Analysis of Return and Beta Trends in Sensex Stocks

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
Ambujacementlt
Cipla
HDFC
L&
T
Relianceenerg
TATAMOTARS
HULRANBAXYHDFC
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
2.2138
20
year 1st
-50
-40
-30
-20
-10
0
ICICI 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
0
20
40
60
80
100
120
140
160
180
ICICIBank
sbi
Satym
TATASteel
M
&
M
infosysTATAMOTARS
BHELRANBAXY
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
0
Ambuja cement ltd Bharthi airtel Cipla Hindalco Ltd HUL
index stocks
return(%)
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
AccBharthiairtelICICIBankJaiprakash
L&
T
M
&
M
infosys
BHEL
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
return(%)
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
AccBharthiairtelICICIBankJaiprakash
L&
T
M
&
M
infosys
BHEL
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
HDFC
Reliance
energy
TATA
MOTARS
RANBAXY
ongc
index stocks
return(%)
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 Bharthi
airtel
HDFC Jaiprakash L&T Satym M &M BHEL ITC Reliance
Industries
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
0
10
20
30
40
G
rasimHindalco
Ltd
Reliance
energyTATA
Steel
TCS
TATA
M
O
TAR
S
NTPC
RANBAXY
W
IPRO
index stocks
return(%)
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
0
20
40
60
80
100
120
Acc
Am
buja
cem
entltdBharthiairtel
Grasim
Jaiprakash
L
&
TReliance
com
M
&
M
BHEL
Reliance
Industries
Ltd
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
-5
0
5
10
15
20
25
30
35
Hindalco
Ltd
Reliance
energy
Satym
TATA
Steel
HU
L
NTPC
RANBAXY
W
IPRO
ITC
ongc
index stocks
return(%)
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
0
50
100
150
200
250
300
350
DLF
HD
FCJaiprakash
L
&
T
Reliance
energy
sbi
TATA
Steel
BHEL
NTPC
Reliance
Industries
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
Ambujacementlt
CiplaMaruthiSuzuk
TCS
M
&
M
infosysTATAMOTARS
HUL
WIPRO
index stocks
return%)
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
Bharthiairtel
Cipla
HD
FC
Satym
infosys
HU
LRANBAXY
W
IPRO
ITC
Reliance
Industries
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
Acc
DLF
Grasim
IC
IC
IBankJaiprakash
L
&
T
M
aruthiSuzuki
Reliance
energy
M
&
M
BHEL
N
TPC
index stocks
return(%)
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 op cs 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 op cs 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 op cs 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 op cs 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 op cs 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

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