EVA Approach Validity

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European Financial Management, Vol. 13, No. 1, 2007, 71100

The Validity of the Economic Value

Added Approach: an EmpiricalApplication

Dimitris Kyriazis and Christos AnastassisDepartment of Banking & Financial Management, University of Piraeus, Karaoli & Dimitriou

80, Piraeus 185 34, Greece

e-mail: [email protected]

Abstract

This study investigates the relative explanatory power of the Economic ValueAdded (EVA) model with respect to stock returns and firms market value,compared to established accounting variables (e.g. net income, operating income),in the context of a small European developing market, namely the Athens Stock

Exchange, in its first market-wide application of the EVA measure. Relativeinformation content tests reveal that net and operating income appear to bemore value relevant than EVA. Additionally, incremental information tests suggest

that EVA unique components add only marginally to the information content ofaccounting profit. Moreover, EVA does not appear to have a stronger correlationwith firms Market Value Added than the other variables, suggesting that forour Greek dataset EVA, even though useful as a performance evaluation tool,need not necessarily be more correlated with shareholders value than establishedaccounting variables.

Keywords: economic value added, residual income,market value added,relative

information content,incremental information content.

JEL classification: G3,G31,M41

1. Introduction

Although, the Economic Value Added (EVA) model was thoroughly applied by SternStewart & Co., for the first time, in the nineties, a similar concept had been contemplated

by economists for many years before that. It was the famous economist Alfred Marshallin 1890, who first spoke about the notion of economic profit, in terms of the real profit

We would like to thank Sudi Sudarsanam, Nick Travlos and an anonymous referee for

many insightful comments on an earlier version of this article and Thomas Syrmos forhis assistance in numerical calculations. We are also grateful to the editor John Doukas forhelpful suggestions Financial support from the Research Centre of the University of Piraeus


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72 Dimitris Kyriazis and Christos Anastassis

that a company makes when it covers, besides the various operating costs, the cost ofits invested capital.

It is clear that under the EVA approach performance measurement gains a newmeaning in contrast with the traditional approach which is merely based on the simple

notions of accounting profits and the relevant ratios derived from them, such as thereturn on equity (ROE) and the return on assets (ROA). The difference is that thetraditional performance measurement benchmarks do not consider the cost of investedcapital (equity and debt) in order to generate the profits made by a company. Thus, underthe traditional approach two companies that have the same ROE would be consideredas equally successful, whereas under the EVA approach the same conclusion could not

be reached if these two firms had a different cost of capital, in other words if theireconomic profit or residual income was different.

Based upon the above meaning of economic profit, Stern Stewart & Co., developedthe concept of the Economic Value Added Model. 1 The basic difference between thenotions of economic value and residual income2 concerns the method for calculating

profits and invested capital. Stern Stewart suggested various adjustments in the financialstatements of the firms, in order to move away from the concept of accounting profitscaused by the application of the Generally Accepted Accounting Principles (GAAP),and approach the notion of real economic value. Considering this, it follows that, ifthe EVA model with the adjustments that Stern Stewart proposes, is closer to the realeconomic value of the firm, then its application will enable management to monitorand control more efficiently the use of invested capital. According to Stern, Stewart

and Chew (1996), EVA is not just another performance measure, but can be the mainpart of an integrated financial management system, leading to decentralised decisionmaking. Thus, the adoption of EVA should indirectly bring changes in management,

which in turn can enhance firm value. In fact, several US companies (e.g. Coca Cola,AT&T, Briggs & Stratton, Quaker Oats etc.) which have adopted EVA as the basis

of management performance measurement, have experienced a significant increase intheir shareholders wealth. However, several academic empirical studies (e.g. Dodd andChen, 1996; Biddle, Bowen and Wallace, 1997, etc.) have offered contradictory resultsregarding the superior informational content of EVA over the traditional measures of

performance, and the necessity for its application.Because the relevant empirical research concerning the US market is inconclusive, the

main motivation in our study was to examine the relative and incremental informationalcontent of EVA in explaining the stock returns of companies in a different environment

with different characteristics.Thus, we chose to investigate the case of the Greek stock market mainly for three

reasons: the first because we would like to examine the explanatory power of the EVAmodel in comparison with the traditional accounting measures of performance (e.g.

net income, operating income), in the case of a small emerging market with differentcharacteristics and different accounting standards from those of the developed andmature markets of the USA and other major European countries. In other words, wewould like to test if the lack of conclusive evidence regarding the superiority in EVAsability to explain stock returns is confined to mature markets or applies also to small

emerging European markets such as the Athens Stock Exchange (ASE).

1 EVA can be defined as the net operating profits after taxt (Weighted Average Cost ofCapitalt

invested capitalt 1)


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The Validity of the Economic Value Added Approach 73

The second reason stems from the fact that during the 19962003 period which isthe focus of our study, the Greek stock market began a transitory phase (which is stillunder way) from the status of an emerging and relatively inefficient European market,to the status of a developed and more efficient one after the year 2000 (when it was

included in the Morgan Stanley Capital International World Index weighting). Thus, wehad an additional motive to examine if the explanatory power of EVA was enhanced bythis shift in the status of the market, leaving scope for the firms managers and outsideinvestors to adopt it as a performance evaluation benchmark in the near future. The thirdreason for choosing the Greek stock market is associated with the different ownershipstructure of ASE listed firms (e.g. family owned companies, large shareholders vs.

small ones, etc.). This implies different agency costs and prevents the occurrence ofhostile takeovers in cases of financial distress, as opposed to the stock markets of theUSA and UK, in which hostile bids are common. From the view of Stern et al.(1996),the adoption of EVA should lead to a more efficient use of corporate resources byencouraging managers to act as shareholders, without the need for the external control

mechanism of the market, i.e. hostile bids and leveraged buy outs (LBOs). We considerthat in any market, with the characteristics of the ASE,3 in which the mitigation of theagency problem cannot occur via hostile takeovers, the necessity for the application ofinternal corporate control mechanisms and EVA based financial management systems isgreater. Under these conditions, there is a stronger interest to investigate the superiorityof EVA versus the traditional EPS based financial management systems, in terms oftheir relevance to stock returns.

To the best of our knowledge it is the first time that the validity of the EVA modelis examined for companies listed in the ASE. We consider that, if our results showthat EVA has at least the same explanatory power of stock returns as the traditional

accounting measures of profits, this leaves scope for the firms managers and outsideinvestors (e.g. domestic and foreign fund managers) to further develop EVA in a more

elaborate way and adopt it as a performance evaluation benchmark.4 As the ASE

continues its course to maturity, more and more investors, and in particular fundmanagers, will realise in the near future the usefulness of the EVA and graduallythis will be reflected in stock prices. Furthermore, with the introduction of fair valueaccounting (the application of IFRS from year 2005) the estimation of EVA will be easierand comparable across countries with different accounting standards. Undoubtedly,

the big differences in the GAAP and corporate taxation issues among the countriesof the European Union prevented us from applying our research to other European

markets.In our study we find, that EVA does not appear to be the most value relevant income

measure with respect to the variability of Greek firms stock returns. Furthermore, theunique components of EVA add only marginally to the information content of accounting

profit measures, suggesting that better estimates of the cost of capital, or the introductionof different accounting adjustments (perhaps more suited to the Greek GAAPs) couldadd significant information content to the EVA measure. Additionally, EVA does appear

3 Some of these characteristics are encountered in many other European markets (excluding

the UK).4 As far as we know it was only a couple of years ago in Greece, that a few large brokeragefirms started to include in their valuation reports estimates about the EVA of the companies


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to have a slightly stronger correlation with the market value added (MVA) 5 of the firmthan the other income variables (albeit not statistically significant), suggesting that itmay yet be useful as a tool of performance evaluation linked with shareholder value inthe Greek market.

The outline of the paper is as follows. Section 2 reviews the results of the previous

research undertaken on the validity and informational content of EVA. Section 3 presentsour hypotheses and methodology. Section 4 describes our data and the construction ofthe variables employed and the adjustments made in the financial statements. Section 5includes our results and develops a discussion. Finally, Section 6 contains the mainconclusions of our research.

