determinants of equity share prices in the indian corporate sector

Determinants Of Equity Share Prices: An Empirical Study

Rakesh D, MBA 2005-07,M.P Birla Institute Of Management, Bangalore - 1-

A RESEARCH PROJECT ON

DETERMINANTS OF EQUITY SHARE PRICES IN THE INDIAN CORPORATE SECTOR : AN

EMPIRICAL STUDY

A Report Submitted in partial fulfillment for the award of

MASTERS IN BUSINESS ADMINISTRATION For Bangalore University

Submitted by

RAKESH D

REG NO: 04XQCM6070

UNDER THE GUIDANCE OF Prof. B V RUDRAMURTHY

M.P.BIRLA INSTITUTE OF MANAGEMENT Associate, Bharatiya Vidya Bhavan

#43.RACE COURSE ROAD, BANGALORE-560001



DECLARATION

&

CERTIFICATES



DECLARATION

I hereby declare that this dissertation entitled “Determinants of

Equity Share Prices in the Indian Corporate Sector: an Empirical Study”

is the result of my own research work carried out under the guidance and

supervision of Prof. Dr T V Narasimha Rao, M P Birla Institute of

Management Bangalore.

I further declare that this dissertation has not been submitted earlier to

any Institute/organization for the award of any degree or diploma.

Place: Bangalore

Date: RAKESH D



PRINCIPAL’S CERTIFICATE

I hereby certify that this dissertation entitled “Determinants Of

Equity Share Prices In The Indian Corporate Sector : An Empirical

Study” is the result of research work carried out by Mr. Rakesh D bearing

the Reg no. 05XQCM6070 under the guidance of Prof. Dr T V Narasimha

Rao, M P Birla Institute of Management, Bangalore.

Place: Bangalore

Date: Dr N.S. MALAVALLI

Principal



GUIDE’S CERTIFICATE

I hereby certify that project work embodied in the dissertation entitled

“Determinants Of Equity Share Prices In The Indian Corporate Sector:

An Empirical Study” is the result of research undertaken and completed by

Mr. RAKESH D bearing the Reg no. 05XQCM6070 under my guidance and

supervision.

Place: Bangalore

Date: Prof. Dr: T. V. N RAO



ACKNOWLEDGEMENT

In these two months I have worked on it & I feel indebted to many and

extend my heartful gratitude and profusely thank those people who not

only gave assistance to me but also participated in the making of this

project. First and foremost I would like to express my sincere gratitude to my

research guide Prof. Dr: T. V. NARASIMHA RAO, Adjunct Faculty,

M.P.Birla Institute of Management, Bangalore for his constant

encouragement and guidance in the course of the research investigation.

Further, I would also like to thank all the faculty members of

MPBIM who have helped me in completing my project. I have gained a lot of

knowledge throughout the course of carrying out this project.

I would like to sincerely thank my Parents and all my Friends who

have helped me in completing this project by providing me with the

psychological and academic support.

RAKESH D (Reg No. 05XQCM6070)



INDEX CONTENT PAGE NO EXECUTIVE SUMMARY…………………………………………..10 CHAPTER 1: INTRODUCTION & THEORETICAL BACKGROUND

1.1 Introduction…………………………………………………11 1.2 Evolution ……………………………………………………13 1.3 Other leading Indian Stock Exchange operations……......14 1.4Security analysis……………………………………………..15 1.5 Financial Analysis…………………………………………..15 1.6Macroeconomic analysis………………………………….…20 1.7Industry analysis…………………………………………….22

CHAPTER 2: LITERATURE REVIEW 2.1 Paper 1 Determinants of equity share prices in the Indian Corporate sector…………………………………………24 2.2 Paper 2 Determinants of stock prices in India………….….29 2.3 Paper 3 Determinants of equity prices: a study of select Indian companies………………………….……………...31 2.4 Problem Statement……………………………………….….37

CHAPTER 3: RESEARCH METHODOLOGY

3.1 Objectives and Scope of Study ……………………………...38 3.2 Sample and Period of Study ………………………………...39 3.3 Sources of Data ………………………………………….…...40 3.4 Period of Data …………………………………………….….40 3.5 Statistical Procedure …………………………………….…..41 3.6 Variables Used In Determining the Equity Share Price …..41 3.7 Limitations of the study ……………………………………..44

CHAPTER 4: ANALYSIS OF DATA & INTERPRETATION OF RESULTS

4.1 Descriptive Analysis ……………………………………….….50 4.2 Correlation Matricies & Regression Tables……....................60 4.4 Interpretation of Results………………………………………88

CHAPTER 5: CONCLUSION ………………………………………….90



CHAPTER 6: BIBILOGRAPHY & ANNEXURES………….…….92 Table 1: list of sectors included in study in Paper 1……………..…25

Table 2 list of industries selected for this study…………………..…39

Table 3 interpretation of results year wise ……………………........88

Table 4 interpretation of results industry wise……………………...89

Table 5 list of companies included in the study………………….....95

Table 6 no of sectors and companies included in the study……......96



EXECUTIVE SUMMARY

Share price is the most important indicator which is readily available to the investors for

their decision to invest or not to invest in a particular share. Financial theories suggests

that share price changes are associated with changes in fundamental variables which are

relevant for share valuation like payout ratio, dividend yield, capital structure, earnings,

size of the firm and its growth. However the actual fundamental factors found to be

relevant may vary from market to market.

The study examines the empirical relationship of explanatory variables namely

dividend payout, earnings per share, book value equity and reserves and surplus, price

earnings ratio, return on capital employed and growth on the market price of the shares.

The relationship between independent variables of 87 companies is studied over a period

of 5 years from 2002 to 2006.

The result revealed that Earnings Per Share the only determinant which is

common in both the analysis (year wise and industry wise). Therefore EPS is an

important determinant of share price.

If we look particularly into the year wise analysis- Book value also influences in

the dependent variable i.e. share price. And looking into industry wise it is found that

Price earning ratio also influences significantly on the dependent variable. The other

independent variables like Return on capital employed and dividend per share remain

insignificant but with a positive value. They are not significant determinants of share

price.

The regression analysis clearly depicts that Growth and payout remains most

insignificant determinant with negative value. They do not have any influence on the

share price. Overall the R2 ranges from 13 % to 56 % (except for automobile industry).

It means less than 50 % of variation in dependent variable is explained by these

independent variables.

Finally it can be concluded that apart from the above variables there are some

other factors which influences the share price. Those factors may be macroeconomic

factors like government policy, federal bank policy, central bank interest rates, business

cycle, demand and supply shocks, GDP, inflation, exchange rates. Etc.



CHAPTER I

INTRODUCTION



INTRODUCTION

The literature on fundamental analysis on valuing stocks is perhaps one of the earliest

developments in the literature on security analysis. It perhaps sought to find an answer to

the age-old adage — ‘What explains stock prices?’ However, various limitations of the

models used in fundamental analysis led to the development of various alternative

valuation models.

Share price is the most important factor readily available to the investors for their

decision to invest not in a particular share. Theories suggest that shire price changes are

associated with changes in fundamental variables with changes in valuation like payout

ratio, dividend yield, capital structure, earnings, size of the firm and its growth.

Investigations of share price changes appear to yield evidence that change in

fundamentals variable(s) should jointly bring about changes in share prices both in

developed and emerging markets. However the actual fundamental factors found to be

relevant may vary from market to market. The changes in asset growth of firms are

significant in case of Japanese shares while earnings appear to be universally a relevant

factor. However, it is widely agreed that a set of fundamental variables as, suggested by

individual theories is no doubt relevant as possible factors affecting share prices changes

in the short and the long run

Knowledge of relative influence of fundamental factors on equity share prices is

helpful to corporate, management, government and investors. To the corporate

management an understanding of the valuation mechanism in stock market is essential for

the sound financial management of the company. An understanding of determinants of

share prices is useful to dividend payment, bonus declaration, right issues, etc. Investors

can also form better judgments and make intelligent and rational investment decisions.

Investors in shares usually make constant use of these various variables (or

gauging the relative merit of a script. These calculations are in no sense, final

determinants of equality and value but they are convenient indicators about the

performance of equity shares.



Due to liberalization, privatization and globalization, Indian capital market has

witnessed considerable changes in 1990s and 2000s. As a consequence, the relative

importance of the variables determining the share prices has also undergone some

changes. All these developments have increased the importance of striving towards the

basic goals of financial management i.e., maximizing the price of firm’s common stock

and therefore shareholders wealth. For a firm whose equity shares are actively traded on

the stock market the wealth of equity shareholder is reflected in its marker value. Hence,

the goal of financial management for such firms should be to maximize the market value

of equity shares.

The decade of 1990s i.e., post reforms era has witnessed radical changes in public

policies in India that can be expected to have an effect on the environment within which

firms operate. The financial sector also experienced deregulatory initiatives in the form of

unfreezing of interest rare controls and public policy initiatives to encourage the growth

of financial markets—for both equity and debt (bonds) instruments. Decisions located

within the boundaries of the firm therefore, play a greater role in driving the equity share

prices, under the new policy regime.

Financial management in any company is largely concerned with two main

functions: Procurement of funds and utilization of funds. There are three major decision

areas in any decisions and the dividend decisions. While procurement of funds is largely

the result of financing decisions, utilization of funds is the result of investment decisions.

Investment is the economic decision of committing a set of fixed monetary resources

with the expectation of receiving a stream of returns over a reasonable long period of

time in the future. Since the decision to invest in securities is revocable, investment ends

are momentary and investment environment is fluctuating, the reliable bases for reasoned

expectation become more and more ambiguous as one envisages of the distant future.

Investment is concerned with the purchase and sale of financial assets and an

attempt of the investor to make logical decisions about the various alternatives in order to

earn suitable return.

The investor has various alternative options for investing savings to flow in

accordance with his preference. Savings are generally flown into investment with an



expectation of return, but savings kept as cash are unproductive (i.e., they do not earn any

reward). Savings arc invested into return yielding assets depending on their risk and

return characteristics.

EVOLUTION Indian Stock Markets are one of the oldest in Asia. Its history dates back to nearly 200

years ago. The earliest records of security dealings in India are meager and obscure. The

East India Company was the dominant institution in those days and business in its loan

securities used to be transacted towards the close of the eighteenth century. By 1830,

business on corporate stocks and shares in Bank and Cotton presses took place in

Bombay. Though the trading list was broader in 1839, there were only half a dozen

brokers recognized by banks and merchants during 1840 and 1850.

The 1850 witnessed a rapid development of commercial enterprise, brokerage

business attracted many men into the field, and by 1860, the number of brokers increased

into 60. In 1860-61 the American Civil War broke out and cotton supply from United

States of Europe was stopped; thus, the 'Share Mania' in India begun. The number of

brokers increased to about 200 to 250. However, at the end of the American Civil War, in

1865, a disastrous slump began (for example, Bank of Bombay Share, which had touched

Rs 2850, could only be sold at Rs. 87).

At the end of the American Civil War, the brokers who thrived out of Civil War

in 1874, found a place in a street (now appropriately called as Dalal Street) where they

would conveniently assemble and transact business. In 1887, they formally established in

Bombay, the "Native Share and Stock Brokers' Association" (which is alternatively

known as .The Stock Exchange "). In 1895, the Stock Exchange acquired a premise in

the same street and it was inaugurated in 1899. Thus, the Stock Exchange at Bombay was

consolidated.



OTHER LEADING CITIES IN STOCK MARKET OPERATIONS

Ahmedabad gained importance next to Bombay with respect to cotton textile industry.

After 1880, many mills originated from Ahmedabad and rapidly forged ahead. As new

mills were floated, the need for a Stock Exchange at Ahmedabad was realized and in

1894, the brokers formed "The Ahmedabad Share and Stock Brokers' Association". The

cotton textile industry was to Bombay and Ahmedabad, the jute industry was to Calcutta.

In addition, tea and coal industries were the other major industrial groups in Calcutta.

After the Share Mania in 1861-65, in the 1870's there was a sharp boom in jute shares,

which was followed by a boom in tea shares in the 1880's and 1890's; and a coal boom

between 1904 and 1908. On June 1908, some leading brokers formed "The Calcutta

Stock Exchange Association".

In the beginning of the twentieth century, the industrial revolution was on the way

in India with the Swadeshi Movement; and with the inauguration of the Tata Iron and

Steel Company Limited in 1907, an important stage in industrial advancement under

Indian enterprise was reached. In 1920, the then demure city of Madras had the maiden

thrill of a stock exchange functioning in its midst, under the name and style of "The

Madras Stock Exchange" with 100 members. However, when boom faded, the number of

members stood reduced from 100 to three, by 1923, and so it went out of existence. In

1935, the stock market activity improved, especially in South India where there was a

rapid increase in the number of textile mills and many plantation companies were floated.

In 1937, a stock exchange was once again organized in Madras - Madras Stock

Exchange Association (Pvt) Limited. Lahore Stock Exchange was formed in 1934 and it

had a brief life. It was merged with the Punjab Stock Exchange Limited, which was

incorporated in 1936.



SECURITY ANALYSIS

FINANCIAL ANALYSIS

The most important quality for financial analysis is the passion to go for, go into and go

beyond numbers. Let us begin by unlearning some common misconceptions. Many

people relate financial analysis to number crunching. There are some others who have set

benchmarks for financial ratios and numbers, like a current ratio of 2 or debt to equity

ratio of 1, etc. Many have a tendency to calculate expected share price by multiplying

EPS with a normative P/E. Were financial analysis such simple arithmetic, we would

have given you a spreadsheet with pre-written formulae rather than this verbose piece.

You have some acquired knowledge and techniques and then it is all upon your judgment

and experience. Yes, numbers are important. Financial analysis starts with numbers. But

it does not end there.

Ratio

A ratio is nothing more than a simple division of two numbers. Often numbers by

themselves do not convey anything until they are related. In financial analysis, we need

qualitative information and try to read between the numbers. We have to ask all the right

questions. Over the years, there are some ratios, which have become more popular and

handy for rule of thumb analysis of financial statements. Our purpose in this note is not

deride them but to advice the reader to use them properly to derive the correct results.

Key Objectives of a Business

Before you look at different ratios, let us look at a firm's objectives in a capitalist market.

The one and only intention of any firm is to maximize shareholders value, which is

effectively done by getting a bigger bang out of the capital employed. Exceptional cases

like charity, passion, hobbies, etc also try to maximize return on capital employed, but

there the definition of capital is different. For the time being, let us stick to financial

capital.

While businesses claim to have multiple objectives such as market share, brand

building and even social objectives, at the end of the day, what really matters is how



much money one makes. All are strategies to maximize return on capital employed,

which is the one and only long term goal of all management. Obviously one will look at

money made in relation to one's investment. If you use 10 times as much capital and

make 5 times more money, it is of no good. If business A earns Rs10 on Rs 100

investment (10%), it is better than another business B that earns Rs50 on Rs1000 (5%).

