determinants of price earnings ration in the indian corporate sector

A RESEARCH PROJECT ON

DETERMINANTS OF PRICE EARNINGS RATIO IN

THE INDIAN CORPORATE SECTOR

Dissertation Submitted in partial fulfillment for the award of

MASTER OF BUSINESS ADMINISTRATION

For Bangalore University SUBMITTED BY

SHILPA.M REG NO: 05XQCM6085

Under the guidance of

Prof. S. SANTHANAM

M P BIRLA INSTITUTE OF MANAGEMENT

(Associate Bharatiya Vidya Bhavan)

#43.RACE COURSE ROAD, BANGALORE-560001

2005-2007

DECLARATION

I hereby declare that this dissertation work entitled “DETERMINANTS OF

PRICE EARNINGS RATIO IN INDIAN CORPORATE SECTOR” is a

bonafide study, completed under the guidance and supervision of Prof. S.

Santhanam and submitted in partial fulfillment for the award of MASTERS

OF BUSINESS ADMINISTRATION degree at Bangalore University.

I further declare that this project is the result of my own effort and that it has

not been submitted to any other university/institution for the award of any

degree or diploma or any other similar title of recognition. BANGALORE SHILPA.M DATE: 17.05.2007 Reg No: 05XQCM6085

PRINCIPAL’S CERTIFICATE I here by certify that this project dissertation report is undertaken and

completed by MS. SHILPA.M bearing Reg. No.05XQCM6085 on

“DETERMINANTS OF PRICE EARNINGS RATIO IN THE INDIAN

CORPORATE SECTOR”. Under the guidance of Prof.S.

SANTHANAM permanent faculty, M P Birla Institute of Management,

Bangalore.

Place: Bangalore Date: Dr Nagesh S Malavalli

CERTIFICATE

I here by certify that project work embodied in the dissertation entitled

is the result of an study undertaken and completed by MS. SHILPA.M

bearing Reg No: 05XQCM6085 on “DETERMINANTS OF PRICE

EARNINGS RATIO IN THE INDIANCORPORATE SECTOR” under my

guidance and supervision.

This has not formed the basis for the award of any Degree/Diploma by

Bangalore University or any other University.

I also certify that he has fulfilled all the requirements under the covenant

governing the submission of dissertation to the Bangalore University for the

award of MBA Degree. Place: Bangalore DATE: PROF. SANTHANAM

ACKNOWLEDGEMENT

As students collect accolades in the form of grades for the success in his/her endeavors

and his/her success depends on adequate preparation and in domination and most

important of all the support received from his/her guide. So the accolades I earn of this

project, I would like to share with all those who have played a notable part in its making.

I take this opportunity to sincerely thank Dr.T.V.N Rao who guided me through out the

project through his Valuable suggestions, without which the project would not have been

successful.

I also thank PROF. S. SANTHANAM & PROF. RUDRA MURTHY for giving me the

opportunity to explore my areas of interest by consistently lending support in terms of his

expertise and also supplying valuable inputs in terms of resources every step of the way.

I also remain grateful to all my friends for their assistance to prepare this project

successfully.

SHILPA.M

(05XQCM6085)

CONTENTS

Chapters Particulars Page No.

Executive summary

1 Introduction

2 Literature review

Forecasting p/e ratios for the Indian capital market

Determinants of price-earnings ratio

3 Research methodology

4 Analysis of data & interpretation

Sector wise analysis

Year wise analysis

5 Summary & conclusion

6 Bibliography

7 Annexure

LIST OF TABLES

TABLE NO TABLE NAME PAGE NO

1 Automobile industry

2 Cement industry

3 Chemical industry

4 Computer and engineering industry

5 Textile industry

6 Miscellaneous Industry

7 Aggregate of sectors analysis

8 Year 2002

9 Year 2003

10 Year 2004

11 Year 2005

12 Year 2006

EXECUTIVE SUMMARY

A systematic analysis of securities for investment is important for making sound

investment decisions. The objective of the study is to examine the factors influencing the

price-earnings ratio (P\E) of Indian equities.

The study has attempted to examine the varying importance of different factors

influencing the P/E ratio of equity shares. The study considers empirical relationship of

explanatory variables namely, corporate size, dividend payout ratio, variability in earning

per share, variability in market price, debt-equity ratio and growth rate in market price on

the price earning ratio.

The study considers a sample of 52 listed companies chosen on the basis of availability of

data over five years ranging from 2001-2002 to 2005-2006. Regression analysis and

correlations are used as statistical tool to find out the determinants of price earning ratio.

In the context of Indian stock market, the result revealed that dividend payout ratio is the

important determinant of price earning ratio, which shows that the companies should

adopt a liberal dividend policy to activate the primary as well 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 but practically growing

companies and companies which have high potential future growth rate they may not

give high dividend and they reserved for future expansion. The corporate size, debt

equity ratio, variability in earning per share, variability in market price being insignificant

variables find no evidence to support theoretical work.

CHAPTER I INTRODUCTION

INTRODUCTION

A systematic analysis of securities for investment is important for making sound

investment decisions. It helps investors select those securities that conform to their

expected risk-return requirements. Security analysis plays an important role in efficient

stock markets as well as in stock markets, which are not claimed to be efficient. Of the

various technique of security analysis, the fundamental analysis is more popular.

Valuation is the central focus in fundamental analysis. Some analysts’ use discounted

cash-flow models to value firms, while others use multiples such as the price-earnings

and price-book value ratios. The use of an industry –average price-earning ratio to value

a firm, the assumption being that the other firms in the industry are comparable to the

firm being valued and that the market, on average, prices these firms correctly.

Many people use it to determine whether the market (or a given stock) is "expensive" or

"cheap". The calculation is very simple. You simply divide the price by the yearly

earnings. One easy way to think of it is the P/E ratio is really just equal is the price

divided by earnings... so:

P/E ratio = Price/Earnings

The market price per share may be the price prevailing on a certain day or the average

price over a period of time. The earnings per share are simply: profit after tax less

preference dividend divided by the number of outstanding equity shares.

For instance, on 10/01/01 the SP500's closing price was 1038.55. Its cumulative earnings

for the 500 companies in the index are $36.79. So the P/E ratio is calculated as 1038.55 /

36.79 = 28.23.

This means that if you are investing in the SP500 via a stock index fund, you are paying

$28.27 for each dollar of earnings that those 500 companies will have this year.

The PE ratio does not work very well as a timing device, but it can give you some idea of

the whether the market is "cheap" or "expensive". And as you can see from the above

chart, it is definitely not cheap right now, even after the large losses that the market has

suffered.

Below is the SP500 price earnings ratio (commonly referred to as the "PE ratio" or the

"P/E ratio") since 1943. You can see the levels we are at now are still very high compared

to historic levels

The Price Earnings ratio (or the Price-Earning multiple as it is commonly referred to) is a

summary measure, which primarily reflects the following factors:

Growth prospects

Risk characteristics

Shareholder orientation

Corporate image

Degree of liquidity

Different Types of P/E Ratios: It's important to understand that all P/E ratios are not

created equally. Some are calculated using earnings from the past four quarters (known as

a trailing P/E). Meanwhile, others use earnings from the last two quarters, plus projected

earnings for the next two quarters (known as a current P/E). Finally, some are calculated

based entirely on future earnings estimates (known as a forward P/E).

Caution must be used when examining forward P/E ratios, as future growth estimates

may ultimately prove to be inaccurate. Also, the underlying earnings used in the P/E

calculation can vary from source to source. Some analysts, for example, choose to work

with adjusted earnings figures, which exclude one-time gains or losses. Meanwhile,

others prefer to use net income figures calculated based on traditional GAAP rules.

This apparent tautology is justified because earnings are the main foundation of stock

prices. Fundamentally, when you buy a stock, you are purchasing an ownership share in

that company. If a company's earnings represent 5% of the total value of all of their

stock, you are purchasing a current yield of 5% on your money invested.

Two other factors influence the amount an investor is willing to pay for a given amount

of earnings:

1. Current and forecast future interest rates, and

2. Investor expectations of future earnings.

Interest rates directly affect the P/E multiple: if interest rates go up, the P/E multiple must

decline, so that an investor is purchasing the company's earnings at a competitive market

rate. For example, suppose a company earns 1 per share. If interest rates were 10%, the

P/E might be 10, with a resulting share price of 10, giving an "earnings yield" of 10% for

the purchased shares. If interest rates were 5%, the P/E might be 20, with a resulting

share price of 20, giving an "earnings yield" of 5% for the purchased shares.

Of course, by purchasing the stock, you take on whatever prospects and risks the

company faces in the future. If the company is consistently growing earnings at 20% per

year, and is expected to continue to do so with little risk, investors are willing to accept a

lower current earnings yield, and hence a higher current P/E ratio, in expectations of

getting much higher earnings in future years. Conversely, if the continued viability of the

company is in question, investors will demand a higher current earnings yield, and hence

a lower current P/E ratio, to purchase the stock.

The P/E multiple model has been the most popular approach to equity valuation in recent

year. The P/E multiple shows the average price the market is willing to pay for

purchasing each unit of a company’s earnings and hence it should reflect the earnings

quality and industry growth potential. There are a number of reasons the P/E ratio is used

so widely in valuation. First, it is an intuitively appealing statistics that relates the price

paid to current earnings. Second, it is simple to compute for the most stocks and is widely

available, making comparisons across stocks simple. Third, P/E ratios are a proxy for

number of other characteristics of the firm, including risk & growth.

While there are good reasons for using a P/E ratio, there is wide potential for misuse. One

reason given for using a P/E ratio is that it eliminates the need to make assumptions about

risk, growth and payout ratio, all of which have to be estimated for DCF valuation. This

is disingenuous, because P/E ratios are ultimately determined by the very same

parameters that determine value in DCF models. Thus the use of P/E ratio is a way for

some analysts to avoid having to be explicit about their assumptions on risk, growth and

payout ratios. This may be convenient, but it is certainly not legitimate reason for using

P/E ratios. Another reason for using P/E ratio of comparable firms is that they are much

more likely to reflect market moods and perceptions. Thus, if investors are upbeat about

retail stocks, the P/E ratios of these stocks will be higher to reflect this optimism. Again,

this can be viewed as a weakness, especially when markets make systematic errors in

valuing entire sectors. If, for instance, investors have overvalued retail stocks, on

average, using the average P/E ratio of these stocks will build in that error into the

valuation.

Never Use P/E Ratios in Isolation: Although a P/E ratio can provide a good

approximation of how "expensive" a particular stock is relative to its underlying earnings

stream, it is by no means a perfect gauge of a company's value. P/E ratios have a number

of drawbacks, including:

Earnings Manipulation: Companies often use a variety of accounting techniques to alter

their reported net income. As a result, the reported earnings figures we read about are

often not entirely representative of a company's true financial situation. Since net income

is a critical component of a firm's P/E ratio, manipulated earnings can lead to misleading

P/E data.

