Eva And Accounting Earnings With Market Value Accounting Essay

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Purpose: The basic objective of this paper is to examine the claim of EVA proponents about its superiority as a financial performance measure compared to traditional performance measures in Indian companies and provide empirical evidences.

Design/Methodology: The paper uses a sample of 873 firms-year observations from the Indian market and applies pooled OLS regression to test the relative and incremental information content of EVA and other earning based measures in explaining the market value added (MVA).

Findings: The results about relative information content test reveal that NOAPT and OCF outperform EVA in explaining the market value of Indian companies. Incremental information content test show that EVA makes a marginal contribution to information content beyond traditional performance measures such as NOPAT, OCF, EPS and RONW etc. Overall our results do not support the hypothesis that EVA is superior than traditional accounting based measures in association with market value of the firm.

Originality/value: Our study concludes that other non- financial variables such employees, product quality, community satisfaction should be considered in order to capture the unexplained variation in the market value of the firm.

Key Words: Economic Value Added (EVA); Traditional performance measures, Market Value Added (MVA), Relative information content, Incremental information content,

1. INTRODUCTION

Maximization of shareholders wealth or value is well accepted objective among corporate financial managers in recent years. Shareholders activism has reached to unprecedented level partially because due to integration of financial markets and partially because of regulatory reforms (in terms of disclosure requirement and investor protection) and this has led to increased pressure on firms to increase shareholders value. The Corporates, which gave the lowest preference to shareholders curiosity, are now bestowing the utmost preference to it (Sharma & Kumar, 2010). However, despite their best efforts, many companies failed to create shareholders wealth (Kim, 2006). Modern value -based performance measures, such as Economic Value Added (EVA) [1] , Cash Flow Return on Investment (CFROI) [2] , Cash Value Added (CVA) [3] , Discounted Economic Profits (EP), Shareholders Value Added (SVA) [4] have been developed recently by various consulting companies to gauge the real performance of companies and also to shift the focus from accounting earnings to cash flows. Traditional performance measures such as such as NOPAT, EPS, ROI, ROE etc. have been criticized due to their inability to incorporate full cost of capital. Hence, accounting income is not a consistent predictor of firm value and cannot be used for measuring corporate performance. On the other hand, value based measures recognise that capital invested in a corporation is not free, and thus make a capital charge for the use of the capital employed by the corporation in its operations (O'Hanlon and Peasnell, 1998). The most popular value-based performance measure is Stern Stewart's Economic Value Added (hereafter EVA).

EVA is financial performance measure that most accurately reflects company's true profit (Stewart, 1991). EVA is the calculated after deducting the cost of equity capital and debt from the operating profits. EVA is a revised version of Residual Income (RI) with a difference the way the economic profit and the economic capital are calculated. Coined and popularized by New York based management consultancy firm Stern Stewart & Co. in 1991, EVA over the years has gained popularity as a reliable measure of corporate performance. In the later years, the concept has received recognition and support from various corporate houses; those adopted it as an internal control measure. The selling point of EVA is that it considers economic profits and economic capital in order to know the value created and destroyed by an organization during a particular period. Economic profit and economic capital is calculated by making certain adjustments into the accounting profits. There exist anomalies in the academic literature about the number of adjustments required to reach economic profit and economic capital. Stern Stewart & company have suggested 164 such accounting adjustments to convert GAAP profits to economic profits. Another important point in calculation of EVA is calculation of the cost of capital (WACC). As suggested by various researchers, cost of equity capital under EVA may be calculated using Capital Assets Pricing Model (CAPM).

