.000 0.608 0.547 0.481 0.000 0.096 0.124 0.056 0.325 0.572 0.792 0.505 0.129 0.150 0.316
model-specific mean values for Verbund comes to 0.64%, comparing model 6.A.1 with models 4.A.3 and 5.A.3; for EVN it comes to 0.37%, comparing model 6.B.1 with model 4.B.1.21 Every event summarized in Table 5.96 led to a significant abnormal return in at least one model at the 10% level (two-sided). For Verbund, the abnormal returns arising from event A1 proved to be significant in all models at the 10%
21
Reference is made here to Table 5.44 of the current work, in which the model descriptions and the model specifications are cited.
126
5 The Primary Empirical Study
Table 5.92 Model summary for EVN – DJ1800 (7.B.3)
Model summary Adj. R2 Standard error of the estimate Model R R2 7.B.3 0.294 0.086 0.070 0.014507
Table 5.93 Coefficient model for EVN – DJ1800 (7.B.3) Coefficients Model Unstandardized coefficients B 7.B.3
(Constant) DJ1800 Dummy_(Constant) Dummy_DJ1800 G1 G2 G4 G5 G6 S1 S2 S3 S4_S5 S6_G3 S7 S8 S10 S12 A1 S13 S15 E1 E2 E3
0.000 0.124 0.000 0.472 0.035 0.002 0.019 0.009 0.007 0.056 0.006 0.008 0.009 0.051 0.023 0.023 0.027 0.013 0.007 0.006 0.008 0.022 0.020 0.014
Standard error 0.001 0.064 0.001 0.097 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.015
Standardized coefficients Beta 0.069 0.008 0.174 0.064 0.003 0.035 0.016 0.013 0.102 0.011 0.014 0.017 0.093 0.043 0.042 0.050 0.024 0.013 0.010 0.015 0.039 0.037 0.025
t
Sig.
0.787 1.923 0.285 4.879 2.390 0.114 1.305 0.604 0.483 3.837 0.411 0.528 0.624 3.478 1.609 1.565 1.858 0.906 0.471 0.379 0.565 1.481 1.381 0.952
0.432 0.055 0.776 0.000 0.017 0.909 0.192 0.546 0.629 0.000 0.681 0.597 0.533 0.001 0.108 0.118 0.063 0.365 0.638 0.705 0.572 0.139 0.168 0.341
¨ level (two-sided). The basic agreement between the regulatory agency and VEO (The Association of Austrian Electricity Companies), as lobbyists from the electricity network operators, on the introduction of an incentive regulation on electricity appears to have been important for the capital market. For events S6, E3 and S14, only for two (S6 and E3) of the 12 models used for calculation or only for one model (S14) could a significant abnormal return be established at the 10% level (two-sided). For EVN, events S6_G3 proved to be significant in all 12 models used for calculation. Events G1 and S10 led to significant abnormal returns in 11 of the 12 models. For event S1, significant abnormal returns could be determined especially with models having a 1-day event window. Events S7 (in 3 of the 12 models), E2 (in 2 of the 12 models) and G4 (in 1 of the 12 models), appear that they can be interpreted as significant only conditionally.
EW ¼ 3 days / with Dummy (%) ATX DJ600 DJ1800
ATX DJ600 0.12 DJ1800 0.13 0.01 EW ¼ 1 day / with Dummy ATX 0.07 0.05 0.06 DJ600 0.17 0.05 0.04 0.10 DJ1800 0.17 0.05 0.04 0.10 0.00 EW ¼ 3 days / without Dummy ATX 0.53 0.41 0.40 0.46 0.36 0.36 DJ600 0.58 0.47 0.46 0.52 0.42 0.42 0.06 DJ1800 0.64 0.53 0.52 0.58 0.48 0.48 0.12 0.06 ATX 0.57 0.45 0.44 0.50 0.40 0.40 0.04 0.02 0.08 EW ¼ 3 days / with Dummy DJ600 0.60 0.48 0.47 0.53 0.43 0.43 0.07 0.02 0.04 0.03 DJ1800 0.64 0.52 0.51 0.57 0.47 0.47 0.11 0.06 0.01 0.07 0.04 a The differences in the model-specific mean values listed arise from: “Model according to description in column” – “Model according to description in row”. The arithmetic mean was used as the mean value. “EW” ¼ event window
EW ¼ 1 day / without Dummy
Table 5.94 Summary of the model-specific mean value differences of abnormal returns for Verbunda Mean differences of AR´s EW ¼ 1 day / without EW ¼ 1 day / with EW ¼ 3 days / without Dummy (%) Dummy (%) Dummy (%) ATX DJ600 DJ1800 ATX DJ600 DJ1800 ATX DJ600 DJ1800 Verbund
