share price n company fundamentals

8/8/2019 Share Price n Company Fundamentals

1/58

1 | P a g e 9 - N o v - 1 0

SOCIAL RESEARCH METHODS

SHARE PRICE AND EQUITY RETURNS ININDIA

BASED ON PRIMARY DATA

Prepared for

Prof. Prahlad MishraProfessor,

Xavier Institute of Management

Bhubaneswar

By:

Rabindra Kumar Jena(U109079)

Santosh Patnaik(U109087)Sarang Ashok Kulkarni(U109088)

Sandeep Swain(U109086)

Venkat Ramana(U109099)

Vikas Gautam(U109100)

Jagrat Kumar Bhanja(U109171)

Anubhav Gupta(U109175)


2/58

2 | P a g e 9 - N o v - 1 0

Table of Contents

Acknowledgement................................ ................................ ........................ Error!Bookmarknotdefined.

Abstract................................ ................................ ................................ ........... Error!Bookmarknotdefined.

Introduction................................ ................................ ................................ ................................ ............................... 5

Research Objective................................ ................................ ................................ ................................ ................... 9

Literature Review................................ ................................ ................................ ................................ ................... 10

Research Hypothesis................................ ................................ ................................ ................................ ............. 19

Methodology ................................ ................................ ................................ ................................ ............................ 20

Data Analysis and Findings................................ ................................ ................................ ................................ .25

Univariate Tools................................ ................................ ................................ ................................ ...................... 27

Bivariate Tools................................ ................................ ................................ ................................ ........................ 29

Multivariate Tools................................ ................................ ................................ ................................ .................. 32

FactorAnalysis................................ ................................ ................................ ................................ ........................ 41

ClusterAnalysis................................ ................................ ................................ ................................ ....................... 46

Conclusion................................ ................................ ................................ ................................ ................................ 47

Appendix................................ ................................ ................................ ................................ ................................ ........

PartA................................ ................................ ................................ ................................ ................................ .49

PartB................................ ................................ ................................ ................................ ................................ .52


3/58

3 | P a g e 9 - N o v - 1 0

Acknowledgement

We are greatly indebted to all those who helped us directly and indirectly in our endeavor to

carry out the research.

We would like to express our deepest gratitude to our esteemed professor, Mr. P. Mishra for

his invaluable support, guidance, motivation and encouragement throughout the period. His

incisive inputs, his concern and assistance during the whole duration have been extremely

helpful. It was his enthusiastic and progressive outlook towards the project which inspired us

throughout our work during this period.

January 07, 2010


4/58

4 | P a g e 9 - N o v - 1 0

Abstract

The importance of investing cannot be overstated. Something worth one rupee one day

could cost significantly more the next day. This is because history shows us that things

always cost more over time. It is obvious that doing nothing with money will cause it to lose

its buying power. It is therefore important to invest to make an individuals money grow

rather than shrink.

Historically it has been observed that equity generates highest return among all the asset

classes. And in National stock exchange itself 1737 companies are listed. Unless the right

companies are selected an investor may find that his investment loses its value instead of

generating superior returns. Hence there is a heightened need to stu dy the fundamentals of

the company, its profit generating ability and ability to pay off all its debts before taking a

call on investing in the companies.

While there have been many studies abroad trying to link equity returns to fundamentals of

the company few such researches have been carried out in India. This project tries to find

out relationship between share price of a company and the various fundamentals. In thelong run, not only companys profit generating abilities are important but also the abi lity to

repay its long term debts is crucial.

For this purpose companies have been chosen such that the entire economy could be

represented. Hence broad-based NIFTY 50 has been taken as the reference and in NIFTY

companies from all the industries have been chosen. Various tools of regression have been

used for the process- Bivariate regression, multivariate regression and factor analysis. Time

trend also has been used to see if any pattern exists in the movement of the share price.


5/58

5 | P a g e 9 - N o v - 1 0

INTRODUCTION

In an emerging stockmarketlike India, investmentanalysts and marketparticipants are

continuously in search for investment strategies that can outperform the market. In

general, Analysis of Investment securitiescan be broadly classified into two categories

as,

y Fundamental analysis and

y Technical analysis.

Whatis technicalanalysis?

A method of evaluating securities by analyzing statistics generated by market activity,

such as past prices and volume. Technical analysts do not attempt to measure asecurity'sintrinsic value, butinstead use charts and othertoolsto identify patternsthat

cansuggestfuture activity. Technical analystsbelieve thatthe historical performance of

stocks and markets are indications of future performance. In a shopping mall, a

fundamental analystwould go to eachstore, study the productthatwasbeing sold, and

then decide whether to buy it or not. By contrast, a technical analyst would sit on a

benchinthe mall and watch people go into the stores. Disregarding the intrinsic value

of the products in the store, the technical analyst's decision would be based on the

patterns or activity of people going into eachstore.

WhatDoes Fundamental Analysis Mean?

A method of evaluating a security that entails attempting to measure its intrinsic value

by examining related economic, financial and other qualitative and quantitative

actors. Fundamental analysts attempt to study everything that can affect the security's

value, including macroeconomic factors (like the overall economy and industry

conditions) and company-specific factors (like financial condition and management).

The end goal of performing fundamental analysisis to produce a value that an

investorcancompare withthe security'scurrentprice, withthe aim of figuring outwhat

sort of position to take with that security (underpriced = buy, overpriced = sell or

short).This method of security analysis is considered to be the opposite of technical

analysis. Fundamental analysis is aboutusing real data to evaluate a security's value.

Although most analysts use fundamental analysis to value stocks, this method of

valuation can be used forjust about any type of security.

For example, an investor can perform fundamental analysis on a bond's value by

looking ateconomic factors, such asinterestrates and the overall state ofthe economy,

and information aboutthe bond issuer, such as potential changes incredit ratings. For


6/58

6 | P a g e 9 - N o v - 1 0

assessing stocks, this method usesrevenues, earnings, future growth, return on equity,

profit margins and other data to determine a company's underlying value and potential

for future growth. In terms of stocks, fundamental analysis focuses on the financial

statements of the company being evaluated. One of the most famous and successful

fundamental analysts is the Oracle of Omaha, Warren Buffett, who is well known for

successfully employing fundamental analysisto picksecurities. His abilitieshave turnedhim into a billionaire.

Fundamental Vs Technical Analysis

Fundamental Analysis involves making investment decisions based on examination of

the economy, an industry, and the target company that lead to an estimate ofintrinsic

value for aninvestmentorsecurity, whichiscompared to the prevailing marketvalue of

the security. Incontrast, technical analysisinvolves examination ofthe pastmarketdata

such as security prices and trading volume, which leads to the estimate future price

trends, and therefore investment decision. In the market, major investment firms

employ both fundamental and technical analysts to provide investment analysis and

advice. And most investment managers complement technical analysis with

fundamental analysisto make a well informed investmentdecision. Therefore, whether

aninvestoris a fan of fundamental analysis ortechnical analysis, itis vital to understand

the basic philosophies and reasoning backing technical approaches. Unlike fundamental

analysts, technical analysts do notspend theirtime in attemptto estimate the intrinsic

value of a stockto see ifits overvalued or undervalued relative to the marketprice, but

would ratherspend their time on market based activity like securitys past prices and

trading volumes.

Techniciansbase their analysis primarily on fourbasic assumptions:

The market value of a security, good, or service is determined solely by the

interaction ofits demand and supply.

The demand and supply ofthe security are governed by many rational and irrational

factors like opinions, moods, and guesses;including economic variables.

The prices of securities and the overall value of the market move in trends, which

persists for a considerable period oftime.

The actual price shiftsinthe demand and supply canbe observed inthe marketplace

sooner or later.


7/58

7 | P a g e 9 - N o v - 1 0

Capital Asset Pricing Model- Tool for technical analysis

CAPM is one of the widely used tools for conducting technical analysis.

Itis a model thatdescribesthe relationship betweenrisk and expected return and that

is used inthe pricing ofrisky securities.

The general idea behind CAPM is that investorsneed to be compensated intwo ways:

time value of money and risk. The time value of money is represented by the risk-free

(rf) rate in the formula and compensates the investors for placing money in any

investment over a period of time. The other half of the formula represents risk and

calculatesthe amount ofcompensationthe investorneeds fortaking on additional risk.

