51395704 analysis-of-beta-returns-in-bse

Analysis Of Return And Beta In Sensex Component

1 M.P. Birla Institute of Management

A RESEARCH STUDY On

“ANALYSIS OF RETURN AND BETA IN SENSEX COMPONENT”

Submitted in partial fulfillment of the requirements for MBA Degree of Bangalore University

Submitted By KIRAN M K

06XQCM6035 Under the Guidance and Supervision

Of Dr. NAGESH S MALAVALLI

M.P.BIRLA INSTITUTE OF MANAGEMENT

Associate Bharatiya Vidya Bhavan

# 43, Race Course Road

Bangalore-560001

2006 – 08



DECLARATION

I hereby declare that the report entitled. “A Study on ANALYSIS OF

RETURN AND BETA IN SENSEX COMPONENT” is prepared under the

guidance of Dr Nagesh S Malavalli ( Principal, M P BIRLA INSTITUTE

OF MANAGEMENT) .I also declare that this project report has not been

submitted to any other University / Institute for the award of any other

degree, diploma, fellowship or other similar title or prizes.

Date: Kiran M K Place: Bangalore (O6XQCM6035)



PRINCIPAL’S CERTIFICATE

This is to certify that this report titled. “A ANALYSIS OF RETURN AND

BETA IN SENSEX COMPONENT " is the result of project work undergone

by Kiran M K, bearing the Register Number 06XQCM6035, under the

guidance of Dr Nagesh S Mallavalli . This has not formed a basis for the

award of any Degree/Diploma for any other University.

Place: Bangalore Dr. Nagesh.S.Malavalli Date :



GUIDE’S CERTIFICATE

This is to certify that Mr. Kiran M K student of M.P.BIRLA INSTITUTE

OF MANAGEMENT Associate Bharatiya Vidya Bhavan, Bangalore, has

successfully completed the research work entitled “A Study on ANALYSIS

OF RETURN AND BETA IN SENSEX COMPONENT” for the partial

fulfilment of the requirements of MASTER OF BUSINESS

ADMINISTRATION degree of BANGALORE UNIVERSITY, under my

guidance and supervision. Date: Dr. Nagesh S Malavalli Place: Bangalore (Internal guide)



ACKNOWLEDGEMENT I am thankful to Dr. Nagesh malavalli, Principal M.P. Birla Institute of Management, Bangalore, who has given his valuable support

during the Study and who has guided me to do this project by giving

valuable suggestions and advice.

My gratitude will not be complete without thanking God and I am most

grateful to my beloved parents who have been a constant source of

aspiration and blessings in my pursuit for studies. Finally, I express

my sincere gratitude to all my friends and well wishers who helped

me to do this project

Kiran M K



CONTENTS

1. Research Extract

2. Introduction

Statement Of Problem

Objective of the study

Hypothesis

Limitations of the Research

3. Literature Review

Theoretical background:

Beta

Beta and risk

Sensex

4. Research Methodology

Research Design

5. Data Analysis and Interpretation

6. Summary of Findings

7. Conclusion

8. Bibliography

9. Annexure



RESEARCH EXTRACT

In partial fulfillment of MBA degree of Bangalore University, I took up a project titled

“ANALYSIS OF RETURN AND BETA IN SENSEX COMPONENT” Hence, I

thought it would be a right time to take such a project and present the findings of

what impact it has made. I have also specified in the end of the project the road for

further study, which other interested persons, can take up a project and augment to

the findings of this project.

The following research has been conducted to find out index stock follows

a particular trend. If stocks of a particular sector gave their investor huge return for one

particular year does it the stock follow the same trend for the next years. In order to find

out the trend the opening price and closing price for a period of eight year from 2001 to

2008 of Sensex index stocks considered. Based on the return arrived each 30 stocks are

ranked using simple ranking method and analyzed for the presence of any particular

trend. It has been found that the Sensex index stock does not follow any particular

trend.



INTRODUCTION

It is widely believed that stock market is related to macroeconomic fundamentals of an

economy, as companies that are listed for trading in stock exchanges are the Ones who

contribute significantly to the economy's growth .Ever since the turn of the century;

world stock markets have been very volatile. In other words there have been significant

movements (up or down) in share prices. This phenomenon has been evidenced by the

collapse in recent years of the share prices of the companies.

The current world political situation is probably the worst it is for many years. World

markets are falling at a rapid pace. What does beta factor analysis teach us about an

investment strategy in this situation? Firstly, however good a company is it likely that in

such circumstances most will encounter falls in their share price.

The beta of an investment is a relative measure of the systematic risk of an investment.

In other words it measures the specific risk of the company's shares relative to the market

as a whole. In general, the sign of the beta indicates whether, on average, the investment's

returns move with the market or in the opposite direction to the market. The scale or

value of the beta indicates the relative volatility of the particular stock.

However during this time a number of alternative investments that have negative beta

factors have appreciated in value. The prime example of this is gold. However in the past

few years it is noticeable that in the political uncertainty that has arisen in the world that

the price of gold has shown material gains at a time when equity markets have recorded

sharp falls.



PROBLEM STATEMENT

To assess the return and beta from Sensex index stocks and analyzing the behavior of

stocks.

On the basis of this problem statement, the following specific objectives have

been crystallized

OBJECTIVE OF THE STUDY

• To study the index stocks behavior from past eight years.

• To know the performance of index stocks in Sensex.

• To find out whether index stocks return having any relationships.

Need of the study:

In Sensex market, index stocks beta factor can have a major influence on the investment

strategies of investors but this factor doesn’t have any specific format. If the analysis is to

be believed then in times of a bull market investors should hold stocks with a high

positive beta factor since they should outperform the market.

Hypothesis:

Null hypothesis (H0): There is no specific format in index stocks in Sensex.

Alternative hypothesis (Ha): There is a specific format in index stocks in Sensex.



LIMITATIONS OF THE RESEARCH

• The sample consider for research is only stocks from BSE index.

• Ideally, the research should have been done throughout the cross Section of the

equity stocks in India, but this would have been beyond my scope of study due to

limited resources.



LITERATURE REVIEW AND

THEORETICAL BACKGROUND



LITERATURE REVIEW

Sharpe (1963) developed a simplified single-index model to predict security

returns. The major characteristic, and the primary shortcoming, of the single index model

is that the only factor influencing a security’s return is its sensitivity to changes in the

market portfolio return (Martin and Klemkosky (1976)). King (1966) published the first

important study proving that stock prices for firms in the same industry exhibit a common

movement that goes beyond the market effect. Employing monthly closing stock prices

for 63 firms in six industries during the June 1927 to December 1960 period, his study

documents that while 50% of stock price movements could be explained by movements

in the market index, 20% of the residual variance was accounted for by industry

affiliation.

Meyers (1973) and Livingston (1977) in similar studies confirmed King’s findings. The

Meyers study involved 60 of the same companies used by King and 60 additional

companies, using data through December 1967. Meyers concluded that although there

were strong industries effects, King may have overstated the percent of residual variance

explained by industry association. Livingston used 50 companies in 10 industry groups

and studied monthly returns from January 1966 through June 1970. He also found strong

co movement among stocks in the same industry, and concluded that 18% of residual

variance was accounted for by industry effects.

The recognition that factors other than movement in the market index affect security

returns led to the development of multi-index models. Several subsequent studies

attempted to determine factors other than the market index which affect security prices.

Sharpe (1982) studied monthly returns for stocks of 2,197 firms from 1931

through 1979. His findings showed that the R2 for a regression model was significantly

improved using dividend yield, company size, and bond beta in addition to a market

index. Pari and Chen (1984) conducted a test of an Arbitrage Pricing Theory (APT)



model for 2,090 firms for the period 1975 to 1980. Using this model, they found that

factors such as the general market index, price volatility of energy, and interest rate risk,

influence stock price. Chen, Roll, and Ross (1986) tested an APT model for significance

of several factors in explaining security returns. Using monthly data for the period 1953-

1983, their results indicate that the following factors are significant in explaining the

variability of a security return: spread between long and short interest rates, expected and

unexpected inflation.

Beginning in the early 1980’s, researchers began applying multi-index An Empirical

Analysis Of Market And Industry Factors In Stock Returns Of U.S. Aerospace Industry

87 CAPMs to identify which factors influenced stock returns. These studies tend to cover

stocks in various industries, mainly focusing on utility industry, and show that various

factors have significant influence on stock returns. In particular, the CAPM approach has

been widely used in the utility industry for determining its cost of capital and the utility’s

rate structure.

Theoretical Background:

Economists have long been fascinated by what influences aggregate stock Market

returns. For example, a distinguished literature has examined the role of Inflation (e.g.

Fama, 1981 Feldstein, 1982; Lintner, 1976; Modigliani and Cohn, 1979; and Stulz,

1986), while recent research has drawn attention to the possible Influence of a exhaustive

list of macroeconomic variables (e.g. Campbell and Hamao, 1989: Engel and Rodrigues,

1988; Giovannini and Jorion, 1989; and Keim and Stambaugh, 1986).

