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Investment Analysis and Portfolio Management
A project report submitted as partial fulfilment of
Summer Internship Project at IDBI Federal Life
Insurance.
Submitted By:-
Pankaj Arora
Summer Intern
Goa Institute of Management, Goa
Email- [email protected]
Submitted To:-
Project Guide-
Ms. Shanthi Yagyanath
(Manager Distribution- Chief, IDBI
Federal)
Project Co-ordinator-
Mr. Sathya Balan M.A.
(Business Mentor, IDBI Federal)
GIM Faculty Guide-
Prof. Nirmalya Bandyopadhyay
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Disclaimer
This document is copyright protected in content, presentation, and intellectual origin,
except where noted otherwise. You may not modify, remove, augment, add to, publish,
transmit, participate in the transfer or sale of, create derivative works from, or in any way
exploit any of the elements of this document, in whole or in part without prior written
permission from IDBI Federal Life Insurance Co. Ltd. © 2011-2012
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Table of Contents Acknowledgement ............................................................................................................................ 3
Executive Summary .......................................................................................................................... 4
Industry Overview..................................................................................................................................5
Company Overview……………………………………………………………………………………………………………….………..11
Financial Markets………………………………………………………………………………………………………………………..….13
Primary and secondary market
Trading in secondary market
Money market
Bond Market........................................................................................................................................15
Macaulay Duration and Modified Duration
Financial Analysis and Valuation…………………………………………………………………………………………………….17
Valuation of stocks
Managing a Portfolio………………………………………………………………………………………………………………………20
The CAPM Model
Calculation of Beta
Arbitrage Pricing Theory
Sharpe Ratio
Treynor Ratio
Jenson Measure or Portfolio Alpha
Analysis of a portfolio
Market Research…………………………………………………………………………………………………………………………….63
Objective
Methodology
Questionnaire
Analysis
Findings
Recommendations………………………………………………………………………………………………………………………..68
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Acknowledgement
I take this opportunity to thank various people who have made it possible for me to
successfully complete my internship program with the project at IDBI Federal Life. I would
like to thank the following people:
Mrs. Shanthi Yagyanath – Manager Distribution, Chief, IDBI Federal who gave me this
wonderful opportunity to work on such a fruitful project.
Mr. Sathya Balan M.A. – Business Mentor, IDBI Federal for guiding and assisting me in
the project and for his valuable feedback at every step of the project.
Mr. Hemanth Nagaraj – Corporate Trainer, IDBI Federal for briefing about the company‘s
background and products and helping me throughout the project.
Also I would like to thank Prof. Nirmalya Bandyopadhyay – Faculty Guide, Goa Institute
of Management for his critical views, suggestions and support during the course of the
project.
Apart from these, I would like to thank the other officers and staff of IDBI Federal Life
Insurance Co. Ltd. where I had received immense support in carrying out my internship
program.
I would like to thank all other people who are in some way or the other involved with my
internship. These include my friends and other colleagues.
Pankaj Arora
Goa Institute of Management
Batch of 2010-12
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Executive Summary
Life Insurance companies collect the money from the policyholders in the form of premiums
and invest this money in various investment opportunities available like fixed bonds,
securities, stocks, mutual funds, etc. The investment strategy depends on the investment
objective and future expectations of cash flow. This includes effective management of a
portfolio of investments by the Insurance company in order to meet the future liabilities.
This research work deals with the review of an existing portfolio of IDBI Federal Life
Insurance Co. Ltd. by calculating the Beta of all the stocks held in the portfolio and hence
giving a critical feedback to the company to how to improvise the portfolio and increase the
returns by mitigating risks.
The findings will give a better combination of stocks to be held as a portfolio in order to
increase the returns.
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Industry Overview
Indian insurance is a flourishing industry, with several national and international players
competing and growing at rapid rates. Thanks to reforms and the easing of policy regulations,
the Indian insurance sector been allowed to flourish, and as Indians become more familiar
with different insurance products, this growth can only increase, with the period from 2010 -
2015 projected to be the 'Golden Age' for the Indian insurance industry.
The insurance sector in India has come a full circle from being an open competitive market to
nationalisation and back to a liberalised market again. Tracing the developments in the Indian
insurance sector reveals the 360-degree turn witnessed over a period of almost two centuries.
Indian insurance companies offer a comprehensive range of insurance plans, a range that is
growing as the economy matures and the wealth of the middle classes increases. The most
common types include: term life policies, endowment policies, joint life policies, whole life
policies, loan cover term assurance policies, unit-linked insurance plans, group insurance
policies, pension plans, and annuities. General insurance plans are also available to cover
motor insurance, home insurance, travel insurance and health insurance.
Due to the growing demand for insurance, more and more insurance companies are now
emerging in the Indian insurance sector. With the opening up of the economy, several
international leaders in the insurance sector are trying to venture into the India insurance
industry.
A brief history of the Insurance sector
The history of the Indian insurance sector dates back to 1818, when the Oriental Life
Insurance Company was formed in Kolkata. A new era began in the India insurance sector,
with the passing of the Life Insurance Act of 1912. The Indian Insurance Companies Act was
passed in 1928. This act empowered the government of India to gather necessary information
about the life insurance and non-life insurance organizations operating in the Indian financial
markets.
Some of the important milestones in the life insurance business in India are:
1912: The Indian Life Assurance Companies Act enacted as the first statute to
regulate the life insurance business.
1928: The Indian Insurance Companies Act enacted to enable the government to
collect statistical information about both life and non-life insurance businesses.
1938: Earlier legislation consolidated and amended to by the Insurance Act with the
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objective of protecting the interests of the insuring public.
1956: 245 Indian and foreign insurers and provident societies taken over by the
central government and nationalised. LIC formed by an Act of Parliament, viz.
LIC Act, 1956, with a capital contribution of Rs. 5 crore from the Government
of India.
The General insurance business in India, on the other hand, can trace its roots to the Triton
Insurance Company Ltd., the first general insurance company established in the year 1850 in
Calcutta by the British.
Some of the important milestones in the general insurance business in India are:
1907: The Indian Mercantile Insurance Ltd. set up, the first company to transact all
classes of general insurance business.
1957: General Insurance Council, a wing of the Insurance Association of India,
frames a code of conduct for ensuring fair conduct and sound business
practices.
1968: The Insurance Act amended to regulate investments and set minimum solvency
margins and the Tariff Advisory Committee set up.
1972: The General Insurance Business (Nationalisation) Act, 1972 nationalised the
general insurance business in India with effect from 1st January 1973.
107 insurers amalgamated and grouped into four companies viz. the National
Insurance Company Ltd., the New India Assurance Company Ltd., the Oriental
Insurance Company Ltd. and the United India Insurance Company Ltd. GIC
incorporated as a company.
Indian Insurance: Sector Reforms
In 1993, Malhotra Committee headed by former Finance Secretary and RBI Governor R.N.
Malhotra was formed to evaluate the Indian insurance industry and recommend its future
direction.The aim of the Malhotra Committee was to assess the functionality of the Indian
insurance sector. This committee was also in charge of recommending the future path of
insurance in India.
The Malhotra committee was set up with the objective of complementing the reforms
initiated in the financial sector. The reforms were aimed at creating a more efficient and
competitive financial system suitable for the requirements of the economy keeping in mind
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the structural changes currently underway and recognizing that insurance is an important part
of the overall financial system where it was necessary to address the need for similar reforms.
In 1994, the committee submitted the report and some of the key recommendations included:
1) Structure
Government stake in the insurance Companies to be brought down to 50%.
Government should take over the holdings of GIC and its subsidiaries so that these
subsidiaries can act as independent corporations.
All the insurance companies should be given greater freedom to operate.
2) Competition
Private Companies with a minimum paid up capital of Rs.1bn should be allowed to
enter the industry.
No Company should deal in both Life and General Insurance through a single entity.
Foreign companies may be allowed to enter the industry in collaboration with the
domestic companies.
Postal Life Insurance should be allowed to operate in the rural market.
Only One State Level Life Insurance Company should be allowed to operate in each
state.
3) Regulatory Body
The Insurance Act should be changed.
An Insurance Regulatory body should be set up.
Controller of Insurance (Currently a part from the Finance Ministry) should be made
independent.
4) Investments
Mandatory Investments of LIC Life Fund in government securities to be reduced from
75% to 50%.
GIC and its subsidiaries are not to hold more than 5% in any company (There current
holdings to be brought down to this level over a period of time).
5) Customer Service
LIC should pay interest on delays in payments beyond 30 days.
Insurance companies must be encouraged to set up unit linked pension plans.
Computerisation of operations and updating of technology to be carried out in the
insurance industry The committee emphasized that in order to improve the
customer services and increase the coverage of the insurance industry should be
opened up to competition.
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But at the same time, the committee felt the need to exercise caution as any failure on the part
of new players could ruin the public confidence in the industry. Hence, it was decided to
allow competition in a limited way by stipulating the minimum capital requirement of Rs.100
crores. The committee felt the need to provide greater autonomy to insurance companies in
order to improve their performance and enable them to act as independent companies with
economic motives. For this purpose, it had proposed setting up an independent regulatory
body.
The Insurance Regulatory and Development Authority Act of 1999 brought about several
crucial policy changes in the insurance sector of India. It led to the formation of the Insurance
Regulatory and Development Authority (IRDA) in 2000.
The goals of the IRDA are to safeguard the interests of insurance policyholders, as well as to
initiate different policy measures to help sustain growth in the Indian insurance sector.
The Authority has notified 27 Regulations on various issues which include Registration of
Insurers, Regulation on insurance agents, Solvency Margin, Re-insurance, Obligation of
Insurers to Rural and Social sector, Investment and Accounting Procedure, Protection of
policy holders' interest etc. Applications were invited by the Authority with effect from 15th
August, 2000 for issue of the Certificate of Registration to both life and non-life insurers. The
Authority has its Head Quarter at Hyderabad.
Major Policy Changes
Insurance sector has been opened up for competition from Indian private insurance
companies with the enactment of Insurance Regulatory and Development Authority Act,
1999 (IRDA Act). As per the provisions of IRDA Act, 1999, Insurance Regulatory and
Development Authority (IRDA) was established on 19th April 2000 to protect the interests of
holder of insurance policy and to regulate, promote and ensure orderly growth of the
insurance industry. IRDA Act 1999 paved the way for the entry of private players into the
insurance market which was hitherto the exclusive privilege of public sector insurance
companies/ corporations. Under the new dispensation Indian insurance companies in private
sector were permitted to operate in India with the following conditions:
Company is formed and registered under the Companies Act, 1956;
The aggregate holdings of equity shares by a foreign company, either by itself or
through its subsidiary companies or its nominees, do not exceed 26%, paid up
equity capital of such Indian insurance company;
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The company's sole purpose is to carry on life insurance business or general insurance
business or reinsurance business.