Thisiscalculated by taking a riskmeasure (beta)thatcomparesthe returns ofthe asset

to the marketover a period oftime and to the marketpremium (Rm-rf).

The CAPM saysthatthe expected return of a security or a portfolio equalsthe rate on a

risk-free security plus a riskpremium. Ifthis expected return doesnotmeetorbeatthe

required return, then the investment should not be undertaken. The security market

line plots the results of the CAPM for all different risks (betas).Using the CAPM

model and the following assumptions, we cancompute the expected return of a stockin

this CAPM example:ifthe risk-free rate is 3%, the beta (riskmeasure) ofthe stockis 2

and the expected marketreturn overthe period is 10%, the stockis expected to return

17% (3%+2(10% -3%)).

EfficientMarketHypothesis (EMH)

An investment theory that states it is impossible to "beat the market" because stock market

efficiency causes existing share prices to always incorporate and reflect all relevant

information. According to the EMH, stocks always trade at their fair value on stock

exchanges, making it impossible for investors to either purchase undervalued stocks or sell

stocks for inflated prices. As such, it should be impossible to outperform the overall market

through expert stock selection or market timing, and that the only way an investor can

possibly obtain higher returns is by purchasing riskier investments. Efficient Market

Hypothesis (EMH) rules out the possibility by anybody to consistently earn extra normal

return in an efficient stock market. According to this hypothesis securities are correctly

priced and return is solely determined by the amount of risk one assumes (as per the

standard Capital asset Pricing Model CAPM). However a plethora of empirical studies

doubts such a phenomenon and documents the availability of extra normal returns by using

investment strategies. A few of the studies are by JANE A. OU* AND STEPHEN H. PENMAN,


8/58

8 | P a g e 9 - N o v - 1 0

Joseph D. Piotroski etc. These empirical evidences have been commonly cited as anomalies

to CAPM based on company fundamentals and popularly known as the size effect (small

capitalization stocks outperform large capitalization-stocks), leverage effect (high debt-

equity stocks outperform low debt-equity stocks), Price Earnings Effect (low P/E stocks

outperform high P/E stocks) and value effect (high book equity to market equity stocks

outperform low book to market equity stocks). Two schools of thought have emerged in

search for possible explanation of persistent departure from the standard CAPM. One

argument is that CAPM is misspecified; there is/are some missing risk factor(s) which beta

fails to capture. Hence there is a move towards multifactor asset pricing framework as

specified by Fama and French (1996). The other school blames the investors' irrationality for

the existence of the phenomenon. Whatever be the cause, the presence of these CAPM

anomalies provide gainful investment opportunities to the investing community. The

robustness of size and value effects in US stock market motivated Fama and French (1992,

1993, 1996) to suggest the inclusion of a size and value factor in asset pricing model. A

number of research studies have explored the economic feasibility of investment strategies

based on fundamental variables, but most of these studies relate to US and other mature

markets. Similar evidence for emerging markets including India is limited and more recent in

origin.

As a result of financial sector reforms initiated since early 1990s the Indian stock market has

witnessed metamorphic changes as regards to the size, structure and turnover. With more

than 5000 listed companies, 25 millions shareowners and a market capitalization of

Rs.50,257,720 million (in 2008 -09) developments in Indian stock markets are now

comparable to those in other mature markets. Hence there is a felt need for a study which

can examine the relationship between various company fundamentals and equity returns inIndian stock market in this changed regime and test for the economic feasibility of

fundamentals based investment strategies in the advent of technological up gradation. The

results of the study are of pertinent use by investment analysts, mutual fund managers as

well as marginal investors in devising fundamentals based investment strategies to earn

extra-normal returns in Indian stock market.


9/58

9 | P a g e 9 - N o v - 1 0

RESEARCH OBJECTIVES

The primary objectives of the study are:

y To examine the relationship between four company fundamentals (EPS, RONW, PAT,

ICR & DER,ROCE) and equity returns in India.

y To test whether the investment strategies based on these company fundamentals

yield any extra risk adjusted returns in Indian stock market.

y To check whether the inclusion of any or more of these fundamental variables can

better explain cross sectional and time series variations in average equity returns in

India. In other words whether a multifactor model can better explain cross -sectional

and time series variations in equity returns in India or not..


10/58

10 | P a g e 9 - N o v - 1 0

Literature Review

Accounting Measurement, Price-Earnings Ratio, and the Information Content of

Security Prices

JANE A. OU* AND STEPHEN H. PENMAN

A number of papers have documented that stock prices lead accounting earnings. These

investigations have led to the conclusions that prices provide information about earnings

ahead of time and that earnings capture events that affect security prices with a lag. The

study of P/E ratios has supported those conclusions and also produced additional insights.

Beaver and Morse [1978] have shown that P/E ratios not only predict future earnings

changes but they also identify transitory aspects of current earnings. Investors utilize other

information in setting prices which provides both a prediction of future earnings and an

indication of whether current earnings are representative of future earnings. Thus a

comparison of price to earnings in a P/E calculation can indicate the extent to which current

earnings are transitory. We compare the ability of prices and appropriate financial

statement variables to predict future earnings. We find that the price changes are poor

earnings predictors relative to predictors based on financial statement information.

However, price-to-earnings comparisons are found to be relatively good predictors. The fact

that both financial statement measures and P/E ratios can predict future earnings (and can

identify transitory current earnings) demonstrates that information about earnings which

can be identified by the P/E comparison is contained in financial statements.

These findings lead us to a different characterization of accounting measurement than

found in the recent literature. The idea is that there are some aspects of accounting

earnings that capture the information in prices about fut ure earnings, and other, the

transitory error component, that do not. Since prices reflect only the "permanent earnings,"

price changes reflect changes in "permanent earnings," that is, accounting earnings purged

of transitory components. We find that the transitory earnings components that are

identified by financial statement information also affect prices and thus are not value-

irrelevant measurement error. Since these transitory components are valued, price changes

contemporaneous with current earnings garble information in financial statements about

future earnings. In addition, financial statements provide information that distinguishes

transitory earnings components which, because of their temporary nature, may be valued

differently.

We find that the predictable returns associated with P/E measures and those associated

with accounting earnings predictors are negatively related. Thus, while P/E ratios and

identified accounting numbers provide similar information about future earnings, they


11/58

11 | P a g e 9 - N o v - 1 0

provide different information about future returns. Either P/E and the financial statement

predictions measure aspects of risk that are negatively correlated, or the predictable returns

indicate market mispricing with respect to information sets that are negatively related. The

latter explanation is plausible since both P/E and accounting information identify transitory

earnings that are negatively correlated with future earnings changes. Thus, predictable

returns associated with accounting earnings predictors indicate that the market

underutilizes information in accounting statements about future earnings, whereas the P/E

anomaly indicates that the market underutilizes information in accounting statements

about current earnings. This suggests that it takes time for the market to appreciate both

the information about transitory earnings and about future earnings and, thus, to reflect the

filtering of these components that is carried out by accountants.

The P/E ratio can be interpreted as a comparison of two information sets, the information

about current and future earnings that is summarized in price (the numerator) and the

information in current earnings alone (the denominator). High (low) price -earnings ratios

indicate that earnings will be higher (lower) in the future. When these higher or lower

subsequent earnings are ultimately recorded, observed P/E ratios revert toward the mean.

Thus P/E ratios are positively correlated with subsequent earnings changes.

They also find that P/E ratios are negatively correlated with current earnings changes

(incorporated in the denominator of the P/E calculation). Thus, P/E ratios indicate reversals

in the direction of earnings changes. This is the so -called Molodovsky effect.

The estimated correlations were obtained from portfolio values based on an assignment of

firms to ten equal-sized portfolios after ranking them in each year of the 11-year sample

period on decreasing values of E/P. E/P ratios are positively correlate d with current earningschanges. The price comparison to current earnings identifies transitory earnings that reflect

temporary phenomena: E/P ratios indicate current earnings that are lower (higher) than in

the past but will likely revert to past levels in the future.

