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Market prices of the shares reflect the performance of the company. Today,

wealth created by the company is more important than the profit it earns or the

dividend it pays to the shareholders. The objective of this research is to study the

relationship that exists between the wealth/value for the shareholders created by the

company and market prices of the shares. Basic thrust of the study is to establish the

supremacy of EVA as a measure of performance over traditional measures.

The hypothesis of the study is that, there is significant relationship

between EVA and share prices and of the 4 chosen performance metrics,

EVA is the most significant measure, which is better reflected in the market

prices of the shares as compared to other traditional measures of

performance. This is tested on 24 companies of the Sensex for a period of 10

years beginning from FY 94-95 to FY 2003-04. Analysis is done using the

Regression tool.

From the analysis, it is found that EVA has no significant relationship

with the Market prices and also is not a major influencing factor among the

performance metrics. EPS is the strong measure reflected in the share prices

of the companies.

CHAPTERS PARTICULARS PAGE NO’S

ABSTRACT

1 INTRODUCTION 1-13

1.1 Introduction 2

1.2 Background of the study 3

1.3 Purpose of the study 3

1.4 Problem statement 3

1.5 Objectives of the study 4

1.6 Research Questions 4

1.7 Theoretical Framework 5-13

2

REVIEW OF LITERATURE 14-17

3

METHODOLOGY 18-26

4.1 Study Type 19

4.2 Population of the study 19

4.3 Sample Frame 19

4.4 Sampling Technique 19

4.5 Sampling Profile 20

4.6 Data Gathering Procedure 21

4.7 Hypothesis of the study 21

4.8 Testing of hypothesis 22

4.9 Description of tools used 23

4.10 Limitations of the study 26

CHAPTERS

CONTENTS

PAGE NO’S

5

ANALYSIS AND INTERPRETATION

27-43

6

DISCUSSIONS AND CONCLUSIONS

44-46

6.1

Discussions 45

6.2

Conclusions 46

BIBLIOGRAPHY 47-49

List of Tables

Table No. PARTICULARS Page No.

1

Market price, NPR, RONW, EPS and EVA for

the 24 companies for the year 2003-2004.

28

2


the 24 companies for the year 2002-2003

29

3



30

4



31

5



32

6



33

7



34

8



35

9



36

10 Market price, NPR, RONW, EPS and EVA for the 24 companies for the year 1994-1995. 37

Table No. PARTICULARS Page No.

11

Co-efficient of regression, t statistic, Adjusted R2 and

F values from the year 1994-2004 for Model 1

38

12



40

13



42

Chapter-1

Introduction

Creation of shareholder’s value is the core of every business. This is

natural since shareholders own the company and as rational investors they

expect good long-term yield on their investment. Commonly used accounting

measures are EPS (Earnings per Share), ROI (Return on Investment), NPR

(Net Profit Ratio) etc. But these metrics may not show a clear picture with

respect to the value created by the company.

Therefore, value based metrics of performance evaluation like EVA

(Economic Value Added), MVA (Market Value Added), CVA (Cash Value

Added) and CFROI (Cash Flow Return on Investment) have come into

existence. These new performance metrics seek to measure the periodic

performance in terms of change in value. Maximizing value means

maximizing long-term yield on shareholders’ investment.

The consulting firm of Stern Stewart published The Real Key to

Creating Wealth (John Wiley & Sons, New York, 1998), which "celebrates a

revolution in management known as economic value added (EVA)." Stern

Stewart claims that:

EVA is the only true indicator of business and management performance;

EVA is "today's hottest financial idea and getting hotter";

EVA "allows all financial decisions to be modeled, monitored, evaluated, communicated, and compensated in terms of a single measure";

"EVA is the only reliable and unambiguous continuous-improvement metric"

EVA must be used to guide every decision;

EVA should be used as the "deciding factor in all business decisions."

In India, a few companies like Infosys publish EVA in its annual report and TISCO uses EVA to measure the performance of its segments.

Background of the study Investors are currently demanding Shareholder value more strongly

than ever. So the value created by the company is more linked to the market

price of the shares. This study is undertaken to show the relationship that

exists between the various performance metrics and the share prices as the

performance of the company is reflected in the share prices in the market.

Purpose of the study

The purpose of the study is to show that EVA has relationship with

share prices and this relationship is significant. This shows that value addition

by the company is also a dependant factor for the movement in the share

prices.

So this study is helpful to the investors for their investment decision as

the study also allows them to easily identify companies that pursue business

strategies with the goal of shareholders value creation.

Problem Statement

There are various views with respect to whether traditional measures

or value-based measures influence the share prices. Considering this, it is

interesting to examine which of the performance measures has more

influence on the share prices.

Question to be addressed is whether there is relationship

between EVA and share prices and whether this relationship is

significant.

Objectives of the study To measure EVA for sample companies.

To analyse the relationship between EVA and share prices.

To find out which of the performance metrics is better reflected in the

share prices.

To establish that EVA is a better measure of performance.

Research Questions The following are the research questions that are addressed in the study:

1. Is there any relationship between EVA and Share prices?

2. Is EVA a strong influencing factor on the share prices as compared to

NPR, RONW and EPS?

Theoretical framework

Investors analyze and interpret the financial statements so that they

can get an insight into the firm’s performance. Ratio analysis is one of the

tools of financial analysis. It shows the relationship between two figures in the

financial statements. There are various types of ratios used. Investors of more

interested in profitability ratios like Net profit ratio, Return on Capital

employed, Return on Equity, etc., and valuation ratios like Price-Earnings

ratio, Earnings per share, Yield, etc.

Net profit ratio NPR shows the earnings left for the shareholders as a percentage of

net sales. It measures the overall efficiency of production, administration,

selling, financing, pricing and provides a valuable understanding of the cost-

profit structure of the company. It enables the analysts to identify the sources

of business efficiency/inefficiency.

NPR= Net Profit * 100

Net Sales

Return on Net worth/Equity

RONW measures the profitability for the shareholders funds invested in

the firm. It is an important measure as it reflects the productivity of the

ownership capital in the firm. This measure is of great interest to the

shareholders.

