WEAKNESSES OF ACCOUNTING INDICATORS AND ECONOMIC METHODS OF VALUE DETERMINATION

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Dodd and Chen’s investigation of the explanatory power of EVA

Dodd and Chen (1996:27) used the 1992 Stern Stewart 1000 database as a starting point and added some supplementary data for the ten years from 1983 to 1992. They gathered complete data for 566 USA companies and set out to test the claim that EVA is a superior measure of shareholder value performance. Although they did find a correlation between share returns and EVA (an r2 of 20%), it was not as high as the r2 of share returns and ROA, for which the r2 was 25%. The r2 for the other accounting measures tested, namely EPS and ROE, were very low (between 5% and 7%). Based on the data of this large number of companies over as long a period as 10 years, it appears that EVA does not relate well to share returns. The results obtained imply that 80% of changes in share returns could not be accounted for by changes in EVA. In this specific study, and bearing in mind that unadjusted data were used, the ROA showed a better explanatory ability than EVA did. Dodd and Chen (1996:27) also found that residual income, which is similar to EVA, except for the adjustments required to deal with the so-called accrual accounting distortions, gave results almost identical to those achieved using EVA. The r2 of residual income relative to share returns was 19%, compared to EVA’s r2 of 20%.

Makelainen’s evidence in support of EVA and related measures

Makelainen (1998:15) studied the evidence of the correlation between EVA and share prices and reviewed the work of Stewart (1991:215) and several other researchers up to 1997. However, she concentrated primarily on the study done on Finnish data by Telaranta (in Makelainen, 1998:15). Teleranta (in Makelainen, 1998:15) used 42 Finnish industrial companies, of which 26 were listed for the full period between 1988 and 1995, to test the ability of different measures to explain market movements. Teleranta (in Makelainen, 1998:15) used MVA, market-to-book ratio and excess return on shares as dependent variables. As independent variables he used two versions of economic profit (residual income) and three versions of the EduardBell-Ohlson figure (close to residual income) as well as traditional accounting based performance measures such as earnings before interest, taxation, depreciation and amortization (EBITDA), operating profit, NOPAT, net earnings and cash flow. The results of Teleranta’s (in Makelainen, 1998:15) study showed that the level of economic profit (as expressed by the r2 ) explained 31% of the level of MVA. Of all the measures used by Teleranta (in Makelainen, 1998:15), economic profit was the measure closest to EVA. The next best measure was NOPAT, which explained 30% of MVA. When the changes in the measures were considered, the change in economic profit was still correlated best with changes in MVA with an r2 of 17%. NOPAT was second best again, with an r2 of just below 17%. #Hall’s study of the relationship between MVA and EVA for South African companies Hall (1998:198) investigated the relationship between MVA and EVA, as well as other financial ratios such as ROA, ROE and EPS in South Africa. The study was done on the top 200 companies listed on the JSE for the period from 1987 to 1996. The sample included only industrial sector companies (financial, investment and mining sector companies were excluded). Companies with thinly traded shares were also not included in the sample, as this would have affected the reliability of the estimated WACC calculations. Hall’s (1998:198) study found relatively low correlation coefficients on the whole. The highest correlation was that between MVA and discounted EVA, with inflation adjustments to the data. He ascribes the low correlation to the fact that no distinction was being made between companies that create wealth and those that destroy wealth. He cites Grant (1997:44), who had done a similar regression exercise and found a more significant correlation after splitting his sample between the top 50 wealth creators and the worst 50 wealth destroyers.

Kleiman’s findings supporting better performance where EVA is

adopted Kleiman (1999:80) argues that research on EVA and other accounting performance measures up to 1999 could not conclusively prove whether EVA or EPS affected market returns most. He judged both EVA and EPS to be more or less equally effective in explaining share returns. The study of Kleiman (1999:80) set out to determine whether companies that adopt EVA as a performance measure add more value for their shareholders than their industry competitors do. He limited his study to companies that had implemented EVA. His sample was 71 companies that had adopted EVA during the period from 1987 to 1996. For the sake of comparison he also identified the “closest-matched industry firm”, namely the firm that was the closest in sales to the EVA company in the year prior to the adoption of EVA.