Industry Differences: Different industries typically have different historical growth

rates, risk levels, etc... and hence different average P/E ratios. Thus, stocks that may

appear cheap in one industry may look expensive when stacked up against another. For

this reason, it is typically more appropriate to compare a firm's P/E ratio to those of other

companies within the same sector.

Other Factors: It's important to remember that P/E ratios only take two items into

account -- a firm's current stock price and its net income. As a result, P/E ratios

completely ignore a variety of other important factors. One of the most notable of these

factors is a firm's projected future growth rate. Two stocks could be identical in every

respect (including on a P/E basis), but if one company is growing at twice the rate of the

other firm, then the high-growth firm will likely make a better investment over the long

haul. With this in mind, many investors prefer to examine PEG ratios as opposed to

traditional P/E ratios.

Volatility and Risk: P/E ratios also ignore such critical items as risk and volatility. Two

firm's may sport identical P/E ratios, but if one firm's revenue and earnings base is

extremely reliable, yet the other firm's earnings are highly uncertain, then the more

reliable firm could make a better investment over the long haul.

With the above limitations in mind, when attempting to assess the value of a particular

security, most experienced investors choose to analyze P/E ratios in conjunction with a

variety of other ratios, including Price/Sales (P/S), Price/Cash Flow (P/CF), etc

Using the P/E ratio to compare companies in the same industry

In addition to helping you determine which industries and sectors are over / under priced

you can use the P/E ratio to compare the prices of companies in the same sector against

each other. For example, if company ABC and XYZ are both selling for 50 a share, one is

not more expensive than the other. Wrong!

Company ABC may have reported earnings of 10 per share, while company XYZ has

reported earnings of 20 per share. Each is selling on the stock market for 50. What does

this mean? Company ABC has a price to earnings ratio of 5, while Company XYZ has a

P/E ratio of 2 1/2. This means that company XYZ is much cheaper on a relative basis.

For every share purchased, the investor is getting 20 of earnings as opposed to 10 in

earnings from ABC. All things being equal, an intelligent investor should opt to purchase

shares of XYZ; for the exact same price (50), he is getting twice the earning power.

Thus P/E multiple is the most widely used and misread of all multiples. Its simplicity

makes is an attractive choice in applications ranging from pricing initial public offerings

to making judgments on relative value, but its relationship to a firm’s financial

fundamentals is often ignored, leading to significant errors in applications. This research

will try to provide some insight into the determinants of P/E ratios.

CHAPTER II LITERATURE

SURVEY

PAPER I: FORECASTING P/E RATIOS FOR THE INDIAN CAPITAL MARKET An empirical study by sanjay sehgal,Balakrishnan & Soumik Basu.

INTRODUCTION

The P/E multiple model has been the most popular approach to equity valuation in recent

years. Under the approach the P/E ratio of a company (normalized for industry factors) is

multiplied with its expected future earnings (proxied by past earnings adjusted for growth

rate) to obtain a fair valuation of corporate stocks. The P/E multiple shows the average

price the market is willing to pay for purchasing each unit of a company’s earnings and

hence it should reflect the earnings quality and industry growth potential. A precise

forecast of P/E can be extremely useful in devising abnormal return investment strategies.

OBJECTIVE OF STUDY

The primary objective of the study is to evaluate quantitative techniques of forecasting

and suggesting the one, which provides best P/E forecasts for the Indian capital

market.

MODEL SPECIFICATION:

The Moving Average and Exponential smoothing methods have been used for P/E

forecasting. The alternative models used in the study are specified below:

1) Simple Moving Average (SMA):

Mt = Y^t+1 = Yt + Yt+1……………..+Yt-n+1/n

Where

Mt = Moving Average at time t

Y^ t+1 = Forecast value for next period

Yt = Actual series value at time period t.

n= No. of term in Moving Average

2) Double Moving Average (DMA):

First stage smoothing

Mt = Yt + Yt-1…………….+ Yt-n+1/n

Second stage smoothing

M`t = Mt + Mt-1……………+ Mt-n+1/n

Raw Forecast

at = 2Mt – M`t

Slope correlation forecast

2(Mt – M`t)/n-1

Final forecasting Equation

Y^t+p = at +btp

Where

n = Number of periods in the Moving Average

Yt = Actual series value at time period t.

p = Number of period ahead to be forecast.

3) Simple Exponential smoothing (SES):

Forecasting Equation

Y^t+1 = ∝ Yt +(1-∝) Y^t

Where

Y^t+1 = Forecast value for the next period

∝ = Smoothing constant (0 < ∝ < 1)

Yt = Actual value of series in period t

Y^t = Old smoothened value to period t-1

4) Double Exponential smoothing (DES):

First stage Exponential smoothing

At = ∝ Yt + (1-∝) at-1

Second stage Exponential smoothing

A`t = ∝ At +(1-∝)A`t-1

Raw Forecast

AT = ∝2At +A`t

Slope correlation factor

bt = (∝/1-∝)(At-A`t)

Final forecasting Equation

Y^t+p = a1 +btp

METHODOLOGY

The P/E ratios of 98 BSE National Index companies for the period January 1995 to

October 2000 in the form monthly time series have been used as sample companies to

analyze by using auto correlation analysis. Under the approach Auto-Correlation

coefficients up to ten lags are calculated for each of the sample time-series.

Summary & concluding remarks The empirical findings of the study can be summarized as follows:

A P/E ratio series for a company can be defined to be stationary in the Indian

context if it exhibits auto-correlation coefficients up to the order four significant

while the higher order auto-correlation coefficients are close to zero.

Majority of the sample companies exhibited a non-stationary time series of P/E

ratios. This implies that the mean PE ratio as well as the variability in PE ratio is

changing over time. The shifting parameter points towards are volatile stock

markets.

Moving average methods based on shorter windows (3 months) outperform

moving average methods employing larger windows (say 6 & 9 months) as per

MSE criterion.

Exponential smoothing procedure using bigger alpha coefficients (α=.8)

outperform those with smaller alpha coefficients (α=.2 & α= .5) as per MSE

criterion.

Exponential smoothing methods in general perform better than moving average

methods using the MSE criterion.

Conclusion For forecasting P/E ratios of companies which exhibit a stable time series, one should

adopt simple exponential smoothing technique with a larger alpha (say α=.8) thereby

giving a greater weightage to current values, in case of companies with non-stationary

time series, a better P/E forecast can be obtained by adopting simple or double

exponential smoothing methods with high alpha (α=.8). the latter method has an edge

as it makes the error pattern more random.

PAPER II: DETERMINANTS OF PRICE-EARNINGS RATIO. An empirical study by Nishi Tuli & R. K. Mittal

INTRODUCTION

A systematic analysis of securities for investments is important for making sound

investment decision. It helps investors select those securities that conform to their

expected risk-return requirements. Security analysis plays an important role in efficient

stock market as well as stock markets, which are not claimed to be efficient. Fundamental

analysis is concerned foremost. It has been prime concern of the fundamental analysts to

determine the appropriate capitalization rate or equivalently the appropriate multiplier to

be used in valuing particular securities.

In one of the early studies, showed that the impact of projected earnings growth, expected

dividend payout ratio, and variability in rates of earnings growth and concluded that P/E

is an increasing function of earning growth and payout and inversely related to variations

in growth of earnings.

P/E ratios of firms are compared using the accelerated depreciation with those firms

using straight-line depreciation. With that they found average P/E ratios were larger for

accelerated depreciation firms and also suggested that the investors are forecasting only

short-lived earnings expectations. They also find that P/E ratio are can vary positively or

negatively with market risk depending upon the market condition there fore risk also

doesn’t supply the explanation for P/E differences across firms. They conclude that

differences in P/E ratios are not because of growth or risk but because of difference

accounting methods

Purpose of the study

The primary purpose of the study is to explain the variability of P/E ratio of Indian

Corporate equities in terms of fundamental factors

The factors covered in this study are corporate size, variability in earnings per share,

variability in market price, debt equity ratio, dividend payout ratio etc.

METHODOLOGY

The sample is based on the Indian private corporate sector and was selected on the basis

of availability of data

A data relating to the market price annual high and low of its shares were not

available for all the years are excluded.

The figures of earning per share were negative in any year. 105 companies are

covered in this study.

The data was collected from the Bombay stock exchange official directory, in the present

study multiple regression technique has been adopted to examine the determinants of P/E

ratio corporate size, variability in earnings per share, variability in market price, debt

equity ratio, dividend payout ratio and growth rate in market price under two different

classifications. Under the first classification, the impact of above explanatory variables

on P/E ratios are has been examined by the taking sample as whole. And at the second

stage, the influence above explanatory variables on P/E has been examined at industry

level. This criterion was adopted to examine whether there are differences in the

determinants of P/E ratio in different industries.

The following log linear multiple regression equation is used for the studying the

influence of explanatory variables on P/E ratio.

Log P/E = Log a + b1 Log CS + b2 Log VEPS + b3 Log VMP + b4 Log

DER + b5 Log GMP + E Where P/E = price earning ratio

CS = corporate size

VEPS = variability in earning per share

VMP = variability in market price

DER = debt equity ratio

DPR = dividend payout ratio

GMP = growth rate in market price

The specification and measurement of those variables is given below:

Price earnings ratio: The measure of this ratio is adopted in the study is average of

annual high and low of market prices in the numerator and cross sectional year’s earning

per share in the denominator the reason for using each year average share price has the

advantage of smoothing out short term fluctuation in share prices and consequently in

P/E ratio. An incidental advantage of relying year average share price, instead of prices at

a particular point of time, was the economy of cost and efforts. And they preferred to use

cross section year’s earning per share in calculating P/E ratio.

Corporate size: Size is expected to influence P/E ratio positively this variable is

measured in terms of total assets and is the arithmetic mean of the value of total assets for

two years proceeding and including cross section year.

Variability in earnings: It is a measure of risk. Risk is expected to have negative

relationship with the P/E ratio of a share. Variability in earnings per share for five years

for proceeding and including cross section year.

Variability in market price: It was hypothesized that higher variation in the market price

should influence P/E ratio in positive way. This variable was obtained by calculating the

standard deviation of mean of annual high and low of market price of equity shares for

five years proceeding and including cross section year.

Debt equity ratio: Debt equity ratio is a measure financial risk. It was expected that the

higher the leverage (debt equity), higher is the risk and lower is the price of equity share

in terms of its earnings.

Dividend payout ratio: It has expected to have positive impact on P/E ratio of a firm.