RONW= Earnings for Equity shareholders * 100

Average equity capital

Earnings per share

EPS is also a measure of the profitability of the firm. It indicates the

profitability on a per-share basis.

EPS = Earnings for Equity shareholders * 100

No. of equity shares

Economic Value Added

The consulting firm Stern Stewart originally proposed it. It is the most popular

value-based measure. It is the surplus left after making an appropriate charge for the

capital employed in the business.

EVA = NOPAT – WACC (Capital Invested)

NOPAT is Net Operating Profit After Taxes

WACC is Weighted Average Cost of Capital

Market Value Added MVA is the difference between the company’s market value and the capital

invested. It is the premium the market awards a company over and above the amount

the investors have invested in it. This helps in the assessment of how the markets view

EVA generating ability of the firm. So, MVA is the market expectations of future

EVAs generated by a firm.

MVA= EVA1 + EVA2 +………………………

(1+WACC)1 (1+WACC)2

Cash Flow Return on Investment

The CFROI model avoids the use of accounting book capital in valuing the

firm’s existing assets and thus provides an accurate estimate in determining

shareholder value.

CFROI = Cash flow - Economic depreciation

Cash Invested

Economic depreciation is the amount of annual sinking fund payment earning the cost

of capital required to replace the assets.

Cash Value Added

CVA is based on cash flows and not on earnings. It is the spread

between CFROI and the real cost of capital, multiplied with the investment in

fixed assets and working capital.

CVA= Cash flows- Economic Depreciation-(Cash invested* WACC)

Background of Economic Value Added

EVA is not a new discovery. An accounting performance measure

called residual income is defined to be operating profit subtracted with capital

charge. EVA is thus one variation of residual income with adjustments to how

one calculates income and capital. According to Wallace (1997, p.1) one of

the earliest to mention the residual income concept was Alfred Marshall in

1890. Marshall defined economic profit as total net gains less the interest on

invested capital at the current rate. According to Dodd & Chen (1996, p.27)

the idea of residual income appeared first in accounting theory literature early

in this century by e.g. Church in 1917 and by Scovell in 1924 and appeared in

management accounting literature in the 1960s. Also Finnish academics and

financial press discussed the concept as early as in the 1970s. It was defined

as a good way to complement ROI-control (Virtanen 1975, p.111). Knowing

this background many academics have been wondering about the big

publicity and praise that has surrounded EVA in the recent years. The EVA-

concept is often called Economic Profit (EP) in order to avoid problems

caused by the trademarking. On the other hand the name "EVA" is so popular

and well known that often all residual income concepts are often called EVA

although they do not include even the main elements defined by Stern

Stewart & Co. For example, hardly any of those Finnish companies that have

adopted EVA calculate rate of return based on the beginning capital as

Stewart has defined it, because average capital is in practice a better estimate

of the capital employed. So they do not actually use EVA but other residual

income measure. This insignificance detail is ignored later on in order to avoid

more serious misconceptions. It is justified to say that the EVA concept

Finnish companies are using corresponds virtually the EVA defined by Stern

Stewart & Co.

In the 1970s or earlier residual income did not got wide publicity and it

did not end up to be the prime performance measure in great deal of

companies. However EVA, practically the same concept with a different

name, has done it in the recent years. Furthermore the spreading of EVA and

other residual income measures does not look to be on a weakening trend.

On the contrary the number of companies adopting EVA is increasing rapidly

(Nuelle 1996, p.39, Wallace 1997, p.24 and Economist 1997/2). We can only

guess why residual income did never gain a popularity of this scale. One of

the possible reasons is that Economic value added (EVA) was marketed with

a concept of Market value added (MVA) and it did offer a theoretically sound

link to market valuations. In the times when investors demand focus on

Shareholder value issues this was a good bite. Perhaps also pertinent

marketing by Stern Stewart & Co. had and has its contribution.

EVA is one measure that is used to monitor the overall value creation in

a business. It is a value based framework that provides unique insight into the

value creation. The idea behind EVA is that shareholders must earn a return

that compensates the risk taken. In other words equity capital has to earn at

least same return as similarly risky investments at equity markets. If that is not

the case, then there is no real profit made and actually the company operates

at a loss from the viewpoint of shareholders. On the other hand if EVA is zero,

this should be treated as a sufficient achievement because the shareholders

have earned a return that compensates the risk. This approach - using average

risk-adjusted market return as a minimum requirement - is justified since that

average return is easily obtained from diversified long-term investments on

stock markets. Average long-term stock market return reflects the average

return that the public companies generate from their operations.

What is EVA and how it is calculated?

EVA is the difference between the company’s net operating profits after

taxes and the cost of capital employed in generating those profits in one

financial year. If EVA is positive, the company creates shareholder wealth. If

EVA is negative, the shareholder wealth is destroyed.

The concept of EVA is based on the principle of residual income, which

states that the real income generated by a company is the residue that remains

after a company’s shareholders and debtors have been paid their annual

required return.

The technique of EVA has acquired acceptance as a tool for assessing

the existing financial status and predicting the future performance of the

company. The most important reason to adopt EVA as the main corporate

financial goal is that it is directly correlated to the intrinsic market value.

Maximising EVA consistently would lead to mazimisation of the market

capitalization.

EVA has two distinct applications

To measure how much shareholder value the firm has created in the

past and to determine investor expectations as they relate to the stock price .

A firm/s present value should equal its invested capital plus the

present value of future EVA.

Basically EVA examines the three fundamental principles of value

creation: Cash flow, Risk, and Sustainability of Returns.

Uses of EVA

In stock selection the EVA tool can be used in four distinct ways.

1. Analyzing historical trends - EVA can measure a company’s historical

success in creating shareholder wealth.

2. Using EVA to forecast a target stock price - EVA can be used to

determine whether a stock is fairly valued based on a forecast of

economic profits. Such a forecast converts discounted EVA into a

share price.

3. To quantify Competitive Advantage Period (CAP) - Stock prices in

many cases may reflect a long competitive advantage period. Analysts

can use the value driver model to look at their assumptions regarding

risk and CAP.

