Sample Report

Assignment On

Financial Management (F206)

Focusing On

Performance Analysis

Advanced Chemical Industries Limited

Date of Submission: May 13, 2006

FINANCIAL PERFORMANCE AND CHARACTERISTICS OF PHARMACEUTICAL AND CHEMICAL INDUSTRY IN BANGLADESH:

MULTINATIONAL VERSUS DOMESTIC CORPORATIONS

by

Shoeb Ahmed ID # 0330056

has been approved May 2008

_________________ Dr. Osman Goni

Assistant Professor School of Business

May 18, 2008 Dr. Osman Goni

Assistant Professor

School of Business,

Independent University, Bangladesh.

Dear Sir:

I, hereby, submit you the report on “Financial Performance and Characteristics of

Pharmaceutical and Chemical Industry in Bangladesh: Multinational versus Domestic

Corporations”, which has been prepared as a partial fulfillment of the degree Bachelors of

Business Administration.

This is the first time a study was performed comprehensively on my part and I have tried my

level best to complete the study in a proper way despite having limitations. It is hoped that proper

assessment will be done on my report considering the limitations of this study. Your benign and

authoritative advice will encourage me to conduct further flawless research in future.

Yours’ sincerely

____________ Shoeb Ahmed.

ID # 0330056

ACKNOWLEDGEMENT

In preparing the long and rigorous internship report, I acknowledge the encouragement and

assistance given by a number of people and institution. I am most grateful to the management of

GlaxoSmithKline Bangladesh Limited for gave me the opportunity to complete my internship in

their organization.

I would like to express my gratitude to my supervisor Dr. Osman Goni for providing me

detailed feedback and technical advice on this report. He always gave me his suggestions in

making this study as flawless as possible.

I would also like to render my sincere thanks to Mr. Sarwar Azam Khan (Finance Director),

Mr. Anisuzzaman (Finance Operation Manager) of GlaxoSmithKline Bangladesh Limited

providing me guidance, inspiration and above all flexibility of work.

Finally, I would like to thank Mr. A.N.M Giasuddin (Deputy Inspector), Law department,

Bangladesh Bank who had given me appointment from his precious time to collect data for my

report also helped me to understand many related matters.

I

Table of Content

Page

List of Tables III

List of Figures III

Executive Summary IV

1.0 Introduction 1

1.1 Purpose of the Study 2

1.2 Problem Statement 2

1.3 Methodology 3

1.3.1 Research Approach 3

1.3.2 Sampling Procedure 3

1.3.3 Instrument 4

1.3.4 Data Collection 5

1.3.5 Data Analysis 5

1.4 Limitations 6

1.5 Significance of the Study 6

1.6 Research Timeline 7

2.0 Literature Review 7

2.1 Capital Asset Pricing Model 8

2.2 The Sharpe Measure 10

2.3 Standard Deviation 10

2.4 The Treynor Measure 11

2.5 Beta Coefficient 12

II

2.6 The Jensen Measure 13

2.7 Systematic Risk 15

2.8 Geometric Mean 16

2.9 Calculation of Geometric Mean 17

2.10 Portfolio 17

2.11 The Risk Free Rate 18

2.12 Capitalization Ratio 18

3.0 Analysis of Performance 19

3.1 Financial Performance 19

3.1.1 Sharpe Measure 19

3.1.2 Treynor Measure 19

3.1.3 Jensen Measure 20

3.2 Financial Characteristic 21

3.2.1 Debt Equity Ratio 21

3.2.2 Average Standard Deviation of Equity 22

3.2.3 Frequency Distribution of Beta 23

3.2.4 Average Total Assets 24

4.0 Summary and Discussion 26

5.0 Conclusion 28

References 29

Bibliography 30

Appendix 32

III

List of tables

1. Sharpe Measure of MNC and DMC 19

2. Treynor Measure of MNC and DMC 19

3. Jensen Measure of MNC and DMC 20

4. Debt equity ratio of MNC and DMC 21

5. Average Standard Deviation of Equity of MNC and DMC 22

6. Frequency Distribution of Beta of MNC and DMC 23

7. Average Total Assets of MNC and DMC 24

8. Correlational Matrix of MNC and DMC 25

9. Financial Performance Comparison of MNC and DMC 26

10. Systematic Risk (β) Comparison of MNC and DMC 26

11. T-bill Rate Comparison of Bangladesh and U.S Government 27

List of Figure

1. Graph of Markowitz Portfolio Selection 7

2. Graph of Capital Market Line 9

3. Diagram of Debt equity ratio of MNC and DMC 22

IV

Executive Summary

The pharmaceutical and chemical industry is known as the fastest growing industry in

