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1. RESEARCH EXTRACTS:

Dissatisfaction with traditional accounting-based performance measures has

spawned a number of alternatives, of which Economic Value Added (EVA) is
currently the most prominent. Today, performance of the company with respect
to wealth creation is more important than the profit it earns or the dividend it
pays to the shareholders. Market prices of the shares reflect the performance of
the company

This research examines suitability of Economic Value Added (EVA) as a

performance measure. EVA is an effective measure of the quality of managerial
decisions as well as a reliable indicator of an enterprise’s value growth in future.

The value-based management performance measure EVA, introduced by Stern

Stewart & Co. is an incarnation of the underlying residual income (RI) concept
EVA and its variability are observed to be strongly affected by the firm's growth
policies because of leverage effects. Methodologically, the paper demonstrates
the advantages of using a controlled simulation approach in financial research.

The EVA is computed and compared with the respective share prices of 20
companies of the SENSEX for a period of 5 years beginning from FY 2002-03
to FY 2006-07.

From the study it is observed that there is there is no relationship between EVA
and share prices but EVA is the true profit that the company is able to generate
which also considers opportunity cost of the capital invested in the business.
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The study adds that EVA is the one of the measure, which is used to measure
the performance of the company but it cannot be used to forecast the share
price of the company.

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2. INTRODUCTION:

2.1. Background of the study:

Performance measurement has, of late, become an area of considerable

practitioner interest. Consulting firms routinely develop proprietary performance
measures for implementation by their client firms and have attempted to outdo
each other with claims of their own metric's "superiority." This "superiority" has
often been defined in terms of the correlation between the metric and realized
stock returns.

Stern Stewart & Co, a consulting firm based in New York, introduced the concept
of EVA as a measurement tool in 1989, and trademarked it. The EVA concept is
often called Economic Profit (EP) to avoid problems caused by the trade marking.
EVA is so popular and well known that all residual income concepts are often
called EVA even though they do not include the main elements defined by Stern
Stewart & Co (Pinto, 2001)
Stern Stewart developed EVA to help managers incorporate two basic principles
of finance into their decision making:
1. The primary objective of maximizing the wealth of its shareholders; and
2. Accepting that the value of a company depends on the extent to which
investors expect future profits to exceed or fall short of the cost of capital.

Today, this mechanism enable all types of firms to determine their value creation
and share holders to determine the value created on their investments. The first
question coming to our mind after reading this is:

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2.2. WHAT IS EVA?

EVA is a value based financial performance measure, an investment decision tool

and a performance measure reflecting the absolute amount of shareholder value
created. It is computed as the product of the “excess return” made on an
investment or investments and the capital invested in that investment or
investments. EVA is the net operating profit minus an appropriate charge for the
opportunity cost of all capital invested in an enterprise or project. It is an estimate
of true economic profit, or the amount by which earnings exceed or fall short of
the required minimum rate of return investors could get by investing in other
securities of comparable risk (Stewart, 1990).

2.3 Profits the way shareholders count them:

By taking all capital costs into account, including the cost of equity, EVA shows
the monetary amount of wealth a business has created or destroyed in each
reporting period. In other words, EVA is the profit as defined by the share holders.

The capital charge is the most distinctive and important aspect of EVA. Under
conventional accounting, most companies appear profitable but many in fact are
not. As Peter Drucker put the matter in a Harvard Business Review article, "Until
a business returns a profit that is greater than its cost of capital, it operates at a
loss. Never mind that it pays taxes as if it had a genuine profit. The enterprise still
returns less to the economy than it devours in resources…Until then it does not
create wealth; it destroys it." EVA corrects this error by explicitly recognizing that
when managers employ capital they must pay for it, just as if it were a wage.

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If the shareholders expect, say, a 10% return on their investment, they "make
money" only to the extent that their share of after-tax operating profits exceeds
10% of equity capital. Everything before that is just building up to the minimum
acceptable compensation for investing in a risky enterprise.

2.4 HOW EVA CAN BE USED:

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

• Analyzing historical trends:

EVA can measure a company’s historical success in creating shareholder
wealth.

• 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.

• 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.

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• 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.

2.5 APPLICATIONS OF EVA:

1. To measure how much shareholder value the firm has created in the past and
2. To determine investor expectations as they relate to the stock price.

2.6 ADVANTAGES OF EVA :

EVA is frequently regarded as a single, simple measure that provides a real

picture of shareholder wealth creation. In addition to motivating managers to
create shareholder value and to serving as a basis for the calculation of
management compensation, there are further practical advantages that value
based measurement systems can offer. According to Roztoci & Needy an EVA
system helps managers to:
• make better investment decisions;
• identify improvement opportunities; and
• consider long-term and short-term benefits for the company.

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EVA is an effective measure of the quality of managerial decisions and a reliable

indicator of a company’s value growth in the future. Constant positive EVA values
over time will increase company values, while negative EVA values might
decrease company values.

Above all, EVA helps in overcoming the ambiguity of financial goals. Most
companies use a plethora of measures to express their financial goals and
objectiv

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2.7 Development of the concept of EVA:

EVA is not new. Residual income, an accounting performance measure, is

defined to be operating profit with a capital charge subtracted. Thus, EVA is
variant of residual income, with adjustments to how one calculates income and
capital.
Up to 1970 residual income did not get wide publicity and it was not the prime
performance measure for companies (Mäkeläinen, 1998). However, in the
1990’s, the creation of shareholder value has become recognized as the ultimate
economic purpose of a corporation.
Firms adopt this concept to track their financial position and to guide
management decisions regarding resource allocation, capital budgeting and
acquisition analysis.

Two views behind the concept of economic profit:

 The financial concept underlying EVA was originally propagated by Adam

Smith, who proclaimed that the social mission of an individual enterprise is
to maximize the value of shareholders.

 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.

At operational level EVA approach leads often to increased shareholder value

through increased capital turnover (Wallace 1997, p.16). In many companies
everything has been done in cutting costs but the capital efficiency has been
ignored. EVA has been helpful because it forces to pay attention to capital
employed and especially to excess working capital. Allocating the capital costs to

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their originators i.e. individual functions of organization can further reinforce this
impact.