4. To examine excess returns and its impact on valuation of a stock -

Another way of exploring the explanatory power of the value drivers in

an EVA model is to perform a regression analysis of invested capital

(ROIC) minus the weighted average cost of capital (WACC) spread as

the independent variable and enterprise value to invest capital as the

dependant variable. The correlation between return spreads and

valuation is quite strong.

Superiority of EVA can be known from the following comparisons

Conceptually, EVA is superior to accounting profits as a measure of

value creation because it recognizes the cost of capital and, hence, the

riskiness of a firm’s operations. Furthermore EVA is constructed so that

maximizing it can be set as a target. Traditional measures do not work that

way. Maximizing any accounting profit or accounting rate of return leads to an

undesired outcome.

• EVA vs. ROCE

1. Return on capital is very common and relatively good performance

measure. Different companies calculate this return with different formulas

and call it also with different names like Return on investment (ROI),

Return on invested capital (ROIC), Return on capital employed (ROCE),

Return on net assets (RONA), Return on assets (ROA) etc. The main

shortcoming with all this rate of return is that maximizing rate of return

does not necessarily maximize the return to shareholders.

2. Also operations should not be guided with the goal to maximize the

rate of return. As a relative measure and without the risk component ROI

fails to steer operations correctly. Therefore capital can be misallocated on

the basis of ROI. First of all ROI ignores the definite requirement that the

rate of return should be at least as high as the cost of capital. Secondly

ROI does not recognize that shareholders’ wealth is not maximized when

the rate of return is maximized. Shareholders want the firm to maximize

the absolute return above the cost of capital and not to maximize

percentages. Companies should not ignore projects yielding more than the

cost of capital just because the return happens to be less than their current

return. Cost of capital is much more important hurdle rate than the

company's current rate of return.

3. Observing rate of return and making decisions based on it alone is

similar to assessing products on the "gross margin on sales" -percentage.

The product with the biggest "gross margin on sales" -percentage is not

necessary the most profitable product. The product profitability depends

also on the product volume. In the same way bare high rate of return

should not be used as a measure of a company's performance. Also the

magnitude of operations i.e. the amount of capital that produces that return

is important. High return is a lot easier to achieve with tiny amount of

capital than with large amount of capital. Almost any highly profitable

company can increase its rate of return if it decreases its size or overlooks

some good projects, which produce a return under the current rate of

return.

• EVA vs. ROE

ROE suffers from the same shortcomings as ROI. Risk component is not

included and hence there is no comparison. The level of ROE does not

tell the owners if company is creating shareholders wealth or destroying

it. With ROE this shortcoming is however much more severe than with

ROI, because simply increasing leverage can increase ROE. As we all

know, decreasing solvency does not always make shareholders’ position

better because of the increased (financial) risk. As ROI , return on equity

(ROE) is also an informative measure but it should not guide the

operations.

• EVA vs. EPS

EPS is raised simply by investing more capital in business. If the

additional capital is equity (cash flow) then the EPS will rise if the rate of

return of the invested capital is just positive. If the additional capital is

debt then the EPS will rise if the rate of return of the invested capital is

just above the cost of debt. In reality the invested capital is a mix of debt

and equity and the EPS will rise if the rate of return of that additional

capital invested is somewhere between cost of debt and zero. Therefore

EPS is completely inappropriate measure of corporate performance and

still it is very common yardstick and even a common bonus base. EPS

and earnings can be increased simply by pouring more money into

business even though the return on that money would be entirely

unacceptable from the viewpoint of owners. EPS, earnings and

earnings/EPS growth should therefore be abandoned as performance

measures.

Chapter-2

Review of literature The share prices in the market can be measured by the MVA-Market value added for the company which is the total market value of the company. This relationship between EVA and MVA has been studied in the recent years in many studies with many methods - and with different results.

Stewart (1990, p.215 - 218) has first studied this relationship with

market data of 618 U.S. companies. Stewart presents the results in his book

"The quest for value". He states that EVA and MVA correspond each other in

reality quite well among US companies (the data was from late 1980’s).

Market value will always reflect the value of assets even though the company

has very low or negative rate of return (and so theoretically it should sell a lot

below book value). On the other hand markets do not believe that the weak

returns can go on forever. Markets are expecting a chance, an improvement,

in the long run. If EVA is positive, the relationship is more direct. Then the

market valuation happens on the basis of return and growth potential and not

on the basis of liquidation or recovery value. Stewart finds also that MVA and

EVA correspond each other best when we talk about changes in EVA and

MVA and not the absolute levels. Changes in EVA and MVA are not affected

so much by accounting distortions and inflation than the absolute values.

Lehn and Makhija (1996) study EVA and MVA as performance

measures and signals for strategic change. Their data consists of 241 U.S.

companies and cover years 1987, 1988, 1992 and 1993. The researchers first

find out that both measures correlate positively with stock returns and that the

correlation is slightly better than with traditional performance measures like

return on assets (ROA), return on equity (ROE) and return on sales (ROS).

Lehn and Makhija conclude that their results suggest EVA and MVA to be

effective performance measures that contain information about the quality of

strategic decisions and serve as signals of strategic change.

Uyemura, Kantor and Pettit (1996) from Stern Stewart & Co present

findings on the relationship between EVA and MVA with 100 bank holding

companies. They calculate regressions to 5 performance measures including

EPS, Net Income, ROE, ROA and EVA. According to their study the

correlations between these performance measures and MVA are: EVA 40%,

ROA 13%, ROE 10%, Net income 8% and EPS 6%. The data is from the ten-

year period 1986 through 1995.

O’Byrne (1996) from Stern Stewart & Co uses capitalized EVA as

independent variable in a regression where market value divided by capital is

the dependent variable. He finds that the level of EVA explains 31% of the

variance in market value, whereas the level of net operating profit after taxes

explains only 17%. When looking at changes in EVA and market value

O’Byrne finds that changes in EVA explain 55% of variations in changes in

market value. Changes in NOPAT explain only 33%.