Bangladesh. At the side of multinational, domestic corporations have been improved range and

quality of their product. Multinational corporations (MNC) have established domineering

presence with advanced technological, financial and administrative base over domestic

corporations (DMC) in pharmaceutical and chemical industry. On the other hand, domestic

corporations have production cost advantage over multinationals operating in developed

countries. This research intends to evaluate systematically the differences of financial

characteristics and performance between multinational and domestic corporations utilizing

risk-adjusted performance measuring tools on the pharmaceutical and chemical industry in

Bangladesh. The risk-adjusted performance measuring tools are Sharpe, Treynor, Jensen

measure and debt equity ratio, average standard deviation of equity, frequency distribution of beta

and average total assets are used to define financial characteristic. The origin of the sample list is

the Dhaka Stock exchange’s industry wise company list. Secondary data like: DSE general index,

risk free rate, stock price etc. have been used for the research. The researcher employed t-test to

find out the significant difference of financial characteristics and performance of two groups. The

researcher also employed correlation and regression analyses to explore any existing relationship

between the size and financial performance tools in context of Bangladesh. The result shows

MNCs are more risk-adjusted with lower returns and DMCs are less risk-adjusted with higher

returns. The report will help investors for better understanding the nature of MNCs and DMCs for

investment in pharmaceutical and chemical industry in Bangladesh. Few unusual findings are

observed and those would be issues for future research.

1.0 Introduction

Pharmaceutical and chemical Industry has grown in Bangladesh in the last two decades at a

significant rate. The national companies account for more than 65% of the pharmaceutical and

chemical business in Bangladesh (www.pharmabiz.com). Following the Drug (Control)

Ordinance of 1982, some of the local pharmaceutical companies improved range and quality of

their products considerably. Square, Beximco, Acme, Incepta, Opsonin, ACI, General Pharma,

Ibn Sina are quite strong and enjoying good market share. Square currently is the number one

company in the industry and enjoys over 12% market share. (www.pharmabiz.com). However,

among the top 20 companies of Bangladesh six are multinationals including GlaxoSmithKline,

Sanofi-aventis, Reckitt Benckiser and Novartis. Almost all the life saving imported products and

new innovative molecules are channeled-into and marketed in Bangladesh through these

multinational companies. According to major economic indicator (2007), the export of

pharmaceutical and chemicals rose to US$ 123.47 million in 2005-06 financial years while it was

US$106.31 million in 2004-05 financial years. The country can expand its economic growth by

investing in their fast growing pharmaceutical and chemical industry which has an annual average

growth rate of 16% and a market size of BDT 30 billion in 2005 according to International

Management System (IMS). In Bangladesh, the production cost of drugs is much lower than that

of large MNCs operating in developed countries. This will give the local products a price

advantage in developed markets as well. On the other hand the multinationals are taking the

advantages of insufficient infrastructures, technological, financial and administrative base over

domestic corporations of pharmaceutical and chemical industry. Bangladesh’s rapid expansion in

pharmaceuticals and chemicals was accompanied by huge investments mainly locally (Firdousi,

2005). Limited foreign investments flowed in through the setup of various multinational

Financial Performance & Characteristics of Pharmaceutical & Chemical Industry in Bangladesh: MNC vs. DMC

2

pharmaceutical and chemical companies (MNCs) that have established a domineering presence in

the local market today. At present, the multinationals have a market share of 15% (Hossain,

2003). This is a favorable trend for Bangladesh since the multinationals have not capitalized on

the local market but have just enough influence to transfer their technology and hire national

employees creating jobs. The establishment of the multinationals is prospective for Bangladesh. It

gives the local companies the opportunity to create partnerships and mergers with the MNCs.

