Chapter 5 Relationship of Economic Value Added and Conventional Performance Measures...
Transcript of Chapter 5 Relationship of Economic Value Added and Conventional Performance Measures...
Chapter 5
Relationship of Economic Value Added and Conventional
Performance Measures with Market Value Added
5.1 Introduction
Although Shareholder Value Creation has become the widely accepted corporate mission,
much debate is taking place at its measurement level. As much as companies intensify to
fulfill their vision of creating value for their shareholders, the quite obvious question
arises i.e. which measurement metric is best among all. Lay investors and even most
companies tend to focus too much on size and income based metrics such as share price
(market value or market capitalization), earnings, growth in earnings, EPS, ROCE and
ROE. But such metrics don‟t consider the cost of equity capital and are influenced by
accrual accounting based conventions. Moreover, these traditional accounting measures
do not take into account how much capital has been poured into the business to generate
the additional income, so it is relatively easy to improve such measures simply by
investing more. Thus, there are so many reasons (refer shortcomings of traditional
financial performance measures discussed in Chapter 1) due to which these traditional
measures have been regularly criticized as misleading, manipulative and incompetent in
disclosing an organization‟s value creating performance.
In turn, proponents of value based measures have responded with specific metrics and
methodologies that claim to provide a better and reliable measurement of shareholder
value creation (Armitage, 1995). For instance, US-based Consultancy firm Stern-Stewart
& Company claimed that „earnings, earning per share and earnings growth are misleading
measures of corporate performance and the best practical periodic performance measure
is Economic Value Added (EVA)‟. Stewart (1991) argued that EVA comes closer than
any other measure to capture the true economic profitability of an enterprise and is the
performance measure, which is most directly linked to the shareholder value over time.
To further support his claim, Stewart (1994) provided empirical evidence that „EVA
stands well out from the crowd as the single best measure of wealth creation on a
contemporaneous basis and is almost 50% better than its closest accounting based
competitors (including EPS, ROE and ROI) in explaining changes in shareholder wealth‟.
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The proponents also claimed that mathematically, the EVA of a company is the net
present value (NPV) of all its future EVAs. Thus, a company that continues to improve
economic value added, year after year, will sooner than later, find favor with investors.
Thus, over the long term, it is an improvement in EVA and not in accounting results that
derives wealth creation. That is one reason why companies world over need to focus on
improving their fundamental economic performance as measured by EVA.
The literature for the relationship between EVA and Market Value involves a
considerable debate regarding the superiority of EVA in comparison to the traditional
performance measures like ROI, EPS, ROCE etc. This chapter of the dissertation is
devoted to identify the result of this metric war (between traditional and value based
measures of performance) empirically. It attempts to investigate “Does EVA dominate
Earnings in Indian corporate sector?” Thus, the present study will be of immense use to
financial analysts, corporate officials, researchers and policy makers who may be
interested in EVA as replacement (or compliments) to earnings as key measure of
corporate performance.
Worth to be mentioned here that in a letter to the editor of Management Accounting,
Stewart criticized studies that evaluated EVA‟s effectiveness in estimating value added
by measuring how it explains stock returns, calling them “meaningless and unimportant
for the purposes of validating EVA. Stewart argued that using EVA as a proxy for MVA
is what carries more importance. Thus, the present study considers MVA as a proxy for
shareholder wealth created or eroded by the sample companies.
5.2 Sample Description and Database
Initially, sample size of the study remained same i.e. 104 Companies (as described in
section 3.2 of Chapter 3). However, while examining data, four companies namely
Satyam Computer Services Ltd., Tata Steel Ltd., Sun Pharmaceutical Industries Ltd.,
Sesa Goa Ltd. and were identified as outliers and had to be deleted. Thus, a final sample
of 100 companies was selected and studied for the subsequent analysis. Secondary data
has been used for a period of 12 years i.e. from 1996 to 2007. All the relevant financial
information has been sourced from the CMIE‟s corporate database Prowess and the data
regarding share prices has been obtained from the Capitacharts of Capital Market
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Publishers of India Ltd. E-Views and Statistical Package for Social Sciences (SPSS) have
been used for the analysis of data.
5.3 Hypothesis of the Study
Relative information content comparisons are appropriate when one desires a ranking of
performance measures by information content or when making mutually exclusive
choices among performance measures i.e. when only one measure can be chosen. In
contrast, Incremental information content comparisons assess whether one measure
provides value-relevant inferences beyond those provided by another measure, evaluating
the benefit of supplemental disclosures in financial reporting (Biddle et al., 1997). As the
literature contains both i.e. studies favorable to EVA as well as those which disagree with
EVA to be the best predictor of MVA, the present study takes a neutral position. It tests
the hypothesis that Value Based Measures as well as Traditional Financial Performance
Measures have equal relative and incremental information content i.e. equal association
with MVA, a surrogate of shareholder value creation.
5.4 Choice of Variables
Nine independent financial variables are chosen for the purpose of the study, of which
five represent Accrual Accounting based traditional performance measures, two are
Value Based Performance Measures and the remaining two are Economic Variables.