The minimum paid up equity capital for life or general insurance business is Rs.100
crores.
The minimum paid up equity capital for carrying on reinsurance business has been
prescribed as Rs.200 crores.
The Authority has notified 27 Regulations on various issues which include Registration of
Insurers, Regulation on insurance agents, Solvency Margin, Re-insurance, Obligation of
Insurers to Rural and Social sector, Investment and Accounting Procedure, Protection of
policy holders' interest etc. Applications were invited by the Authority with effect from 15th
August, 2000 for issue of the Certificate of Registration to both life and non-life insurers. The
Authority has its Head Quarter at Hyderabad.
Insurance companies:
IRDA has so far granted registration to 12 private life insurance companies and 9 general
insurance companies. If the existing public sector insurance companies are included, there are
currently 13 insurance companies in the life side and 13 companies operating in general
insurance business. General Insurance Corporation has been approved as the "Indian
reinsurer" for underwriting only reinsurance business.
Protection of the interest of policy holders:
IRDA has the responsibility of protecting the interest of insurance policyholders. Towards
achieving this objective, the Authority has taken the following steps:
IRDA has notified Protection of Policyholders Interest Regulations 2001 to provide
for: policy proposal documents in easily understandable language; claims procedure in
both life and non-life; setting up of grievance redressal machinery; speedy settlement
of claims; and policyholders' servicing. The Regulation also provides for payment of
interest by insurers for the delay in settlement of claim.
The insurers are required to maintain solvency margins so that they are in a position
to meet their obligations towards policyholders with regard to payment of claims.
It is obligatory on the part of the insurance companies to disclose clearly the benefits,
terms and conditions under the policy. The advertisements issued by the insurers
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should not mislead the insuring public.
All insurers are required to set up proper grievance redress machinery in their head
office and at their other offices.
The Authority takes up with the insurers any complaint received from the
policyholders in connection with services provided by them under the insurance
contract.
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Company Overview
IDBI Federal Life Insurance:-
IDBI Federal Life Insurance Co Ltd is a joint-venture of IDBI Bank, India‘s premier
development and commercial bank, Federal Bank, one of India‘s leading private sector banks
and Ageas, a multinational insurance giant based out of Europe. In this venture, IDBI Bank
owns 48% equity while Federal Bank and Ageas own 26% equity each. At IDBI Federal, we
endeavour to deliver products that provide value and convenience to the customer. Through a
continuous process of innovation in product and service delivery we intend to deliver world-
class wealth management, protection and retirement solutions to Indian customers. Having
started in March 2008, in just five months of inception we became one of the fastest growing
new insurance companies to garner Rs.100 Cr in premiums. The company offers its services
through a vast nationwide network across the branches of IDBI Bank and Federal Bank in
addition to a sizeable network of advisors and partners. As on January 31st 2011, the
company has issued over lakh 2.68 lakh policies with over Rs.14,230 Cr in Sum Assured.
Sponsors of IDBI Federal Life Insurance:-
IDBI Bank Ltd. continues to be, since its inception, India‘s premier industrial development
bank. Created in 1956 to support India‘s industrial backbone, IDBI Bank has since evolved
into a powerhouse of industrial and retail finance. Today, it is amongst India‘s foremost
commercial banks, with a wide range of innovative products and services, serving retail and
corporate customers in all corners of the country from 783 branches and 1328 ATMs. The
Bank offers its customers an extensive range of diversified services including project
financing, term lending, working capital facilities, lease finance, venture capital, loan
syndication, corporate advisory services and legal and technical advisory services to its
corporate clients as well as mortgages and personal loans to its retail clients. As part of its
development activities, IDBI Bank has been instrumental in sponsoring the development of
key institutions involved in India‘s financial sector –National Stock Exchange of India
Limited (NSE) and National Securities Depository Ltd, SHCIL (Stock Holding Corporation
of India Ltd), CARE (Credit Analysis and Research Ltd)
Federal Bank is one of India‘s leading private sector banks, with a dominant presence in the
state of Kerala. It has a strong network of over 739 branches and 797 ATMs spread across
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India. The bank provides over four million retail customers with a wide variety of financial
products. Federal Bank is one of the first large Indian banks to have an entirely automated
and interconnected branch network. In addition to interconnected branches and ATMs, the
Bank has a wide range of services like Internet Banking, Mobile Banking, Tele Banking, Any
Where Banking, debit cards, online bill payment and call centre facilities to offer round the
clock banking convenience to its customers. The Bank has been a pioneer in providing
innovative technological solutions to its customers and the Bank has won several awards and
recommendations.
Ageas is an international insurance company with a heritage spanning more than 180 years.
Ranked among the top 20 insurance companies in Europe, Ageas has chosen to concentrate
its business activities in Europe and Asia, which together make up the largest share of the
global insurance market. They are grouped around four segments: Belgium, United Kingdom,
Continental Europe and Asia. It is an undisputed leader in the Belgian market for individual
life and employee benefits, as well as a leading non-life player, through AG Insurance.
Internationally Ageas has a strong presence in the UK, where it is the second largest player in
private car insurance. The company also has subsidiaries in France, Germany and Hong
Kong. Ageas has a track record in developing partnerships with strong financial institutions
and key distributors in different markets around the world and successfully operates
partnerships in Luxembourg, Italy, Portugal, China, Malaysia, India and Thailand. Ageas
employs more than 13,000 people and has annual inflows of almost EUR 18 billion.
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Financial Markets
Financial markets can mainly be classified into money markets and capital markets.
Instruments in the money markets include mainly short-term, marketable, liquid, low-risk
debt securities. Capital markets, in contrast, include longer-term and riskier securities, which
include bonds and equities. There is also a wide range of derivatives instruments that are
traded in the capital markets.
Both bond market and money market instruments are fixed-income securities but bond
market instruments are generally of longer maturity period as compared to money market
instruments. Money market instruments are of very short maturity period. The equities
market can be further classified into the primary and the secondary market. Derivative market
instruments are mainly futures, forwards and options on the underlying instruments, usually
equities and bonds.
Primary and Secondary Markets:-
A primary market is that segment of the capital market, which deals with the raising of
capital from investors via issuance of new securities. New stocks/bonds are sold by the issuer
to the public in the primary market. When a particular security is offered to the public for the
first time, it is called an Initial Public Offering (IPO). When an issuer wants to issue more
securities of a category that is already in existence in the market it is referred to as Follow-up
Offerings.
The secondary market (also known as ‗aftermarket‘) is the financial market where securities,
which have been issued before are traded. The secondary market helps in bringing potential
buyers and sellers for a particular security together and helps in facilitating the transfer of the
security between the parties. Unlike in the primary market where the funds move from the
hands of the investors to the issuer (company/ Government, etc.), in case of the secondary
market, funds and the securities are transferred from the hands of one investor to the hands of
another. Thus the primary market facilitates capital formation in the economy and secondary
market provides liquidity to the securities.
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Trading in Secondary Markets:-
Trading in secondary market happens through placing of orders by the investors and their
matching with a counter order in the trading system. Orders refer to instructions provided by
a customer to a brokerage firm, for buying or selling a security with specific conditions.
These conditions may be related to the price of the security (limit order or market order or
stop loss orders) or related to time (a day order or immediate or cancel order). Advances in
technology have led to most secondary markets of the world becoming electronic exchanges.
Disaggregated traders across regions simply log in the exchange, and use their trading
terminals to key in orders for transaction in securities.
The Money Market:-
The money market is a subset of the fixed-income market. In the money market, participants
borrow or lend for short period of time, usually up to a period of one year. These instruments
are generally traded by the Government, financial institutions and large corporate houses.
These securities are of very large denominations, very liquid, very safe but offer relatively
low interest rates. The cost of trading in the money market (bid-ask spread) is relatively small
due to the high liquidity and large size of the market. Since money market instruments are of
high denominations they are generally beyond the reach of individual investors.
T-Bills-T-Bills or treasury bills are largely risk-free, short-term, very liquid instruments that
are issued by the central bank of a country. The maturity period for T-bills ranges from 3-12
months. T-bills are circulated both in primary as well as in secondary markets.
Commercial Paper-Commercial papers (CP) are unsecured money market instruments
issued in the form of a promissory note by large corporate houses in order to diversify their
sources of short-term borrowings and to provide additional investment avenues to investors.
Issuing companies are required to obtain investment-grade credit ratings from approved
rating agencies.
Certificate of Deposits- A certificate of deposit (CD), is a term deposit with a bank with a
specified interest rate. The duration is also pre-specified and the deposit cannot be withdrawn
on demand.
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The Bond Market:-
Bond markets consist of fixed-income securities of longer duration than instruments in the
money market. The bond market instruments mainly include treasury notes and treasury
bonds, corporate bonds, Government bonds etc.
T-Notes & T-Bonds- Treasury notes and bonds are debt securities issued by the Central
Government of a country. Treasury notes maturity range up to 10 years, whereas treasury
bonds are issued for maturity ranging from 10 years to 30 years. Another distinction between
T-notes and T-bonds is that T-bonds usually consist of a call/put option after a certain period.
In order to make these instruments attractive, the interest income is usually made tax-free.
State & Municipal Government Bonds- Various State Governments and sometimes
municipal bodies are also empowered to borrow by issuing bonds. They usually are also
backed by guarantees from the respective Government. These bonds may also be issued to
finance specific projects.
Corporate Bonds- Bonds are also issued by large corporate houses for borrowing money
from the public for a certain period.
International Bonds- These bonds are issued overseas, in the currency of a foreign country
which represents a large potential market of investors for the bonds. Bonds issued in a
currency other than that of the country which issues them are usually called Eurobonds.
Others-
Zero Coupon Bonds- Zero coupon bonds (also called as deep-discount bonds or discount
bonds) refer to bonds which do not pay any interest (or coupons) during the life of the bonds.
The bonds are issued at a discount to the face value and the face value is repaid at the
maturity.
Convertible Bonds- Convertible bonds offer a right (but not the obligation) to the
bondholder to get the bond converted into predetermined number of equity stock of the
issuing company, at certain, pre-specified times during its life.
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Callable Bonds- In case of callable bonds, the bond issuer holds a call option, which can be
exercised after some pre-specified period from the date of the issue. The option gives the
right to the issuer to repurchase (cancel) the bond by paying the stipulated call price.
Puttable Bonds- The bondholder has a right (but not the obligation) to sell back the bond to
the issuer after a certain time at a pre-specified price. The right has a cost and hence one
would expect a lower yield in such bonds.