2. Previous Research

The literature relating to EVA, literally begins with the publication of the book The

Quest for Value by Stewart (1991), in which the author exposed his views about theusefulness of EVA as the basis of performance measurement of a company and itsmanagement at a total or a divisional level. In his empirical research he examined

the informational content of EVA canvassing 613 American companies comparingtwo periods, namely 198485 and 198788. He found a strong correlation betweenEVA and MVA, which becomes more apparent when the changes in EVA and MVAare considered giving an R2 of about 97%. However, for companies with a negativeEVA the association becomes less obvious, because of the increased probability ofliquidation or acquisition, which sets a lower limit on the market value of these

companies. In a subsequent study again by Stewart (1994) which investigated theperformance of the largest 1,000 American companies, he reported that the change inEVA explains 50% of the change in MVA (the remaining 50% is explained by thefuture EVA), whereas the change in sales explains only 10% of the change in MVA,comparing it with 1520% of the change in earnings per share (EPS) and 35% ofthe change in ROE. In the same line, the study by OByrne (1996) gave support to

the argument of incremental informational content of EVA. OByrne, using a largesample of American companies for the period 198593, examined the relationship

between the ratio of market value to invested capital (as the dependent variable) andindependent variables, the ratios of EVA/WACC, 6 free cash flow (FCF)/ invested capitaland NOPAT7/invested capital. After several adjustments in the regression equations, suchas setting different coefficients for negative and positive EVAs and creating a dummy

variable which controls for intra-industry differences, OByrne found an R2 of 56%,reaching to the conclusion that EVA explains the value of companies much better thanthe operating profits. Grant (1996), examining a sample of 983 American companiesfor 1983, reached similar conclusions about the validity of EVA by finding that theratio of EVA/WACC explains the 31.6% of the ratio MVA/invested capital. However,he reported that for the 50 companies with the highest EVA (wealth creators) the R2

of regressions increases to 83.2%, while for the 50 companies with the lowest EVA(wealth destroyers) the R2 of regressions falls to 2.7%, implying that investors are less

5

MVA can be defined as the difference between the market value tof the firm (market valueof equity+ book value of debt) and its invested capital t1 (see Appendix 1).6 WACC is the weighted average cost of capital


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likely to proceed to valuations of companies for which they know that they are wealthdestroyers.

However, several other empirical studies offered results arguing against the superiorinformational content of the EVA. For example, Dodd and Chen (1996), who examined

566 American companies for the period 198692, discovered that EVA can explain onlythe 20% of the variability of stock returns, in contrast with ROA which can explainthe 24.5% of the corresponding variability. They found that EVA appeared to havehigher explanatory power when it was compared with ROE and EPS, but when it wascompared with a simple measure of residual income (without the accounting adjustmentsof Stern Stewart) they could not identify any significant incremental informational

content. Peterson and Peterson (1996) for a sample of 282 American companies for theperiod of 198892, evaluated the correlation between traditional performance measures,e.g. ROA and ROE and measures based on added value, such as EVA, MVA, changesin MVA with stock returns. They reported that EVA has a low correlation with stockreturns, while the measures based only on MVA are statistically significantly correlated

with stock returns. Biddle et al. (1997) also offered empirical evidence against thevalidity of EVA, by investigating a large sample of American companies for the 198394

period. They discovered that the earnings before extraordinary items (EBEI) had greaterexplanatory power of stock returns than EVA, residual income and operational cashflows (OCF). Nevertheless, they reported that for companies where their managementremuneration schemes are linked to EVA measures, EVA tends to have a marginally

superior informational content than operating profits. However, this study was criticisedby OByrne (1998) who argued that their model was mispecified, because their variableof profits was not clear from financing costs (interest expenses) and because thedefinition of their independent variable of NOPAT was more like the EVA variable

than operating profits.Furthermore, Bao and Bao (1998) examined the relative informational content of

net income, abnormal economic earnings (their definition of EVA) and value added(defined as sales cost of goods sold depreciation) using a sample of 166 Americancompanies for the period 199293. Their results did not support the argument ofsuperior informational content of the EVA, since they found inconsistent behaviourin the abnormal economic earnings variable, which produced a negative sign when thedependent variable was the value of the firm, and then changed to positive when the

dependent variable was either the stock price or the stock return. The only variable,which consistently generates positive signs with high explanatory power in all three

models, was the value added. In the same category of studies against the validity ofthe EVA, we can also classify that of Chen and Dodd (1998), who found, by using asample of 566 American companies for the period 198392, that EVA did not have an

incremental informational content in explaining the variability of stock returns, whencompared to operating income and simple residual income. However, in the same studythe variable of residual income appeared to have a marginally higher explanatory powerthan operating income.

In a more recent work by Fernandez (2001), who followed a different approach byexamining the correlation coefficients between EVA and MVA for a sample of 582

American companies for the period 198397, it was shown that for 296 firms of the

sample the changes in the NOPAT had higher correlation with the changes in MVA thanthe corresponding changes in EVA, while for 210 sample firms the correlation betweenchanges in EVA and MVA was negative Finally in one of the few published studies


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period 199598, it was reported that the net income variable has a higher informationalcontent than EVA and operating profits, when the dependent variable is the market valueof the companies. However, EVA appeared to have a superior informational content whenthe dependent variable is the MVA. The latter finding implies that EVA may perform

well as a measure of evaluation of management performance, when the goal is themaximisation of shareholders wealth.

Based on the above findings of empirical research carried out so far, we obtain aninconclusive and mixed picture regarding the explanatory power of EVA measures inrelation to stock returns and market value of firms. It also appears from the majority ofthe studies that simple unadjusted measures of residual income (without the accounting

adjustments of Stern Stewart) tend to better explain stock returns. The possible causesfor this phenomenon may be attributed to the likelihood that the market makes differentestimates about the cost of capital, or that the market does not consider appropriatethe accounting adjustments that Stern Stewart suggests. There is also one possibilitythat the empirical studies which offered contradictory results to those conducted by

Stewart (1991, 1994), did not apply correctly all the accounting adjustments applied byStern Stewart. Furthermore, we would like to stress that in emerging markets which arerelatively inefficient, as was the case for Greece before the year 2000, it may be equally

possible that investors are unaware of the true economic value of firms. They solelyrely on traditional measures of performance which are based on GAAP.

What is clear though, from reviewing a number of other studies, is that when the

objective is to examine the performance of firms which have adopted control measuresbased on EVA or MVA, then most researchers (e.g. Lehn and Makhija, 1997; Kleiman,1999) agree that EVA has the highest explanatory power of stock returns than any othervariable and leads to increased operational efficiency (e.g. Wallace, 1996; Lehn and

Makhija, 1997; Zimmerman, 1997). These results imply that EVA may constitute thebasis for establishing an efficient management performance and remuneration system.

3. Hypotheses and Methodology

The first hypothesis we tested refers to the relative information content of EVA. Inparticular, we examined whether the information content of EVA is greater than that ofnet income, operating income and residual income.

Hypothesis 1: EVA explains the variability of stock returns better than net income,

operating income and residual income.The test for the relative information content of EVA was carried out by estimating

for each one of the four measures under comparison the following model. 8

Di t=ai + b1(Ei t/Pi t1) + b2(Ei t1/Pi t1) + ei , (1)

8 This model, which was used by Biddle et al. (1997), correlates unexpected (abnormal)stock returns with the difference between the realised value of the earnings measure (Eit)and its expected value (which is approximated byEit1). According to Biddle et al. (1997), the

positive difference of the realised value of the earnings variable and its expected value shouldlead to higher abnormal stock returns. Model (1) is equivalent to the levels and changes spec-

ification proposed by Easton and Harris (1991) (who argued that both the current level andthe change from last years earnings should be positively correlated with stock returns) sinceit can be easily shown that R = a + b E /P + b (E E )/P + e = = a +


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where

Dit can be either the annual abnormal return of the stock of firm i in yeart(estimatedby the market model) or the raw annual return of the stock

Pit1 is the price of the stock of firmithree months after the beginning of the previous

fiscal year9Eit is the profitability measure (per share) under comparison for firm i in yeart

Eit1 is the profitability measure (per share) under comparison for firm i in theprevious yeart1

eit is the error term (under the usual assumptions of the OLS regression).