The basic objective of this paper is to provide empirical evidence about the superiority of EVA, as claimed by EVA proponents in comparison to traditional accounting based measures in explaining the Market Value Added (MVA). We have examined the association of EVA alongwith five traditional accounting performances measures (NOPAT, ROCE, RONW, EPS and OCF) with MVA using a sample of 97 Indian companies for the period 2000 to 2008 and tested the assertion that EVA is better linked with market value added as compared to traditional performance measures. Contrary to the claim of Stern- Stewart & Co., we report that accounting earning based measures such as NPAT and OCF are better in explaining the market value of the sample companies. We find evidence supporting the earlier work of Peterson and Peterson (1996), Biddle et al. (1997), Chen and Dodd (2001), Kim (2006) and Ismail (2006) that traditional accounting based measures are more associated with market value added than EVA. However, our incremental information content tests reveal that EVA makes a marginal contribution to information content beyond traditional performance measures such as NOPAT, OCF, EPS and RONW etc. Overall, our results do not support the hypothesis that EVA is superior than traditional accounting based measures in association with market value of the firm. The study concludes that other non- financial variables such as employees, product quality community satisfaction should be considered in order to capture the unexplained variation in the market value of the firm.

The remainder of the paper is organized as follows: section 2 presents a brief account of the literature review, section 3 explains the data, variables and hypothesis of the study, section 4 presents model specifications, section 5 presents empirical analysis & findings and the conclusion is presented in section 6.

2. LITERATURE REVIEW

There is a growing debate in the academic literature about which performance measures that best explain change in market value of the firms. Various studies have been conducted during last two decades, initially in the developed markets like US and later in the other countries to know whether it is really better to use modern value-based measures such as EVA, CVA, NPV, shareholders value added (SHV) and other cash flows based measures than traditional accounting based performance measures (NOPAT, ROCE, EPS, RONW) to measure the corporate financial performance. The results are quite mixed and controversial. Several studies have proved the superiority of EVA as a performance measure (Stewart, 1991; Milunovich and Tsuei, 1996; O'Byrne, 1996; Lehn and Makhija, 1997; Worthington and West, 2004; Irla, 2007; Lee and Kim , 2009 ) while others (Biddle et al., 1997; Kramer and Pushner, 1997; Chen and Dodd, 1997; 2000; Chen and Dodd, 2001;Worthington and West, 2001; Ismail, 2006; Kyriazis and Anastassis, 2007; Ramana, 2007; Ismail, 2008) provided different and opposing results. Some of the important studies about the various corporate performance measures are presented in this section.

Stewart (1991) provided evidence of the correlation between EVA and MVA. Using a sample US companies and examining both constant and changes in EVA and MVA, he found that there is a relationship between both the levels of EVA and MVA. Since the correlation between changes in EVA and MVA was high, Stewart suggested that adopting the goal of maximising EVA and EVA growth would in fact build a premium into the market value of the company. In a major study by Stern (1995) argues that the accounting measures such as earnings, earnings growth, dividends, dividend growth, ROE, or even cash flow are not key measures of corporate performance, but in fact EVA is one such measure that is closely linked with market value of company.

Milunovich and Tsuei (1996) investigated the correlation between frequently used financial measures (including EVA) and the MVA of companies in the US computer technology industry. The results clearly state that EVA demonstrated the best correlation and it would be fair to infer that a company that can consistently improve its EVA should be able to boost its MVA and therefore its shareholders' value. O'Byrne (1996) conclude that EVA explains more than twice as much of the variance in market/capital ratio as NOPAT when the EVA model has positive and negative EVA coefficients, and an ln(capital) term. He also showed that EVA changes explain significantly more of the variation in market value changes

Lehn and Makhija (1997) studied the relationship between six performance measures and stock returns. The results revealed that EVA and MVA are effective measures of performance. Moreover, the correlation of EVA with stock returns (.59) was slightly higher than the correlation of MVA (.58), ROE (.46), ROA (.46), or ROS (.39). Thus, EVA and MVA appear to be somewhat better long-run performance measures than conventional accounting performance measures. Irala (2007) analyzed whether Economic Value Added (EVA) has got a better predictive power relative to the traditional accounting measures such as EPS, ROCE, RONW, Capital Productivity (Kp) and Labor Productivity (Lp). Analysis of 1000 companies across 6 years (6000 company years), very much supports the claim that the EVA is the better predictor of market value compared to other accounting measures. EVA is gaining recognition as fundamental measure of company performance despite the fact that it has been in existence for a relatively short span of time.