Accrual accounting based performance measures includes Return on Capital Employed
(ROCE), Return on Net Worth (RONW), Profit after Tax (PAT), Earning per Share
(EPS) and Return on Total Assets (ROTA) whereas Value based measures include
Economic Value Added (EVA) and EVA in percentage terms (EVA%). Employees‟
Productivity (Ep) and Capital Productivity (Cp) are the performance measures
representing Economic Indicators. For testing the hypothesis, Market Value Added
(MVA) has been taken as the dependent variable. A brief description of all these
variables is given in Table 5.1.
5.4.1 Dependent Variable
Market Value Added (MVA): MVA being an absolute measure assesses that how much
capital a company has added to or subtracted from its shareholders‟ investment. It is the
cumulative amount by which a company is perceived to have enhanced or diminished
shareholder wealth. It is based upon the logic that if the total market value of a company
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is more than the capital invested in it, the company has managed to create shareholder
value. However, if the market value of a company comes less than its invested capital,
company has destroyed the shareholder value. MVA thus, measures the value added by
the management over and above the capital invested in the company by its shareholders
and lenders. For the purpose of the study, MVA is obtained by subtracting the economic
capital of a corporation (book value after adjusting for economic anomalies) from its total
market value i.e. what investors can take out of the company.
Mathematically,
MVA = Market Value of the firm – Economic Capital
Hence, the way in which shareholder wealth is maximized is by increasing the difference
between the company‟s market value and its economic capital. Market value of a firm as
represented by market value of its equity is arrived at by multiplying the stock price by
the number of outstanding shares of the firm. Taking share price at the end of the
financial year for the calculation of the market capitalization can be biased. Hence, in the
present study, 364-days average market cap has been taken as proxy for the market value
of equity. Market value of the firm has been taken as the sum of book value of debt and
364-days average market capitalization.
MVA is the perfect summary assessment of corporate performance that shows how
successful a company has been in allocating and managing resources to maximize the
value of the enterprise and the wealth of its shareholders (Stewart, 1994). In the present
study, MVA being the surrogate of shareholder wealth addition has been taken as the
dependent variable.
5.4.2 Explanatory Variables
i. Return on capital employed (ROCE)
ROCE measures the profit which a firm earns on investing a unit of capital and tells
whether the company‟s borrowing policy was wise economically and whether the capital
had been employed fruitfully (Maheshwari, 2004). If the long-term return of a business
enterprise is not satisfactory in any case, then the deficiency needs to be corrected and the
activity can be abandoned for a more favourable one (Kishore, 2002).
Obviously, it is quite impractical to assess profits or profit growth properly without
relating them to the amount of funds (capital) that were employed in making profits.
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ROCE is one of the most important profitability ratios which assess how much the capital
invested has earned during the period. It is determined by dividing net profit or income
by the capital employed or investment made to achieve that profit. The ROCE is
determined by the formula:
Adjusted Net Profit
ROCE = × 100
Capital Employed
Higher a company‟s ROCE, stronger will be its financial position. ROCE has two
components – profit as a percentage of sales (Profit margin) and sales as a percentage of
capital employed (Investment turnover). Alternatively, it can be defined as:
PAT Sales
× × 100 Sales Average Capital Employed
A firm can improve its ROCE by increasing one or both of its components viz., profit
margin i.e. (PAT / Sales) and the investment turnover i.e. productivity of its capital
employed (Sales / ACE). It also indicates that a company with higher operating profit
margin may have a lower ROCE if its asset efficiency is poor. Thus ROCE analysis
provides a strong incentive for optimal utilization of the assets of the company and is
used as a measure of success of a business. For the purpose of the study, it is expected
that ROCE will not only find a significant reflection in the market value addition of a
company but will also be a significant predictor of the same.
ii. Return on Net Worth (RONW)
This ratio measures the relationship between net profits and proprietor‟s funds and thus,
reveals how well the firm has used the resources of owners. So, this ratio is of great
interest to the present as well as prospective shareholders and also of great concern to
management, which has the responsibility of maximizing the owners‟ welfare. Further,
RONW is also capable to reveal the relative performance and strength of the company in
attracting future investments. It is calculated by the formula:
Net profit after interest and taxes
RONW =
Net worth
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Where,
Net worth = Equity Capital + Reserves and surplus + Preference share capital –
Accumulated losses
It is believed that the stock prices and hence the market capitalization reacts favorably to
an improvement in RONW (Misra and Kanwal, 2004). This expected positive
relationship leads to improvement in the market value added of a company. Thus, RONW
is selected as one of the independent variable having positive relationship with MVA of a
company. This variable is also a relative measure and has been expressed in percentage
terms.
iii. Profit after Taxes (PAT)
It is the net profit earned by the company after deducting all expenses like interest,
depreciation and tax. It is defined as:
PAT = EBDIT –Depreciation – Interest – Taxes
Where, EBDIT is earnings before depreciation, interest and taxes.