Since the local companies can produce drugs at a cheaper rate due to low production costs, the

MNCs can outsource their export drug production to the local companies. This is an option that

Bangladesh should consider in order to maximize its growth potential. Therefore, the

multinational and domestic or national pharmaceuticals and chemical companies’ performances

are playing different role for the economy of Bangladesh. As a result, it is important how

multinational corporations (MNCs) and domestic or national corporations (DMCs) are

performing and differs from each other.

1.2 Purpose of the study

The main purpose of this study is to evaluate systematically the differences of financial

characteristics and performance between multinational and domestic corporations utilizing

risk-adjusted performance measuring tools on the pharmaceutical and chemical industry in

Bangladesh.

1.2 Problem Statement

According to Michel and Shaked (1986), if markets are not perfectly integrated, the

multinational corporations are performing a valuable function for investors. It has been frequently

argued that imperfections in the market for products translate into opportunities for MNCs. For

example, Hirsch (1979) suggested a cost saving that permits an increase in the export of

intermediate products as well as entry to markets of new products sharing production economies,


3

this incremental value of being able to arbitrage tax regimes (Agmon & Lessard, 1977). But the

most frequently cited disadvantage of MNCs is that they operate in a more complex environment

than their counterparts, DMCs. MNCs are always exposed to the criticism that they siphon funds

out of countries in which they do business. So, governments are liable to limit the company’s

freedom to repatriate any of its profits. The performances of MNCs’ and DMCs’ were compared

by Gughes, Louge and Sweeney (1975) through various risk measures, such as systematic (β) risk

and unsystematic risk to find out whether MNC provides substantial diversification benefits.

Previous research conducted by Michel and Shaked (1986) compared standard MNCs’ and

DMCs’ performances through performance measures such as Sharpe, Treynor and Jensen

measures.

Therefore, the researcher intends to investigate the differences of financial characteristics and

performance between multinational corporations (MNCs) and domestic corporations (DMCs) in

context of pharmaceutical and chemical industry in Bangladesh.

1.3 Methodology

1.3.1 Research approach

Here in this study two portfolios group were formed. One was for MNCs and another was for

DMCs. After that portfolios’ performances were compared.

1.3.2 Sampling Procedure

As the previous research conducted by Michel and Shaked (1986) included only publicly held

company, so the researcher followed the procedure for the sampling. It facilitates of accounting

data accesses which is most reasonable and standardized information. The genesis of the sample

was the Dhaka Stock Exchange (DSE) industry-wise company list. There were total 25

pharmaceutical and chemical companies listed. There were other large MNCs operating in

Bangladesh, but they were not enlisted in DSE. Those companies are registered and operating in


4

more than one country is selected as sample for multinational companies. As only two

multinational companies (GlaxoSmithKline, Reckitt Benckiser) were there, so they were selected.

According to market share position of year 2006 top three DMCs were selected. They are Square

(15.01%), Beximco (10.28%) and ACI (3.31%).

1.3.3 Instrument

In this study the researcher tries to measure financial performance of MNC and DMC

portfolios through Sharp, Treynor and Jensen performance measurement tools and differentiate

their characteristics through capitalization ratio (debt equity ratio), standard deviation of equity

and frequency distribution beta. The measure tools are as follows:

Sharpe Measure,

Where,

RiG

= Geometric average return on stock i

Rf G = Geometric average return on risk free Security

σi = Standard deviation of yearly rates of return

Treynor Measure,

Where,

RiG


RfG

= Geometric average return on risk free security

βi = Security’s beta

RiG

- RfG

σi Si =

Ti = βi

RiG

- RfG


5

Jensen Measure,

Where,

RiG


RfG



RmG = Geometric average market return

1.3.4 Data collection

For completing the study, secondary data were utilized. For secondary data collection:

companies annual reports, daily trading price of stock and DSE general index, Dhaka stock

exchange’s library was used. The risk free rate (Rf), estimated by the monthly T-bill returns was

obtained from Bangladesh Bank’s website and online published report, like: Major Economic

indicator. Dhaka stock exchange’s web site was also used to collect companies’ yearly

performance data.