To analyze the performance of any business, the key ratio is therefore Return on

Capital Employed (ROCE). We can further analyze this ratio using models popularly

know as The DuPont model.

The model starts with analysis of ROCE in its two constituents

• Profit margin on sales

• Sales per unit of capital invested

To give an example, say business A is one in which Rs100 capital invested in a

year generates sales of Rs100 with net profit margin of 10%. Whereas, in business B

Rs100 investment generates a turnover of Rs500 but with a net profit margin of only 4%.

As you can see, in business B, net profit margin can be lower but is more than

compensated by the fact that turnover generated per unit of capital invested is

significance higher or capital turnover ratio is higher. Return on capital invested is the

product of sales margin and capital turnover ratio. The same can be presented in the

formula as follows.

(Net profit/ sales) * (sales/ capital employed) = Return on capital employed

Profit Margin.

We all know that profit is revenue minus cost. Each element of cost can be presented as a

% of revenue and at different levels of costs; we have different versions of profit, i.e.

EBIDTA, EBIT, EBT, etc. EBITDA margin is a good indicator of operational efficiency

of any company.



Even revenue can be broken up for the purpose of analysis, which is of use in a

multi product, multi division entity. Typically, analysts look at the relative share of other

income, because this item is where most Indian companies show extra ordinary profits to

boost their bottom line.

Return Ratios

There are two types of providers of capital, owners and lenders. As returns to lenders are

fixed, we don't have to calculate any return ratio on debt, as the same is predetermined.

From owners' perspective, the key ratio is return on net worth. Net worth represents

owners' funds, paid up capital and retained profits called as reserves. As an owner, you

would also be interested in knowing how much return is being generated by the total

capital employed. Capital employed consists of net worth plus debt, i.e. owned and owed

money. So when we calculate this ratio we have to add back the cost of debt, i.e. adjust

for interest expenses. This ratio is calculated primarily on pre-tax basis and it is

equivalent to EBIT (Earnings before Interest and Tax) divided by total capital employed.

If we want to calculate it on post-tax basis, we will have to add interest adjusted for tax

i.e.

EBT + interest*(1-T)/ capital employed, where T is the tax rate.

Add Back Interest for ROCE

Because, while calculating ROCE, we have to add back interest. This ratio calculates the

returns to all the providers of capital. As mentioned earlier, capital can be debt or equity.

On debt, we pay interest while entire PAT belongs to equity holders. Therefore, when we

calculate return on capital employed, we have to do so before any payment is made to the

providers of capital. So if we do not add back interest we will be taking profits after

making some payment to the provider of capital thereby distorting the real picture.

Per Share Ratios

An equity share is a legal document representing ownership of any entity. Shares of listed

companies trade in stock markets. It therefore makes sense to look at most profitability

indicators on a per share basis. The key ratio is earnings per share which is net profit (if



the company has issued preference capital, then one must remove preference dividend to

reflect what belongs to the common equity holders only) divided by number of

outstanding shares.

One variant of this ratio of cash earnings per share, which is cash, profited

divided by number of outstanding shares. Cash profit is equivalent to profit after tax plus

depreciation and other non-cash charges.

In stock market, a fraction of ownership known as shares is traded. Therefore in

order to arrive or get a proper picture of the worth of a share (one unit of the company),

we should look at numbers calculated on a per share basis. Earnings per share are profit

after tax (adjusted for preference dividend if any) divided by number of outstanding

shares.

Similarly, you can calculate cash profit per share, sales per share, etc. This will

facilitate valuation and comparison with other companies. The most famous of the

valuation ratios is the Price earnings ratio (P/E ratio), which the current market is priced

of the share divided by the earnings per share.

Dividend per share

The owner can allow profits to remain within business or can withdraw it for other or his

personal use. When he withdraws, it is analogous to dividend payout. In a company, the

management decides on behalf of the owner, whether or not to retain a part of profits

within the company (that is called retained earnings) and gives back a part of profits to

the owners called dividends.

Dividend per share is the total dividend paid per equity share. In case there was a

fresh issue of equity capital in the year, most companies make pro rata payment, i.e.

supposing in a financial year (April to March) there was an issue of equity shares on

October 1. The new shares, which were issued on Oct 1, will be entitled for only 50%

dividend as compared to other shareholders who were there for the full year.



Trends in Some Key Ratios

By trends we mean progress year after year. So one can look at trends in sales, fixed

assets, working capital and trends in various ratios. Trends in some key performance

ratios such as operating margin, return on net worth also convey meaningful results. For

instance, operating margin that was 8% last year and 9% this year.

Comparison

One can make comparisons across years in terms of trends in margins, growth or

comparison across companies within a sector or across a sector, by comparing large

companies in both the sectors and sector aggregates. And firms of the same industry are

compared on various parameters. One can look at aggregate numbers of one industry and

compare them with aggregate numbers of another industry to understand the differences

in performance of various industries. For instance, if you look at the consumer durable

industry which might be generating a return on networth of 8-10%, whereas software

industry may be generating a return on networth of 40-50%. So one can easily conclude

that software industry is doing significantly better than the consumer durables industry.

MACROECONOMIC ANALYSIS

To determine the proper price for a firm’s stock, the security analyst must forecast the

dividend and earnings that can be expected from the firm. This is the heart of

fundamental analysis – that is, the analysis of determinants of value such as earnings

prospects. Ultimately, the business success of the firm determines the dividends it can

pay to shareholders and the price it will command in the stock market. Because the

prospects of the firm are tied to those of the broader economy however, fundamental

analysis must consider the business environment in which the firm operates. For some

firms, macroeconomic and industry circumstances might have a greater influence on

profits than the firm’s relative performance within its industry. In other words, investors

need to keep the big economic picture in mind.

Therefore, in analyzing a firm’s prospects it often makes sense to start with the

broad economic environment, examining the state of the aggregate economy and even the



international economy. From there, one considers the implications of the outside

environment on the industry in which the firm operates. Finally, the firm position within

the industry is examined.

Following are some International factors relevant to firm’s performance:

The Global Economy

The top down analysis of a firm’s prospects must start with the global economy. The

international economy might affect a firm’ exports prospects, the price competition it

faces from competitors, or the profits it makes on investments abroad. Certainly, despite

the fact that the economies of most countries are linked in a global macro economy, there

is considerable variation in the economic performance across countries at any time. It

includes factors like growth rates of respective nations, currency exchange rate, global

industrial output etc.

The Domestic Macro Economy

The macroeconomy is the environment in which all firms operate. The importance of

macroeconomy is determining investment performance to forecasts earnings per share. It

includes Gross Domestic Product, Employment, Inflation, Interest rates, Budget Deficit,

etc

Demand And Supply Shocks

A demand shock is an event that affects the demand for goods and services in the

economy. Examples of positive demand shock are reduction in tax rates, increase in

money supply, increases in government spending or increases in foreign export demand.

A supply shock is an event that influences production capacity and costs.

Examples of supply shocks are changes in the prices of imported oil; freezes,

floods, or droughts that might destroy large quantities of agricultural crops; changes in

educational level of economy’s workforce; or changes in the wage rate at which the labor

force is willing to work.



Federal Government Policy As previous section would suggest, the government has two broad classes of macro

economic tools- those that affect the demand for goods and services and those that affect

the supply. However issues such as government spending, tax levels, monetary policy,

national policies on education, infrastructure (such as communication and transportation

system), research and development also are properly regarded as part of macroeconomic

policy.

Business Cycles

The economy recurrently experiences periods of expansion and contraction, although

length and breadth of those cycles can be irregular. This recurring pattern of recession

and recovery is called business cycle.

As economy passes through different stages of business cycle, relative

performance of different industry groups might be expected to vary.



INDUSTRY ANALYSIS

Industry analysis is important for the same reason that macroeconomic analysis. Is not

surprisingly, industry group exhibits considerable dispersion in their stock market

performance. Even small investors can easily take positions in industry performance by

using mutual funds with an industry focus.

Defining an industry

Although we know what we mean by an “industry” it can be difficult in practice to decide

where to draw the in between one industry and other. Consider for example the financial

industry the forecast for 2002 growth in industry earnings per share was 16.7%, but the

financial “industry” contains firms with widely differing products and prospects. Several

industry classifications are provided by many analysts for example Standard & Poor.

Sensitive to the business cycle

Once analyst forecast the state of macro economic it is necessary to determine the

implication of that forecast for specific industries. Not all industries are equally sensitive

to the business cycle. For example the cigarette industry is largely independent of the

business cycle demand for cigarette doesn’t seem effected by the state of the

macroeconomy in a meaningful way. It is a matter of habit in contrast, are automobile

production is highly volatile. In recession, customers can prolong the lives of their cars

until their income is higher.

Three factors will determine the sensitivity of a firms earnings to the business cycle.

• Sensitivity of sales

• Degree of operating leverage

• Financial average



CHAPTER II

LITERATURE REVIEW

& PROBLEM IDENTIFICATION



REVIEW OF LITERATURE A number of empirical studies have been conducted in India and abroad on relationship

between market price of shares and explanatory variables namely, dividend per share,

earnings per share, book value per share, size, cover, return on capital employed and

payout ratio.

PAPER I: DETERMIINANTS OF EQUITY SHARE PRICES IN THE

INDIAN CORPORATE SECTOR:

Shefali Sharma and Balawinder Singh

This study examines the empirical relationship of explanatory variables namely, dividend

per share, earnings per share, book value per share, size, cover, return on capital

employed and payout ratio on the market price of shares in the post reform era. The

relationship between independent and dependent variables of 160 companies is studied

over a period of five years spanning from 2001 to 2005. The results reveal that earnings

per share and book value per share are important determinants of share price as they are

indices healthy financial position of companies. Dividend per share is the important

indicator of share price which shows that the companies should adopt a liberal dividend

policy to activate the primary as well as secondary market. A high dividend rate may also

help in increasing the market price and result in high capital appreciation to share holders

as depicted by the payout ratio and cover. Price ratio investor reflects investor

expectations of growth in a firm’s earnings that vary from industry to industry.

Database and Research Methodology

The present study deals with fundamental analysis of share valuation as it focuses on

factors relating to company. This section explains in detail the objectives, period, sample

and database of the study.



Sample and Period of Study

The data employed in the study relates to manufacturing sector of companies listed on

Bombay Stock Exchange. 160 companies covering the following industries have been

finally selected for the purpose of the study

Table 1

Industry No. of Companies

General Engineering 28

Cotton Textile 23

Chemical 33

Iron and Steel 26

Electrical 28

Miscellaneous 22

Total 160

While selecting the sample of the companies from six industries, the following criteria is

adopted

The necessary financial data required lot calculating the measures of dependent and

independent variable pertaining to all the years 2001-2005 is available.

The companies did not skip dividend for any two successive years are included

the sample

The companies whose average earnings per share of any three successive years

are not zero or negative is also considered.

Further only those companies whose price data is available are retained in the

sample size.

The listed shares on Bombay Stock Exchange are considered.



Regression model

The linear multiple regression model has been applied primarily to minimize the problem

of multicollinearity. This technique of multivariate analysis was selected because it is the

most appropriate tool for evaluating the individual and combined effect of a set of

independent variables on dependent variables. The significance of the coefficient of a set

of independent variables was tested at 1% and 5% by computing t-values. To determine

the proportion of explained variation in dependent variable, coefficient of multiple

determination (R2) was worked out. The overall significance of regression equation was

tested with the help F-values.

Company Performance Variables and equity share prices

Share Price (SP)

The forces of demand and supply in the market determine the market price of the share.

SPt = (PH + PL) /2

Where PH is the highest market price, PL is the lowest market price during the year,

which relates to‘t’ period.

Book Value (BV)

It is also known as net asset value per share because it measures the amount of assets,

which the corporation has on behalf of each equity share BV shows the net investment

per share made in the business by the share holder. It is calculated as follows:

Book Value Per Share = Equity Share Capital + Shareholders Reserves

Total no. of Equity Shares Outstanding

Cover (C)

It shows the extent to which the dividend per share is protected by the earnings of the

company. Cover has a negative relationship with market price. It is calculated as follows

Cover = Profits after tax and preference dividend

Dividend



Or

Cover = Earnings per share

Dividend per share

Dividend Per Share (DPS)

It refers to the actual amount of dividend (gross) declared per share. The net profit after

taxes belong to shareholders but the income that they really receive is the amount of

earnings distributed and paid as cash dividend. The dividends generally influence the

share price in positive direction as depicted by earlier studies.

Dividend Per Share = Total amount (dividend) paid to equity shareholders

Number of Equity Shares Outstanding

Earnings Per Share (EPS)

The Equity shareholders are the sole claimants to the net earnings of the corporation after

making payment of dividend to the preference shareholders. The significance of this ratio

flows from the fact that higher the earnings per share the more is the scope for a higher

rate of dividend and also of retained earnings, to build up the inner strength of the

company. Therefore, a higher EPS would increase the market price and vice versa. It is

calculated as follows:

EPS= Net Income after interest, income tax & preference dividend

Numbers of Equity Shares Outstanding

Dividend Payout Ratio (D)

Dividend Payout shows the percentage share of the net profits after taxes and preference

dividend paid out as dividend to equity shareholders. It can be calculated by dividing the

total dividend paid to the equity shareholders by the total profits/ earnings available for

them. Alternatively, it can be found out by dividing DPS by EPS. . This predicts direct



relation between payout ratio and the price-earning multiple. Conversely it means that

there is an inverse relation between payout ratio and share price changes.

Dividend Payout = Total Dividend to equity shareholders * 100

Total Net Profit belonging to Equity shareholders

Or

= Dividend per share / Earnings pet share

Price Earning Ratio (P/E)

P/E ratio expresses the relationship between the market price of a company’s share and

it5s earnings per share. It indicates the extent to which the earnings of each share are

covered by its price. The ratio helps an investor to make an approximate calculation of

the time required to recover his investment in a company’s share. The price ratio has a

positive relationship with market price (Dixit, 1986). It was calculated as follows:

P/E = Market price per share * 100

Earnings per share

Return on Capital Employed (ROCE)

The return on investment indicates the efficiency with which a company utilizes funds

invested in it. This ratio reveals how well the resources of a firm are being used, higher

the ratio better are the results. The inter comparison of this ratio determines whether the

investments are attractive or not as the investor would like to invest only where the return

is higher. It generally has positive relationship with marker price of equity share. It is

computed as follows

Total Capital Employed = Profit after tax, plus interest * 100

Total Capital Employed



Size (S)

The size of the firm if captured through total capital employed is expected to influence

the share prices positively as large firms are better diversified than small ones and thus

are less risky (Benishy, 1461). Atiase (1985) showed that as the size of the firm increases,

their share price volatility declines. The large size firms are expected to have higher

market values of their shares. For studying the influence of size on equity share price,

size may be measured in terms of total assets, turnover paid up capital, net worth, sales,

number of shares outstanding, etc. The amount of total assets is taken as a measure of

size because it represents the total resources at the command of the (Sachdeva, VP L994).

