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Stock returns, performance measures and wealth creation

There are many evidences to believe that stock returns have a positively relationship with corporate governance and performance measures. The study of Gompers et al. (2003) showed the relationship between corporate governance and long-term equity returns, firm value and accounting measures of performance, that measured by ROA, the company’s profit and growth. Their findings expose that well-governed firms have greater equity returns, command higher values and their accounting statements explain a better operating performance compared to their poorly governed counterparts. Ali El Mir and Souad Seboui (2008) affirm that basically Corporate Governance mechanism could explain better the gap created between the shareholders’ value and EVA. Brown and Caylor (2005) state that firms with better governance are fairly more profitable and more highly priced, and so that they pay higher dividend to their shareholders.

A large number of consulting companies therefore produce and aggressively market various accounting-based performance measures. These include Economic Value Added (EVA) by Stern Stewart & Co, Holt's Cash Flow Return on Investment (CFRO), Boston Consulting Group's Total Business Return (TBR), McKinsey's Economic Profit, and LEK/Alcar's Shareholder Value Added (SVA). In order to capture the market, many firms tend to focus on designing and implementing compensation schemes aimed at increasing shareholder wealth. Besides, since the expected goal of the new performance measures is to enhance company's value as long as the shareholders wealth, the correlation of those measures with stock returns has become a controversial topic. Gjesdal (1981) shown that there is no strong statistical correlation between performance measures and stock returns. Gjesdal further added that there is no any performance measure could ever have a greater statistical correlation with stock returns than the return itself. Therefore, if correlation was the only goal, firms should only utilize their stock price for compensation and disregard all other measures. Though as argued above, stock returns can be a noisy and even a misleading measure of managers' added value.

Garvey and Milbourn (2000) examined how better the new wealth performance measures could relate with stock returns than other traditional accounting earnings. By doing this, they used a standard agency model with a principal and one agent in which contracts can be relied on any two accounting based performance measures plus the stock price. Their research included 6,800 observations of firms which appeared in the Stern Stewart Performance 1000 list, over the period of 1986 - 1997. The research showed that there is a simple correlation between stock returns and EVA which is a reasonable reliable guide to its value as an incentive contracting tool. Hence, a firm could logically evaluate the merits of adding a measure like EVA by examining its correlation with stock price of the firm.

Kleiman (1999) consisted a sample of 71 companies that had adopted EVA during 1987 to 1996 to determine EVA as a performance measure that can help those companies create more value for their shareholders and showed greater improvement in operating profit margins than their competitors do. The results of the study proved that EVA companies earned an extra total return of 28.8% over four years compared to the industry competitor. These improvements were attributable more to a decline in assets rather than extensive cost cutting.

EVA vs other measure:

The Economic Value Added (EVA) concept is a registered trademark developed by the US firm Stern Stewart & Co in 1990, and it is claimed by Stewart (1991) as the financial performance measure that perform better than other measures in determining and capturing the true economic profit of an enterprise. It is also known as a measure that most directly related to the creation of shareholder wealth overtime. Besides, EVA is also an accounting based measure of operating performance that takes the difference between a firm’s Net Operating Profit after Tax (NOPAT) and the cost of capital into account. Stewart (1994) further argued that EVA stands well out from the crowd as the single best measure of wealth creation and has researched data of EVA which is almost 50% better than its closest accounting-based competitor (including EPS, ROE and ROI) in explaining changes in shareholder wealth. In the real life, more and more firms are now adopting EVA and applying EVA-based management systems. A lot of research has been done on EVA, comparing it to other traditionally performance measurements and naturally two groups of researchers conflicted each other: proponents and opponents of EVA.

In 1991, Stewart published a book, entitled “The Quest for Value: The EVA – Management Guide”. In this book, the author represents his analysis about the application and values of EVA, as an important indicator for the performance measurement of the company. According to his empirical research, Stewart evaluated the informational database of 613 American companies and he found that for companies with a positive EVA, there was a very strong correlation (97%) between EVA and MVA (market value added) levels, both for the changes in values and the average values used. The relationship for the changes in values was even better than that for the average values. While, for the companies with a negative EVA, the correlation between the EVA and MVA was quite low. Stewart’s (1991) explained that the market value of shares always reveals at least the value of net assets, even if the firm has low or negative returns. The potential for liquidation, recovery, recapitalization or a takeover sets a floor on the market value. On the other hand, this mean the market value does not go down far below the net asset value).

A number of studies have indicated the relationship between EVA and other traditional accounting measures relative to market value added (MVA). Some researchers have argued that economic value added (EVA) is more associated with shareholder value (stock returns) than traditional accounting indicators such as earnings per share (EPS), return on equity (ROE), return on assets (ROA), dividends per share (DPS). Supporters of EVA include O’Byrne (1996), Uyemura et al. (1996), Lehn and Makhija (1996), Milunovich and Tsuei (1996) and Grant (1996).