Value Investing: The Use of Historical FinancialStatement

Information


12/58

12 | P a g e 9 - N o v - 1 0

to separate Winners from Losers

Joseph D. Piotroski

Benefits to financial statement analysis are not likely to disappear after accounting for a low

share price effect or additional transaction costs associated with stale prices or thinly traded

securities. Despite an inability to identify strong companies, the analysis can successfully

identify and eliminate firms with extreme negative returns. Although this paper does not

purport to find the optimal set of financial ratios for evaluating the performance prospects

of individual value firms, it convincingly demonstrates that investors can use past

historical information to eliminate firms with poor future prospects from a generic high BM

portfolio. In addition, an investment strategy that buys expected winners and shorts

expected losers generates a 23 percent annual return between 1976 and 1996 and the

strategy appears to be robust across time and to controls for alternative investment

strategies. A positive relationship between the sign of the initial historical information and

both future firm performance and subsequent quarterly earnings announcement reactions

suggests that the market initially under-reacts to the historical information. In particular,

1/6th of the annual return difference between ex ante strong and weak firms is earned over

the four three-day periods surrounding these earnings announcements.

Overall, the results are striking because the observed patterns of long-window and

announcement-period returns are inconsistent with common notions of risk. The evidence

instead supports the view that the financial markets slowly incorporates public historical

information into prices and that the sluggishness appears to be concentrated in low

volume, small, and thinly followed firms. They conjecture that early -stage momentum losers

that continue to post poor performance can become subject to extreme pessimism andexperience low volume and investor neglect (i.e., a late-stage momentum loser).Eventually,

the average late-stage momentum loser does recover and becomes an early-stage

momentum winner. Whether the market behavior documented in this paper equates to

inefficiency or is the result of rational bayesian pricing strategies is a subject for future

research.


13/58

13 | P a g e 9 - N o v - 1 0

Benjamin GrahamSecurity Analysis (1934)

Fundamental analysis , hence, helps us in becoming value investors i.e. someone focused on

the liquidation value of a company, or what it might be worth if all of its assets were sold

tomorrow and who better in the concepts of value investing than the man who coined the

term itself i.e. Benjamin Graham, whose 1934 book Security Analysis (co-written with David

Dodd) is still widely used today. Value investing has very strict, absolute rules governing how

to purchase a company's stock. These rules are usually based on relationships between the

current market price of the company and certain business fundamentals.

According to Ben graham there are passive screeners to identify stocks which are

undervalued these are:

1. PE of the stock has to be less than the inverse of the yield on AAA Corporate Bonds.

2. PE of the stock has to less than 40% of the average PE over the last 5 years.

3. Dividend Yield > Two-thirds of the AAA Corporate Bond Yield4. Price < Two-thirds of Book Value

5. Price < Two-thirds of Net Current Assets

6. Debt-Equity Ratio (Book Value) has to be less than one.

7. Current Assets > Twice Current Liabilities

8. Debt < Twice Net Current Assets

9. Historical Growthin EPS (over last10 years) > 7%

10. No more thantwo years ofnegative earnings overthe previousten years.


14/58

14 | P a g e 9 - N o v - 1 0

Efficacy ofgrahams screens:

1. A study by Oppenheimer concluded that stocks that passed the Graham screens

would have earned a return well in excess of the market.

2. Grahams best claim to fame comes from the success of the students who took his

classes at Columbia University. Among them were Charlie Munger and WarrenBuffett

VALUESCREENS:

1. Price to Book ratios: Buy stocks where equity trades at less than or at

least a low multiple of the book value of equity.

2. Price earnings ratios: Buy stocks where equity trades at a low multiple

of equity earnings.

3. Price to sales ratio: Buy stocks where equity trades at a low multiple of

revenues.

4. Dividend Yields: Buy stocks with high dividend yields.

Investors have long argued that stocks with low price earnings ratios are more likely to be

undervalued and earn excess returns. For instance, this is one of Ben Grahams primary

screens. Studies which have looked at the relationship between PE ratios and excess returns

confirm these priors.

Excess Returns on Low P/E Ratio Stocksby Country: 19891994

Country Annual Premium Earned by LowestP/E Stocks

Australia 3.03%

France 6.40%

Germany 1.06%

Hong Kong 6.60%

Italy 14.16%

Japan 7.30%

Switzerland 9.02%

U.K. 2.40%Annual premium: Premium earned over an index of equally weighted stocks in that

market

between January 1, 1989 and December 31, 1994. These numbers were obtained

from a

Merrill Lynch Survey of Proprietary Indices.


15/58

15 | P a g e 9 - N o v - 1 0

Firms in the lowest PE ratio class earned 10 percent more each year than those in the

highest PE class between 1952 and 1971, about 9 percent more each year between 1971

and 1990, and about 12 percent more each year between 1991 and 2001.

The excess returns earned by low PE ratio stocks also persist in other international markets.

Table above summarizes the results of studies looking at this phenomenon in markets

outside the United States

Fundamental Analysis, Future Earnings, andStock Prices

In another such analysis done by Abarbanell J. S., Bushee B. [1997], Fundamental Analysis,

Future Earnings, and Stock Prices the relationship between fundamental signals and future

earnings is deciphered further.

The fundamental variables selected by the researchers were :

1. Inventory

2. Accounts Receivable

3. Capital expenditure

4. Gross Margin

5. Effective tax rate

6. Selling and Administrative Expenses

7. Labour Factor

The research reveals that inventory ,gross margin, Effective Tax Rate and labour Factor are

significantly related to earnings over a one year period in the direction anticipated ie for

increase in any of the factors the earning would be negatively correlated.

The test further suggests that the fundamental signal have incremental explanatory powerover future earnings ie as the time progresses th e explanatory power increases.


16/58

16 | P a g e 9 - N o v - 1 0

Fundamentals and stock returns on the WarsawStock Exchange .

Monika Witkowska

This research was done by Monika Witkowska showing a relationship between stock returns

and the fundamental indices for companies lis ted on the Warsaw Stock Exchange in Poland.

The fundamental exogenous variables were constructed following the previous research of

Lev and Thiagarajan [1993], Abarbanell and Bushee [1997], Piotroski [2000] and Mohanram

[2004], while the endogenous variable is defined as a one-year-ahead stock return.

Empirical analysis based on a panel data model for 187 companies in years 1999 2003

finds that the future stock returns are significantly related to three fundamental variables,

i.e. gross margin, sales and administrative expenses and return on assets

Firstly, Fama and French [1992, 1993] initiated the studies of the risk factors that de -termine

the value of stock returns. They refined the traditional Capital Asset Pricing Model (Sharpe

[1964]) by adding new explanatory variables: companys size (measured by its mar-ket

capitalization), financial leverage, earnings to price ratio and book-to-market equity (book

value of a firms common stock to its market value). What is more, firms size and book-to-

market equity account for much of the variability in average stock returns in the United

States in years 1963-1990. Many researchers followed Fama and French approach, carrying

out similar studies in UK and Japan (see Chan, Hamao, Lakonishok [1991]).

Also Ou and Pen-man [1989] show that an aggregated measure constructed out of financial

ratios can be used to predict the sign of the future earnings. The explanatory variables are

composed of 68 finan-cial ratios with the use of statistical methods. Their followers,including Lev and Thiagarajan [1993], Abarbanell and Bushee [1997, 1998], Piotroski [2000]

and Mohanram [2004], refine the methodology by introducing a so -called guided search

procedure for variables.


17/58

17 | P a g e 9 - N o v - 1 0

Definitions of the variables as well as their expected relations with stock returns.

Table 1. Fundamantal

signals used as

explanatory variables

forstockreturns

Fundamental signal

Symbol Calculation method

Expected

relationship withst

returns

Inventory inventory Inventoryt Sales

t negative

Accounts Receivable acc_receiv Accounts Receivablet

Salest

negative

Gross Margin grossmargin Salest- Gross

Margint

negative

Sales & Administrative

Expenses

expenses Sales and

Administrative

Expensest Sales

t

negative

Labour Force labour (Salest

/ No of

Employeest)

positive

Return onAssets ROA Net incomet

/ Total

Assetst

positive

Cash Flow from Opera-

tions

cashflow Cash Flow from

Operationst

/ Total

Assetst

positive

Leverage leverage (Long-term Debtt/

Equityt)

negative

Liquidity liquidity (CurrentAssetst/

CurrentLiabilitiest)

positive

- percentage annual change inthe variable from the average of priortwo years

Both fixed and random effects method yield four significant fundamental variables. The

coefficients signs are coincident with the expectations. The goodness of fit of both models

as assessed by R2 is low.