PAT is expressed in absolute (Rupee) terms. It has been selected as an independent
variable as normally it is expected to have a positive correlation with the MVA i.e.
increasing Profitability in a well functioning capital market is likely to give a boost to the
share prices, market capitalization and market value added. A company, whose
profitability is not sufficient to cover up its overall cost of capital, face adverse EVA
situation, the result of which is the decline in its stock prices and therefore, its Market
Value also falls. For the purpose of the study, PAT figures are taken from the corporate
database, Prowess.
iv. Earning per share (EPS)
EPS is an absolute measure of profitability that identifies how much each share has
earned for the shareholders. Investors, in general, look upon earnings per share as the best
yardstick to analyze their investment decisions. It is calculated by the formula:
Net profit after tax – Preference dividend
EPS =
Total number of Outstanding Equity shares
Traditionally it is believed that EPS has a positive relationship with share prices and
hence MVA (Misra and Kanwal, 2004). It is also considered as one of the major factors
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affecting the dividend policy of the firm and market prices of the company (Kishore,
2002). Thus, the study expects a positive association between EPS and MVA of a
company and EPS has been taken as an independent variable affecting market value
addition of a company.
v. Return on Total Assets (ROTA)
The ROTA measures a company's profits before interest and taxes (PBIT) against its total
assets. It is considered as an indicator of how effectively a company is using its assets to
generate earnings before the contractual obligations must be paid. This ratio takes out the
impact of interest and tax to depict clearly how well the operational managers have done
with the assets of the company. ROTA is calculated as:
Profit before Interest and taxes
ROTA = × 100
Total Assets
Greater a company's earnings in proportion to its assets, more efficient that company is
considered in using its assets and contributing towards firm value and shareholders‟
wealth.
vi. Economic Value Added (EVA)
EVA is conceptually a superior measure of performance because it charges management
for using capital at an appropriate risk-adjusted rate, and it eliminates financial and
accounting distortions to the extent it is practical to do so (Stewart, 1994). Economic
Value Added is calculated as the difference between NOPAT and the stakeholders‟
expectations, which is the capital charge for both debt and equity i.e. overall cost of
capital. Operationally defined,
EVA = NOPAT – Capital charge
= NOPAT – WACC × Economic Capital
Where, NOPAT is Net Operating Profits after adjusting for non-operating items, non-
recurring events and other economic adjustments to compute economic profits from
accounting profits. The detailed explanation of these adjustments is given in Chapter 4.
NOPAT = (PAT + non-recurring expenses + revenue expenditure on R & D + interest
expense + goodwill written off + provision for taxes) - non-recurring income
- R & D amortization – cash operating taxes.
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WACC = Weighted average cost of capital.
= Cost of equity × proportion of equity in total capital + Cost of debt ×
proportion of debt in total capital (1 – tax rate) + Cost of preference capital ×
proportion of preference capital in total capital.
Economic Capital = Net Fixed Assets + Investments + Current Assets – (NIBCLs +
Miscellaneous Expenditure not written-off + Intangible Assets) + (Cumulative
Non-Recurring Losses + Capitalized expenditure on R & D + Gross
Goodwill) – Revaluation Reserve – Cumulative Non-Recurring Gains
Grant (2003) emphasized that EVA is the internal performance measure that is most
highly correlated with MVA and provides the most reliable guide to- whether and by how
much, management actions have contributed to shareholder wealth. Grant (2003) also
stated that companies having positive EVA momentum should on balance see their stock
prices go up over times as the increasing profits, net of the overall capital costs lead to a
rise in the company‟s Market Value Added. Moreover, the relationship between MVA
and EVA has also been supported empirically by a number of prominent researchers
(section 2.1 in chapter 2). The purpose of selecting EVA as an important explanatory
variable is to identify in Indian context that to what extent EVA is associated with MVA
and whether EVA dominates other traditional performance measures in explaining the
changes in MVA of sampled companies.
vii. EVA as a percentage of capital employed (EVACE)
It indicates that how much value has been added by the company at given level of capital
employed and is determined by the formula:
EVA
EVA as a % of capital employed = × 100
Invested capital
The logic behind considering this relative measure of value creation as independent
variable is to identify that what explains MVA more; EVA in absolute terms or EVA as a
relative measure. This ratio can assist the policy makers to infer whether the market‟s
response to the relative measures of financial performance is better than that to the
absolute measures of financial performance (Misra and Kanwal, 2004).
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viii. Capital Productivity (Cp)
Capital productivity captures revenue generated per unit of capital employed and
indicates the productivity taken out of the fixed assets of a company. This ratio improves
when a company manages to generate more revenue out of the same assets i.e. through
better utilization of its capital resources. It is calculated as:
(Net Sales + Change in Stock – Raw Material Consumed – Power and Fuel Cost)
Cp =
Net Fixed Assets
Where, Net Sales is Gross Sales net of indirect taxes and Net Fixed Assets are Gross
block net of accumulated depreciation. It is believed that shareholders value can improve
only when capital productivity improves. If fixed assets are efficiently used, it would
generate wealth for the stakeholders in a company and more particularly for equity
shareholders (Misra and Kanwal, 2004). Thus, Cp is expected to be a significant predictor
of market value added of a company.
ix. Employees’ Productivity (Ep)
Employees‟ productivity is the ratio of (the real value of) output to the input of
employees. This is defined as:
(Net Sales + Change in Stock – Raw Material Consumed – Power and Fuel Cost)
Ep =
Salaries & Wages
To calculate Ep, denominator i.e. employees‟ input can be expressed in terms of hours
worked, numbers of employees or expenses on salaries and wages. Based upon Review
of literature (Banerjee and Jain, 1999; Bhatnagar et al., 2004; Misra and Kanwal, 2004
and Singh and Garg, 2004), this study uses salaries and wages expenditure as a surrogate
of employees‟ input. Thus, Ep denotes value addition per rupee of salaries and wages bill.
