Finance Thesis BBA Pakistan

An Empirical Study of Firm Financial Position on its Risk and Return
A case study of Karachi Stock Exchange
SUBMITTED BY:
Muhammad Umar (060021)
ZAIGHUM TANVEER (060035)
A Thesis Submitted In Partial Fulfillment of the Requirements for the

Degree of BBA (HONS)
Department of Business Administration

Faculty of Administrative Sciences
Air University
2010
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Final Project Approval Sheet
Topic of Research: An empirical Study of Firm Financial Position on its Risk and
Return.
Names of Student: Muhammad Umar
Reg No: 060021
Names of Student: Zaighum Tanveer

Reg No: 060035
Program: BBA-S-06-A-47
Approved by:
Project Supervisor
Internal Examiner
Internal Examiner
Dean
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Acknowledgment
This thesis has been the result of research conducted during spring of 2009 within the
division of the Department of Management Sciences at Air University, Islamabad.
All the praise is for Allah, the most merciful and beneficent, who blessed us with the
knowledge, gave us the courage and allowed us to accomplish this research.
We gratefully acknowledge Mr.Farooq Rasheed for his supervision, advice and

crucial contribution which made him a backbone to this thesis. His involvement with
his originality has triggered and nourished our intellectual maturity that we will
benefit from, for a long time to come.
It is also our immense pleasure to express sincere gratitude to Dr.I U Shad and
Mr.Saeed Chodhary whose inspiring guidance, remarkable suggestion, keen interest
and constructive criticism helped us to complete this research efficiently.
We found this research interesting, challenging and most of all rewarding. We hope
the report is informative to anyone who refers to it.
Thanking all the reader(s).
Muhammad Umar
Zaighum Tanveer
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Dedication
We dedicate this research to our parents and teachers, who taught us to think,
understand and express. We earnestly feel that without their inspiration, able guidance
and dedication, we would not be able to pass through the tiring process of this
research.
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ABSTARCT
This study examines the return based performance of the companies of financial
sector of Pakistan in stock market from 1st July 2005 to 30th June 2009. The analysis is
done by the construction of one portfolio consisting of 10 stocks of companies
relating to financial sector of different sector of Karachi Stock Exchange. The risky
ness of each stock of financial sector is measured to analyze whether small cap stocks
of financial sector of Pakistan are more volatile or not as compare to large cap stocks.
This is done by the construction of a manager universe benchmark and volatility of
each stock from its benchmark is analyzed. The analysis is done using non parametric
method, which is much more efficient than parametric method when distributions are
not normal. For this analysis of variation, various tools are used including ANOVA
Test under MET, Test of sources of variation and the Test of descriptive statistics.
The ANOVA Test is based on the comparison of mean returns and the risk associated
with these returns. The results of all the tests have shown that stocks of small
capitalization category have more fluctuation in returns as compared to stocks of large
capitalization category confirming that small cap stocks are more risky as compared
to large cap stocks. In the end, policy recommendations for investments are also
provided to the investors regarding their investments decisions in financial stocks
based on their ability and willingness to take risk.
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Table of Contents
ABSTRACT……………………………………………………………………
CHAPTER ONE ………………………………………………………………1
INTRODUCTION
• Background …………………………………………………………….1
• Stock Market Review…………………………………………………...2
• Purpose of Study………………………………………………………...9
• Significance of Study …………………………………………………..10
CHAPTER TWO ……………………………………………………………….11
LITERATURE REVIEW
• Hypothesis ………………………………………………………………20
CHAPTER THREE……………………………………………………………...21
DATA & METHODOLOGY

• Research Procedure………………………………………………………21
CHAPTER FOUR ……………………………………………………………….26
RESULTS
• Common Effect …………………………………………………………..26
• Fixed Effect ………………………………………………………………27
• Random Effect …………………………………………………………....29
• Test for Equality of Means ……………………………………………….32
• Correlation ………………………………………………………………..38
CHAPTER FIVE ………………………………………………………………….64
CONCLUSION AND RECOMMENDATION

• Conclusion …...……………………………………………………………64
• Recommendation ………………………………………………………….65
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REFERENCES ……………………………………………………………………67
CHAPTER I
INTRODUCTION
The purpose of this study is to investigate the relationship between firms financial
position and its risk and return and how risk affect return in portfolio choices.
1.1 Background:
Modern finance theory started from Markowitz’s (1952) portfolio theory, which
predicts how individual investor allocates their assets by balancing the risk and return
tradeoffs. Based on this theory, Sharpe’s (1963), Lintner (1965) and Black (1965)
developed the so called capital Asset Pricing Model (CAPM). For the first time their
theory clearly prescribes that it are the individual stock’s co-movements with the
overall market variables that determine stocks expected returns (thus the stock prices)
postulating a simple linear relationship between a stock’s expected price/ return and
its risk?
The CAPM has been under intensive scrutiny since birth. Early empirical studies
generally failed to reject the model. However in recent years one of the most
influential papers by Fama and French (1992) questioned the cross-sectional
predictability of the CAPM. Current evidence has shown that other factors have a
consistent and significance effect on common stock prices and return. Despite the
heated debate the CAPM still receives wide attention especially from the
practitioners. At the same time for good or bad we have at least learned that there
might be multiple other factors in determining the asset prices.
The association between size and average stock price is about as important as the
association between risk and average returns. Thus it is not surprising that there ha
seen immense growth in the papers investigating “size effect” and other empirical
regularities in average stock prices.
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The size effect one of the most enigmatic finding in finance first reported by Banz
(1981), seems to provide strong evidence that the shares of firms with small equity
market values have on average higher stock prices than firm with large equity market
values. The apparent persistence of this effect is such that it has been accorded the
status of an anomaly.
Like many emerging markets the Pakistani capital market also suffers from
unsatisfactory corporate governance, dubious accounting practice, market
manipulation, and insider trading problems. Most investor has traded speculatively
with very short holding period. The turnover ratio of stocks at KSE has been very
high, showing that investor were interested more in short gains and ignored long term
investment objectives based on future profitability of a firm. Despite this the Karachi
Stock Exchange of the Pakistani capital market is the biggest and most liquid stock
exchange and was declared the best performing stock exchange of the world for the
year 2002. Such a unique investment environment provides a natural laboratory to
study the securities price issue and its relationship to firms’ size and to know whether
there is a size effect using Pakistani stock data.
1.2 Stock market review
A stock (also known as equity or a share) is a portion of the ownership of a

corporation. . It represents a claim on the company's assets and earnings. A share in a
corporation gives the owner of the stock a stake in the company and its
profits. Important features of the stock is its limited liability, which means that, as an
owner of a stock, one is not personally liable if the company is not able to pay its
debt. Owning stocks means that, no matter what the maximum one can loose is the
value of his investment.
A market is mechanism by which buyers and sellers interact to determine the price
and quantity of goods or services. A stock exchange, securities exchange is a
corporation or mutual organization which provides "trading" facilities for stock
brokers and traders, to trade stocks and other securities. Stock exchanges also provide
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facilities for the issue and redemption of securities as well as other financial
instruments and capital events including the payment of income and dividends. The
securities traded on a stock exchange include: shares issued by companies, unit trusts
and other pooled investment products and bonds. To be able to trade a security on a
certain stock exchange, it has to be listed there. Usually there is a central location at
least for recordkeeping, but trade is less and less linked to such a physical place, as
modern markets are electronic networks, which gives them advantages of speed and
cost of transactions. Trade on an exchange is by members only. The initial offering of
stocks and bonds to investors is by definition done in the primary market and
subsequent trading is done in the secondary market. A stock exchange is often the
most important component of a stock market. Supply and demand in stock markets is
driven by various factors which, as in all free markets, affect the price of stocks.
There is usually no compulsion to issue stock via the stock exchange itself, nor must
stock be subsequently traded on the exchange. Such trading is said to be off exchange
or over-the-counter. This is the usual way that bonds are traded. Increasingly, stock
exchanges are part of a global market for securities.
The size of the world stock market is estimated at about $36.6 trillion US at the
beginning of October 2008. The world derivatives market has been estimated at about
$480 trillion face or nominal value, 12 times the size of the entire world economy.
Historian Fernand Braudel suggests that in Cairo in the 11th century, Muslim and
Jewish merchants had already set up every form of trade association and had
knowledge of many methods of credit and payment, disproving the belief that these
were originally invented later by Italians. In 12th century France the courratiers de
change were concerned with managing and regulating the debts of agricultural
communities on behalf of the banks. Because these men also traded with debts, they
could be called the first brokers. A common misbelieve is that in late 13th century
Bruges commodity traders gathered inside the house of a man called Van der Beurze,
and in 1309 they became the "Brugse Beurse", institutionalizing what had been, until
then, an informal meeting, but actually, the family Van der Beurze had a building in
Antwerp where those gatherings occurred; the Van der Beurze had Antwerp, as most
of the merchants of that period, as their primary place for trading. The idea quickly
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spread around Flanders and neighboring counties and "Beurzen" soon opened in
Ghent and Amsterdam.
In the middle of the 13th century, Venetian bankers began to trade in government
securities. In 1351 the Venetian government outlawed spreading rumors intended to
lower the price of government funds. Bankers in Pisa, Verona, Genoa and Florence
also began trading in government securities during the 14th century. This was only
possible because these were independent city states not ruled by a duke but a council
of influential citizens. The Dutch later started joint stock companies, which let
shareholders invest in business ventures and get a share of their profits - or losses. In
1602, the Dutch East India Company issued the first shares on the Amsterdam Stock
Exchange. It was the first company to issue stocks and bonds.
The Amsterdam Stock Exchange (or Amsterdam Beurs) is also said to have been the
first stock exchange to introduce continuous trade in the early 17th century. The
Dutch "pioneered short selling, option trading, debt-equity swaps, merchant banking,
unit trusts and other speculative instruments, much as we know them. There are now
stock markets in virtually every developed and most developing economies, with the
world's biggest markets being in the United States, Canada, China (Hongkong), India,
UK, Germany, France and Japan.
1.2.1 Karachi Stock Exchange
The Karachi Stock Exchange or KSE is a stock exchange located in Karachi,