In another study by Misra and Kanwal (2007) about Indian companies argued that accounting based metrics are misleading measures of corporate financial performance as they are vulnerable to "accounting distortions". Results of their study reveal that EVA (%) is the most significant determinant of MVA as it explains the variations in share value better than the other conventional accounting based measures of firms' financial performance. Lee and Kim (2009) introduced Refined EVA (REVA) to the hospitality industry and compared it to EVA, market value added (MVA) and other traditional accounting measures (cash flow from operations (CFO), return on assets (ROA), and return on equity (ROE). The study provides interesting and meaningful findings that REVA and MVA can be considered good performance measures throughout the three hospitality sectors (i.e., hotel, restaurant and casino). According to the findings, REVA and MVA significantly explain the market adjusted return by presenting positive coefficients.

On the other hand, Biddle et al. (1997) tested the assertions that EVA is more highly associated with stock returns and firm's value than accrual earnings, and evaluated which component of EVA, if any, contributed to these associations. The results indicated that earnings (R2 =12.8%) were significantly associated with market adjusted annual returns than either Residual Income (R2 = 7.3%) or EVA (R2 = 6.5%) and that all three of these measures dominate cash from operations (R2 =2.8%). The empirical results regarding relative information content, rather suggest that earnings generally outperform EVA. Similar results were revealed by Kramer and Pushner (1997) by analyzing the strength of the relationship between EVA and MVA, using the Stern Stewart 1000 companies for the period between 1982 and 1992.They found that although MVA and NOPAT were positive on average, the average EVA over the period was negative. No clear evidence is found to support the contention that EVA is the best internal measure of corporate success in adding value to shareholders' investment.

Chen and Dodd (2001) empirically examined the value-relevance of three profitability measures- Operating Income (OI), Residual Income (RI), and Economic Value Added (EVA) and concluded that the market may place higher reliance on audited accounting earnings than the unaudited EVA metric. Their findings failed to support the assertion that EVA is the best measure for valuation purposes. Ismail (2006) in a study about UK companies tested the relative and incremental information content of EVA and other performance measures using panel data regression. The results of the study fail to support the Stern- Stewart hypothesis as net operating income after taxes and net income outperform EVA and residual income. The paper concludes that apart from financial variables other factors like employee, customer satisfaction and R&D initiatives must be considered to capture the changes in the stock return.

Similarly, Kim (2006) provides empirical evidence on the relative and incremental information content of EVA and traditional performance measures, earnings, and cash flow of hospitality industry of U.S. The information content of EVA and other explanatory variables indicates that earnings are more useful than cash flow in explaining the market value of hospitality firms. Kyriazis and Anastassis (2007) investigated the relative explanatory power of the Economic Value Added (EVA) model with respect to stock returns and firms' market value. They conclude that net and operating income (NOPAT and OP) appear to be more value relevant than EVA in explaining the market value of firms listed at Athens Stock Exchange (ASE).

Ismail (2008) provide evidence regarding Economic Value Added (EVA) and company performance in Malaysia. The study sought to explain the ability of EVA, compared to traditional tools, in measuring performance under various economic conditions; pre-economic crisis, during economic crisis and post-economic crisis period. The result of the study found that traditional tools particularly EPS is able to correlate and had a relationship with stock returns. Further the study revealed that EVA is also able to correlate with stock returns and is superior in explaining the variations in the stock returns as compared to the traditional tools under varying economic conditions. Maditinos et al. (2009) examined the explanatory power of two value-based performance measurement models, EVA and SVA, compared with three traditional accounting performance measures: earnings per share (EPS), return on investment (ROI), and return on equity (ROE), in explaining stock market returns in the Athens Stock Exchange (ASE). Relative information content tests reveal that stock market returns are more closely associated with EPS than with EVA or other performance measures. However, incremental information content tests suggest that the pairwise combination of EVA with EPS increases significantly the explanatory power in explaining stock market returns.

Thus, literature on various performance measures reveals that earnings generally dominate in explaining stock returns. But performance indicators based on earnings are criticized by various researchers not incorporating full cost of capital in calculation of returns available for shareholders. Of late, various researchers have started according importance to value added measures, EVA being one of them. Results are quite mixed from both, developed and developing markets. Further, most of the studies are from developed countries and very little evidence exists about emerging market like India. This motivates us to analyse the highly controversial but important Stern- Stewart assertion regarding the superiority of EVA in Indian context and contribute to the existing literature.