On the other hand financial economists Investigating the pricing of risky securities have

largely focused on factor Models; the (single factor) CAPM due to Sharpe (1964) Lintner

(1965), and Others, and estimated by, for example, Blume and Friend (1973), and Fama

and Macbeth (1973); and the multi-factor analytic methods which have been used to

Estimate multiple measures of systmatic risk (Roll and Ross, 1980). Typically it Has not

been possible to identify economic variables with those factors (other Than the market

index itself).



Economic Factors and Stock Returns:

Economic variable which influences expected dividends or the discount rate will affect

stock prices. Which affect future anticipated cash flows, and those influences the discount

rate, though such a distinction will be somewhat arbitrary if one considers a complete

structural model of the economy. Expected dividends will be affected by anything, which

influences cash flows. Changes in the expected rate of inflation would affect both

nominal cash flows and interest rates. Clearly changes in industrial production would

influence profits and hence dividends. Fama (1981) finds a correlation between stock

market returns and future growth rates of output.

There is extensive evidence that relative prices change with inflation and hence sectoral

and aggregate performance may change, (see Fischer, 1981: and Driffill, Mizonand Ulph,

1989). Changes in exchange rate will affect the value of foreign earnings and export

performance. Default risk referred to a ‘market risk’ by Chen, Roll and Ross(CRR) may

be captured by the spread between the yield on a corporate debentures and loan price

index and the yield on long government bonds.

The use of interest differential between government stocks and below-investment grade

corporate bond as a measure of risk aversion implicit in the market’s pricing of stocks,

though one could argue that such a variable simply reflects financial leverage. This is the

most important variable (statistically) in the analysis of CCH and CRR.

Other indicators of economic activity like unemployment, stock market turnover, bank

lending, and the trade balance might also have an influence upon expected future cash

flows expected cash flows. Finally, oil price changes also observed to influence industry

costs, and via induced macro policy responses, possibly output and hence revenues.

The appropriate discount rate in equation is constructed from the prevailing risk-free

rate and a risk premium, and an average of rate over time; the changes in the ‘safe’ rate

and yield curve are likely to influence the stock prices. Further, ‘surprises’ in the current

account balance, exchange rates, the money supply, output, oil prices, or even the price of



gold, could all alter the outlook for interest rates, and hence the discount rate. To the

extent that the ‘market risk’ variables capture perceptions of equity market risk, and

unanticipated changes in these will also influence returns.

One of the difficulties of empirically studying the APT is that it does not offer any

theoretical or empirical grounds for identifying the economic nature of the factors. An

alternative to the use of artificially devised factors and their corresponding sensitivities is

to identify factors a priori. Chen, Roll and Ross (1986) adopted this approach using

macro-economic factors.

Returns on securities are influenced by various macro economic activities.

Nevertheless, individual economic variables cannot be taken directly as common factors

in the generating process. First, economic variables are not totally independent of each

other. Including this economic variable in equation to estimate loadings will introduce

multicollinearity.

Second, there is a lack of prior knowledge which economic variables should be included

as factors that determine asset returns. The factors estimated from all economic activities

eliminate multicollinearity among independent variables and reduce the dimension of

independent variables entering the return generating process. These estimated economic

factors preserve most of the relevant information. King (1966) and Cohen and Pogue

(1967), who investigate mainly the industry related impact on asset returns also use a

similar specification of the return generating process. Although these studies have

enhanced the understanding of non-market components of asset returns, the equilibrium

asset-pricing model is not used and major economic variables are not considered.

In India. A total of 30scrip considered in the construction of BSE30 form the basic

sample stocks. While establishing the CAPM, APT and Economic Factor Models, these

30 scrip have been grouped into 15 portfolios of with two assets each,. The first factor

composed of a wide variety of factors from different sectors like Industrial production,

Money Banking and Interest rate, Inflation, External Transactions and select indicators of



Capital Market. The second factor represents primarily the inflation factor and the last

factor represents the select sectors of Industrial Production. Next the systematic risks

(beta coefficients) corresponding to these factors are estimated for each stock using the

three factors by regressing the individual stock return relatives against the factor scores

obtained from Factor Analysis.

The macro economic factor model is based on the general hypothesis that returns

are influenced by three classes of factors – real domestic activity, money and stock

market activity and foreign variables and any change in them are expected to change the

investor’s perceptions of future expected cash flows and likely to affect current asset

prices. While inflation is the most common factor; it was found to be significant in Chen,

Roll and Ross (1986) and Mc Elroy and Burmeister (1988) and others.

Interest rates were also prevalent amongst priced factors in almost all studies. While

monetary variables are found to be less common the foreign influences (exchange rates or

balance of payments) found to play their own role. As the macro economic factor model

is trying to explain the cross sectional variations in average security returns, the results of

this model has been compared with that of the APT. But it should be noted that the APT

and economic factors are not directly comparable to each other because of the differences

in the scope and nature of the factors considered in the analysis.



BETA

Beta is a measure of a stock's volatility in relation to the market. By definition, the

market has a beta of 1.0, and individual stocks are ranked according to how much they

deviate from the market. A stock that swings more than the market over time has a beta

above 1.0. If a stock moves less than the market, the stock's beta is less than 1.0. High-

beta stocks are supposed to be riskier but provide a potential for higher returns; low-beta

stocks pose less risk but also lower returns.

Beta measures a stock's volatility, the degree to which its price fluctuates in relation to

the overall market. In other words, it gives a sense of the stock's market risk compared to

the greater market. Beta is used also to compare a stock's market risk to that of other

stocks. Investment analysts use the Greek letter 'ß' to represent beta.

Beta is a key component for the capital asset pricing model (CAPM), which is used to

calculate cost of equity. Recall that the cost of capital represents the discount rate used to

arrive at the present value of a company's future cash flows. All things being equal, the

higher a company's beta is, the higher its cost of capital discount rate. The higher the

discount rate, the lower the present value placed on the company's future cash flows. In

short, beta can impact a company's share valuation.

The beta of an investment is a relative measure of the systematic risk of an investment.

In other words it measures the specific risk of the company's shares relative to the market

as a whole. In general, the sign of the beta (+/-) indicates whether, on average, the

investment's returns move with the market or in the opposite direction to the market. The

scale or value of the beta indicates the relative volatility of the particular stock.

Indian Capital Market since liberalizations has undergone tremendous Changes and

has evolved as a vibrant system of investment flows. A dynamic Capital market is an

important segment of the financial system of any country as it plays a significant role in

mobilizing savings and channeling them for Productive purposes. The efficient fund

allocation depends on the stock market Efficiency in pricing the different securities

traded in it. The modern financial Theory focuses upon systematic factors as sources of



risk and contemplates that The long run return on an individual asset must reflect the

changes in such Systematic factors. An enquiry into such systematic factors through

different Methodologies suggested in finance literature would help the policy makers,

Investors, to design their investment strategies meaningfully.

BETA FACTORS:

This measure is calculated using regression analysis. A beta of 1 indicates that the

security's price tends to move with the market. A beta greater than 1 indicates that the

security's price tends to be more volatile than the market, and a beta less than 1 means it

tends to be less volatile than the market. Many utility stocks have a beta of less than 1,

and, conversely, many high-tech NASDAQ-listed stocks have a beta greater than 1.

Essentially, beta expresses the fundamental tradeoff between minimizing risk and

maximizing return. Let's give an illustration. Say a company has a beta of 2. This means

it is two times as volatile as the overall market. Let's say we expect the market to provide

a return of 10% on an investment. We would expect the company to return 20%. On the

other hand, if the market were to decline and provide a return of -6%, investors in that

company could expect a return of -12% (a loss of 12%). If a stock had a beta of 0.5, we

would expect it to be half as volatile as the market: a market return of 10% would mean a

5% gain for the company.

Here is a basic guide to various betas:

• Negative beta - A beta less than 0 - which would indicate an inverse relation to

the market - is possible but highly unlikely. Some investors used to believe that

gold and gold stocks should have negative betas because they tended to do better

when the stock market declined, but this hasn't proved to be true over the long

term.

• Beta of 0 - Basically, cash has a beta of 0. In other words, regardless of which

way the market moves, the value of cash remains unchanged (given no inflation).



• Beta between 0 and 1 - Companies with volatilities lower than the market have a

beta of less than 1 (but more than 0). As we mentioned earlier, many utilities fall

in this range.

• Beta of 1 - A beta of 1 represents the volatility of the given index used to

represent the overall market, against which other stocks and their betas are

measured. The S&P 500 is such an index. If a stock has a beta of one, it will move

the same amount and direction as the index. So, an index fund that mirrors the

S&P 500 will have a beta close to 1.

• Beta greater than 1 - This denotes a volatility that is greater than the broad-based

index. Again, as we mentioned above, many technology companies on the

NASDAQ have a beta higher than 1.

Beta greater than 100 - This is impossible as it essentially denotes a volatility that is 100

times greater than the market. If a stock had a beta of 100, it would be expected to go to 0

on any decline in the stock market. If you ever see a beta of over 100 on a research site it

is usually the result of a statistical error, or the given stock has experienced large swings

due to low liquidity, such as an over-the-counter stock. For the most part, stocks of well-

known companies rarely ever have a beta higher than 4.