Dividend payout ratio is calculated as percentage of dividend paid to equity share holders

out of earnings available and is the average of dividend payout ratio of two years

preceding and including cross section years. Growth rate in market price: Growth variable is expected to have positive influence on

P/E ratio of corporate equities. Growth in market price is calculated from a regression of

logarithms of market price against time. The value of market price is arithmetic mean of

higher and lower of market price of a share. The advantage of using regression to

calculate growth rates is that all the observations in time series are considered, as

opposed to calculating the geometric mean growth rate by considering only beginning

and ending values.

REGRESSION RESULTS: TOTAL SAMPLE COMPANIES

Dividend payout ratio and variability in market price are the most important determinants

of P/E ratio as their respective coefficients are positively significance in each of the years

covered. The value of coefficient of variability in earnings per share has the negative sign

in all years but significance in three out of five years.

The corporate size measure has the right sign all through. Although, it is significance in

two out of five years, the general consistency of the signs would suggest that investor.s

value the shares of large companies more than those of smaller ones. The coefficients

associated with growth rate in market price and debt-equity ratio are not found to be

significance while positive direction of growth rate in market price supports the

hypothesis and positive direction of debt-equity ratio is contrary to expectation.

The observed relationship of these variables explained on average 35 percent (R2)

of variability in P/E ratios of company.s equities over a period of 1989-93. The

relevance of (R2) is further supported by F-values being significance at 1 percent level

throughout the study period. All this leads us to conclude that explanatory determinants

used in the study have strong influence on P/E ratio except debt-equity ratio and growth

rate in market price.

Results

The results of parametric ANOVA is it contains mean P/E ratio by ownership pattern,

their corresponding standard deviation, computed F-ratio and critical F-ratio needed for

testing the significance at 5 percent level, The mean P/E ratio for particular ownership

pattern.

Conclusion

The empirical study has attempted to examine the varying importance of different factors

influencing the P/E ratio of equity shares. In the context of Indian stock market, it

appears that variability in market price and dividend payout ratio are the most important

Determinants of P/E ratio, followed by variability in earnings per share. The corporate

size, debt-equity ratio and growth rate in market price being insignificance variable find

no evidence to support the theoretical work. Industry class and ownership pattern

classifications do not have significance impact on P/E ratio.

CHAPTER III RESEARCH

METHODOLOGY

OBJECTIVES AND SCOPE OF STUDY

The main objective of the study is to explain the variability of P\E ratios of Indian

corporate equities in terms of fundamental factors.

To study the empirical relationship of explanatory variables namely, variability in

earnings, corporate size, variability in market price, Debt-equity ratio, dividend

payout ratio and growth in market price on the price earnings ratio.

To know the relationship between dependent and independent variables of 52

Companies over a period of five years spanning from 2001-2002 to 2005-2006.

SAMPLE AND PERIOD OF STUDY Sampling: The sample is based on the companies of Indian private corporate sector

and selected on the basis of availability of data. A company was excluded from the

sample if

The necessary financial data required for calculating the measures of dependent

and independent variable pertaining to all the years 2001-2002 to 2005-2006 is

not available.

The data relating to market price annual high and low of its shares are not

available for all the years under study (2001-2002 to 2005-2006)

The figure of the earnings per share is zero or negative in any year.

The data employed in the study relates to companies listed in Bombay Stock Exchange

and national stock exchange. A sample of 52 companies covering the following industries

have been finally selected for the purpose of the study.

INDUSTRY NO OF COMPANIES

Automobile 10

Cement 8

Chemicals 8

Computer & engineering 10

Textiles 8

Miscellaneous 8

TOTAL 52

SOURCES OF DATA

The data relating to the companies was taken from Capitaline database such as

earning per share, dividend payout ratio, total assets, debt-equity ratio and national stock

exchange official directory for getting the data relating to market price annual high and

low of shares.

PERIOD OF DATA

The study has been conducted for the period of past five years i.e. 2001-2002 to 2005-

2006 and the total sample were of fifty two companies divided into six different

categories as mentioned above.

STATISTICAL PROCEDURE To analyze the determinants of price earning ratio the following model has been used. PERFORMANCE OF INDEPENDENT VARIABLES:

To access the performance of independent variables, in terms of its impact on price

earnings ratio as well as on other variables, (a) coefficient of correlation is used and

(b) Regression equation.

Regression model: multiple regression technique has been adopted to examine the

determinants of P\E ratio, that is variability in earnings, corporate size, variability in

market price, Debt-equity ratio, dividend payout ratio and growth in market price under

three different classifications.

Under the first classification, the impact of above explanatory variables on P/E ratios has

been examined by the taking sample as whole. And at the second stage, the influence

above explanatory variables on P/E has been examined at industry level. This criterion

was adopted to examine whether there are differences in the determinants of P/E ratio in

different industries. And at the third stage, the influence of above explanatory variables

on P/E has been examined year wise.

Multiple Regression Equation For regression analysis, the linear relationship of the variables is been used:

P/E = a + b1X1 + b2X2 + b3X3 + b4X4 + b5X5 + b6X6 + E

Where

P\E = Price-earnings ratio

X1 = Corporate size

X2 = Debt equity ratio

X3 = Dividend payout ratio

X4 = Variability in Market price

X5 = Growth in Market price

X6 = Variability in Earning per share

E = error term

The vales of regression coefficient have been found with the help of t-values both 1% and

5% level.

VARIABLES USED IN DETERMINIG THE PRICE EARNING RATIO:

For the purpose of empirical analysis, price-earning ratio has been assumed to be

dependent variable while other factors have been taken as independent variable.

Price earnings ratio: The measure of price-earnings ratio adopted in the study is average

of annual high and low of market prices in the numerator and cross sectional year’s

earning per share in the denominator the reason for using each year average share price

has the advantage of smoothing out short term fluctuation in share prices and

consequently in P/E ratio. An incidental advantage of relying year average share price,

instead of prices at a particular point of time, was the economy of cost and efforts. And

they preferred to use cross section year’s earning per share in calculating P/E ratio.

P\E ratio = (PH+PL)/ EPS

Where

PH = is the highest market price

PL = is the lowest market price

EPS = earning per share

Corporate size: Size is expected to influence P/E ratio positively this variable is

measured in terms of total assets and is the arithmetic mean of the value of total assets for

two years proceeding and including cross section year. Variability in earnings: It is a measure of risk. Risk is expected to have negative

relationship with the P/E ratio of a share. The Variability in earnings per share is obtained

by calculating the standard deviation of the earning per share for five years preceding and

including cross section year.

Variability in market price: It was hypothesized that higher variation in the market price

should influence P/E ratio in positive way. This variable was obtained by calculating the

standard deviation of mean of annual high and low of market price of equity shares for

five years proceeding and including cross section year.

Debt equity ratio: Debt equity ratio is a measure of financial risk. It was expected that the

higher the leverage (debt equity), higher is the risk and lower is the price of equity share

in terms of its earnings. The variable was calculated by dividing equity by debt and the

arithmetic mean of two years preceding and including cross-section year.

Dividend payout ratio: It has expected to have positive impact on P/E ratio of a firm.

Dividend payout ratio is calculated as percentage of dividend paid to equity share holders

out of earnings available and is the average of dividend payout ratio of two years

preceding and including cross section years.

Growth rate in market price: Growth variable is expected to have positive influence on

P/E ratio of corporate equities.

LIMITATIONS OF THE STUDY

Time constraint and availability of the data.

study covers only five sectors.

Only fifty-two companies are under the study.

CHAPTER IV ANALYSIS OF DATA & INTERPRETATION

DATA ANALYSIS AND INTERPRETATION To determine the Price earning ratio the explanatory variables namely, Variability

in earnings, dividend payout ratio, corporate size, variability in market price, Debt equity

ratio, growth rate in market price, these variables are treated as independent variable and

price earning ratio is considered to be dependent variable.

For the determinants of price earning ratio the data has been collected for five different

sectors for five years from 2001-2002 to 2005-2006.

To analyze the determinants of price earning ratio the following model has been used.

Correlation analysis:

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

Regression model:

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

problem of multi-collinearity. 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 coefficient of multiple determination, R2, obtained from the equation indicate that

the variables was able to explain the dependent variable.

Sector wise Analysis Automobile industry (Table –1)

Correlations

1.000 .042 -.050 .286* .052 .296*. .770 .729 .044 .719 .037

50 50 50 50 50 50.042 1.000 -.032 -.299* -.072 -.353*.770 . .825 .035 .619 .012

50 50 50 50 50 50-.050 -.032 1.000 .208 -.407** .191.729 .825 . .147 .003 .185

50 50 50 50 50 50.286* -.299* .208 1.000 -.024 .839**.044 .035 .147 . .871 .000

50 50 50 50 50 50.052 -.072 -.407** -.024 1.000 -.049.719 .619 .003 .871 . .736

50 50 50 50 50 50.296* -.353* .191 .839** -.049 1.000.037 .012 .185 .000 .736 .

50 50 50 50 50 50

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

CORPSIZE

DERATIO

DPR

VINMP

GMP

VEPS

CORPSIZE DERATIO DPR VINMP GMP VEPS

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

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

Interpretation: There is high correlation between variability in earning per share with variability in

market price for automobile industry and correlation is significant at 1% level. But this

problem is overcome by using the linear multiple regression approach primarily to

minimize the problem of multicollinearity.

Regression analysis

Model Summary

.645a .416 .335 39.5731

.645b .416 .349 39.1316

.644c .415 .363 38.7216

.642d .412 .373 38.4046

.625e .390 .364 38.6785

.596f .355 .341 39.3722

Model123456

R R SquareAdjustedR Square

Std. Error ofthe Estimate

Predictors: (Constant), VEPS, GMP, CORPSIZE,DERATIO, DPR, VINMP

a.

Predictors: (Constant), VEPS, CORPSIZE, DERATIO,DPR, VINMP

b.

Predictors: (Constant), VEPS, DERATIO, DPR, VINMPc.

Predictors: (Constant), DERATIO, DPR, VINMPd.

Predictors: (Constant), DPR, VINMPe.

Predictors: (Constant), DPRf.

ANOVAg

47971.163 6 7995.194 5.105 .000a

67339.375 43 1566.032115310.5 49

47934.277 5 9586.855 6.261 .000b

67376.260 44 1531.279115310.5 49

47839.170 4 11959.792 7.977 .000c

67471.367 45 1499.364115310.5 49

47464.515 3 15821.505 10.727 .000d

67846.022 46 1474.914115310.5 49

44997.176 2 22498.588 15.039 .000e

70313.361 47 1496.029115310.5 49

40902.251 1 40902.251 26.386 .000f

74408.286 48 1550.173115310.5 49

RegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotal

Model1

2

3

4

5

6

Sum ofSquares df Mean Square F Sig.