Conclusions

The results reveal that Earnings per share and book value per share are the important

determinants of share price as they are an index of the sound financial position of the

companies. Dividend per share is important determinant of the share price which shows

that companies should adopt a liberal dividend policy to activate the primary as well as

secondary market. A high dividend rate may also help in increasing the market price and

result in high capital appreciation to the shareholders as depicted by payout ratio and

cover. Price-earnings ratio too showed investors expectation about the growth in the

firm’s earnings that varied from industry to industry.

PAPER 2 DETERMINANTS OF STOCK PRICES IN INDIA

Subir Sen, Rajkumar Ray

In this paper, an attempt has been made to explore the possibility of explaining the P/E

(Price- Earning Ratio) of Indian stocks it terms of certain key variables through a

decomposition study. The statistical model being used is the well known as ‘Whitbeck

kisor Model’ the feasibility of the model has been tested out on the variables as used as

by the authors (Whitbeck and kisor 1963) i.e. earnings per share, payout ratio and

coefficient of variation in explaining variation in stock prices. The findings revealed that

the dividend payout ratio is by far the single most important factor affecting stock prices,



followed by earning per share. Coefficient of variation in earnings per share has a very

weak influence on stock prices.

Research Methodology

This study intends to find a relationship by explaining the P/E of stocks in terms of

certain independent variables with the help of multivariate regression analysis. After

identifying the variables responsible for affecting P/F, a causal relationship between the

dependent and independent variables will he obtained. The significance of each of the -

independent variables together with the overall validity model will he statistically tested.

Then we will attempt to find the difference between a models based on historical growth

in earnings per share vis-à-vis forecasted growth in earnings per share, other variables

remaining the same. The next part will carry out a valuation study to see the robustness of

the model,

For the multivariate regression analysis, the following relationship is proposed.

(P/E) =C1 + C2 (EPS) + C3 (DPR) + C4 (CV)+Ui

Where, EPS=normalized growth in earnings per ,

CV=coefficient of variation in actual earnings per share,

Ui =idiosyncratic error term,

CI =autonomous value of P/E if all other variables were zero,

DPR=dividend payout ratio,

C2, 03 and 04 are the regression coefficients of EPS, DPR and OV

respectively.

Annual growth in EPS is arrived at by regressing the logarithmic (Ln) values of the

actual EPS The slope of the line of best fit obtained through the scatter diagram shows

the normalized’ growth. In the Indian context, it is very difficult to fix a very rigid time

frame for deriving the EPS growth, mainly because of frequent dilution and / or

adjustments in equity, which indirectly suppresses the EPS growth. To overcome this

difficulty we took the sample time frame in which the growth path had a coefficient of

determination (r2) exceeding 80%. All the 30 stocks fulfilled this criterion. The



prospective DPR was estimated on the ratio of dividend payout in 1999 is actual EPS in

the same year. The coefficient of variation (CV) of historical growth in EPS was

calculated by dividing the standard deviation of EPS by its arithmetic mean,

The multivariate regression analysis carried out with P/E as the dependent

variable and the annual growth in EPS. Dividend Payout Ratio (DPR), and coefficient of

variation in EPS as the independent variables, revealed the following results

Conclusion

In this paper, we have attempted to test the validity of the - Kisor Model” the Indian

context, and as such we did not in any way try to modify the original model. It is evident

from the results of the above study that the P/E Model as proposed by Whitbeck and

Kisor (1963), is valid in the Indian context, though to a much lesser extent A look at the

individual variables revealed that the Dividend Payout Ratio (DPR) is by far the single

most important factor that affects P/E of stocks in India. Growth in earnings per share

(EPS) was also found to he relevant, although to a much lesser extent. The only

parameter that was found to have very little significance was Coefficient of variation in

earnings per share (CV) and it could have been excluded from the model without

affecting its validity to any great extent.

PAPER 3. DETERMINANTS OF EQUITY PRICES: A STUDY OF

SELECT INDIAN COMPANIES

Monica Singhania*

In the last one and a half decades, many emerging capital markets have undergone drastic

changes in terms of market microstructure changes, specifically in secondary markets.

One of the policy concerns is the factors determining equity prices in markets. The author

studies the various determinants of equity share prices with reference to Indian stock

market. The mean values have shown that during the period 1997 to 2004, the market

price was far lower due to various uncertainties prevailing in the country.



The correlation analysis shows positive significant (1%) association of only price

earnings ratio with market price. Book value, dividend cover, DPS, EL and growth are

positive but insignificant. At the same time, there is negative insignificant association of

yield with Market Price (MP). While regression analysis depicts that book value,

dividend per share, earnings per share and price earnings ratio are significant

determinants, whereas, dividend cover and yield are insignificant with negative value.

Growth remained insignificant but with positive value.

Finally it can be concluded from correlation and regression analysis that price

earnings ratio, earnings per share, book value and dividend cover are the variables, which

contributed the most in determining share prices followed by dividend per share and

yield.

Research Methodology The study is based primarily on the data collected from the CMIE (Center for Monitoring

of the Indian Economy) Prowess database. The data for the sample companies is obtained

from CMIE is supplemented with information from various financial dailies, magazine

reports, industry reports, annual reports of the companies, etc.

Sample Selection and Period of the Study

The data used in the study are related to those manufacturing companies listed on the

Bombay Stock Exchange (BSE) for which the data is available in the Prowess database.

The analysis is confined to BSE listed companies only because all the listed companies

are required to follow the norms set by SEBI for financial reporting. Another reason for

the selection was the fact that BSE has the second largest number of domestic quoted

companies on any stock exchange in the world after NYSE, and has more quoted

companies than either the London or the Tokyo stock exchange. The period of the study

is from 1997 to 2004. There are basic reasons behind selection of this period as period of

the study. This period relates to the post-Liberalization era for the Indian economy which

is more relevant for study of corporate behavior. Also, this is the period for which

maximum financial information is available in the database.



Statistical Analysis

The data collected relating to the sample companies is analyzed using multiple regression

to study the impact of explanatory variables on equity share prices (i.e., market price). On

the basis of the aforesaid analysis, a suggestive framework is built which may assist in

making future predictions regarding behavior of market price of equity shares.

To achieve the objectives of the research study, the following relationship of

independent variables with dependent variable is formed:

MP =f(BV, DPS, EPS, DC, GH, P/E, DY)

Where,

MP = Marker Price of Equity Share,

BV = Book Value,

DPS = Dividend Per Share,

EPS=Earnings Per Share,

DC = Dividend Cover Growth,

P/E = Price Earnings Ratio,

DY=Dividend Yield.

In order to study the impact of explanatory variables on dependent variable, the

following statistical techniques have been employed:

Mean Values:

Mean values of the dependent and independent variables have been computed. The mean

values are co with the values of the ground data of the different variables over the period

of study and to analyze the effect of explanatory variables on the dependent variables.

Standard Deviation

Standard deviation of dependent and explanatory variables has also been computed to

examine the variation in various variables from their means values and also to analyze the

consistency and homogeneity in data collection.



Correlation

The analysis of the degree of linear association between various variables used was

carried out with the help of Karl Pearson’s correlation method. The lower the value of ‘r’,

the lower is the degree of linear relationship between the variables. The value of’ ‘r’

needs to be interpreted accurately because a low value of ‘r’ may be due to the non-linear

relationship between these variables. Also the high degree of correlation does not imply

cause and effect relationship between two variables. The significance of the correlation

coefficient is tested with the help of t-test distribution at 1% and 5% level of

significance.

Regression

A linear multiple regression in has been selected to measure the combined effects of

explanatory variables on the dependent variable.

The general form of multiple linear equation is:

Y = bo +b1X1+b2X2 +...+bnXn

where,

Y = Dependent Variable,

X1, X2, X3 = Independent Variables,

bo =Regression Constant, and

b1,b2,…… bn = Regression Coefficients of independent variables.

The statistical significance of regression coefficients was worked out and tested

by applying ‘t’ test. The coefficient of determination R2 was computed to determine the

percentage variation in the dependent variables.

Also with a view to account for the loss of degree of freedom resulting from the

inclusion of additional explanatory variables, the adjusted R2 was computed. The ‘F’

value was also computed to test the significance of the R2 with ‘F’ distribution at 1% and

5% significance level.



Research Design

The interpretation and significance of the variable to a very large extent depends upon

how the various dependent and independent variables are measured.

Market Price (MP)

The average price of the share derived from the financial year high and low has been

considered as market price for this study.

MP = High Price + Low Price

2

Where, High Price is Highest market price during the financial year and Low Price

Lowest market price during the financial year

Book Value Per Share (BVPS)

BVPS = Reserves + Equity Capital - Revaluation, Reserves

Number of Outstanding Shares (NOS)


Dividend Per Share (DPS) = Total dividend paid

Number of Outstanding Shares


EPS is defined as the ratio of the profit after tax of the company for any financial year

after payment of preference dividend if any to the number of shares outstanding as on the

last day of the financial year.

Earnings Per Share (EPS) = Net Profit Tax - Preference Dividend

Number of Outstanding Shares (NOS)



.

Dividend Cover (DC)

It shows the extent to which the dividend per share is protected by the earnings of the

company

DC= EPS

DPS

Growth (GH)

Growth is measured in terms of net sales in the present study.

G = St-St-1

St-1

Where, St = Net sales in the current year and

St-1= Net sales in the immediate previous year

Price to Earnings Ratio (P/E)

Using the definition given above of EPS the P/E ratio is defined as under;

P/E Ratio= Marker Price Per Share (MP)


This ratio enables an investor to make an approximate calculation of the time required to

cover his investment in a company’s stock.

Dividend Yield (DY)

This is the return earned by an equity shareholder by way of dividends. Dividend Yield

(DY) is computed as:

DYj,t = DPSj,t X 100

Market pricej,t



Where, DY refers to dividend yield for company j in year t,

DPS refers to dividend per share for company j in year t, and

MP is average price of the sate derived from the financial year high and

low for company.

Summary and Conclusions

The mean values have shown that during the period 1997 to 2004, the market price was

far lower due to various uncertainties prevailing at the time in the country. The

correlation analysis shows positive significant (1%) association of only price earnings

ratio with market price. Book value, dividend cover, DPS, EPS, and growth rate are

positive but insignificant. At the same time there is negative insignificant association of

yield with market price (MP). While regression analysis depicts that book value, dividend

per share, cover and yield are insignificant with negative value. Finally it can be

concluded that from correlation and regression analysis that price earnings ratio, book

value and dividend cover were the variables which contributed most in determining the

share prices followed by dividend per share and yield.

PROBLEM STATEMENT

What are the significant determinants of equity share prices in the Indian corporate sector?

What is the empirical relationship between share prices and various explanatory variables?



Chapter III

RESEARH

METHODOLOGY



RESEARCH METHODOLOGY

OBJECTIVES AND SCOPE OF STUDY

1. To determine the determinants of equity share prices in Indian corporate

sector

2. To examine the empirical relationship between equity share prices and

explanatory variables such as: dividend per share, earnings per share, book

value, payout ratio, price earnings ratio, return on capital employed, growth

and market capitalization(size).

3. To study the significance of above variables in different industries as well as

for grouped data of all these industries.

SAMPLE AND PERIOD OF STUDY The data employed in the study relates to manufacturing companies listed on Bombay

Stock Exchange. A sample of 87 companies covering the following industries have been

finally selected for the purpose of the study.

Table 2

Sector No. of companies

Automobiles 12

Cements 13

Chemicals 15

Pharmaceuticals 19

Textile & cotton 14

Miscellaneous 14

Total 87



SELECTION OF DATA While selecting the sample of the companies from six industries, the following criteria

are adopted:

1. The necessary financial data required for calculating the measures of dependent

and independent variable pertaining to all the years 2002-2006 is available.

2. The companies which did not skip dividend for any two successive years are

included in the sample.

3. The companies whose average earning per share of any three successive years is

not zero or negative is also considered.

4. Further only those companies whose price data is available are retained in the

sample size.

5. The listed shares on Bombay Stock Exchange are considered.

SOURCES OF DATA

1. The data relating to the companies was taken from the CAPITALINE

DATABASE such as earning per share, dividend payout ratio, total assets, gross

block, growth rate, return on capital employed, book value, market capitalization

2. Data regarding the share prices were taken from the website: www.bseindia.com

3. Coefficients of determination for various industries were calculated with the help

of SPSS10 software.

STATISTICAL PROCEDURE

To Analyze The Determinants Of Equity The Following Model Has Been

Used.

Linear Multiple Regression Model:

The linear multiple regression approach has been applied primarily to minimize the

problem of multicollinearity. This technique of multivariate analysis was selected

because it is the most appropriate tool evaluating the individual and combined effect of

set of independent variables on dependent variable. The significance of coefficient of



various explanatory variables was tested at 5% by computing t-values. To determine the

proportion of explained variation in dependent variables, coefficient of multiple

determinations R2 was worked out. The overall significance of regression equation was

tested with the help of F-values.

Variables Used In Determining The Equity Share Prices: For the purpose of empirical analysis, share price has been assumed to be dependent

variable while other factors have been taken as independent variable. To explain the share

prices in the year ‘t’ data used to calculate the values of explanatory variables relate to

the year ‘t’ (t refers to the year, the share price of which is being explained). This is based

on the assumption that the dividend decisions made by a company in a given year as well

as other variables are apt to affect the market price of its share in the following year when

the data is publicly made available.

Share Price (SP)

The forces of demand and supply in the market determine the market price of the share.

SPt = (PH + PL)

2

Where PH is the highest market price, PL is the lowest market price during the year,

which relates to‘t’ period.

Book Value (BV)

It refers to the book value of total shareholders fund. It is extracted from the balance

sheet of the companies.

Book Value = LOG(Total Share Capital + Total Reserves)




It refers to the actual amount of dividend (gross) declared per share. The net profit after

taxes belong to shareholders but the income that they really receive is the amount of

earnings distributed and paid as cash dividend. The dividends generally influence the

share price in positive direction as depicted by earlier studies.

Dividend Per Share = Total amount (dividend) paid to equity shareholders

Number of Equity Shares Outstanding


The Equity shareholders are the sole claimants to the net earnings of the corporation after

making payment of dividend to the preference shareholders. The significance of this ratio

flows from the fact that higher the earnings per share the more is the scope for a higher

rate of dividend and also of retained earnings, to build up the inner strength of the

company. Therefore, a higher EPS would increase the market price and vice versa.