The null hypothesis for each one of the three pairwise comparisons is that EVA doesnot provide more information than net income, operating income and residual income,

respectively in explaining the variability of stock returns.Regarding the coefficients of model (1), we expect a positive sign for b1 (since

the current level of the performance measure should be positively correlated with theabnormal stock return) and a negative sign forb2(since a larger value of the performancemeasure for the previous year, as compared with its current level, should be associatedwith smaller abnormal stock returns).

Equation (1) was initially estimated with panel OLS, combined with annual cross-sectional regressions. The time series cross-section model that was selected was thefixed effects model (or least squares dummy variable model). Examination of theresiduals from the initial OLS regressions revealed serious cross-sectional hetero-skedasticity problems for most of the panel regressions. 10 Thus, our model was re-estimated (when necessary) using weighted least squares. Meanwhile, standard errors

and t-statistics for the annual cross-sectional regressions where computed using Whites(1980) correction for heteroskedasticity.

In order to test for the effect of a potential bias in the coefficients, introduced bythe existence of cross-sectional correlation in the residuals, 11 as well as the stationarityassumption embedded in our pooled models12 we calculated the average coefficients

9 In order to reduce heteroskedasticity in the data, all the explanatory variables are deflatedby the stock price three months after the beginning of the fiscal year (Pt1). We allowed athree month period before taking the stock price in order to anticipate the time lag in the

public release of the financial statements information.10

Cross-sectional heteroskedasticity occurs when the residuals for each cross-sectional unitihave different variances j= i (i

2 =j2, forj= i). The statistic we used to test for cross-

sectional heteroskedasticity in the residuals was the Lagrange Multiplier statistic presentedin Greene (1997).11 The test for the existence of cross-sectional correlation in the residuals was carried outwith the Breusch and Pagan (1980) LM test statistic:

L M=Tn

i=2

i=1j=1

r2i j

where, rij= the correlation coefficient for the residuals.12 This is derived from the assumption that the matrix

1Ts

Tt=s+1

(XtXts )

where X is the matrix of explanatory variables converges to a positive semi definite matrix


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from the cross-sectional regressions. If these averages are statistically different fromzero we can assume that any cross-sectional correlation in the residuals does not cause asignificant bias in the estimated coefficients, and that the stationarity assumption is notviolated.

Hypothesis 1 was tested by comparing the R2 of the pooled regressions withindependent variables, each one of the profitability measures under examination.In order to conduct a formal test on the statistical significance of the differencesin the R2 of the pooled regressions, we carried out Coxs (1961) test for non-nested regressions. Coxs test is based on the estimation of the mean squared errorfrom the regressions of the two sets of explanatory variables under compari-

son (X,Z) with the dependent variable (y), and on the sum of squared residualsfrom two auxiliary regressions: fitted values of y from the regression on X (Xb)regressed on Z, and residuals from the regression of Xb on Z regressed on X.13

Coxs statistic is distributed as standard normal N (0,1). The test is about the nullhypothesis that the model with the set X as independent variables is preferable to

the model with regressors Z, and it is repeated for the reversed hypothesis (regressorset Z is preferable to X). Rejection or non-rejection of both hypotheses is an indi-cation that no set of regressors has more explanatory power than the other. In ourcontext, regressor set X can be [EVAt EVAt1]

while regressor set Z can be any of[NIt NIt1]

[OIt Ot1] [RIt RIt1] (since the comparison is made with respect to

EVA).The second hypothesis we tested refers to the incremental information content

of EVA. A test of incremental information content between accounting variablesgives an answer to the question whether the disclosure, by a firm, of supple-mentary accounting and financial measures of profitability, provides more informa-

tion, relevant to firm value and stock returns, than that which is already includedin traditional accounting variables. In particular, we tested whether EVA has any

incremental information content over net income, operating income and residualincome.

Hypothesis 2: EVA provides information content, useful in explaining the variabilityof stock returns, which is not incorporated in net income, operating income and residualincome.

13

Coxs test statistic is computed as follows:q01 =

c01 s2Xs2Z X

2 bXMZMXMZX bwhere:

c01 =n

21n

s2Z

s2X+ (1/N)bXMZX b

s2X=(1/N)eX

eX=mean squared residual from the regression on y to Xs2Z =(1/N)e

Z

eZ=mean squared residual from the regression on y to ZMX=IX(X

X)1 X

MZ=IZ(ZZ)1 Z

s2Z X=s 2X+s2Z+ (1/N)bXMZX bMZX b = residuals from the regression ofX

bto ZbX M X b = sum of squared residuals from the regression of X b to Z


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In order to test the incremental information content of EVA we developed its basiccomponents, and estimated the following model:

Di t=ai + b1(NIi t/Pi t1) + b2(NIi t1/Pi t1) + b3(OIADJi t/Pi t1)

+ b4(OIADJi t1/Pi t1) b5(CAPCHGi t/Pi t1) b6(CAPCHGi t1/Pi t1)+ b7(STSTADJi t/Pi t1) + b8(STSTADJi t1/Pi t1) + ei t (2)

where

Dit can be either the annual abnormal return of the stockof firm i in year t(estimated by the market model) orthe raw annual return of the stock

Pit1 is the price of the stock of firmi in yeart

NIitand NIit1 is the net income (per share) for firm i at the end ofyeartand at the beginning of yeart respectively

OIADJitand OIADJit1 is the operating income adjustments (operating income net income) (per share) for firmi at the end of yeartand at the beginning of yeart respectively

CAPCHGitand CAPCHGit1 is the capital charge (WACCt total assetst1) for firmi at the end of year t and at the beginning of year trespectively

STSTADJit is the Stern Stewart adjustments (adjustments to profits WACCt adjustments to invested capitalt1) for firm

i at the end of year t and at the beginning of year trespectivelyeit is the error term (under the usual assumptions of the

OLS regression).

Since EVA = NI + OIADJ CAPCHG + STSTEWADJ, we can test the nullhypothesis that EVA does not provide any additional information, useful in explainingthe variability of stock returns, than that which is already incorporated in net income,or operating income, or residual income in pairwise comparisons.

Each hypothesis was tested using a Wald test on the estimated coefficients and the

above restrictions. With respect to the regression coefficients, we expect a positive signfor the coefficient of current years NI and a negative sign for the coefficient of CAPCHG(since a larger capital charge corresponds to a lower residual income, and in theory, tolower stock returns). The coefficients of the one lag variables for NI and CAPCHG areexpected to have the opposite sign, while OIADJ and STSTEWADJ can have any sign.

The test of hypothesis 2 was based on the estimates of the pooled regression, whileannual cross-sectional regressions were also estimated, in order to test the significanceand the consistency of the coefficients.

Our third hypothesis refers to the relationship between market value added (MVA),defined as the difference between the firms market value and its invested capital and

EVA. MVA should equal the discounted present value of the whole EVA a company isexpected to generate in the future, and therefore should be highly correlated with EVA.In particular we tested Stewarts (1991) claim that EVA tracks changes in MVA better


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Hypothesis 3: The correlation between EVA and MVA is higher than the correlationbetween net income, operating income, residual income and MVA.

The test for hypothesis 3 was based on the estimates of the following model:

MVAi t/ICi t1 =ai + b1(Ei t/ICi t1) + ei t (3)

where

MVAit is the market value added of firmi at the end of yeartICit1 is the invested capital of firmi at the beginning of yeart

Eit is a profitability measure of firmi at the end of yearteit is the error term (under the usual assumptions of the OLS regression).