Dodd and Chen (1996) study the correlation between stock returns and

different profitability measures including EVA, non-adjusted residual income,

ROA, EPS and ROE. In their study ROA explained stock returns best with R

squared of 24,5%. The R squared for other metrics are: EVA 20,2%, residual

income 19,4% and EPS, ROE approximately 5-7%. The writers concluded

that firms adopting EVA might as adopt simple residual income concept, while

residual income correlates with share prices almost as well as its adjusted

version called EVA. The study is based on 566 U.S. companies from 1983-

1992.

In India, the concept of EVA is gaining popularity. Research titled Economic Value Added - A General Perspective by Asish K Bhattacharyya

& B.V.Phani examines whether EVA is a superior performance measure both

for corporate reporting and for internal governance. It relied on empirical

studies in U.S.A. and other advance economies. It concluded that though EVA

does not provide additional information to investors, it can be adapted as a

corporate philosophy for motivating and educating employees to differentiate

between value creating and value destructing activities. This would lead to

direct all efforts in creating shareholder value. The paper brings to attention

the dangerous trend of reporting EVA casually that might mislead investors.

Chapter-3

Methodology

Study type

The study type is analytical, quantitative and historical.

Analytical as facts and existing information is used for the analysis.

Quantitative as EVA is calculated and the variables are expressed in

measurable terms.

Historical as the historical information is used for analysis and interpretation.

Study population Population includes all the domestic and foreign companies.

Sampling frame Sampling frame includes all those companies that are listed in the stock

exchange.

Sampling technique Non-probabilistic sampling is used because only particular units are selected

from the sampling frame. Such a selection is undertaken as these units

represent the population in a better way. The companies listed in the stock

exchange are considered since the market prices can be obtained. The

companies in the Sensex are chosen because it is an ideal index and it is a

good proxy for the whole market. Also it is a barometer that indicates the state

and health of the economy.

Sample Profile

Sample includes 24 companies in the BSE Sensex (for which relevant data

was available), for a period of 10 years starting from FY 94-95 to FY 2003-04.

The following are the sample 24 companies:

1. Associated Cement Companies Ltd.

2. Bajaj Auto

3. Bharat Heavy Electricals Ltd.

4. Cipla Ltd.

5. Dr. Reddy’s Laboratories Ltd.

6. Grasim Industries Ltd.

7. Gujarat Ambuja Cements Ltd.

8. Hero Honda Motors

9. Hindalco

10. Hindustan Lever Ltd.

11. Hindustan Petroleum Corporation Ltd.

12. Housing Development Finance Co,

13. ITC Ltd.

14. Infosys Technologies Ltd.

15. Larsen & Tourbro Ltd.

16. Ranbaxy Laboratories Ltd.

17. Reliance Industries

18. Reliance Energy

19. Satyam Computers

20. Tata Iron and Steel Co. Ltd.

21. Tata Motors

22. Tata Power

23. Wipro Ltd.

24. Zee Telefilms

Data gathering procedure and instrumentation Data type: Secondary data

Data: � Historical share prices of the sample companies. � Index values of BSE Sensex � Financial Information of the sample companies.

Data Source: Historical share prices of the sample companies and the index

points for the period has been taken from the database of Capital Market

Publishers (India) Ltd., Capitaline 2000. Financial statements of the sample

companies have also been taken from the same source.

Hypothesis of the study

Hypothesis 1

H0: There is no significant relationship between EVA and Share prices.

H1: There is significant relationship between EVA and Share prices.

Hypothesis 2

H0: EVA is not a strong factor influencing the share prices as compared to

NPR, RONW and EPS.

H1: EVA is a strong factor influencing the share prices as compared to

NPR, RONW and EPS.

Testing of Hypothesis

The first hypothesis is tested using linear regression tool, simple co-efficient of

correlation, co-efficient of determination, t statistic and F statistic.

Co-efficient of correlation is used to describe how well one variable is

explained by the other variable. It reveals the magnitude and direction of

relationship. The magnitude is the degree to which variables move in the

same or opposite direction. The co-efficient’s sign signifies the direction of the

relationship.

Co-efficient of determination measures the extent, or strength of the

association that exists between the two variables.

t-statistic is used for testing the significance of an individual

explanatory variable. If the t statistic is greater than 2 @ 5 % level of

significance, the relationship is considered significant. Regression is the process of predicting one variable from another by statistical means using previous data.

F statistic is used to test the significance of the regression as a whole.

If the Fcal < Ftab, then the null hypothesis is accepted.

The second hypothesis is tested using multiple regression tool, multiple co-

efficient of correlation, co-efficient of determination, t statistic and F statistic.

Multiple regression is a statistical process by which several

independent variables are used to predict one dependant variable.

Multiple co-efficient of correlation is used to describe how well one variable is explained by many independent variables. It reveals the magnitude of influence of the variables on the dependant variables.

Description about the analysis tools used

Simple Regression

Y=a+bx

Where,

Y is the dependant variable – market price of shares

a is the constant

x is the independent variable EVA

b is the coefficient of the independent variable

Multiple Regression

Y=a+b1x1+b2x2+b3x3 +b4x4

Where,

Y is the dependant variable – market price of shares

a is the constant

xi are the independent variables – performance metrics like NPR, RONW,

EPS and EVA

bi are the coefficient of the independent variables

Method adopted to calculate EVA is as follows

(9$ 123$7�:$&&��,QYHV WHG�&DSLWDO� �where-

1. NOPAT is net operating profit after taxes.

2. WACC is weighted average cost of capital (equity and debt)

It is calculated as follows:

WACC = Kd (1-T)*W1+Ke *W2

W1 is weight of debt

W2 is weight of equity

Kd is the effective cost of Debt, which is calculated by dividing the totat

interest by the total debt.

Ke is calculated using the Capital Asset Pricing Model developed by

Modigliani and Miller.

Ke=Rf+Beta (Rm-Rf)

Rf is the risk free rate, i.e., the rate of interest for 1-year government

securities. These rates are obtained from the website of Reserve Bank

of India.

Rm is the return for the market. It is calculated by using the formula

given below for the index values.