It is calculated as follows:

EPS =Net Income after interest, income tax & preference dividend

Numbers of Equity Shares Outstanding

Dividend Payout Ratio (DPR)

Dividend Payout shows the percentage share of the net profits after taxes and preference

dividend paid out as dividend to equity shareholders. It can be calculated by dividing the

total dividend paid to the equity shareholders by the total profits/ earnings available for

them. Alternatively, it can be found out by dividing DPS by EPS. . This predicts direct

relation between payout ratio and the price-earning multiple. Conversely it means that

there is an inverse relation between payout ratio and share price changes.

Dividend Payout = Total Dividend to equity shareholders * 100

Total Net Profit belonging to Equity shareholders



Or

Dividend Payout = Dividend per share * 100

Earnings pet share

Price Earning Ratio (P/E)

P ratio expresses the relationship between the market price of a company’s share and its

earnings per share. It indicates the extent to which the earnings of each share are covered

by its price. The ratio helps an investor to make an approximate calculation of the time

required to recover his investment in a company’s share. The price ratio has a positive

relationship with market price. It was calculated as follows:

P/E = Market price per share

Earnings per share

Return on Capital Employed (ROCE)

The return on investment indicates the efficiency with which a company utilizes funds

invested in it. This ratio reveals how well the resources of a firm are being used, higher

the ratio better are the results. The inter comparison of this ratio determines whether the

investments are attractive or not as the investor would like to invest only where the return

is higher. It generally has positive relationship with marker price of equity share. It is

computed as follows

Total Capital Employed = Profit after tax, plus interest * 100

Total Capital Employed

Size (S)

The size of the firm if captured through total market capitalization or total assets. It is

expected to influence the share prices positively as large firms are better diversified than

small ones and thus are less risky. For studying the influence of size on equity share



price, size may be measured in terms of total assets, number of shares outstanding, etc. In

the present study gross block is taken to measure the size of the company.

Sales Growth(G) Growth is measured in terms of net sales in the present study.

G = St-St-1

St-1

Where, St = Net Sales of the current year

St-1 = Net Sales of the previous year

In the absence of profits, many analysts instead focus on sales growth as a measure of the

future growth potential of such companies, and this is reflected in the sales-to-stock price

ratio

LIMITATIONS OF THE STUDY Some limitations of the study are.

1. Time constraint and availability of the data.

2. Study covers five sectors and rest of them is taken under miscellaneous

category.



CHAPTER IV

ANALYSIS AND INTERPRETATION

OF DATA



DATA ANALYSIS AND INTERPRETATION

To determine the equity share prices the explanatory variables namely, dividend per

share, earnings per share, dividend payout ratio, return on capital employed, price earning

ratio, book value, growth and size these variables are treated as independent variable.

And the market price is considered to be dependent variable. For the determinants of

equity share prices the data has been collected for four different sectors for five years

from 2002-2006.

To Analyze The Determinants Of Equity The Following Model Has Been

Used.

Correlation Analysis. Is a statistical tool we can use to describe the degree to which one variable is linearly

related to another often correlation analysis is used in conjunction with regression

analysis to measure how well the regression line explains the variation of dependent

variable, Y. correlation can also be used by itself, however, to measure the degree of

association between two variables.

Statisticians have developed two measures for describing correlations between two

variables.

Coefficient of determination( r2)

Coefficient of correlation

Regression Model : The regression analysis is concerned with the study of dependence of one variable, the

dependent variable on one or more other variables, the explanatory variables, with a view

to estimating and/or predicting the population mean or average value of former in terms

of the known or fixed ( in repeated sampling) values of the latter.

The linear multiple regression approach has been selected to measure the

combined effects of explanatory variables on dependent variable. The general form of

multiple regression estimating equation is:



Y = α + B1X1 + B2 X2…….. + BnXn

Where, Y = Dependent variable,

X1,X2,X3, =Independent Variables,

α =Regression Constant, and

B1, B2,. Bn =Regression Coefficients of independent variables.

Multiple Regression Analysis The principle advantage of multiple regression is that it allows us to use more of

information available to us to estimate dependent variable. Some times the correlation

between two variables may be insufficient to determine a reliable estimating equation.

The linear multiple regression approach has been applied to minimize the problem of

multicollinearity. This technique is the most appropriate tool evaluating the individual

and combined effect of set of independent variables on dependent variable

Multiple regression and correlation analysis involve a three step process:

Describe the Multiple regression equation

Examine Multiple regression standard error of estimate

Use Multiple correlation analysis to determine how well the regression

equation describes the observed data.

The linear multiple regression approach has been applied primarily to minimize

the problem of multicollinearity. This technique of multivariate analysis was selected

because it is the most appropriate tool evaluating the individual and combined effect of

set of independent variables on dependent variable

The significance of coefficient of various explanatory variables was tested at by

computing ‘t-values’. Also with a view to account for loss of degree of freedom resulting

from inclusion of additional variables, the Adjusted R2 was computed. The ‘F’ value was

also computed to test the significance of the R2 with ‘F’ distribution at 1 and 5%



significance level. The overall significance of regression equation was tested with the

help of F-values

Testing the over all significant of multiple regression.

1. R2 and adjusted R2 An important property of R2 is that it is a non decreasing function of the number of

explanatory variables or regressors present in the model: as the number of regressors

increases, R2 almost invariably increases and never decreases.

R2 = ESS Or R2 = 1 - RSS

TSS TSS

Where,

RSS is Residual Sum of Squares

TSS is Total Sum of Squares

ESS is Explained Sum of Squares

2. F-Test An F-test is any statistical test in which the test statistic has an F-distribution if

the null hypothesis is true. A great variety of hypotheses in applied statistics are tested by

F-tests. The hypothesis is that the means of multiple normally distributed populations, all

having the same standard deviation, are equal. This is perhaps the most well-known of

hypotheses tested by means of an F-test, and the simplest problem in the analysis of

variance (ANOVA).

The F-distribution is formed by the ratio of two independent chi-square variables

divided by their respective degrees of freedom. Since F is formed by chi-square, many of

the chi-square properties carry over to the F distribution.

• The F-values are all non-negative

• The distribution is non-symmetric

• The mean is approximately 1



• There are two independent degrees of freedom, one for the numerator,

and one for the denominator.

• There are many different F distributions, one for each pair of degrees of

freedom.

NOTE: It is found that there is a high correlation between size (gross block) and market capitalization at 5% level of significance. To avoid the problem of multicollinearity both the variables are excluded from the research for the further analysis



CORRELATION MATRIX: YEAR 2002 Correlations

1 -.072 -.008 .083 -.096 -.009 .130.505 .942 .447 .379 .937 .230

87 87 87 87 87 87 87-.072 1 .438** -.098 .038 .197 .225*.505 .000 .364 .729 .068 .036

87 87 87 87 87 87 87-.008 .438** 1 .097 .072 .506** .455**.942 .000 .373 .508 .000 .000

87 87 87 87 87 87 87.083 -.098 .097 1 -.005 .243* .179.447 .364 .373 .962 .023 .096

87 87 87 87 87 87 87-.096 .038 .072 -.005 1 .063 -.018.379 .729 .508 .962 .564 .872

87 87 87 87 87 87 87-.009 .197 .506** .243* .063 1 .062.937 .068 .000 .023 .564 .570

87 87 87 87 87 87 87.130 .225* .455** .179 -.018 .062 1.230 .036 .000 .096 .872 .570

87 87 87 87 87 87 87

Pearson CorrelationSig. (2-tailed)NPearson CorrelationSig. (2-tailed)NPearson CorrelationSig. (2-tailed)NPearson CorrelationSig. (2-tailed)NPearson CorrelationSig. (2-tailed)NPearson CorrelationSig. (2-tailed)NPearson CorrelationSig. (2-tailed)N

payout

eps

dividend

peratio

growth

roce

bookval

payout eps dividend peratio growth roce bookval

Correlation is significant at the 0.01 level (2-tailed).**.

Correlation is significant at the 0.05 level (2-tailed).*.

Interpretation: There is a significant correlation between Dividend & ROCE, Dividend & Book

Value and Dividend & EPS at 1% level of significance. Excluding that no other variables

are correlated. There would not be any problem of multicollinearity because of linear

multiple regression model being used.



REGRESSION RESULTS YEAR 2002

Model Summary

.830a .689 .662 77.61144

.830b .689 .666 77.15247

.829c .687 .668 76.86744

Model123

R R SquareAdjustedR Square

Std. Error ofthe Estimate

Predictors: (Constant), bookval, growth, roce, payout,peratio, eps, dividend

a.

Predictors: (Constant), bookval, roce, payout, peratio,eps, dividend

b.

Predictors: (Constant), bookval, roce, peratio, eps,dividend

c.

ANOVAd

1055609 7 150801.265 25.035 .000a

475859.3 79 6023.5351531468 861055268 6 175877.971 29.547 .000b

476200.3 80 5952.5041531468 861052871 5 210574.255 35.639 .000c

478596.9 81 5908.6031531468 86

RegressionResidualTotalRegressionResidualTotalRegressionResidualTotal

Model1

2

3

Sum ofSquares df Mean Square F Sig.

Predictors: (Constant), bookval, growth, roce, payout, peratio, eps, dividenda.

Predictors: (Constant), bookval, roce, payout, peratio, eps, dividendb.

Predictors: (Constant), bookval, roce, peratio, eps, dividendc.

Dependent Variable: avgpriced.



Coefficientsa

-144.097 33.847 -4.257 .000-.067 .110 -.039 -.607 .545.908 .250 .258 3.638 .000.415 .175 .212 2.374 .020

4.841 .854 .381 5.668 .000.060 .251 .015 .238 .813

2.504 .766 .253 3.269 .00254.084 15.216 .266 3.555 .001

-143.365 33.507 -4.279 .000-.069 .109 -.040 -.635 .528.908 .248 .258 3.660 .000.417 .174 .214 2.405 .018

4.841 .849 .381 5.701 .0002.508 .761 .253 3.294 .001

53.955 15.116 .265 3.569 .001-142.747 33.369 -4.278 .000

.920 .247 .262 3.730 .000

.422 .173 .216 2.444 .0174.817 .845 .379 5.700 .0002.502 .759 .253 3.298 .001

52.593 14.908 .259 3.528 .001

(Constant)payoutepsdividendperatiogrowthrocebookval(Constant)payoutepsdividendperatiorocebookval(Constant)epsdividendperatiorocebookval

Model1

2

3

B Std. Error

UnstandardizedCoefficients

Beta

StandardizedCoefficients

t Sig.

Dependent Variable: avgpricea.

Interpretation (2002): Dividend, EPS, P/E Ratio, ROCE, and Bookvalue the most important determinants of

share price for the year 2002 with T- value being 3.3730 & 2.444, 5.700, 3.298 and 3.528

respectively. When backward model is used and when the irrelevant variables are

removed one after the other based on there significance level the t-value of Dividend,

EPS, P/E Ratio, ROCE, and Bookvalue increases to 3.3730 & 2.444, 5.700, 3.298 and

3.528 respectively . The coefficient of multiple determination, (R2), obtained from the

equations indicate that variables included in the equation could explain 66.2% of the

dependent variable share price. The computed F-value 25.035 is found to be significant at

5% level. The variables Growth and Payout are found to be insignificant.



CORRELATION MATRIX: YEAR 2003

Correlations

1 -.052 .216* .575** .052 .041 .145.634 .045 .000 .634 .708 .180

87 87 87 87 87 87 87-.052 1 .451** -.104 .041 .223* .220*.634 .000 .339 .708 .037 .041

87 87 87 87 87 87 87.216* .451** 1 -.035 .062 .289** .261*.045 .000 .747 .570 .007 .015

87 87 87 87 87 87 87.575** -.104 -.035 1 .090 -.040 .095.000 .339 .747 .405 .710 .382

87 87 87 87 87 87 87.052 .041 .062 .090 1 .017 .040.634 .708 .570 .405 .877 .711

87 87 87 87 87 87 87.041 .223* .289** -.040 .017 1 .193.708 .037 .007 .710 .877 .074

87 87 87 87 87 87 87.145 .220* .261* .095 .040 .193 1.180 .041 .015 .382 .711 .074

87 87 87 87 87 87 87

Pearson CorrelatioSig. (2-tailed)NPearson CorrelatioSig. (2-tailed)NPearson CorrelatioSig. (2-tailed)NPearson CorrelatioSig. (2-tailed)NPearson CorrelatioSig. (2-tailed)NPearson CorrelatioSig. (2-tailed)NPearson CorrelatioSig. (2-tailed)N

payout

eps

dividend

peratio

growth

roce

bookvalu

payout eps dividend peratio growth roce bookvalu



Interpretation: There is a significant correlation between Payout & PE ratio, Dividend & EPS

and Dividend & ROCE at 1% level of significance. Excluding that no other variables are

correlated. Therefore there would not be any problem of multicollinearity because of

linear multiple regression model being used.




Model Summary

.711a .505 .461 121.9857

.710b .504 .467 121.2735

.709c .502 .472 120.7522

.707d .500 .475 120.3311

Model1234



Predictors: (Constant), BOOKVAL, GROWTH,PERATIO, ROCE, EPS, DIVIDEND, PAYOUT

a.

Predictors: (Constant), BOOKVAL, GROWTH,PERATIO, ROCE, EPS, DIVIDEND

b.

Predictors: (Constant), BOOKVAL, GROWTH, ROCE,EPS, DIVIDEND

c.

Predictors: (Constant), BOOKVAL, GROWTH, ROCE,EPS

d.

ANOVAe

1198432 7 171204.635 11.505 .000a

1175560 79 14880.5042373992 861197411 6 199568.440 13.569 .000b

1176582 80 14707.2702373992 861192923 5 238584.679 16.363 .000c

1181069 81 14581.0972373992 861186666 4 296666.567 20.489 .000d

1187326 82 14479.5852373992 86

RegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotal

Model1

2

3

4


Predictors: (Constant), BOOKVAL, GROWTH, PERATIO, ROCE, EPS, DIVIDEND,PAYOUT

a.

Predictors: (Constant), BOOKVAL, GROWTH, PERATIO, ROCE, EPS, DIVIDENDb.

Predictors: (Constant), BOOKVAL, GROWTH, ROCE, EPS, DIVIDENDc.

Predictors: (Constant), BOOKVAL, GROWTH, ROCE, EPSd.

Dependent Variable: AVGPRICEe.