Ep will improve if value addition improves for the same level of salaries and wages i.e. if
the efficiency of workforce improves and/or if same value can be achieved with lower
salaries and wages cost, which implies that the less number of employees can do the same
job with equal efficiency (Misra and Kanwal, 2004). It is expected that the employees‟
productivity significantly affect the profitability of a company and consequently
establishes a significant relationship with shareholder value (MVA) of a company.
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Table 5.1: Variables used in the Data Set and Other Formulas Applied
Variables Name Notations Description
1. Dependent Variable
Market Value Added MVA Market Capitalization-Economic Capital
2. Independent Variables
Economic Value Added EVA NOPAT-( WACC *Economic Capital)
Economic Value Added (%) EVA% (EVA/Economic Capital) * 100
Earning Per Share EPS (PAT-Dividend on Pref. Shares)/No. of outstanding Equity
Shares
Return on Capital
Employed
ROCE (PAT nnrt/Average Capital Employed)*100
Return on Average Net
Worth
RONW (PAT/Average Net Worth)*100
Return on Total Assets ROTA (Profit before Interest and Taxes/Total Assets) x 100
Profit After Taxes PAT The profit earned by the company after accounting for all
expenditures (operational, selling & distribution,
administrative & other overheads and financial costs) is
included under this datafield.
Capital Productivity Cp (Net Sales + Change in Stock – Raw material consumed –
Power and Fuel Costs) / Net Fixed Assets
Employees Productivity Ep (Net Sales + Change in Stock – Raw Material Consumed –
Power and Fuel Cost)/Salaries & Wages
3. Other Formulas Applied
Market Capitalization Market
Cap
365 days Avg. Market Cap. + Average Borrowings
Economic Capital EC Net Fixed Assets + Investments + Current Assets –
(NIBCLs + Miscellaneous Expenditure not written-off +
Intangible Assets) + (Cumulative Non-Recurring Losses +
Capitalized expenditure on R & D + Gross Goodwill) –
Revaluation Reserve – Cumulative Non-Recurring Gains
Net Operating Profit After
Taxes
NOPAT (PAT + non-recurring expenses + revenue expenditure on R
& D + interest expense + goodwill written off + provision
for taxes) - non-recurring income - R & D amortization –
cash operating taxes
Weighted Average Cost of
Capital
WACC
Ke = cost of equity shareholders‟ funds
Kd = cost of debt
Kp = cost of preference capital
E = book value proportion of average shareholders‟ funds
D = book value proportion of average total borrowings
P = book value proportion of average preference capital.
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5.5 Statistical Diagnostic
Initially, the study used Partial Regression Plots as the detection method to identify
observations that were outliers on the dependent variables. The analysis indicated four
companies namely Satyam Computer Services Ltd., Tata Steel Ltd., Sun Pharmaceuticals
Industries Ltd. and Sesa Goa Ltd. to be unrepresentative involving extreme values. Thus,
these four companies had to be eliminated from the further analysis.
Before proceeding to the further analysis, the existence of multicollinearity among
independent variables had also been taken care of. For this purpose, Pearson‟s correlation
matrix was at first formed that signaled high correlation among various independent
variables i.e. ROCE, RONW, ROTA, and EVA%, causing the problem of
multicollinearity. To overcome this problem, various combinations of the independent
variables were created and tested. Finally on the basis of „Best Model Fit Criteria‟, highly
collinear variables i.e. RONW, ROTA, and EVA % were omitted from the model.
Further, two variables i.e. Ep and EPS were also eliminated from the model due to their
negligible and insignificant correlation with the dependent variable.
Table 5.2: Correlation Co-efficient Matrix with Selected Variables (1996-2007)
Variables EVA ROCE PAT CP MVA
EVA 1.000 .526* .357* .162* .520*
ROCE .526* 1.000 .213* .265* .392*
PAT .357* .213* 1.000 -.033 .724*
CP .162* .265* -.033 1.000 .083*
MVA .520* .392* .724* .083* 1.000
* Correlation is significant at the 0.01 level (2-tailed).