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2.8 What others say about EVA? (Articles published on web)

"... There is no profit unless you earn the cost of capital. Alfred Marshall said that
in 1896, Peter Drucker said that in 1954 and in 1973, and now EVA (economic
value added) has systematized this idea, thank God."

- Peter Drucker

University of California, Irvine

"Companies that adopt EVA incentives increased their market-to-book value ratio
by nearly 0.6 more than that of non-EVA peer companies. That means for every
$1 billion in book value an EVA firm creates nearly $600 million more in market
value than the non-EVA peer companies."

- Jim Wallace, A. Professor

"EVA based companies outperform their non-EVA peer companies by 9%, 12%, &
10%, one, two and three years, respectively, following adoption of EVA. Prior to
adoption of EVA, the EVA-based companies demonstrated no significant excess
performance relative to non-EVA peers."

- Robert Kleiman, Professor

"Unlike earnings or ROE or any of those other measures, EVA gets at what we're
really after: the creation of value by earning returns above our required cost of
capital across time."

- Bob Boldt, Senior Investment Officer

Northwest Mutual Fund

"We don't look for companies with the highest EVA for our funds, but for
companies that can positively increase their EVA."

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- Michael Butler, President & CEO

It has been shown that share price has a much higher positive correlation with
EVA than with any other measure of value.

- IPS Advisory, Inc.

WALL STREET RESEARCH ANALYSTS

"The EVA methodology explicitly addresses business and financial risk and
allows the investor to gauge the magnitude and sustainability of returns.
Moreover, this structure examines the three fundamental principles of value
creation: cash flow, risk, and sustainability of returns. Of all financial measures,
it best explains the creation of shareholder value."
- Al Jackson

"The EVA approach to equity analysis has become increasingly popular because
it more accurately reflects economic reality (as opposed to accounting reality)
when compared with many traditional valuation measures, such as earnings per
share (EPS), return on equity (ROE), and free cash flow ..."

- Steven G.Einhorn

"Economic Value Added (EVA) is a superior metric ... EVA has a higher
correlation with wealth creation than do EPS, ROE, or cash flow."

- Steven Milunovic
COMPANIES

COCO-COLA

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“Economic Profit is the way to keep score. Why everybody doesn’t use it is a
mystery to me.”
- Roberto Goizueta, Past CEO

Corporation Bank:

“Economic Value Added is the foundation of everything we do at SPX.”

- John Blystone, CEO

“To help us make all the short- and long-term decisions that affect our company
and help it to grow, we use a highly respected performance indicator,
measurement, and compensation system called Economic Value Added (EVA).”

- Michael Volkema, CEO

2.9 Problem Statement:

There are various views with respect to whether traditional measures or value-
based measures influence change in share price.

The question to be addressed is whether there is relationship between EVA and

Wealth creation and whether this relationship is significant

2.10 Significance of the study:

The research provides a fundamental platform to investors to analyze the

companies in terms of its present position; earning capabilities and the growth

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potential unleashed which leads to value addition and subsequently increase or
decrease of share prices

There is a lot of academic and professional research undergoing these days with
the following question common to every research:

Which measure is better suitable in computing the value created on the

investments?
If the research conducted succeeds in the identification of the direct relationship
that exists between EVA and share, there will be a value addition to available
database.

2.11 The objective of the study is:

1) To describes the theory and characteristics of EVA.

2) To analyze the relationship between EVA and share prices.
3) To judge the relation between EVA and change in market capitalization
4) To establish that EVA is a better measure of performance
5) To give the framework to the investors to use EVA considering both its
favorable and unfavorable features in determination of the value created
on their investments

2.12 Scope of the study:

The scope for the research investigation is restricted only to the following:-
• The research focuses only on twenty companies listed in BSE Sensex.
• The research is limited to the data available from 2002-03 to 2006-07.

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2.13 Calculation of EVA,

Following steps are followed:

1) Calculating NOPAT(Net Operating Profit After Tax) using profit and loss
account.
2) Calculating invested capital using Balance Sheet.
3) Calculating the returns on the market using the indices ,which represent
the Market.
4) Extracting the risk free rate from sources.
5) Calculating cost of debt.
6) Calculating the cost of equity using CAPM model.
7) Using the above information ,calculating EVA.

2.14 EVA calculation:

Every calculations are done under Microsoft Excel mostly with the help of inbuilt
functions.
EVA Calculation

EVA = NOPAT - WACC (Capital Invested)

There are three stages involved in this they are
1. Calculation of NOPAT: Net Operating Profit after Taxes
2. Calculation of WACC: Weighted Average Cost of Capital
3. Calculation of Invested Capital

The Stages of the Process

1. Calculation of NOPAT:

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Getting to NOPAT takes three basic steps:

1. Start with earning before interest and taxes (EBIT)

2. Make the key adjustments - these come in two flavors:
a. Eliminating accounting distortions (convert accrual to cash).
b. Reclassifying some expenses as investments (i.e. Capitalizing them
to Balance sheet)
3. Subtract cash operating taxes

The basic exemption of Stern Stewart is that second step can be adjusted
as per the convenience of the research person but the only condition is that the
same process has to be followed for all the years and all the companies under
research.

The formula adopted to calculate NOPAT is as follows

NOPAT = EBIT (1+ Tax rate)

2. Calculation of WACC:

It refers weighted average cost of capital (equity and debt). WACC used in the
calculations is at book value of equity and debt. It is calculated as follows:

WACC = Ke *W1+ Kd *W2

Where,
W1 is weight of EQUITY
W2 is weight of DEBT
Ke is cost of EQUITY
Kd is cost of DEBT

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W1 = Total equity / Total Capital.

W2 = Total debt / Total capital

Ke = Risk-Free Rate + (Beta x Equity Premium)

Equity Premium = Rf + β (Rm - Rf)
Where
Rf is Risk-Free Rate of Return.
Rm is Average Return from Sensex which is considered as proxy to Market.
β is Beta.
Kd = Interest expences (1- Tax rate) / Amount of Debt

3. Calculation of Invested Capital:

Getting to Capital invested takes three basic steps:

a. Get invested book capital from Balance sheet.

b. Make adjustments that convert accounting accruals to cash.
c. Make adjustments that recognize off-balance sheet sources of
funds. The basic exception of Stern Stewart there are many
processes for calculating but while calculating Invested Capital , the
most important condition is that the same process has to be
followed for all the years and all the companies under research.