The pricing of Bonds:-
The cash inflow for an investor in a bond includes the coupon payments and the payment on
maturity (which is the face value) of the bond. Thus the price of the bond should represent the
sum total of the discounted value of each of these cash flows (such a total is called the present
value of the bond). The discount rate used for valuing the bond is generally higher than the
risk-free rate to cover additional risks such as default risk, liquidity risks, etc.
Bond Price = PV (Coupons and Face Value)
Bond Price= t C(t)/(1+y)t
(Where C(t) is the cash flow at time t and y is the discount rate.)
Or, Bond Price= tT Coupon/(1+y)
t + Face Value/(1+y)
T
Macaulay Duration and Modified Duration:-
The effect of interest rate risks on bond prices depends on many factors, but mainly on
coupon rates, maturity date etc. Unlike in case of zero-coupon bonds, where the cash flows
are only at the end, in the case of other bonds, the cash flows are through coupon payments
and the maturity payment. One needs to average out the time to maturity and time to various
coupon payments to find the effective maturity for a bond. The measure is called as duration
of a bond. It is the weighted (cash flow weighted) average maturity of the bond.
Duration= t=1T t*wt
wt= (CFt/(1+y)t)/Bond Price
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Financial Analysis and Valuation
Investments in capital markets primarily involve transactions in shares, bonds, debentures,
and other financial products issued by companies. The decision to invest in these securities is
thus linked to the evaluation of these companies, their earnings, and potential for future
growth. Valuation is all about how well we predict the cash flows,
their growth in future, taking into account future risks involved.
Income Statement- A profit & loss statement provides an account of the total revenue
generated by a firm during a period, the expenses involved and the money earned.
In its simplest form, revenue generation or sales accrues from selling the products
manufactured, or services rendered by the company.
Balance Sheet- Assets owned by a company are financed either by equity or debt and the
balance sheet of a company is a snapshot of this capital structure of the firm at a point in
time; the sources and applications of funds of the company.
Cash Flow Statement- such a statement is used to track the cash flows in the company over
a period. Cash flows are tracked across operating, investing, and financing activities. Cash
flows from operations include net income generation adjusted for changes in working capital,
and non-core accruals.
Valuation of Stocks:-
The problem of valuing the stock translates into one of predicting the future free cash flow
profile of the company, and then using the appropriate discount factor to measure what they
are worth today. The appropriately named discounted-cash flow technique is also referred to
as absolute valuation, particularly when compared to another widely-followed approach in
valuation, called relative valuation.
Discounted Cash Flow- The discounted cash flow method values the share based on the
expected dividends from the shares. The price of a share according to the discounted cash
flow method is calculated as under:
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P= t=1∞ Divt/(1+r)
t
Constant Dividend Growth- where the dividend amount grows at a constant rate, the
constant dividend growth model states that the share price can be obtained using the simple
formula:
P= Div1/r-g
Present Value of Growth Opportunities- One can split the value of the shares as computed
in the constant growth model into two parts – the present value of the share assuming level
stream of earnings and the present value of growth opportunities.
PVGO =Share Price – Present value of level stream of earnings
=Share price– EPS / r
Discounted Free Cash Flow Valuation Models-
Market value of equity (V0) = Value of the firm + Cash in hand – Debt Value
Earning per Share- Earning per share is the firms‘ net income divided by the average
number of shares outstanding during the year.
EPS= (Net Profit- Dividend on preference Shares)/ Average number of shares
outstanding during the year
Dividend per Share- Dividends are a form of profit distribution to the shareholders. The
firm may not distribute the entire income to the shareholders, but decide to retain some
portion of it for financing growth opportunities. The dividend payout ratio (DPR) measures
the percentage of income that the company pays out to the shareholders in the form of
dividends. The formula for calculating DPR is:
DPR= Dividends/Net Income= DPS/EPS
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Price-Earnings Ratio- Price earning ratio for a company is calculated by dividing the market
price per share with the earnings per share (EPS).
PE ratio= Market Price per share/ Annual earning per share
The Dupont Model- The Du Pont model is widely used to decide the determinants of return
profitability of a company, or a sector of the economy. Returns on shareholder equity are
expressed in terms of a company‘s profit margins, asset turn, and its financial leverage.
DuPont Model breaks the Return on equity as under:
RoE= Return on Equity
= Net Profits/Equity
=Net Profits/Sales * Sales/Assets * Assets/Equity
= Profit Margin * Asset Turnover * Financial Leverage
The first component measures the operational efficiency of the firm through its net margin
ratio. The second component, called the asset turnover ratio, measures the efficiency in usage
of assets by the firm and the third component measures the financial leverage of the firm
through the equity multiplier.
Dividend Yield- Dividend yield is the ratio between the dividend paid during the last 1-year
period and the current price of the share. The ratio could also be used with the forward
dividend yield instead— expected dividends, for either the next 12 months, or the financial
year.
Dividend Yield= Last Year Price/Current Price per Share
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Managing a Portfolio
The age-old wisdom about not putting ―all your eggs in one basket‖ applies very much in the
case of portfolios. Portfolio risk (generally defined as the standard deviation of returns) is not
the weighted average of the risk (standard deviation) of individual assets in the portfolio. This
gives rise to opportunities to eliminate the risk of assets, at least partly, by combining risky
assets in a portfolio.
Let us now examine why and how portfolio risk is different from the weighted risk of
constituent assets. Assume that we have two stocks and the returns of the two hypothetical
stocks behave in opposite directions. When A gives high returns, B does not and vice versa.
For a portfolio with 60% invested in A, the portfolio standard deviation becomes zero.
Although the two stocks involved were risky (indicated by the standard deviations), a
portfolio of the two stocks with a certain weight may become totally risk-free. Intuitively, the
negative deviation in the returns of one stock is getting offset by the positive deviation in the
other stock.
Let us assume that you can form portfolios with two stocks, A & B, having the following
characteristics:
Return on stock A= RA
Mean return on stock A= R*A
Std. deviation of the return of stock A= SDA
Return on Stock B= RB
Mean return on stock B= R*B
Std. deviation of the return of stock B= SDB
Investment in stock A=W
Investment in stock B= (1-W)
Hence, Portfolio Risk:-
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SDP= W2 * SDA
2 + (1-W)
2 * SDB
2 + 2W(1-W)*Cov(A,B)
That is, we would show that the variance of our portfolio, as denoted by the left hand side of
this equation, is dependent on the variance of stock A, that of stock B, and a third term, called
Cov(A,B). It is this third term that denotes the interrelationship between the two stocks.
And the Portfolio Return is given by:-
RP = W*RA + (1-W)*RB
The CAPM Model
The most important insight from the analysis of portfolio risk is that a part of the portfolio
variance can be diversified away (unsystematic or diversifiable risk) by selecting securities
with less than perfect correlation.
The assumptions required are as follows:
• All investors are mean-variance optimizers. This implies that investors are concerned only
about the mean and variance of asset returns. Investors would either prefer portfolios which
offer higher return for the same level of risk or prefer portfolios which offer minimum risk
for a given level of return (the indirect assumption of mean-variance investors is that all other
characteristics of the assets are captured by the mean and variance).
• Investors have homogenous information about different assets. The well-organized financial
markets have remarkable ability to digest information almost instantaneously (largely
reflected as the price variation in response to sensitive information).
• Transaction costs are absent in the market and securities can be bought and sold without
significant price impact.
• Investors have the same investment horizon.
Calculation of Beta:-
Let RM be the required rate of return on the market (market portfolio, M), RF be the required
rate of return on the risk free asset and SDM be the standard deviation of the market portfolio.
Page | 22
The rate of risk premium required for unit variance of the market is estimated as,
(RM – RF)/SDM2
The risk premium on stock is:-
(RM-RF)/SDM2 * Cov(i,M)
where, Cov(i,M), is the covariance between the returns of stock i and the market returns.
The quantity represented by Cov(i,M)/SDM2 is popularly called Beta(β). This measures
the sensitivity of the security compared to the market. A beta of 2.0 indicates that if the
market moves down (up) by 1%, the security is expected to move down (up) 2%. Therefore,
we would expect twice the risk premium as compared to the market.
Therefore, the total required Rate of return on any stock is:-
Ri = RF + (RM-RF)*β
The beta of a stock can be estimated with the formula discussed above. Practically, the beta
of any stock can be conveniently estimated as a regression between the return on stock and
that of the market, represented by a stock index like NIFTY (the dependent variable is the
stock return and the independent variables is the market return).
Accordingly the Regression equation is:-
Ri = αi + βi * RM + ei
where the regression coefficient bi represents the slope of the linear relationship between the
stock return and the market return and aI denote the risk-free rate of return.
The beta of an existing firm traded in the market can be derived directly from the market
prices. However, on many occasions, we might be interested to estimate the required rate of
return on an asset which is not traded in the market. For instances like, pricing of an IPO,
takeover of another firm, valuation of certain specific assets etc.. In these instances, the
required rate of return can be estimated by obtaining the beta estimates from similar firms in
the same industry.
Page | 23
The beta can be related to the nature of the assets held by a firm. If the firm holds more risky
assets the beta shall also be higher. Now, it is not difficult to see why investors like venture
capitalists demand higher return for investing in start-up firms. A firm‘s beta is the weighted
average of the beta of its assets (just as the beta of a portfolio is the weighted average of the
beta of its constituent assets).
The Arbitrage Pricing Theory:-
The CAPM is founded on the following two assumptions (1) in the equilibrium every mean
variance investor holds the same market portfolio and (2) the only risk the investor faces is
the beta. Evidently, these are strong assumptions about the market structure and behaviour of
investors. A more general framework about asset pricing should allow for relaxation of these
strong and somewhat counterfactual assumptions. A number of alternative equilibrium asset
pricing models, including the general arbitrage pricing theory (APT), attempt to relax these
assumptions to provide a better understanding about asset pricing. The arbitrage pricing
theory assumes that the investor portfolio is exposed to a number of systematic risk factors.
Arbitrage in the market ensures that portfolios with equal sensitivity to a fundamental risk
factor are equally priced. It further assumes that the risk factors which are associated with any
asset can be expressed as a linear combination of the fundamental risk factors and the factor
sensitivities (betas). Arbitrage is then assumed to eliminate all opportunities to earn riskless
profit by simultaneously selling and buying equivalent portfolios (in terms of risk) which are
overpriced and underpriced.
Under these assumptions, all investors need not have the same market portfolio as under
CAPM. Hence, APT relaxes the assumption that all investors in the market hold the same
portfolio. Again, as compared to CAPM, which has only one risk dimension, under the APT
characterization of the assets, there will be as many dimensions as there are fundamental
risks, which cannot be diversified by the investors. The fundamental factors involved could
for instance be the growth rate of the economy (GDP growth rate), inflation, interest rates
and any other macroeconomic factor which would expose the investor‘s portfolio to
systematic risk.