Pakistan. Founded in 1947, it is Pakistan's largest and oldest stock exchange, with
many Pakistani as well as overseas listings.
Karachi Stock Exchange is the biggest and most liquid exchange and has been
declared as the “Best Performing Stock Market of the World for the year 2002”. As
on December 31, 2008, 653 companies were listed with the market capitalization of
Rs.1, 858,698.90 billion (US $ 23,527.83 billion) having listed capital of Rs.750.48
billion (US $ 9.50 billion). The KSE 100 Index closed at 5865.01 on December 31,
2008.
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KSE has been well into the 4th year of being one of the Best Performing Markets of
the world as declared by the international magazine “Business Week”. Similarly the
US newspaper, USA Today, termed Karachi Stock Exchange as one of the best
performing bourses in the world.
The exchange has pre-market sessions from 09:15am to 09:30am and normal trading
sessions from 09:30am to 03:30pm. It is the second oldest stock exchange in South
Asia.
Today KSE has emerged as the key institution of the capital formation
in Pakistan with:-
i. Listed companies 653, securities listed on the exchange 692:
ordinary share 653, Preference shares 14 and debt securities
(TFC's) 25.
ii. Listed capital Rs.750, 477.55 million (US$ 9,499.72 million).
iii. Market capitalization Rs.1, 858,698.90 million (US$ 23,527.83
million).
iv. Average daily turnover 146.55 million shares with average daily
trade value Rs.14, 228.35 million (US$ 180.11 million).
v. Membership strength at 200.
vi. Corporate Members are 187 out of which 9 are public listed
companies.
vii. Active Members are 163.
viii. Fully automated trading system with T+2 settlement cycle.
ix. Deliveries through central depository company.
x. National Clearing and Settlement System in place.
KSE began with a 50 shares index. As the market grew a representative index was
needed. On November 1, 1991 the KSE-100 was introduced and remains to this date
the most generally accepted measure of the Exchange. The KSE-100 is a capital
weighted index and consists of 100 companies representing about 86 percent of
market capitalization of the Exchange.
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In 1995 the need was felt for an all share index to reconfirm the KSE-100 and also to
provide the basis of index trading in future. On August 29, 1995 the KSE all share
index was constructed and introduced on September 18, 1995.
KSE has also introduced KSE-30 Index which is calculated using "Free Float Market
Capitalization Methodology". The primary objective of the KSE 30 Index is to have a
bench mark by which the stock price performance can be compared to over a period
of time. In particular, the KSE-30 Index is designed to provide investors with a sense
of how large company's scrip's of the Pakistan's equity market are performing
The stock market is one of the most important sources for companies to raise money.
This allows businesses to be publicly traded, or raise additional capital for expansion
by selling shares of ownership of the company in a public market. The liquidity that
an exchange provides affords investors the ability to quickly and easily sell securities.
This is an attractive feature of investing in stocks, compared to other less liquid
investments such as real estate.
History has shown that the price of shares and other assets is an important part of the
dynamics of economic activity, and can influence or be an indicator of social mood.
An economy where the stock market is on the rise is considered to be an up coming
economy. In fact, the stock market is often considered the primary indicator of a
country's economic strength and development. Rising share prices, for instance, tend
to be associated with increased business investment and vice versa. Share prices also
affect the wealth of households and their consumption. Therefore, central banks tend
to keep an eye on the control and behavior of the stock market and, in general, on the
smooth operation of financial system functions.
Exchanges also act as the clearinghouse for each transaction, meaning that they
collect and deliver the shares, and guarantee payment to the seller of a security. This
eliminates the risk to an individual buyer or seller that the counterparty could default
on the transaction.
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The smooth functioning of all these activities facilitates economic growth in that
lower costs and enterprise risks promote the production of goods and services as well
as employment. In this way the financial system contributes to increased prosperity.
Stock exchanges have multiple roles in the economy, this may include the following:
1 Raising capital for businesses
The Stock Exchange provides companies with the facility to raise capital for
expansion through selling shares to the investing public.
2 Mobilizing savings for investment
When people draw their savings and invest in shares, it leads to a more rational
allocation of resources because funds, which could have been consumed, or kept in
idle deposits with banks, are mobilized and redirected to promote business activity
with benefits for several economic sectors such as commerce and industry, resulting
in stronger economic growth and higher productivity levels and firms.
3 Facilitating company growth
Companies view acquisitions as an opportunity to expand product lines, increase

distribution channels, hedge against volatility, increase its market share, or acquire
other necessary business assets.
4 Redistribution of wealth
Stocks exchanges do not exist to redistribute wealth. However, both casual and
professional stock investors, through dividends and stock price increases that may
result in capital gains, will share in the wealth of profitable businesses.
5 Corporate governance
By having a wide and varied scope of owners, companies generally tend to improve
on their management standards and efficiency in order to satisfy the demands of these
shareholders and the more stringent rules for public corporations imposed by public
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stock exchanges and the government. Consequently, it is alleged that public
companies (companies that are owned by shareholders who are members of the
general public and trade shares on public exchanges) tend to have better management
records than privately-held companies (those companies where shares are not publicly
traded, often owned by the company founders and/or their families and heirs, or
otherwise by a small group of investors). However, some well-documented cases are
known where it is alleged that there has been considerable slippage in corporate
governance on the part of some public companies. The dot-com bubble in the early
2000s, and the subprime mortgage crisis in 2007-08, is classical examples of
corporate mismanagement. Companies like Pets.com (2000)
6 Creating investment opportunities for small investors
As opposed to other businesses that require huge capital outlay, investing in shares is
open to both the large and small stock investors because a person buys the number of
shares they can afford. Therefore the Stock Exchange provides the opportunity for
small investors to own shares of the same companies as large investors.
7 Government capital-raising for development projects
Governments at various levels may decide to borrow money in order to finance

infrastructure projects such as sewage and water treatment works or housing estates
by selling another category of securities known as bonds. These bonds can be raised
through the Stock Exchange whereby members of the public buy them, thus loaning
money to the government.
8 Barometer of the economy
At the stock exchange, share prices rise and fall depending, largely, on market forces.
Share prices tend to rise or remain stable when companies and the economy in general
show signs of stability and growth. An economic recession, depression, or financial
crisis could eventually lead to a stock market crash. Therefore the movement of share
prices and in general of the stock indexes can be an indicator of the general trend in
the economy.
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1.3 Purpose of Study
Risk concerns the expected value of one or more results of one or more future events.
Technically, the value of those results may be positive or negative. However, general
usage tends focus only on potential harm that may arise from a future event, which
may accrue either from incurring a cost ("downside risk") or by failing to attain some
benefit ("upside risk").
Financial risk is normally any risk associated with any form of financing. Risk is
probability of unfavorable condition; in financial sector it is the probability of actual
return being less than expected return. There will be uncertainty in every business; the
level of uncertainty present is called risk.
Depending on the nature of the investment, the type of 'investment' risk will vary.
High risk investments have greater potential rewards, but also have greater potential
consequences.
A common concern with any investment is that the initial amount invested may be
lost (also known as "the capital"). This risk is therefore often referred to as capital
risk.
Many forms of investment may not be readily salable on the open market (e.g.
commercial property) or the market has a small capacity and may therefore take time
to sell. Assets that are easily sold are termed liquid: therefore this type of risk is
termed liquidity risk.
In finance, rate of return (ROR), also known as return on investment (ROI), rate of
profit or sometimes just return, is the ratio of money gained or lost (whether realized
or unrealized) on an investment relative to the amount of money invested. The amount
of money gained or lost may be referred to as interest, profit/loss, gain/loss, or net
income/loss. The money invested may be referred to as the asset, capital, principal, or
the cost basis of the investment. ROI is usually expressed as a percentage rather than a
fraction.
The main purpose of the study is to investigate that is there any relationship between
firms financial position and its risk and return and how risk affects return in portfolioc
choices.
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1.4 Significance of study
Size effect on stock prices represents an unusual coincidence of interest among the
broad group of financial economist.
One of the most enigmatic empirical finding in the finance is the size effect first
reported by Banz which seems to provide strong evidence that the shares of the firm
with small equity market values have on average higher stock prices and returns than
firms with large equity market values. The apparent persistence of this effect is such
that it has been accorded the status of anomaly.
The rest of the thesis is organized as follows