Table 5.2 provides the Correlation Matrix for MVA and the four selected independent
variables for the period 1996-2007. Correlation is an extremely useful tool to estimate the
strength of the relationship between the corresponding pair of variables in a correlation
matrix. The analysis of the table reveals that all the selected variables are positively and
significantly associated with MVA. The highest positive relationship exists between PAT
and MVA at .724. That means, as PAT increases, there would be an increase in the
shareholder value. The similar positive relationship can also be observed between EVA
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and MVA as well as between ROCE and MVA. As far as correlation among independent
variables is concerned, the maximum correlation can be observed between EVA and
ROCE at .526 which is much lesser than the prescribed rule of thumb of 0.8 (Gujarati,
1995). Hence, it can be claimed that multicollinearity does no longer exist in the selected
regression model. In addition, the study also undertakes to consider Average Variance
Inflating Factor (VIF) to detect multicollinearity. Durbin-Watson statistics has been
employed to check the assumption of independent errors (auto-correlation). The White
Procedure is applied to ensure that coefficients are not heteroscedastic.
5.6 Model Development
The next methodological requirement is to specify the regression model used to compare
the relative information content of the competing measures of firm performance (Value
Based Measures as well as Traditional Financial Performance Measures) on the basis of
their association with MVA. The following model has been selected for the purpose of
Panel Data Analysis i.e.
MVAit = α + β1 PATit + β2 ROCEit + β3 Cpit + β4 EVAit + eit ……………. Equation 5.1
The dependent variable in the above equation is the Market Value Added (MVA) for firm
i in period t. The explanatory variables in the model are Profits after Taxes (PAT), Return
on Capital Employed (ROCE), Capital Productivity (Cp) and Economic Value Added
(EVA). Following the literature on the relative information content of various firm
performance measures, the hypothesis suggests positive coefficients for PAT, ROCE, Cp
and EVA when specified as explanatory variables for MVA. It also suggests that the
more closely these measures approximate market value addition, the higher will be the
relative information content of these measures. This model is estimated using a pooled
time-series, cross-sectional least squares regression.
Test for Relative and Incremental Information Content
To assess relative and incremental information content, the study employs a statistical
test from Biddle et al. (1997) that allows a test of the null hypothesis of no difference in
the ability of two competing sets of independent variables to explain variation in the
dependent variable. Using this test, the study makes four univariate regressions (between
MVA and each of the four independent variables) and six pairwise comparisons of
regressions among the value based and accounting performance measures namely EVA,
PAT, ROCE and Cp. The test is constructed as a comparison of R2s (Biddle et al., 1997).
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5.7 Empirical Results and Discussions
To select the most appropriate pooling technique, the study estimated coefficients,
standard errors and t-statistics for the model across three alternative pooling techniques
i.e. pooled results, fixed effects and random effects. In the first instance, a pooled
regression was run the results of which are presented in table 5.3. These results reported
an adjusted R2 of .618 and F statistic was also found to be significant (p<.001).
Table 5.3: Multivariate Results of Pooled Regression Analysis (Restricted Model)
Variable Coefficient Std. Error t-Statistic Prob.
C -509.1082 249.3568 -2.041685 0.0414
EVA 6.756939 0.663294 10.18695 0.0000
PAT 7.617715 0.238220 31.97770 0.0000
ROCE 81.56281 13.07449 6.238315 0.0000
CP 64.34786 38.93651 1.652636 0.0987
R-squared 0.617976
Adjusted R-squared 0.616697
F-statistic 483.2684
Prob (F-statistic) 0.000000
However, the pooled regression does not anticipate the firm or time specific effects. To
consider whether firm-specific or time-specific factors had any significant effect on the
dependent variable-MVA, there was a need to estimate both fixed and random effect
models with firm, time or both effects. Thus, at first, Fixed Effects were observed for
cross sections, the results of which are presented in Table 5.4.
Table 5.4: Multivariate Regression Results with Cross-Sectional Fixed Effects and
No Period Effects
Variable Coefficient Std. Error t-Statistic Prob.
C 296.6956 241.3386 1.229375 0.2192
EVA 2.467593 0.714615 3.453038 0.0006
PAT 8.853163 0.302517 29.26503 0.0000
ROCE 15.73601 13.32593 1.180856 0.2379
CP 33.00524 42.73093 0.772397 0.4400
Effects Specification
Cross-section fixed (dummy variables)
R-squared 0.821695
Adjusted R-squared 0.804938
F-statistic 49.03657
Prob (F-statistic) 0.000000
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To test the joint significance of firm effects in the fixed effects model, the Redundant
Fixed Effects-Likelihood Ratio was obtained.
Table 5.5: Results of the Cross-Sectional Redundant Fixed Effects-Likelihood Ratio
Effects Test Statistic d.f. Prob.
Cross-section F
Cross-section Chi-square
12.648627
914.385465
(99,1096)
99
0.0000
0.0000
Table 5.5 presents the results of the Cross-Section Redundant Fixed Effects that provided
F-statistic being significant at 1% level. It indicated that the Fixed Effects Regression
Model (Least Squares Dummy Variable Regression Model) was valid i.e. the fixed
effects were found to be efficient among cross-sections in the sample. Similarly Fixed
Effects were examined for time period, the results of which are provided in tables 5.6 and
5.7. In this case also, fixed effects were found to be efficient for the study period.
Table 5.6: Multivariate Regression Results with Period Fixed Effects and No Cross-
Sectional Effects
Variable Coefficient Std. Error t-Statistic Prob.