A beta of +0.25 for instance, would indicate that on average, the investment's returns

move one quarter as much as the markets do in the same direction. If the market rose by

10%, the investment would be expected to rise by 2.5% but on the other hand if the

market fell by 10% the investment would be expected to fall by only 2.5%. A beta of -0.1

would indicate that on average, the investment's returns move one tenth as much as the

market's do, but in the opposite direction. If the market rose by 10%, the investment

would be expected to fall by 1%. Hence we can summaries a number of situations:



BETA AND RISK

Of course, there is more to it than that. Risk also implies return. Stocks with a high

beta should have a higher return than the market. If you are accepting more risk, you

should expect more reward.

For example, if the market with a beta of 1 is expected to return 8%, a stock with a beta

of 1.5 should return 12%. If you don’t see that level of return, then the stock is not a good

investment possibility.

Stocks with a beta below 1 may be a safer investment (at least by this one measure) and

you should expect a lower return.

Beta seems to be a great way to measure the risk of any stock. If you look a young,

technology stocks, they will always carry high betas. Many utilities on the other hand,

carry betas below 1. Problems with Beta

While the may seem to be a good measure of risk, there are some problems with relying

on beta scores alone for determining the risk of an investment.

• Beta looks backward and history is not always an accurate predictor of the future.

• Beta also doesn’t account for changes that are in the works, such as new lines of

business or industry shifts.

• Beta suggests a stock’s price volatility relative to the whole market, but that

volatility can be upward as well as downward movement. In a sustained

advancing market, a stock that is outperforming the whole market would have a

beta greater than 1.

Beta is also referred to as financial elasticity or correlated relative volatility, and

can be referred to as a measure of the asset's sensitivity of the asset's returns to market

returns, its non-diversifiable risk, its systematic risk or market risk. On an individual asset

level, measuring beta can give clues to volatility and liquidity in the marketplace. On a

portfolio level, measuring beta is thought to separate a manager's skill from his or her

willingness to take risk.



The beta movement should be distinguished from the actual returns of the stocks. For

example, a sector may be performing well and may have good prospects, but the fact that

its movement does not correlate well with the broader market index may decrease its

beta. However, it should not be taken as a reflection on the overall attractiveness or the

loss of it for the sector, or stock as the case may be. Beta is a measure of risk and not to

be confused with the attractiveness of the investment.

The beta coefficient was born out of linear regression analysis. It is linked to a regression

analysis of the returns of a portfolio (such as a stock index) (x-axis) in a specific period

versus the returns of an individual asset (y-axis) in a specific year. The regression line is

then called the Security Characteristic Line (SCL).

USING BETA FACTORS IN THE PRESENT SITUATION:

The current world political situation is probably the worst it is for many years.

World markets are falling at a rapid pace. What does beta factor analysis teach us about

an investment strategy in this situation? Firstly, however good a company is it likely that

in such circumstances most will encounter falls in their share prices.

However during this time a number of alternative investments that have negative

beta factors have appreciated in value. The prime example of this is gold. Over the past

twenty years when there was a strong equity bull market, However in the past few years

it is noticeable that in the political uncertainty that has arisen in the world that the price of

gold has shown material gains at a time when equity markets have recorded sharp falls.

Beta factor analysis is a useful technique that has enabled many international

investors to achieve satisfactory returns in the past. If one looks at the trends in world

markets then one can see that in a bull market those investors that have followed a

selective aggressive portfolio (i.e. including shares with beta factors of over 1 times) have

generally outperformed the market.

The current political uncertainty has made things extremely difficult for

investors especially in India. Should they get out of world markets since a conflict will



almost certainly mean falling equity prices. Or should investors move to alternative

investments with negative beta factors such as gold and oil? After all in case of a conflict

these commodities will almost certainly rise and will probably go against the trend of

equity prices. The beta is a measure of a stock’s price volatility in relation to the rest of

the market.

How to Use Beta

Investors can find the best use of the beta ratio in short-term decision-making, where

price volatility is important. If you are planning to buy and sell within a short period, beta

is a good measure of risk.

However, as a single predictor of risk for a long-term investor, the beta has too many

flaws. Careful consideration of a company’s fundamentals will give you a much better

picture of the potential long-term risk.

Beta is a measure of the market risk or volatility of investing in a stock. It helps investors

pick stocks that fall in their risk comfort zone.

But what does it really tell you about a stock and what mixed signals do investors get

when three different Web sites report three different betas for the same stock?

The last part of than question came from a reader, Dan R., who wondered why three Web

sites gave him three different answers to the same question about a stock’s beta and one

answer was apparently much different from the other two.

Advantages of Beta

To followers of CAPM, beta is a useful measure. A stock's price variability is important

to consider when assessing risk. Indeed, if you think about risk as the possibility of a

stock losing its value, beta has appeal as a proxy for risk.



Intuitively, it makes plenty of sense. Think of an early-stage technology stock with a

price that bounces up and down more than the market. It's hard not to think that stock will

be riskier than, say, a safe-haven utility industry stock with a low beta.

Besides, beta offers a clear, quantifiable measure, which makes it easy to work with.

Sure, there are variations on beta depending on things such as the market index used and

the time period measured, but broadly speaking, the notion of beta is fairly

straightforward to understand. It's a convenient measure that can be used to calculate the

costs of equity used in a valuation method that discounts cash flows.

Disadvantages of Beta.

For starters, beta doesn't incorporate new information. Consider the electrical utility

company American Electric Power (AEP). Historically, AEP has been considered a

defensive stock with a low beta. But when it entered the merchant energy business and

assumed high debt levels, AEP's historic beta no longer captured the substantial risks the

company took on. At the same time, many technology stocks, such as Google, are so new

to the market they have insufficient price history to establish a reliable beta.

Another troubling factor is that past price movements are very poor predictors of the

future. Betas are merely rear-view mirrors, reflecting very little of what lies ahead.

Furthermore, the beta measure on a single stock tends to flip around over time, which

makes it unreliable. Granted, for traders looking to buy and sell stocks within short time

periods, beta is a fairly good risk metric. But for investors with long-term horizons, it's

less useful.

In investing, beta does not refer to fraternities, product testing or VHS' old competition -

in investing, beta is a measurement of market risk, or volatility. It is because of this risk

that some people don't want to invest in stocks. These risk-averse investors can't stomach

stocks' greater tendency to fluctuate in price. Sure, there is always the possibility that a



stock will lose some or all of its value, but volatility also makes it possible for investors

to make a great deal of money - if they make the right choices.

Why You Should Know What Beta Is

Are you prepared to take a loss on your investments? Many people are not and therefore

opt for investments with low volatility. Other people are willing to take on additional risk

because with it they receive the possibility of increased reward. It is very important that

investors not only have a good understanding of their risk tolerance, but also know which

investments match their risk preferences.

And, by using beta to measure volatility, you can better choose those securities that meet

your criteria for risk. Investors who are very risk averse should put their money into

investments with low betas such as utility stocks and Treasury bills. Those investors who

are willing to take on more risk may want to invest in stocks with higher betas.

Many brokerage firms calculate the betas of securities they trade and then publish their

calculations in a beta book. These books offer estimates of the beta for almost any

publicly-traded company. The problem is that most of us don't have access to these

brokerage books, and the calculation for beta can often be confusing, even for

experienced investors



SENSEX

Established in 1875, BSE is not only the oldest stock exchange in India, but is also the

oldest in Asia. It accounts for over one-third of the total trading volume in the country.

The National Stock Exchange (NSE), located in Bombay, was set up in 1993 to

encourage stock exchange reform through system modernization and competition. It

opened for trading in mid-1994. Since then the NSE has made major strides and is now

the dominant stock exchange in the country. Most other studies on Indian market use the

BSE Sensex index to compute market returns. With NSE being an equally prominent

stock exchange in India, we also use the S&P CNX Nifty index to compute returns.

Between the two exchanges, NSE being demutualized provides a better market quality.

With lower execution cost, lower price volatility and higher liquidity compared to BSE,

NSE has emerged to be superior by providing improved market quality and high

standards of investor protection.

BSE Sensex is a basket of 30 constituent stocks representing a sample of large, liquid and

representative companies. The base year of SENSEX is 1978-79 and the base value is

100. The index is widely reported in both domestic and international markets through

print as well as electronic media. The Index was initially calculated based on the .Full

market capitalization. Methodology but was shifted to the .Free-float methodology with

effect from September 1, 2003.

SENSEX: THE BAROMETER OF INDIAN ECONOMY

For the premier Stock Exchange that pioneered the stock broking activity in India, 128

years of experience seems to be a proud milestone. A lot has changed since 1875 when

318 persons became members of what today is called "The Stock Exchange, Mumbai" by

paying a princely amount of Re1.Since then, the country's capital markets have passed

through both good and bad periods. The journey in the 20th century has not been an easy



one. Till the decade of eighties, there was no scale to measure the ups and downs in the

Indian stock market. The Stock Exchange, Mumbai (BSE) in 1986 came out with a stock

index that subsequently became the barometer of the Indian stock market. SENSEX is not

only scientifically designed but also based on globally accepted construction and review

methodology. First compiled in 1986, SENSEX is a basket of 30 constituent stocks

representing a sample of large, liquid and representative companies.