In the lines of the assumptions of arbitrage pricing theory, a number of multifactor asset
pricing models have been proposed. One such empirically successful model is the so-called
Page | 24
Fama-French three-factor model. The Fama-French model has two more risk factors, viz.,
size, and book-to-market ratio as the additional risk factors along with the market risk as
specified by CAPM. The size risk factor is the difference between the expected returns on a
portfolio of small stocks and that of large stocks. And the book-to-market ratio is the
difference in the expected return of the portfolio of high book-to market-ratio stocks and that
of low book-to market-ratio stocks.
Theoretical and empirical evidence suggests that in the real market, expected returns are
probably determined by a multifactor model. Against this evidence, the most popular and
simple equilibrium model, CAPM, could be regarded as a special case where all investors
hold the same portfolio and their only risk exposure is the market risk.
Sharpe Ratio:-
Sharpe ratio or ‗excess return to variability‘ measures the portfolio excess return over the
sample period by the standard deviation of returns over that period. This ratio measures the
effectiveness of a manager in diversifying the total risk(SDM).
This measure is appropriate if one is evaluating the total portfolio of an investor or a fund, in
which case the Sharpe ratio of the portfolio can be compared with that of the market. The
formula for measuring the Sharpe ratio is:
Sharpe Ratio=(RP* - RF
*)/SDP
Treynor Ratio:-
Treynor‘s measure evaluates the excess return per unit of systematic risks ( b ) and not total
risks. If a portfolio is fully diversified, then b becomes the relevant measure of risk and the
performance of a fund manager may be evaluated against the expected return.
The formula for measuring the Treynor Ratio is:
Treynor Ratio= (RP* - RF
*)/βP
Jensen Measure or Portfolio Alpha:-
The Jensen measure, also called Jensen Alpha, or portfolio alpha measures the average return
on the portfolio over and above that predicted by the CAPM, given the portfolio‘s beta and
the average market returns. It is measured using the following formula:
Page | 25
Portfolio Alpha= RP – [RF + β * (RM – RF)]
Analysis of a Portfolio held by IDBI Federal Life
The stepwise procedure for analysis of the portfolio is:-
Collection of the market index figures for BSE-100 for the past 10 years.
Collection of the opening and closing prices of all the stocks in the portfolio for every
year for the past 10 years.
Comparison of the each stock‘s growth with the market growth by calculating the
covariance between both.
Calculation of the Standard Deviation of the stock‘s price over the 10 year period.
Calculation of the Beta for each stock using the formula:-
Beta= Covariance/Sq. of Std. Deviation
BSE 100
Year Open Price Close Price Price Change
%
Change
2001 2042.15 1557.22 -484.93 -23.75
2002 1557.37 1664.67 107.3 6.89
2003 1668.05 3074.87 1406.82 84.34
2004 3089.58 3580.34 490.76 15.88
2005 3593.58 4953.28 1359.7 37.84
2006 4964.64 6982.56 2017.92 40.65
2007 6999.7 11154.28 4154.58 59.35
2008 11186.45 4988.04 -6198.41 -55.41
2009 5021.58 9229.71 4208.13 83.80
2010 9212.74 10675.02 1462.28 15.87
Aditya Birla Nuvo
Year Open Price Close Price
Price
Change
%age
Growth
%age Market
Change
2001 82 72.65 -9.35 -11.40 -23.75
2002 72 94.05 22.05 30.63 6.89
2003 95 270 175 184.21 84.34
2004 272 388.25 116.25 42.74 15.88
2005 395 667.2 272.2 68.91 37.84
2006 672 1247.5 575.5 85.64 40.65
2007 1241 2017.25 776.25 62.55 59.35
Page | 26
2008 2000 574.65 -1425.35 -71.27 -55.41
2009 575 876.3 301.3 52.40 83.8
2010 881.1 838.95 -42.15 -4.78 15.87
26.546
Column 1 Column 2
Column 1 4134.447663
Column 2 2309.197422 1788.131904
Column1
Standard
Dev. Sq. of S.D. Covariance
44.57368312 1986.813227 2309.197422
Mean 26.546
Standard Error 14.09543624
Beta=Cov./Sq. of S.D
Median 26.86
1.162261953
Mode #N/A
Standard
Deviation 44.57368312
R(f) R(m) R
Sample Variance 1986.813227
8.23 26.54 29.51101636
Kurtosis
-
0.203271606
Skewness
-
0.419478531
Range 139.75
Minimum -55.41
Maximum 84.34
Sum 265.46
Count 10
Ashiana Housing Ltd.
Year Open Price Close Price
Price
Change
%age
Growth
%age Market
Change
2002 2.3 1.5 -0.8 -34.78 6.89
2003 1.35 16.81 15.46 1145.19 84.34
2004 17 17 0 0.00 15.88
2005 17.5 67.55 50.05 286.00 37.84
2006 70.9 243.05 172.15 242.81 40.65
2007 255.2 728.45 473.25 185.44 59.35
2008 731 39.15 -691.85 -94.64 -55.41
2009 39.4 114.15 74.75 189.72 83.8
2010 114 155.05 41.05 36.01 15.87
32.13444444
Page | 27
Column 1 Column 2
Column 1 123228.5997
Column 2 9656.822397 1674.506114
Column1
Standard
Dev. Sq. of S.D. Covariance
43.40298812 1883.819378 9656.822397
Mean 32.13444444
Standard Error 14.46766271
Beta=Cov./Sq. of S.D
Median 37.84
5.13
Mode #N/A
Standard
Deviation 43.40298812
R(f) R(m) R
Sample Variance 1883.819378
8.23 32.13 130.7460214
Kurtosis 1.043543933
Skewness
-
0.775375291
Range 139.75
Minimum -55.41
Maximum 84.34
Sum 289.21
Count 9
Axis Bank
Year Open Price Close Price
Price
Change
%age
Growth
%age Market
Change
2001 37 26.4 -10.6 -28.65 -23.75
2002 25.75 44.8 19.05 73.98 6.89
2003 44.1 135.15 91.05 206.46 84.34
2004 138.4 185.2 46.8 33.82 15.88
2005 187 286.35 99.35 53.13 37.84
2006 292 469.05 177.05 60.63 40.65
2007 469.95 967.1 497.15 105.79 59.35
2008 979.95 504.65 -475.3 -48.50 -55.41
2009 510 988.7 478.7 93.86 83.8
2010 999 1349.5 350.5 35.09 15.87
26.546
Column 1 Column 2
Column 1 4585.463229
Page | 28
Column 2 2545.836757 1788.131904
Column1
Standard
Dev. Sq. of S.D. Covariance
44.57368312 1986.813227 2545.836757
Mean 26.546
Standard Error 14.09543624
Beta=Cov./Sq. of S.D
Median 26.86
1.281366926
Mode #N/A
Standard
Deviation 44.57368312
R(f) R(m) R
Sample Variance 1986.813227
8.23 25.54 30.41046149
Kurtosis
-
0.203271606
Skewness
-
0.419478531
Range 139.75
Minimum -55.41
Maximum 84.34
Sum 265.46
Count 10
Bajaj Electricals
Year Open Price Close Price
Price
Change
%age
Growth
%age Market
Change
2001 39 39 0 0.00 -23.75
2002 35 31.5 -3.5 -10.00 6.89
2003 33 68.5 35.5 107.58 84.34
2004 73.5 121.55 48.05 65.37 15.88
2005 124.5 419.75 295.25 237.15 37.84
2006 422.9 440 17.1 4.04 40.65
2007 459.95 704.15 244.2 53.09 59.35
2008 725 225.2 -499.8 -68.94 -55.41
2009 213.65 817.45 603.8 282.61 83.8
2010 828 240.65 -587.35 -70.94 15.87
26.546
Column 1 Column 2
Column 1 12867.8012
Column 2 3278.13951
1788.13190
4
Page | 29
Column1
Standard
Dev. Sq. of S.D. Covariance
44.5736831
2
1986.81322
7 3278.13951
Mean 26.546
Standard Error
14.0954362
4
Beta=Cov./Sq. of S.D
Median 26.86
1.64994850
4
Mode #N/A
Standard
Deviation
44.5736831
2
R(f) R(m) R
Sample
Variance
1986.81322
7
8.23 26.54 38.4405571
Kurtosis
-
0.20327160
6
Skewness
-
0.41947853
1
Range 139.75
Minimum -55.41
Maximum 84.34
Sum 265.46
Count 10
BHEL
Year Open Price Close Price
Price
Change
%age
Growth
%age Market
Change
2001 166 140.6 -25.4 -0.15 -23.75
2002 140.5 172.6 32.1 0.23 6.89
2003 174 507.95 333.95 1.92 84.34
2004 512 769.9 257.9 0.50 15.88
2005 771 1386.25 615.25 0.80 37.84
2006 1394 2298.15 904.15 0.65 40.65
2007 2302 2584.25 282.25 0.12 59.35
2008 2585 1362.4 -1222.6 -0.47 -55.41
2009 1372 2406.1 1034.1 0.75 83.8
2010 2410 2324.75 -85.25 -0.04 15.87
26.546
Column 1 Column 2
Column 1
0.40181998
7
Column 2 21.3229132 1788.131904
Page | 30
Column1
Standard
Dev. Sq. of S.D. Covariance
44.5736831
2
1986.81322
7 21.3229132
Mean 26.546
Standard
Error
14.0954362
4
Median 26.86
Beta=Cov./Sq. of S.D
Mode #N/A
0.01073221
8
Standard
Deviation
44.5736831
2
Sample
Variance
1986.81322
7
R(f) R(m) R
Kurtosis
-
0.20327160
6
8.23 26.54 8.426506916
Skewness
-
0.41947853
1
Range 139.75
Minimum -55.41
Maximum 84.34
Sum 265.46
Count 10
BPCL
Year Open Price
Close
Price
Price
Change
%age
Growth
%age Market
Change
2001 115 189 74 64.35 -23.75
2002 192 216.75 24.75 12.89 6.89
2003 217.75 450.25 232.5 106.77 84.34
2004 450 458.85 8.85 1.97 15.88
2005 464.8 434.2 -30.6 -6.58 37.84
2006 434 336.8 -97.2 -22.40 40.65
2007 337.55 523.55 186 55.10 59.35
2008 525 375.95 -149.05 -28.39 -55.41
2009 377 632.8 255.8 67.85 83.8
2010 634.4 657.95 23.55 3.71 15.87
26.546
Column 1 Column 2
Column 1
1819.96743
7
Column 2 1024.93653 1788.13190
Page | 31
7 4
Column1
Standard
Dev. Sq. of S.D. Covariance
44.5736831
2
1986.81322
7 1024.936537
Mean 26.546
Standard Error
14.0954362
4
Beta=Cov./Sq. of S.D
Median 26.86
0.51586959
6
Mode #N/A
Standard
Deviation
44.5736831
2
R(f) R(m) R
Sample
Variance
1986.81322
7
8.23 26.54 17.67557231
Kurtosis
-
0.20327160
6
Skewness
-
0.41947853
1
Range 139.75
Minimum -55.41
Maximum 84.34
Sum 265.46
Count 10
Bharti Airtel
Year Open Price Close Price
Price
Change
%age
Growth
%age Market
Change
2002 55 22.9 -32.1 -58.36 6.89
2003 23.5 105.1 81.6 347.23 84.34
2004 106.25 215.6 109.35 102.92 15.88
2005 218.9 345.7 126.8 57.93 37.84
2006 348.9 628.85 279.95 80.24 40.65
2007 635 994.55 359.55 56.62 59.35
2008 1010 715.1 -294.9 -29.20 -55.41
2009 719.7 328.8 -390.9 -54.31 83.8
2010 330 358.4 28.4 8.61 15.87
32.13444444
Column 1 Column 2
Column 1
13595.1939
8
Page | 32
Column 2
2232.53093
6
1674.50611
4
Column1
Standard
Dev. Sq. of S.D. Covariance
43.4029881
2
1883.81937
8 2232.530936
Mean
32.1344444
4
Standard Error
14.4676627
1
Beta=Cov./Sq. of S.D
Median 37.84
1.18510880
7
Mode #N/A
Standard
Deviation
43.4029881
2
R(f) R(m) R
Sample
Variance
1883.81937
8
8.23 32.13 36.5541005
Kurtosis
1.04354393
3
Skewness
-
0.77537529
1
Range 139.75
Minimum -55.41
Maximum 84.34
Sum 289.21
Count 9
Clariant Chem
Year Open Price Close Price
Price
Change
%age
Change
%age Market
Change
2001 44 87.8 43.8 99.55 -23.75
2002 86 238.75 152.75 177.62 6.89
2003 240.5 261.5 21 8.73 84.34
2004 261.3 279.95 18.65 7.14 15.88
2005 280 331.4 51.4 18.36 37.84
2006 333.75 337.15 3.4 1.02 40.65
2007 340 328.95 -11.05 -3.25 59.35
2008 336 154.05 -181.95 -54.15 -55.41
2009 156.65 467.9 311.25 198.69 83.8
2010 472.9 724.15 251.25 53.13 15.87
26.546
Column 1 Column 2
Page | 33
Column 1
6147.87789
7
Column 2
729.236210
7
1788.13190
4
Column1
Standard
Dev. Sq. of S.D. Covariance
44.5736831
2
1986.81322
7 729.2362107
Mean 26.546
Standard Error
14.0954362
4
Beta=Cov./Sq. of S.D
Median 26.86
0.36703813
Mode #N/A
Standard
Deviation
44.5736831
2
R(f) R(m) R
Sample
Variance
1986.81322
7
8.23 26.54 14.95046815
Kurtosis
-
0.20327160
6
Skewness
-
0.41947853
1
Range 139.75
Minimum -55.41
Maximum 84.34
Sum 265.46
Count 10
GIC Housing Fin.