1.3.5 Data analysis

For data analysis, the researcher adopted t-test to find out the significant differences of

risk-adjusted performances and financial characteristics of the two groups, MNCs and DMCs.

Financial characteristics of MNC and DMC portfolios were evaluated by some selected variables

like capitalization ratio (debt equity ratio), standard deviation of equity and frequency distribution

of beta. To find out whether average performance measures are influenced only because of their

size or not, regression analysis was performed. For regression analysis, performance measure

used as the dependent variable, size as independent variable. Other calculations like: standard

αi = RiG

- [ RfG + βi ( Rm

G - RfG )]


6

deviation, stock returns etc. Microsoft Excel was used. SPSS version 12 was used for statistical

analysis

1.4 Limitations

Pharmaceutical and chemical industry is so oversaturated and the number of market players is

so intense that it limits the opportunity to work extensively on the proposed research subject

during the internship period.

The limitations confronted while conducting the research were:

• Availability of data was limited for which data of only six years has been incorporated.

• There are good numbers of multinational and domestic pharmaceutical and chemical

companies are not listed under DSE but those are also key players in the market. Exclusion of

those companies for the study can be attributed not to reflect the real differences between them.

• The proposed model of Michel and Shaked (1986) was measured on basis of monthly

return, but unavailability data of dividend and DSE general index on monthly or quarterly this

research study moved to the yearly return.

• Only three DMCs are included for DMCs’ portfolio and equal weight method was used for

portfolio of both groups, which could be a limitation.

1.5 Significance of the study

The present research is remarkable in various aspects. First of all, it will help investors to

identify the nature of MNCs and DMCs and will also help to take decision regarding investment.

Secondly, future researcher would be able to extent the research by including other indicator.

Further more, for government or authorized department like Drug administration, Board of

Investment etc. it facilitate better understanding of the potentiality of the pharmaceutical and

chemical industry to contribute in development of an economy of a country like Bangladesh.


7

1.6 Research Timeline

2008 March Writing Research Proposal

2008 April Developing Literature Review

2008 April Developing financial model

2008 April Collecting pertinent data

2008 April Analyzing data and interpret findings

2008 May Preparing draft and finalize research paper

2.0 Literature Review

Any discussion of the theory of stock price behavior has to start with Markowitz (1952,

1959). The Markowitz model is a single-period model, where an investor forms a portfolio at the

beginning of the period. The investor's objective is to maximize the portfolio's expected return,

subject to an acceptable level of risk (or minimize risk, subject to an acceptable expected return).

The assumption of a single time period, coupled with assumptions about the investor's attitude

toward risk, allows risk to be measured by the variance (or standard deviation) of the portfolio's

return. Thus, as indicated by the arrow in Figure 1, the investor is trying to go as far northwest as

possible.

Figure 1: Markowitz Portfolio Selection


8

As securities are added to a portfolio, the expected return and standard deviation change in

very specific ways, based on the way in which the added securities co-vary with the other

securities in the portfolio. The best that an investor can do (i.e., the furthest northwest a portfolio

can be) is bounded by a curve that is the upper half of a hyperbola, as shown in Figure 1. This

curve is known as the efficient frontier. According to the Markowitz model, investors select

portfolios along this curve, according to their tolerance for risk. An investor who can live with a

lot of risk might choose portfolio A, while a more risk-averse investor would be more likely to

choose portfolio B. One of the major insights of the Markowitz model is that it is a security's

expected return, coupled with how it co-varies with other securities, that determines how it is

added to investor portfolios (http://www.dfaus.com/library/articles/explaining_stock_returns).