The base year of SENSEX is 1978-79 and the base value is 100. The index is widely

reported in both domestic and international markets through print as well as electronic

media.

The Index was initially calculated based on the "Full Market Capitalization"

methodology but was shifted to the free-float methodology with effect from September 1,

2003. The "Free-float Market Capitalization" methodology of index construction is

regarded as an industry best practice globally. All major index providers like MSCI,

FTSE, STOXX, S&P and Dow Jones use the Free-float methodology.

Due to is wide acceptance amongst the Indian investors; SENSEX is regarded to be the

pulse of the Indian stock market. As the oldest index in the country, it provides the time

series data over a fairly long period of time (From 1979 onwards). Small wonder, the

SENSEX has over the years become one of the most prominent brands in the country.

The growth of equity markets in India has been phenomenal in the decade gone by. Right

from early nineties the stock market witnessed heightened activity in terms of various

bull and bear runs. The SENSEX captured all these events in the most judicial manner.

One can identify the booms and busts of the Indian stock market through SENSEX.



SENSEX Calculation Methodology

SENSEX is calculated using the "Free-float Market Capitalization" methodology.

As per this methodology, the level of index at any point of time reflects the Free-float

market value of 30 component stocks relative to a base period. The market capitalization

of a company is determined by multiplying the price of its stock by the number of shares

issued by the company. This market capitalization is further multiplied by the free-float

factor to determine the free-float market capitalization. The base period of SENSEX is

1978-79 and the base value is 100 index points. This is often indicated by the notation

1978-79=100. The calculation of SENSEX involves dividing the Free-float market

capitalization of 30 companies in the Index by a number called the Index Divisor. The

Divisor is the only link to the original base period value of the SENSEX. It keeps the

Index comparable over time and is the adjustment point for all Index adjustments arising

out of corporate actions, replacement of scrips etc.

During market hours, prices of the index scrips, at which latest trades are executed, are

used by the trading system to calculate SENSEX every 15 seconds and disseminated in

real time.

Maintenance of SENSEX

One of the important aspects of maintaining continuity with the past is to update the base

year average. The base year value adjustment ensures that replacement of stocks in Index,

additional issue of capital and other corporate announcements like 'rights issue' etc. do

not destroy the historical value of the index. The beauty of maintenance lies in the fact

that adjustments for corporate actions in the Index should not per se affect the index

values.

The Index Cell of the exchange does the day-to-day maintenance of the index within the

road index policy framework set by the Index Committee. The Index Cell ensures that

SENSEX and all the other BSE indices maintain their benchmark properties by striking a



delicate balance between frequent replacements in index and maintaining its historical

continuity. The Index Committee of the Exchange comprises of experts on capital

markets from all major market segments. They include Academicians, Fund-managers

from leading Mutual Funds, Finance-Journalists, Market Participants, Independent

Governing Board members, and Exchange administration.

On-Line Computation of the Index

During market hours, prices of the index scrips, at which trades are executed, are

automatically used by the trading computer to calculate the SENSEX every 15 seconds

and continuously updated on all trading workstations connected to the BSE trading

computer in real time. Adjustment for Bonus, Rights and Newly issued Capital: The

arithmetic calculation involved in calculating SENSEX is simple, but problem arises

when one of the component stocks pays a bonus or issues rights shares. If no adjustments

were made, a discontinuity would arise between the current value of the index and its

previous value despite the non-occurrence of any economic activity of substance. At the

Index Cell of the Exchange, the base value is adjusted, which is used to alter market

capitalization of the component stocks to arrive at the SENSEX value. The Index Cell of

the Exchange keeps a close watch on the events that might affect the index on a regular

basis and carries out daily maintenance of all the 14 Indices.

Qualification Criteria:

The general guidelines for selection of constituent scrips in SENSEX are as follows:

A. Quantitative Criteria:

1. Final Rank: The scrip should figure in the top 100 companies listed by Final Rank.

The final rank is arrived at by assigning 75% weightage to the rank on the basis of six-

month average full market capitalisation and 25% weightage to the liquidity rank based

on six-month average daily turnover & six-month average impact cost.



2. Trading Frequency: The scrip should have been traded on each and every trading day

for the last six months. Exceptions can be made for extreme reasons like scrip suspension

etc.

3. Market Capitalization Weightage: The weight of each scrip in SENSEX based on

six-month average Free-Float market capitalization should be at least 0.5% of the Index.

4. Industry Representation: Scrip selection would take into account a balanced

representation of the listed companies in the universe of BSE. The index companies

should be leaders in their industry group.

5. Listed History: The scrip should have a listing history of at least 3 months on BSE.

However, the Committee may relax the criteria under exceptional circumstances.

B. Qualitative Criteria:

Track Record In the opinion of the Committee, the company should have an acceptable

track record.

Index Review Frequency:

The Index Committee meets every quarter to review all BSE indices. However, every

review meeting need not necessarily result in a change in the index constituents. In case

of a revision in the Index constituents, the announcement of the incoming and outgoing

scrips is made six weeks in advance of the actual implementation of the revision of the

Index.



Analysis of Indian stock market BSE Sensex Index

The BSE SENSEX is not only scientifically designed but also based on globally accepted

construction and review methodology. First compiled in 1986, SENSEX is a basket of 30

constituent stocks representing a sample of large, liquid and representative companies.

The base year of SENSEX is 1978-79 and the base value is 100. The index is widely

reported in both domestic and international markets through print as well as electronic

media. The Index was initially calculated based on the "Full Market Capitalization"

methodology but was shifted to the free-float methodology with effect from September 1,

2003. The "Free-float Market Capitalization" methodology of index construction is

regarded as an industry best practice globally. All major index providers like MSCI,

FTSE, STOXX, S&P and Dow Jones use the Free-float methodology. Due to is wide

acceptance amongst the Indian investors; SENSEX is regarded to be the pulse of the

Indian stock market. As the oldest index in the country, it provides the time series data

over a fairly long period of time (From 1979 onwards). Small wonder, the SENSEX has

over the years become one of the most prominent brands in the country.

SENSEX MILESTONES

Rise of the Sensex through Indian stock market history.

1000, July 25, 1990 - On July 25, 1990, the Sensex touched the four-digit figure for the

first time and closed at 1,001 in the wake of a good monsoon and excellent corporate

results.

2000, January 15, 1992 - On January 15, 1992, the Sensex crossed the 2,000-mark and

closed at 2,020 followed by the liberal economic policy initiatives undertaken by the then

finance minister and current Prime Minister Dr Manmohan Singh.



3000, February 29, 1992 - On February 29, 1992, the Sensex surged past the 3000 mark

in the wake of the market-friendly Budget announced by the then Finance Minister, Dr

Manmohan Singh.

4000, March 30, 1992 - On March 30, 1992, the Sensex crossed the 4,000-mark and

closed at 4,091 on the expectations of a liberal export-import policy. It was then that the

Harshad Mehta scam hit the markets and Sensex witnessed unabated selling.

5000, October 11, 1999 - On October 8, 1999, the Sensex crossed the 5,000-mark as the

BJP-led coalition won the majority in the 13th Lok Sabha election.

6000, February 11, 2000 - On February 11, 2000, the infotech boom helped the Sensex

to cross the 6,000-mark and hit and all time high of 6,006.

7000, June 21, 2005 - On June 20, 2005, the news of the settlement between the Ambani

brothers boosted investor sentiments and the scrips of RIL, Reliance Energy, Reliance

Capital and IPCL made huge gains. This helped the Sensex crossed 7,000 points for the

first time.

8000, September 8, 2005 - On September 8, 2005, the Bombay Stock Exchange's

benchmark 30-share index -- the Sensex -- crossed the 8000 level following brisk buying

by foreign and domestic funds in early trading.

9000, December 09, 2005 - The Sensex on November 28, 2005 crossed 9000 to touch

9000.32 points during mid-session at the Bombay Stock Exchange on the back of frantic

buying spree by foreign institutional investors and well supported by local operators as

well as retail investors.

10,000, February 7, 2006 - The Sensex on February 6, 2006 touched 10,003 points

during mid-session. The Sensex finally closed above the 10K-mark on February 7, 2006.



11,000, March 27, 2006 - The Sensex on March 21, 2006 crossed 11,000 and touched a

peak of 11,001 points during mid-session at the Bombay Stock Exchange for the first

time. However, it was on March 27, 2006 that the Sensex first closed at over 11,000

points.

12,000, April 20, 2006 - The Sensex on April 20, 2006 crossed 12,000 and touched a

peak of 12,004 points during mid-session at the Bombay Stock Exchange for the first

time.

13,000, October 30, 2006 - The Sensex on October 30, 2006 crossed 13,000 and still

riding high at the Bombay Stock Exchange for the first time. It took 135 days to reach

13,000 from 12,000. And 124 days to reach 13,000 from 12,500. On 30th October 2006 it

touched a peak of 13,039.36 & closed at 13,024.26.