C -496.3919 253.0658 -1.961513 0.0501
EVA 6.704621 0.662044 10.12716 0.0000
PAT 7.544267 0.241169 31.28208 0.0000
ROCE 81.22347 13.22883 6.139883 0.0000
CP 67.00540 38.90336 1.722355 0.0853
Effects Specification
Period fixed (dummy variables)
R-squared 0.625922
Adjusted R-squared 0.621183
F-statistic 132.0744
Prob (F-statistic) 0.000000
Table 5.7: Results of the Period Redundant Fixed Effects-Likelihood Ratio
Effects Test Statistic d.f. Prob.
Period F
Period Chi-square
2.286455
25.223919
(11,1184)
11
0.0092
0.0085
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Further, the study attempted to choose between fixed and random effects specification.
This was accomplished applying the Hausman test in each case. As explained earlier, the
Hausman test is a test of H0: that random effects would be consistent and efficient, versus
H1: that random effects would be inconsistent. (Here, fixed effects would certainly be
consistent.). As for the fixed effects, the random effects could also be along either the
cross sectional or period dimensions. Thus, at first the random effects were selected for
the firms (i.e. cross-sectional) and not over time. Here, the slope coefficients were quite
different compared with both pooled and fixed effects regressions. Thus, there was a need
to identify whether the random effects model passed the Hausman test for the random
effects being uncorrelated with the explanatory variables. The results of the Hausman test
based on the firm random effects indicated the p-value for the test being less than 1%. It
indicated that the random effects model was not appropriate and fixed effects
specification was to be preferred in cross-sectional analysis. Table 5.8 reports the top
panel of the Hausman test results for firms.
Table 5.8: Results of the Cross-Section Random Effects - Hausman Test
Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.
Cross-section random 34.685493 4 0.0000
Following the similar procedure, next, the random effects were selected for the period
dimension. Here, as presented in table 5.9, the results of the Hausman test indicated an
insignificant p-value of .6488. It indicated the acceptance of null hypothesis that random
effects were efficient and consistent as far as period dimension was concerned.
Table 5.9: Results of the Period Random Effects - Hausman Test
Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.
Period random 2.476603 4 0.6488
Thus, finally the model selected was fixed effects for firms (cross-sectional) dimension
and random effects for period dimension.
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Table 5.10: Results of the Multivariate Panel Data Regression Analysis (Final Model
with Cross-Sectional Fixed Effects and Period Random Effects)
Variable Coefficient Std. Error t-Statistic Prob.
C 576.0233 249.9763 2.304311 0.0214
EVA 2.607736 0.702984 3.709523 0.0002
PAT 8.503470 0.306100 27.78004 0.0000
ROCE 2.783971 13.60174 0.204678 0.8379
CP 27.00575 42.15328 0.640656 0.5219
Effects Specification
S.D. Rho
Cross-section fixed (dummy variables)
Period random 718.8874 0.0424
Idiosyncratic random 3415.066 0.9576
Weighted Statistics
R-squared 0.819675
Adjusted R-squared 0.802728
F-statistic 48.36809
Prob(F-statistic) 0.000000
Unweighted Statistics
R-squared 0.821113
Table 5.10 provides that the variability in the MVA accounted for by the four final
predictors comes out to be 81.97% (R2). A high and positive value of Adjusted R
2 at
80.27% verifies that the cross- validity of this model is very good. F-statistic is found to
be large (48.368) and significant (at 1% level). The results also show that all the selected
independent variables i.e. EVA, PAT, ROCE and Cp have positive slope coefficients (i.e.
β values) showing their positive association with MVA. However, tested on the basis of
t-statistic, just two independent variables i.e. EVA and PAT are identified as the
significant predictors of MVA at 1% confidence level (p<.001). On the other hand ROCE
and Capital Productivity i.e. Cp do not seem to have established a significant statistical
association with MVA. As explained earlier, multicollinearity is also not a cause of
concern in the model and has properly been accounted for. Thus, the above indicators
claim the regression model to be statistically fit and valid.
