THE ECONOMIC VALUE ADDED (EVA): AN ANALYSIS OF MARKET REACTION

THE ECONOMIC VALUE ADDED (EVA): AN ANALYSIS OF MARKET REACTION Bartolom´e Dey´a Tortella and Sandro Brusco ABSTRACT The Economic Value Added (EVA®1 ) is a widely adopted technique for the measurement of value creation. Using different event study methodologies we test the market reaction to the introduction of EVA. Additionally, we analyze the long-run evolution before and after EVA adoption of profitability, investment and cash flow variables. We first show that the introduction of EVA does not generate significant abnormal returns, either positive or negative. Next, we show that firms adopt EVA after a long period of bad performance, and performance indicators improve only in the long run. With respect to the firm investment activity variables, the adoption of EVA provides incentives for the managers to increase firm investment activity, and this appears to be linked to higher levels of debt. Finally, we observe that EVA adoption affects positively and significantly cash flow measures.

INTRODUCTION The Economic Value Added is a technique for the measurement of value creation developed by the Stern Stewart and Company consultant group (Stern, 1985; Stern et al., 1995; Stewart, 1991). Basically, the technique provides a way to compute the economic value created by the firm over a period of time, the key

Advances in Accounting Advances in Accounting, Volume 20, 265–290 Copyright © 2003 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 0882-6110/doi:10.1016/S0882-6110(03)20012-2

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variable which should guide managerial decision making (Bromwich & Walker, 1998; Chen & Dodd, 1997). The Economic Value Added of a firm can be defined as the change in the NOPAT (Net Operating Profit after Taxes) minus the change in the Cost of the Capital used to generate this NOPAT (Rappaport, 1986, 1998). The interaction of two important trends can explain the development and diffusion of EVA. First, during the 1980s it became clear that traditional accounting methods often generated very unsatisfactory measures of firm performance (Kaplan, 1983, 1984). Traditional accounting methods are often influenced by the subjective opinion of the accountant (e.g. FIFO vs. LIFO, depreciation methodology), and this appears to be especially important in the analysis of profitability. As a consequence, managers can easily manipulate accounting performance measures (Dyl, 1989; Gomez-Mejia & Balkin, 1992; Hunt, 1985; Jensen & Murphy, 1990; Verrecchia, 1986). Second, during the 1980s American firms experienced tough competition from Japanese firms (Kaplan, 1983) and, at the same time, financial markets internationalized and experienced a huge expansion. This higher exposure to the challenges and opportunities of international competition increased the need for better performance measures. The EVA technique was developed by Stern Stewart and Company in order to satisfy this need, and it has been widely adopted in the 90s. Important firms like Coca Cola, DuPont, Eli Lilly, Polaroid, Pharmacia (former Monsanto), and Whirlpool have been among the adopters. EVA has received much attention both in academic and practitioner publications (see Biddle et al., 1997; Brickley et al., 1997). The measurement of value creation according to EVA has been used as a guide for investment decisions. Furthermore, following standard agency–theoretic considerations, EVA measures have frequently been used in the determination of managerial compensation. Despite all the positive rhetoric surrounding EVA, and all the positive aspects emphasized by Stern Stewart and Co. and others (O’Byrne, 1997; Stewart, 1991, 1994; Tully, 1993, 1994, 1998, 1999; Walbert, 1994; see also www.sternstewart.com), there are several studies questioning its efficiency (Biddle et al., 1997; Chen & Dodd, 2001; Fernandez, 2001; Haspeslagh et al., 2001; Wallace, 1997). For example, studies analyzing EVA information content (Biddle et al., 1997; Chen & Dodd, 2001; Clinton & Chen, 1998), and its correlation with Market Value Added (Fernandez, 2001; Riceman et al., 2000; Walbert, 1994) have obtained mixed results. These studies do not analyze the stock market reaction when firms adopt EVA, or how key firm variables evolve. If EVA improves managerial decisions we should expect a positive market reaction after the adoption. On the other

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Fig. 1. EVA Adoption Timing.

hand, a lack of reaction would imply that the markets do not consider the adoption of EVA a significant improvement. In order to test these hypotheses, we perform an event study methodology (MacKinlay, 1997; McWilliams & Siegel, 1997), using a sample of firms that adopted EVA technique during the 1980s and 1990s. In order to obtain robust results, we use different methodologies and test statistics. We find that on average a firm does not experience significant abnormal reactions, either positive or negative, before or after EVA adoption. The result appears to be in conflict with Stern Stewart and Company communications and other studies (O’Byrne, 1997; Walbert, 1994), observing that companies that apply the EVA technique have subsequently obtained high returns. This is probably due to the fact that the explosion of the EVA technique occurred in the mid-1990s (see Fig. 1), a period characterized by a strong stock market. It has also been claimed that EVA helps to improve firm performance, operating profits, cash flow measures, the cost of capital, and the firm investment activity (Prober, 2000; Stewart, 1991). In order to check these claims we analyze the evolution, before and after EVA adoption, of three sets of firm variables. First, we look at performance indicators, both accounting based (Return on Assets), and market based (Annual Average Monthly Market Return). Second, we analyze variables measuring investment and financing activity (Price to Book ratio, Tobin-q ratio, Debt to Assets ratio, the R&D Expenses to Sales ratio, and the Total Assets item). Finally, since EVA increases the importance of cash flow versus other accounting variables, we also analyze the evolution of two cash flow variables (Cash Flow Margin and the EBITDA Margin), as well as the dividends per share. In this final study, we observe that EVA companies experience significant cash flow increments after the EVA adoption, but no significant increment in dividends per share.

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We find some weak evidence that the improvement in cash flow is stronger when managerial compensation is linked to EVA, although the small size of our sample makes it difficult to provide robust conclusions. The rest of the paper is organized as follows. We first describe the most important characteristics and properties of the EVA technique. Next we discuss the sample and the methodology used in the analysis. In the central part of the paper, we present the results obtained in the event study and in the company analysis profile before and after the EVA adoption. Finally we summarize and discuss the main findings.

THE ECONOMIC VALUE ADDED (EVA) TECHNIQUE EVA can be defined as the firm operating profit after taxes (NOPAT), less the cost of capital (Rappaport, 1986, 1998). EVA proponents assume that any increment in the firm EVA increases the value of the firm (Chen & Dodd, 1997; Ray, 2001). From the operational point of view we have (Biddle et al., 1997; Fernandez, 2001; Rappaport, 1998): EVA = NOPAT − (D + Ebv) × WACC where NOPAT = EBEI + ATIn and the variables are defined as follows:







NOPAT: Net Operating Profits After Taxes. D: Debt Book Value. Ebv: Equity Book Value. WACC: Weighted Average Cost of Capital. EBEI: Earnings Before Extraordinary Items. ATIn: After Taxes Cost of Interest Expense.