The null hypothesis for each one of the three pairwise comparisons is that thecorrelation between EVA and MVA is not greater than the correlation between netincome and MVA, or not greater than the correlation between operating income and

MVA or not greater than the correlation between residual income and MVA.Hypothesis 3 was tested in the same manner as Hypothesis 1 that is by comparing

the R2s of the pooled regressions, with independent variables, each one of the prof-itability measures under examination, and by conducting the Coxs test for non-nestedregressions. Regarding the coefficient sign, it is expected to be positive, since higherlevels of each profitability measure should be consistent with a greater market value for

a firm.

4. Description of Data and Variables

The data used in this study include the financial statements and adjusted stock pricesfor 121 non-financial publicly traded Greek firms covering a period of eight years,from 1996 to 2003. Based on these data we obtained 968 annual observations for netincome (NI), operating income (OI), the weighted average cost of capital (WACC),EVA, residual income (RI), operating income adjustments (OIADJ), capital charges

(CAPCHG) and Stern Stewart adjustments (STSTADJ), and 847 observations for theabnormal stock returns (D).14 This initial sample was reduced to 856 observations (749for the abnormal stock returns) after 14 firms were omitted from the sample, 12 ofwhich due to extreme observations (outliers)15 and two firms because their fiscal yeardid not end in December.

Data about adjusted stock prices, dividends, and the Athens Stock Exchange generalindex price were obtained from Datastream, while financial reports were obtained fromthe Effect Finance Greek database. Information about 10 year government bond returns,which were used as a proxy for the risk free rate, were also obtained from Datastream.The variables used in this study are defined in Appendix 1.

From the adjustments proposed by Stern Stewart we applied the following:

1. Capitalisation of R&D expenses;2. Capitalisation of provisions;3. Subtraction of the interest tax shield from the operating profits;

14 Due to the computation of the market model parameters, data for the abnormal stockreturns are only available from 1997 to 2003


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4. Subtraction of the taxes from non operating profits;5. Subtraction from the total invested capital of f ixed assets under construction;6. Addition of accumulated depreciation of goodwill.

We did not apply the adjustments of capitalisation of operational lease payments and

the conversion of the LIFO method to FIFO of valuing inventory because, according tothe Greek GAAP, the relevant data are not reported in the published financial statements.The adjustment of deferred taxes was not made because again, according to the Greek

GAAP, there is no corresponding account in the financial statements of the Greekcompanies. The change of depreciation method from straight line to increasing wasnot applied as long as this concerns fixed assets with long life, something that cannot

be detected from published financial statements in Greece. Finally, the adjustment forthe unrecorded goodwill was not made because retrospective recognition of goodwillrequires data compiled at the time of the acquisition, making the collection of such

data an extremely tedious task requiring internal information from each company

individually. Moreover, the number of M&As involving the firms in our samplewas not large enough for the adjustment to have a material impact on the EVAcalculated.

Table 1 depicts some descriptive statistics of the variables employed in our analysisfor the sample of the 19972003 period for which we reported our results.

Table 1

Descriptive statistics of the variables used

Descriptive statistics on net income (NI), operating income (OI), residual income (RI), economic

value added (EVA), operating income adjustments (OIADJ), capital charge (CAPCHG), SternStewart

adjustments (STSSTADJ), annual abnormal return (D) and market value added (MVA). All theprofitability variables were divided by the price at the beginning of period t and were estimated

for the total observations of our sample across firms and time. MVA is divided by the firms adjusted

capital at the beginning of the year

NI OI RI EVA OIADJ CAPCHG STSTADJ D MVA

Mean 0.040 0.081 0.108 0.094 0.041 0.189 0.014 0.044 1.920

Median 0.044 0.053 0.042 0.032 0.013 0.110 0.005 0.108 0.704

Maximum 1.425 1.155 0.598 0.413 2.306 2.164 0.519 2.709 67.461

Minimum 2.449 1.381 2.454 2.364 1.143 0.003 0.526 2.798 1.746

S.D. 0.266 0.158 0.258 0.230 0.226 0.233 0.061 0.694 4.536

The operating profit shows the highest mean value among the income variables,whereas the residual income has the lowest mean value, because of the high positivevalues of the Stern Stewart adjustments in profits and invested capital. The EVA has

a negative mean value. Apart from the MVA, which appears to be the most volatilevariable in our study, net income has the highest standard deviation among the other

profitability measures.

5. Results and Discussion

With respect to the three hypotheses set in section 3 we first decided to report


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relation to the other profitability variables, and second to report the results based on asensitivity analysis which will use raw annual stock returns as the dependent variable,the first differences in the explanatory variables and add the market risk premium inthe explanatory variables. At the end of this section we report the results of our MVA

models.

5.1. Basic analysis

Relative information content. Panel A of Table 2 presents the results of the regressionsof annual abnormal returns on each of the profitability measures under comparison.

Examining panel A of Table 2 we observe that all pooled regressions have statisticallysignificant coefficients for each profitability measure under examination. Meanwhile,the t-test on the average annual coefficients reveals that in all regressions bothcoefficients are statistically different from zero, with the exception of the NI model,

where only the current year coefficient is statistically different from zero. As a result,we can assume that the presence of cross-sectional correlation in the residuals does notintroduce any bias problems in the annual coefficients of OI, NI and EVA.

Hypothesis 1, regarding the informational content of EVA, was tested initially withcomparisons of the R2 of the panel regressions. The highest R2 is observed in theregression with OI as the explanatory variable (R2 = 16.85%) (although the coefficientfor lagged OI has the opposite sign to what was expected), which is followed by

NI (R2 = 9.31%), while RI (R2 = 7.91%) and EVA (R2 = 6.89%) appear to have

the smallest explanatory power with respect to abnormal stock returns.The results of the Cox tests on the four pooled regressions are presented in detail

in Appendix 2. The p-values reported represent the probability that the null hypothesisof one income variable outperforming the other in terms of information content iscorrect. The zero p-values in the comparison of EVA with OI, suggest that EVA and

OI have equal information content. On the other hand RI appears to outperform EVA(p-value = 0.4509085). However, NI seems to have more value relevance than EVA(p-value = 0.0343061).

The first modification to our initial specification was the use of the raw annual stockreturn as the dependent variable. This modification was chosen for two reasons. Thefirst was to avoid potential problems caused by a wrong assessment of the information

content of the earnings variables that would be the result of a wrong estimation of the

market model parameters a andb. The second was to obtain results compatible withthose of other researchers like Chen and Dodd (1998) and Bao and Bao (1998) who,in the spirit of Easton and Harris (1991), correlated raw stock returns with the currentlevel and the change in the companys earnings.

Panel B of Table 2 presents the results of the regressions of the raw annual stock

returns on each of the profitability measures under comparison. An examination of theR2 of the panel regressions reveals that OI still appears to have the greatest relativeexplanatory power (R2 = 16.65%) over the other profitability measures, even thoughthe coefficient for lagged OI again has the wrong sign. OI is again followed by the

NI model (R2 =8.22%). RI comes third (R2 =7.61%), while EVA seems to have the

least explanatory power with respect to raw stock returns. (R2

=5.80%)According to the Cox test results on the panel regressions, we cannot support the

argument that EVA has greater information content than the other variables under


13/31


Ta

ble2

Regressionsofannualstock

returnsonprofitabilitymeasures

derlying

equationisDit=

ai+

b1(Eit/Pit

1)+

b2(Eit

1/Pit

1)+

eit(1),w

here,forpanelA,Ditistheannu

alabnormalreturn(ascomputed

bytheresiduals

hemark

etmodel),whileforpanelB,Ditistheannualrawstockreturn,E

it/Pit1isoneoftheprofitability

measures(pershare)undercomparison(NI,OI,

orEVA

)scaledbythestockpriceatthe

beginningoftheyear.Thefirste

ightrowsforeachpanelrepresen

tresultsforthefixedeffectspoo

ledregressions,

reforeachcross-sectionadifferentinterceptisestimated.Thenumberofobservationsforthepooledregressionsis749forpanelAand8

56forpanelB.

second

partofeachpaneldepictstheaveragevalueofthecoefficientsfromtheannualregressions,whichwasestimatedinordertotestforthepotential

intheestimatedcoefficientsinducedby

thecross-sectionalcorrelationintheresiduals.valuesinthefirst,th

ird,fifthandseventhrowrepresenttheestimated

fficientsofthefourregressions,witheachoneoftheIncomeMeasuresastheexplanatoryvariable.FisthevalueoftheF-statisticandR2isth

ecoefficientof

rminationforeachregression.Thet-statisticsarepresentedinparentheses,with,

,and

denotingstatisticalsignificanceatthe0.01,0.05and0.10level

ectively.Significancelevelsarecompute

dforone-sidedt-tests,withN-2d

egreesoffreedomexceptfortheinterceptwhereatwo-sidedtestis

conducted.For

average

thedegreesoffreedomare6for

panelAand7forpanelB.

nelA:R

egressionsofannualabnorma

lreturnsonprofitabilitymeasures

Intercept

NIt

NIt1

OIt

OIt1

RIt

RIt1

EVAt

EVAt1

F

R2(%)

ol

f.e.

