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 theDegree of BBA (HONS)

Department of Business AdministrationFaculty of Administrative Sciences

Air University2010

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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

REFERENCES ……………………………………………………………………67

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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

facilities for the issue and redemption of securities as well as other financial

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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

9

http://en.wikipedia.org/wiki/Fernand_Braudel

http://en.wikipedia.org/wiki/Antwerp

http://en.wikipedia.org/wiki/Bruges

http://en.wikipedia.org/wiki/Stock_broker

http://en.wikipedia.org/wiki/France

http://en.wikipedia.org/wiki/Industry_trade_group

http://en.wikipedia.org/wiki/Radhanite

http://en.wikipedia.org/wiki/Muslim

http://en.wikipedia.org/wiki/Cairo

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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http://en.wikipedia.org/wiki/1947

http://en.wikipedia.org/wiki/Stock_exchange

http://en.wikipedia.org/wiki/Japan

http://en.wikipedia.org/wiki/France

http://en.wikipedia.org/wiki/Germany

http://en.wikipedia.org/wiki/UK

http://en.wikipedia.org/wiki/India

http://en.wikipedia.org/wiki/Hongkong

http://en.wikipedia.org/wiki/Canada

http://en.wikipedia.org/wiki/United_States

http://en.wikipedia.org/wiki/Speculation

http://en.wikipedia.org/wiki/Trust_law

http://en.wikipedia.org/wiki/Merchant_bank

http://en.wikipedia.org/wiki/Options_strategies

http://en.wikipedia.org/wiki/Short_(finance)

http://en.wikipedia.org/wiki/Amsterdam_Stock_Exchange

http://en.wikipedia.org/wiki/Bond_(finance)



http://en.wikipedia.org/wiki/Dutch_East_India_Company

http://en.wikipedia.org/wiki/Shareholder

http://en.wikipedia.org/wiki/Joint_stock_company

http://en.wikipedia.org/wiki/Florence

http://en.wikipedia.org/wiki/Genoa

http://en.wikipedia.org/wiki/Verona

http://en.wikipedia.org/wiki/Pisa

http://en.wikipedia.org/wiki/Venice

http://en.wikipedia.org/wiki/Amsterdam

http://en.wikipedia.org/wiki/Ghent

http://en.wikipedia.org/wiki/Flanders

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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http://en.wikipedia.org/wiki/Efficiency_(economics)

http://en.wikipedia.org/wiki/Management

http://en.wikipedia.org/wiki/Capital_gain

http://en.wikipedia.org/wiki/Stock_price

http://en.wikipedia.org/wiki/Dividend

http://en.wikipedia.org/wiki/Stock_investor

http://en.wikipedia.org/wiki/Asset

http://en.wikipedia.org/wiki/Market_share

http://en.wikipedia.org/wiki/Product_line

http://en.wikipedia.org/wiki/Productivity_(economics)

http://en.wikipedia.org/wiki/Economic_growth

http://en.wikipedia.org/wiki/Industry

http://en.wikipedia.org/wiki/Commerce

http://en.wikipedia.org/wiki/Business

http://en.wikipedia.org/wiki/Bank

http://en.wikipedia.org/wiki/Deposit_account

http://en.wikipedia.org/wiki/Rationality

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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http://en.wikipedia.org/wiki/Securities

http://en.wikipedia.org/wiki/Privately-held_company

http://en.wikipedia.org/wiki/Public_companies

http://en.wikipedia.org/wiki/Public_companies

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

17

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

18

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

20

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

Risk Return

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

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.

3.1 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.

32

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

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

33

_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

_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

34

_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

Durbin-Watson stat 1.818021

4.3 Random Effect

Table 4.3

Random Effect


Method: GLS (Variance Components)

Sample: 1 22

35

Included observations: 22



Variable Coefficient

Std.

Error

t-

Statistic Prob.

C -0.000389 0.001359 -0.28626 0.7747

RISK? 0.000973 0.00026 3.750213 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

36

_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

_8_FAUJI--C 0.000253

_5_ATKCEMET--C 0.000346

_8_ATKCEMET--C 0.000501

_7_JSCL--C 0.000578

_9_ATKCEMET--C 0.000578

_5_JSCL--C 0.00073

_6_OGDC--C 0.000753

_6_FAUJI--C 0.000773

_5_ALFALAH--C 0.000874

_8_HABIB--C 0.000966

_7_ALFALAH--C 0.001086

_9_INDUS--C 0.001318

_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

Durbin-Watson stat 1.740366

37

4.4 Tests for Equality of Means

Test for Equality of Means Between

Series

Return alfalah Test for Equality of Means Between

Series

Risk alfalah

Sample: 1 30 Sample: 1 30

Included observations: 30 Included observations: 30

Metho

d

df Value Probability Method df Value Probability

Anova F-

statistic

(4, 103) 0.880093 0.4787 Anova F-

statistic

(4, 103) 21.72775 0

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.