Predictors: (Constant), VEPS, GMP, CORPSIZE, DERATIO, DPR, VINMPa.

Predictors: (Constant), VEPS, CORPSIZE, DERATIO, DPR, VINMPb.

Predictors: (Constant), VEPS, DERATIO, DPR, VINMPc.

Predictors: (Constant), DERATIO, DPR, VINMPd.

Predictors: (Constant), DPR, VINMPe.


Dependent Variable: PEg.

Coefficientsa

-13.534 15.535 -.871 .388-1.03E-03 .004 -.030 -.241 .811

-4.968 4.581 -.138 -1.085 .2841.125 .236 .626 4.766 .000-.122 .083 -.318 -1.469 .149

-8.914 58.083 -.020 -.153 .879.948 1.866 .113 .508 .614

-14.141 14.855 -.952 .346-1.05E-03 .004 -.031 -.249 .804

-4.904 4.511 -.136 -1.087 .2831.140 .214 .635 5.334 .000-.123 .082 -.321 -1.501 .141.967 1.841 .115 .525 .602

-14.726 14.515 -1.015 .316-5.096 4.398 -.141 -1.159 .2531.147 .210 .638 5.469 .000-.125 .081 -.325 -1.544 .130.901 1.803 .107 .500 .620

-10.450 11.631 -.898 .374-5.530 4.276 -.153 -1.293 .2021.151 .208 .641 5.537 .000

-9.20E-02 .047 -.240 -1.977 .054-16.776 10.628 -1.578 .121

1.142 .209 .636 5.459 .000-7.40E-02 .045 -.193 -1.654 .105

-22.990 10.120 -2.272 .0281.070 .208 .596 5.137 .000

(Constant)CORPSIZEDERATIODPRVINMPGMPVEPS(Constant)CORPSIZEDERATIODPRVINMPVEPS(Constant)DERATIODPRVINMPVEPS(Constant)DERATIODPRVINMP(Constant)DPRVINMP(Constant)DPR

Model1

2

3

4

5

6

B Std. Error

UnstandardizedCoefficients

Beta

Standardized

Coefficients

t Sig.

Dependent Variable: PEa.

Interpretation Dividend payout ratio is the most important determinate of price earning ratio for

automobile sector with T- value being 4.766, when backward model is used and when the

irrelevant variable is removed one after the other based on there significant level the T-

value of dividend payout ratio increases to 5.537. The coefficient of multiple

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

equation could explain 33.55% of the difference in P\E ratios. The computed F-value

5.105 is found to be significant at 5% level. The coefficient associated with corporate

size, debt-equity ratio, variability in market price and growth in market price are not

found to be significant as their T-values are negative. All this tends to confirm that

dividend payout ratio is the most important determinant of P/E ratio.

Cement industry (Table –2)

Correlations

1.000 -.328* -.099 .010 .018 .192. .039 .545 .949 .911 .236

40 40 40 40 40 40-.328* 1.000 .157 -.249 -.050 -.490**.039 . .333 .121 .761 .001

40 40 40 40 40 40-.099 .157 1.000 -.163 .256 -.131.545 .333 . .315 .111 .421

40 40 40 40 40 40.010 -.249 -.163 1.000 -.442** -.126.949 .121 .315 . .004 .439

40 40 40 40 40 40.018 -.050 .256 -.442** 1.000 .200.911 .761 .111 .004 . .216

40 40 40 40 40 40.192 -.490** -.131 -.126 .200 1.000.236 .001 .421 .439 .216 .

40 40 40 40 40 40


CS

DE

DPS

VMP

GMP

VEPS

CS DE DPS VMP GMP VEPS


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

Model Summary

.714a .510 .421 37.6593

.714b .510 .438 37.1040

.714c .509 .453 36.5836

.706d .499 .457 36.4506

.690e .476 .448 36.7606

Model12345



Predictors: (Constant), VEPS, VMP, CS, DPS, GMP, DEa.

Predictors: (Constant), VEPS, VMP, DPS, GMP, DEb.

Predictors: (Constant), VEPS, VMP, DPS, GMPc.

Predictors: (Constant), VEPS, DPS, GMPd.

Predictors: (Constant), DPS, GMPe.

ANOVAf

48672.058 6 8112.010 5.720 .000a

46801.468 33 1418.22695473.525 3948665.473 5 9733.095 7.070 .000b

46808.052 34 1376.70795473.525 3948631.036 4 12157.759 9.084 .000c

46842.489 35 1338.35795473.525 3947642.194 3 15880.731 11.953 .000d

47831.331 36 1328.64895473.525 3945473.926 2 22736.963 16.825 .000e

49999.599 37 1351.34195473.525 39

RegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotal

Model1

2

3

4

5


Predictors: (Constant), VEPS, VMP, CS, DPS, GMP, DEa.

Predictors: (Constant), VEPS, VMP, DPS, GMP, DEb.

Predictors: (Constant), VEPS, VMP, DPS, GMPc.

Predictors: (Constant), VEPS, DPS, GMPd.

Predictors: (Constant), DPS, GMPe.

Dependent Variable: PEf.

Coefficientsa

16.910 25.742 .657 .5162.343E-04 .003 .009 .068 .946

2.624 15.559 .027 .169 .867.337 .082 .531 4.103 .000

1.431E-02 .017 .123 .837 .409117.115 43.865 .382 2.670 .012

-.679 .721 -.139 -.942 .35317.577 23.455 .749 .459

2.329 14.724 .024 .158 .875.337 .081 .530 4.167 .000

1.421E-02 .017 .122 .846 .403117.035 43.202 .382 2.709 .010

-.679 .710 -.139 -.955 .34620.828 11.137 1.870 .070

.338 .079 .532 4.251 .0001.324E-02 .015 .114 .860 .396

116.143 42.232 .379 2.750 .009-.736 .601 -.151 -1.224 .229

23.412 10.685 2.191 .035.333 .079 .524 4.218 .000

101.649 38.579 .331 2.635 .012-.764 .598 -.157 -1.277 .210

13.994 7.799 1.794 .081.352 .078 .555 4.509 .000

89.648 37.736 .292 2.376 .023

(Constant)CSDEDPSVMPGMPVEPS(Constant)DEDPSVMPGMPVEPS(Constant)DPSVMPGMPVEPS(Constant)DPSGMPVEPS(Constant)DPSGMP

Model1

2

3

4

5

B Std. Error


Beta

Standardized

Coefficients

t Sig.


Interpretation Dividend payout ratio and growth in market price are the most important determinate of

price earning ratio for cement sector with T- value being 4.103 & 2.67 respectively, when

backward model is used and when the irrelevant variable is removed one after the other

based on there significant level the T-value of dividend payout ratio and growth in market

price increases to 4.509 & 2.376. The coefficient of multiple determination, (R2),

obtained from the equations indicate that variables included in the equation could explain

42.1% of the dependent variable P\E ratios. The computed F-value 5.72 is found to be

significant at 5% level. The coefficient associated with corporate size, debt-equity ratio,

variability in market price & variability in earning per share are not found to be

significant.

Chemical industry (Table –3)

Correlations

1.000 .269 -.230 -.065 .461** -.156. .093 .154 .689 .003 .335

40 40 40 40 40 40.269 1.000 -.100 -.336* .152 -.183.093 . .540 .034 .350 .258

40 40 40 40 40 40-.230 -.100 1.000 -.177 -.159 -.161.154 .540 . .274 .326 .321

40 40 40 40 40 40-.065 -.336* -.177 1.000 -.075 .930**.689 .034 .274 . .646 .000

40 40 40 40 40 40.461** .152 -.159 -.075 1.000 -.018.003 .350 .326 .646 . .910

40 40 40 40 40 40-.156 -.183 -.161 .930** -.018 1.000.335 .258 .321 .000 .910 .

40 40 40 40 40 40


CS

DE

DPR

VMP

GMP

VEPS

CS DE DPR VMP GMP VEPS



Interpretation There is high correlation between variability in earning per share with variability in

market price for automobile industry and correlation is significant at 1% level. But this

problem is overcome by using the linear multiple regression approach primarily to

minimize the problem of multicollinearity

Regression model

Model Summary

.642a .413 .306 29.8382

.642b .413 .326 29.3968

.640c .410 .342 29.0469

.636d .404 .354 28.7818

.625e .390 .357 28.7177

.607f .368 .352 28.8412

Model123456



Predictors: (Constant), VEPS, GMP, DPR, DE, CS, VMPa.

Predictors: (Constant), VEPS, GMP, DPR, DE, CSb.

Predictors: (Constant), VEPS, DPR, DE, CSc.

Predictors: (Constant), VEPS, DPR, CSd.

Predictors: (Constant), DPR, CSe.


ANOVAg

20653.959 6 3442.326 3.866 .005a

29380.492 33 890.31850034.450 3920652.633 5 4130.527 4.780 .002b

29381.818 34 864.17150034.450 3920504.260 4 5126.065 6.076 .001c

29530.190 35 843.72050034.450 3920212.370 3 6737.457 8.133 .000d

29822.081 36 828.39150034.450 3919520.228 2 9760.114 11.835 .000e

30514.223 37 824.70950034.450 3918425.576 1 18425.576 22.151 .000f

31608.874 38 831.81250034.450 39


Model1

2

3

4

5

6


Predictors: (Constant), VEPS, GMP, DPR, DE, CS, VMPa.

Predictors: (Constant), VEPS, GMP, DPR, DE, CSb.

Predictors: (Constant), VEPS, DPR, DE, CSc.

Predictors: (Constant), VEPS, DPR, CSd.

Predictors: (Constant), DPR, CSe.



Coefficientsa

-18.988 18.176 -1.045 .3044.408E-02 .046 .171 .950 .349

-6.091 11.973 -.086 -.509 .6141.036 .221 .663 4.700 .000

-4.17E-03 .108 -.018 -.039 .96921.054 56.672 .060 .372 .713

2.920E-02 .108 .122 .270 .789-19.264 16.458 -1.170 .250

4.324E-02 .040 .167 1.070 .292-5.832 9.775 -.083 -.597 .5551.037 .216 .664 4.794 .000

21.789 52.585 .062 .414 .6812.522E-02 .033 .106 .766 .449

-21.385 15.456 -1.384 .1755.040E-02 .036 .195 1.396 .171

-5.677 9.651 -.080 -.588 .5601.033 .214 .661 4.837 .000

2.598E-02 .032 .109 .800 .429-25.719 13.463 -1.910 .064

4.568E-02 .035 .177 1.310 .1981.042 .211 .667 4.938 .000

2.904E-02 .032 .122 .914 .367-21.613 12.663 -1.707 .096

3.926E-02 .034 .152 1.152 .2571.003 .206 .642 4.865 .000

-10.109 7.820 -1.293 .204.948 .201 .607 4.706 .000

(Constant)CSDEDPRVMPGMPVEPS(Constant)CSDEDPRGMPVEPS(Constant)CSDEDPRVEPS(Constant)CSDPRVEPS(Constant)CSDPR(Constant)DPR

Model1

2

3

4

5

6

B Std. Error


Beta

Standardized

Coefficients

t Sig.