1.12

0.74

76.67

9.31

(4.

87)

(3.69)

f.e.

1.79

0.23

151.38

16.

85

(6.

27)

(0.

82)

f.e.

0.61

0.88

64.16

7.91

(2.

79)

(4.47)

f.e.

0.48

0.76

55.27

6.89

(2.

34)

(4.21)

erage

0.04

0.60

0.03

-

5.82

(0.21)

(4.

04)

(0.04)

0.09

2.86

0.96

-

7.65

(0.58)

(2.

40)

(3.08)

0.06

0.67

1.36

-

6.16

(0.39)

(3.

63)

(3.06)

0.06

0.32

1.11

-

5.22

(0.38)

(3.

09)

(4.05)


14/31


nelB:R

egressionsofannualrawstockreturnsonprofitabilitymeasu

res

Intercept

NIt

NIt1

OIt

OIt1

RIt

RIt1

EVAt

EVAt1

F

R2(%)

ol

f.e.

1.10

0.52

76

.

47

8.22

(3.

90)

(2.13)

f.e.

1.70

0.39

170

.

55

16.

65

(4.

63)

(1.

07)

f.e.

0.62

0.89

46

.

05

7.61

(2.

99)

(4.67)

f.e.

0.36

0.68

52

.

52

5.80

(2.

02)

(3.87)

erage

0.04

0.76

0.19

-

7.47

(0.19)

(3.

52)

(0.50)

0.00

2.16

1.08

-

9.87

(0.01)

(3.

60)

(2.41)

0.09

0.97

0.93

-

7.42

(0.42)

(4.

09)

(2.29)

0.09

0.72

0.74

-

7.19

(0.40)

(3.

94)

(1.85)


15/31


Incremental information content. Panel A of Table 3 depicts the estimated output ofregressions of annual abnormal returns on the components of EVA.

An examination of panel A of Table 3 reveals that the panel regression is statisticallysignificant as shown by the value of the F-statistic. In addition to this, all variables have

at least one average annual coefficient statistically different from zero, which allowsus to assume that the significance of the earnings coefficients for those variables is

possibly not a result of potential cross-sectional correlation in the residuals. Examiningthe coefficients of the panel regression, we observe that only the coefficients for current

NI, OIADJ and STSTADJ are significant at the 10% level at least, while both coefficientsfor CAPCHG are non-significant.

The test for the incremental information content of EVA was carried out using Waldtests on the coefficients of each variable in the pooled regression (4). The resultsof the pairwise F-tests on each of the hypotheses under examination are presentedin Appendix 2. According to these results (zero p-values in the first two tests), theassumption that EVA does not have any incremental information content over NI, OI

is rejected. However the result of the third test (p-value = 0.1554) suggests that thehypothesis that EVA does not have any incremental information content over RI cannot

be rejected, implying that the Stern Stewart adjustments (at least those applied in thisstudy) do not add any useful information to the RI measure.

Taking the results of the regression estimation and the Wald tests as a whole, wecan conclude that for the elements unique to EVA, both the capital charge (which is

also included in RI) and the Stern Stewart adjustments do not have any value relevantinformation additional to that which is already incorporated in the traditional accountingvariables.

The estimated output of regressions of raw stock returns on the components of

EVA is depicted in Panel B of Table 3. As we can see by looking at panel B, thelagged coefficients for NI and OIADJ and the current year coefficient for CAPCHG are

not statistically significant, while both Stern Stewart adjustments coefficients are notstatistically significant. On the other hand, all variables, with the exception of the capitalcharge, have at least one average annual coefficient significantly different from zero,implying that any cross-sectional residual correlation does not alter the significance ofthe estimated coefficients.

Based on the results of the Wald tests for the incremental information content of

EVA, the assumption that EVA does not contain any additional information over NI,OI is again rejected, while the hypothesis that EVA does not have any incremental

information content over RI is not rejected, as was the case in our basic analysis.Considering the results of the above information content tests as well as the regressionoutputs, we conclude that, even when the raw stock returns are the dependent variable, we

cannot argue that the capital charge and the Stern Stewart adjustments add significantlyto the information content of EVA, since the coefficients for the two variables are notconsistently significant through both models.

5.2. Sensitivity analysis changes analysis models

The second modification we applied to our initial model was the inclusion (as an

explanatory variable) of only the first difference of the profitability measure undercomparison. The reason behind this modification lies in the examination of the resultsin our initial 1 lag levels model which revealed that in a very few cases both coefficients


16/31


Ta

ble3

Regressionsofannualstoc

kreturnsonEVAcomponents

derlying

equationisDit=

ai+

b1(NIit/Pit1)+

b2(NIit1/Pit1)+

b3(OIAD

Jit/Pit1)+

b4(OIADJit1/Pit1)+

b5(CAPCHGit/Pit1)+

b6(CAP

CHGit1/Pit1)

7(STSTEWADJit/Pit1)+

b8(STSTADJit1/Pit1)+

eit(2),whereDitisthe

annualabnormalreturn(ascomp

utedbytheresidualsofthemarketmodel),while

PanelB

,Ditistheannualrawstockreturn,NIisthefirmsnetincome,OIADJisoperatingincomeadjustments(i.e.thedifferencebetweenN

Iandoperating

ome),C

APCHGisthecapitalcharge(W

ACCt

investedcapitalt1),and

STSTEWADJaretheSternStewa

rtadjustmentsonoperatingprofitsandinvested

tal.All

variablesarescaledbythestockpriceatthebeginningoftheyear.

Thefirsttworowsrepresentresultsforthefixedeffectspooledreg

ressions,where

eachcross-sectionadifferentinterceptisestimated.Thenumberofobse

rvationsforthepooledregressionsis749forpanelAand856forpanelB.The

ondpartofeachPaneldepictstheaverag

evalueofthecoefficientsfrom

theannualregressions,whichwa

sestimatedinordertotestforth

epotentialbias

heestim

atedcoefficientsinducedbythecross-sectionalcorrelationintheresiduals.Thet-statisticsarepresentedinparentheses,with,

,a

nd

denoting

sticalsignificanceatthe0.010.05and0.10levelrespectively.Significancelevelsarecomputedforone-sidedt-tests,withN-8degreesoffreedomexceptfor

intercep

twhereatwo-sidedtestisconducted.Fortheaveragethedegreesoffreedomare6forpanelAand7forpanelB..

nelA.R

egressionsofannualabnormalreturnsonEVAcomponents

a1

b1

b2

b3

B4

b5

b6

b7

b8

F

R2(%)

ol

f.e.

1.97

0.27

1.18

0.13

0.05

0.55

0.80

0.19

29.389

21.73

(5.

69)

(0.76)

(3.

47)

(0.

34)

(0.34)

(0.11)

(1.

66)

(0.

48)

erage

0.29

2.98

0.52

3.03

1.09

0.31

1.56

1.68

0.47

-

23.62

(1.81)

(3.

25)

(0.87)

(2.

60)

(1.96)

(0.73)

(2.68)

(3.

21)

(0.

64)

nelB.R

egressionsofannualrawstockreturnsonEVAcomponents

a1

b1

b2

b3

b4

b5

b6

b7

b8

F

R2(%)

ol

f.e.