The study confirms that statistically significant associations can be found between some

fundamental factors and future stock returns for companies listed on the Warsaw Stock

Exchange. The importance of the fundamental variables is more evident in the long term.

Three fundamentals (indices referring to gross margin, sales and administrative expenses,

return on assets) were found significant in case of one -year-ahead stock returns.


18/58

18 | P a g e 9 - N o v - 1 0

All the above researches have tried to bring out a relationship between the fundamental

variables and the share price .Though the variables are different the purpose remains the

same.


19/58

19 | P a g e 9 - N o v - 1 0

RESEARCH HYPOTHESES

Following hypotheses have been tested in the study, with regard to the company

fundamentals and equity returns in India.

Null Hypothesis-

There is no relation between share price(Market value) and fundamental parameters of a

company.

Alternate Hypothesis-

There is some relation between share price(Market value) and fundamental parameters of

a company. Namely,

(i) Low DER stocks outperform the stocks of high DER.

(ii) High EPS stocks outperform the stocks of low EPS.

(iii) High ICR stocks outperform the stocks of low ICR.(iv)High RONW stocks outperform the stocks of low RONW.

(v) High ROCE stocks outperform the stocks of low ROCE


20/58

20 | P a g e 9 - N o v - 1 0

Methodology

The objective of the project was to find out if there existed a relationship between the share

price of a company and other independent variables, various models had to be tried out in

order to find the best fitting one. Therefore the data to be used for this purpose had to besuch that it satisfied all types of models and analyses. Since the project dealt with the share

price and other independent variables, the data had to be from different companies.

DATA:

For the purpose of this study, the indicators of a company that are supposed to affect the

share prices of a company were considered. The data was considered for 19 years from

1991 -2009. Those indicators can be clearly categorized into two types:

1.) Profitability Indicators:

These ratios, much like the operational performance ratios, give users a goodunderstanding of how well the company utilized its resources in generating profit

and shareholder value.

EPS:

It is the portion of a company's profit allocated to eac h outstanding share of

common stock.

PAT:

It the net profit earned by the company after deducting all expenses like interest,

depreciation and tax.

RONW:

This ratio indicates the return on stockholder's total equity.

2.) Solvency Indicators:

These indicate the ability of a company to pay its long term debts.

ICR (Interest Coverage Ratio) :

A ratio used to determine how easily a company can pay interest on outstanding

debt.

DER:

It indicates what proportion of equity and debt the company is using to finance its

assets.

All the above ratios except DER behave in a similar fashion with respect to the share price of

a company. They vary directly. However, DER has an inverse relationship with share price.


21/58

21 | P a g e 9 - N o v - 1 0

Source of Data:

The source that we used to collect the data from was the CMIE Prowess (Centre for

Monitoring Indian Economy) database. Prowess is a database of large and medium Indian

firms. It contains detailed information on over 23,000 firms. These comprise:

y All companies traded on India's major stock exchanges

y Several others including the central public sector enterprises.

The database covers most of the

y Organized industrial activities

y Banking

y Organized financial and other services sectors in India.

The companies covered in Prowess account for

y 75 per cent of all corporate taxes

y Over 95 per cent of excise duty collected by the Government of India.

Prowess provides detailed information on each company. This includes a normalised

database of the financials covering 1,500 data items and ratios per company. Besides, it

provides quantitative information on

y Production

y Sales

y Consumption of raw material

y Energy.

Totally, the number of indicators per company is close to two thousand. You will also find

useful

y Contact information

y Share holding pattern

y List of bankers

y Auditors

y News abstracts


22/58

22 | P a g e 9 - N o v - 1 0

Itpackages a normalized database in a versatile and amazingly powerful software. The

software permits unlimited querying powerto the user.

Sample Selection:

The sample selected was such that it was representative of the entire economy of the

country.

Steps followed:

y All the fifty companies of Nifty50 were segregated based onthe type ofindustry

they belonged to. The following figure is a snapshotthatrepresentsthe number

ofcompanies of eachsectorthatare listed in Nifty.


23/58

23 | P a g e 9 - N o v - 1 0

y As can be seen above, a total of 11 industries were selected such that they

effectively represented the primary, secondary and the tertiary sectors of the

economy.

y From among these different industries, 15 companies were listed. The criteria

for selecting these companies being that, they held the maximum market

capitalization.

y The above mentioned data were then collected for the following companies

underthese differentsectors:

Primary Secondary Tertiary

1) Mining Sterlite,

HINDALCO

1) Energy- RIL,

NTPC

2) Construction-

L&T

3) Metals-TataSteel,

SAIL

4) Automobile-

Maruti

5) Industrial Mfg-

BHEL

6) Pharma-SUN

7) Consumer

goods-HUL, ITC

1) Financial Services SBI

2) IT Infosys

3)Telecom - Airtel

Company for Time Series data:

Since a single companys data had to be taken for the Time Series analysis, the company


24/58

24 | P a g e 9 - N o v - 1 0

selected was SBI from the Financial Services industry.

Reason for selecting SBI:

As the performance of an economy depends on the performance of the industries

comprising of different companies. So, the performance of the companies is a direct

indicator of how the economy is performing. Banks being the lenders for these companies,

the performance of a bank directly affects the company and vice-versa. Thus, it is clear that

by tracking the performance of a bank, the performance of the economy of a country can be

easily judged.

SBI being the one of the largest banks with the maximum market capitalization, its data can

be taken for a time-series analysis using various models.

Data Collection for Cluster Analysis:

For carrying out the cluster analysis, the data used was different from that used for the

purpose of other studies. Primary data was used for this purpose. The data were collected

through a questionnaire which was circulated among a group of respondents.

The questionnaire was aimed at capturing the usage patterns of premium soaps in a

population with varying demographics.

The questionnaire required the respondents to fill in the following details:

Name, Age, Gender, Annual Family Income and were then asked to rate the characteristics

that they preferred the most in a soap. The rating was to be done on a seven-point Likert

scale. The characteristics to be rated were as follows:

Freshness, Price, Perfume, Moisturizer, Colour, Size, Lather, Package, Hygiene, C leansing

effect, Availability, Advertisement, Brand and Life.


25/58

25 | P a g e 9 - N o v - 1 0

Findings

What is Regression?

Regression analysis includes any techniques for modeling and analyzing several variables,

when the focus is on the relationship between a dependent variable and one or more

independent variables. More specifically, regression analysis helps us understand how the

typical value of the dependent variable changes when any one of the independent variables

is varied, while the other independent variables are held fixed.

Applications of regression

Regression analysis is widely used for prediction (including forecasting of time-series data).

Use of regression analysis for prediction has substantial overlap with the field of machine

learning. Regression analysis is also used to understand which among the independent

variables are related to the dependent variable, and to explore the forms of these

relationships. In restricted circumstances, regression analysis can be used to infer causal

relationships between the independent and dependent variables.

Description of univariate analysis

Univariate analysis is concerned with the description or summarization of individual

variables in a given data set. The following data analysis situatio ns can be visualized

depending upon the data.

Measurement

Level

Statistical

Technique

IDAMS

Module

Nominal Frequencies,

Proportions

TABLES

Ordinal Median, Mode,

Range

TABLES

Preference Ranking RANKIntervalscale Mean, Standard

Deviation, Gini

TABLES

QUANTILE


26/58

26 | P a g e 9 - N o v - 1 0

Univariate analysis explores each variable in a data set, separately. It looks at the range of

values, as well as the central tendency of the values. It describes the pattern of response to

the variable. It describes each variable on its own.

Descriptive statistics describe and summarize data. Univariate descriptive statistics describe

individual variables.