The change in EVA is therefore equal to
EVA =
(NOPAT − (D + Ebv) × WACC) The information needed to compute EVA is obtained mainly from accounting data. However, accounting information has to go through some adjustments. Some of these adjustments are to add back deferred tax reserves and bad debt reserves, goodwill amortization, and LIFO reserve increase (Prober, 2000; Rappaport, 1998). These adjustments are made to correct the “distortions” in accounting information, and in order to get a better approximation of firm cash flows (Stewart, 1994). Basically, the EVA methodology uses modifications of GAAP earnings in addition to a capital charge (Wallace, 1997).

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In order to provide incentives for managers to use EVA in making investment decisions, some firms link part of the managerial compensation to some measure of EVA (Haspeslagh et al., 2001; Pettit & Ahmad, 2000; Riceman et al., 2000; Wallace, 1997). The introduction of EVA into managerial compensation usually occurs some time after the adoption of EVA. However, we observe that most firms do not introduce EVA in the compensation system (Ittner & Larcker, 1998).

Controversy in EVA Literature The literature on EVA has produced mixed results. Some papers have found that EVA increases shareholders value (Pettit, 2000; Stern et al., 1995; Stewart, 1991, 1994; see also: www.sternstewart.com). In this line, we can also find studies that observe a positive and significant correlation between EVA and Market Value Added (Walbert, 1994), and shareholder returns (O’Byrne, 1997). The financial press has also devoted some attention to the positive properties of EVA (e.g. Davies, 1996; Tully, 1993, 1994, 1998, 1999; Walbert, 1993). However, as several authors point out (Chen & Dodd, 2001; Ray, 2001), in most of the cases these papers only expose anecdotal stories about the spectacular stock price evolution of EVA firms after the EVA adoption. Other papers have put forward theoretical arguments and empirical evidence questioning the properties of EVA. The main issue is whether EVA is a really useful technique or it is only a management fashion that will fade away with time, as was the case with ABC (Carmona & Gutierrez, 2003) and TQM (Abrahamson & Fairchild, 1999). Brickley et al. (1997) classify EVA as a management fashion. O’Hanlon and Peasnell (1998) discuss the main characteristics of the EVA, but at the same time question its utility, and posit that we will have to wait some time to evaluate if EVA is a really useful technique. Some empirical studies have questioned the efficiency of EVA. Fernandez (2001), using a representative sample of American and European firms based on data provided by Stern Stewart and Company, analyzes the correlation between the MVA (Market Value Added) and the EVA, NOPAT, and WACC. Fernandez observes a low (and sometimes negative) correlation between EVA and MVA, and concludes that NOPAT and WACC present higher levels of correlation with the increase in the MVA.2 The results are in the line with those obtained by Biddle et al. (1997), and Riceman et al. (2000). Motivated by the huge increase in the use of EVA, Biddle et al. (1997) analyze the EVA information content with respect to other accounting-based measures using data provided by Stern Stewart and Company. They find evidence that accounting earnings and operating cash flows are more closely associated to stock

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market returns or firm values than EVA. In the same line, Chen and Dodd (2001) examine the information content (in terms of value-relevance) of operating income, residual income, and EVA. Using different statistical testing methodologies, they find that operating income and residual income present higher information content than the EVA measure. Clinton and Chen (1998) obtain similar results. Wallace (1997) observes that firms adopting residual income compensation plans do not present statistically significant abnormal returns over the market portfolio. At the same time he observes that firms adopting such plans present highly significant increments in their residual income measures, a “you get what you measure and reward” kind of effect. Our paper is in the same line as Wallace’s study, but there are some important differences. First, Wallace analyzes all the firms that adopt residual income-based compensation plans, while we focus only on firms that apply the EVA technique. Second, Wallace uses monthly stock return data, while our event study uses daily stock return data. Finally, the time period of the study is different. Wallace only analyzes firms that adopt residual incomebased compensation plans over the ten-year period ending fiscal year 1994. Our paper analyzes firms that adopt the EVA technique during the period 1982–1999. We supplement the event study with an analysis of the evolution of three sets of variables: performance measures (ROA, and Annual Average Monthly Market Return), investment activity indicators (Price to Book ratio, Tobin-q ratio, Debt to Assets, R&D to Sales, and Total Assets), and cash flow measures (Cash Flow Margin, the EBITDA Margin and Dividends per Share). In order to obtain a long-term perspective we analyze how these three sets of variables evolve during the period starting from five years before and ending five years after the adoption.

SAMPLE AND METHODOLOGY Sample The list of EVA firms and their adoption day is obtained from Stern Stewart and Company marketing brochures. We began with an initial list of 66 events/firms, all the events being from different firms. Starting from this initial firm list, we apply several filters. First, we require that firm information is available in the Compustat Database and CRSP Database. Second, we want to have a sufficiently long estimation period for the market model. The estimation period is the time between (−330, −30), where moment 0 is the adoption day. We establish a minimum of 200 daily returns for the estimation period. As usual, any non-trading date is converted to the next trading day.

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Fig. 2. Sector Distribution.

Applying both restrictions we end up with a sample of 61 firms for the company analysis and 55 firms/events for the event study (the list of firms is in the Appendix). Following the Standard Industrial Classification code, we analyze the sample sector distribution (see Figs 2 and 3). The sample includes firms from many sectors, with the manufacturing sector being the most important one. Inside manufacturing, we can observe that the electronic and computer sector presents the highest level of adoption.

Fig. 3. Manufacturing Sector Distribution.

Event Study Methodology As pointed out before, the estimation period is set between (−330, −30) (where 0 is the EVA adoption day). The event window is set between day −30 to day +100. We compute the CAAR from a set of windows embedded in this event window. Specifically, we analyze the windows (−30, 0), (−20, 0), (−10, 0), (0, +20), (0, +30), (0, +50), (0, +60), (0, +90), (0, +100). According to the traditional market model, we can represent the stock return of the firm j in day t as: r j,t = ␣ + ␤ · r m,t + ␧j,t

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Thus, we can define the abnormal return of j-firm (ARj,t ), as: ARj,t = r j,t − ␣ˆ − ␤ˆ · r m,t ˆ are the parameters estimated during the estimation period. The where (␣, ˆ ␤) Average Abnormal Return of period t (AARt ), is defined as:
J j=1 ARj,t AARt = J Finally, the Cumulative Average Abnormal Return in the window (T1 , T2 ) (CAART 1 ,T 2 ), is:
J
T 2 j=1 t=T 1 ARj,t CAART 1 ,T 2 = J In order to obtain robust results, we apply different methodologies and test statistics (MacKinlay, 1997; McWilliams & Siegel, 1997). We develop the traditional Event Study Methodology and compute the traditional t-statistics for each daily return AARt , and for each CAART 1 ,T 2 . Additionally, we also apply the Standardized Abnormal Return method, and compute the z-statistic proposed by Patell (1976). This method is based on the concept of standardized abnormal return that can be defined as: SARj,t =