Table 4.5


Series

Return Atk

Cement


Series

Risk Atk

cement



Method df Value Probability Method df Value Probability

ANOVA F-

statistic

(4, 100) 0.238812 0.9158 ANOVA F-

statistic

(4, 100) 0.238812 0.9158

38

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.

Test for Equality of Means Between Series

ret atlas Batrey

Test for Equality of Means Between Series

Risk Atlas Batry

Sample: 1 30

Sample: 1 30



Method df Value Probability Method df Value ProbabilityANOVA F-statistic

(4, 84) 1.8976 0.1184 ANOVA F-statistic

(4, 84) 19.61763 0

Table 4.6

Explanation:

HO: µ1, µ2, µ3, µ4, µ5

H1: µ1≠µ≠2µ≠3µ≠4µ≠5


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.

39

Table 4.7


Series

Return Fauji Test for Equality of Means Between

Series

Risk Fauji



Method do Value Probability Method do Value Probability

ANOVA F-

statistic

(4, 103) 0.246575 0.9111 ANOVA F-

statistic

(4, 103) 18.06381 0

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


Series

Return

Habib

Test for Equality of Means Between Series Risk

Habib



Method Df Value Probability Method df Value Probability

ANOVA F-

statistic

(4, 102) 0.764547 0.5507 ANOVA F-

statistic

(4, 102) 26.4811 0

40

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


Series

ret Indus Test for Equality of Means Between

Series

Risk Indus

Sample: 1

30

Sample: 1 30

Included observations:

30



ANOVA F-

statistic

(4, 42) 0.431743 0.7849 ANOVA F-

statistic

(4, 42) 0.957245 0.4409

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.

41


Indus Dying is not found to be equal in Year 2005, 2006, 2007, 2008, 2009.

Table 4.10


Series

Return Jscl Test for Equality of Means Between

Series

Risk Jscl

Sample: 1

30

Sample: 1 30



ANOVA F-

statistic

(4, 100) 2.983227 0.0226 ANOVA F-

statistic

(4, 100) 50.39303 0

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


Series

Return

OGDCL


Series Risk OGDCl

Sample: 1

30 Sample: 1 30



ANOVA (4, 100) 0.338873 0.8512 ANOVA F- (4, 100) 41.80957 0

42

F-statistic statistic

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

PkRfnry


Series

Risk

PkRfnry




ANOVA F-

statistic

(4, 101) 0.736857 0.569 ANOVA F-

statistic

(4, 101) 20.70222 0

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.

43


Pak Refinery is not found to be equal in Year 2005, 2006, 2007, 2008, 2009.

Table 4.13


Series

Return PSO Test for Equality of Means Between

Series

Risk PSO




ANOVA F-

statistic

(4, 103) 0.202956 0.9362 ANOVA F-

statistic

(4, 103) 16.64375 0

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:

44

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.

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

45

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.

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.

46

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


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.

47





find to be 17% 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 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.

48



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.



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

49

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

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







50











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


51








2007 Of Pak Refinery is find to be -30% correlated with the return of Attock Cement

in year 2007. The table shows that return in year 2007 Of Pak Refinery is find to be




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



52


2007.

Table 4.17

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.






53



The table shows that return in year 2008 Of Indus Dying is find to be -9% correlated



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.









54



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%

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

55

Explanation:

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%














56


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.



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

in year 2009. The table shows that return in year 2009 Of Pak Refinery is find to be

57




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.

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

58

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

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


59

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

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%

60

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 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:

61

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.





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

RISK of Bank AlFalah in year 2006. The table shows that RISK in year 2006 Of


62






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







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



63






to be 83% correlated with the RISK of Fauji Fertilizer 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.

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.

64

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.











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




65



OGDC is find to be -45% correlated with the RISK of Attock Cement in year 2007.












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








shows that RISK in year 2007 Of PSO is find to be 24% correlated with the RISK of

Atlas Battery in year 2007. The table shows that RISK in year 2007 Of PSO is find to

be 82% correlated with the RISK of Fauji Fertilizer in year 2007. The table shows


66

Securities in year 2007.The table shows that RISK in year 2007 Of PSO is find 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 year

2007. The table shows that RISK in year 2007 Of PSO is find to be 73% correlated

with the RISK of OGDC in year 2007.The table shows that RISK in year 2007 Of

PSO is find to be 83% correlated with the RISK of Pak Refinery in year 2007.

Table 4.22

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%



67


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



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.

68



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

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











to be -62% correlated with the RISK of Fauji Fertilizer in year 2008. The table shows


Securities in year 2008.The table shows that RISK in year 2008 Of PSO is find 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

69

year 2008. The table shows that RISK in year 2008 Of PSO is find to be 11%

correlated with the RISK of OGDC in year 2008.The table shows that RISK in year

2008 Of PSO is find to be 30% correlated with the RISK of Pak Refinery in year

2008.

70

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.

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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.

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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.

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Finance Thesis BBA Pakistan

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