14,000, December 5, 2006 - The Sensex on December 5, 2006 crossed 14,000 and

touched a peak of 14028 at 9.58AM (IST) while opening for the day December 5, 2006.

15,000, July 6, 2007- The Sensex on July 6, 2007 crossed another milestone and reached

a magic figure of 15,000. it took almost 7 month and 1 day to touch such a historic

milestone.

16,000, September 19, 2007- The Sensex on September 19, 2007 crossed the 16,000

mark and reached a historic peak of 16322 while closing. The bull hits because of the rate

cut of 50 bps in the discount rate by the Fed chief Ben Bernanke in US.

17,000, September 26, 2007- The Sensex on September 26, 2007 crossed the 17,000

mark for the first time, creating a record for the second fastest 1000 point gain in just 5

trading sessions. It failed however to sustain the momentum and closed below 17000.

The Sensex closed above 17000 for the first time on the following day. Reliance group

has been the main contributor in this bull run, contributing 256 points. This also helped

Mukesh Ambani's net worth to grow to over $50 billion or Rs.2 trillion. It was also



during this record bull run that the Sensex for the first time zoomed ahead of the Nikkei

of Japan.

18,000, October 9, 2007- The Sensex crossed the 18k mark for the first time on October

9, 2007. The journey from 17k to 18k took just 8 trading sessions which is the third

fastest 1000 point rise in the history of the Sensex. The Sensex closed at 18,280 at the

end of day. This 788 point gain on 9th October was the second biggest single day

absolute gains.

19,000, October 15, 2007- The Sensex crossed the 19k mark for the first time on

October 15th 2007. It took just 4 days to reach from 18k to 19k. This is the fastest 1000

points rally ever and also the 640 point rally was the second highest single day rally in

absolute terms. This made it a record 3000 point rally in 17 trading sessions overall.

20,000, October 29, 2007- The Sensex crossed the 20k mark for the first time with a

massive 734.5 point gain but closed below the 20k mark. It took 11 days to reach from

19k to 20k. The journey of the last 10,000 points was covered in just 869 sessions as

against 7,297 sessions taken to touch the 10,000 mark from 1,000 levels. In 2007 alone,

there were six 1,000-point rallies for the Sensex.



RESEARCH METHODALOGY



RESEARCH DESIGN

Study Type: The study type is analytical, quantitative and historical.

Analytical because facts and existing information is used for the analysis,

Quantitative as relationship is examined by expressing variables in measurable

terms and also Historical as the historical information is used for analysis and

interpretation.

Sampling frame: Sampling Frame would be 30 stocks of SENSEX INDEX

STOCKS.

Sampling technique: simple random sampling is used because only index

stocks units are selected from the sampling frame. Such a selection is undertaken

as these units represent the population in a better way and reflect better

relationship with the other variable.

Data gathering procedures and instruments:

Data: Historical yearly share prices of index stocks from 2001 to 2008

Data Source: Historical share prices of the sample companies and the index points

for the period has been taken from the database of capitaline.com

Sensex has been taken because BSE Sensex is considered as trust worthy indices

of India.



Sample description : to assessing the beta following list of index stocks to be

taken Name of company Name of industries

Acc Cement industries

Ambuja cement ltd Cement industries

Bharthi airtell telecommunication

Cipla Pharmaceutical industries

DLF Infrastructure development

Grasim Textile industries

HDFC Banking

Hindalco Ltd Iron and steel industries

ICICI Bank Banking industries

Jaiprakash Consulting and broking

L & T Infrastructure development

Maruthi Suzuki Automobile industries

Reliance com Information technology

Reliance energy Oil and power

SBI Banking industries

Satym Information technology

TATA Steel Iron and steel industries

TCS Information technology

M & M Automobile industries

Infosys Information technology

TATA MOTARS Auto mobile industries

BHEL Power

HUL Marketing

NTPC power and fuel

RANBAXY Pharmaceutical industries

WIPRO Information technology

ITC FMCG

Reliance Industries Ltd Diversified

HDFC Bank Banking

ONGC Oil and power



ANALYSIS

AND

INTERPRETATION



Table1: show that ranking of index stocks according to returns (%).

YEAR/returns (%)

NAME OF INDEX STOCKS 1ST year

ranks

2ND year

ranks

3RD year

ranks

4TH year

ranks

5TH year

ranks

6TH year

ranks

7TH year

ranks

8TH year

ranks

Acc 11 14 19 9 9 1 24 21

Ambuja cement ltd 2 21 15 11 12 6 21 18

Bharthi airtel 24 1 1 8 4 13 8

Cipla 5 23 21 19 15 16 28 3

DLF 8 29

Grasim 13 11 3 12 26 2 16 25

HDFC 1 15 17 20 10 19 10 7

Hindalco Ltd 15 20 8 26 25 25 17 12

ICICI Bank 23 4 12 7 11 11 15 22

Jaiprakash 3 1 3 3 30

L & T 10 13 7 2 2 10 2 27

Maruthi Suzuki 9 17 17 14 20 24

Reliance com 9 12 19

Reliance energy 9 12 10 24 24 29 1 28

sbi 12 5 14 18 16 18 7 17

Satym 18 9 22 23 5 20 25 5

TATA Steel 21 2 4 14 27 22 6 14

TCS 16 22 15 26 16

M & M 22 7 2 8 3 5 23 23

infosys 19 10 23 5 13 13 30 6

TATA MOTARS 3 3 6 22 20 17 29 13

BHEL 16 8 5 4 4 8 5 26

HUL 6 22 24 28 18 27 22 1

NTPC 15 21 24 9 20

RANBAXY 7 6 16 21 28 28 19 2

WIPRO 20 16 25 13 23 21 27 9

ITC 17 17 20 10 7 23 18 4

Reliance Industries Ltd 14 19 13 27 6 7 4 10

HDFC Bank 8 18 18 6 19 12 11 11

ongc 4 1 11 25 14 26 14 15



INTERPRETATION:

the above table states that , in year 2001 HDFC got 1st rank (22.33%). but later

it started to vary like 1,15,17,20,10,19,10,7 rank according to year

2002,2003,2004,2005,2006,2007,2008 respectively. Ranking is based on

percentage of return. So that, it states proper format is not there in index

stocks and also there is no relationship with past ranking.

Ambuja cement ltd having a second rank in 2001 but in 2002, rank 21st so

that it indicate that past ranking not influencing the future ranks

In 2006, ACC ranked 1st with a return of 101.775% and year 2007,ranked 24th

with a negative return of 6.63%, it indicates that ranking of post rank will not

influence future rank in index stocks.

This table shows that there is no specific format in index stocks rank so that

portfolio manager and investor not invest according the past rank in index

stock.

According to research, in BSE 30 there is no proper format in index stocks and in this

company who had 1st rank may not be same for the future years. So that by using simple

ranking technique null hypothesis is accepted.



Table2: show that ranking of index stocks according to Beta of stock.

YEAR/beta

NAME OF INDEX

STOCKS 1ST year

ranks

2ND year

ranks

3RD year

ranks

4TH year

ranks

5TH year

ranks

6TH year

ranks

7TH year

ranks

8TH year

ranks

Acc 4 8 9 16 12 14 12 12

Ambuja cement ltd 15 13 18 14 27 17 27 27

Bharthi airtel 14 22 15 9 22 10 26

Cipla 18 24 24 22 17 25 28 27

DLF 3 4

Grasim 12 12 13 20 22 5 20 19

HDFC 23 23 25 28 26 28 24 23

Hindalco Ltd 22 21 19 21 13 4 14 3

ICICI Bank 5 11 15 18

Jaiprakash 3 5 2 4 1

L & T 7 16 14 27 18 8 7 9

Maruthi Suzuki 4 8 8 7 13 28

Reliance com 3 6 6

Reliance energy 17 22 10 1 10 18 1 2

sbi 13 12 12 4 3 24 2 13

Satym 1 1 1 16 1 12 19 20

TATA Steel 8 7 5 2 6 1 8 10

TCS 19 19 23 14

M & M 9 4 6 12 14 16 16 22

infosys 3 2 3 19 7 20 22 15

TATA MOTARS 10 3 7 5 4 6 15 21

BHEL 11 9 17 2 15 10 5 11

HUL 16 10 11 24 20 13 26 24

NTPC 13 23 26 18 5

RANBAXY 14 17 21 26 21 23 25 25

WIPRO 2 6 2 7 2 9 17 16

ITC 19 18 20 23 24 11 29 18

Reliance Industries Ltd 6 5 8 9 11 15 9 7

HDFC Bank 20 20 23 25 25 27 21 17

ONGC 21 15 16 10 16 21 11 8



INTERPRETATION:

The above table shows that ranking of sensex index stocks according to beta of

stocks. This analysis prove that no specific format there in index stocks. Because

this research revels that if index stocks having higher Beta (1st rank) in previous

year but it may have low ranking in current year.

In this analysis also states that specific format is not there. Beta analysis reveals

that all the index stocks having a beta more than 0.5.

Beta analysis reveals that there is no consistency in Volatility of index stocks

example: in 2001, M&M having 0.993 Beta and 2002 it will 1.2, in 2003 it will

1.2262, in 2004 it will 1.05, in 2005 it will 0.97.