 In Chapter II Literature Review
 In Chapter III Data Methodology
 In Chapter IV Results
 In Chapter V Conclusion and Recommendations
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CHAPTER II
LITERATURE REVIEW
Imperial research over past years has provided evidence of the cross sectional
relationship between stock prices and certain fundamental variables being studied
extensively. In general, a positive relationship has been found between stock prices
and earning yields, cash flows yield and book to market ratio and size. Specially,
voluminous are the studied that document the size and prices effects and studies that
try to descent angle the two effects.
Basu (1977) finds that price earning ratios and risk adjusted returns are related , a
study perform by Letzemberger and Ramaswomy (1979) shows a significant positive
relationship between dividend yield and prices on common stocks. The existence of
the size effects some specific implication for both the CAPM and the efficient market
hypothesis. CAPM assumes that expected return from an asset is a function of its
price variance. This figure is usually reported as beta and is synonymous with risk.
This relationship is thought to be linear and positive, hence the adage high “high risk,
high returns”. Several assumptions were made by Sharp. (1964), Lintner (1965) and
moss in (1966) when they developed CAPM. First they assumed investor’s portfolio
will maintain a constant proportion between risky and risk free asset. The second
assumption is that all investors can lend or borrow money at the risk free rate.
A more establish theory known as the efficient market hypothesis also conflict with
the Banz’s (1981). A capital market is said to be efficient if it fully correctly reflects
all relevant information in determining security prices. Thus it is impossible to make
economic profit by trading on the basis of such information. This is implied because
people are assumed to be rational. An indication of abnormally high profit well attract
investors and increase the demand for that security. In turn the price for that security
will increase eliminating access profits. Since the size of the company is public
information buying stocks on the basis of firm size should not lead to higher prices.
However, Banz’s study indicates other wise. Banz’s several approaches to testing this
size effect. One in particular seems to eliminate most econometric problems and yield
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the most reliable results. First the companies are split into five portfolios depending
on size. Banz’s significant and negative parameters for size, thus indicating that firms
with large market values have smaller results than small firms with comparable beta
figures.
A number of papers have analyzes the statistical tests in the papers of Benz
&Reinganum (1981). In particular Roll (1981) suggests that the stocks of small firms
are traded less frequently than the stocks of large firm so the estimates of risks from
stock prices will be biased downward. Christie & Hertzel (1981) argue that the size
effect could be due to non stationary in risk measures. The risk of a stock of levered
firm increases and the sock price decreases. Historical estimates that assume risk is
constant over time, understate the risk of levered stocks whose prices has fallen; and
thus average returns for stocks with low current value should be positive because risk
is underestimated. Still, adjusting for bias in risk estimates does not discount the size
effect.
Roll (1982) and Blume &Stambaugh (1983) examine the effects of the different
portfolio strategies implicit in alternative estimators of prices to portfolios of firms
stocks depending on the market equity. Since the magnitude of the ‘size effect’ is
apparently sensitive to the technique used to calculate current value (price) both Roll
and Blume & Stamaugh question the empirical importance of this phenomenon. In
sum, several papers have attempted to explain the results of Banz & Reinganum. Basu
(1983) re-examines Reinganum’s results using a different sample period and a
different procedure for creating portfolios of stocks ranked on both size and E/P
ratios. He found that prices of stocks of firms with low market values are riskier than
larger firms stocks. Basu contradicts Reinganum and finds that both the size and the
E/P effect are indications of deficiencies in the CAPM, not a sign of market
inefficiency.
Keim (1983) and Brown, Keidon & Marsh (1983) provide new evidence to the ‘time
series’ behavior of the size effect. Keim notes that the average price of a portfolio of
small firms stocks is large in January and much smaller for rest of the year. About
half of the annual size effect occurs in January and about 25% during the first five
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days of trading of January. Keim finds that ‘size effect’ exhibits seasonality. Brown,
Kleidon &Marsh examine the behaviors of the ‘size effects’ over time, using data
from different sample periods, thus speculate about the type of explanations that are
consistent with a ‘time varying size effect’.
Several papers examine the “January size effect’ using international data. The size
effect has also been identified empirically for the UK by Levis (1985&1989) and
Fong (1993). Brown Keim, Kleidon & Marsh analyze the prices of Australian stocks,
since the typical fiscal year end for tax purposes in June 30 in Australia. Other papers
that examine the relation between firm size, tax-loss selling and seasonality in stock
prices include Gultekin & Gultekin (1982) who examine prices of Toronto and
Montreal stock exchanges and find higher average prices in January especially for
small stocks. However, this phenomenon seems to exist both before and after 1972,
when Canada imposed the capital gains tax. Thus they concur that the tax effect does
not fully explain the size effect.
Fama and French(1992) argued that size play a dominant role in explaining cross
sectional differences in expected prices and returns from firms and they proposed an
alternative model that includes apart from market factor, a factor related to size and a
factor related to B/M(Book value/Market value)
Lakonishok, Schleifer, and Vishny (1994) suggest that the high prices associated with
high market equity stocks are generated by investors who incorrectly extrapolate the
past earning growth rates of firms. They suggest that investors are overly optimistic
about firms, which have done well in the past and overly pessimistic about those that
have done poorly. Lakonishok, Schleifer, and Vishny also suggest that high market
equity stocks are more glamorous then low market equity stocks and may thus attract
naïve investors who push up prices and raise the expected returns of these securities.
In other words NSV find evidence that values stregies higher prices not because
fundamentally riskier, But because these straggles explode the sub optimal behaviors
of the typical investors. The LSV story also supported by cai (1997) and cahangy,
Mcleavey and Rhee (1995) for Japan and by Gregory, harris, and moich (2003) for the
UK.
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Knez and Ready (1997) used the Robest Fama and Macbeth (1973) procedure in order
to postulate the influential to help to uncover why size and market worth appear to be
useful for explaining cross sectional variation and prices and returns. They find that
the risk premium of size that was estimated by Fama and French completely disappear
when the one percent most extreme observations are tempt each month. finally they
argued that further investigation are these result could lead to end and understanding
of economic forces underplaying the size effect and may also yield important inside
into how firms growth. On the other hand, Daniel and Titmen, (1997) find evidence
that the return premium on small capitalization and high book-to-market stocks does
not arise because of the co-movements of these stocks with pervasive factors. It is the
characteristic rather then the covariance structure (risk) of returns that appear to
explain the cross sectional variation in stock prices and return.
Lew and Bassalou (2000) provides that firm size and market equity are related to
future economic growth , furthermore, Vassalou shows that much of the ability of size
and equity to explain asset is due to news related to future gross domestic product
growth.
For developing capital markets in general and Pakistani markets in particular

empirical evidence on equilibrium models are few. Khilji (1993) and Hussain and
Uppal (1998) investigated the distributional characteristics of stock return in the
Karachi Stock Exchange, concluding that the return behaviour cannot be adequately
modeled by a normal distribution. Hussain (2000) found no evidence of the day of the
week anomaly and concluded that for the period January 1989 to December 1993 the
absence of this predictability pattern implied efficiency of the market. Ahmad and
Zaman (2000) using sectoral monthly data from July 1992 to March 1997 found that
some of the CAPM implications are valid in the Karachi Stock Market. They found
evidence in favor of positive expected return for investors but speculative bubbles
were also indicated. Khilji (1994) found that the majority of return series are
characterized by non-linear dependence. Ahmad and Rosser (1995) used an ARCH-
in-Mean specification to study risk return relationship using sectoral indices.
Attaullah (2001) tested APT in the Karachi Stock Exchange using 70 randomly
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selected stocks employing monthly data from April 1993 to December 1998. Out of
11 macroeconomic factors he found unexpected inflation, exchange rate, trade
balance and world oil prices were sources of systematic risk. He used Iterative Non
Linear Seemingly Unrelated Regressions technique. The present study provides more
recent evidence from monthly data from January 1997 to December 2003. With a
relatively greater sample this study employs two different factor analysis techniques
and stability analysis is also performed. Moreover macroeconomic variables used are
also greater in number and regional market indices are also included.
Javid and Ahmed (2008) an attempt to empirically investigate the size and return
(price) relationship of individual stocks traded at Karachi Stock Exchange (KSE), the
main equity market in Pakistan. The analysis is based on daily as well as monthly data
of 49 companies and KSE 100 index is used as market factor covering the period from
July 1993 to December 2004. The natural starting point of this study is to test the
adequacy of the standard Capital Asset Pricing Model (CAPM) of Sharpe (1964) and
Lintner (1965). The empirical findings do not support the standard CAPM model as a
model to explain assets pricing in Pakistani equity market. The critical condition of
CAPM—that there is a positive trade-off between risk and return—is rejected and
residual risk plays some role in pricing risky assets. This allows for the return
distribution to vary over time. The empirical results of the conditional CAPM, with
time variation in market risk and risk premium, are more supported by the KSE data,
where lagged macroeconomic variables, mostly containing business cycle
information, are used for conditioning information. The information set includes the
first lag of the following business cycle variables: market return, call money rate, term
structure, inflation rate, foreign exchange rate, growth in industrial production, growth
in real consumption, and growth in oil prices. In a nutshell, the results confirm the
hypothesis that risk premium is time-varying type in Pakistani stock market and it
strengthens the notion that rational asset pricing is working, although inefficiencies
are also present in unconditional and conditional settings.
According to Clarkson, Guedes, Thompson (1996), this paper reexamines how risk
return relationships are affected by investor uncertainty about the exact parameters of
the joint rate of return distribution. In this the authors have, attempt to clarify results
21
relating to three central issues. First, they address the issue of diversification, focusing
on an APT, factor model framework. Second, they have discussed the observablity of
estimation risk and describe research experimental designs that should encompass the
existence of estimation risk and reveal it in the data. Finally, they suggested how
exploiting contemporaneous return observations on high and low information
securities to aid in the measurement of return parameters for low information
securities.
According to the analysis of Little (2008), Small cap stocks are risky because the
economic changes or economic reversals have a great impact on smaller companies
which usually do not have enough resources to survive during difficult time. It means
that the chance of failure of small cap companies is more than large cap companies.
On the other hand, there are various benefits associated with the investment in small
cap stocks. The return of small cap stocks is higher than that of large cap stocks
because of higher risk associated with these stocks due to higher fluctuations in the
price. Small cap stocks are more nimble and react quickly to any market and
technological changes.
Huang (2004) analyzed cross country return correlations and conducted asset pricing
test on three different size based portfolios over the stocks of nine different countries
for the period of 1980 to 2004. He found that large cap stocks show significant co-
movement with across countries while on the other hand, small cap stocks show small
average correlation relative to both small cap and large cap stocks across countries.
The asset pricing test showed that large cap stocks are priced globally while the global
pricing is rejected for small cap stocks.
Early studies relating the small cap and large cap stocks support the initial hypothesis.
Solnik (1974) and Stehle (1977) conducted a test on large cap stocks from U.S and
other developed countries and found that large cap stocks carry fewer variations in
their price as compared to other stocks.
Fedorov and Sarkissian (2000) analyzed the variation of small cap stocks and large
cap stocks of Russian equity market. They found that the degree of variation is
22
weaker for portfolios of less diversified industries and for smaller-sized portfolios, but
is stronger for stocks that have overseas listings. According to Huang (2004), large
cap stocks of many countries are more likely to be cross-listed in foreign equity
markets, so these stocks have more investor recognition and face less direct or indirect
investment barriers as compared to small cap stocks. While large cap stocks are
exposed to more risk as compared to small cap stocks which only face local risk.
Guidolin and Nicodano (2005) investigated the effect of variance risk on the portfolio
choices of investors, considering the assets of European and North American small
equity portfolios. According to Guidolin and Nicodano , small cap stocks are well
known to show asymmetric risk across bull and bear markets. They found that small
cap stocks imply above-average levels of variance risk, which may significantly
reduce their appeal in the portfolio. Various researches on small cap stocks show that
the cross-sectional distribution of the equity risk premium is related to variance risk
[Harvey and Siddique, 2000; Barone-Adesi, Gagliardini, and Urga (2004)]. The size
of the U.S. small cap premium has been examined for more than twenty years. Pastor
(2000) reported that a small cap portfolio (consisting of small firms) paid 0.17% per
month in excess of the risk-adjusted return on a large cap portfolio (composed of large
firms) from 1927 to 1996.
There has been a number of recent studies of the FED model, including Asness
(2003), Durré and Giot (2005), Estrada(2006), Gwilym et al (2006), Hjalmarsson
(2004), Jansen and Wang (2004), Koivu et al (2005), Maio (2005), Malkiel (2003),
Salomons (2004) and Thomas (2005). Asness who studied the period from 1926 to
2002 found no long term (10 and 20 years) predictive power of absolute real stock
returns using the FED model.
For shorter periods, the predictive power was better but still at very low levels. For
the more recent period, the FED model did a better job explaining actual market
behavior than in the earlier period. Like Campbell and Shiller (1998, 2001) before
him, Asness found that for long-term predictions of absolute stock returns, P/E alone
did a better job than the FED model.
23
According to Chang and Thomas (1989), the author states that, this study examines
the impact of diversification strategy on risk and return in diversified firms. Following
an assessment of previous research on strategic risk, relationships between risk,
return, and diversification strategy are hypothesized. Regression analysis shows that
differences in risk-return performance among diversified firms are more closely
associated with structural factors associated with markets and businesses than with the
particular diversification strategy chosen. Returns also influence the choice of
diversification strategies which, in turn, do not get rewarded with higher profits. A
curvilinear risk-return relationship is also observed which is consistent with previous
theoretical suggestions. Implications for the strategic management of risk are then
drawn.
According to Bettis and Mahajan (1985), they have studies many firms on the base
sample of 80 firms; this paper examines the risk/return performance of related and
unrelated diversified firms at the level of accounting data. The results suggest that
although on the average related diversified firms outperform unrelated diversified
firms, related diversification offers no guarantee of a favorable risk/return
performance. (Many low performers are related diversifiers.) In fact, different
diversification strategies can result in similar risk/return performance. However, a
favorable risk/return performance is extremely hard to achieve with unrelated
diversification. The study identifies diversified firms that have managed to
simultaneously reduced risks and increase returns. The results indicate that these firms
differ from other firms on some managerially useful dimensions. The differences
suggest clues to guide other diversified firms to improve their risk/return
performance.
Analysis of Bell, (1995), expected utility theory is widely acknowledged to be a