5.3 Empirical Investigation: Event Study 127
EW ¼ 3 days / with Dummy (%) ATX DJ600 DJ1800
ATX DJ600 0.11 DJ1800 0.18 0.07 EW ¼ 1 day / with Dummy ATX 0.06 0.05 0.12 DJ600 0.15 0.04 0.03 0.09 DJ1800 0.14 0.03 0.04 0.08 0.01 EW ¼ 3 days / without Dummy ATX 0.19 0.30 0.37 0.25 0.34 0.33 DJ600 0.16 0.27 0.34 0.22 0.31 0.30 0.03 DJ1800 0.14 0.24 0.31 0.19 0.28 0.28 0.06 0.02 EW ¼ 3 days / with Dummy ATX 0.16 0.26 0.33 0.21 0.30 0.30 0.03 0.00 0.02 DJ600 0.12 0.23 0.30 0.18 0.27 0.26 0.07 0.04 0.01 0.04 DJ1800 0.13 0.24 0.31 0.19 0.28 0.27 0.06 0.03 0.00 0.03 0.01 a The differences in the model-specific mean values listed arise from: “Model according to description in column” – “Model according to description in row”. The arithmetic mean was used as the mean value. “EW” ¼ event window
EW ¼ 1 day / without Dummy
Table 5.95 Summary of the model-specific mean value differences of abnormal returns for EVNa Mean differences of AR´s EW ¼ 1 day / without EW ¼ 1 day / with EW ¼ 3 days / without Dummy (%) Dummy (%) Dummy (%) ATX DJ600 DJ1800 ATX DJ600 DJ1800 ATX DJ600 DJ1800 EVN
128 5 The Primary Empirical Study
Table 5.96 Summary of the significant events for Verbund and EVN Enterprise Event Model ATX EW ¼ 1 day EWa ¼ 3 days B B Verbund S6 without Dummy 0.010 0.025* with Dummy 0.009 0.024* A1 without Dummy 0.016** 0.037*** with Dummy 0.016** 0.037*** S14 without Dummy 0.013 0.019 with Dummy 0.014* 0.018 E3 without Dummy 0.012 0.028* with Dummy 0.011 0.026* EVN G1 without Dummy 0.015* 0.033** with Dummy 0.014* 0.033** G4 without Dummy 0.015 0.015 with Dummy 0.016* 0.015 S1 without Dummy 0.012 0.051*** with Dummy 0.013* 0.053*** S6_G3 without Dummy 0.019** 0.047*** with Dummy 0.020** 0.049*** S7 without Dummy 0.012 0.020 with Dummy 0.012 0.021 S10 without Dummy 0.015* 0.032** with Dummy 0.015* 0.031** E2 without Dummy 0.011 0.026* with Dummy 0.010 0.023* a EW ¼ event window *Sig. 0.10 (2-tailed) **Sig. 0.05 (2-tailed) ***Sig. 0.01 (2-tailed) DJ600 EW ¼ 3 days EW ¼ 1 day B B 0.010 0.023 0.010 0.022 0.017* 0.034** 0.018** 0.035** 0.010 0.020 0.013 0.018 0.009 0.019 0.009 0.020 0.015* 0.036** 0.014* 0.035** 0.011 0.014 0.013 0.014 0.011 0.054*** 0.013 0.055*** 0.019** 0.048*** 0.019** 0.050*** 0.013 0.025* 0.013 0.024* 0.014* 0.027* 0.014* 0.027* 0.009 0.022 0.009 0.020
DJ1800 EW ¼ 3 days EW ¼ 1 day B B 0.009 0.02 0.009 0.021 0.017* 0.033** 0.018** 0.034** 0.006 0.02 0.008 0.018 0.018 0.010 0.009 0.02 0.014* 0.035** 0.013 0.035** 0.010 0.016 0.014 0.019 0.014* 0.057*** 0.015* 0.056*** 0.020** 0.049*** 0.020** 0.051*** 0.013 0.025* 0.012 0.023 0.014 0.027* 0.014* 0.027* 0.009 0.022 0.008 0.020
5.3 Empirical Investigation: Event Study 129
130
5 The Primary Empirical Study
In summary, it can hence be held that event A1 led to significant abnormal returns for Verbund in all twelve models, however, no significant abnormal returns could be established from this event in the models calculated for EVN. Conversely, event S6 or S6_G3 acted this way: this event proved to be significant for EVN in all models, however, only in two of the twelve models for Verbund. Of the remaining five gas network events, none of them led to significant results. Events concerning the extension of the terms of the managing directors from the regulatory agency (E2) or the members of the ECK (E3) led to significant negative abnormal returns for both Verbund and EVN – for Verbund in two of the twelve models for event E3, for EVN in two of the twelve models for event E2.