Gates’s study on strategic performance measurement systems

Gates (2000:44) performed a study on companies that had adopted strategic performance measurement (SPM) systems in order to evaluate management’s success in improving operating efficiency and adding value for shareholders. The survey focused on the SPM practices of publicly traded industrial and service companies based (mainly) in the USA and Europe. Of the 113 companies that responded, more than a half said they had formal SPM systems and more than two thirds said they expected to have such systems in place within three years. Gates (2000) wanted to find out what the most popular measures in these SPM systems were. For instance, were those measures mainly financial or were they non-financial or a mix of both? Regarding the emphasis of the SPM system, companies were almost evenly divided: 41% said they used a value-based approach and 40% said they used a balanced scorecard approach. There was also no significant difference between the share price performance of companies with “value-based” SPMs and those with balanced scorecard-type systems.

CONTENTS :

• Acknowledgements
• Summary
• List of abbreviations
• CHAPTER INTRODUCTION
  • 1.1 BACKGROUND
  • 1.2 RATIONALE FOR THE STUDY
  • 1.3 RESEARCH OBJECTIVES
  • 1.4 LITERATURE REVIEW
  • 1.5 LIMITATIONS OF THE STUDY
  • 1.6 OUTLINE OF THE STUDY
  • 1.7 CONCLUSION
• CHAPTER WEAKNESSES OF ACCOUNTING INDICATORS AND ECONOMIC METHODS OF VALUE DETERMINATION
  • 2.1 INTRODUCTION
  • 2.2 THE ACCOUNTING MODEL OF VALUATION
  • 2.3 THE ECONOMIC MODEL
  • 2.4 THE ACCOUNTING MODEL VERSUS THE ECONOMIC MODEL
  • 2.4.1 LIFO versus FIFO
  • 2.4.2 Amortisation of goodwill
  • 2.4.3 Research and development expenditure
  • 2.4.4 Deferred taxation
  • 2.4.5 EPS
  • 2.4.6 Earnings growth
  • 2.4.7 Dividends
  • 2.4.8 ROE
  • 2.5 ECONOMIC METHODS OF VALUATION
  • 2.5.1 NPV
  • 2.5.2 SVA
  • 2.5.3 The economic profit model
  • 2.6 CONCLUSION
• CHAPTER EVA AND MVA AND ADJUSTMENTS TO FINANCIAL STATEMENTS TO REFLECT VALUE CREATION
  • 3.1 INTRODUCTION
  • 3.2 DEFINITION OF EVA AND MVA
  • 3.3 RESEARCH IN SUPPORT OF EVA AS A DRIVER OF MVA
  • 3.3.1 The pioneering studies of Stewart
  • 3.3.2 Finegan’s extensions of the EVA and MVA applications
  • 3.3.3 Stern’s comparison of EVA with popular accounting measures
  • 3.3.4 Lehn and Makhija’s work on EVA, MVA, share price performance and CEO turnover
  • 3.3.5 O’Byrne’s findings on EVA’s link to market value and investor expectations
  • 3.3.6 Uyemura, Kantor and Petit – EVA and wealth creation
  • 3.3.7 Grant’s analysis of relative EVA and relative capital invested
  • 3.3.8 Dodd and Chen’s investigation of the explanatory power of EVA
  • 3.3.9 Milunovich and Tsuei’s study on the use of EVA and MVA in the USA computer industry
  • 3.3.10 Makelainen’s evidence in support of EVA and related measures
  • 3.3.11 Hall’s study of the relationship between MVA and EVA for South African companies
  • 3.3.12 Kleiman’s findings supporting better performance where EVA is adopted