ARj,t S ARj,t

where (S ARj,t ) is the maximum likelihood estimate of the variance of ARj,t . Under a set of conditions (see Patell, 1976), the test statistic for the hypothesis (CAART 1 ,T 2 = 0), follows (under the null hypothesis) a standard normal distribution. The traditional event study t-statistic uses the standard error from the time series standard deviation of estimation period. Following Pilotte (1992), we apply the cross-sectional method, consisting in computing the t-statistic using for each event date the cross-sectional (across securities) standard deviation. Finally, we also compute the standardized cross-sectional test proposed by Mikkelson and Partch (1988). This test corrects the test of Patell (1976), and adjusts the CAAR for the possible serial correlation of the abnormal returns of each stock. As Cowan (1993) points out, serial correlation may be an important factor for long windows (e.g. windows of 100 days’ length). These statistics also follows a standard normal distribution. The daily returns needed for the event study are obtained from CRSP Database. The value weighted and the equally weighted index used in the event study are the NYSE-AMEX-Nasdaq indices provided by CRSP Database using all stocks.

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We also performed the study using the SandP500 and Composite market indexes, obtaining identical results. Company Analysis Methodology EVA proponents claim that EVA helps to improve firm performance, operating profits, cash flow measures, the cost of capital and the firm investment activity (Prober, 2000; Stewart, 1991). In order to test these claims, and also in order to analyze how the EVA company profiles evolve, we use annual data to capture the long-term firm evolution around the adoption date. The data are obtained from the Compustat Database. We analyze the cross time evolution of three sets of key firm variables in the period from five years before to five years after the adoption. First, we analyze profitability measures such as the Return on Assets, and the Annual Average Monthly Market Return. ROA is obtained directly from Compustat Database, and is the standard measure used in the literature to capture the firm’s performance evolution. Additionally, in order to analyze both accounting-based and market-based performance measures, we also consider the Annual Average Monthly Market Return. The second set of measures analyzed in this section is related to the firm investment activity. We analyze the Price to Book ratio and the Tobin-q ratio (using the approximation proposed by Chung & Pruitt, 1994). Both measures are used in the literature as proxies for the firm investment opportunities set (Fenn & Liang, 2001; Martin, 1996; Wright et al., 1996). We also use the Debt to Assets ratio, to analyze how the investment activity is financed. We also broke down the ratio Debt to Assets into Long and Short Term Debt to Assets ratios. The R&D to Sales ratio is frequently used in the literature to measure directly the firm investment activity. In order to analyze the impact of EVA on company size before and after the adoption, we also analyze the time evolution of the Total Assets item. Lastly, we take a look at cash flow measures. We analyze the Cash Flow Margin (Income Before Extraordinary Items plus Depreciation and Amortization, scaled by Sales) and the EBITDA Margin (Earnings Before Interest, Taxes and Depreciation, scaled by Sales), since some studies use this variable as a proxy for the free cash flow (Fenn & Liang, 2001). We also consider the evolution of the Dividends per Share, a measure of how much cash is given back to shareholders.

RESULTS Abnormal Market Returns Around the EVA Adoption As discussed before, in order to obtain robust conclusions we apply different event studies methodologies and test statistics. In Table 1, we develop the traditional

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Table 1. Daily Abnormal Returns. Day

AAR (%)

Pos:Neg

t-Stat (1)

z-Stat (2)

CS-t-Stat (3)

Gen. Sign z (4)

−30 −29 −28 −27 −26 −25 −24 −23 −22 −21 −20 −19 −18 −17 −16 −15 −14 −13 −12 −11 −10 −9 −8 −7 −6 −5 −4 −3 −2 −1 +0 +1 +2 +3 +4 +5 +6 +7 +8 +9 +10 +11 +12

−0.04 0.02 0.31 −0.26 0.29 −0.01 0.36 −0.15 0.11 0.31 0.27 0.08 −0.16 −0.24 0.10 0.29 −0.08 0.19 −0.28 0.24 0.20 −0.53 0.26 0.07 0.18 0.32 0.46 −0.21 0.36 −0.26 0.10 0.27 −0.47 0.21 0.01 0.07 0.52 −0.17 −0.39 0.11 0.29 −0.03 0.13

24:31 25:30 29:26 22:33 27:28 22:33 28:27 19:36 30:25 29:26 30:25 22:33 19:36 20:35 30:25 28:27 26:29 29:26 25:30 33:22 32:23 23:32 34:21 30:25 32:23 31:24 35:20 23:32 31:24 19:36 27:28 27:28 22:33 27:28 27:28 26:29 30:25 21:34 18:37 28:27 28:27 23:32 24:31

−0.160 0.080 1.270 −1.060 1.190 −0.040 1.480 −0.620 0.450 1.270 1.110 0.330 −0.660 −0.970 0.410 1.190 −0.330 0.780 −1.150 0.990 0.820 −2.180∗ 1.070 0.290 0.740 1.310 1.890$ −0.860 1.480 −1.070 0.400 1.100 −1.910$ 0.860 0.040 0.290 2.110∗ −0.680 −1.600 0.450 1.200 −0.130 0.540

−0.139 −0.089 0.735 −0.611 1.201 −0.317 1.519 −0.352 −0.230 0.887 1.667$ −0.332 −0.993 −1.221 0.239 0.950 −0.243 1.145 −0.942 1.259 1.331 −1.646$ 1.539 −0.022 1.025 1.476 1.976∗ −0.704 1.069 −1.220 0.492 0.624 −1.378 0.391 0.038 0.315 2.130∗ −0.685 −1.696$ 0.452 1.121 −0.327 0.684

−0.191 0.069 1.401 −1.318 1.162 −0.037 1.835$ −0.680 0.412 1.287 1.241 0.267 −0.803 −0.870 0.435 0.919 −0.431 0.795 −0.969 0.883 0.781 −1.808$ 1.113 0.303 0.718 1.475 2.026∗ −0.994 1.391 −1.124 0.441 1.109 −1.703$ 0.597 0.054 0.318 2.609∗∗ −0.972 −2.344∗ 0.546 1.105 −0.105 0.440

−0.540 −0.270 0.810 −1.081 0.270 −1.081 0.540 −1.891$ 1.080 0.810 1.080 −1.081 −1.891$ −1.621 1.080 0.540 0.000 0.810 −0.270 1.890$ 1.620 −0.811 2.160∗ 1.080 1.620 1.350 2.431∗ −0.811 1.350 −1.891$ 0.270 0.270 −1.081 0.270 0.270 0.000 1.080 −1.351 −2.161∗ 0.540 0.540 −0.811 −0.540