rational approach to making decisions involving risk. Yet the methodology gives no
explicit role to measures of risk and return. In this paper we identify those families of
utility functions that are compatible with a risk-return interpretation. From these
families we deduce utility-compatible measures of risk. (Risk; Return; Utility;
Investments)
24
According Fong and Vasicek, (2000), the target value of an immunized portfolio at
the horizon date defines the portfolio's target rate of return. If interest rates change by
parallel shifts for all maturities, the portfolio's realized rate of return will not be below
the target value. To the extent that non-parallel rate changes occur, however, the
realized return may be less than the target value. The relative change in the end-of-
horizon value of an immunized portfolio resulting from such an arbitrary rate change
will be proportional to the value of its immunization risk. Immunization risk equals
the weighted variance of times to payment around the horizon date, hence depends on
portfolio composition. For example, immunization risk will be low if portfolio
payments cluster around the end of the horizon and high if payments are widely
dispersed in time. One may minimize the extent to which a portfolio's realized return
differs from its target return by minimizing the portfolio's immunization risk (while
keeping the portfolio's duration equal to the remaining horizon length). Although risk
minimization is the traditional objective of immunization, the immunization risk
measure may also be used to optimize the risk-return tradeoff. The standard deviation
of an immunized portfolio's rate of return over the investment horizon will be
proportional to the value of its immunization risk. Thus an investor may choose from
immunized portfolios of equal duration a portfolio with a high level of immunization
risk in order to maximize his expected return.
The empirical studies have shown the importance of the FED Model by emphasizing
the how much this model is considered important by the investors due to mostly one
reason that is the simplicity of the model. “Among practitioners, the use of the
original FED model has been more as an illustrative tool of market sentiment rather
than a positivistic prediction model. Furthermore, the market uses the FED model
mostly as a relative valuation tool rather than as an absolute valuation model.”
(Michael Clemens, 2007)
Empirical studies have being done on a large collection of countries including

Australia, Austria, Belgium, Canada, Denmark, France, Germany, Italy, Japan,
Switzerland, Netherlands, United Kingdom and the United States ( Durr´e and Giot,
2004).
25
2.1 Hypothesis
We intend to test the hypothesis that does risk affect return in portfolio choices that
differ with various characteristic like size, type and volume of trade.
DV IV
Risk Return
26
CHAPTER III
DATA & METHODOLOGY
The data for the analysis is collected from Karachi stock exchange. As the stocks of
financial sector are analyzed dynamically and risk is measured by classifying the
stocks of financial sector into small cap and large cap stocks, so the stocks of the
companies of financial sector listed on Karachi stock exchange are selected on the
basis of their market capitalization. For the analysis of variation, non parametric
method is used. According to Siegel (2004), non parametric methods are the statistical
procedures for hypothesis testing that do not require a normal distribution.
Furthermore, non parametric method is more efficient than parametric methods when
distributions are not normal [Siegel (2004)]. In the first step, the stocks are divided
into two portfolios. The portfolio consists of 10 stocks and data has been collected for
the last 5 years that are 2005, 2006,2007,2008,2009. It is determined from analysis
that market capitalization of these selected stocks did not remain same during last five
years, that is why the assumptions on the market capitalization value of these stocks is
made on the basis of market capitalization value on 6th march 2009. The data price
data of 20 stocks is collected from 1 July 2005 to 30 June 2009.
RESEARCH PROCEDURE
The first step after data calculation was calculation of 10 listed stocks. In order to
evaluate the risk of small cap stocks and large cap stocks of financial sector of
Pakistan stock market, different tools are used. The analysis is started using basic risk
measuring tools including mean, median, Maximum and minimum value of stock
prices, standard deviation, skew ness coefficient. The results of stock price variations
of each company’s stock are compared with the other stocks in order to measure the
risky ness of each stock of selected stocks. Afterwards ANOVA test under MET is
applied on the data. The results of ANOVA Test are also tested with Durban Watson
Statistics.
The index is calculated using market-value weighted index method. In this method,
index is calculated using market capitalization value of each stock. The market
27
capitalization value is obtained by multiplying the number of shares outstanding with
current market price. In this method, a base year is selected and on this base year, a
base value is selected. The index for a particular date is calculated by using the
following formula [Reilly and Brown (2007)].
According to Walpole (2000), Mean is the average value of series and is obtained by
adding up series and dividing it by the number of observations. Median is the middle
value or is the average of two middle values of the series. The median is a strong
measure of the center of the distribution that is less sensitive to outliers than the mean.
The difference between the mean value of each stock and stock-40 index shows the
riskiness of that particular stock. Furthermore, the difference between mean value and
median value of stocks of each stock and stock-40 index also shows the risk as well as
the return of each stock [Walpole (2000)].
3.1.1 Equality Test
Afterwards the hypothesis test by classification is done on the data for which mean
equality test is used. This test allows to analyze the equality of the means, medians,
and variances across sub samples (or subgroups) of a single series. The tests assume
that the sub samples are independent.
3.1.2 Mean Equality Test
This test is based on a single-factor, between-subjects, analysis of variance

(ANOVA). The purpose of this test is that if the subgroups have the same mean, then
the variability between the sample means (between groups) should be the same as the
variability within any subgroup (within group).
Denote the i-th observation in subgroup as , where for

groups . The between and within sums of squares are defined as [Siegel
(2004)]:
28
In the above equation is the sample mean within group and is the overall
sample mean. According to Siegel (2004), The F-statistic for the equality of means
according to the assumption that the subgroup means are identical is computed as:
In the above equation is the total number of observations. The F-statistic has an F-
distribution with numerator degrees of freedom and denominator degrees
of freedom under the null hypothesis of independent and identical normal distribution,
with equal means and variances in each subgroup.
When the subgroup variances are heterogeneous, the Welch (1951) version of the test
statistic is used. The purpose is to create a modified F-statistic that accounts for the
unequal variances. Using the Cochran (1937) weight function,
In the above equation, is the sample variance in subgroup , the modified F-

statistic can be formed as
In the above equation is a normalized weight and is the weighted grand mean,
The numerator of the adjusted statistic is the weighted between-group mean squares
and the denominator is the weighted within-group mean squares[Cochran (1937)].
29
Under the null hypothesis of equal means but possibly unequal variances, has an
approximate F-distribution with degrees-of-freedom, where
3.1.3 Pool Regression
Typically time-series regression models need a sufficient history of data to yield

robust results (you need at least 2 years of data to get sensible results). If you have
less than 2 years of data, but you have this for multiple groups, like stores or similar
products, then you can still build a "pooled" model by combining time-series
observations across several groups.
Pooled Regression is usually carried out on Time-Series Cross-Sectional data- data

that has observations over time for several different units or ‘cross-sections’. For
example concatenating Monthly Net Income data for different companies with
Quarterly GDP information allows an analyst to model the relationship between Net
Income and GDP even with limited Quarters of data per company, since
concatenating across companies increases observations, yielding greater degrees of
freedom.
Pooled regression works similar to regular regression, except an extra intercept or

‘dummy’ is added for each store. It is important to remember that Pooled Regression
Coefficients do not measure demand effect separately for each store, but yield an
‘overall’ measure of demand.
This technique can also be used with product groups instead of stores provided the
products are similar. In this case it is important to remember that the model doesn’t
really measure demand effects of the variables for a specific product, but instead are
measures of overall cross-product demand.
This approach can be used when the groups to be pooled are relatively similar or
homogenous. Level differences can be removed by 'mean-centering' (similar to
Within-Effects Model) the data across the groups (subtracting the mean or average of
30
each group from observations for the group). The model can be directly run using
Ordinary Least Squares on the concatenated groups. If the model yields large standard
errors (small T-Stats), this could be a warning flag that the groups are not all that
homogenous and a more advanced approach like Random Effects Model may be more
appropriate.
3.1.4 Correlation
In statistics, correlation and dependence are any of a broad class of statistical

relationships between two or more random variables or observed data values.
Familiar examples of dependent phenomena include the correlation between the

physical statures of parents and their offspring, and the correlation between the
demand for a product and its price. Correlations are useful because they can indicate a
predictive relationship that can be exploited in practice. For example, an electrical
utility may produce less power on a mild day based on the correlation between
electricity demand and weather. Correlations can also suggest possible causal, or
mechanistic relationships; however statistical dependence is not sufficient to
demonstrate the presence of such a relationship.
31
Chapter IV
Results
4.1 Common Effect
Table 4.1
COMMON EFFECT
Dependent Variable: RET?
Method: Pooled Least Squares
Sample(adjusted): 1 22
Included observations: 22 after adjusting
endpoints
Number of cross-sections used: 50
Total panel (unbalanced) observations: 988
Variable Coefficient Std. Error t-Statistic Prob.
C -0.000202 0.001278 -0.158017 0.8745