Relationship of Economic Value Added and Conventional Performance Measures with Market Value Added
162
Table 5.11: Summary of the Results of the Association of Independent Variables with Market Value Added
Models
EVA PAT ROCE Cp
F Adj. R2 Estimated
Coefficient
Standard
Error
t-
statistics
Estimated
Coefficient
Standard
Error
t-
statistics
Estimated
Coefficient
Standard
Error
t-
statistics
Estimated
Coefficient
Standard
Error
t-
statistics
I 13.148 .674 19.50
(.000)
53.719
(.000) 0.3454
II 9.066 .243 37.258
(.000)
152.44
(.000) 0.6025
III 80.587 18.472 4.363
(.000)
16.805
(.000) 0.5940
IV 36.301 59.314 .6120
(.5406) 16.756 (.000)
0.1362
V 2.708 0.651 4.157 (.000)
8.509 0.304 28.009 (.000)
49.413 (.000)
0.8031
VI 9.169 0.252 36.430
(.000) 23.780 12.469
1.907
(.0568)
48.682
(.000) 0.8007
VII 13.591 0.749 18.145
(.000) -40.452 17.510
-2.310
(.021)
24.620
(.000) 0.6881
VIII 9.070 0.243 37.324
(.000) 60.654 40.229
1.508
(.132)
140.837
(.000) 0.6026
IX 81.991 18.763 4.370
(.000) -26.947 62.445
-0.432
(.666)
16.645
(.000) 0.5937
X 13.181 0.676 19.491 (.000)
-36.727 51.775 -0.709 (.478)
49.620 (.000)
0.3452
XI 2.608 0.703 3.710
(.000) 8.503 0.306
27.780
(.000) 2.784 13.602
0.205
(.838) 27.006 42.153
0.641
(.522)
48.368
(.000) 0.8027
Note: The dependent variable is Market Value Added (MVA) and explanatory variables represent one value based measure i.e. Economic Value Added (EVA), and
three accounting based measures namely Profit after Taxes (PAT), Return on Capital Employed (ROCE) and Capital Productivity (Cp). The first four models (I to
IV) present the results of the univariate association of each independent variable with the dependent variable- MVA. In the next six models (V to X), independent
variables are specified in pair-wise combinations and finally considered jointly in the last model (XI).
Relationship of Economic Value Added and Conventional Performance Measures with Market Value Added
163
Relative and Incremental Information Content Tests
Table 5.11 presents the estimated co-efficient, standard errors, t-statistics, F-statistic and
adjusted R2 for each model. The dependent variable is Market Value Added (MVA) and
explanatory variables represent one value based measure i.e. Economic Value Added
(EVA), and three accounting based measures namely Profit after Taxes (PAT), Return
on Capital Employed (ROCE) and Capital Productivity (Cp). The first four models (I to
IV) present the results of the univariate association of each independent variable with the
dependent variable- MVA. In the next six models (V to X), independent variables are
specified in pair-wise combinations and finally considered jointly in the last model (XI).
The detailed regression results for each of univariate and bivariate (pair-wise) models are
given in table 1 through table 10 of Appendix B.
Table 5.11 clearly shows that PAT is the most significant predictor of MVA when it is
considered univariately as well as when paired with EVA. Similarly, ROCE is also found
to be significant by itself and when compared with PAT. The pair-wise regressions that
best explain the variations in MVA are EVA/PAT (80.31%), PAT/ROCE (80.07%),
EVA/ROCE (68.81%), PAT/Cp (60.26%), ROCE/Cp (59.37%) and EVA/Cp (34.52%).
Here, EVA comes twice among the best three pair-wise regressions which evidence EVA
to be a highly significant explanatory variable. However, profit after taxes (PAT) can
clearly be observed as the best predictor of MVA and is thus, recognized as the most
legitimate and reliable measure of shareholder value creation. Further PAT is followed by
ROCE, which depicts a slightly less explanatory power of 59.40% in comparison to
60.25% for PAT. These results show that traditional measures of performance have
emerged as the more dominating determinants of MVA during the study period.
Table 5.12 presents the summary results of regressions based on the Relative and
Incremental Information Content tests. Panel A of the table summarizes the significant
differences in the relative information content between accounting and value based
measures. The results of single coefficient regressions clearly show that R2 (PAT)> R
2
(ROCE)> R2 (EVA)> R
2 (Cp) where R
2 depicts the percentage variation in shareholder
wealth (MVA), as explained by each particular explanatory variable.
Relationship of Economic Value Added and Conventional Performance Measures with Market Value Added
164
Table 5.12: Results of Relative and Incremental Information Content Tests
Panel A: Results of Relative Information Content Test
PAT > ROCE > EVA > Cp
60.25% 59.40% 34.51% 13.62%
Panel B: Results of Incremental Information Content Test
PAT/EVA PAT/ROCE EVA/ROCE PAT/Cp ROCE/Cp EVA/Cp
(80.27-34.54) (80.00-59.40) (68.8-59.4) (60.26-13.62) (59.37-13.62) (34.51-13.62)
45.73% 20.60% 9.4% 46.64% 45.75% 20.89%
EVA/PAT ROCE/PAT ROCE/EVA Cp/PAT Cp/ROCE Cp/EVA
(80.27-60.25) (80.00-60.25) (68.8-34.54) (60.26-60.25) (59.37-59.40) (34.51-34.54)
20.02% 19.75% 34.26% .01% -0.03 -0.03
Note: The comparison of adjusted R
2 of the first four models in table 5.11 (where each explanatory variable is specified univariately) provides the
results of relative information content test. For the incremental information content test, the adjusted R2 of earlier univariate regressions have
been subtracted from the adjusted R2 of each pair-wise (bivariate) regressions in models V to X in table 5.11.
Relationship of Economic Value Added and Conventional Performance Measures with Market Value Added
165
The results in Panel B provide the results of incremental information content tests for the
pair-wise comparisons of all the four explanatory variables. For this purpose, the adjusted
R2 of earlier univariate regressions have been subtracted from the adjusted R
2 of each
pair-wise (bivariate) regressions to identify the incremental information provided by each
explanatory variable in relation to other variables. For instance, in panel B, PAT/EVA
(45.73%) is equal to the information content of the pairwise comparison of PAT and
EVA (80.27%) minus the information content of EVA (34.54%) from table 5.11.