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Table 1. (Continued ) Day

AAR (%)

Pos:Neg

t-Stat (1)

z-Stat (2)

CS-t-Stat (3)

Gen. Sign z (4)

+13 +14 +15 +16 +17 +18 +19 +20 +21 +22 +23 +24 +25 +26 +27 +28 +29 +30

−0.33 −0.35 −0.23 −0.07 0.01 0.04 −0.09 −0.09 0.67 0.05 0.15 −0.16 −0.15 −0.07 −0.12 0.05 0.09 0.26

17:38 24:31 29:26 24:31 27:28 25:30 25:30 24:31 30:25 32:23 24:31 25:30 22:33 31:24 23:32 28:27 31:24 36:19

−1.350 −1.420 −0.920 −0.280 0.060 0.170 −0.390 −0.380 2.760∗∗ 0.210 0.620 −0.660 −0.620 −0.280 −0.510 0.200 0.370 1.070

−1.563 −1.417 −0.515 −0.582 0.053 0.214 −0.436 −0.544 2.472∗ 0.355 0.008 −0.393 −0.905 0.112 −0.733 −0.131 0.737 1.419

−1.370 −1.232 −1.009 −0.288 0.067 0.221 −0.453 −0.338 2.349∗ 0.207 0.494 −0.613 −0.623 −0.373 −0.361 0.235 0.485 1.079

−2.431∗ −0.540 0.810 −0.540 0.270 −0.270 −0.270 −0.540 1.080 1.620 −0.540 −0.270 −1.081 1.350 −0.811 0.540 1.350 2.701∗∗

Note: Equally Weighted Index. $ significant at 10%, ∗ significant at 5%, and ∗∗ significant at 1%.

event study methodology, computing the t-statistics (column 1), both for the daily and cumulative average abnormal returns (AAR and CAAR, respectively). Additionally, we develop the Standardized Residual Methodology proposed by Patell (1976). This methodology uses the z-statistic instead of the traditional t-statistic to test the null hypothesis that daily and cumulative average abnormal returns are equal to zero (column 2). Following Pilotte (1992), we compute the t-statistic using the cross-sectional methodology (CS-t-statistic), using the cross-sectional standard deviation (column 3). We also compute the Generalized Sign z-statistic for the proportion of positive and negative abnormal returns (column 4). We consider both an Equally Weighted (EW) index and a Value Weighted (VW) index for market return. The results are presented in Tables 1 and 3 (using EW index), and 2 and 4 (using VW index), for daily and cumulative abnormal returns, respectively. According to Tables 1 and 2 there is no significant market reaction before or after EVA adoption. This result is obtained looking at the traditional t-statistic (column 1), the z-statistic (column 2), the cross-sectional t-statistic (column 3), and the Generalized Sign test (column 4). Before the adoption we observe a

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Table 2. Daily Abnormal Returns. Day

AAR (%)

Pos:Neg

t-Stat (1)

z-Stat (2)

CS-t-Stat (3)

Gen. Sign z (4)

−30 −29 −28 −27 −26 −25 −24 −23 −22 −21 −20 −19 −18 −17 −16 −15 −14 −13 −12 −11 −10 −9 −8 −7 −6 −5 −4 −3 −2 −1 +0 +1 +2 +3 +4 +5 +6 +7 +8 +9 +10 +11 +12

−0.03 −0.04 0.25 −0.35 0.34 0.01 0.28 −0.07 0.14 0.36 0.27 0.05 −0.15 −0.18 0.16 0.26 −0.14 0.10 −0.37 0.23 0.13 −0.57 0.21 0.11 0.24 0.32 0.48 −0.28 0.32 0.09 0.02 0.27 −0.47 0.28 −0.02 0.00 0.49 −0.12 −0.36 0.06 0.25 −0.06 0.13

24:31 24:31 27:28 20:35 31:24 20:35 30:25 21:34 29:26 33:22 21:24 20:35 20:35 22:33 32:23 27:28 26:29 28:27 23:32 32:23 31:24 23:32 34:21 29:26 31:24 31:24 36:19 24:31 33:22 24:31 24:31 26:29 23:32 29:26 28:27 27:28 34:21 20:35 18:37 28:27 27:28 22:33 27:28

−0.120 −0.170 1.040 −1.450 1.410 0.040 1.160 −0.290 0.580 1.500 1.120 0.200 −0.620 −0.750 0.660 1.080 −0.580 0.410 −1.540 0.960 0.540 −2.370∗ 0.870 0.460 0.990 1.330 1.990∗ −1.160 1.330 0.370 0.100 1.100 −1.940$ 1.170 −0.070 0.020 1.990∗ −0.480 −1.500 0.250 1.030 −0.230 0.520

−0.049 −0.229 0.336 −0.994 1.460 −0.265 1.112 0.025 0.009 1.185 1.758$ −0.451 −0.913 −0.938 0.479 0.763 −0.452 0.807 −1.289 1.200 0.971 −1.819$ 1.359 0.022 1.270 1.547 2.009∗ −1.008 0.851 0.182 0.085 0.544 −1.513 0.700 −0.128 0.043 2.066∗ −0.586 −1.667$ 0.260 0.958 −0.430 0.659

−0.140 −0.185 1.106 −1.769$ 1.327 0.031 1.427 −0.322 0.547 1.518 1.262 0.164 −0.713 −0.662 0.676 0.803 −0.718 0.431 −1.264 0.819 0.552 −1.975∗ 0.889 0.472 0.956 1.423 2.113∗ −1.417 1.300 0.380 0.118 1.107 −1.721$ 0.829 −0.077 0.021 2.541∗ −0.726 −2.255∗ 0.317 0.991 −0.194 0.423

−0.550 −0.550 0.260 −1.630 1.340 −1.630 1.070 −1.360 0.800 1.881$ 1.340 −1.630 −1.630 −1.090 1.611 0.260 −0.010 0.530 −0.820 1.611 1.340 −0.820 2.151∗ 0.800 1.340 1.340 2.691∗∗ −0.550 1.881$ −0.550 −0.550 −0.010 −0.820 0.800 0.530 0.260 2.151∗ −1.630 −2.170∗ 0.530 0.260 −1.090 0.260

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Table 2. (Continued ) Day

AAR (%)

Pos:Neg

t-Stat (1)

z-Stat (2)

CS-t-Stat (3)

Gen. Sign z (4)

+13 +14 +15 +16 +17 +18 +19 +20 +21 +22 +23 +24 +25 +26 +27 +28 +29 +30

−0.31 −0.36 −0.27 −0.13 0.09 −0.03 −0.25 −0.06 0.69 0.04 0.13 −0.19 −0.17 −0.05 −0.14 0.08 0.02 0.15

