Post on 27-Mar-2015
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.
Predictors: (Constant), DPRf.
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
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
CS
DE
DPS
VMP
GMP
VEPS
CS DE DPS VMP GMP VEPS
Correlation is significant at the 0.05 level (2-tailed).*.
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
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
Sum ofSquares df Mean Square F Sig.
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
UnstandardizedCoefficients
Beta
Standardized
Coefficients
t Sig.
Dependent Variable: PEa.
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
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
CS
DE
DPR
VMP
GMP
VEPS
CS DE DPR VMP GMP VEPS
Correlation is significant at the 0.01 level (2-tailed).**.
Correlation is significant at the 0.05 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 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
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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.
Predictors: (Constant), DPRf.
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
RegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotal
Model1
2
3
4
5
6
Sum ofSquares df Mean Square F Sig.
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.
Predictors: (Constant), DPRf.
Dependent Variable: PEg.
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
UnstandardizedCoefficients
Beta
Standardized
Coefficients
t Sig.
Dependent Variable: PEa.
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
determination, (R2), obtained from the equations indicate that variables included in the
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
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
CS
DE
DPR
VMP
GMP
VEPS
CS DE DPR VMP GMP VEPS
Correlation is significant at the 0.01 level (2-tailed).**.
Correlation is significant at the 0.05 level (2-tailed).*.
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
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotal
Model1
2
3
4
5
Sum ofSquares df Mean Square F Sig.
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.
Dependent Variable: PEf.
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
UnstandardizedCoefficients
Beta
Standardized
Coefficients
t Sig.
Dependent Variable: PEa.
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
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
CS
DE
DPR
VMP
GMP
VEPS
CS DE DPR VMP GMP VEPS
Correlation is significant at the 0.01 level (2-tailed).**.
Correlation is significant at the 0.05 level (2-tailed).*.
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
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
Sum ofSquares df Mean Square F Sig.
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
UnstandardizedCoefficients
Beta
Standardized
Coefficients
t Sig.
Dependent Variable: PEa.
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
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
CS
DE
DPR
VMP
GMP
VEPS
CS DE DPR VMP GMP VEPS
Correlation is significant at the 0.01 level (2-tailed).**.
Correlation is significant at the 0.05 level (2-tailed).*.
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
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotal
Model1
2
3
4
5
Sum ofSquares df Mean Square F Sig.
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.
Dependent Variable: PEf.
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
UnstandardizedCoefficients
Beta
Standardized
Coefficients
t Sig.
Dependent Variable: PEa.
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
determination, (R2), obtained from the equations indicate that variables included in the
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
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
VMP
GMP
VEPS
CORPSIZE DERATIO DPR VMP GMP VEPS
Correlation is significant at the 0.01 level (2-tailed).**.
Correlation is significant at the 0.05 level (2-tailed).*.
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
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
RegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotal
Model1
2
3
4
5
Sum ofSquares df Mean Square F Sig.
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.
Predictors: (Constant), GMP, DPRe.
Dependent Variable: PEf.
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
UnstandardizedCoefficients
Beta
Standardized
Coefficients
t Sig.
Dependent Variable: PEa.
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
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
CS
DE
DPR
VMP
GMP
VEPS
CS DE DPR VMP GMP VEPS
Correlation is significant at the 0.05 level (2-tailed).*.
Correlation is significant at the 0.01 level (2-tailed).**.
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
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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.
Predictors: (Constant), DPRf.
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
RegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotal
Model1
2
3
4
5
6
Sum ofSquares df Mean Square F Sig.
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.
Predictors: (Constant), DPRf.
Dependent Variable: PEg.
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
UnstandardizedCoefficients
Beta
Standardized
Coefficients
t Sig.
Dependent Variable: PEa.
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),
obtained from the equations indicate that variables included in the equation could explain
54% of the dependent variable P\E ratio. The computed F-value 10.962 is found to be
significant at 5% level. The coefficient associated with corporate size, debt-equity ratio,
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
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
CS
DE
DPR
VMP
GMP
VEPS
CS DE DPR VMP GMP VEPS
Correlation is significant at the 0.01 level (2-tailed).**.
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
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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.