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3. REVIEW OF LITRATURE:

 Stern Stewart in 1990 has first studied this relationship with market data
of 618 U.S. companies. Stewart presents the results in his book "The
quest for value". Stewart has studied the relationship between EVA and
market value of the company and he has produced a list of companies’
EVA annually since 1982, its coverage is limited to the largest 1,000
companies.

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"

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• EVA must be used to guide every decision;
• EVA should be used as the "deciding factor in all business decisions.

• Grant in 1996 showed that

EVA significantly impacts the market value added of a firm and that this wealth
effect stems from the company’s positive residual return on capital. He calculates
regression statistics between the MVA-to-capital and EVA-to-capital ratios from
the data of 983 firms.

• Peterson in 1996 showed that

Value-added measures are slightly but not significantly more correlated with
stock returns than traditional performance measures.

• Lehn and Makhija in 1996 conducted a study on 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 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).

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Additionally they study how companies’ performance, as measured in terms of
EVA and MVA, affect on the CEO firings. Finally they examine the relationship
between EVA/MVA and corporate focus. Lehn and Makhija find an inverse
relation between EVA/MVA and abnormal CEO turnover.

They also find that firms with greater focus on their business activities have
significantly higher MVA than their less focused counterparts.

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.”

• Myers study of 1997 states that

EVA is the most widely used Value-Based performance measure probably just
because it happens to be an easier concept compared to the others. In
implementing EVA, one of the most important things is to get the people in
organizations to commit to EVA and thereby also to understand EVA. Even as
easy concept as EVA seems to be quite hard to communicate down the
organization. That is why complicated measures do not work very well.

• A research conducted by Dodd & Chen in 1998 suggests that

“Some Value-Based measures have been found to correlate better with share
prices than EVA and find that Cash Flow Return on investment (CFROI) explains
share price movements better than EVA. Of course EVA can also be modified in
order to avoid some accounting distortions and to correlate better with share
prices, but then we have almost as complicated measure as CFROI. The best

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possible correlation with share prices is not however the main point, especially
when the differences in correlations are quite small and also disputable.

All the shareholder value metrics are said to be identical to discounted free cash
flow -method, so it is no wonder that we can relate the results of these metrics "to
the fourth decimal points". With the complicated shareholder wealth -measures it
is not always the toughest part to communicate these to people, but to calculate
these in day-to-day operations. E.g. CFROI calls for taking into account the
effects of inflation to asset values and this in turn takes time and resources.

In other words: it takes money. Hence it can perhaps in many occasions be

stated that the other metrics do not pass a prudent cost-benefit analysis; the
additional costs with implementing them instead of EVA are often more than the
incremental information achieved with them.”

• A research conducted by Ashok Banerjee on “Economic Value Added and

Shareholder Wealth - An empirical study of relationship” on nine industries
in 1999 concludes that

“EVA has proved to be an important explanatory variable with the observation

and it is true that companies, which have started disclosing EVA results in their
Annual Reports, would see a more direct impact on stock price.”

• Another research conducted by Dr. Srinivasan, Professor, National

Institute of Industrial Engineering (NITIE) on “Economic Value Added
(EVA) - An Emerging Tool for Value Creation” in 2000 suggests that

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 The study points Out that EVA alone cannot be an ‘Indicator of
Value.’ At the best it can be used an additional tool. However the
use of EVA analysis in isolation can lead to misleading results.

 In India, there is no big correlation between EVA, management

innovation and stock market analysis. EVA serves a useful indicator
only at the conceptual stage rather than on the operational stage.
As regards stock market indicator it cannot be relied upon as a sole
measure.

 As far as India is concerned the stock trends depend on a number

of variables of which EVA is one among them. It is clear that only a
blend of EVA and other variables can give a holistic future on stock
market.

• Another research conducted by Gerald T. Garvey and Todd T.

Milbourn states that

“They advance the literature by ascertaining the relative weights that firms should
use in a realistic setting where they do not know all of the relevant attributes of
alternative performance measures a priori.

More surprisingly, they show that the apparently simplistic idea of comparing the
relative ability of alternative measures to explain stock returns is both
theoretically defensible and a reasonable representation of practice. Therefore,
firms contemplating the adoption of EVA would be well advised to begin with an
examination of EVA's R2 with its stock returns.”

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• One more research conducted by Madhav V. Rajan on “EVA versus

Earnings: Does It Matter Which Is More Highly Correlated with Stock
Returns?” In 2000 suggests that

“Implementing EVA in a form would have access to sufficient data

about the metrics to again obviate the need for R2 information. This paper
concludes with the message that a researcher who is unaware of the parameters
governing the metrics for a firm can make use of a complex nonlinear function
of the metrics' correlation with price to compute the value of adopting
EVA. These are obviously not the same things.”

• A research conducted by M Geyser & IE Liebenberg on “CREATING A

NEW VALUATION TOOL” on agricultural co-operatives in South Africa
in 2003 concludes that

“EVA as a value enhancement measure for agricultural co-operatives. EVA

measures the excess return on existing assets. It is important to remember when
using EVA as a value enhancement measure that it will not work unless there is a
commitment on the part of managers to make value maximization their primary
objective.

Finally, there are no magic bullets that create value. Value creation is hard work
in competitive markets and almost always involves a trade off between costs and
benefits. Everyone has a role in value creation and it certainly is not the sole
domain of financial analysts. In fact, the value created by financial engineers is
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smaller and less significant than the value created by good strategic marketing,
production and personnel decisions.”

Learning form the review of literature:

From the study of above mentioned research papers, one can understand that
there is only a confusion but not any clear idea regarding the usage or non-
usage of EVA in forecasting the share prices of the company.
Here is the research gap an the main objective behind this research

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4. Research Methodology

4.1 Type of Research

The research type is historical analytical and quantitative.