Rm=Average of return on market for all the 10 years

Return = Closing index value-opening index value * 100

Opening index value

Beta values for all the sample companies for all the 10 years are

calculated by finding the slope between log normal of share prices of

all the companies and log normal of the index values.

Log normal of the values is considered to remove abnormalities if any

and convert them into normal distribution.

3. Invested Capital is the total long term funds and includes equity

shares and the total debt as at the end of the year.

Explanation

A firm’s invested capital multiplied by WACC gives the minimum level

of operating profits the firm should generate to satisfy stakeholders. EVA

measures how much net operating profit (adjusted for taxes) or NOPAT

exceed the capital charge (WACC * Invested capital). EVA correlates

extremely well with the market values.

While analyzing the EVA performance of a company, we look at ROIC-

WACC spread achieved by the company every year. The higher the spread,

the highest the Enterprise Value/Invested Capital multiple the company is

likely to enjoy.

Issues relating to calculation of EVA

�WACC used in the calculations is at book value of equity and debt.

�There are no consistent definitions of NOPAT or capital employed.

Different values of capital and NOPAT can be arrived by interpreting

them differently. �

EVA can be manipulated by capitalizing the items appearing in the

profit and loss account. Any item capitalized and carried forward to the

balance sheet instead of being shown as an expense in the profit &

loss account will increase the net operating income. At the same time

inclusion of the item in the balance sheet will also increase the capital

employed. However, the increase in the capital charges as a result is

always much less than the increase in the net operating income as it is

factored by cost of capital. Thus accounting ambiguities can be used in

increasing the EVA. �

Under the operating approach of capital employed calculation, different

depreciation policies (SLM and WDV here) will yield two different

values. In the calculations above the effect of changing the

depreciation policy on capital employed is shown. �

As EVA does not measure cash flows and uses accounting measures

like accrued net income, it is prone to manipulations.

�Managers could stop doing long-term planning and only work on the

short run if they are paid on the basis of EVA. �

EVA requires research and development expenditures to be

capitalized. Managers could capitalize (record expenditures as assets

rather than expenses or losses) expenditures that have no future value

to manipulate EVA. �

Changes in inventory valuation policy (e.g., FIFO to LIFO) or in

accounting for deferred taxation can have a significant impact on

operating profit and capital employed, which affects EVA. �

EVA allows managers to hold back expenditures in suspension (asset)

accounts. Managers could therefore record as assets, expenditures

that will provide no expected future benefit. �

EVA does not really measure value creation in terms of effectiveness

with which the resources deployed are utilized. �

EVA cannot be used as a measure for divisional performance

appraisal. Different divisions (businesses) will have different levels of

risks and therefore should have different cost of capital. The

companies, though, use a standard cost of capital. Also, if somehow a

divisional measure is used it will inculcate a bias among divisional

managers who may be unwilling to invest in the required physical asset

creation to sustain corporate growth. �

EVA by definition is biased against companies, which are capital

intensive. Hence, capital-intensive industries will always have a low

EVA compared to a FMCG companies. This measure is also biased

towards companies and industries that are in the matured stage of

business cycle and do not require asset creation.

Limitations of the study

o The Study is limited to the 24 companies that are included in BSE-

SENSEX.

o Availability of dressed data with respect to the financial statements of

the companies

Chapter-4

Data Analysis

The performance measures are based on the returns generated by the

business. Therefore, to obtain a meaningful conclusion, the performance of

different companies is compared cross-sectionally over the study period.

The following tables indicate the descriptive statistics with respect to

Market prices and the performance measures considered for the study-Net

Profit Ratio, Return on Networth, Earnings per share and Economic value

added.

Table No. 1

Market price, NPR, RONW, EPS and EVA for the 24 companies for the year

2003-2004.

Company Name Market Price NPR RONW EPS EVA

Associated cement companies ltd 202.9 5.15 16.48 10.78 133.0363

Bajaj auto 809.975 13.43 21.1 69.09 532.4924 Bhel 425.925 8.88 15.58 26.11 347.6986 Cipla ltd. 192.47 15.53 26.51 49.22 245.8825 Dr.Reddy's laboratories ltd. 1137.13 16.27 14.7 36.37 182.2263 Grasim industries ltd. 778.2 12.71 23.7 83.21 112.8462 Gujarat ambuja cements ltd. 270.78 14.61 18.51 17.75 221.691 Herohonda motors 490.2 27.67 26.46 32.81 1008.065 Hindalco industries 360.525 11.16 65.11 33.91 659.0971 Hindustan Lever ltd. 1014.13 12.78 12.86 88.6 505.744 Hindustan Petroleum Corp. ltd 160.9 15.99 61.14 6.35 1642.06

Housing Development Finance co 400.68 3.29 26.4 53.36 1485.427

I T C ltd 1104.68 27.76 44.83 68.82 1021.574 Infosys technologies ltd 901.625 13.48 27.34 61.75 1265.878 Larsen & Loubro ltd. 395.55 5.38 16.99 40.77 401.1886 Ranbaxy laboratories ltd. 1073.675 21.42 37.91 40.66 539.836 Reliance energy 432.05 9.17 17.39 36.31 3257.88 Reliance industries 511.3 10.46 10.61 20.43 45.79024 Satyam computers 260.48 21.87 23.57 17.06 359.9342 Tata Iron and Steel co. Ltd. 490.025 5.34 26.2 21.93 631.3833 Tata motors 268.075 12.38 10.78 25.72 258.0984 Tata power 187.185 14.65 45.36 46.02 1526.184 Wipro ltd. 436.78 17.63 26.76 35.59 662.4929 Zee telefilms 117.15 21.41 3.86 2.66 -5.90491

Table No. 2

Market price, NPR, RONW, EPS and EVA for 24 companies for the year

2002-2003.