Interpretation: Dividend payout ratio is the most important determinate of price earning ratio for

chemical sector with T- value being 4.7, when backward model is used and when the

irrelevant variable is removed one after the other based on there significant level the T-

value of dividend payout ratio increases to 4.938. The coefficient of multiple


equation could explain 30.6% of the dependent variable P\E ratio. The computed F-value

3.866 is found to be significant at 5% level. The coefficient associated with corporate

size, debt-equity ratio, variability in market price, growth in market price & variability in

earning per share are not found to be significant.

Computer and engineering industry (Table –4)

Correlations

1.000 .075 -.096 .201 .413** -.114. .602 .506 .162 .003 .430

50 50 50 50 50 50.075 1.000 .072 -.318* -.168 -.326*.602 . .621 .025 .244 .021

50 50 50 50 50 50-.096 .072 1.000 -.310* -.287* -.104.506 .621 . .028 .044 .471

50 50 50 50 50 50.201 -.318* -.310* 1.000 .479** .006.162 .025 .028 . .000 .969

50 50 50 50 50 50.413** -.168 -.287* .479** 1.000 -.041.003 .244 .044 .000 . .780

50 50 50 50 50 50-.114 -.326* -.104 .006 -.041 1.000.430 .021 .471 .969 .780 .

50 50 50 50 50 50


CS

DE

DPR

VMP

GMP

VEPS




Regression analysis

Model Summary

.426a .182 .068 24.9600

.426b .182 .089 24.6775

.422c .178 .105 24.4575

.412d .170 .116 24.3067

.392e .153 .117 24.2843

Model12345



Predictors: (Constant), VEPS, VMP, CS, DPR, DE, GMPa.

Predictors: (Constant), VEPS, VMP, CS, DE, GMPb.

Predictors: (Constant), VEPS, VMP, DE, GMPc.

Predictors: (Constant), VEPS, VMP, GMPd.

Predictors: (Constant), VEPS, GMPe.

ANOVAf

5950.675 6 991.779 1.592 .173a

26789.142 43 623.00332739.817 49

5944.675 5 1188.935 1.952 .105b

26795.142 44 608.98032739.817 49

5822.095 4 1455.524 2.433 .061c

26917.722 45 598.17232739.817 49

5562.360 3 1854.120 3.138 .034d

27177.457 46 590.81432739.817 49

5022.690 2 2511.345 4.258 .020e

27717.127 47 589.72632739.817 49


Model1

2

3

4

5


Predictors: (Constant), VEPS, VMP, CS, DPR, DE, GMPa.

Predictors: (Constant), VEPS, VMP, CS, DE, GMPb.

Predictors: (Constant), VEPS, VMP, DE, GMPc.

Predictors: (Constant), VEPS, VMP, GMPd.

Predictors: (Constant), VEPS, GMPe.


Coefficientsa

13.620 12.164 1.120 .269-7.47E-04 .002 -.068 -.441 .661

-5.476 9.401 -.091 -.583 .563-2.10E-02 .214 -.015 -.098 .9225.057E-03 .007 .116 .693 .492

45.093 28.187 .275 1.600 .117.703 .546 .191 1.288 .205

12.776 8.512 1.501 .141-7.51E-04 .002 -.068 -.449 .656

-5.400 9.263 -.090 -.583 .5635.213E-03 .007 .120 .740 .463

45.569 27.451 .278 1.660 .104.710 .535 .193 1.328 .191

11.532 7.976 1.446 .155-5.988 9.088 -.100 -.659 .513

5.064E-03 .007 .117 .726 .47240.980 25.247 .250 1.623 .112

.723 .529 .197 1.366 .1798.227 6.163 1.335 .188

6.356E-03 .007 .146 .956 .34441.617 25.073 .254 1.660 .104

.843 .494 .229 1.706 .09510.119 5.830 1.736 .08953.099 21.987 .324 2.415 .020

.856 .493 .233 1.735 .089

(Constant)CSDEDPRVMPGMPVEPS(Constant)CSDEVMPGMPVEPS(Constant)DEVMPGMPVEPS(Constant)VMPGMPVEPS(Constant)GMPVEPS

Model1

2

3

4

5

B Std. Error


Beta

Standardized

Coefficients

t Sig.


Interpretation: None of the variable is the determinate of price earning ratio for computer & engineering

sector, when backward model is used and when the irrelevant variable is removed one

after the other based on there significant level the T-value of growth in market price is

2.415, so has some influence on P/E ratio. The coefficient of multiple determination,

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

explain 6.8% of the dependent variable P\E ratio. The computed F-value 1.592 is found

to be significant at 5% level.

Textile industry (Table –5) Correlations

1.000 -.680** .059 .792** -.103 .627**. .000 .717 .000 .528 .000

40 40 40 40 40 40-.680** 1.000 -.078 -.524** .362* -.448**.000 . .631 .001 .022 .004

40 40 40 40 40 40.059 -.078 1.000 .036 .094 .038.717 .631 . .826 .566 .818

40 40 40 40 40 40.792** -.524** .036 1.000 -.118 .332*.000 .001 .826 . .467 .037

40 40 40 40 40 40-.103 .362* .094 -.118 1.000 -.126.528 .022 .566 .467 . .440

40 40 40 40 40 40.627** -.448** .038 .332* -.126 1.000.000 .004 .818 .037 .440 .

40 40 40 40 40 40


CS

DE

DPR

VMP

GMP

VEPS




Interpretation There is high correlation between variability in market price & corporate size and

variability in earning per share & corporate size for Textile industry and correlation is

significant at 1% level. But this problem is overcome by using the linear multiple

regression approach primarily to minimize the problem of multicollinearity.

Regression Model Summary

.343a .118 -.042 12.1706

.340b .115 -.015 12.0068

.338c .114 .013 11.8428

.316d .100 .025 11.7702

.287e .082 .033 11.7247

.204f .042 .017 11.8211

.000g .000 .000 11.9201

Model1234567



Predictors: (Constant), VEPS, DPR, GMP, VMP, DE, CSa.

Predictors: (Constant), VEPS, DPR, GMP, VMP, DEb.

Predictors: (Constant), VEPS, DPR, GMP, VMPc.

Predictors: (Constant), VEPS, GMP, VMPd.

Predictors: (Constant), GMP, VMPe.

Predictors: (Constant), GMPf.

Predictor: (constant)g.

ANOVAh

653.439 6 108.907 .735 .625a

4888.058 33 148.1235541.498 39

639.972 5 127.994 .888 .500b

4901.526 34 144.1635541.498 39

632.720 4 158.180 1.128 .359c

4908.778 35 140.2515541.498 39

554.151 3 184.717 1.333 .279d

4987.346 36 138.5375541.498 39

455.124 2 227.562 1.655 .205e

5086.374 37 137.4705541.498 39

231.435 1 231.435 1.656 .206f

5310.063 38 139.7385541.498 39

.000 0 .000 . .g

5541.498 39 142.0905541.498 39

RegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotal

Model1

2

3

4

5

6

7


Predictors: (Constant), VEPS, DPR, GMP, VMP, DE, CSa.

Predictors: (Constant), VEPS, DPR, GMP, VMP, DEb.

Predictors: (Constant), VEPS, DPR, GMP, VMPc.

Predictors: (Constant), VEPS, GMP, VMPd.

Predictors: (Constant), GMP, VMPe.

Predictors: (Constant), GMPf.

Predictor: (constant)g.

Dependent Variable: PEh.

Coefficientsa

13.697 8.665 1.581 .1232.079E-03 .007 .117 .302 .765

1.844 5.529 .082 .334 .741-3.73E-02 .053 -.115 -.697 .4916.426E-02 .089 .208 .722 .475

22.229 20.124 .202 1.105 .277-.330 .455 -.163 -.725 .474

14.837 7.692 1.929 .0621.092 4.867 .048 .224 .824

-3.75E-02 .053 -.116 -.710 .4838.404E-02 .059 .271 1.416 .166

23.613 19.331 .214 1.222 .230-.251 .368 -.124 -.683 .500

16.390 3.303 4.962 .000-3.87E-02 .052 -.120 -.748 .4597.812E-02 .052 .252 1.490 .145

25.149 17.830 .228 1.410 .167-.278 .343 -.138 -.812 .422

15.366 2.988 5.143 .0007.682E-02 .052 .248 1.475 .149

23.793 17.629 .216 1.350 .186-.288 .340 -.142 -.845 .403

14.054 2.543 5.526 .0006.266E-02 .049 .202 1.276 .210

25.169 17.486 .228 1.439 .15815.763 2.179 7.234 .00022.529 17.506 .204 1.287 .20614.322 1.885 7.599 .000

(Constant)CSDEDPRVMPGMPVEPS(Constant)DEDPRVMPGMPVEPS(Constant)DPRVMPGMPVEPS(Constant)VMPGMPVEPS(Constant)VMPGMP(Constant)GMP(Constant)

Model1

2

3

4

5

6

7

B Std. Error


Beta

Standardized

Coefficients

t Sig.


Interpretation None of the variable is the determinate of price earning ratio for Textile sector, The

coefficient of multiple determination, (R2), obtained from the equations indicate that

variables included in the equation could not explain dependent variable P\E ratio as it has

negative R2 which is –4.2%.

Miscellaneous Industry (Table –6)

Correlations

1.000 .906** .001 -.216 .118 -.283. .000 .994 .181 .469 .077

40 40 40 40 40 40.906** 1.000 -.238 -.379* .215 -.376*.000 . .140 .016 .183 .017

40 40 40 40 40 40.001 -.238 1.000 .259 -.673** -.092.994 .140 . .106 .000 .570

40 40 40 40 40 40-.216 -.379* .259 1.000 -.209 .240.181 .016 .106 . .195 .136

40 40 40 40 40 40.118 .215 -.673** -.209 1.000 .307.469 .183 .000 .195 . .054

40 40 40 40 40 40-.283 -.376* -.092 .240 .307 1.000.077 .017 .570 .136 .054 .