3. DATA AND VARIABLES

3.1 Sample Selection

Our sample consists of 97 non-financial companies all listed on Bombay Stock Exchange (BSE). Initially, a sample of top 200 companies was selected from BT-500 (India's most valuable companies, Business Today, 2006). The rationale behind selecting BT-500 as sample base is that these companies are ranked on the basis of market capitalization in the Indian stock market and hence, can be considered as India's most valuable companies. The final sample was constructed using following criteria; Firms must be available during the study period of year 2000 to year 2008. Because of the specific nature of their activities, firms related to banking, financial and non- banking financial institutions are excluded from the sample. Some firms with missing data were also removed from the sample. The final sample, after considering any missing data, consists of 97 firms. Thus a balanced panel set of 873 firm- year observations was obtained, with observation of 97 firms over the period 2000 to 2008. The data required for the purpose of this study has been taken from the Prowess and Capitaline database. Further, data related to EVA and MVA has been taken from BT-SS survey (Business Today, 2000) and computed using the methodology used in the survey.

3.2 Variables definition

The study examines the association of EVA and traditional earning based with the market value of the companies. To achieve this, market value added (MVA) is used as dependent variable. Market value of the company includes both the market value of the equity as well as debt. MVA measures the value added by the management over and above the capital invested in the company by its shareholders and lenders. It is the cumulative amount by which a company has enhanced or diminished shareholders wealth (Kaur and Narang, 2009). Similar variable was used by Kim, 2006; Vijayakumar and Selvi, 2007) in their studies about U.S. hospitality industry and Indian automobile industry respectively. Alongwith market value added as dependent variable, economic value added (EVA), return on capital employed(ROCE), return on net worth(RONW), earnings per share(EPS),net operating profits after taxes (NOPAT) and cash flow from operations (CFO) are used as explanatory variables. Table I. summarizes the variable definitions and calculations.

3.3 Hypotheses of the study

The prime purpose of our study is to provide evidences about the superiority of EVA over the traditional performance measures. To achieve this, relative and incremental information content of EVA and traditional performance measures are analysed. Relative information content comparisons examine if one measure provides greater information content than another. On the other hand, incremental information content comparisons assess whether one measure provide more information content than another. The following hypotheses were formulated in the present study to examine the relative and incremental information content of various performance measures.

Hypothesis 1: The relative information content of EVA is superior to traditional performance measures (NOPAT, RONW, ROCE, EPS and OCF) in explaining market value of Indian companies.

Hypothesis 2: EVA adds information content to that provided by NOPAT, RONW, ROCE, EPS and OCF in explaining market value of firms

4. THE MODEL SPECIFICATION

The present study examines the relative and incremental information content of various performance measures and their association with MVA using OLS regression analysis. To achieve this, we developed six simple regression models to compare the relative power of each explanatory variable. Our methodology is based on the similar work of Kim, 2006; Irala, 2007; Ismail, 2008 and Vijayakumar and Selvi, 2008. The simple regression models used are as follow:

MVAit =β0 + β1EVAit + εit …………………….. (1)

MVAit =β0 + β1NOPATit + εit ……………………. (2)

MVAit =β0 + β1ROCEit + εit ……………………..(3)

MVAit =β0 + β1RONWit + εit ……………………. (4)

MVAit =β0 + β1EPSit + εit ……………………. (5)

MVAit =β0 + β1OCFit + εit ……………………….(6)

Where: MVAit ,amount of market value added for the firm i in period t; EVAit, amount of economic value added of firm i in period t; NOPATit, net operating profits after taxes for firm i in period t; ROCEit, ratio of earning before taxes to capital employed for firm i in period t; RONWit, ratio of net income after tax to networth for firm i in period t; EPSit, net income to total number of shares outstanding for firm i in period t; OCFit cash flow from operations for firm i in period t; εit ,stochastic error term for firm i at time t and i= 1,…….97 and t= 1,……9.

Further, to test hypothesis regarding the incremental content of EVA, NOPAT, ROCE, RONW, EPS and OCF, multiple linear regression models are used. The present study use two separate multiple regression models, one with all explanatory variables and another after exclusion of EVA.