2.1 Capital Asset Pricing Model

Building on the Markowitz framework, Sharpe (1964), Lintner (1965) and Mossin (1966)

independently developed what has come to be known as the Capital Asset Pricing Model

(CAPM). This model assumes that investors use the logic of Markowitz in forming portfolios. It

further assumes that there is an asset (the risk-free asset) that has a certain return. With a risk-free

asset, the efficient frontier in Figure 1 is no longer the best that investors can do. The straight line

in Figure 2, which has the risk-free rate as its intercept and is tangent to the efficient frontier, is

now the northwest boundary of the investment opportunity set. Investors choose portfolios along

this line (the capital market line), which shows combinations of the risk-free asset and the risky

portfolio M. In order for markets to be in equilibrium (quantity supplied = quantity demanded),

the portfolio M must be the market portfolio of all risky assets. So, all investors combine the

market portfolio and the risk-free asset, and the only risk that investors are paid for bearing is the

risk associated with the market portfolio. This leads to the CAPM equation:


9

CAPM equation:

E(Rj) = Rf + βj [E(Rm) - Rf]

E(Rj) and E(Rm) are the expected returns to asset j and the market portfolio, respectively, Rf is

the risk free rate, and βj is the beta coefficient for asset j. βj measures the tendency of asset j to co-

vary with the market portfolio. It represents the part of the asset's risk that cannot be diversified

away, and this is the risk that investors are compensated for bearing. The CAPM equation says

that the expected return of any risky asset is a linear function of its tendency to co-vary with the

market portfolio. So, if the CAPM is an accurate description of the way assets are priced, this

positive linear relation should be observed when average portfolio returns are compared to

portfolio betas. Further, when beta is included as an explanatory variable, no other variable

should be able to explain cross-sectional differences in average returns. Beta should be all that

matters in a CAPM world.

Figure 2: Capital Market Line

Based on the capital market theory and recognizing the necessity to incorporate return and

risk into the analysis, three researchers – William Sharpe, Jack Treynor, and Michael Jensen

developed measures of portfolio performance in the 1960s. These measures are often referred as


10

the composite (risk-adjusted) measures of portfolio performance, meaning they incorporate both

realized return and risk into the evaluation (Jones, 2004).

2.2 The Sharpe Measure

William Sharpe, introduce a risk-adjusted measure of portfolio performance called the reward-

to-variability ratio (RVAR) based on his work in capital market theory, dealing specially with the

capital market line (CML). The Sharpe portfolio performance measure (designated by S) is stated

as follows:

Sharpe Measure,

Where,

Ri = Average return on stock i

Rf = Average return on risk free Security

σi = Standard deviation of monthly/yearly rates of return

Shape’s measure divides average portfolio excess return (or the return above the risk free rate)

over the sample provided by the standard deviation of returns over that period (Shapre, 1966). In

other words it seeks to measure the total risk of portfolio by including the standard deviation of

returns.

The Sharpe ratio is used to measure how well the return if an asset compensates the investor

for the risk taken. When comparing tow assets each with the average return against the same

benchmark with return Rf , the asset with higher Sharpe ratio gives more return for the same risk.

2.3 Standard Deviation

Standard deviation (σ) is the statistical measure of the degree to which an individual value in a

probability distribution tends to vary from the mean of the distribution.

In finance, standard deviation is a representation of the risk associated with a given security

(stock, bonds, property, etc.) or the risk of a portfolio of securities. Risk is an important factor in

Ri - Rf

σi Si =


11

determining how to efficiently manage a portfolio if investments because it determines the

variations in returns on the assets and/or portfolio and gives investors a mathematical basis for

investment decisions. The overall concept of risk is that as it increases, the expected return on the

asset will increase as a result of the risk premium earned - in other words, investors should expect

a higher return on an investment when said investment carries a higher level of risk. As this study

is on historical data of stocks so, historical returns has been calculated and then standard

deviation or the returns has been calculated.

2.4 The Treynor Measure

At approximately the same time as Sharpe’s measure was developed (the mid 1960s), Jack

Treynor presented a similar measure called the reward-to-volatility ratio (RVOL). Like Sharpe,

Treynor sought to relate the return on a portfolio to its risk. Treynor, however, distinguished

between total risk and systematic risk. He used as a benchmark the ex-post security line.