RISK? 0.000915 0.000247 3.699824 0.0002
R-squared 0.013693
Adjusted R-squared 0.012693
S.E. of regression 0.031327 Sum squared resid 0.967631
Durbin-Watson stat 1.717658
Explanation:
HO: Beta is equal to zero

H1: Beta is not equal to zero.
From the table 4.1, we found that the coefficient of Risk, is positive but the
statistically it is significant. Thus the test has been rejected.
At 10% significance level Ho is rejected. So we can say that β is significant.
R-Squared = Coefficient of determination , which tells or explains us how much

variations, in Dependent Variable are explained in the effect in variation in
32
Independent Variable. It explains percentage of variation in dependent variable of the
model because of independent variable. It explains % of variation in dependent
variable of the model because of independent variable.
In our case the explained variable are 13%, which is a not a good sign.
Durbin Watson = Durbin Watson test the presence of the problem of auto correlating
in the error terms.
In our case Durbin Watson statistics, is 1.71, above 1.5, which implies that there are
very minor chances of error of auto correlation.
4.2 Fixed Effect
Table 4.2
Fixed Effect
Method: Pooled Least Squares
Sample(adjusted): 1 22
Included observations: 22 after adjusting
endpoints
Variable Coefficient Std. Error t-Statistic Prob.
RISK? 0.002271 0.000462 4.91158 0
_7_HABIB--C -0.034892
_9_JSCL--C -0.026105
_7_PAKREFNRY--C -0.025954
_9_PSO--C -0.02274
_5_HABIB--C -0.019143
_7_PSO--C -0.013909
_8_PSO--C -0.00984
_6_PSO--C -0.009595
_9_ATLAS--C -0.00864
_5_ATLAS--C -0.007303
_5_PSO--C -0.00682
_6_ATKCEMET--C -0.006522
33
_8_ALFALAH--C -0.006093
_9_ALFALAH--C -0.005972
_8_INDUS--C -0.005941
_5_FAUJI--C -0.005636
_9_HABIB--C -0.005019
_6_HABIB--C -0.004966
_7_INDUS--C -0.004232
_6_ATLAS--C -0.003829
_7_OGDC--C -0.003673
_5_OGDC--C -0.003628
_7_ATLAS--C -0.003001
_9_OGDC--C -0.002431
_7_ATKCEMET--C -0.001821
_9_FAUJI--C -0.001814
_7_JSCL--C -0.001714
_8_OGDC--C -0.001623
_6_JSCL--C -0.001436
_7_FAUJI--C -0.000427
_5_ATKCEMET--C 0.000922
_8_FAUJI--C 0.001114
_8_ATKCEMET--C 0.001141
_6_ALFALAH--C 0.001668
_9_ATKCEMET--C 0.002117
_5_JSCL--C 0.002187
_8_HABIB--C 0.002519
_6_OGDC--C 0.002751
_6_FAUJI--C 0.004909
_5_ALFALAH--C 0.005234
_7_ALFALAH--C 0.006532
_9_INDUS--C 0.007353
_8_ATLAS--C 0.009207
_6_INDUS--C 0.009563
_5_INDUS--C 0.010652
_8_JSCL--C 0.012596
Fixed Effects
R-squared 0.074544
S.E. of regression 0.031128
4.3 Random Effect
Table 4.3
34
Random Effect
Method: GLS (Variance Components)
Sample: 1 22
Included observations: 22
Std. t-
Variable Coefficient Error Statistic Prob.
0.00135
C -0.000389 9 -0.28626 0.7747
3.75021
RISK? 0.000973 0.00026 3 0.0002
_7_HABIB--C -0.002384
_9_PSO--C -0.001427
_9_JSCL--C -0.001251
_9_ALFALAH--C -0.000725
_5_ATLAS--C -0.000707
_9_ATLAS--C -0.000637
_5_FAUJI--C -0.000636
_7_PSO--C -0.000594
_8_ALFALAH--C -0.00059
_6_ATKCEMET--C -0.000535
_8_INDUS--C -0.000519
_5_PSO--C -0.000414
_6_ATLAS--C -0.000315
_5_OGDC--C -0.000309
_7_INDUS--C -0.000267
_8_PSO--C -0.000262
_7_ATLAS--C -0.000205
_5_HABIB--C -0.000194
_6_PSO--C -0.000179
_9_FAUJI--C -0.00015
_8_OGDC--C -4.32E-05
_5_PAKREFNRY--C -3.57E-05
_6_HABIB--C 7.17E-05
_9_OGDC--C 7.82E-05
_9_HABIB--C 7.83E-05
_7_FAUJI--C 9.73E-05
_6_JSCL--C 9.99E-05
_7_OGDC--C 0.00019
_7_ATKCEMET--C 0.000235
35
_8_FAUJI--C 0.000253
_5_ATKCEMET--C 0.000346
_8_ATKCEMET--C 0.000501
_7_JSCL--C 0.000578
_9_ATKCEMET--C
Test for Equality of Means Between Return alfalah 0.000578
Test for Equality of Means Between Risk alfalah
_5_JSCL--C 0.00073
Series _6_OGDC--C Series
0.000753
Sample: 1 30 _6_FAUJI--C Sample:
0.0007731 30
Included observations: 30
_5_ALFALAH--C Included
0.000874 observations: 30
Method df_8_HABIB--C Value Probability Method
0.000966 df Value Probability
Anova F- (4, 103)
_7_ALFALAH--C 0.880093 0.4787 Anova F-
0.001086 (4, 103) 21.72775 0
statistic _9_INDUS--C 0.001318
statistic
_6_INDUS--C 0.001509
_5_INDUS--C 0.001734
_8_ATLAS--C 0.001885
_8_JSCL--C 0.003432
Random Effects
R-squared 0.027113
S.E. of regression 0.031113
4.4 Tests for Equality of Means
Table 4.4
Explanation:
HO: µ1, µ2, µ3, µ4, µ5

H1: µ1≠µ≠2µ≠3µ≠4µ≠5
In the case of Return, probability is 48%, therefore, Ho is accepted, this means that
return of Bank AlFalah, found to be equal in year 2005-6-7-8-2009.
In the case of Risk, probability is 0%, therefore, Ho is rejected, and this means that
Bank AlFalah is not found to be equal in Year 2005, 2006, 2007, 2008, 2009.
36
Table 4.5
Test for Equality of Means Between Return Atk Test for Equality of Means Between Risk Atk
Series Cement Series cement
Sample: 1 30 Sample: 1 30
Included observations: 30 Included observations: 30
Method df Value Probability Method df Value Probability
ANOVA F- (4, 100) 0.238812 0.9158 ANOVA F- (4, 100) 0.238812 0.9158
statistic statistic
Explanation:
HO: µ1, µ2, µ3, µ4, µ5

H1: µ1≠µ≠2µ≠3µ≠4µ≠5
return of Attock Cement, found to be equal in year 2005-6-7-8-2009.
In the case of Risk, probability is 92%, therefore, Ho is accepted and this means that
Attock Cement is found to be equal in Year 2005, 2006, 2007, 2008, 2009.
Table 4.6
Test for Equality of Means Between ret atlas Test for Equality of Means Between Risk Atlas Batry
Series Batrey Series
Sample: 1 Sample: 1 30
30
Included observations: Included observations: 30
30
ANOVA F- (4, 84) 1.8976 0.1184 ANOVA F- (4, 84) 19.61763 0
statistic statistic
Explanation:
HO: µ1, µ2, µ3, µ4, µ5

H1: µ1≠µ≠2µ≠3µ≠4µ≠5
37
return of Atlas Battery, found to be equal in year 2005-6-7-8-2009.
Atlas Battery is not found to be equal in Year 2005, 2006, 2007, 2008, 2009.
Table 4.7
Test for Equality of Means Between Return Fauji Test for Equality of Means Between Risk Fauji
Series Series
Method do Value Probability Method do Value Probability
ANOVA F- (4, 103) 0.246575 0.9111 ANOVA F- (4, 103) 18.06381 0

statistic statistic
Explanation:
HO: µ1, µ2, µ3, µ4, µ5

H1: µ1≠µ≠2µ≠3µ≠4µ≠5
return of Fauji Fertilizer, found to be equal in year 2005-6-7-8-2009.
Fauji Fertilizer is not found to be equal in Year 2005, 2006, 2007, 2008, 2009.
Table 4.8
Test for Equality of Means Between Return Test for Equality of Means Between Risk
Series Habib Series Habib
Method Df Value Probability Method df Value Probability
ANOVA F- (4, 102) 0.764547 0.5507 ANOVA F- (4, 102) 26.4811 0
38
statistic statistic
Explanation:
HO: µ1, µ2, µ3, µ4, µ5

H1: µ1≠µ≠2µ≠3µ≠4µ≠5
return of Habib Securities, found to be equal in year 2005-6-7-8-2009.
Habib Securities is not found to be equal in Year 2005, 2006, 2007, 2008, 2009.
Table 4.9
Test for Equality of Means Between ret Indus Test for Equality of Means Between Risk Indus
Series Series
30
Included observations: Included observations: 30
30
ANOVA F- (4, 42) 0.431743 0.7849 ANOVA F- (4, 42) 0.957245 0.4409
statistic statistic
Explanation:
HO: µ1, µ2, µ3, µ4, µ5

H1: µ1≠µ≠2µ≠3µ≠4µ≠5
return of Indus Dying, found to be equal in year 2005-6-7-8-2009.
39
Indus Dying is not found to be equal in Year 2005, 2006, 2007, 2008, 2009.
Table 4.10
Test for Equality of Means Between Return Jscl Test for Equality of Means Between Risk Jscl
Series Series
30
ANOVA F- (4, 100) 2.983227 0.0226 ANOVA F- (4, 100) 50.39303 0
statistic statistic
Explanation:
HO: µ1, µ2, µ3, µ4, µ5