1.84

0.08

1.26

0.32

0.37

0.97

1.03

0.27

31.41

20.59

(4.

16)

(0.21)

(3.

08)

(0.

78)

(0.

99)

(2.63)

(1.

58)

(0.51)

erage

0.02

2.54

1.29

2.54

1.74

0.14

0.82

1.01

0.26

-

20.31

(0.09)

(4.

36)

(5.67)

(3.

53)

(2.74)

(0.

39)

(1.89)

(2.

31)

(1.31)


17/31


Table 4

Regressions of annual abnormal returns on the f irst difference of profitability measures

Underlying equation isD it=ai + b1(Eit/Pit1) + eit (4), whereD itis the annual abnormal return

(as computed by the residuals of the market model), Eit/Pit1 is the first difference of one of the

profitability measures (per share) under comparison (NI, OI, RI, or EVA) scaled by the stock price atthe beginning of the year. The first eight rows represent results for the fixed effects panel regressions,

where for each cross-section a different intercept is estimated. The number of observations for the

panel regressions is 749. The second part of the table depicts the average value of the coefficients

from the annual regressions, which was estimated in order to test for the potential bias in the estimated

coefficients induced by the cross-sectional correlation in the residuals. Values in the first, third, fifth and

seventh row represent the estimated coefficients of the four regressions, with each one of the Income

Measures as the explanatory variable. R2 is the coefficient of determination for each regression. The

t-statistics are presented in parentheses, with , , and denoting statistical significance at the

0.01, 0.05 and 0.10 level respectively. Significance levels are computed for one-sided t-tests, with N-1

degrees of freedom except for the intercept where a two-sided test is conducted. For the average the

degrees of freedom are 6.

Intercept NIt OIt RIt EVAt R2(%)

Pool f.e. 0.88 7.61

(4.42)

f.e. 0.88 5.17

(3.28)

f.e. 0.83 6.93

(3.91)

f.e. 0.71 6.13

(3.66)

Average 0.03 0.20 3.19(0.19) (0.57)0.02 1.31 3.46

(0.13) (3.50)

0.02 1.05 3.49(0.14) (3.58)

0.03 0.52 2.72(0.16) (4.98)

used in the present study, only the change in the income measure should have a significant

explanatory power with respect to the abnormal stock returns.

Relative information content. Table 4 illustrates the results of the regressions of annualabnormal returns on the first difference of the profitability measures under comparison.

What is made clear by looking at Table 4, is that all variables are statistically significantat the 1% level. However, the average annual coefficients are statistically different fromzero for OI, RI and EVA, suggesting that we should be very cautious interpreting theresults concerning NI due to the potential bias introduced by the presence of cross-

sectional correlation in the residuals. With the exception of the NI model, which explains7.61% of the variability of abnormal returns, the differences in R2 for the rest of the

pooled models are very small, implying statistically non-significant differences in theinformation content of RI, OI and EVA.

A di t th C t t lt t j t th h th i th t EVA d t


18/31


Table 5

Regressions of annual abnormal returns on the first difference of EVA components

Underlying equation isD it=a i +b 1(NIit/Pit1)+ b 2 (OIADJit/Pit1)+ b 3(CAPCHGit/Pit1)

+ b4 (STSTADJit/Pit1) + eit (5) where Dit is the annual abnormal return (as computed by the

residuals of the market model), NI is the firms net income, OIADJ is operating income adjustments(i.e. the difference between NI and operating income), CAPCHG is the capital charge (=WACCt invested capitalt1), and STSTEWADJ are the SternStewart adjustments on operating profits and

invested capital. All variables are scaled by the stock price at the beginning of the year. The first two

rows represent results for the f ixed effects panel regressions, where for each cross-section a different

intercept is estimated. The number of observations for the panel regressions is 749. The second part

of the table depicts the average value of the coefficients from the annual regressions, which was

estimated in order to test for the potential bias in the estimated coefficients induced by the cross-

sectional correlation in the residuals. The t-statistics are presented in parentheses, with , , and

denoting statistical significance at the 0.01, 0.05 and 0.10 level respectively. Significance levels are

computed for one-sided t-tests, with N-4 degrees of freedom except for the intercept where a two-sided

test is conducted. For the average the degrees of freedom are 6.

a1 b1 b2 b3 b4 F R2 (%)

Pool f.e. 0.96 0.31 0.63 0.28 23.52 8.65

(3.44) (1.16) (1.69) (0.81)Average 0.04 1.28 1.62 0.97 0.40 8.88

(0.26) (4.45) (2.54) (2.75) (1.26)

pairwise comparisons are zero. In addition when compared to RI EVA appears to have aslight relative information disadvantage (p-value= 0.2700222 vs 0.032490).

Incremental information content. Table 5 presents the results of the regressions ofannual abnormal returns on the first difference of EVA components.

An examination of Table 5 reveals that only when the first differences of the EVAcomponents are included in the regression, then only NI is statistically significant atthe 1% level, while capital charge is significant at the 10% level. This result impliesthat, contrary to our expectations, the change in operating income adjustments and Stern

Stewart adjustments is of no importance to the market, whereas, as shown in the previoussections, their levels are statistically significant.Based on the results of the Wald tests for the incremental information of the first

difference of EVA components, we find that the hypothesis that EVA does not containincremental information over NI, OI or RI cannot be rejected (p-values of 0.1728, 0.2075and 0.4203 respectively).

According to the evidence of the above tests the first difference of OIADJ, CAPCHGand STSTADJ does not add any useful information to that already incorporated in thechange in NI.

Taken as a whole, the tests on the first differences model do not allow us todraw definite conclusions regarding the relative importance of the income measuresunder comparison, since the differences in explanatory power are too small to matter

statistically. We can thus conclude that the change in any income measure is equally


19/31


5.3. Sensitivity analysis an extension of the market model using profitabilityvariables

The third modification to our initial model entails the inclusion of the excess return

of the market as an additional explanatory variable, combined with the replacement

of the abnormal stock return with the excess stock return (over the risk free rate).In essence we attempted to expand our initial returns earnings model with a marketvariable, ending up with an expanded version of the excess return market model, usingfirm specific profitability variables. This modification aims at enhancing the robustnessof our comparison of profitability measures, since adding a market parameter in our

initial models would significantly increase the explanatory power of the regressions.The model we estimated was the following:

Ri t r ft=ai + b1(Et/Pi t1) + b2(Et1/Pi t1) + b3(Rm t r ft) + ei t (6)

where

Rit is the raw stock returns for firm i in yeartrft is the annual risk free rate (10 year government bond yield) in yeart

Pit1 is the stock price for firm i at the beginning of yeartEit is the income measure under comparison (per share) for firmi in yeart

Eit1 is the income measure under comparison (per share) for firm i in yeart1Rmt is the annual return of the market (general market index) in yeart

eit is the error term (under the usual assumptions of the OLS regression).

Relative information content. The results of the estimation of model (6) for each ofthe four income variables under comparison are presented in Table 6.

An examination of Table 6 reveals that all variables have both coefficients statistically

significant. Also, as expected, the addition of the market premium variable improvesgreatly the explanatory power of the models. However, it is worth noting that all the

profitability measures of our analysis remain significant despite the addition of a verystrong explanatory variable. As far as the R2 of the competing models are concerned,the EVA model seems to have a small advantage over the other variables, but thedifferences in their explanatory power are too small to allow us to draw any specific

results without a formal test.

Based on the results of the Cox tests, we cannot reject the null hypothesis of equalinformation content for the two-way tests between EVA and the other variables beinganalysed. Meanwhile the p-values for the tests between EVA and RI are almost equal,

being close to 0.05 with a small but insignificant advantage of EVA (p-value = 0.05 vs.0.045).