The primary objectives of the use of univariate tools are

1. To introduce the sample to the reader and

2. To examine the nature of the variable in terms of its distribution.

Uses of some of these tools are as follows:

1. Frequency Tables

The objective of a frequency table is to summarize the raw data in a concise, systematic and

meaningful way. Cumulative frequency tables, Histogram, Pie-Charts, distributions can be

prepared from this.

Broad conclusions on the nature of the distribution of the data can be drawn from these

tools.

2. Summary Statistics (Descriptive Statistics)

Often the researcher is interested to represent a set of data in single number/figure withrespect to a variable.

For example: A researcher has a set of observation on income of a group of persons.

He wants to summarize the variable for the group in terms of average and deviation from

the average.

Such statistical tools are known as descriptive statistics since the number/figure describe

the distribution of the variables.

Some of the summary statistics are:

1) Measures of Central Tendencies (AM, HM, GM)

2) Measures of Dispersion (Range, Mean Deviation, Variance and Standard D eviation)

3) Measures of Peakedness (Platykurtic, Mesokurtic and Leptokurtic)


27/58

27 | P a g e 9 - N o v - 1 0

Univariate analysis ofthe projectdata

We chose SBI for our univariate analysis. The performance of a bank depends on the

performance of other industries in the economy and SBI has the highest market

capitalization among all the banks. So the performance of SBI indicates the performance of

the economy in general and therefore we had chosen SBI.

As per the available data for SBI we did the time series analysis where we did a trend

analysis using the values of the share price of SBI for 19 years (between 1991and 2008).

We performed a linear regression taking time as the independent variable and share price

as the dependent variable.

Following are some of the results that we re obtained after the linear regression:

ModelSummary

Model R R Square

Adjusted R

Square

Std. Error of

the Estimate

Durbin-

Watson

1 .748a .559 .533 272.96516 .674

a. Predictors: (Constant), Year

b. Dependent Variable: Share Price

The value of the R2

was found to be .559 which shows that the linear relationship between

the time and the share price is not much explained. A higher value of R2

would have

indicated a stronger linear relationship.

ANOVAs

Model

Sum of

Squares df Mean Square F Sig.

1 Regression 1605776.166 1 1605776.166 21.551 .000a

Residual 1266669.595 17 74509.976

Total 2872445.761 18

a. Predictors: (Constant), Year

b. DependentVariable: Share Price


28/58

28 | P a g e 9 - N o v - 1 0

Coefficientsa

Model

UnstandardizedCoefficients

StandardizedCoefficients

t Sig.B Std. Error Beta

1 (Constant) -

105674.76622866.590 -4.621 .000

Year 53.077 11.433 .748 4.642 .000

a. DependentVariable: Share Price

Residuals Statisticsa

Minimum Maximum Mean

Std.

Deviation N

Predicted Value 1.2268 956.6100 4.7892E2 298.68007 19

Residual -

3.68249E26.95317E2 .00000 265.27445 19

Std. Predicted

Value-1.599 1.599 .000 1.000 19

Std. Residual -1.349 2.547 .000 .972 19

a. DependentVariable: Share Price

From the above results of the regression analysis we can formulate the following:

Yi = -105674.66 + 53.077 * X

(Significance) (0.00) (0.00)

Y = Share Price

X = Year Here, R2 = 0.559 (low R2 value)

Conclusion

The relatively low value of R2 shows that the share price of SBI does not exactly follow a

linear trend (with respect to time). While the share price has increased over the years there

have been multiple business cycles and share prices have fallen in that period.


29/58

29 | P a g e 9 - N o v - 1 0

BivariateAnalysis

Bivariate analysis is concerned with the relationships between pairs of variables (X, Y) in adata set.

Some of the bivariate tools are:

a) Cross Tables, Graphs and Scatter Plots: Gives an idea about the nature of relationship

between the variables

b) Correlations (Rank and Simple): These two types of correlation differ with respect to the

types of data used. Ordinal scale (rank order) data are used for rank correlation where as

metric data are used in simple correlation. Both of these use a specific formulae to calculate

the correlation coefficient which ranges from -1 to 1. The correlation coefficient speaks

about the direction and the extent of correlation. No cause and effect relationship is

examined, but it should have construct validity.

c) Bivariate linear and non-linear regression: The simplest relationship between two

variables is a linear one which can be specified as follows:

Yi = + Xi + Ui , where Y - Dependent variable,,X- Independent variable, U- Error term or

disturbance term

A scatter plot gives us some idea about the relationship between two variables. There could

be alternative lines representing the relationship between the variables. Consider e i and

ei2

about the alternative lines. ei

2will be non-negative and will vary with the spread of the

points from the lines. Now, each line has and values . Therefore ei2

will be a function

of and . Therefore we need to minimize this with respect to and which will identify

the line which will give the least square error.

ei = Actual Observation on Y - Estimated Y

Therefore, ei2

= (yi - y^i)2

= [yi - ( + Xi)]2

ei2

= [yi - ( + Xi)]2

This has to be minimized with respect to and to get the best fitted line which represents

the relationship between X and Y. The process of minimization gives two normal equations

with two unknowns. By solving the equations we get the formula for estimating the values

of and .


30/58

30 | P a g e 9 - N o v - 1 0

= xiyi/ xi 2 ( In deviation form)

= Mean Y - Mean X

These estimates are known an Least Square Estimates.

With the help of these estimated values of the intercept and the slope we can write the

equation of the line of best fit.

A Null Hypothesis which is commonly tested is Ho : = 0

This means that there is no relation between X and Y Or the line is a straight line parallel to

the X axis. This null hypothesis (Ho) is rejected if the computed 't' value is more than the

tabulated 't' value with a certain d egree of freedom and significance level

Three quantities can be calculated from the line of regression with respect to the given Yand X values.

TSS: Total Sum of Squares of the deviations

ESS: Explained sum of Squares

RSS: Residual Sum of Squares

R2

= Explained sum of Squares / Total Sum of Squares.

When RSS declines ESS tends to TSS and R2

approaches 1. This is known as the explanatory

power of the model


31/58

31 | P a g e 9 - N o v - 1 0

Dependent Variables Constant

(Significance)

Co-efficient

(Significance)

R Square

RONW 21.049

(0.682)

113.943

(0.000)

0.888

DER 543.492

(0.001)

-28.499

(0.486)

0.029

ICR -2523.674

(0.002)

2525.421

(0.000)

0.533

PAT 123.780

(0.111)

0.139

(0.000)

0.718

EPS 8.00

(0.950)

7.936

(0.000)

0.833

RONW, EPS, PAT, ICRare all highly correlated with share price

Significance is high forall these variables

Relevant findings pertaining to our project

Yi = 8 + 7.936 * Xi where Y = Share Price, X = EPS

Here, R2 = 0.833 (high R2 value), Significance = 0.00

This shows that though the significance value of the co-efficient of X is high, and the model

does explain the variation in share price.


32/58

32 | P a g e 9 - N o v - 1 0

MULTIVARIATE ANALYSIS

It can be broadly defined as all the statistical methods that simultaneously analyse multiple

measurements on the variable under consideration. As such, these are extensions of

univariate and bivariate analysis. Many other techniques like the factor analysis are uniquelydesigned to deal with multivariate issues like identifying the structure underlying a set of

variables.

To be considered truly multivariate, however, all the variables must be random and

interrelated in such ways that their different effects cannot meaningfully be interpreted

separately. The function of multivariate analysis is to measure, explain and predict the

degree of relationship among variates. Thus the multivariate character lies in the multiple

variates, and not only in the number of variables or observations.

Variate: It is the building block for any multivariate analysis. It is a linear combination of

variables with empirically determined weights. The variables are specified by the researcher

and the weights are determined by a multivariate technique to meet a specific objective.

Variate value= w1X1+w2X2++wnXn

Where, Xn: observed variable wn: Weight

The variate is a single value representing a combination of the entire set of variables that

best achieves the objective of the specific multivariate analysis.

Types of Multivariate Analysis:

1. Principal Component and Common Factor Analysis

2. Multiple Regression

3. Multiple Discriminant Analysis

4. Multivariate Analysis of Variance and Covariance (MANOVA)

5. Conjoint Analysis

6. Canonical Analysis

7. Cluster Analysis

8. Structural Equation Modelling (SEM)

Classification of Multivariate Techniques:

1. Dependence Technique in which a variable is defined as the dependent variable to be

predicted or explained by other variables defined as independent variables.