15:40 24:31 23:32 22:33 26:29 25:30 25:30 27:28 32:23 32:23 24:31 26:29 20:35 28:27 23:32 27:28 26:29 33:22

−1.270 −1.480 −1.100 −0.520 0.360 −0.110 −1.040 −0.240 2.820∗∗ 0.180 0.550 −0.790 −0.710 −0.210 −0.580 0.330 0.080 0.620

−1.468 −1.483 −0.661 −0.820 0.340 0.067 −1.185 −0.405 2.561∗ 0.243 −0.118 −0.486 −1.008 0.121 −0.812 −0.035 0.388 1.005

−1.245 −1.317 −1.208 −0.514 0.420 −0.138 −1.181 −0.229 2.369∗ 0.176 0.430 −0.746 −0.708 −0.290 −0.422 0.373 0.107 0.644

−2.981∗∗ −0.550 −0.820 −1.090 −0.010 −0.280 −0.280 0.260 1.611 1.611 −0.550 −0.010 −1.630 0.530 −0.820 0.260 −0.010 1.881$

Note: Value Weighted Index. $ significant at 10%, ∗ significant at 5%, and ∗∗ significant at 1%.

positive significant abnormal return for the day −4, and a negative significant abnormal return for the day −9. The rest of abnormal returns before adoption are not significant, but we observe 19 positive daily abnormal returns and 11 negative abnormal returns. For the period after the EVA adoption we observe positive significant abnormal returns for the day 6 and 21. Days 2 and 8 present significant negative abnormal returns. The rest of daily abnormal returns after the adoption are not significant, with a fair number of them being negative (14 daily negative abnormal returns and 16 positive). The results are consistent for both the Equally Weighted Index (Table 1), and the Value Weighted Index (Table 2). We also compute the CAAR of a set of windows before and after the EVA adoption in Table 3 (using EW index), and Table 4 (using VW index). In the windows beginning right after the adoption day, we do not observe any significant positive CAAR. Some negative CAAR, although not significant, is also observed. Looking at the windows ending at the adoption date, we can observe some significant positive CAAR (see columns 3 and 4), although the effects appear to be weak.

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Table 3. Cumulative Abnormal Returns. Window (−30, 0) (−20, 0) (−10, 0) (0, +20) (0, +30) (0, +50) (0, +60) (0, +90) (0, +100)

CAAR (%) 2.29 1.36 0.95 −0.44 0.31 2.08 1.81 2.20 1.39

Pos:Neg 30:25 36:19 34:21 23:32 29:26 29:26 26:29 30:25 32:23

t-Stat (1) 1.69$ 1.22 1.18 −0.40 0.23 1.20 0.95 0.95 0.57

z-Stat (2) 1.69$ 1.49 1.60 −0.58 0.05 1.30 1.18 1.20 0.92

CS-t-Stat (3) 1.74$ 1.36 1.44 −0.42 0.26 1.29 1.14 0.99 0.58

Gen. Sign z (4) 1.08 2.70∗∗ 2.16∗ −0.81 0.81 0.81 0.00 1.08 1.62

Note: Equally Weighted Index. $ significant at 10%, ∗ significant at 5%, and ∗∗ significant at 1%.

As noted above, we use the Mikkelson and Partch (1988) methodology to correct cumulative abnormal returns for stock serial dependence. This only affects the CAAR z-statistics. The results are shown in Table 5 (using EW index), and Table 6 (using VW index). When the computation of CAAR is adjusted for serial dependence, all the values turn out not to be significantly different from zero. Thus, we conclude that the weak positive effects found in the previous analysis do not appear to be very robust. To sum up, from the different event studies, we find that there is no significant market reaction to EVA adoption. There are some weakly positive abnormal returns before adoption, but these results are not robust. The same results are obtained using the S&P500 or Composite as market indexes. Table 4. Cumulative Abnormal Returns. Window

CAAR (%)

Pos:Neg

t-Stat (1)

z-Stat (2)

CS-t-Stat (3)

Gen. Sign z (4)

(−30, 0) (−20, 0) (−10, 0) (0, +20) (0, +30) (0, +50) (0, +60) (0, +90) (0, +100)

2.18 1.30 1.06 −0.83 −0.28 1.37 0.66 0.48 −0.99

29:26 33:22 36:19 24:31 22:33 28:27 25:30 27:28 29:26

1.61 1.17 1.32 −0.75 −0.21 0.79 0.35 0.21 −0.41

1.62 1.40 1.64$ −1.01 −0.50 0.76 0.42 0.32 −0.20

1.79$ 1.33 1.77$ −0.78 −0.23 0.82 0.39 0.21 −0.41

0.80 1.88$ 2.69∗∗ −0.55 −1.09 0.53 −0.28 0.26 0.80

Note: Value Weighted Index. $ significant at 10%, and ∗∗ significant at 1%.

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Table 5. Cumulative Abnormal Returns Corrected by Serial Dependence. Window

CAAR-EW (%)

CAAR-PW (%)

Median CAR (%)

SD-z

Pos:Neg

Gen. Sign z

(−30, 0) (−20, 0) (−10, 0) (0, +20) (0, +30) (0, +50) (0, +60) (0, +90) (0, +100)

2.29 1.36 0.95 −0.44 0.31 2.08 1.81 2.20 1.39

2.02 1.46 1.14 −0.56 0.06 1.99 1.98 2.46 1.99

0.44 1.78 1.80 −0.56 0.86 0.73 −0.84 2.74 2.96

1.62 1.45 1.57 −0.57 0.04 1.19 1.06 1.03 0.78

30:25 36:19 34:21 23:32 29:26 29:26 26:29 30:25 32:23

1.08 2.70∗∗ 2.16∗ −0.81 0.81 0.81 0.00 1.08 1.62

Note: Equally Weighted Index. ∗ significant at 5%, and ∗∗ significant at 1%.

Table 6. Cumulative Abnormal Return corrected by Serial Dependence. Window

CAAR-VW (%)

CAAR-PW (%)

Median CAR (%)

SD-z

Pos:Neg

Gen. Sign z

(−30, 0) (−20, 0) (−10, 0) (0, +20) (0, +30) (0, +50) (0, +60) (0, +90) (0, +100)

2.18 1.30 1.06 −0.83 −0.28 1.37 0.66 0.48 −0.99

1.90 1.35 1.15 −0.98 −0.58 1.15 0.69 0.64 −0.42

1.07 1.40 1.22 −0.85 −1.10 0.34 −1.94 −0.21 0.85

1.55 1.36 1.62 −1.00 −0.50 0.70 0.38 0.27 −0.19

29:26 33:22 36:19 24:31 22:33 28:27 25:30 27:28 29:26

0.80 1.88$ 2.69∗∗ −0.55 −1.09 0.53 −0.28 0.26 0.80

Note: Value Weighted Index. $ significant at 10%, and ∗∗ significant at 1%.