H1: µ1≠µ≠2µ≠3µ≠4µ≠5
return of J.S.C.L, found to be equal in year 2005-6-7-8-2009.
J.S.C.L is not found to be equal in Year 2005, 2006, 2007, 2008, 2009.
Table 4.11
Test for Equality of Means Between Return Test for Equality of Means Between
Series OGDCL Series Risk OGDCl
Sample: 1
30 Sample: 1 30

ANOVA ANOVA F-
F-statistic (4, 100) 0.338873 0.8512 statistic (4, 100) 41.80957 0
40
Explanation:
HO: µ1, µ2, µ3, µ4, µ5

H1: µ1≠µ≠2µ≠3µ≠4µ≠5
return of O.G.D.C, found to be equal in year 2005-6-7-8-2009.
O.G.D.C is not found to be equal in Year 2005, 2006, 2007, 2008, 2009.
Table 4.12
Test for Equality of Means Between Series Return Test for Equality of Means Between Risk
PkRfnry Series PkRfnry
ANOVA F- (4, 101) 0.736857 0.569 ANOVA F- (4, 101) 20.70222 0
statistic statistic
Explanation
HO: µ1, µ2, µ3, µ4, µ5

H1: µ1≠µ≠2µ≠3µ≠4µ≠5
return of Pak Refinery, found to be equal in year 2005-6-7-8-2009.
Pak Refinery is not found to be equal in Year 2005, 2006, 2007, 2008, 2009.
Table 4.13
Test for Equality of Means Between Return PSO Test for Equality of Means Between Risk PSO
41
Series Series
ANOVA F- (4, 103) 0.202956 0.9362 ANOVA F- (4, 103) 16.64375 0
statistic statistic
Explanation:
return of PSO, found to be equal in year 2005-6-7-8-2009.
PSO is not found to be equal in Year 2005, 2006, 2007, 2008, 2009.
4.5 Correlation
4.5.1 Correlation (Return)
Table 4.14
Explanation:
It shows that return in year 2005 of Attock Cement is find to be 56% correlated with
the return of Bank AlFalah in year 2005.
The table shows that return in year 2005 of Atlas Battery is find to be -31% correlated
with the return of Bank AlFalah in year 2005. The table shows that return in year
2005 of Atlas Battery is find to be -26% correlated with the return of Attock Cement
in year 2005.
42
The table shows that return in year 2005 of Fauji Fertilizer is find to be 83%
correlated with the return of Bank AlFalah in year 2005. The table shows that return
in year 2005 of Fauji Fertilizer is find to be 67% correlated with the return of Attock
Cement in year 2005. The table shows that return in year 2005 of Fauji Fertilizer is
find to be -55% correlated with the return of Atlas Battery in year 2005.
The table shows that return in year 2005 of Habib Securities is find to be -26%
in year 2005 of Habib Securities is find to be -66% correlated with the return of
Attock Cement in year 2005. The table shows that return in year 2005 of Habib
Securities is find to be 27% correlated with the return of Atlas Battery in year 2005.
correlated with the return of Fauji Fertilizer in year 2005.
The table shows that return in year 2005 Of Indus Dying is find to be 4% correlated
2005 Of Indus Dying is find to be -22% correlated with the return of Attock Cement
in year 2005. The table shows that return in year 2005 Of Indus Dying is find to be
-18% correlated with the return of Atlas Battery in year 2005. The table shows that
return in year 2005 Of Indus Dying is find to be 15% correlated with the return of
Fauji Fertilizer in year 2005. The table shows that return in year 2005 Of Indus Dying
is find to be 35% correlated with the return of Habib Securities in year 2005.
The table shows that return in year 2005 Of JSCL is find to be -11% correlated with
the return of Bank AlFalah in year 2005. The table shows that return in year 2005 Of
JSCL is find to be -68% correlated with the return of Attock Cement in year 2005. .
The table shows that return in year 2005 Of JSCL is find to be -1% correlated with the
return of Atlas Battery in year 2005. . The table shows that return in year 2005 Of
JSCL is find to be -37% correlated with the return of Fauji Fertilizer in year 2005. .
The table shows that return in year 2005 Of JSCL is find to be 71% correlated with
the return of Habib Securities in year 2005. . The table shows that return in year 2005
Of JSCL is find to be 25% correlated with the return of Indus Dying in year 2005.
43
The table shows that return in year 2005 Of OGDC is find to be -16% correlated with
OGDC is find to be -66% correlated with the return of Attock Cement in year 2005.
The table shows that return in year 2005 Of OGDC is find to be 8% correlated with
the return of Atlas Battery in year 2005. The table shows that return in year 2005 Of
OGDC is find to be -40% correlated with the return of Fauji Fertilizer in year 2005.
the return of Habib Securities in year 2005. The table shows that return in year 2005
Of OGDC is find to be -39% correlated with the return of Indus Dying in year 2005.
the return of JSCL in year 2005.
The table shows that return in year 2005 Of Pak Refinery is find to be 34% correlated
2005 Of Pak Refinery is find to be 20% correlated with the return of Attock Cement
in year 2005. The table shows that return in year 2005 Of Pak Refinery is find to be
-18% correlated with the return of Atlas Battery in year 2005 The table shows that
return in year 2005 Of Pak Refinery is find to be 35% correlated with the return of
Fauji Fertilizer in year 2005. The table shows that return in year 2005 Of Pak
Refinery is find to be -3% correlated with the return of Habib Securities in year 2005.
The table shows that return in year 2005 Of Pak Refinery is find to be -27% correlated
with the return of Indus Dying in year 2005. The table shows that return in year 2005
Of Pak Refinery is find to be -24% correlated with the return of JSCL in year 2005.
with the return of OGDC in year 2005.
The table shows that return in year 2005 Of PSO is find to be -24% correlated with
PSO is find to be -56% correlated with the return of Attock Cement in year 2005.The
table shows that return in year 2005 Of PSO is find to be 11% correlated with the
return of Atlas Battery in year 2005. The table shows that return in year 2005 Of PSO
is find to be -39% correlated with the return of Fauji Fertilizer in year 2005. . The
44
return of Habib Securities in year 2005.The table shows that return in year 2005 Of
PSO is find to be 31% correlated with the return of Indus Dying in year 2005.The
return of JSCL in year 2005. The table shows that return in year 2005 Of PSO is find
to be 84% correlated with the return of OGDC in year 2005.The table shows that
Bank AlFalah in year 2005.
Table 4.15
Explanation:
The table shows that return in year 2006 of Atlas Battery is find to be -22%
in year 2006 of Atlas Battery is find to be 8% correlated with the return of Attock
Cement in year 2006.
find to be 17% correlated with the return of Atlas Battery in year 2006.
45
The table shows that return in year 2006 of Habib Securities is find to be 32%
in year 2006 of Habib Securities is find to be 29% correlated with the return of Attock
Cement in year 2006. The table shows that return in year 2006 of Habib Securities is
find to be 29% correlated with the return of Atlas Battery in year 2006. The table
shows that return in year 2006 of Habib Securities is find to be 98% correlated with
the return of Fauji Fertilizer in year 2006.
2006 Of Indus Dying is find to be 17% correlated with the return of Attock Cement in
year 2006. The table shows that return in year 2006 Of Indus Dying is find to be 5%
correlated with the return of Atlas Battery in year 2006. The table shows that return in
year 2006 Of Indus Dying is find to be 66% correlated with the return of Fauji
Fertilizer in year 2006. The table shows that return in year 2006 Of Indus Dying is
find to be 68% correlated with the return of Habib Securities in year 2006.
JSCL is find to be 42% correlated with the return of Attock Cement in year 2006. .
the return of Atlas Battery in year 2006.The table shows that return in year 2006 Of
JSCL is find to be 25% correlated with the return of Fauji Fertilizer in year 2006.The
table shows that return in year 2006 Of JSCL is find to be 42% correlated with the
JSCL is find to be 46% correlated with the return of Indus Dying in year 2006.
OGDC is find to be 8% correlated with the return of Attock Cement in year 2006. The
table shows that return in year 2006 Of OGDC is find to be 13% correlated with the
return of Atlas Battery in year 2006. The table shows that return in year 2006 Of
OGDC is find to be 93% correlated with the return of Fauji Fertilizer in year 2006.
46
Of OGDC is find to be 56% correlated with the return of Indus Dying in year 2006.
2006 Of Pak Refinery is find to be 5% correlated with the return of Attock Cement in
year 2006. The table shows that return in year 2006 Of Pak Refinery is find to be 15%
correlated with the return of Atlas Battery in year 2006 The table shows that return in
year 2006 Of Pak Refinery is find to be 83% correlated with the return of Fauji
Fertilizer in year 2006. The table shows that return in year 2006 Of Pak Refinery is
find to be 75% correlated with the return of Habib Securities in year 2006. The table
shows that return in year 2006 Of Pak Refinery is find to be 32% correlated with the
return of Indus Dying in year 2006. The table shows that return in year 2006 Of Pak
Refinery is find to be -9% correlated with the return of JSCL in year 2006. The table
return of OGDC in year 2006.
The table shows that return in year 2006 Of PSO is find to be 12% correlated with the
return of Bank AlFalah in year 2006. The table shows that return in year 2006 Of PSO
is find to be -11% correlated with the return of Attock Cement in year 2006.The table
shows that return in year 2006 Of PSO is find to be 14% correlated with the return of
Atlas Battery in year 2006. The table shows that return in year 2006 Of PSO is find
to be 91% correlated with the return of Fauji Fertilizer in year 2006. . The table shows
that return in year 2006 Of PSO is find to be 84% correlated with the return of Habib
Securities in year 2006.The table shows that return in year 2006 Of PSO is find to be
46% correlated with the return of Indus Dying in year 2006.The table shows that
return in year 2006 Of PSO is find to be -2% correlated with the return of JSCL in
year 2006. The table shows that return in year 2006 Of PSO is find to be 86%
correlated with the return of OGDC in year 2006.The table shows that return in year
47
2006 Of PSO is find to be 89% correlated with the return of Pak Refinery in year
2006.
Table 4.16
Explanation:
in year 2007 of Atlas Battery is find to be -14% correlated with the return of Attock
48
year 2007. The table shows that return in year 2007 Of Indus Dying is find to be -22%
JSCL is find to be -42% correlated with the return of Attock Cement in year 2007.The
return of Atlas Battery in year 2007.The table shows that return in year 2007 Of
JSCL is find to be -6% correlated with the return of Indus Dying in year 2007.
OGDC is find to be 5% correlated with the return of Attock Cement in year 2007. The
table shows that return in year 2007 Of OGDC is find to be 16% correlated with the
return of Atlas Battery in year 2007. The table shows that return in year 2007 Of
49
2007 Of Pak Refinery is find to be -30% correlated with the return of Attock Cement
60% correlated with the return of Atlas Battery in year 2007 The table shows that
Refinery is find to be 80% correlated with the return of Habib Securities in year 2007.
Of Pak Refinery is find to be 5% correlated with the return of JSCL in year 2007. The
table shows that return in year 2007 Of Pak Refinery is find to be 2% correlated with
the return of OGDC in year 2007.
return in year 2007 Of PSO is find to be 65% correlated with the return of JSCL in
2007.
Table 4.17
50
Explanation:
The table shows that return in year 2008 of Atlas Battery is find to be 13% correlated
2008 of Atlas Battery is find to be 18% correlated with the return of Attock Cement in
year 2008.
find to be -27% correlated with the return of Atlas Battery in year 2008.
The table shows that return in year 2008 Of Indus Dying is find to be -9% correlated
51
year 2008. The table shows that return in year 2008 Of Indus Dying is find to be -10%
JSCL is find to be -27% correlated with the return of Attock Cement in year 2008.The
JSCL is find to be -1% correlated with the return of Fauji Fertilizer in year 2008.The
table shows that return in year 2008 Of JSCL is find to be -25% correlated with the
JSCL is find to be -43% correlated with the return of Indus Dying in year 2008.
OGDC is find to be 15% correlated with the return of Attock Cement in year 2008.
2008 Of Pak Refinery is find to be 5% correlated with the return of Attock Cement in
year 2008. The table shows that return in year 2008 Of Pak Refinery is find to be 15%
52
correlated with the return of Atlas Battery in year 2008 The table shows that return in
year 2008 Of Pak Refinery is find to be 83% correlated with the return of Fauji
Fertilizer in year 2008. The table shows that return in year 2008 Of Pak Refinery is
find to be 75% correlated with the return of Habib Securities in year 2008. The table
return of Indus Dying in year 2008. The table shows that return in year 2008 Of Pak
Refinery is find to be -9% correlated with the return of JSCL in year 2008. The table
return of OGDC in year 2008.
return in year 2008 Of PSO is find to be -2% correlated with the return of JSCL in
correlated with the return of Pak Refinery in year 2008.
Table 4.18
Explanation:
53
It shows that return in year 2009 of Attock Cement is found to be -89% correlated
with the return of Bank AlFalah in year 2009.
in year 2009 of Atlas Battery is find to be 98% correlated with the return of Attock
The table shows that return in year 2009 of Fauji Fertilizer is find to be -67%
year 2009. The table shows that return in year 2009 Of Indus Dying is find to be 19%
find to be -57% correlated with the return of Habib Securities in year 2009.
54
JSCL is find to be 81% correlated with the return of Attock Cement in year 2009.The
table shows that return in year 2009 Of JSCL is find to be -7% correlated with the
JSCL is find to be 86% correlated with the return of Indus Dying in year 2009.
OGDC is find to be 99% correlated with the return of Attock Cement in year 2009.
2009 Of Pak Refinery is find to be 82% correlated with the return of Attock Cement