Ex.: Multiple Regression Analysis

2. Interdependence Technique in which simultaneous analysis of all variables is done.

Ex: Factor Analysis


33/58

33 | P a g e 9 - N o v - 1 0

PROJECT:

As per the a priori reasoning, the share price should depend on the companys fundamental

performance parameters. Using Trend Analysis, we found out that the share price do not

follow any specific trend with respect to time. Hence, time and share price do not show any

high correlation. (Relatively low correlation coefficient of 0.559)

Bivariate Analysis shows that RONW, EPS, PAT and ICR have high degree of correlation with

the share price. All these analyses assume that the independent variables have a separate

effect on the share prices of the company.

Actually, there are many factors which work simultaneously and affect the share prices. To

check this real time scenario we opt for multivariate analysis with multiple regression

method.

The analysis will be done for both, time series as well as cross sectional data.

The results and analysis from the data collected are follows:

TIMESERIES DATA:Additive Model

The correlation between the dependent variable and the independent variables (RONW,

DER, ICR, PAT, and EPS) is checked by multiple regression method.

The data was collected for SBI over 19 years.

The additive model shows us whether there is any linear relationship between the

dependent and independent variables.

General Equation:

Y= aX + bY+ cP

Where Y= Dependent Variable (Share Price)

X, Y and P are the independent variable

a, b and c are the coefficients.

After running the analysis in SPSS, the results obtained can be summarized as shown in the

table below:

Table 3.1 Summary of SPSS results (Time Series-Additive Model)

The correlationbetweenthe variablescanbe seen from the CORRELATION MATRIX

obtained:


34/58

34 | P a g e 9 - N o v - 1 0

The correlation matrix shows how the different variables are correlated between

themselves. A high degree of corr elation between two variables means that they both affect

the dependent variable in the same way and hence one of them can be eliminated.

By this way, we follow the principle of Parsimony and try to satisfactorily explain the

dependent variable with least no. of possible independent variables.

The numbers in the brackets show the value or the Type I error value for the given

variable. A low level of signifies a higher explanatory power of the variable over the share

price.

Analysis:

From table 3.1 we see that the Adjusted R2 is high (0.914). Hence there is a high degree of

correlation between the variables and the share price.

But not all variables have a significant effect on the share price. In other words, not all

variables have significant explanatory power over the share price.

We now analyse the table column by column.

Y: Dependent variable: Share Price

Constant: Coeffecient: 407.477 with = 0.591.

The constant accounts for all the other factors apart from the variables which affect the

share prices. As =0.591, the constant does not have a significant explanatory power over

the share price.

RONW: Coefficient: 0.073 with = 0.065.


35/58

35 | P a g e 9 - N o v - 1 0

As value is low, RONW (Return on Net Worth) has a significant explanatory power over the

share prices. This means that changes in RONW can have significant effects on the share

prices. Similarly, DER, ICR and EPS with =0.00, 0.059 and 0.196 respectively have significant

effects on the share prices. The high value (0.500) for PAT indicates that it does not affect

the share prices

The negative coefficient for DER (-115.115) shows the inverse relation between DER and

share prices. This is in accordance to the A Priori reasoning given in the previous chapter

that as the Debt to Equity ratio increases the solvency of the company decreases. The

company is dependent on loans to run its business, so the share prices decrease.

Conclusion: All variables except PAT have a high explanatory power over the share prices.

Multiplicative Model

The multiplicative model in the time series data helps us to determine the elasticity relation

between the share price and the variables.General Equation:

Y= Xa+Z

b+P

c

Log Y =a log X+ b log Y + c log P

Where Y= Dependent variable (Share Price)

X, Y and P are the independent variables.

After running the analysis in SPSS, the results obtained can be summarized as shown in the

table below:

Table 3.2 Summary of SPSS results (Time Series- Multiplicative Model)


36/58

36 | P a g e 9 - N o v - 1 0

The correlationbetweenthe variablescanbe seen from the CORRELATION MATRIX

obtained:


themselves. A high degree of correlation between two variables means that they both affect

the dependent variable in the same way and hence one of them can be elimi nated.



The numbers in the brackets show value or the Type I error value for the given variable. A

low level of signifies a higher explanatory power of the variable over the share price.

Analysis:

From table 3.2 we see that the Adjusted R2 is high (0.928). Hence there is a high degree of

correlation between the variables and the share price.



All the variables except PAT have a high explanatory value and hence explain share price in asignificant manner

Conclusion: All variables except PAT have a high explanatory power over the share prices.

Also the Multiplicative model is able to capture slightly more of the effects on the share

price than the additive model.


37/58

37 | P a g e 9 - N o v - 1 0

CROSSSECTION DATA:

In the time series data we have seen the effect of different variables on the share price of a

single company (State Bank of India) from the data collected for the past 19 years.

In the cross sectional analysis, we will study the effect of these variables on the share pr ices

of different companies from different sectors.The companies were selected from NIFTY index and are the leaders in their area of business.

The details of the company for which the analysis was done are:

Primary Sector:

1) Mining: Sterlite, HINDALCO

Secondary Sector:

1) Energy: RIL, NTPC

2) Construction: L&T

3) Maetals: Tata Steel, SAIL

4) Automobile: Maruti

5) Industrial Goods: BHEL

6) Pharma: SUN

7) Consumer Goods: HLL, ITC

Tertiary Sector:

1) Financial Services: SBI

2) IT: Infosys

3) Telecom: Bharti Airtel

Data was collected for these companies over the period of 3 years. (2006 -2008).The

multivariate analysis was done by multiple regression method. The results of the analysis as

run in SPSS for 3 years can be summarized as given in the table:

Table 3.3 Summary of SPSS results (Cross Sectional Data)


38/58

38 | P a g e 9 - N o v - 1 0

The correlation between the variables can be seen from the CORRELATION MATRIX

obtained:

Year2007:

Year2008:


39/58

39 | P a g e 9 - N o v - 1 0

Year2009:


themselves. A high degree of correlation between two variables means that they both affect

the dependent variable in the same way and hence one of them can be eliminated.



The numbers in the brackets show value or the Type I error value for the given variable. A

low level of signifies a higher explanatory power of the variable over the share price.

Analysis:

From table 4.1 we see that the Adjusted R2 is relatively higher (>0.6) for all the years. Hence

there is a high degree of correlation between the variables and the share price of all the

companies.



As seen from the table, only EPS and RONW have a significantly related to the share prices.

The correlation matrices for all the years show that there is no significant correlation

between the independent variable.

Conclusion: RONW and EPS have high explanatory power. It can be seen that for cross

sectional data DER and ICR are not significant even though they were significant in the time


40/58

40 | P a g e 9 - N o v - 1 0

series analysis.

This shows that the effect on share prices due to these variables may differ from company

to company and industry to industry.


41/58

41 | P a g e 9 - N o v - 1 0

FactorAnalysis

Factor analysis is used to uncover the latent structure (dimensions) of a set of variables. It

reduces attribute space from a larger number of variables to a smaller number of factors

and as such is a "non-dependent" procedure (that is, it does not assume a dependentvariable is specified). Factor analysis could be used for any of the following purp oses:

y To reduce a large number of variables to a smaller number of factors for modeling

purposes, where the large number of variables precludes modeling all the measures

individually. As such, factor analysis is integrated in structural equation

modeling (SEM), helping create the latent variables modeled by SEM. However,

factor analysis can be and is often used on a stand -alone basis for similar purposes.

y To select a subset of variables from a larger set, based on which original variables

have the highest correlations with the principal component factors.

y To create a set of factors to be treated as uncorrelated variables as one approach tohandling multi-collinearity in such procedures as multiple regression

Factor loadings

The factor loadings, also called component loadings in PCA, are the correlation coefficients

between the variables (rows) and factors (columns). Analogous to Pearson's r, the squared

factor loading is the percent of variance in that variable explained by the factor. To get the

percent of variance in all the variables accounted for by each factor, add the sum of the

squared factor loadings for that factor (column) and divide by the number of variables.