In 2001, 30% of index stocks having Beta more than 1. In 2002, 32% of index

stocks having Beta more than 1. In 2003, 40% of index stocks having beta more

than 1 and in2007, it will increase to 50%. This states that volatility of index

stocks don’t have any consistency.

According to research, in BSE 30 there is no proper format in index stocks and in this 1st

rank may not be same for future years. So that by using simple ranking technique null

hypothesis is accepted.



Table3: show that top 10 ranking index stocks in 2001

1st year Return (%) Beta Ranks

Ambuja cement ltd 17.322835 0.738 2

Cipla 8.4047619 0.6474 5

HDFC 22.335793 0.2866 1

L & T -2.84264 1.0422 10

Reliance energy -0.326633 0.6629 9

TATA MOTARS 11.63311 0.9925 3

HUL 7.7831325 0.73 6

RANBAXY 1.961433 0.83 7

HDFC Bank 0.8301548 0.5941 8

ONGC 10.16845 0.5691 4

-5

0

5

10

15

20

25

Am buja c ement lt C ip laHDFC

L & T

Rel ianc e energ

TATA MO TARS HUL

RANB AXY

HDFC Bank ongc

Series1

In 2001 out of 30 stocks, only 13.33% of index stocks giving more than 10% of return

and 50 % index stocks giving negative return and HDFC giving more return and ranked 1

and. If you are invested in market in this period you can get negative return.



Table4: show that top 20 and below 20 ranking index stocks in 2001

year 1

return % Beta

ranks

ICICI Bank -42.671 1.1162 23

TATA Steel -33.6861

1.04 21

M & M -40.4933

0.993 22

WIPRO -33.2216

2.213820

year 1st

-50

-40

-30

-20

-10

0ICICI Bank TATA Steel M & M WIPRO

index stocks

return (%) Series1




2nd year Return (%)

Beta Ranks

ICICI Bank 56.166667 0.8766 4

SBI 54.459632 0.8204 5

Satym 18.964041 2.0251 9

TATA Steel 71.475221 1.12 2

M & M 25.222222 1.2 7

Infosys 17.08155 1.3617 10

TATA MOTARS 59.673429 1.2568 3

BHEL 22.846975 0.9588 8

RANBAXY 35.90049 0.709 6

ONGC 153.47826 0.7956 1

020406080

100120140160180

IC IC I Ba nk sbiSat ym

TATA S teelM & M

in fosy s

TATA MO TARS BHE L

RANBAXY ongc

Series1




year 2

return %

Beta ranks

Ambuja cement ltd -14.0828 0.8113 21

Bharthi airtel -58.3636 0.8065 24

Cipla -22.132 0.237 23

Hindalco Ltd -7.51174 0.377 20

HUL -18.6801 0.9464 22

2nd year

-70

-60

-50

-40

-30

-20

-10

0Ambuja cement ltd Bharthi airtel Cipla Hindalco Ltd HUL

index stocks

retu

rn(%

)

Series1




3 rd year

Return

(%)

Beta Ranks

Bharthi airtell 347.23404 0.5418 1

Grasim 217.80627 0.9572 3

Hindalco Ltd 142.75243 0.6722 8

L & T 146.77585 0.92 7

Maruthi Suzuki 137.56313 1.3526 9

Reliance energy 130.55054 1.0677 10

TATA Steel 191.94376 1.3 4

M & M 244.11817 1.2262 2

TATA MOTARS 180.14865 1.2113 6

BHEL 191.93103 0.8968 5

0

20

40

60

80

100

120

Acc

Bha rthi airtel

IC IC I Ba nk

Jaiprakash L & TM & M

in fosysBHE L ITC

HDFC Bank

Series1




year 3 return % Beta ranks

Cipla 45.7135 0.5109 21

Satym 31.15316 1.9873 22

Infosys 16.83494 1.5784 23

HUL 12.28744 0.9962 24

WIPRO 5.670291 1.7877 25

ITC 48.30547 0.6616 20

3 rd year

0

10

20

30

40

50

60

Cipla Satym infosys HUL WIPRO ITC

index stocks

retu

rn (%

)

Series1




4 th year

Return

(%)

Beta

Ranks

Acc 37.738918 1.0873 9


ICICI Bank 42.760878 0.9693 7

Jaiprakash 62.350427 1.4335 3

L & T 85.283019 0.49 2

M & M 38.903061 1.0563 8

Infosys 49.080114 0.9167 5

BHEL 50.371094 1.3626 4

ITC 31.763996 0.7584 10

HDFC Bank 43.328729 0.6697 6

0

20

40

60

80

100

120

Acc

Bha rthi airtel

IC IC I Ba nk

Jaiprakash L & TM & M

in fosysBHE L ITC

HDFC Bank

Series1





HDFC 18.13416 0.4293 20

Hindalco Ltd 0.14177 0.7719 26


Satym 10.78378 1.0202 23

TATA MOTARS 11.02198 1.3794 22

HUL -30.3567 0.7058 28

RANBAXY 13.7533 0.5373 21

Reliance Industries

Ltd -6.51617

1.201

27

ONGC 1.217117 1.1246 25

4 th year

-40

-30

-20

-10

0

10

20

30

HD

FC

Rel

ianc

een

ergy

TATA

MO

TAR

S

RA

NB

AX

Y

ongc

index stocks

retu

rn (%

)

Series1




5 th year

Return (%)

Beta Ranks

Acc 57.279552 1.0433 9


HDFC 56.742523 0.4678 10

Jaiprakash 109.1129 1.1597 1

L & T 86.527764 0.8942 2

Satym 79.07767 1.32 5

M & M 86.267734 0.9746 3

BHEL 79.800259 0.9723 4

ITC 60.815402 0.7751 7

Reliance Industries Ltd 71.067383 1.0525 6

0

20

40

60

80

100

120

Acc Bharthiairtel

HDFC Jaiprakash L & T Satym M & M BHEL ITC RelianceIndustries

Ltd

Series1





Grasim 4.213483 0.7923 26

Hindalco Ltd 5.869324 0.9848 25


TATA Steel -2.73751 1.12 27

TCS 26.12683 0.8586 22

TATA MOTARS 28.10201 1.17 20

NTPC 27.98635 0.792 21

RANBAXY -42.1166 0.799 28

WIPRO 23.09429 1.12 23

5 th year

-50-40-30-20-10

010203040

Grasim

Hindalco

Ltd

Reliance ene

rgy

TATA Steel

TCS

TATA MOTARS

NTPC

RANBAXY

WIPRO

index stocks

retu

rn(%

)

Series1




6 th year

Return (%)

Beta Ranks

Acc 101.77509 1.0429 1



Grasim 100.51042 1.21 2

Jaiprakash 87.216495 1.3876 3

L & T 56.417344 1.14 10

Reliance com 62.517241 1.3041 9

M & M 76.923828 0.9867 5

BHEL 64.860832 1.08 8


020406080

100120

Acc

Ambuja ce

ment lt

d

Bharthi a

irtel

Grasim

Jaiprakas

hL & T

Reliance

com

M & MBHEL

Reliance

Industr

ies Lt

d

Series1





Hindalco Ltd 17.63514 1.25 25


Satym 30.67369 1.052 20

TATA Steel 26.25437 1.479 22

HUL 9.923858 1.05 27

NTPC 21.78571 0.7682 24

RANBAXY 7.532931 0.8291 28

WIPRO 30.29095 0.8294 21

ITC 23.47368 1.0978 23

ONGC 11.06927 0.8571 26

6th year

-20-15-10-505

101520253035

Hindalco

Ltd

Relianc

e energy

Satym

TATA Stee

lHUL

NTPC

RANBAXY

WIPRO ITC

ongc

index stocks

retu

rn (%

)

Series1




7th year Return (%)

Beta Ranks

DLF 84.364261 1.233 8

HDFC 77.860681 1.2166 10

Jaiprakash 192.43599 1.2 3

L & T 197.98929 1.3115 2


SBI 90.136583 1.188 7

TATA Steel 118.53376 1.21 6

BHEL 124.52215 0.834 5

NTPC 81.854545 0.6241 9


050

100150200250300350

DLFHDFC

Jaipr

akas

hL &

T

Relianc

e energy

sbi

TATA Stee

l

BHELNTPC

Relianc

e Indu

stries

Ltd

Series1





Acc -6.82439 1.0302 24


Cipla -16.101 1.0043 28

Maruthi Suzuki 6.217144 0.8251 20

TCS -13.332 0.7124 26

M & M -5.61404 0.908 23

Infosys -21.124 0.71 30

TATA MOTARS -17.5824 0.5422 29

HUL -1.88073 0.57 22

WIPRO -13.5384 0.89 27

7 th year

-25

-20

-15

-10

-5

0

5

10

Acc

Am buja cement lt C ip la

Maruthi Suzuk TCSM & M

in fosy s

TATA MOTARS HULWIP RO

index stocks

retu

rn%

)

Series1




8th year

Return (%)

