Economic Value Added

EVA is a way of measuring a firm’s profitability. EVA is NOPAT minus a charge for all capital invested in the business (Byrne 1). A more intuitive way to think of EVA is as the difference between a firms NOPAT and its total cost of capital (Kramer & Pushner 40). Stern Staurt’s numerical definition of EVA is calculated for any year by multiplying a firm’s economic book value of capital © at the beginning of the year by the spread between its return on capital ® and its cost of capital (K): EVA=(Rt-Kt)*Ct-1 (Kramer &Pushner 41). EVA is a notion of residual income (Ehrbar Xi).
Investors demand a rate of return proportional to the amount of risk incurred. Operating profits determine residual income by plotting them against the required rate of return, a product of both debt and equity. EVA takes into account all capital invested. Peter Druker says in his Harvard Business Review article, “EVA is based on something we have known for a long time: What we call profits, the money left to service equity, is not profit at all. Until a business returns a profit that is greater than its cost of capital, it operates at a loss. Never mind that it pays taxes if it had a genuine profit.
The enterprise still returns less to the economy than it devours in resources…. Until then it does not create wealth but destroys it” (Ehrbar 2). EVA is a measure of wealth creation or destruction after all costs are capitalized. Companies use EVA as a measure of corporate performance, as an incentive system and as a link between shareholder and management/employee goals. Stock price indicates investor’s certainty concerning current and future earnings potential. EVA is a static measure of corporate performance; MVA is a dynamic, forward looking market performance measure.

MVA is a market generated number calculated by subtracting the Capital invested in a firm © from the sum (V) of the total market value of the firm’s equity and book value of debt: MVA=Vt-Ct” (Kramer & Pushner 42). Al Ehrbar describes MVA as exactly equivalent to the stock market’s estimate of the NPV of a company. In 1998 CSX Corperation introduced EVA criteria to the fast growing but low margin CSX Intermodal business, where trains deliver freight to waiting trucks or cargo ships. Large amounts of capital are required to power a mammoth fleet of locomotive, containers and railcars.
Figuring in capital costs, CSX Intermodal lost $70 million in 1988. “The CEO issued an ultimatum, et EVA up or be sold” (Fortune, 39). CSX Intermodal freight volume increased by 25%, yet they dramatically reduced their capital cost by reducing the number of container and trailers by 22%, reducing their locomotive fleet by 33%, and reducing fuel costs. EVA in 1992 was $10 million dollars, and was expected to triple the following year. Wall Street responded: CSX stock price rose from $28 before EVA to a 1993 price of $75.
CSX concluded that investors care more about their net cash return on capital than accounting figures such as EPS, ROE and ROA. Companies that adopt EVA as a performance measure found tie-in compensation plans very useful in aligning management behavior and shareholder needs. Typical plans consist of two familiar parts, a bonus and stock incentives, applied in new ways (Fortune 50). Bonus targets are established by a percent increase in EVA and recalculated each year by averaging the prior year’s goal and the prior year’s result.
Bonus have no limits, but the manager incurs operating risk because some of the bonus is put in a “bank,” say, for five years. If over the next five years management performs poorly, and EVA drops, the “bank” account is depleted. Management incurs the risks and benefits just as owners do. Joel Stern notes that in cases without an EVA incentive plan, employees suffer from a common problem. On average their fixed pay, salaries and pension, are too high, and their variable pay, profit sharing and share options, are to low (Ehbar XIX).
Stern adds that size, not value, drives employees in typical incentive programs because size is positively correlated with increases in fixed pay and closely thereafter, variable pay, even if it destroys shareholder wealth. EVA protects shareholder interests by depositing variable pay into a deferred account that can be lost if value is eroded. EVA, as a corporate measure and a predictive tool, generates mixed reviews in the business and academic worlds. AT&T’s Jim Meen says, “The correlation between MVA and EVA is very high.
So when your driving your business toward EVA, your really driving the correlation with market value” (Kramer & Pushner, 43). Stern Stewart finds an R squared value of 60% based on 20 groupings of firms (Kramer & Pushner, 41). Contenders site statistical evidence to the contrary. BCG-Holt calculates an R square, after removing 21 outliers, of 27%. Dodd and Chen report that EVA accounts for only 20. 2% of the variation in stock returns for a sample of 500 companies, while ROA explains 24. 5% of market returns (Kramer & Pushner, 43).
In their paper “An Empirical Analysis of Economic Value Added as a Proxy for Market Value Added,” Kramer and Pushner test the hypothesis that EVA is highly correlated with MVA. Simple regression analysis is used to test this hypothesis and other market determinants of market value such as NOPAT. First Kramer and Pushner test the relationship between the level of MVA and the level of EVA using the SS1000. In all cases the level of MVA positively relates to both NOPAT and EVA in the same and prior periods. However, in all cases, NOPAT explains more of the total variation in MVA than EVA” (O’Byrne & Stewart 44).