Lehn and Makhija (1996) used the database of 452 large US companies over a period of 10 years from 1985 to 1994 to determine the value relevance of the traditional performance measures like return on equity (ROE) return on assets (ROA), EVA and MVA. They found out that EVA has correlated positively with stock returns (0.59) and that the correlation is higher than return on sales ROS (0.39), ROE (0.46), and ROA (0.46).

Uyemura et al. (1996) present findings on the relationship between MVA with EVA, ROE, ROA, EPS and Net Income (NI) by estimating the top 100 USA bank holding companies over the period 1986 through 1995. According to their study, EVA is the performance measure that can correlate the best by far with shareholder wealth creation compared to others. EVA explains 40% of the variation in MVA while other accounting measures are much lower: ROA 13%, ROE 10%, Net income 8% and EPS 6%.

In addition, another researcher proved that EVA explains more variations of MVA when compared to NOPAT (O’Byrne, 1996). He collected the information content to observe the explanatory power of NOPAT, FCFs (free cash flows) and capitalized EVA (which is EVA/Cost of Capital), by regressing firm value on earnings and EVA. The study used database of companies in the 1993 Stern Stewart Performance 1000 from 1985 and 1993. By evaluating the changes in the variables, he proved that changes in EVA explained 56% of the changes in market value, and only 33% explained by NOPAT. The author concluded that EVA is the best link to the market value and that EVA could help the company understand the investor expectations that directly affect to current share price of company.

Grant (1996) used the database of 983 firms chosen from the Stern Stewart Performance 1000 in 2 years 1993 – 1994 to evaluate the correlation between EVA/Capital ratio and MVA/Capital ratio. The results explained that the r2 were in turn 83% (1993) and 74% (1994) for the 50 largest wealth creators while were only 3% (1993) and 8% (1994) for the 50 largest wealth destroyers. These results revealed that firms with a positive EVA have a high level of correlation between MVA and EVA compared with low levels of correlation for firms with a negative EVA. He concluded that EVA has an important influence on MVA of a company. And the company’s value responds to variations in both the near-term EVA outlook and movements in the long-term EVA growth rate.

Milunovich and Tsuei (1996) compared the database of a number of companies in the computer industry over the period of 1990-1995 to examine the correlation between MVA and some conventional performance measures. Their result determined a relatively weak correlation between free cash flow (18%) and MVA that can lead to a misleading indicator. And that the growth in EPS is not enough to create value to shareholders, unless such returns are greater than cost of capital. They conclued that EVA could correlate rather better with MVA (42%) than the other measures in evaluating share value.

However, other researchers have disproved these argues by contributing data in support of those traditional accounting performance measures. A number of studies show that traditional measures play a significant role in the firm’s performance measurement in such a long time. Farsio et al. (2000), tested the relationship between EVA and stock returns. Their sample consisted of 397 companies from the S&P 500 and and the Dow Jones Industrial Average (DJIA) from 1994 to 1998. Moreover, their findings showed that EVA is not a good measure of stock performance and represents just one of many measures available, explaining only a part of the inconsistency in stock return.

Dodd and Chen (1996) study the correlation between stock returns and different profitability measures including EVA, RI, ROA, EPS and ROE respectively based on 566 U.S. companies from 1983 to 1992. In their study ROA explained stock returns with the highest ratio of 24,5% of corresponding variability, in contrast with : EVA 20,2%, residual income 19,4% and EPS 5% and ROE approximately 7%. They found that EVA showed a stronger explanatory ability when it was compared with ROA, ROE while with a single measure of residual income, excluding the accounting adjustments required to deal with the accrual accounting distortions, they could not identify any critical incremental information.

Clinton & Chen (1998) also realized that residual cash-flow (RCF) is a better indicator for the firms’ performance measurement than EVA and Kramer & Pushner (1997) found that NOPAT explains more of the variations of MVA than EVA. Moreover, in a recent study, Fernandez (2001) observed a low and sometimes negative correlation between MVA and EVA . He found that there is a higher correlation between the changes in the NOPAT (net operational profit after taxes) and the changes in MVA than the equivalent changes in EVA applying for more than 50% of firms. Also, The study using European data by Peixoto (2002) found that EVA and operating profits has a lower informational content than the net income variable, when the dependent variables is the market value of the companies.

In order to correctly measure shareholders’ value (stock returns), Bacidore et al. (1997) suggested the use of a modified measure as refined EVA (REVA) made using a sample size of 600 companies listed in the Stern Steward Performance data of 1000 firms from 1982 to 1992. The study reveals that the firm should use the market value of capital employed rather than book value of the assets in determining cost of capital. The researchers also compared the relationship between REVA and EVA in term of explanatory power for abnormal returns. They found that REVA had better explanatory power and even more value relevant than EVA.

However, EVA is realized to have a superior informational content when the dependent variable is the MVA. The last finding involve that EVA may perform well as a measure of evaluation of management performance in order to maximize the shareholders’ wealth. In order to examine how well the performance of the firms which have adopted EVA or MVA, Lehn and Makhiija (1997) and Kleiman (1999) agree that EVA has the highest explanatory power of stock returns than any other measures and leads to better operational efficiency (Wallace, 1996; Zimmerman, 1997).

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