Historical as the historical information is used for analysis and interpretation.
Analytical as facts and existing information is used for the analysis.
Quantitative as EVA is calculated and the variables are expressed in measurable
terms.

4.2 Sampling Technique

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The Sampling method used here is Simple Random Sampling. 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 considered to be a good proxy for the whole market. Also it is a
barometer that indicates the state and health of the economy.

4.3 Sample Description

Out of 30 companies included in the list of BSE Sensex, our Sample includes 20
companies for which relevant data is available, for a period of 5 years starting
from FY 2002-03 to FY 2006-07.

At irregular intervals, the Bombay Stock Exchange (BSE) authorities review and
modify its composition to make sure it reflects current market conditions.
Therefore only 20 companies are considered for the research as the data of all
the 30 companies for all the five years is unavailable.

4.4 Sample Profile

The following are the sample 20 companies:
Sl.no Company Name Sector
1 Associated Cement Companies Ltd. Housing Related
2 Bharati Airtel. Telecom
3 Bharat Heavy Electricals Ltd. Capital Goods
4 Cipla Ltd. Healthcare
5 Grasim Industries Ltd. Diversified
6 Gujarat Ambuja Cements Ltd. Housing Related
7 HDFC Finance Finance
8 HDFC Bank Finance
9 Hindalco Metal, Metal Products &
Mining
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10 Hindustan Lever Ltd. FMCG
11 Infosys Technologies Ltd Information Technology
12 ITC Ltd. FMCG
13 Larsen & Tourbro Ltd. Capital Goods &
Construction
14 Mahindra & Mahindra Transport Equipments
15 Maruti Transport Equipments
16 ONGC Oil & Gas
17 Ranbaxy Laboratories Ltd. Healthcare
18 Reliance Industries Oil & Gas
19 Reliance Energy Power
20 Wipro Ltd. Information Technology

4.5 Data gathering procedure and instrumentation

4.6 Data type: Secondary data

4.7 Required data:

• Balance Sheet
• Profit and loss Account
• History of stock prices
• History of market index
• Risk free rate of return
• Corporate tax rate

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4.8 Data Source

• Balance sheet and Profit and loss Account are taken from the financial
statements of the sample companies from the database of Capital Market.
• History of stock prices and history of market index are downloaded from
database of Capital Market and Bombay stock exchange.

• Risk free rate of return of relevant years is taken from the RBI bulletin
As 365 days Treasury bill rate is considered to be the proxy for risk free
rate of return.

• Corporate tax rate is taken from the Indian annual budget statements and
KPMG survey 2006,

4.9 Hypothesis

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

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

Tools used for testing of hypothesis:

The hypothesis is tested using linear regression tool, simple co-efficient of
correlation, co-efficient of determination and t 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

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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 dependent variable over the
independent variable.

There are two methods of testing the relationship with the help of t-statistics.
They are
1. To compare the values of t- calculated with that of t- tabulated.
In this case if the calculated t-value is greater than that of table value null
hypothesis has to be rejected and alternate hypothesis has to be accepted.

2. To compare the p- value with that of level of significance:

In this research report this method is adopted and
• If the p-value is greater than or equal to level of significance the null
hypothesis is accepted.
• If the p-value is less than the level of signigicance, the null
hypothesis is rejected.

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TABLE 1: BETA of sample companies

SI NO Company Name 2003 2004 2005 2006 2007

1 ACC 0.261 0.262 0.144 0.154 0.609
-0.25
2 Ambuja 5 0.419 -10.4 0.352 0.676
-0.24
3 Airtel 8 0.156 0.657 0.295 0.332
4 BHEL 0.454 0.472 0.391 0.309 0.355
-0.07
5 Cipla 9 0.604 0.247 0.126 0.506
-0.43
6 Grasim 3 0.371 0.323 0.245 0.795
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7 HDFC Bank 0.079 0.405 0.463 0.313 0.428

8 HDFC FINANCE 0.377 0.912 0.587 0.551 0.334
9 Hindalco 0.454 0.794 0.391 0.847 0.032
10 HUL 0.117 0.06 0.497 0.02 0.399
11 Infosys Tech 0.402 0.324 0.57 0.086 -0.01
-0.03
12 ITC 1 0.134 0.861 0.63 0.148
13 L&T 0.199 0.38 1.202 0.06 0.189
14 M&M 0.511 0.34 0.491 0.289 0.475
15 Maruti 0.135 -0.99 0.875 0.126 0.384
-0.08
16 ONGC 2 0.335 0.694 0.307 0.277
-0.23
17 Ranbaxy 3 0.587 0.652 0.575 0.29
18 Reliance Industries 0.562 0.482 0.912 0.144 -0.29
19 Reliance Energy 0.294 -0.43 0.695 0.478 0.359
20 Wipro 0.454 0.472 0.391 0.309 0.355

TABLE 2: INVESTED CAPITAL of sample companies

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SI NO Company Name 2003 2004 2005 2006 2007

1 ACC 2982.88 3317.69 3347.51 4100.38 4647.71

2 Ambuja 3367.863 3291.37 3305.84 4355.96 4991.29

3 Airtel 4825.67 5451.22 9527.89 12129.72 16724.01

4 BHEL 5337.89 5835.87 6563.87 5835.87 8877.59

5 Cipla 1164.86 1474.63 1744.83 2454.18 3359.83

6 Grasim 5053.38 5676.38 6336.69 6961.75 9181.6

7 HDFC Bank 91319.29 73586.87 51505.98 42379.98 30482.45

8 HDFC FINANCE 26296.14 32077.83 40530.5 51189.68 62744.42

9 Hindalco 8586.11 9422.5 11466.58 14509.7 19786.64

10 HUL 3843.03 3563.83 2362.56 2796.09 1427.77

11 Infosys Tech 2,860.65 3,253.43 5,242.00 6,897.00 11,162.00

12 ITC 5482.6 6530.91 8140.97 9181.21 10637.96

13 L&T 6738.54 4099.39 5228.19 6093.74 7846.18

14 M&M 2709.68 2504.84 3039.15 3792.25 5188.91

15 Maruti 3554 3903.1 4686.4 5524.3 7484.7

16 ONGC 36539.19 51950.89 56761.64 66682.28 77033

17 Ranbaxy 36539.19 51950.89 56761.64 66682.28 77033

18 Reliance Industries 50132.72 55397.11 59187.91 71699.87 91792.86

19 Reliance Energy 3209.88 7159.54 9532.66 12075.51 15222.2

20 Wipro 3,399.96 3,608.28 4,954.53 6,470.61 9,554.90

TABLE 3: NOPAT of sample companies

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SI NO Company Name 2003 2004 2005 2006 2007