MVAit =β0 + β1EVAit+ β2 NOPATit + β3 ROCEit + β4 RONWit + β5 EPSit + β6 OCFit + εit

……………… (7)

MVAit =β0 + β1NOPATit+ β2ROCEit+ β3 RONWit + β4 EPSit + β5 OCFit + εit

……………… (8)

Where: MVAit ,amount of market value added for the firm i in period t as above ; EVAit, amount of economic value added of firm i in period t; NOPATit, net operating profits after taxes for firm i in period t; ROCEit, ratio of earning before taxes to capital employed for firm i in period t; RONWit, ratio of net income after tax to networth for firm i in period t; EPSit, net income to total number of shares outstanding for firm i in period t; OCFit cash flow from operations for firm i in period t; εit ,stochastic error term for firm i at time t ; and i= 1,…….97 and t= 1,……9.

5. EMPIRICAL RESULTS AND ANALYSIS

5.1 Descriptive Statistics

Table II provides summary of descriptive statistics of MVA (dependent variable) and six explanatory variables used in the study. It is evident from the Table that all performance measures considered in the present study have a positive mean value. MVA has 2623.32 as mean value of Indian companies whereas mean value of EVA is 10.95, which implies that most of Indian companies included in the study are able to earn more than the cost of capital. Table further reveals that median EVA value is negative (-0.59), whereas median value of all other measures exhibit positive values. Another important observation is that MVA has the largest mean and median followed by OCF, NOPAT, EPS, ROCE, RONW and EVA. These results are partially consistent with many international studies with similar and different variables. Low values of EVA are noticed, since in the long term, companies cannot continue to earn more than the cost of capital due to competitiveness of markets as companies cannot earn supernormal growth over long time.

Pair-wise correlations between dependent variable and independent variables are presented in Table III. We observe that all the variables are positively correlated with each other. EVA is positively correlated with MVA (0.483) with statistical significant value at 1 percent level but lower correlation as compared to NOPAT and OCF. Highest correlation value can be observed between NOPAT, OCF and ROCE, RONW. It is important to note from the table that EVA under-perform traditional accounting measures (NOPAT and OCF), which reject the claim of EVA advocates (e.g. Stewart, 1990; O'Byrne, 1996; Makelainen, 1998; Taufik et.al, 2008) that it is highly associated with MVA or stock returns.

5.2 Relative Information content test

In Table IV, we report the results of relative information content test of every independent variable. The assessment is made by analyzing six separate regressions for each performance measures i.e. EVA, NOPAT, ROCE, RONW, EPS and OCF. This estimation is done by OLS regressions based on equations (1) to (6). Table IV reveals coefficients and adjusted R2 values of each variables alongwith F statistics. Firstly, we find that all of the regressions, except EPS are significant according to F statistics at the .01 level. Similarly, the coefficients result suggest that in terms of relative information content of all six explanatory variables, except EPS (p=.465), all performance measures are statistically significant at the level of .01. Relative information content test as measured by adjusted R2 of six regressions is also presented in the Table IV. The test results suggest that accounting earnings (NOPAT) have the greatest ability to explain market value of Indian companies with adjusted R2 of 44.98 percent. Next, OCF has significantly larger adjusted R2 (31.44%) followed by EVA, RONW and ROCE. Economic value added, a value based performance measures stand third with adjusted R2 in terms of explanatory power and thereby confirming that earnings dominate in explaining the variations in market value of Indian companies. One important observation from the Table IV is that EPS, accounting earning based performance measure contribute negatively (adjusted R2 = -0.48) in terms of explanatory power of the performance measures. Moreover the coefficients value about EPS is not statistically significant at any level of significance. The result of the present study fails to confirm Hypothesis 1 that relative information content of EVA is superior to traditional performance measures (NOPAT, RONW, ROCE, EPS and OCF) in explaining firm value of Indian companies. Our results related to relative information test are consistent with many international studies (Chen and Dodd, 1997; Biddle et al., 1998; Ray, 2001; Worthington & West, 2001; Peixoto, 2002; de Wet, 2005; Ismail, 2006; Kim, 2006; Kyriazis and Anastassis, 2007; Vijayakumar and Selvi, 2007; Visaltanachoti et al., 2008; Maditinos et al., 2009) but different from many studies (Irla, 2007; Sunitha, 2008, Taufik et al., 2008)

Finally, the results of our OLS regressions lead to the conclusion that EVA does not outperform NOPAT and OCF. So, our relative information content tests discard the claim of EVA proponents that EVA is far best performance measure that explains market value of a firm.