Year Open Price Close Price
Price
Change
%age
Change
%age Market
Change
2001 8.1 8.5 0.4
4.93827160
5 -23.75
2002 8.45 11.35 2.9
34.3195266
3 6.89
2003 11.25 37.55 26.3
233.777777
8 84.34
2004 37.6 34.05 -3.55
-
9.44148936
2 15.88
2005 34.1 50.7 16.6
48.6803519
1 37.84
Page | 34
2006 51.1 44.95 -6.15
-
12.0352250
5 40.65
2007 46 98.1 52.1
113.260869
6 59.35
2008 105.1 37.65 -67.45
-
64.1769743
1 -55.41
2009 38 91 53
139.473684
2 83.8
2010 91.25 118.9 27.65
30.3013698
6 15.87
26.546
Column 1 Column 2
Column 1
6882.95307
9
Column 2
2970.57735
3
1788.13190
4
Column1
Standard
Dev. Sq. of S.D. Covariance
44.5736831
2
1986.81322
7 2970.577353
Mean 26.546
Standard Error
14.0954362
4
Beta=Cov./Sq. of S.D
Median 26.86
1.49514675
7
Mode #N/A
Standard
Deviation
44.5736831
2
R(f) R(m) R
Sample
Variance
1986.81322
7
8.23 26.54 35.60613713
Kurtosis
-
0.20327160
6
Skewness
-
0.41947853
1
Range 139.75
Minimum -55.41
Maximum 84.34
Sum 265.46
Count 10
Page | 35
GAIL
Year Open Price Close Price
Price
Change
%age
Change
%age Market
Change
2001 52.25 63 10.75 20.57 -23.75
2002 63 70.2 7.2 11.43 6.89
2003 70.75 260.35 189.6 267.99 84.34
2004 262.65 230.7 -31.95 -12.16 15.88
2005 235 265.8 30.8 13.11 37.84
2006 266.7 261.55 -5.15 -1.93 40.65
2007 264.35 542.05 277.7 105.05 59.35
2008 548 206 -342 -62.41 -55.41
2009 208 413.1 205.1 98.61 83.8
2010 412 510.8 98.8 23.98 15.87
26.546
Column 1 Column 2
Column 1
7637.16703
7
Column 2
2842.94664
1
1788.13190
4
Column1
Standard
Dev. Sq. of S.D. Covariance
44.5736831
2
1986.81322
7 2842.946641
Mean 26.546
Standard Error
14.0954362
4
Beta=Cov./Sq. of S.D
Median 26.86
1.43090784
9
Mode #N/A
Standard
Deviation
44.5736831
2
R(f) R(m) R
Sample
Variance
1986.81322
7
8.23 26.54 34.42992272
Kurtosis
-
0.20327160
6
Skewness
-
0.41947853
1
Range 139.75
Minimum -55.41
Maximum 84.34
Page | 36
Sum 265.46
Count 10
Gujarat Appollo
Year Open Price Close Price
Price
Change
%age
Change
%age Market
Change
2001 50 40.7 -9.3 -18.60 -23.75
2002 32.7 49 16.3 49.85 6.89
2003 48.5 79.1 30.6 63.09 84.34
2004 82 172.2 90.2 110.00 15.88
2005 181 145.45 -35.55 -19.64 37.84
2006 145 220.65 75.65 52.17 40.65
2007 224 368.5 144.5 64.51 59.35
2008 368 57.45 -310.55 -84.39 -55.41
2009 57 195.8 138.8 243.51 83.8
2010 200 168.7 -31.3 -15.65 15.87
26.546
Column 1 Column 2
Column 1
7305.41985
9
Column 2
2608.40835
7
1788.13190
4
Column1
Standard
Dev. Sq. of S.D. Covariance
44.5736831
2
1986.81322
7 2608.408357
Mean 26.546
Standard Error
14.0954362
4
Beta=Cov./Sq. of S.D
Median 26.86
1.31286037
5
Mode #N/A
Standard
Deviation
44.5736831
2
R(f) R(m) R
Sample
Variance
1986.81322
7
8.23 26.54 32.26847346
Kurtosis
-
0.20327160
6
Skewness
-
0.41947853
Page | 37
1
Range 139.75
Minimum -55.41
Maximum 84.34
Sum 265.46
Count 10
HCL Tech.
Year Open Price Close Price
Price
Change
%age
Change
%age Market
Change
2001 655 274.3 -380.7 -58.12 -23.75
2002 272.4 186.6 -85.8 -31.50 6.89
2003 187 306.4 119.4 63.85 84.34
2004 307.5 343.25 35.75 11.63 15.88
2005 355 539.05 184.05 51.85 37.84
2006 539.5 648.5 109 20.20 40.65
2007 648.4 331.4 -317 -48.89 59.35
2008 333.8 115.2 -218.6 -65.49 -55.41
2009 116.9 371.35 254.45 217.66 83.8
2010 372 456.05 84.05 22.59 15.87
26.546
Column 1 Column 2
Column 1
6286.79993
1
Column 2
2396.34303
7
1788.13190
4
Column1
Standard
Dev. Sq. of S.D. Covariance
44.5736831
2
1986.81322
7 2396.343037
Mean 26.546
Standard Error
14.0954362
4
Beta=Cov./Sq. of S.D
Median 26.86
1.20612396
Mode #N/A
Standard
Deviation
44.5736831
2
R(f) R(m) R
Sample
Variance
1986.81322
7
8.23 26.54 30.31412971
Kurtosis
-
0.20327160
Page | 38
6
Skewness
-
0.41947853
1
Range 139.75
Minimum -55.41
Maximum 84.34
Sum 265.46
Count 10
HDFC Bank
Year Open Price Close Price
Price
Change
%age
Change
%age Market
Change
2001 222 224.7 2.7 1.22 -23.75
2002 224.6 219 -5.6 -2.49 6.89
2003 219.75 366.65 146.9 66.85 84.34
2004 362 518.85 156.85 43.33 15.88
2005 522 707.45 185.45 35.53 37.84
2006 710.9 1069.75 358.85 50.48 40.65
2007 1070 1727.8 657.8 61.48 59.35
2008 1728 997.6 -730.4 -42.27 -55.41
2009 1007.25 1700.4 693.15 68.82 83.8
2010 1690.25 2346.5 656.25 38.83 15.87
26.546
Column 1 Column 2
Column 1
1162.04766
5
Column 2
1350.86643
6
1788.13190
4
Column1
Standard
Dev. Sq. of S.D. Covariance
44.5736831
2
1986.81322
7 1350.866436
Mean 26.546
Standard Error
14.0954362
4
Beta=Cov./Sq. of S.D
Median 26.86
0.67991616
8
Mode #N/A
Standard
Deviation
44.5736831
2
R(f) R(m) R
Page | 39
Sample
Variance
1986.81322
7
8.23 26.54 20.67926504
Kurtosis
-
0.20327160
6
Skewness
-
0.41947853
1
Range 139.75
Minimum -55.41
Maximum 84.34
Sum 265.46
Count 10
ICICI Bank
Year Open Price Close Price
Price
Change
%age
Change
%age Market
Change
2001 147.5 88 -59.5 -40.34 -23.75
2002 90 140.55 50.55 56.17 6.89
2003 141.7 295.7 154 108.68 84.34
2004 299.7 370.75 71.05 23.71 15.88
2005 374.85 584.7 209.85 55.98 37.84
2006 586.25 890.4 304.15 51.88 40.65
2007 889 1232.4 343.4 38.63 59.35
2008 1235 448.35 -786.65 -63.70 -55.41
2009 455 875.7 420.7 92.46 83.8
2010 888 1144.65 256.65 28.90 15.87
26.546
Column 1 Column 2
Column 1
2549.81059
6
Column 2
1978.97527
5
1788.13190
4
Column1
Standard
Dev. Sq. of S.D. Covariance
44.5736831
2
1986.81322
7 1978.975275
Mean 26.546
Standard Error
14.0954362
4
Beta=Cov./Sq. of S.D
Median 26.86
0.99605501
Page | 40
3
Mode #N/A
Standard
Deviation
44.5736831
2
R(f) R(m) R
Sample
Variance
1986.81322
7
8.23 26.54 26.46776729
Kurtosis
-
0.20327160
6
Skewness
-
0.41947853
1
Range 139.75
Minimum -55.41
Maximum 84.34
Sum 265.46
Count 10
Infosys
Year Open Price Close Price
Price
Change
%age
Change
%age Market
Change
2001 6000 4073.6 -1926.4 -32.11 -23.75
2002 4075 4771.15 696.15 17.08 6.89
2003 4762 5563.7 801.7 16.84 84.34
2004 5605 2089 -3516 -62.73 15.88
2005 2099 2996 897 42.73 37.84
2006 3000 2240 -760 -25.33 40.65
2007 2242 1768.4 -473.6 -21.12 59.35
2008 1758 1117.85 -640.15 -36.41 -55.41
2009 1125 2605.25 1480.25 131.58 83.8
2010 2606 3445 839 32.19 15.87
26.546
Column 1 Column 2
Column 1
2773.71823
8
Column 2
1252.74438
8
1788.13190
4
Column1
Standard
Dev. Sq. of S.D. Covariance
44.5736831
2
1986.81322
7 1252.744388
Page | 41
Mean 26.546
Standard Error
14.0954362
4
Beta=Cov./Sq. of S.D
Median 26.86
0.63052951
9
Mode #N/A
Standard
Deviation
44.5736831
2
R(f) R(m) R
Sample
Variance
1986.81322
7
8.23 26.54 19.77499549
Kurtosis
-
0.20327160
6
Skewness
-
0.41947853
1
Range 139.75
Minimum -55.41
Maximum 84.34
Sum 265.46
Count 10
ITC
Year Open Price Close Price
Price
Change
%age
Change
%age Market
Change
2001 925.75 676.8 -248.95 -26.89 -23.75
2002 670.55 660.4 -10.15 -1.51 6.89
2003 663.9 984.55 320.65 48.30 84.34
2004 994 1309.8 315.8 31.77 15.88
2005 1324.5 142 -1182.5 -89.28 37.84
2006 142.5 175.95 33.45 23.47 40.65
2007 177.9 210.3 32.4 18.21 59.35
2008 212 171.45 -40.55 -19.13 -55.41
2009 172.5 250.85 78.35 45.42 83.8
2010 251 174.5 -76.5 -30.48 15.87
26.546
Column 1 Column 2
Column 1
1627.87328
4
Column 2
824.844124
1
1788.13190
4
Page | 42
Column1
Standard
Dev. Sq. of S.D. Covariance
44.5736831
2
1986.81322
7 824.8441241
Mean 26.546
Standard Error
14.0954362
4