1 ACC 244.0322 360.3113 500.2317 1129.85 1336.173

2 Ambuja 253.466 331.8299 406.8937 1303.285 1858.893

3 Airtel 2100.105 1440.739 125424.9 170181.8 325578.9

4 BHEL 571.4686 716.5625 1108.709 1748.781 2519.679

5 Cipla 211.3306 271.9536 350.8642 483.9642 546.1206

6 Grasim 448.6824 820.801 961.6947 872.9836 1558.878

7 HDFC Bank 1175.265 1286.738 1529.743 2122.113 3212.294

8 HDFC FINANCE 188064.7 193432.3 214596.1 270334.8 376085.5

9 Hindalco 691.1346 948.5941 1382.836 1553.991 2498.138

10 HUL 1489.901 1076.707 1095.341 1465.567 1568.945

11 Infosys Tech 773.1587 980.8624 1486.741 1824.758 2768.805

12 ITC 1397.697 1568.905 2052.236 2163.622 2628.625

13 L&T 511.8523 596.0165 937.9069 1036.732 1427.851

14 M&M 208.9079 343.4305 486.2981 751.0109 958.9079

15 Maruti 223.2112 542.1604 893.978 1180.326 1545.011

16 ONGC 1082423 910278.8 1313489 1458806 1580592

17 Ranbaxy 282.6992 448.8008 514.7158 452.6917 400.2217

18 Reliance Industries 4353.131 5157.498 7025.245 7721.119 10473.43

19 Reliance Energy 152.6476 324.8496 469.4301 648.9124 748.3974

20 Wipro 622.751 723.683 1174.56 1561.95 2122.37

TABLE 4: WACC of sample companies

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SI NO Company Name 2003 2004 2005 2006 2007

1 ACC 0.036731 0.13519 0.061706 0.122107 0.098063

2 Ambuja 0.070143 0.227303 -1.10177 0.220899 0.10404

3 Airtel 0.108575 0.137775 0.077988 0.13344 0.058657

4 BHEL -0.00258 0.344622 0.115767 0.21883 0.089169

5 Cipla 0.066694 0.402723 0.08706 0.10587 0.091843

6 Grasim 0.079147 0.211514 0.086544 0.133423 0.079735

7 HDFC Bank 0.029419 0.04445 0.066036 0.053043 0.025049

8 HDFC FINANCE 0.052051 0.100203 0.047112 0.072123 0.047996

9 Hindalco 0.033439 0.050369 0.034674 0.048176 0.042004

10 HUL 0.027629 0.368551 0.384665 0.16136 0.251075

11 Infosys Tech 0.183898 0.51752 0.432994 0.254079 0.03325

12 ITC 0.066481 0.139045 0.194284 0.406061 0.077633

13 L&T 0.037451 0.248903 0.177805 0.086431 0.063063

14 M&M 0.014036 0.232312 0.093619 0.161593 0.056665

15 Maruti 0.003005 -0.62752 0.189957 0.13167 0.081784

16 ONGC 0.074989 0.222361 0.140176 0.179076 0.082744

17 Ranbaxy 1.203276 0.362417 0.159759 0.376678 0.083349

18 Reliance Industries 0.002959 0.278467 0.155931 0.095325 0.047575

19 Reliance Energy 0.042215 -0.13845 0.111844 0.240156 0.075251

20 Wipro -0.00279 0.361657 0.116327 0.22912 0.085144

TABLE 5: SHARE PRICES of sample companies

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SI NO Company Name 2003 2004 2005 2006 2007

1 ACC 148.6025 204.3667 289.4408 487.4683 927.9225

2 Ambuja 23.36667 32.2275 43.05583 72.89083 117.9617

3 Airtel 29.895 78.55667 171.5175 507.8867 520.17

4 BHEL 87.97917 198.0733 315.5742 626.155 1118.903

5 Cipla 73.1 79.8225 104.6858 153.6733 246.0817

6 Grasim 314.2883 727.4717 1158.968 1332.09 2359.734

7 HDFC Bank 217.5967 301.9808 438.1608 659.4875 914.3142

8 HDFC FINANCE 329.5208 501.585 662.8167 1018.214 1408.893

9 Hindalco 241.615 94.3425 305.6117 291.6358 172.8783

10 HUL 136.05 153.35 134.7933 176.8075 231.5117

11 Infosys Tech 482.9508 531.025 863.8875 1258.33 1880.815

12 ITC 43.1391 57.3225 75.275 128.1108 178.2592

13 L&T 92.2483 181.6175 402.6267 764.5017 1326.798

14 M&M 49.9025 136.3542 237.8483 397.59 719.2775

15 Maruti 266.47 328.9178 427.8058 575.4542 870.47

16 ONGC 236.0683 399.19 516.6467 684.5133 819.08

17 Ranbaxy 236.0683 399.19 516.6467 684.5133 819.08

18 Reliance Industries 222.1617 348.5825 421.955 605.1217 1154.897

19 Reliance Energy 218.7675 411.07 598.27 581.3517 504.8458

20 Wipro 241.615 207.303 305.612 409.492 543.657

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Interpretation:
TALBLE 1: BETA
It can be seen that beta for most of the companies has increased from year to
year from Table-1. Earlier, in 2003 it was on an average of 0.15 when all the
companies are taken together. But slowly it has increased to about 0.40 by the
end of 2007. Thus we can say that riskiness of the stock is increased during the
period of 2003 to 2007.

TABLE 2: INVESTED CAPITAL

Total capital employed of almost companies is increased consistently from
2003 to 2007. While, HUL has reduced its capital fund from 3843.03 crore to
1427.77 crore in 2007. L&T and AMBUJA have also reduced its capital fund
during 2004 but it has again increased the capital fund in 2005 and 2006.