Incremental information content. Table 7 presents the results of our EVA componentsmodel extended with the excess market return. Equation (7), which is described inTable 7, is the same as equation (2) with the excess return over the risk-free rate insteadof the abnormal stock return (Dit) as the dependent variable plus the risk premium

variable (Rm rf) as an additional independent variable.From the results presented in Table 7, we observe that NI, OIADJ and CAPCHG have

at least one statistically significant coefficient with the exception of STSTADJ which


20/31


Ta

ble6

Excessreturnmarketm

odelwithincomevariables

derlying

equationisRit

rft=

ai+

b1(E

t/Pit

1)+

b2(Et1/Pit

1)+

bi3(Rmt

rft)+

eitt(6),whereRitistheannualrawstockreturn,rfistheriskfreerate

yieldo

fa10yeargovernmentbond),Rm

istheannualreturnofthemarket

andEit/Pit1isoneoftheprofitabilitymeasures(pershare)undercomparison(NI,

RI,orE

VA)scaledbythestockpriceatthebeginningoftheyear.Thenum

berofobservationsforthepanelregressionsis856.Valuesinthe

first,third,fifth

seventh

rowrepresenttheestimatedcoefficientsofthefourregressions,w

itheachoneoftheIncomeMeasuresastheexplanatoryvariable.Fisthevalueof

F-statisticandR2isthecoefficientofde

terminationforeachregression.T

hecoefficientspresentedinthetenthcolumnaretheaveragebetasofthefirms,

eadifferentcoefficientwascomputedfo

reachcross-section.Thet-statisticsarepresentedinparentheses,w

ith,

,and

denotingstatisticalsignificance

he0.01,

0.05and0.10levelrespectively.Significancelevelsarecomputedforone-sidedt-tests,withN-109degreesoffreedomexceptforthe

interceptwhere

wo-sided

testisconducted.

ercept

NIt

NIt1

OIt

OIt1

RIt

RIt1

EVAt

EVAt1

Rmt

rft

F

R2(%)

0.49

0.38

1.27

13.

61

66.

03

(3.

02)

(2.50)

(5.

04)

0.76

0.68

1.28

13.

71

66.

20

(3.

50)

(2.91)

(4.

99)

0.7

3

0.39

1.31

14.

02

66.

96

(4.1

1)

(2.29)

(5.

17)

0.72

0.32

1.31

14.

10

67.

09

(4.

13)

(2.03)

(5.

21)


21/31


Ta

ble7

RegressionsofexcessstockreturnsonEVAcomponentsandtheexcess

marketreturn

derlying

equationisRit

rft=

ai+

b1(NIit/Pit1)+

b2(NIit1/P

it1)+

b3(OIADJit/Pit1)+

b4(OIADJit1/Pit1)+

b5(CAPC

HGit/Pit1)+

CAPCHGit1/Pit1)+

b7(STSTEWADJit/Pit1)+

b8(STSTADJit1/Pit1)+

b9(Rmt

rft)+

eit(7),whereRitistheannualrawstockr

eturn,rfisthe

freerate(theyieldofa10yeargovernmentbond),Rmistheannualreturn

ofthemarket,NIisthefirmsnetincome,OIADJisoperatinginco

meadjustments

thedifferencebetweenNIandoperatin

gincome),CAPCHGisthecapitalcharge(WACCt

investedcapitalt1),andSTSTEWADJaretheSternStewart

ustmentsonoperatingprofitsandinvestedcapital.Thenumberofobservationsforthepanelregressionsis856.Valuesinthefirst,third,fifthandseventhrow

esentth

eestimatedcoefficientsofthefourregressions,witheachoneofth

eIncomemeasuresastheexplanatoryvariable.FisthevalueoftheF-statisticand

sthecoefficientofdeterminationforeachregression.Thecoefficientspresentedinthetenthcolumnaretheaveragebetasofthefirms,sinceadifferent

fficient

wascomputedforeachcross-section.Thet-statisticsarepresented

inparentheses,with,

,and

denotingstatisticalsignificanceatthe0.01,0.05

0.10levelrespectively.Significanceleve

lsarecomputedforone-sidedt-tests,withN-109degreesoffreedo

mexceptfortheinterceptwhere

atwo-sidedtest

onducte

d. b1

b2

b3

b4

b5

b6

b7

b8

b9

F

R2(%)

0.92

0.40

0.82

0.23

0.65

0.21

0.17

0.21

1.33

15.

30

68.

87

(3.42)

(1.51)

(3.10)

(0.80)

(2.

64)

(0

.

81)

(0.

39)

(0.69)

(5.

13)


22/31


The results produced by the Wald tests conducted for this specification are consistentwith those performed for our initial models and justify the rejection of the null hypothesesthat EVA does not contain incremental information over NI or OI, while the assumptionthat EVA has no significant incremental information content over RI cannot again

be rejected. This result is consistent with our initial assertion that the Stern Stewartadjustments, which is the only component unique to EVA, do not seem to add significantvalue relevant information to the RI measure.

Taking into account both types of tests, we cannot argue that the marginal advantageof EVA over RI in the Cox tests could be attributed to the Stern Stewart adjustmentssince they appear to be non-significant. Meanwhile, the results of the tests between all

the other variables reveal that there is no dominant profitability measure in terms ofinformation content. This result implies that when a very powerful market variable(Rm rf) is included in our model, it is very difficult to make comparisons on the valuerelevance of income measures, even though all the variables tested were statisticallysignificant.

5.4. Final assessment of the sensitivity tests

Considering the results of the sensitivity analysis as a whole, we can argue that our initialresults regarding the relative value relevance of EVA have substantial validity. For mostof our re-specifications, EVA does not appear to outperform the other variables in ouranalysis in terms of their association to stock returns. Meanwhile, EVA does not appearto have any significant advantage over RI in all our settings, suggesting that the Stern

Stewart adjustments applied by the present study do not add significant information to

the residual income measure.With respect to the variable with the greatest value relevance, based on the R2s of thepanel regressions, we observe that OI appears to have the greatest explanatory powerin our one lag levels models, while NI has the edge when only the first differencesare taken into account. Still, the test results between the two variables show that the

difference in explanatory power is not statistically significant, suggesting that bothaccounting variables are equally well associated with stock returns.

The EVA unique components do not appear to be consistently statistically significantin all our respecifications. All the tests imply that their significance is probably small, aslong as this is not enough to give EVA greater information content than the traditional

income variables.

5.5. MVA models

Table 8 depicts the estimated output of regressions of MVA on the four profitability

measures under comparison.We can clearly see from Table 8, that all variables of our analysis are significant at

the 1% level, and each one explains about one third of the variability of the firms MVA.In addition, all the annual cross-sectional averages are significantly different from zero,suggesting that the significance of the pooled coefficients is unlikely to be the result of

cross-sectional correlation in the residuals.Regarding the relative explanatory power of the variables under comparison, EVA

(R2 = 42 71%) seems to dominate NI OI and RI which follow with R 2s of 38 05%


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Table 8

Regressions of market value added on profitability measures

Underlying equation isMVAit /ICit1=ai +b1(Eit/ICit1) +e it (3),whereMVAitis the market value

added per share of the firm (i.e. the difference between the market value and the book value of the

firm),Eit/Pit1is one of the profitability measures (per share) under comparison (NI, OI, RI, or EVA)scaled by the invested capital at the beginning of the year. The first eight rows represent results for

the fixed effects panel regressions, where for each cross-section a different intercept is estimated.

The number of observations for the panel regressions is 880. The second part of the table depicts the

average value of the coefficients from the annual regressions, which was estimated in order to test

for the potential bias in the estimated coefficients induced by the cross-sectional correlation in the

residuals. Values in the first, third, fifth and seventh row represent the estimated coefficients of the

four regressions, with each one of the income measures as the explanatory variable. F is the value

of the F-statistic and R2 is the coefficient of determination for each regression. The t-statistics are

presented in parentheses, with , , and denoting statistical significance at the 0.01, 0.05 and 0.10

level respectively. Significance levels are computed for one-sided t-tests, with N-1 degrees of freedom

except for the intercept where a two-sided test is conducted. For the average the degrees of freedomare 7.