Coefficients a

-180.143 50.672 -3.555 .001-.188 .716 -.027 -.262 .794.574 .401 .131 1.431 .156

4.082E-02 .058 .068 .701 .486.243 .405 .059 .599 .551

-1.062 .526 -.161 -2.021 .0474.284 .921 .390 4.654 .000

106.393 21.558 .414 4.935 .000-183.866 48.355 -3.802 .000

.589 .394 .134 1.494 .1393.625E-02 .055 .060 .656 .514

.180 .327 .044 .552 .582-1.060 .522 -.161 -2.028 .0464.284 .915 .390 4.680 .000

106.074 21.398 .413 4.957 .000-183.996 48.147 -3.822 .000

.565 .390 .129 1.448 .1513.605E-02 .055 .060 .655 .514

-1.033 .518 -.157 -1.994 .0494.266 .911 .388 4.684 .000

107.523 21.145 .419 5.085 .000-190.779 46.856 -4.072 .000

.664 .358 .151 1.855 .067-1.018 .516 -.154 -1.975 .0524.381 .890 .399 4.920 .000

109.699 20.810 .427 5.271 .000

(Constant)PAYOUTEPSDIVIDENDPERATIOGROWTHROCEBOOKVAL(Constant)EPSDIVIDENDPERATIOGROWTHROCEBOOKVAL(Constant)EPSDIVIDENDGROWTHROCEBOOKVAL(Constant)EPSGROWTHROCEBOOKVAL

Model1

2

3

4

B Std. Error


Beta

Standardized

Coefficients

t Sig.

Dependent Variable: AVGPRICEa.

Interpretation (2003): Book value and ROCE are the most important determinants of share price for the year

2003 with positive t- values. When backward model is applied, variables are removed

one after the other based on there significance level the t-value of Book value, ROCE,

EPS increases to 5.271 & 4.920 respectively. The coefficient of multiple determination,

(R2), obtained from the equations indicate that variables included in the equation could

explain 46.1% of the dependent variable share price. The computed F-value 11.505 is

found to be significant at 5% level. The variables Growth and Payout are found to be

insignificant with negative values.



CORRELATION MATRIX: YEAR 2004 Correlations

1 -.050 -.026 .056 -.023 -.067 -.120.647 .814 .608 .830 .538 .269

87 87 87 87 87 87 87-.050 1 .232* -.096 .081 .017 .020.647 .030 .377 .456 .876 .851

87 87 87 87 87 87 87-.026 .232* 1 .108 -.153 .309** .496**.814 .030 .321 .156 .004 .000

87 87 87 87 87 87 87.056 -.096 .108 1 -.057 .179 .146.608 .377 .321 .603 .097 .177

87 87 87 87 87 87 87-.023 .081 -.153 -.057 1 .081 -.177.830 .456 .156 .603 .457 .100

87 87 87 87 87 87 87-.067 .017 .309** .179 .081 1 .217*.538 .876 .004 .097 .457 .044

87 87 87 87 87 87 87-.120 .020 .496** .146 -.177 .217* 1.269 .851 .000 .177 .100 .044

87 87 87 87 87 87 87


payout

eps

dividend

peratio

growth

roce

bookval




Interpretation: There is a significant correlation between Dividend & EPS and Dividend & Book

value at 1% level of significance. Excluding that no other variables are correlated.

Therefore there would not be any problem of multicollinearity because of linear multiple

regression model being used..



REGRESSION RESULTS YEAR 2004 Model Summary

.689a .475 .429 196.6439

.689b .475 .436 195.4157

.689c .475 .443 194.2152

.688d .474 .448 193.3043

.686e .470 .451 192.7421

Model12345



Predictors: (Constant), BOOKVAL, EPS, PAYOUT,PERATIO, GROWTH, ROCE, DIVIDEND

a.

Predictors: (Constant), BOOKVAL, EPS, PAYOUT,PERATIO, ROCE, DIVIDEND

b.

Predictors: (Constant), BOOKVAL, EPS, PERATIO,ROCE, DIVIDEND

c.

Predictors: (Constant), BOOKVAL, PERATIO, ROCE,DIVIDEND

d.

Predictors: (Constant), BOOKVAL, PERATIO, ROCEe.

ANOVAf

2766912 7 395273.152 10.222 .000a

3054837 79 38668.8255821749 862766765 6 461127.520 12.075 .000b

3054984 80 38187.3015821749 862766465 5 553292.983 14.669 .000c

3055284 81 37719.5595821749 862757691 4 689422.714 18.450 .000d

3064058 82 37366.5665821749 862738340 3 912779.992 24.570 .000e

3083409 83 37149.5095821749 86

RegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotal

Model1

2

3

4

5


Predictors: (Constant), BOOKVAL, EPS, PAYOUT, PERATIO, GROWTH, ROCE,DIVIDEND

a.

Predictors: (Constant), BOOKVAL, EPS, PAYOUT, PERATIO, ROCE, DIVIDENDb.

Predictors: (Constant), BOOKVAL, EPS, PERATIO, ROCE, DIVIDENDc.

Predictors: (Constant), BOOKVAL, PERATIO, ROCE, DIVIDENDd.

Predictors: (Constant), BOOKVAL, PERATIO, ROCEe.

Dependent Variable: AVGPRICEf.



Coefficients a

-323.455 92.239 -3.507 .001-2.70E-02 .302 -.007 -.090 .9295.805E-02 .122 .041 .475 .636

.141 .256 .056 .549 .5853.836 1.512 .214 2.538 .013

-7.36E-02 1.195 -.005 -.062 .9515.640 1.378 .361 4.093 .000

157.395 39.859 .381 3.949 .000-325.327 86.554 -3.759 .000

-2.66E-02 .300 -.007 -.089 .9305.721E-02 .121 .040 .474 .637

.143 .252 .056 .566 .5733.840 1.501 .214 2.558 .0125.627 1.353 .360 4.160 .000

157.681 39.342 .382 4.008 .000-327.224 83.353 -3.926 .000

5.779E-02 .120 .040 .482 .631.141 .250 .056 .565 .574

3.830 1.488 .213 2.574 .0125.635 1.341 .361 4.201 .000

158.140 38.760 .383 4.080 .000-321.706 82.177 -3.915 .000

.173 .240 .068 .720 .4743.752 1.472 .209 2.549 .0135.612 1.334 .359 4.206 .000

156.306 38.392 .379 4.071 .000-341.317 77.301 -4.415 .000

3.757 1.468 .209 2.560 .0125.837 1.293 .374 4.514 .000

169.009 33.994 .409 4.972 .000

(Constant)PAYOUTEPSDIVIDENDPERATIOGROWTHROCEBOOKVAL(Constant)PAYOUTEPSDIVIDENDPERATIOROCEBOOKVAL(Constant)EPSDIVIDENDPERATIOROCEBOOKVAL(Constant)DIVIDENDPERATIOROCEBOOKVAL(Constant)PERATIOROCEBOOKVAL

Model1

2

3

4

5

B Std. Error


Beta

Standardized

Coefficients

t Sig.


Interpretation (2004):

Book value P/E ratio and ROCE are the highly significant determinants for year 2004

with positive t- values. . The coefficient of multiple determination, (R2), obtained from

the equations indicate that variables included in the equation could explain 42.9 % of the


5% level. The variables Growth and Payout are found to be insignificant with negative t-

values.




Correlations

1 -.165 .088 .154 .149 .133 -.028.126 .420 .154 .167 .218 .799

87 87 87 87 87 87 87-.165 1 .486** -.134 .132 .283** .574**.126 .000 .218 .224 .008 .000

87 87 87 87 87 87 87.088 .486** 1 -.045 .080 .273* .467**.420 .000 .680 .461 .011 .000

87 87 87 87 87 87 87.154 -.134 -.045 1 .109 -.014 -.130.154 .218 .680 .317 .901 .232

87 87 87 87 87 87 87.149 .132 .080 .109 1 -.038 .022.167 .224 .461 .317 .725 .840

87 87 87 87 87 87 87.133 .283** .273* -.014 -.038 1 .231*.218 .008 .011 .901 .725 .031

87 87 87 87 87 87 87-.028 .574** .467** -.130 .022 .231* 1.799 .000 .000 .232 .840 .031

87 87 87 87 87 87 87


payout

eps

dividend

peratio

growth

roce

bookval




Interpretation: There is a significant correlation between Dividend & EPS Dividend & Book value and

EPS & Book value at 1% level of significance. Excluding that no other variables are

correlated. There would not be any problem of multicollinearity because of linear





Model Summary

.709a .502 .458 247.2454

.705b .498 .460 246.8029

.700c .491 .459 246.9405

Model123



Predictors: (Constant), BOOKVALU, GROWTH,PAYOUT, PERATIO, ROCE, DIVIDEND, EPS

a.

Predictors: (Constant), BOOKVALU, GROWTH,PERATIO, ROCE, DIVIDEND, EPS

b.

Predictors: (Constant), BOOKVALU, GROWTH,PERATIO, ROCE, EPS

c.

ANOVAd

4868595 7 695513.565 11.378 .000a

4829294 79 61130.3069697889 864824957 6 804159.449 13.202 .000b

4872932 80 60911.6569697889 864758541 5 951708.159 15.607 .000c

4939348 81 60979.6099697889 86


Model1

2

3


Predictors: (Constant), BOOKVALU, GROWTH, PAYOUT, PERATIO, ROCE,DIVIDEND, EPS

a.

Predictors: (Constant), BOOKVALU, GROWTH, PERATIO, ROCE, DIVIDEND, EPSb.

Predictors: (Constant), BOOKVALU, GROWTH, PERATIO, ROCE, EPSc.

Dependent Variable: AVGPRICEd.



Coefficientsa

-225.169 117.251 -1.920 .058.890 1.054 .072 .845 .401

5.077 1.455 .377 3.489 .001.167 .185 .087 .905 .368

1.085 .622 .142 1.745 .085-2.292 1.111 -.170 -2.063 .0424.736 1.656 .245 2.860 .005

120.815 53.909 .227 2.241 .028-207.406 115.144 -1.801 .075

4.748 1.400 .353 3.393 .001.190 .182 .099 1.044 .300

1.144 .617 .150 1.855 .067-2.122 1.091 -.158 -1.945 .0554.985 1.627 .258 3.065 .003

123.028 53.749 .231 2.289 .025-237.576 111.522 -2.130 .036

5.138 1.350 .382 3.807 .0001.165 .617 .153 1.888 .063

-2.072 1.090 -.154 -1.900 .0615.232 1.610 .271 3.250 .002

137.412 51.982 .258 2.643 .010

(Constant)PAYOUTEPSDIVIDENDPERATIOGROWTHROCEBOOKVALU(Constant)EPSDIVIDENDPERATIOGROWTHROCEBOOKVALU(Constant)EPSPERATIOGROWTHROCEBOOKVALU

Model1

2

3

B Std. Error


Beta

Standardized

Coefficients

t Sig.


Interpretation (2005): Book value, EPS and ROCE are the highly significant determinants for year 2005 with

positive t- values. . The coefficient of multiple determination, (R2), obtained from the

equations indicate that variables included in the equation could explain 45.8 % of the


5% level. The variables Dividend and Payout are found to be insignificant with negative

t-values.




Correlations

1 -.217* .078 .291** -.208 .052 -.002.044 .471 .006 .053 .633 .984

87 87 87 87 87 87 87-.217* 1 .540** .063 .036 .346** .513**.044 .000 .564 .737 .001 .000

87 87 87 87 87 87 87.078 .540** 1 .030 -.026 .358** .512**.471 .000 .781 .810 .001 .000

87 87 87 87 87 87 87.291** .063 .030 1 .237* -.014 .099.006 .564 .781 .027 .898 .360

87 87 87 87 87 87 87-.208 .036 -.026 .237* 1 .097 .148.053 .737 .810 .027 .373 .171

87 87 87 87 87 87 87.052 .346** .358** -.014 .097 1 .274*.633 .001 .001 .898 .373 .010

87 87 87 87 87 87 87-.002 .513** .512** .099 .148 .274* 1.984 .000 .000 .360 .171 .010

87 87 87 87 87 87 87


payou

epss

dividend

peratio

growth

roce

bookval

payou epss dividend peratio growth roce bookval



Interpretation: There is a significant correlation between Payout & EPS & Book value, EPS and

Dividend & Book value at 1% level of significance. Excluding that no other variables are






Model Summary

.877a .770 .749 289.0781

.877b .770 .752 287.2748

.877c .769 .755 285.7508

.877d .768 .757 284.5056

.874e .765 .756 285.0996

Model12345



Predictors: (Constant), BOOKVAL, PAYOU, GROWTH,ROCE, PERATIO, DIVIDEND, EPSS

a.

Predictors: (Constant), BOOKVAL, PAYOU, GROWTH,ROCE, PERATIO, EPSS

b.

Predictors: (Constant), BOOKVAL, PAYOU, GROWTH,PERATIO, EPSS

c.

Predictors: (Constant), BOOKVAL, PAYOU, PERATIO,EPSS

d.

Predictors: (Constant), PAYOU, PERATIO, EPSSe.

ANOVAf

22046977 7 3149568.089 37.690 .000a

6601725 79 83566.14228648702 8622046556 6 3674426.071 44.524 .000b

6602145 80 82526.81828648702 8622034765 5 4406952.976 53.971 .000c

6613937 81 81653.54328648702 8622011342 4 5502835.513 67.984 .000d

6637360 82 80943.41228648702 8621902315 3 7300771.605 89.821 .000e

6746387 83 81281.77128648702 86


Model1

2

3

4

5


Predictors: (Constant), BOOKVAL, PAYOU, GROWTH, ROCE, PERATIO, DIVIDEND,EPSS

a.

Predictors: (Constant), BOOKVAL, PAYOU, GROWTH, ROCE, PERATIO, EPSSb.

Predictors: (Constant), BOOKVAL, PAYOU, GROWTH, PERATIO, EPSSc.

Predictors: (Constant), BOOKVAL, PAYOU, PERATIO, EPSSd.

Predictors: (Constant), PAYOU, PERATIO, EPSSe.