67% correlated with the return of Atlas Battery in year 2009 The table shows that
Refinery is find to be -6% correlated with the return of Habib Securities in year 2009.
Of Pak Refinery is find to be 100% correlated with the return of JSCL in year 2009.
with the return of OGDC in year 2009.
55
The table shows that return in year 2009 Of PSO is find to be -99% correlated with
PSO is find to be 95% correlated with the return of Attock Cement in year 2009.The
return of Atlas Battery in year 2009. The table shows that return in year 2009 Of PSO
is find to be 77% correlated with the return of Fauji Fertilizer in year 2009. . The table
Habib Securities in year 2009.The table shows that return in year 2009 Of PSO is find
to be 8% correlated with the return of Indus Dying in year 2009.The table shows that
return in year 2009 Of PSO is find to be 58% correlated with the return of JSCL in
2009.
4.5.2 Correlation (Risk)
Table 4.19
Explanation:
It shows that RISK in year 2005 of Attock Cement is found to be 12% correlated with
the RISK of Bank AlFalah in year 2005.
The table shows that RISK in year 2005 of Atlas Battery is find to be 19% correlated
with the RISK of Bank AlFalah in year 2005. The table shows that RISK in year 2005
56
of Atlas Battery is find to be 17% correlated with the RISK of Attock Cement in year
2005.
The table shows that RISK in year 2005 of Fauji Fertilizer is find to be 17%
correlated with the RISK of Bank AlFalah in year 2005. The table shows that RISK in
year 2005 of Fauji Fertilizer is find to be 80% correlated with the RISK of Attock
Cement in year 2005. The table shows that RISK in year 2005 of Fauji Fertilizer is
find to be 23% correlated with the RISK of Atlas Battery in year 2005.
The table shows that RISK in year 2005 of Habib Securities is find to be 47%
year 2005 of Habib Securities is find to be 28% correlated with the RISK of Attock
Cement in year 2005. The table shows that RISK in year 2005 of Habib Securities is
find to be -5% correlated with the RISK of Atlas Battery in year 2005. The table
shows that RISK in year 2005 of Habib Securities is find to be 45% correlated with
the RISK of Fauji Fertilizer in year 2005.
The table shows that RISK in year 2005 Of Indus Dying is find to be -33% correlated
Of Indus Dying is find to be -45% correlated with the RISK of Attock Cement in year
2005. The table shows that RISK in year 2005 Of Indus Dying is find to be 43%
correlated with the RISK of Atlas Battery in year 2005. The table shows that RISK in
year 2005 Of Indus Dying is find to be -29% correlated with the RISK of Fauji
Fertilizer in year 2005. The table shows that RISK in year 2005 Of Indus Dying is
find to be -36% correlated with the RISK of Habib Securities in year 2005.
The table shows that RISK in year 2005 Of JSCL is find to be 39% correlated with
the RISK of Bank AlFalah in year 2005. The table shows that RISK in year 2005 Of
JSCL is find to be 7% correlated with the RISK of Attock Cement in year 2005.The
table shows that RISK in year 2005 Of JSCL is find to be -10% correlated with the
RISK of Atlas Battery in year 2005.The table shows that RISK in year 2005 Of
JSCL is find to be 41% correlated with the RISK of Fauji Fertilizer in year 2005.The
table shows that RISK in year 2005 Of JSCL is find to be 39% correlated with the
57
RISK of Habib Securities in year 2005.The table shows that RISK in year 2005 Of
JSCL is find to be -8% correlated with the RISK of Indus Dying in year 2005.
The table shows that RISK in year 2005 Of OGDC is find to be 34% correlated with
OGDC is find to be 25% correlated with the RISK of Attock Cement in year 2005.
The table shows that RISK in year 2005 Of OGDC is find to be -50% correlated with
the RISK of Atlas Battery in year 2005. The table shows that RISK in year 2005 Of
OGDC is find to be 19% correlated with the RISK of Fauji Fertilizer in year 2005.
the RISK of Habib Securities in year 2005. The table shows that RISK in year 2005
Of OGDC is find to be -74% correlated with the RISK of Indus Dying in year 2005.
the RISK of JSCL in year 2005.
The table shows that RISK in year 2005 Of Pak Refinery is find to be 42% correlated
Of Pak Refinery is find to be 17% correlated with the RISK of Attock Cement in year
2005. The table shows that RISK in year 2005 Of Pak Refinery is find to be 4%
correlated with the RISK of Atlas Battery in year 2005 The table shows that RISK in
year 2005 Of Pak Refinery is find to be 5% correlated with the RISK of Fauji
Fertilizer in year 2005. The table shows that RISK in year 2005 Of Pak Refinery is
find to be -7% correlated with the RISK of Habib Securities in year 2005. The table
shows that RISK in year 2005 Of Pak Refinery is find to be 18% correlated with the
RISK of Indus Dying in year 2005. The table shows that RISK in year 2005 Of Pak
Refinery is find to be -39% correlated with the RISK of JSCL in year 2005. The table
shows that RISK in year 2005 Of Pak Refinery is find to be -37% correlated with the
RISK of OGDC in year 2005.
The table shows that RISK in year 2005 Of PSO is find to be 31% correlated with the
RISK of Bank AlFalah in year 2005. The table shows that RISK in year 2005 Of
PSO is find to be 46% correlated with the RISK of Attock Cement in year 2005.The
table shows that RISK in year 2005 Of PSO is find to be -25% correlated with the
58
RISK of Atlas Battery in year 2005. The table shows that RISK in year 2005 Of PSO
is find to be 46% correlated with the RISK of Fauji Fertilizer in year 2005. The table
shows that RISK in year 2005 Of PSO is find to be -64% correlated with the RISK of
Indus Dying in year 2005.The table shows that RISK in year 2005 Of PSO is find to
be 25% correlated with the RISK of JSCL in year 2005. The table shows that RISK
in year 2005 Of PSO is find to be 89% correlated with the RISK of OGDC in year
2005.The table shows that RISK in year 2005 Of PSO is find to be -47% correlated
with the RISK of Pak Refinery in year 2005.
Table 4.20
Explanation:
It shows that RISK in year 2006 of Attock Cement is found to be -52% correlated
with the RISK of Bank AlFalah in year 2006.
of Atlas Battery is find to be 22% correlated with the RISK of Attock Cement in year
2006.
The table shows that RISK in year 2006 of Fauji Fertilizer is find to be -9% correlated
of Fauji Fertilizer is find to be 43% correlated with the RISK of Attock Cement in
year 2006. The table shows that RISK in year 2006 of Fauji Fertilizer is find to be
32% correlated with the RISK of Atlas Battery in year 2006.
59
find to be 24% correlated with the RISK of Atlas Battery in year 2006. The table
Of Indus Dying is find to be 59% correlated with the RISK of Attock Cement in year
2006. The table shows that RISK in year 2006 Of Indus Dying is find to be 31%
year 2006 Of Indus Dying is find to be 56% correlated with the RISK of Fauji
find to be 67% correlated with the RISK of Habib Securities in year 2006.
The table shows that RISK in year 2006 Of JSCL is find to be -2% correlated with the
JSCL is find to be 44% correlated with the RISK of Indus Dying in year 2006.
OGDC is find to be 7% correlated with the RISK of Attock Cement in year 2006. The
table shows that RISK in year 2006 Of OGDC is find to be -45% correlated with the
RISK of Atlas Battery in year 2006. The table shows that RISK in year 2006 Of
60
The table shows that RISK in year 2006 Of Pak Refinery is find to be -10% correlated
2006. The table shows that RISK in year 2006 Of Pak Refinery is find to be -7%
find to be 68% correlated with the RISK of Habib Securities in year 2006. The table
Refinery is find to be 24% correlated with the RISK of JSCL in year 2006. The table
shows that RISK in year 2006 Of PSO is find to be 45% correlated with the RISK of
Habib Securities in year 2006.The table shows that RISK in year 2006 Of PSO is find
to be 34% correlated with the RISK of Indus Dying in year 2006.The table shows that
RISK in year 2006 Of PSO is find to be 30% correlated with the RISK of JSCL in
year 2006. The table shows that RISK in year 2006 Of PSO is find to be 90%
correlated with the RISK of OGDC in year 2006.The table shows that RISK in year
2006 Of PSO is find to be 50% correlated with the RISK of Pak Refinery in year
2006.
61
Table 4.21
Explanation:
It shows that RISK in year 2007 of Attock Cement is found to be -19% correlated
with the RISK of Bank AlFalah in year 2007.
of Atlas Battery is find to be -41% correlated with the RISK of Attock Cement in year
2007.
The table shows that RISK in year 2007 of Fauji Fertilizer is find to be 34%
find to be -20% correlated with the RISK of Atlas Battery in year 2007.
62
2007. The table shows that RISK in year 2007 Of Indus Dying is find to be -69%
find to be -14% correlated with the RISK of Habib Securities in year 2007.
The table shows that RISK in year 2007 Of JSCL is find to be 18% correlated with
JSCL is find to be -17% correlated with the RISK of Fauji Fertilizer in year 2007.The
OGDC is find to be -45% correlated with the RISK of Attock Cement in year 2007.
63
2007. The table shows that RISK in year 2007 Of Pak Refinery is find to be 55%
find to be 81% correlated with the RISK of Habib Securities in year 2007. The table
shows that RISK in year 2007 Of Pak Refinery is find to be -21% correlated with the
table shows that RISK in year 2007 Of PSO is find to be 24% correlated with the
to be -7% correlated with the RISK of Indus Dying in year 2007.The table shows that
RISK in year 2007 Of PSO is find to be 30% correlated with the RISK of JSCL in
2007.
Table 4.22
64
Explanation:
It shows that RISK in year 2008 of Attock Cement is found to be -9% correlated with
the RISK of Bank AlFalah in year 2008.
of Atlas Battery is find to be -20% correlated with the RISK of Attock Cement in year
2008.
The table shows that RISK in year 2008 of Fauji Fertilizer is find to be -30%
find to be 11% correlated with the RISK of Atlas Battery in year 2008.
shows that RISK in year 2008 of Habib Securities is find to be -12% correlated with
The table shows that RISK in year 2008 Of Indus Dying is find to be 12% correlated
65
2008. The table shows that RISK in year 2008 Of Indus Dying is find to be -32%
find to be 21% correlated with the RISK of Habib Securities in year 2008.
The table shows that RISK in year 2008 Of JSCL is find to be -23% correlated with
JSCL is find to be -1% correlated with the RISK of Attock Cement in year 2008.The
table shows that RISK in year 2008 Of JSCL is find to be -2% correlated with the
OGDC is find to be 28% correlated with the RISK of Attock Cement in year 2008.
OGDC is find to be -19% correlated with the RISK of Fauji Fertilizer in year 2008.
Of OGDC is find to be --7% correlated with the RISK of Indus Dying in year 2008.
Of Pak Refinery is find to be -29% correlated with the RISK of Attock Cement in
year 2008. The table shows that RISK in year 2008 Of Pak Refinery is find to be
66
39% correlated with the RISK of Atlas Battery in year 2008 The table shows that
RISK in year 2008 Of Pak Refinery is find to be -2% correlated with the RISK of
Fauji Fertilizer in year 2008. The table shows that RISK in year 2008 Of Pak Refinery
is find to be -9% correlated with the RISK of Habib Securities in year 2008. The table
is find to be -62% correlated with the RISK of Fauji Fertilizer in year 2008. The table
to be 28% correlated with the RISK of Indus Dying in year 2008.The table shows that
RISK in year 2008 Of PSO is find to be -21% correlated with the RISK of JSCL in
2008.
67
Chapter V
Conclusion and Recommendations
5.1 Conclusion
In this study we make several contributions to our understanding of how investors can
minimize their risk and maximize their returns. In this study, the return based
performance of the companies of financial sector of Pakistan in stock market is
examined. The risky ness of each stock of financial sector is measured to analyze
whether small cap stocks of financial sector of Pakistan are more volatile or not as
compare to large cap stocks. This is done by the construction of a manager universe
benchmark and volatility of each stock from its benchmark is analyzed. For this
analysis of variation, various tools are used including F- TEST, R-Squared statistics,
Durbin –Watson Statistics, Pooled Regression Test, Test for Equality of Means
Between series, Correlation.
In our test, we found that the coefficient of Risk, is positive but the statistically it is
significant. Thus the test has been rejected.
In our case the R- Squared are 13%, which is not a good sign.
In our case Durbin Watson statistics, is 1.71, above 1.5, which implies that there are
very minor chances of error of auto correlation.
While applying the Test for Equality of Means between Series, we find out that, all
returns of 10 listed companies at KSE, all the returns of 2005, 2006,2007,2008,2009
are equal.
While applying the Test for Equality of Means Between Series, we find out that ,
most of listed companies at KSE , risks , al the risks of 2005,2006,2007,2008,2009
are not equal.
68
These results supported the argument that small cap stocks of financial sector of
Pakistan are more volatile as compared to large cap stocks which means that small
cap stocks are more risky as compared to larger cap stocks. The long term average
return of large cap stocks is higher than the average return of small cap stocks. These
results lead me to recommend that the investors who want to invest for long period of
time pursuing minimum risk and high return, should invest in large cap stocks while
those investors who want to invest for shorter period of time and are willing to take
risk are recommended to invest in small cap stocks, they will be able to get higher
returns as compared to large cap stocks. So, the crux is that large cap stocks are
suitable for long term investments while small cap stocks are suitable for short term
investments.
5.2 Recommendations
1. Further researchers should be made on the topic, for the sake of continuing the
working of this research.
2. Purchase of common stock should be done when a company is up-grading