40 40 40 40 40 40


CS

DE

DPR

VMP

GMP

VEPS




Interpretation There is high correlation between debt-equity ratio for miscellaneous industry and

correlation is significant at 1% level. But this problem is overcome by using the linear

multiple regression approach primarily to minimize the problem of multicollinearity Regression analysis

Model Summary

.650a .422 .317 22.7956

.649b .422 .337 22.4689

.649c .421 .355 22.1565

.646d .418 .369 21.9072

.623e .388 .355 22.1592

Model12345



Predictors: (Constant), VEPS, DPR, CS, VMP, GMP, DEa.

Predictors: (Constant), VEPS, DPR, CS, GMP, DEb.

Predictors: (Constant), DPR, CS, GMP, DEc.

Predictors: (Constant), DPR, GMP, DEd.

Predictors: (Constant), DPR, GMPe.

ANOVAf

12531.276 6 2088.546 4.019 .004a

17148.174 33 519.64229679.450 3912514.552 5 2502.910 4.958 .002b

17164.898 34 504.85029679.450 3912497.548 4 3124.387 6.364 .001c

17181.902 35 490.91129679.450 3912402.084 3 4134.028 8.614 .000d

17277.367 36 479.92729679.450 3911511.259 2 5755.630 11.721 .000e

18168.191 37 491.03229679.450 39


Model1

2

3

4

5


Predictors: (Constant), VEPS, DPR, CS, VMP, GMP, DEa.

Predictors: (Constant), VEPS, DPR, CS, GMP, DEb.

Predictors: (Constant), DPR, CS, GMP, DEc.

Predictors: (Constant), DPR, GMP, DEd.



Coefficientsa

-13.995 12.773 -1.096 .2811.476E-03 .004 .165 .408 .686

-3.176 4.255 -.331 -.746 .461.787 .226 .752 3.487 .001

-2.25E-03 .013 -.027 -.179 .859120.421 38.413 .626 3.135 .004

.139 .688 .033 .202 .841-14.727 11.930 -1.234 .226

1.309E-03 .003 .147 .380 .706-2.932 3.974 -.306 -.738 .466

.789 .222 .754 3.551 .001121.363 37.507 .631 3.236 .003

.124 .673 .030 .184 .855-13.538 9.878 -1.370 .179

1.457E-03 .003 .163 .441 .662-3.211 3.621 -.335 -.887 .381

.788 .219 .753 3.596 .001123.776 34.639 .643 3.573 .001-14.430 9.560 -1.509 .140

-1.717 1.260 -.179 -1.362 .182.841 .181 .804 4.637 .000

127.585 33.167 .663 3.847 .000-17.980 9.304 -1.932 .061

.873 .182 .834 4.797 .000124.135 33.451 .645 3.711 .001

(Constant)CSDEDPRVMPGMPVEPS(Constant)CSDEDPRGMPVEPS(Constant)CSDEDPRGMP(Constant)DEDPRGMP(Constant)DPRGMP

Model1

2

3

4

5

B Std. Error


Beta

Standardized

Coefficients

t Sig.


Interpretation: Dividend payout ratio and growth in market price are the most important determinate of

price earning ratio for miscellaneous sector with T- value being 3.487 & 3.135

respectively, when backward model is used and when the irrelevant variable is removed

one after the other based on there significant level the T-value of dividend payout ratio

and growth in market price increases to 4.797 & 3.711. The coefficient of multiple


equation could explain 31.7% of the dependent variable P\E ratios. The computed F-

value 4.019 is found to be significant at 5% level. The coefficient associated with

corporate size, debt-equity ratio, variability in market price & variability in earning per

share are found to be insignificant.

Aggregate of sectors analysis (Table –7)

Correlations

1.000 .424** -.013 .167** .217** -.048. .000 .836 .007 .000 .442

260 260 260 260 260 260.424** 1.000 -.025 -.205** .041 -.053.000 . .689 .001 .508 .392260 260 260 260 260 260

-.013 -.025 1.000 -.049 -.052 -.050.836 .689 . .431 .404 .422260 260 260 260 260 260

.167** -.205** -.049 1.000 .121 .132*

.007 .001 .431 . .051 .034260 260 260 260 260 260

.217** .041 -.052 .121 1.000 .004

.000 .508 .404 .051 . .946260 260 260 260 260 260

-.048 -.053 -.050 .132* .004 1.000.442 .392 .422 .034 .946 .260 260 260 260 260 260


CORPSIZE

DERATIO

DPR

VMP

GMP

VEPS

CORPSIZE DERATIO DPR VMP GMP VEPS



Regression analysis

Model Summary

.484a .234 .216 31.6585

.484b .234 .219 31.5968

.482c .233 .221 31.5583

.480d .230 .221 31.5448

.474e .224 .218 31.6060

Model12345



Predictors: (Constant), VEPS, GMP, DERATIO, DPR,VMP, CORPSIZE

a.

Predictors: (Constant), GMP, DERATIO, DPR, VMP,CORPSIZE

b.

Predictors: (Constant), GMP, DERATIO, DPR, VMPc.

Predictors: (Constant), GMP, DERATIO, DPRd.

Predictors: (Constant), GMP, DPRe.

ANOVAf

77441.459 6 12906.910 12.878 .000a

253571.2 253 1002.258331012.6 259

77430.507 5 15486.101 15.512 .000b

253582.1 254 998.355331012.6 259

77051.580 4 19262.895 19.342 .000c

253961.0 255 995.926331012.6 259

76273.707 3 25424.569 25.550 .000d

254738.9 256 995.074331012.6 259

74285.163 2 37142.582 37.182 .000e

256727.5 257 998.940331012.6 259


Model1

2

3

4

5


Predictors: (Constant), VEPS, GMP, DERATIO, DPR, VMP, CORPSIZEa.

Predictors: (Constant), GMP, DERATIO, DPR, VMP, CORPSIZEb.

Predictors: (Constant), GMP, DERATIO, DPR, VMPc.

Predictors: (Constant), GMP, DERATIO, DPRd.



Coefficientsa

6.342 3.445 1.841 .067-7.03E-04 .001 -.039 -.607 .545

-1.272 1.677 -.048 -.758 .449.416 .050 .457 8.280 .000

5.688E-03 .006 .059 .990 .32340.752 14.596 .158 2.792 .006

3.474E-03 .033 .006 .105 .9176.392 3.405 1.877 .062

-7.11E-04 .001 -.040 -.616 .538-1.271 1.674 -.048 -.759 .448

.416 .050 .457 8.300 .0005.769E-03 .006 .060 1.015 .311

40.753 14.567 .158 2.798 .0066.064 3.359 1.805 .072

-1.758 1.474 -.067 -1.192 .234.415 .050 .456 8.298 .000

4.835E-03 .005 .050 .884 .37839.024 14.277 .152 2.733 .007

7.434 2.978 2.496 .013-2.035 1.440 -.078 -1.414 .159

.413 .050 .454 8.268 .00040.668 14.149 .158 2.874 .004

5.581 2.680 2.083 .038.415 .050 .456 8.286 .000

39.868 14.165 .155 2.814 .005

(Constant)CORPSIZEDERATIODPRVMPGMPVEPS(Constant)CORPSIZEDERATIODPRVMPGMP(Constant)DERATIODPRVMPGMP(Constant)DERATIODPRGMP(Constant)DPRGMP

Model1

2

3

4

5

B Std. Error


Beta

Standardized

Coefficients

t Sig.


Interpretation: When all the sectors for all the years is taken dividend payout ratio and growth in market

price are the most important determinate of price earning ratio with T- value being 8.28

& 7.262 respectively, The coefficient of multiple determination, (R2), obtained from the

equations indicate that variables included in the equation could explain 21.6% of the

dependent variable P\E ratios. The computed F-value 12.878 is found to be significant at

5% level. The coefficient associated with corporate size, debt-equity ratio, variability in

market price & variability in earning per share are found to be insignificant.

Year wise analysis Year 2002 (Table –8)

Correlations

1.000 .318* .045 .114 .207 .053. .021 .749 .420 .140 .709

52 52 52 52 52 52.318* 1.000 .029 -.203 .009 -.270.021 . .838 .148 .951 .053

52 52 52 52 52 52.045 .029 1.000 -.121 -.004 -.036.749 .838 . .392 .977 .800

52 52 52 52 52 52.114 -.203 -.121 1.000 .371** .224.420 .148 .392 . .007 .110

52 52 52 52 52 52.207 .009 -.004 .371** 1.000 -.079.140 .951 .977 .007 . .576

52 52 52 52 52 52.053 -.270 -.036 .224 -.079 1.000.709 .053 .800 .110 .576 .

52 52 52 52 52 52


CS

DE

DPR

VMP

GMP

VEPS




Regression analysis

Model Summary

.771a .594 .540 23.5869

.770b .594 .549 23.3362

.768c .590 .555 23.1786

.764d .584 .558 23.1052

.761e .578 .561 23.0252

.748f .560 .551 23.2903

Model123456



Predictors: (Constant), VEPS, DPR, CS, GMP, DE, VMPa.

Predictors: (Constant), DPR, CS, GMP, DE, VMPb.

Predictors: (Constant), DPR, CS, GMP, VMPc.

Predictors: (Constant), DPR, GMP, VMPd.

Predictors: (Constant), DPR, VMPe.


ANOVAg

36591.602 6 6098.600 10.962 .000a

25035.314 45 556.34061626.915 5136576.250 5 7315.250 13.433 .000b

25050.665 46 544.58061626.915 5136376.198 4 9094.050 16.927 .000c

25250.717 47 537.24961626.915 5136001.997 3 12000.666 22.479 .000d

25624.919 48 533.85261626.915 5135649.089 2 17824.544 33.621 .000e

25977.827 49 530.16061626.915 5134505.053 1 34505.053 63.611 .000f

27121.862 50 542.43761626.915 51


Model1

2

3

4

5

6


Predictors: (Constant), VEPS, DPR, CS, GMP, DE, VMPa.

Predictors: (Constant), DPR, CS, GMP, DE, VMPb.

Predictors: (Constant), DPR, CS, GMP, VMPc.


Predictors: (Constant), DPR, VMPe.



Coefficientsa

-.534 7.611 -.070 .944-2.04E-03 .002 -.098 -.939 .353

1.529 2.854 .058 .536 .595.538 .067 .767 7.988 .000

1.111E-02 .009 .130 1.195 .23821.241 24.644 .091 .862 .393

-9.10E-02 .548 -.017 -.166 .869-1.149 6.579 -.175 .862

-2.10E-03 .002 -.101 -.986 .3291.652 2.726 .062 .606 .547

.538 .067 .767 8.076 .0001.079E-02 .009 .127 1.200 .236

22.000 23.959 .095 .918 .363.198 6.151 .032 .974

-1.66E-03 .002 -.080 -.835 .408.537 .066 .766 8.121 .000

9.426E-03 .009 .111 1.090 .28122.478 23.785 .097 .945 .349-1.857 5.618 -.331 .742

.534 .066 .762 8.114 .0009.081E-03 .009 .107 1.055 .297

18.974 23.337 .082 .813 .420-4.658 4.424 -1.053 .298

.536 .066 .765 8.186 .0001.169E-02 .008 .137 1.469 .148

-1.610 3.952 -.407 .686.525 .066 .748 7.976 .000

(Constant)CSDEDPRVMPGMPVEPS(Constant)CSDEDPRVMPGMP(Constant)CSDPRVMPGMP(Constant)DPRVMPGMP(Constant)DPRVMP(Constant)DPR

Model1

2

3

4

5

6

B Std. Error


Beta

Standardized

Coefficients

t Sig.