COMPANY PROFILE BEFORE AND AFTER THE EVA ADOPTION Profitability Measures We analyze the evolution of two measures, one accounting based (Return on Assets), and the other one market based (Annual Average Monthly Market Return) (Table 7).

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Table 7. Profitability Measures. Year −5 Year −4 Year −3 Year −2 Year −1 Year 0 Year 1 Year 2 Year 3 Year 4 Year 5 ROA Average Median Std. Dev.

6.439 5.699 6.003

5.266 4.930 3.591

4.742 4.944 3.933

3.913 3.769 4.045

4.419 3.744 5.004

5.339 5.832 4.522

5.544 5.540 4.250

5.172 3.976 6.105

4.139 4.464 7.946 3.683 4.612 7.820 5.686 10.292 5.513

Annual average monthly return Average 2.018 1.074 Median 1.808 1.055 Std. Dev. 2.014 2.911

0.704 0.812 2.581

0.878 1.192 2.248

1.848 1.398 2.408

1.837 1.786 3.247

1.716 1.843 2.737

1.064 0.598 4.072

0.927 1.201 3.459

0.540 1.853 0.656 2.242 3.659 2.601

Both measures appear to be declining up to year −2, when they start improving until year zero. However, following the introduction no clear positive improving trend emerges. Only in year 5 after the adoption does the ROA appear to be clearly better than before the adoption. This is also true for the median monthly return, but not for the average. The good results of year 5 may not be representative. Many firms in our sample adopt EVA in 1994 and 1995, so that the fifth year coincides with a very strong market.

Investment Activity Measures We now check whether the EVA introduction affects the company investment and financing activity. We analyze five measures: the Price to Book ratio, Tobin-q ratio, Debt to Assets, the R&D to Sales ratio, and the firm Total Assets. The Price to Book and the Tobin-q ratios are used in the literature as proxies of the firm investment opportunities set. Debt to assets ratio measures the firm leverage, and the R&D to sales ratio measures directly the R&D firm activity (Table 8). The Price to Book and Tobin-q ratios show significant increases after the adoption of the EVA technique. This reveals that firms increase their investment activity, investing in new projects that expand the firm investment opportunity set. This fact is corroborated by the evolution of the R&D to sales ratio analyzed in the study. However, if we analyze the evolution of the Total Assets item, we observe that this increment in the firm investment activity is not followed by an increase in the firm size. Additionally, we can observe an increment in the Debt to Assets ratio. This can be interpreted in the sense that this increment in the firm investment activity after EVA adoption is usually financed through higher levels of debt. When we broke down debt into short-term and long-term, a slightly more pronounced role for long-term debt seems to emerge, although the patterns are not very clear.

Year −4

Year −3

Year −2

Year −1

Price to book Average Median Std. Dev.

2.226 1.784 1.150

2.233 1.930 1.427

2.100 1.916 1.100

2.155 1.870 1.245

2.210 2.308 1.891

2.606 2.266 2.600

2.900 2.325 3.310

2.862 2.385 3.130

2.952 2.265 7.328

3.982 2.948 4.138

3.971 2.797 3.597

Tobin-q ratio Average Median Std. Dev.

1.035 0.832 0.607

0.982 0.911 0.525

0.903 0.831 0.521

0.939 0.858 0.580

1.022 0.931 0.628

1.227 0.945 0.828

1.329 0.953 1.022

1.315 1.015 1.172

1.538 0.986 1.726

1.495 0.895 1.509

1.661 1.080 1.501

Debt to assets Average Median Std. Dev.

24.574 27.167 13.196

25.106 25.970 13.545

25.817 27.746 15.036

24.269 24.009 15.031

26.371 28.031 13.978

27.308 27.431 14.607

27.009 28.820 14.142

27.421 28.351 13.637

27.035 28.747 13.676

28.037 31.575 12.942

26.807 28.737 11.969

Long-term debt to assets Average 20.690 Median 22.154 Std. Dev. 13.923

20.228 20.859 14.002

20.526 21.349 15.118

19.606 19.859 15.050

20.599 20.667 13.231

22.230 21.905 14.613

22.152 21.987 13.731

22.286 22.186 12.825

21.802 23.272 13.339

21.167 23.451 11.349

20.840 20.782 11.383

Short term debt to assets Average 4.596 Median 3.306 Std. Dev. 4.559

5.894 3.921 5.328

5.778 4.746 5.523

5.193 4.685 5.187

5.772 3.802 7.537

5.078 3.185 5.360

4.857 3.677 5.140

5.135 3.911 5.279

5.233 3.020 5.052

6.870 4.680 6.161

5.967 3.502 5.950

R&D to sales Average Median Std. Dev.

2.962 1.739 3.319

3.029 1.329 3.490

3.423 1.356 4.238

3.202 1.385 4.000

3.216 1.718 4.041

3.186 1.597 3.989

4.132 1.469 5.947

5.634 1.759 11.405

4.100 1.723 5.343

4.215 1.720 5.492

2592.3 1507.1 3182.5

2739.0 1787.9 3208.8

2914.0 1974.9 3315.4

3314.8 1992.8 4013.8

3685.2 2143.8 4529.4

4244.9 2164.2 5487.6

4374.1 2197.7 5320.4

3891.6 1930.0 4554.9

3524.7 1800.0 3999.5

3.059 1.963 3.395

Total assets (in millions of $) Average 2329.6 Median 1252.0 Std. Dev. 2807.1

Year 0

Year 1

Year 2

Year 3

Year 4

Year 5

4057.8 1881.6 4828.5

281

Year −5

The Economic Value Added (EVA)

Table 8. Investment Activity Measures.