This suggests that the level of NOPAT is not only a better proxy but also a better predictor of corporate performance than the level of EVA. Results for weighted least squares, change in MVA and variations are described graphically in appendix 1. Kramer and Pushner conclude that there is no clear evidence that EVA is the best measure of corporate success in adding value to shareholder investments (Kramer and Pushner, 47). Stephen F. O’Byrne and Stern Stewart and Co. tested a similar hypothesis. Their objective is to show that EVA provides a theoretical and practical measure of operating performance.
O’Byrne and Stewart substantiate the explanatory power of EVA relative to earnings because, unlike previous studies, they recognize two important characteristics: Multiples of positive EVA are significantly higher than multiples of negative EVA, which implies that companies with negative EVAs have values that are higher than what would be expected if the market valued EVA at the same multiple. Multiples of capital tend to decline with company size, which suggest that the market assigns higher multiples to a given level of EVA for smaller companies. Stewart, 117).
O’Byrne and Stewart suggest at first glance that earnings and EVA have about the same level of success in explaining market value. The variance explained ranges around 32%. Taking into account the two characteristics listed above, the explanatory power of their model increases to 42%. Five-year changes in EVA explain 55% of the variation in market value, and ten-year changes in EVA explain 74% of the variation in ten-year changes. The NOPAT model has 15%-20% less explanatory power. The results of O’Byrne and Stewart research appear in appendix 2.
They conclude that because EVA is systematically linked to market value, it proves to be a better predictor of market value than other performance measures. Proponents of EVA also argue that GAAP standards distort true economic reality, produce unreliable corporate standards and serve as an unproductive compensation system. Harvard business school professor Baruch Lev states that; “Overall, the fragile association between accounting data and capital market’s values suggest that usefulness of financial reports is rather limited” (Ehrbar, 161).
Some differences in GAAP and economic reality stem from a bias toward conservative estimates, compounded by SEC requirements driving conservative financial policies. The principal divergence is GAAP’s treatment of equity. The cost of equity should be capitalized. The cost of borrowed capital shows up in a companies interest expense. “But the cost of equity capital, which the shareholders have contributed, typically appears nowhere in any financial statement-and equity is extraordinarily expensive” (Fortune, 38).
Ehrbar contends that GAAP distorts economic reality in areas such as R&D, strategic management, expense recognition, depreciation, restructuring charges, taxes and balance sheet adjustments (64). R&D under GAAP standards require Corporations to immediately expense R&D in the period in which they occur, where as managers and investors see R&D as an investment. GAAP’s treatment of R&D reduces book value by writing down the asset to $0; EVA would capitalize R&D and amortize it over a period of time.
Lastly, GAAP incentives can be ineffective motivators. For example, a retiring officer’s pension plan is linked to earnings. During their last year they might skimp on R&D to boost earnings because their pension plan is tied to performance. Operating earnings often serve as the benchmark for management compensation. Management has the incentive to negotiate a target that is easy to beat. Managers aim low, insuring their bonus. Trade loading is a second example of how GAAP can affect management decisions concerning bonuses and owner interests.
EVA as a measure of financial performance is positively related MVA, but depending on the methodology, the result vary. Kramer and Pushner used simple univariate regressions to compare EVA with other measures explaining EVA. Their results were mixed, NOPAT’s explanatory power in Ordinary Least Squares Regressions outperformed EVA by 9%, however when weighted, EVA’s explanatory power was higher overall and surpassed NOPAT by 6%. Kramer and Pushner note that the market focuses on profits rather than EVA.
Investors rely on earnings estimates that are consistently calculated within the industry. This is not the case for FCF or EVA. Lastly, Kramer and Pushner observe, “investors certainly need to be aware of capital structure, they should already by familiar with the opportunity cost of their investment and may not need to incorporate this into the measure of performance” (Kramer and Pushner 47). Investors may be familiar with the opportunity cost of their investments, although EVA analysis can illuminate problems, such as those created by GAAP accounting, that may not be recognized otherwise.
Stephen O’Byrne and Stern Stewarts calculation required the recognition of two important characteristics that drastically changed the explanatory power of EVA. They note that simple a simple regression model, similar to the one used by Kramer and Pushner, depresses the predictive power of EVA and inflates the predictive power of earnings (Stewart 120). EVA with industry coefficients explains and impressive “56% of the variation in actual market/capital ratios” (Stewart 121). It also produces notable results for changes in EVA and market value over time. Far better results than NOPAT.
My results using a simple linear regression model parallels Kramer and Pushner’s results. EVA in 1997 has the highest R square factor, at 33%, but is far from the results calculated by Stewart. EVA’s R squared increased dramatically since 1992. This is consistent with the economic trend of the 90’s, so the increase may not necessarily reflect an increase in EVA due to internal factors, but an external factor, such as the greatest economic expansion in recorded history. All four factors consistently increase from 1992 to 1997. EVA could be a valuable tool if it is tailored to the company and industry.
This requires an understanding and adjustment for different EVA multiples for positive and negative EVA and different capital multiples for different size companies. This requires complex calculations, a regularly cited problem. However, in this context EVA lives up to its reputation as a great measure of corporate performance. Other functions, such as aligning employee and shareholder goals, the basis for an incentive system and a more realistic picture of economic reality, makes EVA more attractive. I would recommend using Stern Stewart model to calculate EVA.