Interpretation: Dividend payout ratio is the most important determinate of price earning ratio with T-

value being 7.988 in year 2002, when backward model is used and when the irrelevant

variable is removed one after the other based on there significant level the T-value of

dividend payout ratio increases to 8.186. The coefficient of multiple determination, (R2),


54% of the dependent variable P\E ratio. The computed F-value 10.962 is found to be


variability in market price, growth in market & variability in earning per share are found

to be insignificant.

Year 2003 (Table –9)

Correlations

1.000 .372** -.011 .166 .176 -.042. .007 .940 .239 .213 .769

52 52 52 52 52 52.372** 1.000 -.001 -.221 .038 -.186.007 . .996 .115 .789 .187

52 52 52 52 52 52-.011 -.001 1.000 -.087 .034 -.031.940 .996 . .538 .809 .827

52 52 52 52 52 52.166 -.221 -.087 1.000 .229 .098.239 .115 .538 . .103 .488

52 52 52 52 52 52.176 .038 .034 .229 1.000 -.007.213 .789 .809 .103 . .958

52 52 52 52 52 52-.042 -.186 -.031 .098 -.007 1.000.769 .187 .827 .488 .958 .

52 52 52 52 52 52


CS

DE

DPR

VMP

GMP

VEPS



Regression analysis

Model Summary

.383a .146 .033 47.8399

.383b .146 .054 47.3191

.382c .146 .073 46.8248

.377d .142 .089 46.4371

.370e .137 .101 46.1115

.352f .124 .106 45.9853

Model123456



Predictors: (Constant), VEPS, GMP, DPR, CS, VMP, DEa.

Predictors: (Constant), VEPS, GMP, DPR, VMP, DEb.

Predictors: (Constant), GMP, DPR, VMP, DEc.

Predictors: (Constant), GMP, DPR, DEd.

Predictors: (Constant), DPR, DEe.


ANOVAg

17677.189 6 2946.198 1.287 .282a

102989.7 45 2288.659120666.9 51

17668.275 5 3533.655 1.578 .185b

102998.6 46 2239.100120666.9 51

17616.504 4 4404.126 2.009 .109c

103050.4 47 2192.561120666.9 51

17159.254 3 5719.751 2.652 .059d

103507.6 48 2156.408120666.9 51

16479.568 2 8239.784 3.875 .027e

104187.3 49 2126.271120666.9 51

14934.427 1 14934.427 7.062 .011f

105732.4 50 2114.649120666.9 51


Model1

2

3

4

5

6


Predictors: (Constant), VEPS, GMP, DPR, CS, VMP, DEa.

Predictors: (Constant), VEPS, GMP, DPR, VMP, DEb.

Predictors: (Constant), GMP, DPR, VMP, DEc.

Predictors: (Constant), GMP, DPR, DEd.

Predictors: (Constant), DPR, DEe.



Coefficientsa

6.231 13.914 .448 .656-2.71E-04 .004 -.010 -.062 .951

-3.510 5.861 -.095 -.599 .552.295 .114 .360 2.599 .013

8.495E-03 .019 .069 .457 .650-35.791 56.870 -.090 -.629 .532

-.103 .691 -.021 -.149 .8826.087 13.572 .448 .656

-3.662 5.274 -.099 -.694 .491.295 .112 .360 2.627 .012

8.215E-03 .018 .067 .460 .648-36.198 55.880 -.091 -.648 .520

-.104 .684 -.021 -.152 .8805.044 11.590 .435 .665

-3.528 5.145 -.095 -.686 .496.296 .111 .361 2.660 .011

8.056E-03 .018 .065 .457 .650-36.080 55.291 -.091 -.653 .517

7.881 9.703 .812 .421-4.086 4.956 -.110 -.824 .414

.290 .110 .354 2.649 .011-29.825 53.125 -.075 -.561 .57710.010 8.869 1.129 .265-4.192 4.918 -.113 -.852 .398

.288 .109 .352 2.650 .0116.328 7.725 .819 .417

.288 .109 .352 2.658 .011

(Constant)CSDEDPRVMPGMPVEPS(Constant)DEDPRVMPGMPVEPS(Constant)DEDPRVMPGMP(Constant)DEDPRGMP(Constant)DEDPR(Constant)DPR

Model1

2

3

4

5

6

B Std. Error


Beta

Standardized

Coefficients

t Sig.







33% of the dependent variable P\E ratio. The computed F-value 1.287 is found to be





Correlations

1.000 .442** -.034 .179 .213 -.105. .001 .812 .203 .130 .458

52 52 52 52 52 52.442** 1.000 -.083 -.202 .114 -.154.001 . .560 .152 .421 .275

52 52 52 52 52 52-.034 -.083 1.000 .037 -.165 -.013.812 .560 . .795 .242 .930

52 52 52 52 52 52.179 -.202 .037 1.000 .018 .046.203 .152 .795 . .902 .749

52 52 52 52 52 52.213 .114 -.165 .018 1.000 .079.130 .421 .242 .902 . .577

52 52 52 52 52 52-.105 -.154 -.013 .046 .079 1.000.458 .275 .930 .749 .577 .

52 52 52 52 52 52


CS

DE

DPR

VMP

GMP

VEPS



Regression analysis

Model Summary

.719a .517 .452 8.4397

.719b .517 .464 8.3477

.719c .517 .476 8.2593

.710d .505 .474 8.2743

Model1234



Predictors: (Constant), VEPS, DPR, VMP, GMP, DE, CSa.

Predictors: (Constant), VEPS, DPR, VMP, GMP, DEb.

Predictors: (Constant), VEPS, DPR, VMP, GMPc.

Predictors: (Constant), DPR, VMP, GMPd.

ANOVAe

3428.499 6 571.417 8.022 .000a

3205.259 45 71.2286633.758 513428.273 5 685.655 9.839 .000b

3205.485 46 69.6846633.758 513427.619 4 856.905 12.562 .000c

3206.139 47 68.2166633.758 513347.457 3 1115.819 16.298 .000d

3286.301 48 68.4656633.758 51

RegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotal

Model1

2

3

4


Predictors: (Constant), VEPS, DPR, VMP, GMP, DE, CSa.

Predictors: (Constant), VEPS, DPR, VMP, GMP, DEb.

Predictors: (Constant), VEPS, DPR, VMP, GMPc.

Predictors: (Constant), DPR, VMP, GMPd.

Dependent Variable: PEe.

Coefficientsa

3.054 2.556 1.195 .238-4.28E-05 .001 -.007 -.056 .955-6.05E-02 1.051 -.007 -.058 .954

.271 .042 .679 6.449 .0005.667E-03 .003 .199 1.786 .081

19.910 10.666 .202 1.867 .068-.103 .097 -.112 -1.064 .2933.035 2.506 1.211 .232

-8.86E-02 .915 -.010 -.097 .923.271 .042 .679 6.520 .000

5.613E-03 .003 .197 1.877 .06719.799 10.366 .201 1.910 .062

-.103 .096 -.112 -1.074 .2882.923 2.202 1.327 .191

.271 .041 .680 6.607 .0005.670E-03 .003 .199 1.956 .056

19.676 10.179 .199 1.933 .059-.101 .093 -.110 -1.084 .2841.974 2.024 .975 .334

.271 .041 .680 6.597 .0005.531E-03 .003 .194 1.906 .063

18.824 10.168 .191 1.851 .070

(Constant)CSDEDPRVMPGMPVEPS(Constant)DEDPRVMPGMPVEPS(Constant)DPRVMPGMPVEPS(Constant)DPRVMPGMP

Model1

2

3

4

B Std. Error


Beta

Standardized

Coefficients

t Sig.







45.2% of the dependent variable P\E ratio. The computed F-value 8.022 is found to be





Correlations

1.000 .480** -.044 .199 .196 -.082. .000 .759 .157 .165 .562

52 52 52 52 52 52.480** 1.000 -.069 -.203 .100 -.055.000 . .626 .149 .479 .699

52 52 52 52 52 52-.044 -.069 1.000 .059 -.238 -.146.759 .626 . .677 .090 .301

52 52 52 52 52 52.199 -.203 .059 1.000 -.026 .145.157 .149 .677 . .856 .305

52 52 52 52 52 52.196 .100 -.238 -.026 1.000 .013.165 .479 .090 .856 . .925

52 52 52 52 52 52-.082 -.055 -.146 .145 .013 1.000.562 .699 .301 .305 .925 .

52 52 52 52 52 52


CS

DE

DPR

VMP

GMP

VEPS



Regression analysis

Model Summary

.556a .309 .217 23.9736

.555b .308 .233 23.7304

.554c .307 .248 23.4962

.547d .299 .255 23.3736

.540e .292 .263 23.2560

Model12345



Predictors: (Constant), VEPS, GMP, DE, VMP, DPR, CSa.

Predictors: (Constant), VEPS, GMP, VMP, DPR, CSb.

Predictors: (Constant), GMP, VMP, DPR, CSc.

Predictors: (Constant), GMP, DPR, CSd.


ANOVAf

11559.049 6 1926.508 3.352 .008a

25862.923 45 574.73237421.972 5111517.975 5 2303.595 4.091 .004b

25903.997 46 563.13037421.972 5111474.723 4 2868.681 5.196 .002c

25947.249 47 552.06937421.972 5111198.359 3 3732.786 6.833 .001d

26223.613 48 546.32537421.972 5110920.785 2 5460.392 10.096 .000e

26501.187 49 540.84137421.972 51


Model1

2

3

4

5


Predictors: (Constant), VEPS, GMP, DE, VMP, DPR, CSa.

Predictors: (Constant), VEPS, GMP, VMP, DPR, CSb.

Predictors: (Constant), GMP, VMP, DPR, CSc.

Predictors: (Constant), GMP, DPR, CSd.