itself: as it is the time when the company’s share price is lower and its future
earnings will be more.
3. Decisions for stock purchase should not be made by just considering the
market value of equity: as the company’s other internal and external factors
have high significance in determining stock returns.
4. If annual sales of a company are high, the wrong decision regarding its stock
purchase should not be made.
5. A company high variation in the market value of equity should not be

considered good for investment.
69
6. If a Company is having negative correlation of Market Value of Equity with
EPS, it should be avoided for investment, because it may have bad future
prospects.
7. Software should be made which would be an easy predictor of stock returns on

the basis of Market Value of Equity and other economic factors.
70
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What is the topic of research discussed in the document?

What is the topic of research discussed in the document?

What methods were used to analyze the data?

What methods were used to analyze the data?

An Empirical Study of Firm Financial Position on its Risk and Return

A case study of Karachi Stock Exchange

Muhammad Umar (060021)

ZAIGHUM TANVEER (060035)

A Thesis Submitted In Partial Fulfillment of the Requirements for the

Department of Business Administration

Names of Student: Muhammad Umar

Reg No: 060021

Names of Student: Zaighum Tanveer

We gratefully acknowledge Mr.Farooq Rasheed for his supervision, advice and

Thanking all the reader(s).

CHAPTER ONE ………………………………………………………………1

CHAPTER TWO ……………………………………………………………….11

DATA & METHODOLOGY

CHAPTER FOUR ……………………………………………………………….26

CHAPTER FIVE ………………………………………………………………….64

CONCLUSION AND RECOMMENDATION

1.2 Stock market review

A stock (also known as equity or a share) is a portion of the ownership of a

1.2.1 Karachi Stock Exchange

The Karachi Stock Exchange or KSE is a stock exchange located in Karachi,

x. National Clearing and Settlement System in place.

1 Raising capital for businesses

2 Mobilizing savings for investment

3 Facilitating company growth

Companies view acquisitions as an opportunity to expand product lines, increase

6 Creating investment opportunities for small investors

7 Government capital-raising for development projects

Governments at various levels may decide to borrow money in order to finance

8 Barometer of the economy

The rest of the thesis is organized as follows

For developing capital markets in general and Pakistani markets in particular

Analysis of Bell, (1995), expected utility theory is widely acknowledged to be a

Empirical studies have being done on a large collection of countries including

3.1.1 Equality Test

3.1.2 Mean Equality Test

This test is based on a single-factor, between-subjects, analysis of variance

Denote the i-th observation in subgroup as , where for

In the above equation, is the sample variance in subgroup , the modified F-

3.1.3 Pool Regression

Typically time-series regression models need a sufficient history of data to yield

Pooled Regression is usually carried out on Time-Series Cross-Sectional data- data

Pooled regression works similar to regular regression, except an extra intercept or

In statistics, correlation and dependence are any of a broad class of statistical

Familiar examples of dependent phenomena include the correlation between the

4.1 Common Effect

C -0.000202 0.001278 -0.158017 0.8745

HO: Beta is equal to zero

At 10% significance level Ho is rejected. So we can say that β is significant.

R-Squared = Coefficient of determination , which tells or explains us how much

4.2 Fixed Effect

4.3 Random Effect

4.4 Tests for Equality of Means

HO: µ1, µ2, µ3, µ4, µ5

HO: µ1, µ2, µ3, µ4, µ5