Beta=Cov./Sq. of S.D
Median 26.86
0.41515936
8
Mode #N/A
Standard
Deviation
44.5736831
2
R(f) R(m) R
Sample
Variance
1986.81322
7
8.23 26.54 15.83156803
Kurtosis
-
0.20327160
6
Skewness
-
0.41947853
1
Range 139.75
Minimum -55.41
Maximum 84.34
Sum 265.46
Count 10
IDFC
Year Open Price
Close
Price
Price
Change
%age
Change
%age Market
Change
2005 49.9 73.15 23.25 46.59 37.84
2006 73.5 77.5 4 5.44 40.65
2007 77.8 228.45 150.65 193.64 59.35
2008 229.5 66.8 -162.7 -70.89 -55.41
2009 67.7 154.15 86.45 127.70 83.8
2010 155.9 182.2 26.3 16.87 15.87
30.35
Column 1 Column 2
Column 1
7385.96439
5
Column 2
3113.56535
3
1904.090
1
Page | 43
Column1
Standard
Dev. Sq. of S.D. Covariance
47.8007125
5
2284.9081
2 3113.565353
Mean 30.35
Standard Error
19.5145591
8
Beta=Cov./Sq. of S.D
Median 39.245
1.36266545
1
Mode #N/A
Standard
Deviation
47.8007125
5
R(f) R(m) R
Sample
Variance 2284.90812
8.23 30.35 38.37215977
Kurtosis
2.19969354
6
Skewness
-
1.25025841
7
Range 139.21
Minimum -55.41
Maximum 83.8
Sum 182.1
Count 6
KPR Mill
Year Open Price Close Price
Price
Change
%age
Change
%age Market
Change
2007 201.2 190.4 -10.8 -5.37 59.35
2008 193 48.35 -144.65 -74.95 -55.41
2009 49 100.3 51.3 104.69 83.8
2010 101.9 204.5 102.6 100.69 15.87
25.9025
Column 1 Column 2
Column 1
5708.60236
2
Column 2
2741.51428
1
2795.80736
9
Column1
Standard
Dev. Sq. of S.D. Covariance
61.0552467 3727.74315 2741.514281
Page | 44
7 8
Mean 25.9025
Standard Error
30.5276233
9
Beta=Cov./Sq. of S.D
Median 37.61
0.7354354
Mode #N/A
Standard
Deviation
61.0552467
7
R(f) R(m) R
Sample
Variance
3727.74315
8
8.23 25.9 21.22514352
Kurtosis
-
0.01578194
3
Skewness
-
0.89961777
6
Range 139.21
Minimum -55.41
Maximum 83.8
Sum 103.61
Count 4
LnT
Year Open Price Close Price
Price
Change
%age
Change
%age Market
Change
2001 209.25 191.4 -17.85 -8.53 -23.75
2002 192.8 213.55 20.75 10.76 6.89
2003 213.7 527.35 313.65 146.77 84.34
2004 530 982 452 85.28 15.88
2005 988.7 1844.2 855.5 86.53 37.84
2006 1845 1442.95 -402.05 -21.79 40.65
2007 1400 4171.85 2771.85 197.99 59.35
2008 4191 774.4 -3416.6 -81.52 -55.41
2009 777.05 1679.4 902.35 116.13 83.8
2010 1698 1979.05 281.05 16.55 15.87
26.546
Column 1 Column 2
Column 1
6652.14779
9
Column 2
2810.82673
6
1788.13190
4
Page | 45
Column1
Standard
Dev. Sq. of S.D. Covariance
44.5736831
2
1986.81322
7 2810.826736
Mean 26.546
Standard Error
14.0954362
4
Beta=Cov./Sq. of S.D
Median 26.86
1.41474130
4
Mode #N/A
Standard
Deviation
44.5736831
2
R(f) R(m) R
Sample
Variance
1986.81322
7
8.23 26.54 34.13391328
Kurtosis
-
0.20327160
6
Skewness
-
0.41947853
1
Range 139.75
Minimum -55.41
Maximum 84.34
Sum 265.46
Count 10
Lupin
Year Open Price Close Price
Price
Change
%age
Change
%age Market
Change
2001 13 94.75 81.75 628.85 -23.75
2002 96.95 145.65 48.7 50.23 6.89
2003 147.5 699.95 552.45 374.54 84.34
2004 720 685.45 -34.55 -4.80 15.88
2005 694 766.85 72.85 10.50 37.84
2006 769.9 612.05 -157.85 -20.50 40.65
2007 616.1 633.7 17.6 2.86 59.35
2008 645 617.85 -27.15 -4.21 -55.41
2009 618 1490.3 872.3 141.15 83.8
2010 1486.35 480.45 -1005.9 -67.68 15.87
26.546
Column 1 Column 2
Column 1
43991.7146
6
Page | 46
Column 2
-
184.640639
5
1788.13190
4
Column1
Standard
Dev. Sq. of S.D. Covariance
44.5736831
2
1986.81322
7 -184.6406395
Mean 26.546
Standard Error
14.0954362
4
Beta=Cov./Sq. of S.D
Median 26.86
-
0.09293306
3
Mode #N/A
Standard
Deviation
44.5736831
2
R(f) R(m) R
Sample
Variance
1986.81322
7
8.23 26.54 6.52839561
Kurtosis
-
0.20327160
6
Skewness
-
0.41947853
1
Range 139.75
Minimum -55.41
Maximum 84.34
Sum 265.46
Count 10
M&M
Year Open Price Close Price
Price
Change
%age
Change
%age Market
Change
2001 130 89.25 -40.75 -31.35 -23.75
2002 90 112.7 22.7 25.22 6.89
2003 113.05 389.05 276 244.14 84.34
2004 392 544.5 152.5 38.90 15.88
2005 549.8 512.05 -37.75 -6.87 37.84
2006 512 905.85 393.85 76.92 40.65
2007 912 860.8 -51.2 -5.61 59.35
2008 862 274.85 -587.15 -68.11 -55.41
2009 279 1080.8 801.8 287.38 83.8
Page | 47
2010 1095 777.55 -317.45 -28.99 15.87
26.546
Column 1 Column 2
Column 1
12853.7590
1
Column 2
3794.46989
3
1788.13190
4
Column1
Standard
Dev. Sq. of S.D. Covariance
44.5736831
2
1986.81322
7 3794.469893
Mean 26.546
Standard Error
14.0954362
4
Beta=Cov./Sq. of S.D
Median 26.86
1.90982717
5
Mode #N/A
Standard
Deviation
44.5736831
2
R(f) R(m) R
Sample
Variance
1986.81322
7
8.23 26.54 43.19893558
Kurtosis
-
0.20327160
6
Skewness
-
0.41947853
1
Range 139.75
Minimum -55.41
Maximum 84.34
Sum 265.46
Count 10
Nilkamal
Year Open Price Close Price
Price
Change
%age
Change
%age Market
Change
2001 21.5 21.55 0.05 0.23 -23.75
2002 22 28.4 6.4 29.09 6.89
2003 28.85 65 36.15 125.30 84.34
2004 64.7 80.55 15.85 24.50 15.88
2005 80.15 161.9 81.75 102.00 37.84
Page | 48
2006 164.5 152.9 -11.6 -7.05 40.65
2007 152 335.2 183.2 120.53 59.35
2008 330 66.35 -263.65 -79.89 -55.41
2009 65 233.35 168.35 259.00 83.8
2010 233.35 380.55 147.2 63.08 15.87
26.546
Column 1 Column 2
Column 1
7902.10129
4
Column 2
3210.63315
1
1788.13190
4
Column1
Standard
Dev. Sq. of S.D. Covariance
44.5736831
2
1986.81322
7 3210.633151
Mean 26.546
Standard Error
14.0954362
4
Beta=Cov./Sq. of S.D
Median 26.86
1.61597129
9
Mode #N/A
Standard
Deviation
44.5736831
2
R(f) R(m) R
Sample
Variance
1986.81322
7
8.23 26.54 37.81843449
Kurtosis
-
0.20327160
6
Skewness
-
0.41947853
1
Range 139.75
Minimum -55.41
Maximum 84.34
Sum 265.46
Count 10
NTPC
Year Open Price Close Price
Price
Change
%age
Change
%age Market
Change
2004 70 87.35 17.35 24.79 15.88
Page | 49
2005 87.9 112.1 24.2 27.53 37.84
2006 112 136.4 24.4 21.79 40.65
2007 137.5 250.05 112.55 81.85 59.35
2008 254 181 -73 -28.74 -55.41
2009 182 235.7 53.7 29.51 83.8
2010 238.7 200.6 -38.1 -15.96 15.87
28.28285714
Column 1 Column 2
Column 1
1095.41399
5
Column 2
1001.37922
8
1657.71570
6
Column1
Standard
Dev. Sq. of S.D. Covariance
43.9772857
1934.00165
7 1001.379228
Mean
28.2828571
4
Standard Error
16.6218516
1
Beta=Cov./Sq. of S.D
Median 37.84
0.51777578
6
Mode #N/A
Standard
Deviation 43.9772857
R(f) R(m) R
Sample
Variance
1934.00165
7
8.23 28.28 18.61140451
Kurtosis
2.03266793
9
Skewness
-
1.05946539
9
Range 139.21
Minimum -55.41
Maximum 83.8
Sum 197.98
Count 7
ONGC
Year Open Price Close Price
Price
Change
%age
Change
%age Market
Change
Page | 50
2001 158 134.4 -23.6 -14.94 -23.75
2002 138 349.8 211.8 153.48 6.89
2003 352.45 799.5 447.05 126.84 84.34
2004 809.7 819.55 9.85 1.22 15.88
2005 827.9 1174.95 347.05 41.92 37.84
2006 1175 870.05 -304.95 -25.95 40.65
2007 878 1236.5 358.5 40.83 59.35
2008 1248.8 667.65 -581.15 -46.54 -55.41
2009 675 1177.55 502.55 74.45 83.8
2010 1188 1293.4 105.4 8.87 15.87
26.546
Column 1 Column 2
Column 1
3878.09610
5
Column 2
1448.08788
1 1788.131904
Column1
Standard
Dev. Sq. of S.D. Covariance
44.5736831
2
1986.81322
7 1448.087881
Mean 26.546
Standard
Error
14.0954362
4
Beta=Cov./Sq. of S.D
Median 26.86
0.72884952
7
Mode #N/A
Standard
Deviation
44.5736831
2
R(f) R(m) R
Sample
Variance
1986.81322
7
8.23 26.54 21.57523485
Kurtosis
-
0.20327160
6
Skewness
-
0.41947853
1
Range 139.75
Minimum -55.41
Maximum 84.34
Sum 265.46
Count 10
Power Grid Corp.