(Note the number of variables equals the sum of their variances as the variance of astandardized variable is 1.) This is the same as dividing the factor's eigenvalue by the

number of variables.

Factor, component, pattern, and structure matrices: In SPSS, the factor loadings are found

in a matrix labeled Factor Matrix if PFA is requested, or in one labeled Component Matrix if

PCA is requested.

In oblique rotation , one gets both a pattern matrix and a structure matrix. The structure

matrixis simply the factor loading matrix as in orthogonal rotation, representing the

variance in a measured variable explained by a factor on both a unique and commoncontributions basis. The pattern matrix, in contrast, contains coefficients which just

represent unique contributions. The more factors, the lower the pattern coefficients as a

rule since there will be more common contributions to variance explained. For oblique

rotation, the researcher looks at both the structure and pattern coefficients when

attributing a label to a factor.


42/58

42 | P a g e 9 - N o v - 1 0

Eigenvalues: Also called characteristic roots. The eigenvalue for a given factor measures the

variance in all the variables which is accounted for by that factor. The ratio of eigenvalues is

the ratio of explanatory importance of the factors with respect to the variables. If a factor

has a low eigenvalue, then it is contributing little to the explanation of variances in the

variables and may be ignored as redundant with more important factors.

Thus, eigenvalues measure the amount of variation in the total sample accounted for by

each factor. Note that the eigenvalue is not the percent of variance explained but rather a

measure of amount of variance in relation to total variance (since variables are standardized

to have means of 0 and variances of 1, total variance is equal to the number of variables).

SPSS will output a corresponding column titled '% of variance'. A factor's eigenvalue may be

computed as the sum of its squared factor loadings for all the variables.

Initial eigenvalues and eigenvalues after extraction (listed by SPSS as "Extraction Sums of

Squared Loadings") are the same for PCA extraction, but for other extraction methods,eigenvalues after extraction will be lower than their initial counterparts. SPSS also prints

"Rotation Sums of Squared Loadings" and even for PCA, these eigenvalues will differ from

initial and extraction eigenvalues, though their total will be the same.

Factor scores: Also called component scores in PCA, factor scores are the scores of each case

(row) on each factor (column). To compute the factor score for a given case for a given

factor, one takes the case's standardized score on each variable, multiplies by the

corresponding factor loading of the variable for the given factor, and sums these pr oducts.

Computing factor scores allows one to look for factor outliers. Also, factor scores may be

used as variables in subsequent modeling.

Criteria for determining the number of factors, roughly in the order of frequency of use in

social science

Scree plot: The Cattell scree test plots the components as the X axis and the corresponding

eigenvalues as the Y axis. As one moves to the right, toward later components, the

eigenvalues drop. When the drop ceases and the curve makes an elbow toward less steep

decline, Cattell's scree test says to drop all further components after the one starting the

elbow. This rule is sometimes criticised for being amenable to researcher -controlled

"fudging." That is, as picking the "elbow" can be subjective because the curve has multipleelbows or is a smooth curve, the researcher may be tempted to set the cut-off at the

number of factors desired by his or her research agenda. Even when "fudging" is not a

consideration, the scree criterion tends to result in even more factors than the Kaiser

criterion.


43/58

43 | P a g e 9 - N o v - 1 0

Variance explained criteria: Some researchers simply use the rule of keeping enough factors

to account for 90% (sometimes 80%) of the variation. Where the researcher's goal

emphasizes parsimony (explaining variance with as few factors as possible), the criterion

could be as low as 50%.

Rotation Methods: Rotation serves to make the output more understandable and is usually

necessary to facilitate the interpretation of factors. The sum of eigenvalues is not affected

by rotation, but rotation will alter the eigenvalues (and percent of variance explained) of

particular factors and will change the factor loadings. Since alternative rotations may explain

the same variance (have the same total eigenvalue) but have different factor loadings, and

since factor loadings are used to intuit the meaning of factors, this means that different

meanings may be ascribed to the factors depending on the rotation - a problem some cite as

a drawback to factor analysis. If factor analysis is used, the researcher may wish to

experiment with alternative rotation methods to see which leads to the most interpretable

factor structure.

No rotation is the default in SPSS, but it is a good idea to select a rotation method, usually

varimax. The original, unrotated principal components solution maximizes the sum of

squared factor loadings, efficiently creating a set of factors which explain as much of the

variance in the original variables as possible. The amount explained is reflected in the sum

of the eigenvalues of all factors. However, unrotated solutions are hard to interpret because

variables tend to load on multiple factors.

Varimax rotation is an orthogonal rotation of the factor axes to maximize the variance of

the squared loadings of a factor (column) on all the variables (rows) in a factor matrix, whichhas the effect of differentiating the original variables by extracted factor. Each factor will

tend to have either large or small loadings of any particular variable. A varimax solution

yields results which make it as easy as possible to identify each variable with a single factor.

This is the most common rotation option.

Quartimax rotation is an orthogonal alternative which minimizes the number of factors

needed to explain each variable. This type of rotation often generates a general factor on

which most variables are loaded to a high or medium degree. Such a factor structure is

usually not helpful to the research purpose.

Equimax rotation is a compromise between Varimax and Quartimax criteria.

Adequate sample size. At a minimum, there must be more cases than factors


44/58

44 | P a g e 9 - N o v - 1 0

Companies Selected

TATA Steel

SBI

Infosys

HUL

BPCL

Relevantfindings pertaining toourproject


45/58

45 | P a g e 9 - N o v - 1 0

Final Factors:

Factor 1 (Solvency) : ICR, RONW, DER, CR

Factor 2 (Market Price) : Share Price

Factor 3 (Valuation) : PAT, P/E Factor 4 (Profitability) : EPS


46/58

46 | P a g e 9 - N o v - 1 0

Cluster Analysis

Cluster analysis is a technique, which groups persons/objects/occasions into unknown num -

ber of groups such that the members of each group are having similar characteristics/

attributes. The different techniques of cluster analysis form groups such that the similarityamong the members of the groups is maximized. The main difference of the cluster analysis

from the discriminant analysis is that the number of groups and the characteristics of the

groups formed in clustering analysis are not known in advance.

Assumptions

y Data are interval in level or are true dichotomies for hierarchical and k-means clustering,

though two-step clustering can handle categorical data. When at least one variable is

categorical, two-step clustering must be used.y Independent observations.

y Standardization of variables is not required but is often recommended so that all

variables have equal impact on the computation of distances. Some computer

algorithms, including two-step clustering in SPSS, make standardization the default.

y The same assumptions ascorrelation, regression,factor analysis, and other members of

the multiple linear general hypothesis family of procedures. However, significance

testing is not performed on cluster membership apart from exploratory/descriptive

purposes, so violation of these assumptions may be less important to the research

purpose.


We carried out a survey relating to preferences of people in selecting a soap, based on the

response for the parcipants following are the clusters derived after carring out the cluster

analysis.

Characteristics Cluster1 Cluster2 Cluster3 Cluster4 Cluster5

SEX 0.5 0.75 1 0.76 0.56

INCOME 4 2.25 1.87 3.82 3.87

PERFUME 6.25 7 5.62 4.64 5.87

SIZE 2 5.75 3.87 3.29 4.45

LATHER 6 6.75 4.875 3.94 5.29PACKAGE 3.5 2.75 3.375 4.05 4.95

ADVERTISEMENT 1.75 5.75 3.25 4.30 5.10

BRAND 5.5 6 3.60 5.20 5.70


47/58

47 | P a g e 9 - N o v - 1 0

CONCLUSION

Univariate-The share price of SBI increases with time. Though the significance is high it is still

not able to explain fully the movement in share price. This is obvious because in

last twenty years there have been multiple business cycles and the share price of

SBI fell in 1997, 2001 and 2007.

Bivariate-

Following five variables were considered for bivariate analysis:

o EPS

o DER

o ICR

o RONW

o PAT

Bivariate analysis is done with each of the above variables one at a time, these

variables are considered as independent and the share price as dependent.The data

is taken for a period of 19 years from 1991-2009, for SBI. Based on the results the

conclusion is that RONW, EPS, PAT, ICR are all highly correlated with share price.