Looking at the pairwise combinations, it can be observed that over the PAT measure
alone, explanatory power has increased by 46.64% and 45.73%. Similarly the
explanatory power has improved by 45.75% and 34.26% respectively over the ROCE
measure alone. Combining both of these measures i.e. PAT and ROCE, the incremental
information content of PAT (20.60%) is slightly more than the incremental information
content of ROCE (19.75%).
As far as the comparison between value based and accounting based measures is
concerned, the results clearly depict that explanatory power improves by 20.89%, 20.02%
and 9.4% respectively over the EVA measure alone. Although it is lesser than the
incremental information provided by the traditional measures PAT and ROCE, yet it
provides the most logical pairing of information variables in explaining MVA i.e. Models
V (that best explains MVA) and XI (all variables considered jointly). Thus individually,
EVA explains as much as 34.54% of the variation in MVA and in combinations, it also
evidences increment information content (although lesser than that of PAT and ROCE).
Thus, the results provide the sufficient evidence that traditional measures of firm
performance (both absolute and relative measures i.e. PAT and ROCE respectively) are
highly associated with its shareholder value creation as measured in terms of MVA.
Finally, the present study denies the hypothesis of equal relative and incremental
information content and identifies that „Earnings‟ dominate EVA (the Value Based
Measure) in explaining the variations in firm value and hence shareholder wealth.
5.8 Potential Factors Contributing to the Failure of EVA to Dominate
Earnings
The present study finds no clear evidence to support Stern & Stewart‟s claim that EVA is
superior to the traditional performance competitors in its association with MVA. On the
contrary, the evidence suggests that the Indian market seems more focused on „Profits‟
Relationship of Economic Value Added and Conventional Performance Measures with Market Value Added
166
than value based measure „EVA‟. The study empirically finds that although EVA and
PAT both depict highly positive and significant association with MVA yet PAT‟s
explanatory power is greater than the explanatory power of EVA. Further, the results also
provide the sufficient evidence that traditional measures of firm performance (both
absolute and relative measures i.e. PAT and ROCE respectively) are highly associated
with its shareholder value creation as measured in terms of MVA. That means Indian
market is more responsive to accounting based metrics whether these are expressed in
absolute terms or in relative terms. So, accrual accounting based numbers can
undoubtedly be continued for evaluating corporate financial performance.
As the key findings of the study evidence the Earnings‟ superiority to EVA in relative
information content test (in their association with MVA), the study identifies the potential
factors contributing to the failure of EVA to dominate Earnings in explaining the
variations in shareholder value creation. Kramer and Pushner (1997) explained that with
the market being fed almost constant news on earnings, it is not surprising that it is not
much responsive to EVA in the short-run. Another reason might be that accounting
adjustments and estimates of the capital charge given by the proponents may contain
measurement error relative to what the market uses for valuing firms. Biddle et al. (1997)
observes that in attempting to estimate economic profits, adjustments made by Stern &
Stewart may remove accruals that market participants use to infer firm‟s future prospects.
Thus, while computing EVA, the true measure of company‟s economic profitability is
determined but its association with market returns is lost. Moreover, another reason for
the comparatively weak value- relevance of EVA might be the prevalent notion of
„earnings myopia‟. Biddle et al. (1997) viewed that some adopters of EVA feel that they
must still base their external performance on earnings because this is the measure on
which financial analysts continue to focus. As a result, market fails to recognize the
reporting benefits of EVA. However, the present study does not question the
effectiveness of EVA because inspite of non-availability of detailed financial data
required for EVA related computations and non-mandatory disclosure of EVA
Statements in annual reports of Indian Companies, market seems to be quite responsive to
EVA performance of a company. Thus, the findings advocate adoption of EVA for
management compensation, external communication and security analysis and also
Relationship of Economic Value Added and Conventional Performance Measures with Market Value Added
167
suggest disclosure of EVA in financial reporting, to align management objectives with
shareholders‟ interests and facilitate value-based performance monitoring (Holler, 2008).
5.9 Conclusion
Analyzing a pooled, time series, cross-sectional data of 100 Indian companies for a
period of twelve years i.e. from 1996 to 2007, this study has attempted to examine
whether the value based measures of firms performance are more highly associated with
firm‟s MVA than other long established traditional measures. The results indicate that the
variability in the MVA accounted for by the four final predictors comes out to be 80.27%
(adjusted R2). However the study found no clear evidence to support Stern & Stewart‟s
claim that EVA is superior to the traditional performance competitors in its association
with MVA. The empirical evidence suggests that Indian market seems to be more
focused on „Profits‟ than value based measure „EVA‟. Relative tests show the dominance
of PAT and ROCE over EVA; and incremental tests find that solely accounting based
measures provide considerable and significant additional information, whereas EVA
provides comparatively lower incremental information. Thus, Indian market being less
responsive to EVA than PAT needs more ongoing investigation.