5.3 Incremental Information content test

As discussed earlier, incremental information content comparisons assess whether one measure provide more information content than another. Table V shows the results of the incremental information content test of all six explanatory variables. Before running the OLS regression models, we detect the presence of first-order autocorrelation among the residuals. For this purpose, we used Durbin Watson (D-W) statistics. The D-W statistics of the residuals report 1.92 and 1.85 respectively for regression equations (7) and (8). Durbin-Watson statistic, ranges in value from 0 to 4 with an ideal value of 2 indicating that errors are not correlated (eNumerys, 2009). Analysis of D-W statistics suggests no presence of auto-correlation in the data. To detect the presence of multicollinearity, variance inflation factor (VIF) was also analysed. A general rule is that the VIF should not exceed 10 (Belsley, Kuh, & Welsch, 1980).VIF values of all independent variables was in range with a highest value of 6.253 for NOAPT indicating a low degree of multicollinearity among the variables.

In order to determine the incremental information content of EVA, we used two regression models (equation 7 and 8), with all variables and another regression model except EVA. The overall model results suggest that both model 1 and 2 are significant with F-values (34.199 and 31.064) statistically significant at .01 levels. Result about coefficients reveals that only NOPAT, OCF and EVA are statistically significant and can be included in the model. OCF has negative association whereas NOPAT and EVA are positively related with MVA. We also observe increase in the value of R2 (coefficient of determination) from 65.3 percent to 67.4 percent in model 1 and model 2 respectively. Further, the overall increase in adjusted R2 is minimal (1.9%) between the first model with NOPAT, RONW, ROCE, EPS and OCF and the second regression on all six independent variables. Thus, we can conclude that although, the contribution of EVA in explaining market value of Indian companies is slight but increased R2 is statistically significant. So, our results accept Hypothesis 2 and thereby concluding that EVA still adds incremental information to that provided by NOPAT, RONW, OFC, ROCE and EPS in explaining the MVA of Indian companies.

6. SUMMARY AND CONCLUSION

EVA has gained massive popularity in the academia and attracted some of the largest corporation to implement EVA as performance measurement system. There is growing debate about what influence the market value of the company. Various researchers have criticized earnings based performance measures due to their inability to incorporate full cost of capital. Since then, there is growing amount of literature on the efficiency of the various performance measures and their relationship with market value of the company. These results of the studies are mixed suggesting that sometime traditional measures outperform value based measures and another supporting the superiority of value added measures in terms of their associations with market value of the company. The prime objective of the present study is to find empirical evidence about the association of EVA along with traditional performance measures with market value added and contribute to the existing literature. To achieve this, we test the relative and increment information content of all six explanatory variables (NOPAT, EVA, ROCE, RONW, FCF and EPS) about 97 Indian companies for the period from 2000 to 2008.

The empirical results of the study do not support the claim that EVA is a better performance indicator than traditional accounting measures in explaining market value. Our relative information content test reveals that NOPAT and OCF outperform EVA in their association with market value. Our findings regarding relative information content support the findings of many international studies that EVA is not superior to traditional accounting measures in its association with firm values. The results regarding incremental information content test of various performance measures revealed that EVA slightly adds to incremental information to that provided by NOPAT, RONW, OFC, ROCE and EPS in explaining the MVA of Indian companies. Our findings, in sum, do not support the claim of Stern Stewart & Company that EVA is superior to other measures in explaining MVA. It was also evident from the results that one-variable EVA model is not able to capture more than 23 percent of the variations in the market value of Indian companies. This implies that there are other factors that drive market value and should be taken into account for shareholders' value creation or for performance measurement. As suggested by Chen and Dodd (2001) there are various factors related to customers, employees and community satisfaction, product quality, R&D innovations those affect the market value of firms apart from financial variables.