  • 3.3.13 Gates’s study on strategic performance measurement systems
  • 3.3.14 Milano: EVA in the “new economy”
  • 3.3.15 Kramer and Peters: EVA as a proxy for MVA
  • 3.3.16 Hatfield: how EVA affects R&D
  • 3.4 CRITICISMS OF EVA AND MVA
  • 3.4.1 Kaplan and Norton’s preference for the balanced scorecard
  • 3.4.2 De Villiers’s view of the effects of inflation on EVA
  • 3.4.3 Kramer and Pushner’s findings against EVA
  • 3.4.4 Makelainen’s criticism regarding EVA and wrong periodization
  • 3.4.5 Biddle, Bowen and Wallace’s lack of support for EVA
  • 3.4.6 Brealy and Myers: EVA’s bias towards certain projects
  • 3.4.7 Keef and Roush’s comments on the incompatibility of EVA and MVA
  • 3.4.8 Ramezani et al.: EVA’s failure to account for growth opportunities
  • 3.4.9 Paulo: Questionable basis for the calculation of EVA
  • 3.4.10 Ooi and Liow: Some limitations of EVA for property companies
  • 3.4.11 Copeland’s preference for expectations-based management to EVA
  • 3.5 ADJUSTMENTS TO FINANCIAL STATEMENTS
  • 3.6 SPECIFIC ITEMS TO BE ADJUSTED
  • 3.6.1 R&D costs
  • 3.6.2 Marketing costs
  • 3.6.3 Strategic investments
  • 3.6.4 Accounting for acquisitions (goodwill)
  • 3.6.5 Depreciation
  • 3.6.6 Restructuring charges
  • 3.6.7 Taxation
  • 3.6.8 Marketable investments
  • 3.6.9 Off-balance sheet items
  • 3.6.10 Free financing
  • 3.6.11 Intangible capital
  • 3.7 EXAMPLE OF EVA ADJUSTMENTS
  • 3.8 LINK BETWEEN EVA AND MVA
  • 3.8.1 No future growth in EVA
  • 3.8.2 Constant future growth rate in EVA
  • 3.8.3 Abnormal growth initially followed by constant growth
  • 3.9 LINK BETWEEN EVA, MVA AND NPV
  • 3.10 CONCLUSION
• CHAPTER THE RELATIONSHIP BETWEEN LEVERAGE AND EVA AND MVA
  • 4.1 INTRODUCTION
  • 4.2 OPERATIONAL LEVERAGE, FINANCIAL LEVERAGE AND TOTAL LEVERAGE
  • 4.3 LINK BETWEEN EVA, MVA AND LEVERAGE
  • 4.4 SPREADSHEET MODEL
  • 4.5 MODEL ASSUMPTIONS AND INPUTS
  • 4.6 MODEL OUTPUT AND LEVERAGE FACTORS
  • 4.7 RESULTS OF THE ANALYSIS
  • 4.8 CONCLUSION
• CHAPTER CALCULATING EVA COMPONENTS
  • 5.1 INTRODUCTION
  • 5.2 RETURN ON INVESTED CAPITAL (ROIC)
  • 5.3 WEIGHTED AVERAGE COST OF CAPITAL (WACC)
  • 5.3.1 Weighting sources of finance
  • 5.3.2 Optimal capital structure
  • 5.3.2.1 No taxes and no financial distress costs
  • 5.3.2.2 Income taxes and no financial distress costs
  • 5.3.2.3 Taxes and financial distress costs
  • 5.3.2.4 Factors affecting the capital structure decision
  • 5.3.3 Component cost of equity
  • 5.3.3.1 Dividend discount model
  • 5.3.3.2 Capital Asset Pricing Model (CAPM)
  • 5.3.3.3 Arbitrage pricing theory (APT) model
  • 5.3.4 The component cost of preference share capital
  • 5.3.5 The component cost of debt
  • 5.4 THE PERFORMANCE SPREAD
  • 5.5 INVESTED CAPITAL (IC)
  • 5.6 CONCLUSION
• CHAPTER GROWTH IN SALES AND VALUE CREATION IN TERMS OF THE FINANCIAL STRATEGY MATRIX
  • 6.1 INTRODUCTION
  • 6.2 FINANCING REQUIRED FOR SALES GROWTH
  • 6.3 SUSTAINABLE GROWTH RATE (SGR)