Beta Ranks

Bharthi airtel -8.386139 0.6216 8

Cipla 4.3255814 0.5417 3

HDFC -7.897436 0.6924 7

Satym -0.313901 0.738 5

Infosys -4.069966 0.8109 6

HUL 16.596639 0.6808 1

RANBAXY 11.348837 0.6452 2

WIPRO -10.68966 0.8 9

ITC 0.3537736 0.79 4

Reliance Industries Ltd -11.0339 1.1618 10

-15

-10

-5

0

5

10

15

20

Bharth

i airte

lCipla

HDFCSaty

minf

osys HUL

RANBAXY

WIPRO ITC

Reliance

Indu

stries

Ltd

Series1




year 8

return %

Beta Ranks

Acc -24.372 0.9892 21

DLF -36.3762 1.4226 29

Grasim -26.9269 0.7827 25

ICICI Bank -25.8178 * 22

Jaiprakash -42.8605 1.76 30

L & T -29.1016 1.0462 27

Maruthi Suzuki -26.6418 0.4284 24


M & M -26.2471 0.7015 23

BHEL -27.7872 1.02266 26

NTPC -24.1732 1.3381 20

8 th year

-50

-40

-30

-20

-10

0

AccDLF

Grasim

ICICI Ban

k

Jaiprakas

hL & T

Maruthi S

uzuki

Reliance

energy

M & MBHEL

NTPC

index stocks

retu

rn (%

)

Series1



SUMMARY OF FINDINGS



SUMMARY OF FINDINGS:

Infrastructure industries, power and fuel industries, pharmaceutical industries, and

automobile industries stocks in sensex index giving more returns.

Important finding in research is, in index stocks there is no specific format or

trend in return of index stocks that is if the company having 1st rank may not be

the same for next years.

There is no consistency in the return of the stocks for example in 2001 HDFC

giving 22.3% of return and 2002 it will 6.23%. in 2003.

Bharthi airtell giving 350% of return and in 2004 it will 102.9% and also Beta of

this scrip is also varying from 0.5413 to 1.034. So that study indicates that there is

no trend or format in index stocks.

All the index stocks are more volatile because most of index stocks having a beta

more than 1.



CONCLUSION



CONCLUSION

In India the investors being irrational play a lot with the numbers when comes to

investing decisions rather than analyzing the various factors that affect the stock prices.

Because of this investment decisions they are losing a lot of money. According to the

research we can conclude that there is no specific format or trend when comes to Beta

and returns in the BSE Index.

So being a portfolio manager one has to analyze and to do various fundamental and

technical analysis to come up being rational than to speculate by being considering

historical status for investing decisions. Because of the volatility and varying nature of

the stock market proper analysis should be done for investing approach From our

research we can conclude that the Infrastructure, Banking, Iron and Steel, Information

Technology and Automobile sectors doing wonderfully well with good returns but has

beta more than one which indicates that it also difficult to assess its stock prices as it is

very much volatile

Finally we can conclude from the research is that there is no specific format or trend

in BSE Index stocks.



BIBLIOGRAPHY



WEBSITES:

• www.google.com

• www.Capitaline.com

• www.moneycontrol.com

• www.jstor.com

• www.wikipedia.com

BOOKS:

• FINANCIAL MANAGEMENT: Prof Prasanna Chandra

• SECURITY ANALYSIS AND PORTFOLIO MANAGEMENT: Prof Prasanna

Chandra.