TABLE 3: NOPAT
NOPAT is increased consistently increased and improved from year to year of
almost companies. There is some variations in case of Airtel, HUL, ONGC and
Ranbaxy.

TABLE 4: WACC
From the table it is concluded that weightage average cost of capital (WACC) of
the all companies during 2003 and 2004 is very low. WACC is around 0.02
during these years. But in 2004 WACC is increased drastically because of
increasing beta . In case of Infosys there is only equity fund employed and they
are known as debt free company.

TABLE 5: SHARE PRICES

From the table it is clear that the share prices went on increasing from year to
year because of increase in beta. It is because as the market is becoming more
and more risky investors expected return is going on a hike.

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Forecasted Eva & Share price for the year 2008.

Sl No Company EVA SHARE PRICE
1 ACC 1032.674 908.8834
2 Ambuja 2086.779 60.73627
3 Airtel 368196.9 645.0349
4 BHEL 1583.445 799.9634
5 Cipla 320.6328 167.6571
6 Grasim 734.6403 1894
7 HDFC Bank 2324.705 918.177
8 HDFC FINANCE -2153.76 838.026
9 Hindalco 1749.137 230.4962
10 HUL 983.0629 172.6005
11 Infosys Tech 1769.17 1563.617
12 ITC 274.7111 96.18761
13 L&T 941.7613 1119.56
14 M&M 596.8598 574.6667
15 Maruti 589.3737 496.8845
16 ONGC
17 Ranbaxy
18 Reliance Industries
19 Reliance Energy
20 Wipro 1011.646 421.6915

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Before going on with the further part of the analysis of the data here are the
concepts used in the analysis which forms a basis for all the analytical
interpretation

Multiple R: It refers to the correlation,

• If there is no relationship between the two
variables the correlation coefficient is 0 or very Correlation Negative Positive
low. Small −0.3 to −0.1 0.1 to 0.3
• A perfect fit gives a coefficient of 1.0. Medium −0.5 to −0.3 0.3 to 0.5
Large −1.0 to −0.5 0.5 to 1.0

Thus the higher the correlation coefficient the better.

R Square: It refers to coefficient of determination.

Adjusted R Square:

ACC:
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Year EVA(X) Share price(Y)

2003 134.466784 148.6025
2004 -88.205619 204.3667
2005 293.671408 289.4408
2006 629.166754 487.4683
2007 880.402994 927.9225

Regression
Statistics

→Large positive correlation. If EVA moves by one, share

price will move by 0.9189 in the same direction.
Multiple R 0.918902

→84.44% of change in share price is due to change in EVA.

R Square 0.84438

Adjusted R
Square 0.792507

The t-statistics of this is 4.034568

The p- value in this case is 0.027384which is less than 5%.

Therefore Alternate hypothesis is accepted.
As such there is relationship between EVA and share price

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Airtel:

Year EVA(X) Share price(Y)

2003 1576.15597 29.895
2004 689.694416 78.55667
2005 124681.872 171.5175
2006 168563.252 507.8867
2007 324597.963 520.17

Regression
Statistics

→Large positive correlation. If EVA moves by one, share

price will move by 0.9189 in the same direction.
Multiple R 0.895222

→84.44% of change in share price is due to change in EVA.

R Square 0.801423

Adjusted R
Square 0.73523

The t-statistics of this is 3.479578

The p- value in this case is 0.040067 which is less than 5%. Therefore
Alternate hypothesis is accepted.
As such there is relationship between EVA and share price

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BHEL:

Year EVA(X) Share price(Y)

2003 585.241841 87.97917
2004 -1294.6058 198.0733
2005 348.829449 315.5742
2006 471.716669 626.155
2007 1728.07144 1118.903

Regression
Statistics

→Large positive correlation. If EVA moves by one, share

price will move by 0.708 in the same direction.
Multiple R 0.708188

→50%of change in share price is due to change in EVA.

R Square 0.50153

Adjusted R
Square 0.335373

The t-statistics of this is 1.737359

The p- value in this case is 0.180718which is more than 5%. Therefore

Null hypothesis is accepted.
As such there is no relationship between EVA and share price.

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CIPLA:

Year EVA(X) Share price(Y)

2003 133.641809 73.1
2004 -321.91415 79.8225
2005 198.95983 104.6858
2006 224.140919 153.6733
2007 237.544622 246.0817

Regression
Statistics

→Large positive correlation. If EVA moves by one, share

price will move by 0.529 in the same same direction.
Multiple R 0.528854

→27.97% of change in share price is due to change in EVA.

R Square 0.279687

Adjusted R
Square 0.039582

The t-statistics of this is 1.079284

The p- value in this case is 0.3595 which is more than 5%. Therefore
Null hypothesis is accepted.
As such there is no relationship between EVA and share price.

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GRASIM:

Year EVA(X) Share price(Y)

2003 48.7218383 314.2883
2004 -379.8311 727.4717
2005 413.294052 1158.968
2006 -55.874877 1332.09
2007 826.780733 2359.734

Regression
Statistics

→Large positive correlation. If EVA moves by one, share

price will move by 0.7636 in the same same direction.
Multiple R 0.7636

→58.31% of change in share price is due to change in EVA.

R Square 0.583084

Adjusted R
Square 0.444112

The t-statistics of this is 2.04834

The p- value in this case is 0.132976 which is more than 5%. Therefore
Null hypothesis is accepted.
As such there is no relationship between EVA and share price.

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Gujarat Ambuja

Year EVA(X) Share price(Y)

2003 17.233118 23.36667
2004 -416.30818 32.2275
2005 4049.15398 43.05583
2006 341.058943 72.89083
2007 1339.60156 117.9617

Regression
Statistics
→Small positive correlation. If EVA moves
by one, share price will move by 0.129 in the same
Multiple R 0.129352 direction.

→1.67% of change in share price is due to change in EVA.

R Square 0.016732

AdjustedR
Square -0.31102

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The t-statistics of this is 0.225943

The p- value in this case is 0.835764 which is more than 5%. Therefore
Null hypothesis is accepted.
As such there is no relationship between EVA and share price.