Predictors: (Constant), DPRf.
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
RegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotal
Model1
2
3
4
5
6
Sum ofSquares df Mean Square F Sig.
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.
Predictors: (Constant), DPRf.
Dependent Variable: PEg.
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
UnstandardizedCoefficients
Beta
Standardized
Coefficients
t Sig.
Dependent Variable: PEa.
Interpretation: Dividend payout ratio is the most important determinate of price earning ratio with T-
value being 2.599 in year 2003, 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 2.66. The coefficient of multiple determination, (R2),
obtained from the equations indicate that variables included in the equation could explain
33% of the dependent variable P\E ratio. The computed F-value 1.287 is found to be
significant at 5% level. The coefficient associated with corporate size, debt-equity ratio,
variability in market price, growth in market & variability in earning per share are found
to be insignificant.
Year 2004 (Table –10)
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
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
CS
DE
DPR
VMP
GMP
VEPS
CS DE DPR VMP GMP VEPS
Correlation is significant at the 0.01 level (2-tailed).**.
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
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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
Sum ofSquares df Mean Square F Sig.
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
UnstandardizedCoefficients
Beta
Standardized
Coefficients
t Sig.
Dependent Variable: PEa.
Interpretation: Dividend payout ratio is the most important determinate of price earning ratio with T-
value being 6.449 in year 2004, 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 6.607. The coefficient of multiple determination, (R2),
obtained from the equations indicate that variables included in the equation could explain
45.2% of the dependent variable P\E ratio. The computed F-value 8.022 is found to be
significant at 5% level. The coefficient associated with corporate size, debt-equity ratio,
variability in market price, growth in market & variability in earning per share are found
to be insignificant.
Year 2005 (Table –11)
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
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
CS
DE
DPR
VMP
GMP
VEPS
CS DE DPR VMP GMP VEPS
Correlation is significant at the 0.01 level (2-tailed).**.
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
R R SquareAdjustedR Square
Std. Error ofthe Estimate
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.
Predictors: (Constant), GMP, DPRe.
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
RegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotal
Model1
2
3
4
5
Sum ofSquares df Mean Square F Sig.
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.
Predictors: (Constant), GMP, DPRe.
Dependent Variable: PEf.
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
UnstandardizedCoefficients
Beta
Standardized
Coefficients
t Sig.
Dependent Variable: PEa.
Interpretation: Dividend payout ratio is the most important determinate of price earning ratio with T-
value being 4.238 in year 2005, 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 4.434. The coefficient of multiple determination, (R2),
obtained from the equations indicate that variables included in the equation could explain
21.7% of the dependent variable P\E ratio. The computed F-value 3.352 is found to be
significant at 5% level. The coefficient associated with corporate size, debt-equity ratio,
variability in market price, growth in market & variability in earning per share are found
to be insignificant.
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
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
CS
DE
DPR
VMP
GMP
VEPS
CS DE DPR VMP GMP VEPS
Correlation is significant at the 0.01 level (2-tailed).**.
Correlation is significant at the 0.05 level (2-tailed).*.
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
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), VEPS, DE, DPR, GMP, VMP, CSa.
Predictors: (Constant), DE, DPR, GMP, VMP, CSb.
Predictors: (Constant), DPR, GMP, VMP, CSc.
Predictors: (Constant), DPR, GMP, VMPd.
Predictors: (Constant), DPR, GMPe.
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
RegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotalRegressionResidualTotal
Model1
2
3
4
5
Sum ofSquares df Mean Square F Sig.
Predictors: (Constant), VEPS, DE, DPR, GMP, VMP, CSa.
Predictors: (Constant), DE, DPR, GMP, VMP, CSb.
Predictors: (Constant), DPR, GMP, VMP, CSc.
Predictors: (Constant), DPR, GMP, VMPd.
Predictors: (Constant), DPR, GMPe.
Dependent Variable: PEf.
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
UnstandardizedCoefficients
Beta
Standardized
Coefficients
t Sig.
Dependent Variable: PEa.
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),
obtained from the equations indicate that variables included in the equation could explain
23.8% of the dependent variable P\E ratio. The computed F-value 3.661 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.
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