Chapter 6
Summary of the Work
Chapter 2 presented the calculation of the costs of equity in regulated companies as to its intrinsic meaning – on one hand for determining an appropriate cost of capital and on the other as a prognosis basis for estimating future cash flow. Chapter 3 presented the increasingly observable change from cost-based to price-based regulatory systems, based on reflections on efficiency. The potentially contradictory effects outlined due to a regulatory system shift (regulatory lag and buffering effect) were explained. In addition, it was shown that the first empirical investigations from the 1980s came to the conclusion that a change from a costbased regulatory system to a price-based regulatory system, attributable to the strengthening of the regulatory lag effect, is evaluated by the capital market as negative. In Chap. 4, a secondary data analysis was conducted in which the results published from empirical studies on the interrelationship between changes in regulatory parameters and the capital markets reaction to them were included as a database. A significant difference could be established in the variance of abnormal returns, attributable to regulatory risk, between regulatory systems on profitability and multiple-period incentive regulatory systems. The spread of abnormal returns proved to be significantly larger for multiple-period incentive regulation models than for regulation on profitability. A regulatory system shift was interpreted as a structural break in the regression line of the market model. Chapter 5 investigated whether this type of structural break can be established for Austrian system network operators, attributable to the regulatory system shift carried out in the electricity network industry. The investigations came to the conclusion that, introducing the incentive regulation on electricity in July 2005 led to structural breaks in the market models examined for Verbund and EVN. Significant increases in the beta factor, significant reductions of unsystematic risk and significant increases in the total risk were established. Support of this interpretation of the results lies in the fact that these observations cannot be made for the control portfolio DJ600UTIL. Additionally, an event study was conducted in Chap. 5 to establish which changes in the Austrian electricity and gas system network regulation policies M. Hierzenberger, Price Regulation and Risk, Lecture Notes in Economics and Mathematical Systems 641, DOI 10.1007/978-3-642-12047-3_6, # Springer-Verlag Berlin Heidelberg 2010
131
132
6 Summary of the Work
have led to significant abnormal returns and how different specifications from the market models have an effect on the measurement results. The results show that selecting the event window is of special importance, whereas selecting market portfolios, considering or not considering potential structural breaks in the market model, only exert a little influence on the measurement results from the event study. ¨ Event A1, the agreement between the regulatory agency E-Control and VEO (The Association of Austrian Electricity Companies) on introducing the incentive regulation on electricity, led to significant abnormal returns in all 12 models calculated for Verbund. No significant abnormal returns could be established from this event in the models calculated for EVN. Conversely, event S6 or S6_G3 acted in this way: this event proved to be significant for EVN in all models, however, only in two of the twelve models for Verbund. Among the events involving the gas system network, which were considered only for EVN, event G1 proved to be significant in eleven of the twelve models. Of the remaining five gas system network events, none of them led to significant results. In total, 10 of the 25 events proved to be significant in at least one of the twelve models used for calculation (cf. Table 111). As a general problem for the interpretation of results from the structural break analysis conducted and the event study, it must be noted that companies examined are not exclusively active in the electricity and gas network system industries. The influences exerted on the variable examined from other business fields could not be controlled. However, this problem does not only exist for Austrian electricity and gas network operators. Capital market data, which are not intrinsic to this problem, are only available for regulated network operators from Great Britain for the 1990s, to the knowledge of the author of this work. Empirical studies that have utilized this data and whose results were utilized for the empirical secondary data analysis in Chap. 5 came to similar conclusions as those of the present work. In summary, it can be held that, bearing in mind the empirical studies published thus far as well as the results of this work concerning an upcoming regulatory system shift as a part of business valuation, appropriate adjustments are to be made for discount interest. Provided that change is expected from a rate of return to a multiple-period incentive regulatory system, the discount interest should be increased and vice versa. Providing a generally valid recommendation on the rate of adjustment for the cost of equity does not seem to be expedient to the author of this work. This must be decided on a case-by-case basis because multiple-period incentive regulations require a large number of parameters by which each of their characteristics and hence the economic consequences can differ. The same applies for fixing financing costs allowed by the regulatory agency for the regulated system network operator. By way of suggestion for additional works, may it be noted that the structural break analysis in Sect. 5.2 of this work could only be conducted within the scope of the introduction of the incentive regulation on electricity because the database for an analogous investigation on the gas system network was not present. Additionally, only the market model was applied in the empirical studies in this work. The pressing question is what results would have been reached if, for instance, the FF3F
6 Summary of the Work
133
model would have been applied? Moreover, other investigations could still be done on what adjustments could be made for the beta factor, as explained in the adjustment procedure in Sect. 2.2.1.4.4 in Chap. 2, and what relationship these adjustments have with the results of the structural break analysis in Sect. 5.2 in Chap. 5 of this work.
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