HDFC Bank:

Year EVA(X) Share price(Y)

2003 -1511.2649 217.5967
2004 -1984.2215 301.9808
2005 -1871.5028 438.1608
2006 -125.84661 659.4875
2007 2448.75096 914.3142

Regression
Statistics

→Large positive correlation. If EVA moves by one, share

price will move by 0.9252 in the same direction.
Multiple R 0.925221

→85.56% of change in share price is due to change in EVA.

R Square 0.856033

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Adjusted R
Square 0.808045
 EVA & SHAREPRICE 2
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The t-statistics of this is 4.223525

The p- value in this case is 0.02427 which is less than 5%. Therefore
Alternate hypothesis is accepted.
As such there is relationship between EVA and share price.

HDFC Finance:

Year EVA(X) Share price(Y)

2003 186695.968 329.5208
2004 16127.9931 501.585
2005 212686.62 662.8167
2006 266642.834 1018.214
2007 373074.025 1408.893

Regression
Statistics

Multiple R 0.240021

R Square 0.05761
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Adjusted R
Square -0.25652
 EVA & SHAREPRICE 2
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→Small positive correlation. If EVA moves by one, share price will move
by 0.24 in the same direction.

→5.76% of change in share price is due to change in EVA.

The t-statistics of this is -0.42825.

The p- value in this case is 0.697356 which is more than 5%. Therefore
Null hypothesis is accepted.
As such there is no relationship between EVA and share price.

Hindalco:

Year EVA(X) Share price(Y)

2003 404.026483 241.615
2004 473.991341 94.3425
2005 985.24634 305.6116667
2006 854.968669 291.6358333
2007 1667.0164 172.8783333

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Regression
Statistics

→Small positive correlation. If EVA moves by one, share

price will move by 0.0612 in the same direction.
Multiple R 0.061177

→.374% of change in share price is due to change in EVA.

R Square 0.003743

Adjusted R
Square -0.32834

The t-statistics of this is 0.106161

The p- value in this case is 0.922156 which is more than 5%. Therefore
Null hypothesis is accepted.
As such there is no relationship between EVA and share price.

HUL:

Year EVA(X) Share price(Y)

2003 1383.72394 136.05
2004 -236.74715 153.35
2005 186.546737 134.7933
2006 1014.38882 176.8075
2007 1210.46748 231.5117

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Regression
Statistics

→Medium positive correlation. If EVA moves by one,

share price will move by 0.392784 in the same direction.
Multiple R 0.392784

→15.43% of change in share price is due to change in EVA.

R Square 0.154279

Adjusted R
Square -0.12763

The t-statistics of this is 0.739777

The p- value in this case is 0.513066 which is more than 5%. Therefore
Null hypothesis is accepted.
As such there is no relationship between EVA and share price.

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Infosys:

Year EVA(X) Share price(Y)

2003 247.089698 482.9508
2004 -702.85272 531.025
2005 -783.01145 863.8875
2006 72.375037 1258.33
2007 2397.6686 1880.815

Regression
Statistics

→Large positive correlation. If EVA moves by one, share

price will move by 0.9189 in the same direction.
Multiple R 0.815042

→84.44% of change in share price is due to change in EVA.

R Square 0.664294

Adjusted R
Square 0.552392

The t-statistics of this is 2.43647

The p- value in this case is 0.092792 which is more than 5%. Therefore

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 EVA & SHAREPRICE 2
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Null hypothesis is accepted.
As such there is no relationship between EVA and share price.

ITC:

Year EVA(X) Share price(Y)

2003 1033.20737 43.1391
2004 660.817448 57.3225
2005 470.572946 75.275
2006 -1564.5084 128.1108
2007 1802.76922 178.2592

Regression
Statistics

→Small positive correlation. If EVA moves by one, share

price will move by 0.0254 in the same direction.
Multiple R 0.025407

→.0646% of change in share price is due to change in EVA.

R Square 0.000646

Adjusted R
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Square -0.33247
 EVA & SHAREPRICE 2
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The t-statistics of this is

The p- value in this case is 0.967654 which is more than 5%. Therefore
Null hypothesis is accepted.
As such there is no relationship between EVA and share price.

L & T:

Year EVA(X) Share price(Y)

2003 259.48926 92.2483
2004 -424.3343 181.6175
2005 8.30691828 402.6267
2006 510.042795 764.5017
2007 933.05102 1326.798

Regression
Statistics

Multiple R 0.839389

R Square 0.704574
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Adjusted R
Square 0.606099
 EVA & SHAREPRICE 2
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→Large positive correlation. If EVA moves by one, share price will move
by 0.8394 in the same direction.

→70.46% of change in share price is due to change in EVA.

The t-statistics of this is

The p- value in this case is 0.075378 which is more than 5%. Therefore
Null hypothesis is accepted.
As such there is no relationship between EVA and share price.

M & M:

Year EVA(X) Share price(Y)

2003 170.875309 49.9025
2004 -238.47406 136.3542
2005 201.775889 237.8483
2006 138.209798 397.59
2007 664.878032 719.2775

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Regression
Statistics

→Large positive correlation. If EVA moves by one, share

price will move by 0.7924 in the same direction.
Multiple R 0.792358

→62.78% of change in share price is due to change in EVA.

R Square 0.627831

Adjusted R
Square 0.503774

The t-statistics of this is

The p- value in this case is 0.109975which is more than 5%. Therefore

Null hypothesis is accepted.
As such there is no relationship between EVA and share price.

MARUTI:

Year EVA(X) Share price(Y)

2003 212.532858 266.47
2004 2991.42119 328.9178
2005 3.76448448 427.8058
2006 452.940567 575.4542
2007 932.882226 870.47

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Regression
Statistics

→Medium positive correlation. If EVA moves by one,

share price will move by 0.14631 in the same direction.
Multiple R 0.146309

→2.14% of change in share price is due to change in EVA.

R Square 0.021406

Adjusted R
Square -0.30479

The t-statistics of this is

The p- value in this case is 0.81438 which is more than 5%. Therefore
Null hypothesis is accepted.
As such there is no relationship between EVA and share price.