Bajaj auto 458.05 11.28 17.63 51.42 347.1624

Bhel 184.48 6.92 11.45 17.65 193.4523

Cipla ltd. 170.79 15.99 25.55 40.03 190.4426

Dr.Reddy's laboratories ltd. 908.93 24.53 24.02 50.6 288.9769

Grasim industries ltd. 318.15 9.22 17.57 38.82 373.5762

Gujarat ambuja cements ltd. 179.55 10.97 13.79 13.4 146.8731

Herohonda motors 336.36 23.2 24.02 26.83 903.7652

Hindalco industries 279.25 11.38 75.09 26.78 530.2646

Hindustan Lever ltd. 620.43 12.31 12.08 61.23 297.1086

Hindustan Petroleum Corp. ltd 207.65 16.19 52.82 8.04 1549.177


I T C ltd 1018.39 26.12 40.68 170.01 775.7628

Infosys technologies ltd 651.78 12.44 28.41 53.48 1058.922

Larsen & Loubro ltd. 185.3 4.36 12.67 16.45 267.392

Ranbaxy laboratories ltd. 515.23 20.65 35.88 32.34 -1507.4

Reliance energy 262.3 8.19 15.58 28.62 2445.064

Reliance industries 214.23 4.18 6.19 11.21 18.51805

Satyam computers 233.9 20.64 20.55 9.49 159.9132

Tata Iron and Steel co. Ltd. 144.6 2.84 11.86 8.87 246.1611

Tata motors 114.03 12.46 11.98 26.27 314.2687

Tata power 82.83 11.99 35.41 26.48 882.2611

Wipro ltd. 494.88 20.13 27.74 34.84 607.4649

Zee telefilms 124.55 19.76 2.38 2.23 -144.53

Table No. 3

Market price, NPR, RONW, EPS and EVA for the sample companies for the

year 2001-2002.



Bajaj auto 355.88 10.58 15.94 51.5 363.4351

Bhel 150.63 7.94 14.28 19.12 238.4659

Cipla ltd. 211.07 16.97 29.51 39.2 187.8532








Housing Development Finance co 211 1.77 12.73 23.26 555.5485

I T C ltd 912.97 26.44 38.78 142.76 686.5728




Reliance energy 305.18 7.14 17.63 30.78 2116.849



Tata Iron and Steel co. Ltd. 102.73 -0.1 -0.29 0 15.81908

Tata motors 122.03 8.53 8.02 25.68 357.9784

Tata power 68.2 4.59 8.47 5.51 93.91668

Wipro ltd. 459.22 24.91 39.29 37.26 715.3063

Zee telefilms 124.275 23.93 2.41 2.36 -126.336

Table No. 4


year 2000-2001.


Associated cement companies ltd 139.43 2.8 7.26 2.57 -8.05185

Bajaj auto 309.13 8.39 10.33 25.13 130.6638

Bhel 140.2 7.1 12.58 12.47 117.011

Cipla ltd. 186.06 17.09 28.01 29.4 144.7737









I T C ltd 1912.14 31.03 46.57 121.32 555.4118

Infosys technologies ltd 730 11.59 32.43 39.98 874.3234



Reliance energy 363.43 11.49 23.05 24.63 1836.134



Tata Iron and Steel co. Ltd. 106.15 -6.31 -14.28 0 -654.301

Tata motors 108.68 10.23 12.19 19.69 262.1287

Tata power 80.24 10.44 17.7 14.46 340.841

Wipro ltd. 1202.27 21.31 52.94 28.6 562.6748

Zee telefilms 587.18 36 3.66 3.3 -68.2917

Table No. 5


year 1999-2000.


Associated cement companies ltd 196.27 -2.72 -6.81 0 -89.5514

Bajaj auto 444.5 13.57 17.03 50.31 507.5519

Bhel 228.3 8.76 17.94 24 482.2678

Cipla ltd. 214.95 17.51 26.19 21.86 111.2881



Gujarat ambuja cements ltd. 270.3 12.09 12 27.74 390.1593



Hindustan Lever ltd. 768 26.75 17.36 80.96 442.6265



I T C ltd 1841.03 33.09 56.57 93.73 270.086



Ranbaxy laboratories ltd. 452.07 12.02 13.58 16.16 34.02998

Reliance energy 240.68 15.17 23.37 22.04 1940.954



Tata Iron and Steel co. Ltd. 218.7 -2.15 -4.98 2.23 -39.4298

Tata motors 80.68 13.84 10.93 20.08 214.3706

Tata power 81.76 6.13 9.86 11.02 321.0664

Wipro ltd. 1744.22 12.38 55.1 10.68 178.2421

Zee telefilms 824 -28.46 -4.35 1.92 -19.592

Table No. 6


year 1998-1999.


Associated cement companies ltd 105.92 2.48 6.41 39.89 -7.21773

Bajaj auto 584 15.33 22.43 44.39 421.9145

Bhel 311.65 7.78 19.13 21.98 403.3937

Cipla ltd. 72.45 18.63 28.58 56.68 95.47326









I T C ltd 272.01 33.27 41.7 43.87 99.2675


Larsen & Loubro ltd. 210 5.43 11.24 18.23 335.4946


Reliance energy 152.2 16.04 18.3 17.56 973.6726

Reliance industries 177 14.78 13.52 18.5 169.8355

Satyam computers 75.77 19.25 54 27.68 63.45146

Tata Iron and Steel co. Ltd. 207.15 -0.15 -0.26 3.48 -89.7714

Tata motors 96.9 14.59 10.37 14 113.4555

Tata power 76.87 4.5 6.86 7.23 80.87423

Wipro ltd. 174.29 9.27 46.55 24.16 78.15441

Zee telefilms 70.13 27.03 36.48 32.04 51.32311

Table No. 7


year 1997-1998.


Associated cement companies ltd 107.76 -0.01 -0.03 8.32 -44.7202

Bajaj auto 568.17 14.7 23.93 38.08 349.2606

Bhel 366.25 10.96 31.53 29.15 576.4844

Cipla ltd. 46.45 19.82 33.09 50.46 82.42989









I T C ltd 46.99 26.58 36.2 40.54 50.34894




Reliance energy 175.89 17 18.78 16.94 941.7349



Tata Iron and Steel co. Ltd. 344 4.11 7.96 10.97 118.7025

Tata motors 124 13.9 11.11 13.92 108.0169

Tata power 109.36 6.21 9.95 8.36 111.3388

Wipro ltd. 30.81 7.38 37.83 18.54 56.05531

Zee telefilms 19.68 25.2 36.88 22.58 37.3622

Table No.8


year 1996-1997.