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Table 9. Cash Flow Measures. Year −5 Year −4 Year −3 Year −2 Year −1 Year 0 Year 1 Year 2 Year 3 Year 4 Year 5 Cash flow margin Average 12.166 Median 9.154 Std. Dev. 10.081

10.982 8.739 7.892

10.709 8.020 7.440

9.670 8.625 6.444

10.464 9.544 7.167

Dividends per share Average 1.502 Median 0.460 Std. Dev. 2.957

1.571 0.501 3.194

1.643 0.492 3.482

2.031 0.460 4.307

1.825 0.430 4.114

EBIDTA margin Average 16.710 Median 15.024 Std. Dev. 11.336

16.318 13.787 11.307

16.224 12.727 11.387

16.761 14.484 11.543

17.730 15.281 11.990

12.919 11.667 11.806 8.503 11.159 14.453 9.948 10.420 10.779 9.010 11.455 13.280 14.112 9.182 11.783 14.547 9.434 8.093 1.818 0.345 3.913

1.614 0.354 3.555

1.660 0.337 3.675

2.267 0.320 5.849

1.434 0.306 3.088

1.935 0.511 3.592

17.895 17.926 17.208 15.308 19.054 21.173 16.887 16.499 16.552 15.852 17.926 18.299 9.755 9.351 10.092 20.824 9.827 12.002

Cash-Flow Measures Given the emphasis put by EVA on cash flow, we thought it interesting to analyze the evolution of the Cash Flow Margin, the EBITDA Margin and Dividends per Share (Table 9). We observe that the EVA adoption has generated significant important positive effects over Cash Flow measures. This stands in contrast with the lack of significant improvement in other performance measures, such as ROA. Dividends per Share also do not appear to be positively influenced by EVA. The median dividend is lower in all years following the adoption (except the fifth) than in the years preceding the adoption. In order to provide incentives for managers, some firms that adopt EVA link part of managerial compensation to the firm economic value added. This in turn provides incentives for managers to improve cash flow measures. This behavior is very similar to the one observed with respect to accounting measures. Various empirical studies have documented that when managerial compensation is tied to firm accounting benefits, the main objective for managers becomes to increase such measures, sometimes manipulating them (Dyl, 1989; Gomez-Mejia & Balkin, 1992; Hunt, 1985; Jensen & Murphy, 1990; Verrecchia, 1986). In order to test this hypothesis we analyze how the cash flow measures evolve around the year in which EVA is introduced in the executive compensation system. In order to determine when firms introduce the EVA methodology in their executive compensation system we use the Edgar Online SEC Documents Database. Through this database we analyze the 10-K files and proxy statements of each sample firm, to determine when (if at all) they introduce EVA measures in the executive compensation system. We observe that not all the sample firms

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Table 10. Firms Introducing EVA in the Compensation System. Year −5 Year −4 Year −3 Year −2 Year −1 Year 0 Year 1 Year 2 Year 3 Year 4 Year 5 Cash flow margin Average 10.282 Median 8.322 Std. Dev. 7.651

9.789 7.543 6.728

8.620 7.027 5.644

9.958 9.117 6.299

10.617 8.631 8.904

10.604 10.527 9.014 9.499 6.650 7.025

EBIDTA margin Average 14.360 Median 13.435 Std. Dev. 8.278

14.301 11.498 8.485

15.062 11.627 9.891

16.331 13.778 10.210

16.730 13.674 10.286

17.478 17.148 17.661 18.806 21.209 16.966 15.073 15.680 15.986 17.194 17.835 14.335 9.969 9.873 10.690 10.222 12.248 9.548

8.792 11.135 13.193 10.556 7.327 11.720 10.596 8.812 7.616 10.437 7.376 8.096

introduce the EVA in the compensation system; when they do, it usually happens one or two years after the EVA adoption. From the original sample of 61 firms, 45 introduce EVA in the compensation system, while the remaining 16 firms only introduce the EVA as a measure for guiding investment. Thus, we divide the initial sample in two subgroups, the first with the 45 firms that introduce EVA in managerial compensation, and another one with the 16 firms that do not. We compare the evolution of cash flow measures in the two groups. In the first group, year 0 denotes the moment when the firm introduces the EVA system in the compensation system. In the second group, we fix year 0 at the year in which the firm adopts EVA (Tables 10 and 11). The evolution of the EBITDA Margin in the first group (firms that introduce EVA in the compensation system) appears to be better than the one in the second group. All the values of the median for the EBITDA margin in years from 0 to 5 are superior to the values obtained before adoption, and the same is true for the average. This does not happen for firms that do not introduce EVA in the managerial compensation. On the other hand, no clear difference in pattern between the two groups emerges when we analyze the Cash Flow Margin. We must also point out that the small size of the two sub-samples does not let us provide robust conclusions. Table 11. Firms not Introducing EVA in the Compensation System. Year −5 Year −4 Year −3 Year −2 Year −1 Year 0 Year 1 Year 2 Year 3 Year 4 Year 5 Cash Flow margin Average 18.532 Median 13.784 Std. Dev. 13.617

15.420 10.157 11.026

14.322 8.572 8.991

13.040 10.887 7.894

13.679 11.671 8.421

18.830 15.546 17.083 7.810 15.050 16.479 13.842 11.632 12.745 13.408 13.031 14.566 24.003 13.931 19.905 27.188 7.926 7.772

EBIDTA margin Average 23.946 Median 18.296 Std. Dev. 16.467

22.994 15.922 17.585

22.554 15.978 16.588

22.514 17.714 14.936

24.082 19.661 15.118

20.913 19.605 19.398 10.724 21.663 21.693 18.933 18.350 19.069 19.449 19.256 19.363 8.178 7.476 10.429 38.761 9.461 12.025

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DISCUSSION AND CONCLUSIONS Discussion Our event study shows that EVA adoption does not generate significant market abnormal returns, neither before nor after the adoption. We observe significant positive abnormal returns only in days −4, 6 and 21, and negative in days −9, 2 and 8. In the case of CAAR, we observe that windows beginning right after the adoption day do not present significant CAAR. We observe some significant positive CAAR for windows ending at the adoption date. It is reasonable to expect that, if there is a positive effect, this should occur before the adoption date. In fact, it is likely that the information about EVA adoption become public in the market sometimes before the formal adoption, so any effect of this information should be incorporated into prices before date 0. At any rate, we remark that the effects that we find are very weak. We observe that all the studies and test statistics generate similar results, both using an equally weighted and a value weighted market index, and conclude that the market does not appear to consider EVA adoption as likely to lead to a significant increase in the value of the firm. Analyzing the evolution of company variables around adoption, we obtain several important conclusions. The analysis of profitability measures reveals that companies adopt the EVA technique after a long period of declining firm performance. After the adoption we can observe that these measures do not improve in the short run, but only (and sometimes not very significantly) in the long run. However, the good results of year 5 may not be representative; many firms in our sample adopt EVA in 1994 and 1995. Thus, years 4 and 5 correspond to 1999 and 2000, a period characterized by a strong economy, as well as a strong stock market. Therefore, at least part of the long-term improvement is probably due to the general economic situation rather than to EVA adoption. The analysis of the investment activity measures indicates an increase in company investment activity after the EVA adoption. The Price to Book, R&D to Sales, and Tobin-q ratios show significant increases after the adoption of EVA. Analyzing the firm Total Assets we observe that the increment in the firm investment activity is not followed by an increase in the firm size. This can be interpreted in the sense that the EVA methodology provides incentives for managers to increment firm investment activity, not with the objective to increase firm size (which may generate inefficiencies), but in order to improve firm economic value and future perspectives. In the case of R&D expenses to sales, some comments are in order. According to U.S. accounting rules (SFAS 2), R&D expenses are considered as expenses in