Coefficients a

-402.480 134.069 -3.002 .0042.633 1.450 .116 1.816 .073

19.879 1.860 .793 10.690 .000-2.15E-02 .303 -.005 -.071 .944

5.799 1.518 .232 3.821 .000.656 1.410 .028 .465 .643.816 2.135 .023 .382 .703

58.956 60.819 .066 .969 .335-399.589 126.923 -3.148 .002

2.615 1.418 .115 1.844 .06919.831 1.721 .791 11.523 .000

5.806 1.505 .233 3.857 .000.662 1.399 .028 .473 .637.791 2.091 .022 .378 .706

57.651 57.606 .064 1.001 .320-394.659 125.581 -3.143 .002

2.714 1.387 .120 1.957 .05420.025 1.635 .798 12.251 .000

5.731 1.484 .230 3.862 .000.738 1.377 .031 .536 .594

59.418 57.111 .066 1.040 .301-387.398 124.303 -3.117 .003

2.475 1.307 .109 1.894 .06219.901 1.611 .793 12.352 .000

5.985 1.400 .240 4.275 .00064.918 55.936 .073 1.161 .249

-263.512 63.827 -4.129 .0002.644 1.302 .117 2.031 .045

20.870 1.381 .832 15.113 .0006.050 1.402 .243 4.317 .000

(Constant)PAYOUEPSSDIVIDENDPERATIOGROWTHROCEBOOKVAL(Constant)PAYOUEPSSPERATIOGROWTHROCEBOOKVAL(Constant)PAYOUEPSSPERATIOGROWTHBOOKVAL(Constant)PAYOUEPSSPERATIOBOOKVAL(Constant)PAYOUEPSSPERATIO

Model1

2

3

4

5

B Std. Error


Beta

Standardized

Coefficients

t Sig.


Interpretation (2006):

Payout, Payout and P/E ratio are the highly significant determinants for year 2006 with





t-values.



INDUSTRY –WISE CORRELATIONS

CORRELATION MATRIX: AUTOMOBILE

Correlations

1 -.379** -.117 .154 -.166 -.326* -.011.003 .375 .241 .205 .011 .936

60 60 60 60 60 60 60-.379** 1 .900** .154 .239 .233 .626**.003 .000 .240 .066 .073 .000

60 60 60 60 60 60 60-.117 .900** 1 .249 .256* .173 .715**.375 .000 .055 .049 .187 .000

60 60 60 60 60 60 60.154 .154 .249 1 .174 .042 .015.241 .240 .055 .183 .748 .908

60 60 60 60 60 60 60-.166 .239 .256* .174 1 .331** .120.205 .066 .049 .183 .010 .361

60 60 60 60 60 60 60-.326* .233 .173 .042 .331** 1 -.091.011 .073 .187 .748 .010 .491

60 60 60 60 60 60 60-.011 .626** .715** .015 .120 -.091 1.936 .000 .000 .908 .361 .491

60 60 60 60 60 60 60

Pearson CorrelatSig. (2-tailed)NPearson CorrelatSig. (2-tailed)NPearson CorrelatSig. (2-tailed)NPearson CorrelatSig. (2-tailed)NPearson CorrelatSig. (2-tailed)NPearson CorrelatSig. (2-tailed)NPearson CorrelatSig. (2-tailed)N

payout

eps

dividend

peratio

growth

roce

bookval




Interpretation: There is a significant correlation between Payout & EPS and EPS, Dividend & Book

value at 1% level of significance. Excluding that no other variables are correlated. There

would not be any problem of multicollinearity because of linear multiple regression

model being used.



REGRESSION RESULTS AUTOMOBILE INDUSTRY

Model Summary

.953a .909 .897 128.1784

.953b .908 .898 127.2799

.952c .906 .898 127.4936

Model123



Predictors: (Constant), BOOKVAL, PAYOUT, PERATIO,GROWTH, ROCE, EPS, DIVIDEND

a.

Predictors: (Constant), BOOKVAL, PERATIO,GROWTH, ROCE, EPS, DIVIDEND

b.

Predictors: (Constant), BOOKVAL, PERATIO, ROCE,EPS, DIVIDEND

c.

ANOVAd

8527504 7 1218214.873 74.147 .000a

854344.1 52 16429.6949381848 598523239 6 1420539.914 87.687 .000b

858608.7 53 16200.1659381848 598504099 5 1700819.722 104.636 .000c

877749.6 54 16254.6229381848 59


Model1

2

3


Predictors: (Constant), BOOKVAL, PAYOUT, PERATIO, GROWTH, ROCE, EPS,DIVIDEND

a.

Predictors: (Constant), BOOKVAL, PERATIO, GROWTH, ROCE, EPS, DIVIDENDb.

Predictors: (Constant), BOOKVAL, PERATIO, ROCE, EPS, DIVIDENDc.




Coefficientsa

57.184 101.578 .563 .576.727 1.427 .029 .509 .613

6.870 2.533 .338 2.712 .0094.835 .785 .798 6.162 .0004.873 1.895 .119 2.572 .013

-1.197 1.178 -.047 -1.016 .314-2.428 1.472 -.080 -1.649 .105

-132.561 33.577 -.258 -3.948 .00089.393 78.949 1.132 .263

6.089 2.003 .299 3.040 .0045.021 .690 .829 7.280 .0005.012 1.862 .122 2.692 .009

-1.264 1.163 -.049 -1.087 .282-2.579 1.431 -.085 -1.802 .077

-131.752 33.304 -.256 -3.956 .00090.204 79.078 1.141 .259

6.115 2.006 .300 3.048 .0044.973 .689 .821 7.213 .0004.759 1.850 .116 2.572 .013

-3.041 1.369 -.100 -2.221 .031-132.957 33.342 -.259 -3.988 .000

(Constant)PAYOUTEPSDIVIDENDPERATIOGROWTHROCEBOOKVAL(Constant)EPSDIVIDENDPERATIOGROWTHROCEBOOKVAL(Constant)EPSDIVIDENDPERATIOROCEBOOKVAL

Model1

2

3

B Std. Error


Beta

Standardized

Coefficients

t Sig.


Interpretation (AUTOMOBILE INDUSTRY):

EPS, Dividend and P/E ratio are the highly significant determinants for auto industry with





t-values.



CORRELATION MATRIX: CEMENTS

Correlations

1 -.130 -.050 .201 -.162 .048 .089.302 .695 .109 .197 .703 .482

65 65 65 65 65 65 65-.130 1 .394** -.111 -.009 .153 .242.302 .001 .378 .944 .224 .052

65 65 65 65 65 65 65-.050 .394** 1 -.002 .196 .498** .459**.695 .001 .990 .118 .000 .000

65 65 65 65 65 65 65.201 -.111 -.002 1 .196 -.081 .134.109 .378 .990 .117 .522 .286

65 65 65 65 65 65 65-.162 -.009 .196 .196 1 .504** .286*.197 .944 .118 .117 .000 .021

65 65 65 65 65 65 65.048 .153 .498** -.081 .504** 1 .381**.703 .224 .000 .522 .000 .002

65 65 65 65 65 65 65.089 .242 .459** .134 .286* .381** 1.482 .052 .000 .286 .021 .002

65 65 65 65 65 65 65


payout

eps

dividend

peratio

growth

roce

bookvalu




Interpretation: There is a significant correlation between Dividend & EPS, Dividend & Book value and

ROCE & Dividend at 1% level of significance. Excluding that no other variables are


multiple regression model is being used for the further analysis.



REGRESSION RESULTS CEMENTS INDUSTRY

Model Summary

.593a .351 .271 362.1219

.592b .351 .284 359.0226

.592c .350 .295 356.1585

.588d .345 .302 354.4937

.585e .342 .310 352.4704

Model12345



Predictors: (Constant), BOOKVAL, PAYOUT, PERATIO,EPS, GROWTH, DIVIDEND, ROCE

a.

Predictors: (Constant), BOOKVAL, PERATIO, EPS,GROWTH, DIVIDEND, ROCE

b.

Predictors: (Constant), PERATIO, EPS, GROWTH,DIVIDEND, ROCE

c.

Predictors: (Constant), PERATIO, EPS, DIVIDEND,ROCE

d.

Predictors: (Constant), PERATIO, EPS, DIVIDENDe.



ANOVAf

4044862 7 577837.418 4.407 .001a

7474541 57 131132.29711519403 644043365 6 673894.250 5.228 .000b

7476037 58 128897.19611519403 644035321 5 807064.112 6.362 .000c

7484082 59 126848.85211519403 643979457 4 994864.168 7.917 .000d

7539946 60 125665.77011519403 643941043 3 1313681.002 10.574 .000e

7578360 61 124235.40711519403 64


Model1

2

3

4

5


Predictors: (Constant), BOOKVAL, PAYOUT, PERATIO, EPS, GROWTH, DIVIDEND,ROCE

a.

Predictors: (Constant), BOOKVAL, PERATIO, EPS, GROWTH, DIVIDEND, ROCEb.

Predictors: (Constant), PERATIO, EPS, GROWTH, DIVIDEND, ROCEc.

Predictors: (Constant), PERATIO, EPS, DIVIDEND, ROCEd.

Predictors: (Constant), PERATIO, EPS, DIVIDENDe.




Coefficientsa

-31.633 192.675 -.164 .870-6.52E-02 .610 -.012 -.107 .915

4.269 1.589 .320 2.686 .0093.148 1.168 .377 2.694 .0091.449 .975 .175 1.486 .1431.316 2.379 .076 .553 .582

-7.431 9.558 -.116 -.778 .44023.974 92.356 .033 .260 .796

-30.329 190.642 -.159 .8744.291 1.563 .322 2.746 .0083.162 1.151 .379 2.747 .0081.421 .933 .172 1.524 .1331.397 2.234 .081 .625 .534

-7.665 9.224 -.120 -.831 .40922.677 90.770 .031 .250 .804

8.790 107.883 .081 .9354.337 1.540 .325 2.817 .0073.243 1.095 .389 2.963 .0041.457 .915 .176 1.593 .1171.461 2.202 .084 .664 .510

-7.346 9.062 -.115 -.811 .421-9.068 103.985 -.087 .9314.290 1.531 .322 2.802 .0073.196 1.087 .383 2.939 .0051.622 .876 .196 1.852 .069

-4.300 7.777 -.067 -.553 .582-52.751 67.219 -.785 .436

4.346 1.519 .326 2.862 .0062.903 .944 .348 3.074 .0031.671 .866 .202 1.928 .058

(Constant)PAYOUTEPSDIVIDENDPERATIOGROWTHROCEBOOKVAL(Constant)EPSDIVIDENDPERATIOGROWTHROCEBOOKVAL(Constant)EPSDIVIDENDPERATIOGROWTHROCE(Constant)EPSDIVIDENDPERATIOROCE(Constant)EPSDIVIDENDPERATIO

Model1

2

3

4

5

B Std. Error


Beta

Standardized

Coefficients

t Sig.


Interpretation (CEMENT INDUSTRY): EPS and Dividend are the highly significant determinants for cement industry with positive t- values. . The coefficient of multiple determination, (R2), obtained from the equations indicate that variables included in the equation could explain 27.1 % of the dependent variable share price. The computed F-value 4.407 is found to be significant at 5% level. The variables Dividend and Payout are found to be insignificant with negative t-values.



CORRELATION MATRIX: CHEMICALS

Correlations

1 -.108 .336** .223 -.033 -.044 .068.357 .003 .055 .781 .709 .565

75 75 75 75 75 75 75-.108 1 .484** -.093 .009 .085 -.007.357 .000 .426 .939 .466 .954

75 75 75 75 75 75 75.336** .484** 1 -.134 -.133 .494** .399**.003 .000 .250 .257 .000 .000

75 75 75 75 75 75 75.223 -.093 -.134 1 .166 -.224 -.179.055 .426 .250 .153 .053 .124

75 75 75 75 75 75 75-.033 .009 -.133 .166 1 -.001 -.152.781 .939 .257 .153 .995 .192

75 75 75 75 75 75 75-.044 .085 .494** -.224 -.001 1 .501**.709 .466 .000 .053 .995 .000

75 75 75 75 75 75 75.068 -.007 .399** -.179 -.152 .501** 1.565 .954 .000 .124 .192 .000

75 75 75 75 75 75 75


payout

eps

dividend

peratio

growth

roce

bookvalu



Interpretation: There is a significant correlation between Dividend & EPS, Dividend & ROCE, EPS and

ROCE & Book Value at 1% level of significance. Except that no other variables are





REGRESSION RESULTS CHEMICALS INDUSTRY

Model Summary

.450a .202 .119 105.1989

.450b .202 .132 104.4230

.447c .200 .142 103.8175

.438d .192 .145 103.6049

.430e .185 .151 103.2922

.394f .155 .132 104.4412

Model123456



Predictors: (Constant), BOOKVAL, EPS, PAYOUT,GROWTH, PERATIO, ROCE, DIVIDEND

a.

Predictors: (Constant), BOOKVAL, EPS, PAYOUT,PERATIO, ROCE, DIVIDEND

b.

Predictors: (Constant), BOOKVAL, EPS, PAYOUT,PERATIO, DIVIDEND

c.

Predictors: (Constant), BOOKVAL, PAYOUT, PERATIO,DIVIDEND

d.

Predictors: (Constant), BOOKVAL, PERATIO, DIVIDENDe.

Predictors: (Constant), PERATIO, DIVIDENDf.



ANOVAg

188007.8 7 26858.257 2.427 .028a

741476.9 67 11066.819929484.7 74188001.2 6 31333.534 2.874 .015b

741483.5 68 10904.168929484.7 74185797.5 5 37159.495 3.448 .008c

743687.2 69 10778.075929484.7 74178105.7 4 44526.430 4.148 .005d

751378.9 70 10733.985929484.7 74171965.9 3 57321.975 5.373 .002e

757518.7 71 10669.278929484.7 74144111.8 2 72055.882 6.606 .002f

785372.9 72 10907.957929484.7 74

RegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotal

Model1

2

3

4

5

6


Predictors: (Constant), BOOKVAL, EPS, PAYOUT, GROWTH, PERATIO, ROCE,DIVIDEND

a.

Predictors: (Constant), BOOKVAL, EPS, PAYOUT, PERATIO, ROCE, DIVIDENDb.

Predictors: (Constant), BOOKVAL, EPS, PAYOUT, PERATIO, DIVIDENDc.

Predictors: (Constant), BOOKVAL, PAYOUT, PERATIO, DIVIDENDd.

Predictors: (Constant), BOOKVAL, PERATIO, DIVIDENDe.

Predictors: (Constant), PERATIO, DIVIDENDf.

Dependent Variable: AVGPRICEg.