Coefficientsa

-4.756 8.012 -.594 .556-1.03E-03 .002 -.080 -.518 .607

-.774 2.896 -.040 -.267 .790.717 .169 .549 4.238 .000

4.559E-03 .009 .069 .502 .61859.721 32.564 .239 1.834 .073

1.048E-02 .037 .036 .284 .777-5.274 7.696 -.685 .497

-1.32E-03 .002 -.102 -.792 .432.719 .167 .550 4.292 .000

5.400E-03 .008 .082 .640 .52559.942 32.223 .240 1.860 .069

1.010E-02 .036 .035 .277 .783-4.811 7.438 -.647 .521

-1.38E-03 .002 -.106 -.841 .405.711 .164 .544 4.345 .000

5.817E-03 .008 .088 .708 .48359.980 31.904 .240 1.880 .066-3.926 7.294 -.538 .593

-1.14E-03 .002 -.088 -.713 .479.718 .163 .549 4.414 .000

58.789 31.694 .235 1.855 .070-5.359 6.976 -.768 .446

.717 .162 .549 4.434 .00054.478 30.955 .218 1.760 .085

(Constant)CSDEDPRVMPGMPVEPS(Constant)CSDPRVMPGMPVEPS(Constant)CSDPRVMPGMP(Constant)CSDPRGMP(Constant)DPRGMP

Model1

2

3

4

5

B Std. Error


Beta

Standardized

Coefficients

t Sig.











Year 2006 (Table –12) Correlations

1.000 .477** -.049 .292* .208 -.071. .000 .728 .036 .139 .615

52 52 52 52 52 52.477** 1.000 -.093 -.250 -.010 -.054.000 . .510 .074 .942 .701

52 52 52 52 52 52-.049 -.093 1.000 -.018 -.143 -.105.728 .510 . .900 .311 .458

52 52 52 52 52 52.292* -.250 -.018 1.000 .081 .433**.036 .074 .900 . .568 .001

52 52 52 52 52 52.208 -.010 -.143 .081 1.000 -.181.139 .942 .311 .568 . .198

52 52 52 52 52 52-.071 -.054 -.105 .433** -.181 1.000.615 .701 .458 .001 .198 .

52 52 52 52 52 52


CS

DE

DPR

VMP

GMP

VEPS




Regression analysis

Model Summary

.573a .328 .238 37.7141

.573b .328 .255 37.3021

.572c .327 .270 36.9173

.552d .304 .261 37.1502

.540e .291 .262 37.1214

Model12345



Predictors: (Constant), VEPS, DE, DPR, GMP, VMP, CSa.

Predictors: (Constant), DE, DPR, GMP, VMP, CSb.

Predictors: (Constant), DPR, GMP, VMP, CSc.



ANOVAf

31244.485 6 5207.414 3.661 .005a

64005.790 45 1422.35195250.275 5131243.831 5 6248.766 4.491 .002b

64006.445 46 1391.44495250.275 5131194.426 4 7798.607 5.722 .001c

64055.849 47 1362.89095250.275 5129003.617 3 9667.872 7.005 .001d

66246.658 48 1380.13995250.275 5127728.527 2 13864.264 10.061 .000e

67521.748 49 1377.99595250.275 51


Model1

2

3

4

5


Predictors: (Constant), VEPS, DE, DPR, GMP, VMP, CSa.

Predictors: (Constant), DE, DPR, GMP, VMP, CSb.

Predictors: (Constant), DPR, GMP, VMP, CSc.




Coefficientsa

-8.747 12.163 -.719 .476-3.11E-03 .003 -.181 -1.061 .294

.881 4.724 .030 .187 .853

.816 .223 .460 3.667 .0013.729E-02 .036 .175 1.033 .307

147.231 50.548 .379 2.913 .006-1.45E-03 .068 -.003 -.021 .983

-8.760 12.015 -.729 .470-3.09E-03 .003 -.180 -1.113 .271

.859 4.559 .030 .188 .851

.817 .218 .460 3.744 .0013.688E-02 .030 .173 1.213 .231

147.446 49.001 .379 3.009 .004-7.849 10.887 -.721 .475

-2.77E-03 .002 -.162 -1.268 .211.812 .215 .458 3.785 .000

3.421E-02 .027 .161 1.285 .205146.092 47.971 .376 3.045 .004-10.504 10.752 -.977 .333

.817 .216 .461 3.787 .0002.470E-02 .026 .116 .961 .341

134.611 47.406 .346 2.840 .007-6.604 9.949 -.664 .510

.816 .216 .460 3.784 .000138.225 47.220 .356 2.927 .005

(Constant)CSDEDPRVMPGMPVEPS(Constant)CSDEDPRVMPGMP(Constant)CSDPRVMPGMP(Constant)DPRVMPGMP(Constant)DPRGMP

Model1

2

3

4

5

B Std. Error


Beta

Standardized

Coefficients

t Sig.


Interpretation: Dividend payout ratio & growth in market price are the most important determinate of

price earning ratio with T- value being 3.667 & 2.913 respectively in year 2006, when

backward model is used and when the irrelevant variable is removed one after the other

based on there significant level the T-value of dividend payout ratio & growth in market

price increases to 3.785 & 3.045. The coefficient of multiple determination, (R2),




variability in market price & variability in earning per share are found to be insignificant.

CHAPTER V SUMMARY

& CONCLUSION

Summary of year wise regression results

Y indicates variable has influence on P/E ratio at 5% significance, N indicates variable has no influence on P/E ratio. CS= corporate size, DE = debt-equity ratio, DPR=dividend payout ratio, VMP=variability in market price, GMP= Growth in market price, VEPS= variability in earning per share

Interpretation It is clearly shows from the regression results from various years that Dividend payout

ratio is the most important of Price Earning ratio among the different variables. And the

coefficient of respective years is positively significance at 5% level. And for the

aggregate of all the industry for the years (2001-2002 to 2005-2006) even growth in

market price has influence on P/E ratio. And their respective coefficients are positively

significance at 5% level.

Summary of sector wise regression results

Y indicates variable has influence on P/E ratio at 5% significance, N indicates variable has no influence on P/E ratio. Y* indicates that variable has influence on P/E ratio when backward model is used and irrelevant variables are removed. CS= corporate size, DE = debt-equity ratio, DPR=dividend payout ratio, VMP=variability in market price, GMP= Growth in market price, VEPS= variability in earning per share

Interpretation

It is clearly shows from the regression results from various sectors that dividend payout

ratio and growth in market price has influence on P/E ratio. And the coefficient of

respective years is positively significance at 5% level. For textile industry none of the

variables taking in the study is the determinant of price earning ratio.

Year CS DE DPR VMP GMP VEPS ADJ R2 F- Value 2002 N N Y N N N .54 10.962 2003 N N Y N N N .033 1.287 2004 N N Y N N N .452 8.022 2005 N N Y N N N .217 3.352 2006 N N Y N N N .238 3.661 Aggregate N N Y N Y N .216 12.878

Sector CS DE DPR VMP GMP VEPS ADJ R2 F- Value Automobile N N Y N N N .335 5.105 Cement N N Y N Y N .421 5.72 Chemical N N Y N N N .306 3.866 Comp&Eng N N N N Y* N .068 1.592 Textile N N N N N N -.042 .735 miscellenous N N Y N Y N .317 4.019

Conclusion

The empirical study has attempted to examine the varying importance of different factors

influencing the P/E ratio of equity shares. The variables being corporate size, Debt-equity

ratio, dividend payout ratio, variability in market price, growth in market price,

variability in earning per share. The relationship between these independent variables and

dependent variable being P/E ratio of 52 companies is studied over five years ranging

from 2001-2002 to 2005-2006.

In the context of Indian stock market, the result revealed that dividend payout ratio is the

important determinant of price earning ratio, which shows that the companies should

adopt a liberal dividend policy to activate the primary as well 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 but practically growing

companies and companies which have high potential future growth rate they may not

give high dividend and they reserved for future expansion. The corporate size, debt

equity ratio, variability in earning per share, variability in market price being insignificant

variables find no evidence to support theoretical work.

BIBILOGRAPHY

JOURNALS:

Tuli Nishi and Mittal R K, (2001), “Determinants of price Earning Ratio”, finance India, vol.15, No. 4, pp. 1235-1250.

Keith Anderson and Chris Brooks, (2006) “The long term Price Earnings

Ratio”,Journal of Business Finance & Accounting, 33(7) & (8), 1063-

1086,sep/oct 2006.

Sanjay Sehgal, Balakrishnan & Soumik Basu (2001), “Forecasting P/E Ratios

For the Indian Capital Market”, Decision, Vol.28 No.1, Jan-June, 2001

pages-131-44.

S.Basu(1977), “Investment performance of common stocks in relation to

their Price Earning Ratios: a test of the efficient market hypothesis”, the

journal of Finance, Vol XXXII, No.3 June, 1977 pages-663-81. Lianzan Xu & Francis Cai , “Price-To-Earnings Multiples & Mergers &

Acquisitions” , Competitiveness Review Vol. 16. No. 1, 2006 pages-32-37.

SOFTWARE USED: Prowess software

Capitaline

SPSS 10

WEBSITES: www.nseindia.com

www.google.com

www.yahoo.com/finance

www.equitymaster.com

www.capitaline.com

ANNEXURE LIST OF COMPANIES UNDER THE STUDY

Bajaj Auto Ltd. Kakatiya Cements Sugar & Industries Ltd

Hero Honda Motors Ltd. Shree cements Ltd.

Mahindra & Mahindra Ltd. Deccan cements ltd

Ashokleyland ltd ACC Ltd.

Punjab Tractors Ltd. Grasim Industries Ltd.

Maharashtra Scooters Ltd. Ramco industries ltd.

Eicher Motors Ltd OCL India Ltd (cements)

Apollo Tyres Ltd. Dalmia cement ltd

VST Tillers Tractors Ltd Deepak nitrate ltd

Ucal Fuel Systems Ltd Ciba speciality chemicals

Satyam Computer Services Ltd. Indian humpe pipeline company ltd

Siemens Ltd. India Glycols Ltd

Wipro Ltd. Tanfac Industries Ltd

ABB Ltd. Pidilite Industries Ltd

Alfalaval Ltd. Hindustan Sanitaryware & Industries Ltd

Bharat Heavy Electricals Ltd. Aarti Industries Ltd.

Elgi equipments ltd Raymond Ltd

Kirloskar oil engines ltd Patspin India Ltd

Larsen & Toubro Ltd. Eurotex Industries and Exports Ltd

Reliance indus infrastructure Ltd BSL Ltd

Britannia ltd Garden Silk Mills Ltd

Bluestar ltd Siyaram Silk Mills Ltd

Hindlever ltd Nahar Spinning Mills Ltd

LIC housing finance ltd Abirlanuvo Ltd

Ranbaxy Ltd Cipla Ltd

Sunpharma Ltd Nicholas piramal ltd

determinants of price earnings ration in the indian corporate sector

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