Multivariate

For multivariate, the regression is done with all the above variables as independent,

and share price as dependent variable. Multivariate is done for both time series data

and cross section data. In the case of time series regression is done on19 years data

of SBI. For cross section regression, 15 companies were selected representing all the

major industries in the Indian economy.

For SBI-

Multiplicative model had slightly higher explanatory power than the additive model.

All the variables had high explanatory power except PAT. The reason being PAT is not

a direct measure of return to the shareholders. Instead ROCE and ROE which


48/58

48 | P a g e 9 - N o v - 1 0

measure return on capital or equity are better able to capture the share price

movement.

The explanatory power of RONW and share price is 0.88 and that in multivariate is

0.92. Thus the explanatory power of the model increased only slightly as compared

to bivariate analysis implying because of the interference casued by the correlationbetween individually paired variables.

Cross section-

For cross section regression, 15 companies were selected representing all the major

industries in the Indian economy. The regression was done for 3 years 2007,2008

and 2009.

The findings are as follows-

Only PAT and EPS are significantly related to share price and both of them are

positively correlated. Due to variation in the size and scale of the companies and

variation in the capital structures, other variables like DER, ICR have very little

explanatory power

Adjusted R2 is able to capture more than 2/3rd the variance of share price

For cross section analysis, adjusted R2 is 0.67 which is acceptable but still some more

variables like GDP and inflation needs to be factored in to make better explanation


49/58

49 | P a g e 9 - N o v - 1 0

Appendix A

SBI Data for Univariate, Bivariate, Multivariate and Factor Analysis

ROCE Debt equity ratio Interest cover BSE closing price EPS

107.01 0.94 8.85 1.02 300 53.57

175.05 1.53 5.99 1.03 300 87.47

212.04 1.75 7.42 1.03 300 106.02

275.04 1.59 1.53 1.04 237.5 8.04

715.5 0.96 2.09 1.11 176 20.92

831.6 1.93 2.5 1.1 237.75 24.32

1349.25 2.1 1 1.26 272.75 25.64

1861.2 2.4 0.95 1.2 282 35.36

1027.8 1.83 0.97 1.12 213.4 19.53

2051.55 1.74 0.92 1.21 201.1 38.98

1604.25 1.71 1.35 1.15 200.25 30.48

2431.62 2.56 0.9 1.17 219.8 46.2

3105 4.67 0.77 1.22 269.9 593681 6.34 0.85 1.26 605.7 69.94

4304.52 7.84 0.94 1.31 656.95 81.79

4406.67 8.63 1.29 1.34 968.05 83.73

4541.31 8.48 1.79 1.36 992.9 86.29

6729.12 11.52 1.49 1.33 1598.85 106.56

9121.23 7.83 1.45 1.33 1066.55 143.67


50/58

50 | P a g e 9 - N o v - 1 0

Questionaire for Cluster Anaysis


51/58

51 | P a g e 9 - N o v - 1 0


52/58

52 | P a g e 9 - N o v - 1 0

Appendix B

Assignment1

Bivariate using differentmethods

Log Linear Model: It is an exponential regression model ( known as double log or log linear

model).

Ln Y i = ln + ln Xi + ei

This model is popular in applied work since the slope coefficient measures elasticity of Y

with respect to X(% change in Y due to % change in X)


Log Y = 417.945+

0.005 * Log X where Y = Share Price, X = EPS

Here, R2 = 0.487 (low R2 value), Significance = 0.150


53/58

53 | P a g e 9 - N o v - 1 0

This shows that though the significance value of the co-efficient of X is low, it can be said

that with low R square value, the model does not explain the v ariation in share price. So, the

relation between EPS and share price cannot be explained properly.

Semi-log Regression Model: The semi log model could be used to measure growth rate of avariable over a time period. This model is specified as

ln Yi = ln + t + u

This is known as semi log model since only one variable appears in log form. It is also known

as log-linear model.In the semi log model the slope coefficient measures the constantproportion or relative change in Y for a given absolute change in X ( 't' in the above

equation). Slope x 100 will give the point of time change in Y with respect to change in X.

Compound growth rate can be found by the formulae: [ Antilog - 1] x 100


Log Yi = 176.923 + .173 * T where Y = Share Price, X = EPS

Here, R2 = 0.741 (low R2 value), Significance of X = 0.000

This shows that though the significance value of the co-efficient of X is low, it can be said

that with R square value is pretty high, the model explains th e variation in share price. So,

the relation between EPS and share price can be explained to a certain extent.

Quadratic/ Cubic Model:

The forms of a quadratic or a cubic model could be

Y= a+bx+cX2

+ u or Y= a+bx+cX2

+dX3+u

Since these models use one independent variable they can be categorized under the two

variable regression equations. The quadratic models are used to examine whether minima

or maxima exits in the curve depicting the relationship between X and Y. Example: Total

Revenue Curve, Average Cost Curves etc. Cubic models are used in Total cost functions etc.


Quadratic Model:

Y = -233.26 + 28.578 * X + 0.071* X

2where Y = Share Price, X = EPS

Here, R2

= 0.784 (high R2

value), Significance of X = 0.014, Significance of X2

= 0.018


54/58

54 | P a g e 9 - N o v - 1 0

This shows that though the significance value of the co-efficient of X is high, it can be said

that with high R square value, the model explains the variation in share price. We can

observe that though R square is high the coeff of x square is low, this implies that there is a

linear trend.

d) Trend Lines: The linear growth of a variable can be calculated using a simple regressionmodel such as Y = + t + u, where Y is the variable under consideration & t is the time or

trend variable. The + ive or - ive trend of the variable over the time period is determined by

looking at the sign of the slope or .


Yi = -125.132 + 104.093 * T where Y = Share Price, X = EPS

Here, R2

= 0.704 (low R2

value), Significance of X = 0.000

This shows that though the significance value of the co-efficient of T is high, it can be said

that with high R square value, the model explains the variation in share price. So, the

relation between EPS and share price can be explained properly.

Factor Analysis Output

KMOand Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .595

Bartlett's Test of Sphericity Approx. Chi-Square 614.495

df 45

Sig. .000

Communalities

Initial Extraction

PAT 1.000 .939

RONW 1.000 .871

CR 1.000 .501

DER 1.000 .640

ICR 1.000 .855

SharePrice 1.000 .686

EPS 1.000 .111

PE 1.000 .787


55/58

55 | P

CE

.

.

CE

.

.

E

t

ti

t

: P

i

i

l

Component Anal

i

.

Rotated ! omponent Matrixa

Component

" #

$

PA%

. "&

" .' ( )

.1 # )

2

3

4 5

.' 1 '

. "' 6

.1 ) 1

CR .6 & 1

.$ 1 #

.1 1 &

7

ER .(

"

(

. "$ )

.(

' 6

ICR .' # 1

.1

"

6

.1 '

8

S9

arePri@

e . "# #

.$ 8 (

.8 # '

EPS .1 ( &

.1

"

(

.$ # )

PE ."

$ #

.$ # 6

.)

"

(

CE . "8

#

.'

(

&

.1 $ $

ROCE .'

(

)

.1 6 1

. "8 (


56/58

56 | P a g e 9 - N o v - 1 0

ComA

onent MatriBa

Component

1 2 3

PAT .158 .953 -.068

RONW .885 .203 .214

CR -.596 .290 -.249

DER -.684 .185 .371

ICR .898 .022 .219

SharePrice -.126 .443 .688

EPS .162 -.015 -.291

PE -.406 -.250 .748

CE -.157 .950 -.116

ROCE .962 .059 .155


57/58

57 | P a g e 9 - N o v - 1 0

Cluster Analysis Output


58/58

Characteristics Cluster1 Cluster2 Cluster3 Cluster4 Cluster5

SEX 0.5 0.75 10.76 0.56

INCOME 4 2.251.87 3.82 3.87

PERFUME6.25 7 5.62 4.64 5.87

SIZE2 5.75

3.87 3.29 4.45

LATHER6 6.75 4.875

3.94 5.29

PACKAGE3.5 2.75 3.375

4.05 4.95

ADVERTISEMENT1.75 5.75 3.25 4.30 5.10

BRAND 5.5 6 3.60 5.20 5.70

share price n company fundamentals

Documents