Coefficientsa

-26.949 79.284 -.340 .735-.556 .517 -.146 -1.077 .285

-7.25E-02 .080 -.129 -.907 .368.984 .421 .428 2.340 .022

1.679 .694 .285 2.420 .018-7.14E-03 .293 -.003 -.024 .981

-1.070 2.453 -.064 -.436 .66455.841 42.429 .175 1.316 .193

-27.236 77.827 -.350 .727-.557 .512 -.146 -1.087 .281

-7.27E-02 .079 -.130 -.920 .361.986 .413 .429 2.386 .020

1.676 .680 .284 2.464 .016-1.080 2.402 -.064 -.450 .65455.964 41.819 .175 1.338 .185

-31.599 76.771 -.412 .682-.491 .488 -.129 -1.006 .318

-6.46E-02 .076 -.115 -.845 .401.901 .365 .392 2.466 .016

1.698 .675 .288 2.518 .01450.235 39.599 .157 1.269 .209

-48.575 73.943 -.657 .513-.345 .456 -.090 -.756 .452.714 .290 .311 2.460 .016

1.679 .673 .285 2.495 .01559.812 37.864 .187 1.580 .119

-56.635 72.950 -.776 .440.634 .269 .276 2.353 .021

1.536 .644 .260 2.386 .02060.947 37.720 .191 1.616 .11156.977 19.649 2.900 .005

.801 .251 .348 3.187 .0021.393 .645 .236 2.160 .034

(Constant)PAYOUTEPSDIVIDENDPERATIOGROWTHROCEBOOKVAL(Constant)PAYOUTEPSDIVIDENDPERATIOROCEBOOKVAL(Constant)PAYOUTEPSDIVIDENDPERATIOBOOKVAL(Constant)PAYOUTDIVIDENDPERATIOBOOKVAL(Constant)DIVIDENDPERATIOBOOKVAL(Constant)DIVIDENDPERATIO

Model1

2

3

4

5

6

B Std. Error


Beta

Standardized

Coefficients

t Sig.


Interpretation (CHEMICALS INDUSTRY): Dividend and PE ratio are the significant determinants for cement industry with positive t- values. . The coefficient of multiple determination, (R2), obtained from the equations indicate that variables included in the equation could explain 11.9 % variation of the dependent variable share price. The computed F-value 6.606 is found to be significant at 5% level. The variables Growth, ROCE and EPS are found to be insignificant with negative t-values.



CORRELATION MATRIX: PHARMACEUTICALS

Correlations

1 -.123 .275** .471** -.112 -.147 .297**.236 .007 .000 .279 .154 .003

95 95 95 95 95 95 95-.123 1 .525** .030 -.067 .608** .457**.236 .000 .775 .519 .000 .000

95 95 95 95 95 95 95.275** .525** 1 .146 -.165 .467** .660**.007 .000 .157 .111 .000 .000

95 95 95 95 95 95 95.471** .030 .146 1 .046 -.062 .194.000 .775 .157 .659 .549 .059

95 95 95 95 95 95 95-.112 -.067 -.165 .046 1 -.067 -.058.279 .519 .111 .659 .520 .578

95 95 95 95 95 95 95-.147 .608** .467** -.062 -.067 1 .167.154 .000 .000 .549 .520 .105

95 95 95 95 95 95 95.297** .457** .660** .194 -.058 .167 1.003 .000 .000 .059 .578 .105

95 95 95 95 95 95 95


payout

eps

dividend

peratio

growth

roce

bookvalu




ROCE, Dividend & Book Value at 1% level of significance. Except that no other

variables are correlated. Therefore there would not be any problem of multicollinearity

because of linear multiple regression model being used.



REGRESSION RESULTS PHARMACEUTICALS INDUSTRY Model Summary

.870a .756 .737 163.0839

.870b .756 .740 162.1557

.870c .756 .743 161.2473

.867d .752 .741 161.6533

Model1234



Predictors: (Constant), BOOKVALU, GROWTH, ROCE,PERATIO, PAYOUT, EPS, DIVIDEND

a.

Predictors: (Constant), BOOKVALU, ROCE, PERATIO,PAYOUT, EPS, DIVIDEND

b.

Predictors: (Constant), BOOKVALU, ROCE, PERATIO,PAYOUT, EPS

c.

Predictors: (Constant), ROCE, PERATIO, PAYOUT, EPSd.

ANOVAe

7178678 7 1025525.428 38.559 .000a

2313883 87 26596.3589492561 947178647 6 1196441.104 45.502 .000b

2313914 88 26294.4839492561 947178500 5 1435700.038 55.218 .000c

2314061 89 26000.6859492561 947140701 4 1785175.138 68.314 .000d

2351861 90 26131.7849492561 94


Model1

2

3

4


Predictors: (Constant), BOOKVALU, GROWTH, ROCE, PERATIO, PAYOUT, EPS,DIVIDEND

a.

Predictors: (Constant), BOOKVALU, ROCE, PERATIO, PAYOUT, EPS, DIVIDENDb.

Predictors: (Constant), BOOKVALU, ROCE, PERATIO, PAYOUT, EPSc.

Predictors: (Constant), ROCE, PERATIO, PAYOUT, EPSd.




Coefficientsa

-198.132 94.480 -2.097 .03911.809 1.769 .452 6.676 .00013.154 1.540 .665 8.541 .000

3.046E-02 .390 .007 .078 .9385.723 1.425 .244 4.015 .000

2.965E-02 .863 .002 .034 .9732.375 1.656 .105 1.434 .155

-47.306 45.133 -.081 -1.048 .297-197.778 93.381 -2.118 .037

11.802 1.745 .452 6.762 .00013.152 1.530 .665 8.594 .000

2.872E-02 .385 .006 .075 .9415.728 1.408 .245 4.069 .0002.376 1.646 .105 1.443 .152

-47.181 44.729 -.081 -1.055 .294-201.364 79.619 -2.529 .013

11.836 1.676 .453 7.060 .00013.167 1.509 .666 8.725 .000

5.724 1.399 .245 4.092 .0002.423 1.509 .107 1.606 .112

-45.404 37.657 -.078 -1.206 .231-282.541 42.608 -6.631 .000

11.131 1.575 .426 7.066 .00012.271 1.317 .620 9.317 .000

5.713 1.402 .244 4.074 .0002.659 1.500 .118 1.773 .080

(Constant)PAYOUTEPSDIVIDENDPERATIOGROWTHROCEBOOKVALU(Constant)PAYOUTEPSDIVIDENDPERATIOROCEBOOKVALU(Constant)PAYOUTEPSPERATIOROCEBOOKVALU(Constant)PAYOUTEPSPERATIOROCE

Model1

2

3

4

B Std. Error


Beta

Standardized

Coefficients

t Sig.


Interpretation (PHARMACY INDUSTRY): PE ratio, EPS and Payout are the significant determinants of share price for pharmacy

industry with positive t- values. . The coefficient of multiple determination, (R2),

obtained from the equations indicate that variables included in the equation could explain

73.7 % variation of the dependent variable share price. The computed F-value 38.559 is

found to be significant at 5% level. The variables book value is found to be insignificant

with negative t-values.



CORRELATION MATRIX: TEXTILE

Correlations

1 -.099 -.030 .022 .153 -.162 -.046.413 .803 .854 .206 .180 .704

70 70 70 70 70 70 70-.099 1 .778** -.113 .096 .664** .262*.413 .000 .353 .430 .000 .029

70 70 70 70 70 70 70-.030 .778** 1 -.081 -.058 .546** .459**.803 .000 .504 .631 .000 .000

70 70 70 70 70 70 70.022 -.113 -.081 1 .206 -.131 -.036.854 .353 .504 .087 .279 .766

70 70 70 70 70 70 70.153 .096 -.058 .206 1 .064 -.071.206 .430 .631 .087 .599 .561

70 70 70 70 70 70 70-.162 .664** .546** -.131 .064 1 -.154.180 .000 .000 .279 .599 .202

70 70 70 70 70 70 70-.046 .262* .459** -.036 -.071 -.154 1.704 .029 .000 .766 .561 .202

70 70 70 70 70 70 70


payout

eps

dividend

peratio

growth

roce

bookvalu





ROCE, Dividend & Book Value at 1% level of significance. Except that no variables are

correlated. Therefore there would not be any problem of multicollinearity analysis

because of linear multiple regression model is being used for the further analysis to

overcome this problem



REGRESSION RESULTS TEXTILE INDUSTRY

Model Summary

.774a .598 .553 89.5228

.773b .598 .560 88.8656

.772c .596 .565 88.3641

.769d .591 .566 88.2094

Model1234



Predictors: (Constant), BOOKVAL, PERATIO, PAYOUT,GROWTH, ROCE, EPS, DIVIDEND

a.

Predictors: (Constant), BOOKVAL, PERATIO, PAYOUT,GROWTH, ROCE, EPS

b.

Predictors: (Constant), BOOKVAL, PAYOUT, GROWTH,ROCE, EPS

c.

Predictors: (Constant), BOOKVAL, GROWTH, ROCE,EPS

d.

ANOVAe

740358.9 7 105765.554 13.197 .000a

496889.0 62 8014.3381237248 69739731.4 6 123288.572 15.612 .000b

497516.4 63 7897.0861237248 69737521.9 5 147504.377 18.891 .000c

499726.0 64 7808.2181237248 69731489.2 4 182872.289 23.503 .000d

505758.7 65 7780.9031237248 69


Model1

2

3

4


Predictors: (Constant), BOOKVAL, PERATIO, PAYOUT, GROWTH, ROCE, EPS,DIVIDEND

a.

Predictors: (Constant), BOOKVAL, PERATIO, PAYOUT, GROWTH, ROCE, EPSb.

Predictors: (Constant), BOOKVAL, PAYOUT, GROWTH, ROCE, EPSc.

Predictors: (Constant), BOOKVAL, GROWTH, ROCE, EPSd.




Coefficients a

-164.599 64.268 -2.561 .013-.114 .129 -.075 -.885 .3802.246 1.140 .292 1.970 .053

.197 .705 .043 .280 .781

.121 .245 .041 .495 .6222.753 .812 .294 3.389 .001

-4.895 2.514 -.255 -1.947 .056131.238 25.896 .558 5.068 .000

-171.718 58.583 -2.931 .005-.107 .126 -.070 -.853 .3972.419 .952 .315 2.541 .014

.128 .242 .044 .529 .5992.701 .785 .288 3.440 .001

-4.654 2.345 -.242 -1.985 .052134.957 22.062 .574 6.117 .000

-168.475 57.933 -2.908 .005-.110 .125 -.072 -.879 .3832.403 .946 .313 2.540 .0142.792 .762 .298 3.666 .001

-4.758 2.323 -.248 -2.048 .045134.660 21.931 .573 6.140 .000

-177.166 56.983 -3.109 .0032.373 .944 .309 2.514 .0142.687 .751 .287 3.578 .001

-4.452 2.293 -.232 -1.941 .057136.074 21.833 .579 6.232 .000

(Constant)PAYOUTEPSDIVIDENDPERATIOGROWTHROCEBOOKVAL(Constant)PAYOUTEPSPERATIOGROWTHROCEBOOKVAL(Constant)PAYOUTEPSGROWTHROCEBOOKVAL(Constant)EPSGROWTHROCEBOOKVAL

Model1

2

3

4

B Std. Error


Beta

Standardized

Coefficients

t Sig.


Interpretation (TEXTILES INDUSTRY): EPS, book value and Growth are the significant determinants of share price for textiles

industry with positive t- values. . The coefficient of multiple determination, (R2),

obtained from the equations indicate that variables included in the equation could explain

55.3 % variation of the dependent variable share price. The computed F-value 13.197 is

found to be significant at 5% level. The variables Dividend, Payout and ROCE are found

to be insignificant with negative t-values.



CORRELATION MATRIX: MISCELLANEOUS

Correlations

1 -.151 .276* .203 -.306* .602** .087.211 .021 .092 .010 .000 .476

70 70 70 70 70 70 70-.151 1 .588** -.167 .192 .104 .480**.211 .000 .167 .111 .391 .000

70 70 70 70 70 70 70.276* .588** 1 -.061 .069 .166 .268*.021 .000 .617 .573 .170 .025

70 70 70 70 70 70 70.203 -.167 -.061 1 -.062 .219 -.021.092 .167 .617 .612 .068 .865

70 70 70 70 70 70 70-.306* .192 .069 -.062 1 -.061 -.018.010 .111 .573 .612 .618 .881

70 70 70 70 70 70 70.602** .104 .166 .219 -.061 1 .053.000 .391 .170 .068 .618 .664

70 70 70 70 70 70 70.087 .480** .268* -.021 -.018 .053 1.476 .000 .025 .865 .881 .664

70 70 70 70 70 70 70


payout

eps

dividend

peratio

growth

roce

bookvalu




Interpretation: There is a significant correlation between Dividend & EPS, ROCE, Payout & Book

Value at 1% level of significance. Except that no other variables are correlated. There

would not be any problem of multicollinearity because of linear multiple regression

model being used for the further analysis.



REGRESSION RESULTS MISCELLANEOUS INDUSTRY

Model Summary

.641a .411 .345 385.4094

.641b .411 .355 382.3881

.633c .401 .354 382.8139

.626d .392 .354 382.6930

.619e .383 .355 382.4791

Model12345



Predictors: (Constant), BOOKVALU, GROWTH,PERATIO, ROCE, DIVIDEND, PAYOUT, EPS

a.

Predictors: (Constant), BOOKVALU, GROWTH,PERATIO, ROCE, PAYOUT, EPS

b.

Predictors: (Constant), BOOKVALU, PERATIO, ROCE,PAYOUT, EPS

c.

Predictors: (Constant), BOOKVALU, PERATIO, ROCE,EPS

d.

Predictors: (Constant), PERATIO, ROCE, EPSe.

ANOVAf

6435284 7 919326.250 6.189 .000a

9209504 62 148540.39015644788 69

6432885 6 1072147.533 7.332 .000b

9211903 63 146220.67915644788 69

6265814 5 1253162.829 8.551 .000c

9378974 64 146546.46615644788 69

6125285 4 1531321.132 10.456 .000d

9519503 65 146453.89915644788 69

5989631 3 1996543.573 13.648 .000e

9655157 66 146290.26115644788 69


Model1

2

3

4

5


Predictors: (Constant), BOOKVALU, GROWTH, PERATIO, ROCE, DIVIDEND,PAYOUT, EPS

a.

Predictors: (Constant), BOOKVALU, GROWTH, PERATIO, ROCE, PAYOUT, EPSb.

Predictors: (Constant), BOOKVALU, PERATIO, ROCE, PAYOUT, EPSc.

Predictors: (Constant), BOOKVALU, PERATIO, ROCE, EPSd.

Predictors: (Constant), PERATIO, ROCE, EPSe.