  • 6.3.1 SGR with no debt and no dividends
  • 6.3.2 SGR with no debt and some dividend payment
  • 6.3.3 SGR with debt and dividend payments
  • 6.3.4 Factors that determine the SGR
  • 6.3.5 Short formula for SGR
  • 6.4 SALES GROWTH RATES ABOVE AND BELOW THE SGR
  • 6.5 VALUE CREATION AND GROWTH MANAGEMENT
  • 6.5.1 The financial strategy matrix
  • 6.5.1.1 Quadrant A: positive EVA and cumulative cash surpluses
  • 6.5.1.2 Quadrant B: positive EVA and cumulative cash deficits
  • 6.5.1.3 Quadrant C: negative EVA and cumulative cash surpluses
  • 6.5.1.4 Quadrant D: negative EVA and cumulative cash deficits
  • 6.5.2 Example of companies placed in each quadrant
  • 6.6 CONCLUSION
• CHAPTER RESEARCH DESIGN AND PLACEMENT OF COMPANIES ON A FINANCIAL STRATEGY MATRIX
  • 7.1 INTRODUCTION
  • 7.2 DATA COLLECTION METHOD
  • 7.3 MOST IMPORTANT VARIABLES
  • 7.4 RANKING OF COMPANIES
  • 7.5 PLACEMENT OF COMPANIES AND SECTORS IN THE FINANCIAL STRATEGY MATRIX
  • 7.5.1 Summary of the results for three individual companies
  • 7.5.2 Summary of results for the sub-sectors
  • 7.5.3 Summary of results for all companies
  • 7.5.4 Summary of results of the sub-sectors for three periods
  • 7.5.5 Summary of results comparing company to sector to all companies
  • 7.6 CONCLUSION
• CHAPTER STATISTICAL TESTS OF THE VALIDITY OF THE FINANCIAL MATRIX MODEL AND THE MAIN DRIVERS OF EVA
  • 8.1 INTRODUCTION
  • 8.2 THE IMPACT OF SPREADS AND SALES GROWTH MINUS THE SGR PERCENTAGE ON MVA AND CHANGES IN MVA
  • 8.2.1 Regression of spreads and sales growth minus the SGR percentage relative to the “growth differentials”
  • 8.2.2 Regression of spreads and sales growth minus the SGR percentage relative to changes in MVA
  • 8.2.3 Regression of spreads and sales growth minus the SGR percentage relative to changes in MVA divided by IC (at beginning of year)
  • 8.3 REGRESSION OF MVA AND THE MAIN DRIVERS OF EVA
  • 8.3.1 Regression of MVA and EVA and main drivers of EVA
  • 8.3.2 Regression of MVA/ICbeg and spreads and main drivers of EVA
  • 8.3.3 Regression of change in MVA and EVA and main drivers of EVA
  • 8.3.4 Regression over periods longer than one year
  • 8.3.5 Regression using natural logarithms
  • 8.3.6 Regression of median values for the period from 1993 to
  • 8.3.7 Stepwise multiple linear regression
  • 8.4 CONCLUSION
• CHAPTER CONCLUSIONS AND RECOMMENDATIONS
  • 9.1 INTRODUCTION
  • 9.2 APPROACH FOLLOWED
  • 9.3 RESEARCH RESULTS
  • 9.3.1 Theoretical research
  • 9.3.2 Empirical research
  • 9.4 RECOMMENDATIONS AND AREAS FOR FURTHER RESEARCH
  • 9.5 CONCLUSION
  • References
  • Appendix A: List of companies in final database
  • Appendix B: List of sub-sectors
  • Appendix C: Ranking of companies in terms of spreads
  • Appendix D: Ranking of companies in terms of median spreads – 1993 to
  • Appendix E: Ranking of companies in terms of median spreads – 1998 to
  • Appendix F: Ranking of companies in terms of median spreads – 1993 to

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