Bajaj auto 806.62 13.95 27.81 54.35 349.6885

Bhel 230.63 7.96 26.45 18.72 428.4591

Cipla ltd. 182.4 15.66 30.54 35.04 45.37688









I T C ltd 17.35 23.43 42.24 37.24 32.04288




Reliance energy 122.91 20.53 15.86 28.85 749.4766




Tata motors 158.88 10.02 8.87 9.91 65.96369

Tata power 131.52 8.31 13.68 12.32 267.8789

Wipro ltd. 12.34 5.06 29.29 41.33 30.48168

Zee telefilms 12.58 32.39 34.27 15.08 24.84089

Table No.9


year 1995-1996.



Bajaj auto 499.8 15.4 33.73 52.53 336.8847

Bhel 100 7.14 25.08 14.31 316.6089

Cipla ltd. 30 8.02 20.41 14.49 18.02364


Grasim industries ltd. 588 10.37 18.71 45.89 447.0293





Hindustan Petroleum Corp. ltd 59 6.36 31.26 16.4 197.3089


I T C ltd 9.35 26.77 38.69 50.94 16.28309




Reliance energy 114.88 22.79 16.6 27.87 783.5743




Tata motors 127.5 19.34 19.57 20.46 172.5012

Tata power 133.44 10.94 19.92 15.41 333.0439

Wipro ltd. 11.43 4.13 27.37 31.26 35.98982

Zee telefilms 18.43 26.11 33.5 12.02 18.37406

Table No. 10


year 1994-1995.



Bajaj auto 476 14.23 39.11 38.34 216.9741

Bhel 145 3.39 11.81 5.76 101.4728

Cipla ltd. 57.73 8.39 35.31 13.31 18.49161


Grasim industries ltd. 687 12.87 22.95 42.68 307.6644







I T C ltd 10.24 23.73 29.53 28.94 7.289069




Reliance energy 169.26 19.76 18.48 23.34 371.1108




Tata motors 159.75 11.61 14.04 11.03 62.22149

Tata power 169.6 6.08 10.79 8.35 177.4811

Wipro ltd. 8.66 4.04 33.47 21.07 20.60014

Zee telefilms 30.25 44.1 39.24 10.88 13.98905

Cross-sectional Regression results

Model 1

Y=a+bx

Y is the Market Price

X is the EVA

a is the constant

b is the co-efficient

Table No. 11

Co-efficient of regression, t statistic, Adjusted R2 and F values from the year

1994-2004.

Numbers within the brackets indicate t values = Correlation/Standard Error * indicates t values greater than 2 @ 5% significance level.

Years a b Adjusted R2 F

2003-04 96.183 (5.52)

0.096 (0.19) -0.044 0.036

2002-03 338.08 (5.40)

0.00107 (0.01) -0.045 0

2001-02 267.806 (4.36)

0.111 (1.19) 0.017 1.409

2000-01 365.55 (3.35)

0.152 (0.84) -0.013 0.709

1999-2000 549.473 (4.27)

-0.179 (0.79) -0.016 0.628

98-99 172.865 (3.42)

0.221 (1.63) 0.067 2.64

97-98 134.273 (2.42)

0.371 (2.22)* 0.146 4.944

96-97 130.807 (1.72)

0.511 (2.05)* 0.123 4.215

95-96 90.062 (1.81)

0.455 (2.52)* 0.188 6.339

94-95 85.07 (1.98)

0.941 (3.49)* 0.327 12.167

We can see that there exists no relationship between market prices

and EVA, excepting for the years 94-98 where the t values are statistically

significant. This is surprising because though EVA is a new concept, the

relationship is reflected in the earlier years, but not in the recent past.

In the recent years the Fcal < Ftab, so the null hypothesis is accepted.

But for the years 94-98 the Fcal > Ftab, indicating that there is relationship.

The R2 values for the years 94-98 are 0.146, 0.123, 0.188 and 0.327

respectively. This indicates that the relationship is not very significant, and

also not consistent.

Therefore, we cannot infer any strong relationship between Market

prices and EVA.

Model 2

Y=a+b1x1+b2x2+b3x3

Y is Market price

X1 is NPR

X2 is RONW

X3 is EPS

a is the constant

bi are the co-efficients

Table No. 12


1994-2004.

Numbers within the brackets indicate t values = Correlation/Standard Error * indicates t values greater than 2 @ 5% significance level

Years a b1 b2 b3 Adjusted

R2 F

2003-04 35.689 (0.22)

12.49 (1.57)

-2.196 (0.64)

9.403 (4.23)* 0.432 6.83

2002-03 49.179 (0.74)

10.096 (2.14)*

-0.628 (0.32)

5.142 (5.15)* 0.669 16.489

2001-02 55.935 (1.28)

2.931 (0.90)

1.688 (1.20)

5.642 (6.57)* 0.778 27.848

2000-01 -82.164 (0.89)

12.399 (1.96)

4.483 (1.45)

8.704 (4.13)* 0.674 16.869

1999-2000

157.651 (1.07)

-7.002 (0.64)

11.561 (2.05)*

5.183 (0.94) 0.19 2.794

98-99 216.579 (2.58)*

3.061 (0.56)

-0.887 (0.32)

0.13 (0.16) -0.132 0.107

97-98 234.042 (2.16)*

4.257 (0.65)

-3.35 (0.91)

0.284 (0.27) -0.079 0.436

96-97 337.003 (2.07)*

2.08 (0.28)

-5.967 (1.09)

0.519 (0.33) -0.072 0.483

95-96 278.062 (1.75)

0.719 (0.15)

-5.094 (0.88)

0.498 (0.74) -0.08 0.43

94-95 254.059 (2.05)*

-2.024 (0.56)

-1.76 (0.37)

0.543 (0.63) -0.094 0.341

We can see that among NPR, RONW and EPS, EPS has a better relationship with market prices.

The t values for EPS are more than t values for NPR and RONW. Also

in the recent 4 years from 2000-2004, the t values for EPS is highly

significant.