Coefficients a

-149.592 218.262 -.685 .496-3.410 3.430 -.157 -.994 .3245.181 2.208 .378 2.347 .022

-2.28E-02 .180 -.018 -.127 .8997.168 2.354 .311 3.045 .003

-2.230 2.145 -.110 -1.040 .3036.294 2.322 .365 2.711 .009

85.033 80.696 .122 1.054 .296-145.408 214.073 -.679 .499

-3.632 2.926 -.167 -1.241 .2194.998 1.660 .365 3.011 .0047.166 2.336 .311 3.068 .003

-2.261 2.115 -.111 -1.069 .2896.370 2.227 .369 2.861 .006

86.574 79.153 .124 1.094 .278-217.989 203.245 -1.073 .288

-2.753 2.812 -.127 -.979 .3314.761 1.647 .347 2.891 .0057.153 2.338 .310 3.059 .0036.092 2.214 .353 2.752 .008

91.923 79.083 .132 1.162 .249-238.084 202.143 -1.178 .243

5.299 1.552 .386 3.415 .0017.095 2.337 .308 3.036 .0034.739 1.730 .275 2.740 .008

74.028 76.918 .106 .962 .339-65.329 92.908 -.703 .484

6.018 1.359 .439 4.427 .0007.253 2.330 .315 3.113 .0034.717 1.728 .273 2.729 .008

(Constant)PAYOUTEPSDIVIDENDPERATIOGROWTHROCEBOOKVALU(Constant)PAYOUTEPSPERATIOGROWTHROCEBOOKVALU(Constant)PAYOUTEPSPERATIOROCEBOOKVALU(Constant)EPSPERATIOROCEBOOKVALU(Constant)EPSPERATIOROCE

Model1

2

3

4

5

B Std. Error


Beta

Standardized

Coefficients

t Sig.


Interpretation (MISCELLANEOUS INDUSTRY): EPS, PE ratio and ROCE are the significant determinants of share price for textiles

industry with positive t- values. The coefficient of multiple determination, (R2), obtained

from the equations indicate that variables included in the equation could explain 35.5 %

variation of the dependent variable share price. The computed F-value 6.819 is found to

be significant at 5% level. The variables Dividend, Payout and Growth are found to be

insignificant with negative t-values.



CORRELATION MATRIX: AGGREGATE OF ALL INDUSTIES

Correlations

1 -.048 .044 .133** -.040 .004 .019.314 .365 .006 .402 .931 .693

435 435 435 435 435 435 435-.048 1 .192** -.045 .044 .081 .107*.314 .000 .347 .361 .093 .025435 435 435 435 435 435 435.044 .192** 1 .003 .040 .289** .370**.365 .000 .958 .405 .000 .000435 435 435 435 435 435 435.133** -.045 .003 1 .105* .020 .051.006 .347 .958 .028 .670 .290435 435 435 435 435 435 435

-.040 .044 .040 .105* 1 .046 .026.402 .361 .405 .028 .342 .585435 435 435 435 435 435 435.004 .081 .289** .020 .046 1 .202**.931 .093 .000 .670 .342 .000435 435 435 435 435 435 435.019 .107* .370** .051 .026 .202** 1.693 .025 .000 .290 .585 .000435 435 435 435 435 435 435

Pearson CorrelaSig. (2-tailed)NPearson CorrelaSig. (2-tailed)NPearson CorrelaSig. (2-tailed)NPearson CorrelaSig. (2-tailed)NPearson CorrelaSig. (2-tailed)NPearson CorrelaSig. (2-tailed)NPearson CorrelaSig. (2-tailed)N

payout

eps

dividend

peratio

growth

roce

bookval




Interpretation: There is a significant correlation between Dividend & Book Value at 1% level of

significance. Except that no other variables are correlated. There would not be any

problem of multicollinearity analysis because of linear multiple regression model being

used for the further analysis.



REGRESSION RESULTS AGGREGATE INDUSTRY

Model Summary

.583a .339 .329 290.0709

.582b .339 .330 289.7461

.581c .338 .330 289.6770

Model123



Predictors: (Constant), BOOKVAL, PAYOUT, GROWTH,EPS, PERATIO, ROCE, DIVIDEND

a.

Predictors: (Constant), BOOKVAL, PAYOUT, EPS,PERATIO, ROCE, DIVIDEND

b.

Predictors: (Constant), BOOKVAL, EPS, PERATIO,ROCE, DIVIDEND

c.

ANOVAd

18454963 7 2636423.250 31.333 .000a

35928266 427 84141.13954383229 43418451440 6 3075239.959 36.631 .000b

35931789 428 83952.77854383229 43418384641 5 3676928.153 43.818 .000c

35998588 429 83912.79354383229 434


Model1

2

3


Predictors: (Constant), BOOKVAL, PAYOUT, GROWTH, EPS, PERATIO, ROCE,DIVIDEND

a.

Predictors: (Constant), BOOKVAL, PAYOUT, EPS, PERATIO, ROCE, DIVIDENDb.

Predictors: (Constant), BOOKVAL, EPS, PERATIO, ROCE, DIVIDENDc.




Coefficientsa

-360.676 54.144 -6.661 .000-.250 .277 -.036 -.901 .368.271 .165 .066 1.636 .102.279 .093 .132 2.990 .003

2.040 .469 .174 4.349 .000-.111 .541 -.008 -.205 .8385.552 .928 .248 5.986 .000

191.844 23.455 .349 8.179 .000-362.254 53.532 -6.767 .000

-.247 .277 -.035 -.892 .373.269 .165 .066 1.632 .103.279 .093 .132 2.989 .003

2.030 .466 .173 4.358 .0005.546 .926 .247 5.989 .000

191.836 23.428 .349 8.188 .000-369.038 52.976 -6.966 .000

.277 .165 .067 1.681 .094

.275 .093 .130 2.949 .0031.975 .462 .169 4.279 .0005.555 .926 .248 6.000 .000

191.853 23.423 .349 8.191 .000

(Constant)PAYOUTEPSDIVIDENDPERATIOGROWTHROCEBOOKVAL(Constant)PAYOUTEPSDIVIDENDPERATIOROCEBOOKVAL(Constant)EPSDIVIDENDPERATIOROCEBOOKVAL

Model1

2

3

B Std. Error


Beta

Standardized

Coefficients

t Sig.


Excluded Variablesc

-.008a -.205 .838 -.010 .982-.006b -.156 .876 -.008 .985-.035b -.892 .373 -.043 .978

GROWTHGROWTHPAYOUT

Model23

Beta In t Sig.Partial

Correlation Tolerance

Collinearity

Statistics

Predictors in the Model: (Constant), BOOKVAL, PAYOUT, EPS, PERATIO, ROCE,DIVIDEND

a.

Predictors in the Model: (Constant), BOOKVAL, EPS, PERATIO, ROCE,DIVIDEND

b.

Dependent Variable: AVGPRICEc.



INTERPRETATION OF REGRESSION RESULTS: YEAR-WISE

Table 3

Y *significant determinant

N *not significant determinant

INTERPRETATION: The regression analysis for aggregate of industries for all the year, clearly depicts

Book value, ROCE and EPS are the most important determinants of market price

among the among all the variables with a high positive values at 1 % level of

significance. At the same time there is a positive significant relationship between PE

ratio and market price of share at 5% level of significance. Where as Dividend Per

Share, Payout ratio and Growth remains insignificant with a negative values. They do

not have any influence on the market share price.

Year

Payout EPS DIV P/E R ROCE Growth BV Adj r 2 F-Value

2002

N Y Y Y Y N Y .662 35.639

2003

N Y N N Y N Y .475 20.489

2004

N N N Y Y N Y .451 24.570

2005

N Y N N Y N Y .459 15.607

2006

Y Y N Y N N Y .756 89.821



INTERPRETATION OF REGRESSION RESULTS: INDUSTRY WISE

Table 4

Y *significant determinant

N *not significant determinant

INTERPRETATION: The regression analysis for various industries in aggregate depicts that there earnings per

share(EPS) and price earning ratio(P/E R) are the significant determinants of equity

share price with highly positive t – values. Dividend is significant at a low positive t-

value. Where as Book value (BV), Growth (G), Return on Capital Employed

(ROCE) and Payout has no any influence on the market share price. They are

insignificant with negative t-values.

Except for automobile industry, the R2 ranges from 13.2% to 56.6%. it means less

than 56% of variation in dependent variable is explained by the independent variables.

Industry

Payout

EPS DIV P/E R ROCE Growth

BV AdjR 2

F-value

Automobile

N Y Y Y N N N .897 104.636

Cements

N Y Y N N N N .310 10.574

Chemical

N N Y Y N N N .132 6.606

Pharmacy

Y Y N Y N N N .541 66.314

Textiles

N Y N N N Y Y .566 23.503

Miscellaneous

N Y N Y Y N N .345 13.648



CHAPTER V

CONCLUSION



CONCLUSION

The present study attempts to examine the empirical relationship of explanatory

variables namely, dividend per share, earning per share , price earning ratio, book

value, return on capital employed, growth and payout ratio on market price of

shares from the year 2002 to 2006 in the post reform era of liberalization. The

relationship between independent variables and dependent variable of 87 companies

(randomly selected) of six industries are studied.

The results reveal that Earnings Per Share is the only determinant which is

common in both the analysis (year wise and industry wise). Therefore EPS is an

important determinant of share price. If look particularly into the year wise

analysis- Book value also influences the share price. And looking into industry wise

it is found that Price earning ratio also influences significantly on the dependent

variable.

The other independent variables like Return on capital employed and

dividend per share remain insignificant but with a positive value. They are not

significant determinants of share price.

The regression analysis clearly depicts that Growth and payout remains most

insignificant determinant with negative value. They do not have any influence on the

share price. Overall the R2 ranges from 13 % to 56 % (except for automobile

industry). It means less than 50 % of variation in dependent variable is explained by

these independent variables.

Finally it can be concluded that apart from the above independent variables

there are some other factors which influences the market price of the share. Those

factors may be macroeconomic factors like government policy, federal bank policy,

central bank interest rates, business cycle, demand and supply shocks, GDP,

inflation, exchange rates. Etc.



CHAPTER V1

BIBLIOGRAPHY

&

ANNEXTURES



Bibliography Journal References:

⇒ Shefali Sharma & Balwinder, (2006),”Determinants of equity share prices in the Indian corporate sector”, The ICFAI Journal of applied finance, Vol. 12 No.4 pp 21-31.

⇒ Monica Singania (2006), “Determinants of equity prices: a study of select

Indian companies”, The ICFAI Journal of applied finance, Vol.12, No.9 pp 39-50.

⇒ Subir Sen, Rajendra Ray (2003), “Key determinants of stock prices in

India”, The ICFAI Journal of applied finance, Vol. 9, No.7 pp 35-40. ⇒ A. James Heins; Stephen L. Alison(1966), “Some factors affecting stock

price variability”, The Journal of Business, vol. 39, No. 1, pp. 19-23 ⇒ James L. Bickler (1969), “Empirical tests of the compatibility of selected

equity share price equations with a Descriptive model, The Journal of applied finance, Vol. 24, No.1pp 106-108.

Books Referred:

Investments- Bodie, Kane, Marcus

Modern Portfolio Theory And Investment Analysis- Elton & Gruber

Security Analysis And Portfolio Management - Prasanna Chandra

Econometrics – Ashwath Damodaran

Statistics- S.C Gupta



Software Used:

⇒ Prowess

⇒ Capitaline

⇒ SPSS 10

⇒ MS Excel

WEBLIOGRAPHY:

⇒ www.jstor.org

⇒ www.nseindia.com

⇒ www.icfaipress.org

⇒ www.bseindia.com

⇒ www.capitaline.com



ANNEXTURES Table 5: LIST OF THE COMPANIES UNDER THE STUDY

IP RINGS LTD MADRAS CEMENT LTD HI-TECH GEARS LTD SHREE CEMENT LTD MENON PISTONS LTD EVEREST INDUSTRIES LTD SAMKRG PISTONS & RINGS LTD RAMCO INDUSTRIES LTD UCAL FUEL SYSTEMS LTD DALMIA CEMENTS SWARAJ MAZDA LTD DECCAN CEMENTS LTD ASHOK LEYLAND LTD SAGAR CEMENTS LTD BAJAJ AUTO LTD OCL INDIA LTD (CEMENTS) MAHINDRA & MAHINDRA LTD HINDUSTAN SANITARYWARE &

INDUSTRIES LTD PUNJAB TRACTORS LTD ORIENT CERAMICS & INDUSTRIES

LTD VST TILLERS TRACTORS LTD KAKATIYA CEMENTS SUGAR &

INDUSTRIES LTD LUMAX INDUSTRIES LTD ALPS INDUSTRIES LTD DEEPAK NITRITE LTD RAYMOND LTD THIRUMALAI CHEMICALS SKY INDUSTRIES LTD CIBA SPECIALITY CHEMICALS UNIPRODUCTS (INDIA) LTD INDIAN HUMPE PIPE LINE COMPANY LTD CHESLIND TEXTILES LTD INDIA GLYCOLS LTD PATSPIN INDIA LTD AARTI INDUSTRIES LTD EUROTEX INDUSTRIES AND

EXPORTS LTD ALKYL AMINES CHEMICALS LTD ADITYA BIRLA NUVO LTD TANFAC INDUSTRIES LTD LOYAL TEXTILES MILLS LTD HIKAL LTD HIMATSINGKA SEIDE LTD PIDILITE INDUSTRIES LTD PIONEER EMBROIDERIES LTD PUNJAB CHEMICALS & CROP PROTECTION LTD CHEVIOT COMPANY LTD SRF POLYMERS LTD DONEAR INDUSTRIES LTD GODREJ INDUSTRIES LTD BSL LTD JAYANT AGRO ORGANICS LTD AARTI DRUGS LTD CLARIANT CHEMICALS (INDIA) LTD DIVIS LABORATORIES LTD ALCHEMIST LTD GRANULES INDIA LTD CRISIL LTD LUPIN LTD PANACEA BIOTEC LTD NEULAND LABORATORIES LTD



Continued….. HINDALCO INDUSTRIES LTD SHASUN CHEMICALS & DRUGS LTD MADRAS ALUMINIUM CO LTD SUVEN LIFE SCIENCES LTD BRITANNIA INDUSTRIES LTD WYETH LTD NESTLE INDIA LTD NOVARTIS INDIA LTD GRASIM INDUSTRIES LTD GLAXOSMITHKLINE PHARMA LTD ELECTROSTEEL CASTINGS LTD SOLVAY PHARMA INDIA LTD INFOSYS TECHNOLOGIES LTD MERCK LTD APOLLO TYRES LTD THEMIS MEDICARE LTD LARSEN & TOUBRO LTD ALEMBIC LTD BHARAT EARTH MOVERS LTD CADILA HEALTHCARE LTD MANUGRAPH INDIA LTD DR REDDYS LABORATORIES LTD ACC J B CHEMICALS &

PHARAMACEUTICALS LTD AMBUJA CEMENTS LTD NICHOLAS PIRAMAL INDIA LTD

RANBAXY LABORATORIES LTD

Table 6: NO OF SECTORS AND COMPANIES TAKEN UNDER THE

STUDY

SECTOR NO. OF COMPANIES AUTOMOBILES 12 CEMENTS 13 CHEMICALS 15 PHARMACEUTICALS 19 TEXTILE & COTTON 14 MISCELLANEOUS 14

TOTAL 87

determinants of equity share prices in the indian corporate sector

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