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Table I. Definition of independent variables

Variable

Definition

Symbol used

Economic value added

Net operating profits adjusted for cost of capital (NOPAT- Total Cost of capital)

EVA

Net operating profits after taxes

Profits after taxes

NOPAT

Return on capital employed

Ratio of earnings before interest and taxes to capital employed

ROCE

Return on net worth

Ratio of net income after taxes to net worth

RONW

Earning per share

Ratio of net income to number of shares outstanding

EPS

Cash flow from operations

(operating cash flows)

Company's net cash flow resulting directly from its regular operations

(NOPAT adjusted for items).

OCF

Table II. Descriptive statistics of dependent and explanatory variables

Variable

Obs.

Mean

Median

S.D

Minimum

Maximum

MVA

97

2623.324433

630.94

6002.853

-394.21

254462.5

EVA

97

10.95546392

-0.59

195.9894

-774.15

1062.68

RONW

97

22.90106529

20.78333

12.44447

3.112222

2221.403

NOPAT

97

371.2425165

113.7389

844.0958

-79.5367

36010.52

ROCE

97

26.14649485

20.79667

16.78594

4.1

2536.21

EPS

97

31.87802978

20.09778

48.65719

1.371111

3092.169

OCF

97

438.8973998

144.3756

1152.521

-615.67

10467.66

Notes: MVA- market value added; EVA- economic value added; RONW- return on net worth; NOPAT-net operating profit after taxes; ROCE- return on capital employed; EPS- earning per share; OCF- operating cash flows

Table III. Correlation Matrix

MVA

EVA

RONW

NOPAT

ROCE

EPS

OCF

MVA

1

EVA

.483**

1

RONW

.396**

.546**

1

NOPAT

.675**

.161

.148

1

ROCE

.336**

.519**

.917**

.102

1

EPS

.075

.087

.061

.114

.111

1

OCF

.567**

.050

.065

.975**

.035

.097

1

Notes: **, correlation is significant at the 0.01 level, MVA- market value added; EVA- economic value added; RONW- return on net worth; NOPAT-net operating profit after taxes; ROCE- return on capital employed; EPS- earning per share; OCF- operating cash flows

Table IV. Test results of the relative information content of EVA, RONW, NOPAT, ROCE, EPS and OCF using OLS regression measures

NOPAT

OCF

EVA

RONW

ROCE

EPS

Coefficients

1.042(8.915)*

0.965(6.710)*

0.733(5.373)*

0.447(4.202)*

0.391(3.479)*

0.098(0.734)

P-value

0.000

0.000

0.000

0.000

0.001

0.465

F

79.472*

45.030*

28.864*

17.660*

12.105*

0.539

R2 (percent)

45.55

32.16

23.30

15.68

11.30

0.56

Adj. R2 (percent)

44.98

31.44

22.50

14.79

10.37

-0.48

Notes: MVA-measure market value added; EVA- economic value added; RONW- return on net worth; NOPAT-net operating profit after taxes; ROCE- return on capital employed; EPS- earning per share; OCF- operating cash flows; * statistically significant at 1% level.

Table V. Test results of the incremental information content of EVA, RONW, NOPAT, ROCE, EPS and OCF

Model 1

Model 2

Independent variables

RONW

.130(.708)

.083(.461)

NOPAT

3.425(7.246)*

2.943(5.880)*

ROCE

.075(.407)

.029(.159)

EPS

-.046 (-.562)

-.051(-.633)

OCF

-2.725(-5.300)*

-2.216(-4.088)*

EVA

-

.292(2.449)*

R2

.653

.674

Adjusted R2

.634

.653

F-value

34.199*

31.064*

∆ R2

-

.019

Durbin-Watson

1.92

1.85

Notes: MVA-measure market value added; EVA- economic value added; RONW- return on net worth; NOPAT-net operating profit after taxes; ROCE- return on capital employed; EPS- earning per share; OCF- operating cash flows; t-statistics are provided in parenthesis; *statistically significant at 1 percent level.

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