The R2 values for the years 1999-2004 are positive. This indicates that

the relationship is very significant.

This clearly shows that in India, investors’ decision is based on the EPS.

Model 1 shows the nature of relationship between Market prices and

EVA and Model 2 shows that EPS is the strong influencing factor among the

three measures considered. So the next Model is used by integrating both the

models and it considers all the four measures- NPR, RONW, EPS and EVA,

to explain the relative comparisons.

Model 3

Y=a+b1x1+b2x2+b3x3 +b4x4

Y is Market price

X1 is NPR

X2 is RONW

X3 is EPS

X4 is EVA

a is the constant

bi are the co-efficients

Table No. 13


1994-2004.

Numbers within the brackets indicate t values = Correlation/Standard Error * indicates t values greater than 2 @ 5% significance level

Years a b1 b2 b3 b4 Adjusted

R2 F

2003-04 46.371 (0.28)

12.029 (1.46)

-1.676 (0.44)

9.479 (4.15)*

-0.0289 (0.37) 0.406 4.936

2002-03 59.121 (0.84)

9.478 (1.91)

-0.384 (0.19)

5.235 (5.07)*

-0.02303 (0.51) 0.656 11.971

2001-02 50.37 (1.08)

3.333 (0.95)

1.417 (0.90)

5.551 (6.14)*

0.02128 (0.41) 0.768 20.063

2000-01 -73.309 (0.79)

11.327 (1.78)

6.489 (1.82)

9.089 (4.28)*

-0.136 (1.10) 0.678 13.087

1999-2000

235.442 (1.60)

-6.13 (0.59)

12.711 (2.34)*

5.893 (1.12)

-0.347 (1.74) 0.264 3.064

98-99 161.287 (1.87)

4.682 (0.88)

-1.702 (0.63)

-0.451 (0.55)

0.256 (1.77) -0.031 0.83

97-98 152.345 (1.46)

5.905 (0.99)

-4.252 (1.27)

-0.128 (0.13)

0.401 (2.30)* 0.112 1.723

96-97 188.524 (1.10)

4.832 (0.69)

-5.401 (1.05)

0.0071 (0.01)

0.517 (1.93) 0.057 1.346

95-96 83.196 (0.49)

1.623 (0.37)

-1.267 (0.23)

0.417 (0.68)

0.444 (2.19)* 0.092 1.583

94-95 84.883 (0.72)

0.268 (0.09)

-0.349 (0.09)

0.208 (0.28)

0.932 (3.03)* 0.224 2.662

We can see that the traditional measure EPS has better relationship

with the market prices in the recent years--- 2000-2004.

The t values for EPS are statistically significant. But in the earlier years

94-98, the t value for EVA is statistically significant. This shows that there is

significant relationship between EVA and market prices.

In the recent years the Fcal > Ftab, so the null hypothesis is accepted.

But for the years 95-99 the Fcal < Ftab, indicating that there is relationship.

So EPS is still a measure used by the investors to predict the share

prices of the company and to take investment decisions.

The R2 values for all the years, excepting 1998-1999 are positive. This

indicates that the relationship is very significant.

Chapter-5

DISCUSSIONS Creating shareholder value is a fundamental requirement for all the

companies. Therefore, most of the leading companies adopt the mantra of

shareholder value to meet the increasing expectations of the share holders.

According to various researches conducted in the recent past in India

and worldwide, the conclusion is, even though EVA is correlated to stock

returns, it is not much greater than the correlation between accounting profit

and stock return. Therefore, though EVA might be incrementally better over

other measures, it does not really provide any significant informational

advantage.

To see whether this relationship holds good the study has been

undertaken by taking 24 companies which are listed in BSE and are a part of

SENSEX, for a period of 10 years. The market prices of the shares are

compared with the 4 performance measures - NPR, RONW, EPS and EVA, to

analyse the extent of influence of each on the share prices.

A cross sectional analysis using regression is used to test the

relationship. The results show that there is no significant relationship between

EVA and market prices; also EPS is strong influencing factor among the

variables. Investor’s decisions rely on the EPS as it is well published

information and is directly related to P-E multiple which is used by the fund

managers for predicting the share prices of the companies.

CONCLUSIONS

Though academicians, professional communities and evaluating firms

give importance to EVA, investors do not consider this for their investment

decisions.

There is no significant relationship between EVA and market prices.

Though there exists relationship between the variables during earlier periods,

this may be because of chance factor and not because of cause factor.

Among the various performance measures, EPS has a better

relationship with the market prices.

Investors do not use all the available information. Investors should be

cautious while taking investment decisions and should consider other

measures which indicate the value created by the firm rather than the

earnings they get.

EPS is not the only measure which indicates the company’s

performance as it can mislead the investors, and can be increased by

reducing the no of shares.

The investors depend more on the readily available and easily

computable measures rather than complicated measure like EVA as it

requires lot of adjustments and calculations, unlike other measures which can

be easily calculated or available from the financial statements.

Bibliography

Text Books

o Financial Management, Theory and Practice

-Prasanna Chandra

(Sixth Edition)

o Research Methodology

-Donald Cooper and Pamela Schindler

(Eighth Edition)

o Statistics for Management

-Richard I Levin and David S Rubin

(Seventh Edition)

Websites

www.investopedia.com

www.valuebasedmanagement.net

www.evanomics.com

www.rbi.org.in

www.bseindia.com

www.indiainfoline.com

References

Articles of ICFAI

(The Institute of Chartered Financial Analysts of India)

o “Computation of EVA in Indian Banks”, by Roji George, the ICFAI

Journal of Bank Management, Vol IV, No. 2 , May 2005, Pg No. 30-44.

o “Value-based management: An Introduction to the concept of

Shareholder value”, by David Gowther, ICFAI Journal of Applied

Finance, , Vol VII, No. 1 , Jan 2002, Pg No. 12-18.

o “Facets of Shareholder Value creation”, by V.V.Gopal, The ICFAI

Reader, May 2005, Pg No. 33-37.

Others o Database of Capital Market Publishers (India) Ltd., Capitaline 2000

Documents

EVA Share Prices