Year Open Price Close Price %age %age Market
Page | 51
Price Change Change Change
2007 85 143.8 58.8 69.18 59.35
2008 145 83.2 -61.8 -42.62 -55.41
2009 83.25 110.1 26.85 32.25 83.8
2010 111 98.2 -12.8 -11.53 15.87
25.9025
Column 1 Column 2
Column 1
1804.08109
1
Column 2
1940.59742
3
2795.80736
9
Column1
Standard
Dev. Sq. of S.D. Covariance
61.0552467
7
3727.74315
8 1940.597423
Mean 25.9025
Standard Error
30.5276233
9
Beta=Cov./Sq. of S.D
Median 37.61
0.52058238
5
Mode #N/A
Standard
Deviation
61.0552467
7
R(f) R(m) R
Sample
Variance
3727.74315
8
8.23 25.9 17.42869074
Kurtosis
-
0.01578194
3
Skewness
-
0.89961777
6
Range 139.21
Minimum -55.41
Maximum 83.8
Sum 103.61
Count 4
RIL
Page | 52
Year Open Price Close Price
Price
Change
%age
Change
%age Market
Change
2001 362.5 305.15 -57.35 -15.82 -23.75
2002 307 297.7 -9.3 -3.03 6.89
2003 300 573 273 91.00 84.34
2004 571 533.8 -37.2 -6.51 15.88
2005 520.05 889.65 369.6 71.07 37.84
2006 893.45 1270.35 376.9 42.18 40.65
2007 1252.55 2881.05 1628.5 130.01 59.35
2008 2950 1230.25 -1719.75 -58.30 -55.41
2009 1240.05 1089.4 -150.65 -12.15 83.8
2010 1094 1058.25 -35.75 -3.27 15.87
26.546
Column 1 Column 2
Column 1
3034.25238
6
Column 2
1596.37301
8 1788.131904
Column1
Standard
Dev. Sq. of S.D. Covariance
44.5736831
2
1986.81322
7 1596.373018
Mean 26.546
Standard
Error
14.0954362
4
Beta=Cov./Sq. of S.D
Median 26.86
0.80348419
1
Mode #N/A
Standard
Deviation
44.5736831
2
R(f) R(m) R
Sample
Variance
1986.81322
7
8.23 26.54 22.94179554
Kurtosis
-
0.20327160
6
Skewness
-
0.41947853
1
Range 139.75
Minimum -55.41
Maximum 84.34
Sum 265.46
Count 10
Page | 53
Rural Elect.
Year Open Price
Close
Price
Price
Change
%age
Change
%age Market
Change
2008 125 73 -52 -41.60 -55.41
2009 73.6 243.5 169.9 230.84 83.8
2010 245 298.2 53.2 21.71 15.87
14.75333333
Column 1 Column 2
Column 1
13552.0133
2
Column 2
6293.97997
7
3230.52748
9
Column1
Standard
Dev. Sq. of S.D. Covariance
69.6117176
4
4845.79123
3 6293.979977
Mean
14.7533333
3
Standard Error
40.1903439
2
Beta=Cov./Sq. of S.D
Median 15.87
1.29885496
Mode #N/A
Standard
Deviation
69.6117176
4
R(f) R(m) R
Sample
Variance
4845.79123
3
8.23 14.75 16.69853434
Kurtosis #DIV/0!
Skewness
-
0.07216754
8
Range 139.21
Minimum -55.41
Maximum 83.8
Sum 44.26
Count 3
SBI
Year Open Price Close Price
Price
Change
%age
Change
%age Market
Change
2001 268 182.55 -85.45 -31.88 -23.75
2002 183 282.65 99.65 54.45 6.89
Page | 54
2003 283 538.5 255.5 90.28 84.34
2004 540.9 652.45 111.55 20.62 15.88
2005 655 907.45 252.45 38.54 37.84
2006 909.8 1245.9 336.1 36.94 40.65
2007 1247 2371 1124 90.14 59.35
2008 2381 1288.25 -1092.75 -45.89 -55.41
2009 1294.45 2269.45 975 75.32 83.8
2010 2265 2811.05 546.05 24.11 15.87
26.546
Column 1 Column 2
Column 1
1945.88608
3
Column 2
1726.07236
8
1788.13190
4
Column1
Standard
Dev. Sq. of S.D. Covariance
44.5736831
2
1986.81322
7 1726.072368
Mean 26.546
Standard Error
14.0954362
4
Beta=Cov./Sq. of S.D
Median 26.86
0.86876428
3
Mode #N/A
Standard
Deviation
44.5736831
2
R(f) R(m) R
Sample
Variance
1986.81322
7
8.23 26.54 24.13707402
Kurtosis
-
0.20327160
6
Skewness
-
0.41947853
1
Range 139.75
Minimum -55.41
Maximum 84.34
Sum 265.46
Count 10
Sundaram Fin.
Page | 55
Year Open Price Close Price
Price
Change
%age
Change
%age Market
Change
2006 415 413.35 -1.65 -0.40 40.65
2007 420 745.65 325.65 77.54 59.35
2008 770 182 -588 -76.36 -55.41
2009 186 343.75 157.75 84.81 83.8
2010 346.45 595.05 248.6 71.76 15.87
28.852
Column 1 Column 2
Column 1
3846.80599
8
Column 2
2504.64901
7
2271.44409
6
Column1
Standard
Dev. Sq. of S.D. Covariance
53.2851303
8
2839.3051
2 2504.649017
Mean 28.852
Standard Error
23.8298347
5
Beta=Cov./Sq. of S.D
Median 40.65
0.88213450
5
Mode #N/A
Standard
Deviation
53.2851303
8
R(f) R(m) R
Sample
Variance 2839.30512
8.23 28.85 26.4196135
Kurtosis
1.37155532
1
Skewness
-
1.11412710
8
Range 139.21
Minimum -55.41
Maximum 83.8
Sum 144.26
Count 5
Supreme Infra.