ONGC:

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Year EVA(X) Share price(Y)

2003 -2719.0387 236.0683
2004 -11537.433 399.19
2005 -6702.3579 516.6467
2006 -10239.35 684.5133
2007 -3118.1909 819.08

Regression
Statistics

→Very small positive correlation. If EVA moves by one,

share price will move by 0.0130 in the same direction.
Multiple R 0.012974

→0.0168% of change in share price is due to change in

EVA.
R Square 0.000168

Adjusted R
Square -0.33311

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The t-statistics of this is

The p- value in this case is 0.983482which is more than 5%. Therefore

Null hypothesis is accepted.
As such there is no relationship between EVA and share price.

Ranbaxy:

Year EVA(X) Share price(Y)

2003 -1979.8566 236.0683
2004 -404.34376 399.19
2005 92.5464295 516.6467
2006 -830.58204 684.5133
2007 -60.508 819.08

Regression
Statistics

→Very small positive correlation. If EVA moves by one,

Multiple R 0.012974
share price will move by 0.0130 in the same direction.

R Square 0.000168
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Adjusted R
Square -0.33311
 EVA & SHAREPRICE 2
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→0.0168% of change in share price is due to change in EVA.

The t-statistics of this is

The p- value in this case is 0.983482 which is more than 5%. Therefore
Null hypothesis is accepted.
As such there is no relationship between EVA and share price.

Reliance industries:

Year EVA(X) Share price(Y)

2003 4204.79832 222.1617
2004 -10268.775 348.5825
2005 -2203.991 421.955
2006 886.327229 605.1217
2007 6106.42638 1154.8971

Regression
Statistics

Multiple R 0.50152

R Square 0.251522
[Type the company name] | Error! No text of specified style in document. 63

Adjusted R
Square 0.002029
 EVA & SHAREPRICE 2
008

→Medium positive correlation. If EVA moves by one, share price will

move by 0.50152 in the same direction.

→25.15% of change in share price is due to change in EVA.

The t-statistics of this is

The p- value in this case is 0.389327which is more than 5%. Therefore

Null hypothesis is accepted.
As such there is no relationship between EVA and share price.

Reliance Energy:

Year EVA(X) Share price(Y)

2003 17.1439079 411.07
2004 -10268.775 348.5825
2005 -596.73733 598.27
2006 -2251.0993 581.3517
2007 -397.08924 504.8458

[Type the company name] | Error! No text of specified style in document. 64

 EVA & SHAREPRICE 2
008

Regression
Statistics

→Large positive correlation. If EVA moves by one, share

price will move by 0.5480 in the same direction.
Multiple R 0.548017

→30% of change in share price is due to change in EVA.

R Square 0.300323

Adjusted R
Square 0.067097

The t-statistics of this is

The p- value in this case is 0.33894 which is more than 5%. Therefore
Null hypothesis is accepted.
As such there is no relationship between EVA and share price

Wipro:

Year EVA(X) Share price(Y)

2003 632.23431 241.615
2004 -581.2762 207.3025
2005 598.2125 305.6117
2006 79.405193 409.4917
2007 1308.8339 543.6567

[Type the company name] | Error! No text of specified style in document. 65

 EVA & SHAREPRICE 2
008

Regression
Statistics

→Large positive correlation. If EVA moves by one, share

price will move by 0.5480 in the same direction.
Multiple R 0.683551

→30% of change in share price is due to change in EVA.

R Square 0.467242

Adjusted R
Square 0.289656

The t-statistics of this is

The p- value in this case is 0.203243which is more than 5%. Therefore

Null hypothesis is accepted.
As such there is no relationship between EVA and share price

[Type the company name] | Error! No text of specified style in document. 66

Findings form the study:

• Investing in the market is becoming more and more risky as the beta of the
individual securities is increasing year by year.

• The increase in beta is increasing weighted average cost of capital, it specifies

that capital is becoming more and more costly.

• Majority of the companies are increasing their capital to meet the increased
expenses.

• As the market is becoming more and more risky, investors expectations are
increasing and therefore share prices of almost all the companies for almost all the
years showed an increasing trend.

• Data analysis of 20 companies shows that

 Share prices of two companies’ shows high correlation of coefficient and
coefficient of determination with EVA.
 Share prices of eighteen companies’ shows low correlation of coefficient
and coefficient of determination with EVA.

This specifies that there is no relationship between EVA and share price.
Conclusion from the study:

This report explains the importance of using EVA as a tool for measuring financial
performance. The study reveals that there is no strong pattern of EVA of selected
companies during the period.

The wealth created by most companies in year 2004 is negative because of

higher cost of capital. The central idea of EVA is subtracting the cost of capital
from the firm's profits to measure, as the term indicates, the economic additional
value produced by the firm to its owners over the weighted cost of the capital
employed. It reveals that the the decision of managers between using equity fund
or debt fund will influence EVA to the greater extent. This raised the question of
the effect of the debt and equity cost components on the behavior of EVA. It was
observed that the cost of debt has little effect on the EVA's. On the other hand, as
is expected, EVA behaves in a linear fashion with respect to the
cost of equity

It is also observed that there is no strong relation between EVA and market
prices of the companies. Thus, it can be understood that investor do not give so
importance to EVA for its investment decision. Extensive study is required to
establish the influence of other factors like non-fund based income, spread,
deployment of funds, market price, etc. It is also expected that the usage of EVA
as a financial performance tool will also be more in India.

The study adds that EVA is the one of the measure, which can be used to
measure the performance of the company but it cannot be used to forecast the
share price of the company.
Suggestions for further research:

This research has a limited scope, that is only five years and the data of only
twenty companies is taken into consideration. These five years had showed a lot
of variations with respect to beta, invested capital, weighted average cost of
capital and share prices.

To overcome this limitation of the study, the sample size and the years of
consideration for the research has to be increased, so as to decrease the
variations with respect to the above mentioned variables.

Extensive study is required to establish the influence of other factors like

expectations of investors with respect to mergers and acquisitions, sold outs and
various other factors which influence investors to undertake investing decisions.
It is because in these days all the above mentioned factors are common and it is
expected that it will also influence share prices.