The Economic Value Added (EVA)

285

the fiscal year in which they are done and must appear in the P&L account. In contrast EVA considers R&D expenses as an asset acquisition (Stewart, 1991). Therefore, accounting NOPAT is adjusted adding back the R&D expense and deducting the amortization of the R&D asset (Stewart, 1991, pp. 28–30). This can explain why EVA firms experience an increase in the R&D to sales ratio after the EVA adoption. We also observe a significant increment in the Debt to Assets ratio. This indicates that the increment in the firm investment activity observed after the EVA adoption is frequently financed through higher levels of debt. This can be explained by the fact that NOPAT (which is directly used to compute EVA) adds back the after tax effect of debt financing charges (interest expense) included in EBEI (Biddle et al., 1997). Thus some firms can have incentives to increase their debt ratio, since this will increase their NOPAT, and therefore their EVA. For example, M. A. Volkema, President and CEO of Herman Miller, Inc., states that (see www.sternstewart.com): EVA analysis has enabled us to identify waste in both our costs and overuse of capital. (. . .) EVA analysis demonstrated that debt capital was cheaper than equity capital. Thus our Board set a new debt to capital ratio of 30% to 35% (. . .).

Finally, we also analyze how the cash flow measures evolve around adoption. In this final study we observed that EVA companies experience significant cash flow increments after the EVA adoption, while this does not happen when dividends per share are considered. This result may be influenced by the fact that many firms in our sample adopted EVA in the middle of the 90s, so that the long-run we analyze coincides with the end of 90s, a period characterized by a strong economy. Another possibility is that when managerial compensation is linked to EVA, the managers have strong incentives to increase the firm’s cash flows, an important determinant of EVA. In order to test this hypothesis we divide the sample in two groups. The first group comprises those firms that introduce EVA in managerial compensation, and the other group by those firms that don’t. As expected, the evolution of cash flow measures appear to be better in the first group, although the small size of the groups makes it difficult to provide robust conclusions. Comparing the evolution of the EBITDA Margin we observe that in the first group it appears to be better than in the second group, but no clear differences emerge between the two groups when we analyze the Cash Flow Margin.

CONCLUSIONS This paper, using an event study methodology, has analyzed the market reaction to the adoption of the EVA technique. Using a sample of firms adopting EVA

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during the period 1983–1998, we do not observe a significant market reaction to EVA adoption. This result appears to be in conflict with some other studies (i.e. O’Byrne, 1997; Walbert, 1994), that observe that EVA companies have high levels of stock market returns. This difference is probably due to the fact that the explosion of the EVA technique occurs in the middle and the second part of the 90s, coinciding with a strong stock market. Probably, the positive stock market evolution observed in these studies can be attributed to the stock market tendency and not to the EVA properties. Our paper focuses on abnormal returns, and we do not observe the positive market response observed by those studies. Our results are in the line of Chen and Dodd (2001) and Biddle et al. (1997). Both papers observe that the market price evolution may rely more on audited accounting earnings than on the non-audited EVA. Additionally, we also analyze how the company profile evolves around the EVA adoption. The main objectives of this second exercise is to analyze the long-term firm evolution, and to check whether EVA helps to improve operating profits, the cost of capital and the investment activity (Prober, 2000; Stewart, 1991). We analyze three sets of firm variables: firm performance variables, investment variables, and cash flow variables. In the first set, we observe that firms usually adopt EVA after a long period of bad performance. After the adoption, performance measures appear to improve only in the long run, a result probably influenced by the favorable evolution of the general economic situation. Analyzing firm investment variables, we observe that EVA adoption increases firm investment activity. We also observe increases in the debt ratios. Finally, since EVA focuses on firm cash flow, we also analyze the evolution of company cash flow variables. We observe a positive impact on the Cash Flow Margin and the EBITDA after the adoption. This may be due to the fact that managerial compensation depends positively on these variables; to check this hypothesis we analyze separately the evolution of cash flow variables for firms that introduce EVA in managerial compensation and firms that do not. The evolution of the EBITDA margin is more favorable in firms introducing EVA in the compensation system. However, the small size of our sample does not let us provide robust conclusions.

NOTES 1. The abbreviation EVA is a trademark of Stern Stewart & Company. 2. MVA is defined as the Equity’s Market Value less the Equity’s Book Value.

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ACKNOWLEDGMENTS The authors would like to thank, without implicating, the anonymous referees and especially Salvador Carmona for many valuable comments that helped to improve the paper. Bartolom´e Dey´a Tortella acknowledges the financial support received from the Caja de Ahorros de Baleares “Sa Nostra,” and from the CICYT, Project BEC 20012553-C03-03. Sandro Brusco acknowledges financial help from DGES, Project SEC 2001-0445.

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APPENDIX Sample Firm List ACXIOM CORP ADAPTIVE BROADBAND CORP ADC TELECOMMUNICATIONS INC ALEXANDER and BALDWIN INC ALLTRISTA CORPa ARMSTRONG HOLDINGS INC BALL CORP BARD (C.R.) INC BAUSCH and LOMB INC BECTON DICKINSON and CO BEST BUY CO INC BOISE CASCADE CORP BOWATER INC BRIGGS and STRATTON CDI CORP CENTURA BANKS INC COCA-COLA CO COLUMBUS MCKINNON CORP COX COMMUNICATIONS – CL A CRANE CO DONNELLEY (R R) and SONS CO DUN and BRADSTREET CORPa EQUIFAX INC FEDERAL-MOGUL CORP FLEMING COMPANIES INC

GC COMPANIES INC GEORGIA-PACIFIC GROUP GRAINGER (W W) INC GUIDANT CORPa HERSHEY FOODS CORP INTL MULTIFOODS CORP JONSON OUTDOORS INC – CL A KANSAS CITY POWER and LIGHT LILLY (ELI) and CO MANITOWOC CO MATERIAL SCIENCES CORP MIDAMERICAN ENERGY HOLDINGS MILLENNIUM CHEMICALS INCa MILLER (HERMAN) INC MONTANA POWER CO NOBLE DRILLING CORP OLIN CORP PENNEY (J C) CO PERKINELMER INC PHARMACIA CORP PMI GROUP INCa POLAROID CORP PULTE HOMES INC QUAKER OATS CO

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APPENDIX (Continued ) RYDER SYSTEM INC SILICON VY BANCSHARES SPRINT FON GROUP SPX CORP STANDARD MOTOR PRODS TENET HEALTHCARE CORP TOYS R US INC a Not

TUPPERWARE CORPa VULCAN MATERIALS CO WEBSTER FINL CORP WATERBURY WELLMAN INC WHIRLPOOL CORP

included in the event study (not enough data for the estimation period).