ANNEXURE



2001

NAME OF STOCK o\p c\s return % Rank Beta

Acc 160 151.8 -5.125 11 1.464

Ambuja cement ltd 21.59 25.33 17.32283 2 0.738

Bharthi airtel 0

Cipla 84 91.06 8.404762 5 0.6474

DLF 0

Grasim 290.5 274.6 -5.47332 13 0.9123

HDFC 271 331.53 22.33579 1 0.2866

Hindalco Ltd 70.06 60.8 -13.2172 15 0.4359

ICICI Bank 153.5 88 -42.671 23 1.1169

Jaiprakash 0

L & T 98.5 95.7 -2.84264 10 1.0422

Maruthi Suzuki 0

Reliance com 0

Reliance energy 199 198.35 -0.32663 9 0.6629

Sbi 182.1 172.24 -5.41461 12 0.8451

Satym 159.85 118.15 -26.087 18 2.3433

TATA Steel 77.48 51.38 -33.6861 21 1.04

TCS 0

M & M 75 44.63 -40.4933 22 0.993

infosys 714.88 509.2 -28.7713 19 1.9598

TATA MOTARS 89.4 99.8 11.63311 3 0.9925

BHEL 81.25 70.3 -13.4769 16 0.9153

HUL 207.5 223.65 7.783133 6 0.73

NTPC 0

RANBAXY 211.58 215.73 1.961433 7 0.83

WIPRO 399.83 267 -33.2216 20 2.2138

ITC 59.94 45.12 -24.7247 17 0.6208

Reliance Industries Ltd 276.38 248.09 -10.2359 14 1.1028

HDFC Bank 222.85 224.7 0.830155 8 0.5941

ONGC 81.33 89.6 10.16845 4 0.5691

sensex 3990.65 3262.33 -18.2507



2002

NAME OF STOCK o\p c\s return % ranks Beta

Acc 152 165.1 8.618421 14 1.1039

Ambuja cement ltd 25.35 21.78 -14.0828 21 0.8113

Bharthi airtel 55 22.9 -58.3636 24 0.8065

Cipla 92.4 71.95 -22.132 23 0.237

DLF 0

Grasim 276 315.25 14.22101 11 0.5904

HDFC 335 358.2 6.925373 15 0.2386

Hindalco Ltd 59.64 55.16 -7.51174 20 0.3777

ICICI Bank 90 140.55 56.16667 4 0.8766

Jaiprakash 0

L & T 96.4 106.78 10.76763 13 0.76

Maruthi Suzuki 0

Reliance com 0

Reliance energy 198.8 222.25 11.79577 12 0.3701

Sbi 172.66 266.69 54.45963 5 0.8204

Satym 116.8 138.95 18.96404 9 2.0251

TATA Steel 52.06 89.27 71.47522 2 1.12

TCS 0

M & M 45 56.35 25.22222 7 1.2

infosys 509.38 596.39 17.08155 10 1.3617

TATA MOTARS 101.05 161.35 59.67343 3 1.2568

BHEL 70.25 86.3 22.84698 8 0.9588

HUL 223.5 181.75 -18.6801 22 0.9464

NTPC 0

RANBAXY 218.27 296.63 35.90049 6 0.709

WIPRO 265.21 271.77 2.473512 16 1.1204

ITC 44.71 44.03 -1.52091 17 0.6396

Reliance Industries

Ltd 249.59 242.03 -3.02897 19 1.17

HDFC Bank 224.6 219 -2.49332 18 0.513

ONGC 92 233.2 153.4783 1 0.7956

sensex 3262.01 3377.28 3.533711



2003


Acc 165.5 245.55 48.36858 19 1.1255


Bharthi airtel 23.5 105.1 347.234 1 0.5418

Cipla 72.32 105.38 45.7135 21 0.5109

DLF 0

Grasim 315.9 1003.95 217.8063 3 0.9572

HDFC 358 644.35 79.98603 17 0.2939

Hindalco Ltd 54.57 132.47 142.7524 8 0.6727

ICICI Bank 141.7 295.7 108.6803 12 0.914

Jaiprakash 0

L & T 106.85 263.68 146.7759 7 0.92

Maruthi Suzuki 158.4 376.3 137.5631 9 1.3526

Reliance com 0

Reliance energy 221.6 510.9 130.5505 10 1.0677

Sbi 267.02 508.09 90.28163 14 0.9902

Satym 140.05 183.68 31.15316 22 1.9873

TATA Steel 89.62 261.64 191.9438 4 1.3

TCS 0

M & M 56.53 194.53 244.1182 2 1.2262

infosys 595.25 695.46 16.83494 23 1.5784

TATA MOTARS 161.45 452.3 180.1487 6 1.2113

BHEL 87 253.98 191.931 5 0.8968

HUL 182.3 204.7 12.28744 24 0.9962

NTPC 0

RANBAXY 298.9 549.1 83.70693 16 0.6508

WIPRO 274.06 289.6 5.670291 25 1.7877

ITC 44.26 65.64 48.30547 20 0.6616

Reliance Industries

Ltd 243.9 465.85 91.00041 13 1.187

HDFC Bank 219.775 366.65 66.82971 18 0.5222

ONGC 234.97 533 126.8375 11 0.9131

sensex 3383.85 5838.96 72.55375



2004


Acc 245.9 338.7 37.73892 9 1.0873


Bharthi airtel 106.25 215.6 102.9176 1 1.0346

Cipla 107.12 126.9 18.46527 19 0.7709

DLF 0

Grasim 1010 1322.35 30.92574 12 0.9124

HDFC 648.5 766.1 18.13416 20 0.4293

Hindalco Ltd 134.02 134.21 0.14177 26 0.7719

ICICI Bank 259.7 370.75 42.76088 7 0.9693

Jaiprakash 23.4 37.99 62.35043 3 1.4335

L & T 265 491 85.28302 2 0.49

Maruthi Suzuki 376 461.25 22.67287 17 1.3146

Reliance com 0

Reliance energy 514.95 524.4 1.83513 24 1.4851

Sbi 510.35 615.6 20.6231 18 1.4293

Satym 185 204.95 10.78378 23 1.0202

TATA Steel 265.14 340.66 28.48307 14 1.438

TCS 538 667.75 24.1171 16 1.01

M & M 196 272.25 38.90306 8 1.0563

infosys 700.63 1044.5 49.08011 5 0.9167

TATA MOTARS 455 505.15 11.02198 22 1.3794

BHEL 256 384.95 50.37109 4 1.3626

HUL 206.05 143.5 -30.3567 28 0.7058

NTPC 70 87.35 24.78571 15 1.0473

RANBAXY 550.05 625.7 13.7533 21 0.5373

WIPRO 290.73 374 28.6417 13 1.3237

ITC 66.27 87.32 31.764 10 0.7584

Reliance Industries

Ltd 464.23 433.98 -6.51617 27 1.201

HDFC Bank 362 518.85 43.32873 6 0.6697

ONGC 539.8 546.37 1.217117 25 1.1246

sensex 5872.48 6602.69 12.43444



2005


Acc 339.65 534.2 57.27955 9 1.0433

Ambuja cement ltd 54 79.6 47.40741 12 0.09557

Bharthi airtel 218.9 345.7 57.92599 8 1.0845

Cipla 128 177.36 38.5625 15 0.8952

DLF 0

Grasim 1335 1391.25 4.213483 26 0.7923

HDFC 769 1205.35 56.74252 10 0.4678

Hindalco Ltd 135.45 143.4 5.869324 25 0.9848

ICICI Bank 374.85 584.7 55.98239 11

Jaiprakash 37.2 77.79 109.1129 1 1.1597

L & T 494.35 922.1 86.52776 2 0.8942

Maruthi Suzuki 461 636.5 38.06941 17 1.1034

Reliance com 0

Reliance energy 525 604.5 15.14286 24 1.0602

Sbi 618.01 856.2 38.54145 16 1.2292

Satym 206 368.9 79.07767 5 1.32

TATA Steel 345.57 336.11 -2.73751 27 1.11

TCS 674.9 851.23 26.12683 22 0.8586

M & M 274.9 512.05 86.26773 3 0.9746

Infosys 1049.5 1498.38 42.77084 13 1.1085

TATA MOTARS 509.75 653 28.10201 20 1.1752

BHEL 385.5 693.13 79.80026 4 0.9726

HUL 144.2 197.25 36.78918 18 0.8578

NTPC 87.9 112.5 27.98635 21 0.792

RANBAXY 626 362.35 -42.1166 28 0.7995

WIPRO 376.5 463.45 23.09429 23 1.2961

ITC 88.3 142 60.8154 7 0.7751

Reliance Industries

Ltd 422.81 723.29 71.06738 6 1.0525

HDFC Bank 522 707.45 35.52682 19 0.6942

ONGC 551.94 783.3 41.9176 14 0.9194

sensex 6626.49 9397.93 41.82365



2006

NAME OF STOCK o\p c\s returns % ranks Beta

Acc 538 1085.55 101.7751 1 1.0429

Ambuja cement ltd 80 141.3 76.625 6 0.97701

Bharthi airtel 348.9 628.85 80.23789 4 0.84

Cipla 178 250.7 40.8427 16 0.7983

DLF 0

Grasim 1391 2789.1 100.5104 2 1.21

HDFC 1212 1624.55 34.03878 19 0.5776

Hindalco Ltd 148 174.1 17.63514 25 1.25

ICICI Bank 586.25 890.4 51.8806 11

Jaiprakash 77.6 145.28 87.21649 3 1.3876

L & T 922.5 1442.95 56.41734 10 1.14

Maruthi Suzuki 634.9 927.35 46.06237 14 1.1497

Reliance com 290 471.3 62.51724 9 1.3041

Reliance energy 607 519.75 -14.374 29 0.9632

Sbi 858.41 1175.53 36.94272 18 0.8074

Satym 370.35 483.95 30.67369 20 1.052

TATA Steel 337.62 426.26 26.25437 22 1.479

TCS 853.5 1218.6 42.7768 15 0.9273

M & M 512 905.85 76.92383 5 0.9867

infosys 1500 2240.5 49.36667 13 0.9085

TATA MOTARS 650 900.25 38.5 17 1.1802

BHEL 697 1149.08 64.86083 8 1.08

HUL 197 216.55 9.923858 27 1.0514

NTPC 112 136.4 21.78571 24 0.7682

RANBAXY 364.4 391.85 7.532931 28 0.8294

WIPRO 464 604.55 30.29095 21 1.0978

ITC 142.5 175.95 23.47368 23 1.0683

Reliance Industries

Ltd 726.38 1270.35 74.8878 7 1.0399

HDFC Bank 710.9 1069.75 50.47827 12 0.715

ONGC 783.34 870.05 11.06927 26 0.8571

sensex 9422.7 13786.91 46.31592



2007

NAME OF STOCK o\p c\s return% ranks Beta

Acc 1099 1024 -6.82439 24 1.0302

Ambuja cement ltd 144.8 146 0.828729 21 0.5531

Bharthi airtel 635 994.55 56.62205 13 1.0834

Cipla 253.4 212.6 -16.101 28 0.5486

DLF 582 1073 84.36426 8 1.233

Grasim 2800 3651.6 30.41429 16 0.8

HDFC 1615 2872.45 77.86068 10 0.649

Hindalco Ltd 175.4 214.85 22.49145 17 0.9593

ICICI Bank 899 1232.4 37.08565 15

Jaiprakash 145.69 426.05 192.436 3 1.2166

L & T 1400 4171.85 197.9893 2 1.2

Maruthi Suzuki 932.1 990.05 6.217144 20 1.0043

Reliance com 471 746.5 58.49257 12 1.2007

Reliance energy 523 2134.6 308.1453 1 1.3115

Sbi 1176.57 2237.09 90.13658 7 1.2519

Satym 486 449.16 -7.58025 25 0.8251

TATA Steel 427.76 934.8 118.5338 6 1.188

TCS 1250 1083.35 -13.332 26 0.7124

M & M 912 860.8 -5.61404 23 0.908

infosys 2242 1768.4 -21.124 30 0.7182

TATA MOTARS 900.9 742.5 -17.5824 29 0.9422

BHEL 1151 2584.25 124.5222 5 1.21

HUL 218 213.9 -1.88073 22 0.57

NTPC 137.5 250.05 81.85455 9 0.834

RANBAXY 393 425.95 8.384224 19 0.6241

WIPRO 607.9 525.6 -13.5384 27 0.89

ITC 177.9 210.3 18.21248 18 0.5433

Reliance Industries

Ltd 1252.55 2881.05 130.0148 4 1.0995

HDFC Bank 1070 1727.8 61.47664 11 787

ONGC 878 1236.5 40.83144 14 1.0374

sensex 13827.77 20286.99 46.71194



2008

NAME OF STOCK o\p c\s return ranks Beta

Acc 1035 782.75 -24.372 21 0.9892

Ambuja cement ltd 147.55 114.8 -22.1959 18 0.3595

Bharthi airtel 1010 925.3 -8.38614 8 0.6216

Cipla 215 224.3 4.325581 3 0.5417

DLF 1051.1 668.75 -36.3762 29 1.4226

Grasim 3625 2648.9 -26.9269 25 0.7827

HDFC 2925 2694 -7.89744 7 0.6924

Hindalco Ltd 218.9 189 -13.6592 12 1.4325

ICICI Bank 1235 916.15 -25.8178 22

Jaiprakash 430 245.7 -42.8605 30 1.76

L & T 4191 2971.35 -29.1016 27 1.0462

Maruthi Suzuki 1005 737.25 -26.6418 24 0.4284

Reliance com 749.7 577.05 -23.0292 19 1.2276

Reliance energy 2134.6 1358.9 -36.3394 28 1.7464

Sbi 2246.52 1750.1 -22.0973 17 0.8558

Satym 446 444.6 -0.3139 5 0.738

TATA Steel 938 803.3 -14.3603 14 1.038

TCS 1065.1 889.8 -16.4585 16 0.8149

M & M 862 635.75 -26.2471 23 0.7015

infosys 1758 1686.45 -4.06997 6 0.8109

TATA MOTARS 742.5 639.1 -13.9259 13 0.7206

BHEL 2585 1866.7 -27.7872 26 1.0266

HUL 214.2 249.75 16.59664 1 0.6808

NTPC 254 192.6 -24.1732 20 1.3381

RANBAXY 430 478.8 11.34884 2 0.6452

WIPRO 522 466.2 -10.6897 9 0.8

ITC 212 212.75 0.353774 4 0.7951

Reliance Industries

Ltd 2950 2624.5 -11.0339 10 1.1618

HDFC Bank 1728 1498.1 -13.3044 11 0.7982

ONGC 1248 1055 -15.4647 15 1.0625

sensex 20325.27 17125.98 -15.7405

51395704 analysis-of-beta-returns-in-bse

Business

prof prasanna

shown material

future cash

major index

studied monthly

initially

bombay stock

full market