Year Open Price Close Price
Price
Change
%age
Change
%age Market
Change
Page | 56
2007 189 188.2 -0.8 -0.42 59.35
2008 189.95 32.25 -157.7 -83.02 -55.41
2009 32.5 193.3 160.8 494.77 83.8
2010 212 245.45 33.45 15.78 15.87
25.9025
Column 1 Column 2
Column 1
51583.5592
4
Column 2
8806.04051
4
2795.80736
9
Column1
Standard
Dev. Sq. of S.D. Covariance
61.0552467
7
3727.74315
8 8806.040514
Mean 25.9025
Standard Error
30.5276233
9
Beta=Cov./Sq. of S.D
Median 37.61
2.36229808
2
Mode #N/A
Standard
Deviation
61.0552467
7
R(f) R(m) R
Sample
Variance
3727.74315
8
8.23 25.9 49.97180711
Kurtosis
-
0.01578194
3
Skewness
-
0.89961777
6
Range 139.21
Minimum -55.41
Maximum 83.8
Sum 103.61
Count 4
TCS
Year Open Price Close Price
Price
Change
%age
Change
%age Market
Change
2004 1076 1335.5 259.5 24.12 15.88
2005 1349.8 1702.45 352.65 26.13 37.84
Page | 57
2006 1707 1218.6 -488.4 -28.61 40.65
2007 1250 1083.35 -166.65 -13.33 59.35
2008 1065.1 478.1 -587 -55.11 -55.41
2009 485 749.75 264.75 54.59 83.8
2010 750.7 1165.05 414.35 55.20 15.87
28.28285714
Column 1 Column 2
Column 1
1536.82540
8
Column 2
877.207968
4
1657.71570
6
Column1
Standard
Dev. Sq. of S.D. Covariance
43.9772857
1934.00165
7 877.2079684
Mean
28.2828571
4
Standard Error
16.6218516
1
Beta=Cov./Sq. of S.D
Median 37.84
0.45357146
7
Mode #N/A
Standard
Deviation 43.9772857
R(f) R(m) R
Sample
Variance
1934.00165
7
8.23 28.28 17.32410791
Kurtosis
2.03266793
9
Skewness
-
1.05946539
9
Range 139.21
Minimum -55.41
Maximum 83.8
Sum 197.98
Count 7
Tata Motors
Year Open Price Close Price
Price
Change
%age
Change
%age Market
Change
2001 102.5 99.8 -2.7 -2.63 -23.75
Page | 58
2002 101.05 161.35 60.3 59.67 6.89
2003 161.45 452.3 290.85 180.15 84.34
2004 455 505.15 50.15 11.02 15.88
2005 509.75 653 143.25 28.10 37.84
2006 650 900.25 250.25 38.50 40.65
2007 900.9 742.1 -158.8 -17.63 59.35
2008 742.5 159.05 -583.45 -78.58 -55.41
2009 158 792.6 634.6 401.65 83.8
2010 791 1306.3 515.3 65.15 15.87
26.546
Column 1 Column 2
Column 1
16348.6423
2
Column 2
3827.60089
5
1788.13190
4
Column1
Standard
Dev. Sq. of S.D. Covariance
44.5736831
2
1986.81322
7 3827.600895
Mean 26.546
Standard Error
14.0954362
4
Beta=Cov./Sq. of S.D
Median 26.86
1.92650262
4
Mode #N/A
Standard
Deviation
44.5736831
2
R(f) R(m) R
Sample
Variance
1986.81322
7
8.23 26.54 43.50426305
Kurtosis
-
0.20327160
6
Skewness
-
0.41947853
1
Range 139.75
Minimum -55.41
Maximum 84.34
Sum 265.46
Count 10
Page | 59
VST Ind.
Year Open Price Close Price
Price
Change
%age
Change
%age Market
Change
2001 103.25 155.5 52.25 50.61 -23.75
2002 158 104.45 -53.55 -33.89 6.89
2003 103 193.5 90.5 87.86 84.34
2004 192.5 237.1 44.6 23.17 15.88
2005 260 500 240 92.31 37.84
2006 487 396.7 -90.3 -18.54 40.65
2007 399.8 376 -23.8 -5.95 59.35
2008 380 210 -170 -44.74 -55.41
2009 220 531.65 311.65 141.66 83.8
2010 522.05 625.25 103.2 19.77 15.87
26.546
Column 1 Column 2
Column 1
3357.59690
8
Column 2
1510.35219
7
1788.13190
4
Column1
Standard
Dev. Sq. of S.D. Covariance
44.5736831
2
1986.81322
7 1510.352197
Mean 26.546
Standard Error
14.0954362
4
Beta=Cov./Sq. of S.D
Median 26.86
0.76018831
4
Mode #N/A
Standard
Deviation
44.5736831
2
R(f) R(m) R
Sample
Variance
1986.81322
7
8.23 26.54 22.14904803
Kurtosis
-
0.20327160
6
Skewness
-
0.41947853
1
Range 139.75
Minimum -55.41
Maximum 84.34
Sum 265.46
Page | 60
Count 10
Wipro Ltd.
Year Open Price Close Price
Price
Change
%age
Change
%age Market
Change
2001 2385 1602.5 -782.5 -32.81 -23.75
2002 1591.25 1630.65 39.4 2.48 6.89
2003 1644.4 1737.6 93.2 5.67 84.34
2004 1744.4 748 -996.4 -57.12 15.88
2005 753 463.45 -289.55 -38.45 37.84
2006 464 604.55 140.55 30.29 40.65
2007 607.9 525.6 -82.3 -13.54 59.35
2008 522 233.55 -288.45 -55.26 -55.41
2009 236 679.4 443.4 187.88 83.8
2010 697.7 490.25 -207.45 -29.73 15.87
26.546
Column 1 Column 2
Column 1
4619.37964
1
Column 2
1769.02965
9
1788.13190
4
Column1
Standard
Dev. Sq. of S.D. Covariance
44.5736831
2
1986.81322
7 1769.029659
Mean 26.546
Standard Error
14.0954362
4
Beta=Cov./Sq. of S.D
Median 26.86
0.89038548
5
Mode #N/A
Standard
Deviation
44.5736831
2
R(f) R(m) R
Sample
Variance
1986.81322
7
8.23 26.54 24.53295823
Kurtosis
-
0.20327160
6
Skewness
-
0.41947853
1
Page | 61
Range 139.75
Minimum -55.41
Maximum 84.34
Sum 265.46
Count 10
The consolidated table with each stock‘s Beta, Return, Alpha Measure and Exposure to the
market is as follows:-
(all figures in %age)
S.No. Stock Beta Return Market Return Alpha/Jensen Measure Exposure
1 Aditya Bira Nuvo 1.16 29.51 26.54 2.97 0.8
2 Ashiana Housing Ltd. 5.13 130.74 32.12 98.62 0.91
3 Axis Bank 1.28 30.4 26.54 3.86 2.94
4 Bajaj Electricals 1.64 38.44 26.54 11.9 0.82
5 BHEL 0.01 8.42 26.54 -18.12 3.27
6 BPCL 0.51 17.67 26.54 -8.87 1.05
7 Bharti Airtel 1.18 36.55 32.12 4.43 1.96
8 Clariant Chem 0.36 14.95 26.54 -11.59 1.6
9 GIC Housing Fin. 1.49 35.6 26.54 9.06 0.78
10 GAIL 1.43 34.42 26.54 7.88 1.01
11 Gujarat Appollo 1.31 32.26 26.54 5.72 0.83
12 HCL Tech. 1.2 30.3 26.54 3.76 1.32
13 HDFC Bank 0.67 20.67 26.54 -5.87 2.65
14 ICICI Bank 0.99 26.46 26.54 -0.08 6.21
15 Infosys 0.63 19.77 26.54 -6.77 6.59
16 ITC 0.41 15.83 26.54 -10.71 0.87
17 IDFC 1.36 38.37 30.35 8.02 4.39
18 KPR Mill 0.73 21.22 25.9 -4.68 0.86
19 LnT 1.41 34.13 26.54 7.59 3.75
20 Lupin
-
0.09 6.52 26.54 -20.02 0.92
21 M&M 1.9 43.19 26.54 16.65 2
22 Nilkamal 1.61 37.81 26.54 11.27 1.2
23 NTPC 0.51 18.61 28.28 -9.67 1.47
24 ONGC 0.72 21.57 26.54 -4.97 2.58
25 Power Grid Corp. 0.52 17.42 25.9 -8.48 1.3
26 RIL 0.8 22.94 26.54 -3.6 6.59
27 Rural Elect. 1.29 16.69 14.75 1.94 1.04
28 SBI 0.86 24.13 26.54 -2.41 3.88
29 Sundaram Fin. 0.88 26.41 28.85 -2.44 1.27
30 Supreme Infra. 2.36 49.97 25.9 24.07 1.33
31 TCS 0.45 17.32 28.28 -10.96 3.82
Page | 62
32 Tata Motors 1.92 43.5 26.54 16.96 1.79
33 VST Ind. 0.76 22.14 26.54 -4.4 1.58
34 Wipro Ltd. 0.89 24.53 26.54 -2.01 1.34
Page | 63
Market Research
Objective- To gauge the investment preferences prevailing among individual investors.
Methodology- Online questionnaire survey filled by 130 respondents.
Questionnaire-
1. Please rate your investment objectives in the scale below:- * (1 means "most important"
and 5 means "least important".)
1 2 3 4 5
Preserving the money
Growing the money
Growth of money
with some income
Guaranteed regular
income
High regular
income(not
guaranteed)
2. If you expect regular income from investments that you make, how often should it be? *
(You can choose only 1 option)
Monthly
Quarterly
Half Yearly
Annually
I don't expect regular income
3. When investing in an insurance policy, when do you generally expect it to mature? * (You
can choose only 1 option)
In 5 years
Between 5 to 10 years
Between 10 to 15 years
Between 15 to 20 years
In more than 20 years
Page | 64
4. What is your average expected rate of return? * (Given that--> Average rate of return on
savings account=4%, Average rate of return on long term bonds=8.5%, Average rate of
return of Indian stock market=14.3%)
5. How would you rate your tolerance for risk? *
Very Low Low Medium High
Very
High
Tolerance for risk
6. In which of the following options, you DO NOT want your money to be invested? * (You
can choose more than 1 option)
Stocks
Bonds
Government Securities
Real Estate
Oil and Gas
Venture Capital
Precious Metals
Derivatives and Futures
I am comfortable with all the options above
7. How much importance do you give to Tax Saving while planning your investments? *
Very Low Low Medium High
Very
High
Importance to Tax
Saving
Page | 65
Analysis-
Investment Objective Preferences
Frequency of returns from investments
16%
33%
26%
13%
12% Preserving the money
Growing the money
Growth of money withsome income
Guaranteed regularincome
Not guaranteed
8%
11%
30% 32%
19% Annually
Half Yearly
Quaterly
Monthly
I don't expect regularincome
Page | 66
Preferred Maturity of an Insurance Policy
Tolerance for Risk
19%
39%
26%
8%
8%
In 5 years
Between 5 to 10 years
Between 10 to 15 years
Between 15 to 20 years
In more than 20 years
1%
22%
55%
16%
6%
Very High
High
Medium
Low
Very Low
Page | 67
Importance to Tax Benefits
Findings-
Majority of the retail investors have the objective of growing their money over a
period of time, while they also give importance to preserving their money and
avoiding excessive risks.
Majority of the investors expect monthly/quarterly returns from the investments they
make.
Majority of the investors who take insurance policies prefer that the policies mature
within a period of 5 to 10 years.
Majority of the investors are mediocrely exposed to risks. They neither prefer high
risk nor too low risk.
Majority of the investors give high preference to tax benefits.
18%
46%
26%
7% 3%
Very High
High
Medium
Low
Very Low
Page | 68
Recommendations
Recommendations of the study for the portfolio management:-
The amount paid back to the policyholders can be given away in monthly or quarterly
instalments rather than giving only annual instalments. This fact was found during the
Market Research conducted.
Investments should be varied depending upon the alpha calculated for each stock
using the 10-year previous history.
More investments should be directed towards stocks with high Alpha and low Beta.
The stocks with negative alpha value can be eliminated from the portfolio as they are
currently giving less returns than the average returns of the market averaged over 10
years.
Majority investors have medium openness towards risk, so it is advisable to continue
the conservative investment approach being adopted by IDBI Federal Life.