Intercept NIt OIt RIt EVAt R2 (%)

Pool f.e. 3.30 38.05

(13.56)

f.e. 5.76 33.69

(20.66)

f.e. 6.52 36.57

(22.49)

f.e. 8.30 42.71

(23.73)

Average 1.25 2.72 7.35(2.74) (3.05)

0.82 6.53 14.06(2.17) (3.81)

1.67 5.35 10.19(3.20) (4.08)

1.62 7.58 13.26(3.30) (3.23)

should equal the present value of all EVAs expected to be earned by the company in thefuture and therefore should be more highly correlated with MVA than the traditionalaccounting variables. However, based on the results of the Cox tests on these regressionswe cannot reject the null hypothesis that EVA does not have a significantly greater

correlation with MVA than the other variables under comparison. Consistent with theresults of our previous returns-based analysis, EVA does not appear to explain thevariability of MVA significantly better than NI, OI or RI (p-values in all pairwisecomparisons are zero). Even though the EVA model has the largest explanatory power(as represented by the high R2, this difference does not appear to be statistically

significant, according to the Cox test results. This finding fails to provide adequatesupport for Stewarts (1991) claim that EVA tracks changes in MVA better than anyother performance measure since it appears that the other earnings measures are equally


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6. Conclusions

Motivated by the absence of a similar study for a small emerging European market, such

as the ASE, which is passing through a transitory phase from emerging status to mature,this paper investigated the information content of EVA and unadjusted residual income,

in comparison with two established accounting measures of performance, namely thenet income and the operating income.

Our findings based on our ASE dataset, do not support Stern Stewarts claims that EVAis more correlated with stock market returns. In fact, net income and operating income

appear to have the greatest relative information content with respect to both abnormaland raw stock returns, a result which is consistent through most of our sensitivity analyseswhich involved different specifications of our initial statistical models. Furthermore, theEVA unique components, capital charge and Stern Stewart adjustments, do not appearto have significant incremental information content, and thus they do not add greatervalue relevance to the EVA measure.

These results obtained from a sample of companies with different characteristics(e.g. transitory nature of emerging status, small size, different accounting standardsand ownership structure) than those of the US market, seem to be consistent with thefindings of Biddleet al. (1997), Peterson and Peterson (1996), Chen and Dodd (1998)for a sample of US companies, which also found no evidence of EVA outperforming

various specifications of Operating Income, with respect to their association with stockreturns.

In addition, we examined the relationship between the firms MVA and EVA, to findthat EVA does not outperform significantly the other variables in our analysis, failingto provide adequate support to one of Stern Stewarts basic claims, according to which

MVA for each period should equal the current realisation of EVA plus the present valueof all expected future EVAs.

In an attempt to answer the question, why we failed to detect stronger value relevancefor EVA, we can think of four possible explanations. The first one is related to theunderlying assumptions in estimating the betas, the cost of debt and the WACC, whichmay be different from those used by the market participants. The second one is associatedwith the adjustments proposed by Stern Stewart, which we applied to operating profit and

invested capital and may be close to the true economic profit. In fact they could evenreduce the amount of useful information which market participants would otherwiseextract from the published and audited traditional accounting measures of income.

Furthermore, it is possible that in the case of the ASE and for the period of time underexamination in our study, the majority of investors did not take into account economicprofit measures because they did not recognise the importance of estimating the total

cost of capital, as well as other relevant residual income measures, which incorporate animplied charge for equity and debt. Finally, a fourth explanation may be offered by thefact, that EVA, even if it is a good proxy of the real economic value of a firm, reflectsa current realisation of this value, while stock returns, and especially abnormal returns,are a result of a shift in the markets expectations for the firms future cash flows.

However, we would like to note that, although the EVA model in our analysis did notseem to have a superiority in explaining the stock returns of ASE listed companies, it was

proved to have some explanatory power in relation to the other traditional accountingmeasures. We believe that, as the Greek stock market becomes more mature and stable,this may have serious implications for the managers of Greek listed companies and


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their investment decisions on the basis of economic profit variables, along with thetraditional measures of performance.

Whether the use of EVA, or simpler residual income measures, as a tool forinternal management control can benefit Greek companies is an assumption which

requires further empirical research. More and more data about firms adoptingsuch measures will become publicly available in the future. The collection ofinformation about the precise date of the adoption of economic profit measureswill enable researchers to investigate whether or not the market appreciated suchstrategies leading to unexpectedly higher stock returns.

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Breusch, T. and Pagan, A., The LM test and its applications to model specification in econometrics,

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Chen, S. and Dodd, J. L., Usefulness of operating income, residual income and EVA: a value-relevance

perspective,Working Paper(Clarion University and Drake University, 1998).Cox, D., Tests of separate families of hypotheses, Proceedings of the Fourth Berkeley Symposium on

Mathematical Statistics and Probability, Vol. 1 (Berkeley: University of California Press, 1961).

Dodd, J. L. and Chen, S., EVA: a new panacea ?, Business and Economic Review, Vol. 42, no. 4,

1996, pp. 2628.

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Greene, W. H., Econometric Analysis, 3rd ed., (Prentice-Hall, 1997).Kleiman, R., Some new evidence on EVA companies, Journal of Applied Corporate Finance,

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Stern, J. M., Stewart, G. B. III and Chew, D. H., Jr., The EVA Financial Management System,

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Appendix 1

Definitions of variables used

Annual abnormal return (Dt) The compound annual total shareholderreturn (defined as the sum of the weeklylogarithmic raw returns,152

t=11n(Pt+di vt

Pt1)) minus the expected

annual return, as derived from themarket model,2 computed for a periodending three months after the fiscal year

end.Net income (NI) Net income before appropriation from the

balance sheetOperating income(OI) Operating profits before taxes from the

balance sheet

Residual income (RI) Operating incomet (WACCt

totalassetst1)

Economic value added (EVA) Net operating profits after taxt weightedaverage cost of capitalt

investedcapitalt1 [NOPATt (WACCt

ICt1)]Net operating profits after taxes (NOPAT) Net profits after taxes+ () increase

(decrease) of provisions + depreciationof goodwill (if it exists) + increase incapitalised R&D expenses interest taxshields (=tax rate interest expense)+taxes in extraordinary income

Invested capital (IC) Short-term debt (book value)+ long-termdebt (book value) + share capital (bookvalue) + provisions accounts payable taxes payable accruals+depreciation of goodwill (if it exists)+capitalised R&D expenses fixed

assets under construction marketablesecurities


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Relative information content raw stock returns

H1a: H0:EVA vs NI H0: NI vs EVA

cox-stat 52.199251 cox-stat 27.148717

p-value 0.0000000 p-value 0.0000000

H1b: H0:EVA vs OI H0: OI vs EVA

cox-stat 92.599180 cox-stat 32.628388p-value 0.0000000 p-value 0.0000000

H1c: H0:EVA vs RI H0: RI vs EVA


Incremental information content abnormal stock returns

H2a: (b3=b4=b5=b6=b7=b8=0)

F-statistic 21.27533 p-value 0.0000

X2 127.6520 p-value 0.0000

H2b: (b5=b6=b7=b8=0)


X2 50.57878 p-value 0.0000

H2c: (b7=b8=0)


X2 3.732509 p-value 0.1547

Incremental information content raw stock returns

H2a: (b3=b4=b5=b6=b7=b8=0)


X2 124.1691 p-value 0.0000

H2b: (b5=b6=b7=b8=0)


X2 38.24202 p-value 0.0000

H2c: (b7=b8=0)


X2 2.512374 p-value 0.2847


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Sensitivity analysis changes analysis models

Relative information content







Incremental information content

H2a: (b6=b7=b8=0)


X2 4.999726 p-value 0.1718

H2b: (b7=b8=0)


X2

3.152366 p-value 0.2068H2c: (b8=0)


X2 0.650059 p-value 0.4201

Sensitivity analysis an extension of the market model using profitability variables









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Incremental information content

H2a: (b3=b4=b5=b6=b7=b8=0)


X2

31.28271 p-value 0.0000

H2b: (b5=b6=b7=b8=0)


X2 25.63453 p-value 0.0000

H2c: (b7=b8=0)


X2 0.865882 p-value 0.6486

Market value added models









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EVA Approach Validity

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