Treynor’s measure relates the average excess return on the portfolio during some period to its

systematic risk as measured by the portfolio’s beta. The Treynor portfolio performance measure

(designated T) is stated as follows:

Treynor Measure,

Where,


Rf = Average return on risk free security


In measuring portfolio performance Treynor introduce the concept of the characteristic line,

which uses to partition a security’s return into its systematic and no systematic components. The

Ti = βi

Ri - Rf


12

slope of the characteristic line measures the relative volatility of the fund’s returns. As we know,

the slope of this line is the beat coefficient, which is a measure of the volatility of the portfolio’s

returns in the relation to those of the market index (Treynor, 1965). A larger T value indicates a

larger slope and a better portfolio for all investors, regardless of their risk preferences. Because

the numerator of this ratio ( ) is the risk premium and the denominator is a measure of

risk, the total express indicates the portfolios risk premium return per unit of the risk. All risk-

averse investors would prefer to maximize this value.

2.5 Beta Coefficient

The beta coefficient (β) measures an investment's relative volatility or impact of a per-unit

change in the independent variable (market) on the dependable variable (portfolio) holding all

else constant.

The Beta coefficient, in terms of finance and investing, is a measure of volatility of a stock or

portfolio in relation to the rest of the financial market

(http://en.wikipedia.org/wiki/Beta_%finince%29). An asset with a beta of 0 means that its price is

not at all correlated with the market; that asset is independent. A positive beta means that the

asset generally follows the market. A negative beta shows that the asset inversely follows the

market; the asset generally decreases in value if the market goes up. By definition, the market

itself has an underlying beta of 1.0, and individual stocks are ranked according to how much they

deviate from the macro market (for simplicity purposes, the DSE general index is usually used as

a proxy for the market as a whole). A stock that swings more than the market (i.e. more volatile)

over time has a beta above 1.0. If a stock moves less than the market, the stock's beta is less than

1.0. More specifically, a stock that has a beta of 2 follows the market in an overall decline or

growth, but does so by a factor of 2; meaning when the market has an overall decline of 3% a

stock with a beta of 2 will fall 6%. (Betas can also be negative, meaning the stock moves in the

Ri - Rf


13

opposite direction of the market: a stock with a beta of -3 would decline 9% when the market

goes up 3% and conversely would climb 9% if the market fell by 3 %.)

The beta coefficient is a key parameter in the capital asset pricing model (CAPM). It

measures the part of the asset's statistical variance that cannot be mitigated by the diversification

provided by the portfolio of many risky assets, because it is correlated with the return of the other

assets that are in the portfolio. Higher-beta stocks mean greater volatility and are therefore

considered to be riskier, but are in turn supposed to provide a potential for higher returns; low-

beta stocks pose less risk but also lower returns. In the same way a stock's beta shows its relation

to market shifts, it also is used as an indicator for required returns on investment (ROI).

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

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

movement does not correlate well with the broader market index may decrease its beta. However,

it should not be taken as a reflection on the overall attractiveness or the loss of it for the sector, or

stock as the case may be. Beta is a measure of risk and not to be confused with the attractiveness

of the investment.

2.6 The Jensen Measure

The measure was first used in the evaluation of mutual fund managers by Michael Jensen in

the 1970’s. In finance, Jensen’s alpha or Jensen’s measure is used to determine the excess return

of a stock, other security, or portfolio over the security’s required rate of return as determined by

the Capital Asset Pricing Model. This model is used to adjust for the level of beta risk, so that

riskier securities are expected to have higher returns. (http://en.wikipedia.org/wiki/Jensen_ratio).


14

Jensen Measure,

Where,


Rf = Average return on risk free security


Rm = Average market return

This equation indicate that the realized rate of return on a security or portfolio should be a

linear function of the risk free rate of return, plus a risk premium that depends on the systematic

risk of the security. By subtracting risk free return from both sides we get following equation:

Where,

= The risk premium on the stock i

In this form, it would not be expected any interception for the regression if all assets and

portfolio were in equilibrium. Alternatively, certain superior portfolio managers who could

forecast market turns or consistently select under valued securities would earn higher risk

premiums than those implied by this model. To detect and measure this superior and/ or inferior

performance Jensen agreed to add an intercept (a non-zero constant) term (alpha) that measures

any positive and/or negative differences from the model. Consistent positive difference would

case a positive intercept, whereas consistent negative differences cause a negative intercept. So

with an intercept the earlier equation becomes:

Superior performances will evident by significantly positive alpha and inferior performances

will evident by significantly negative alpha. If alpha is insignificantly different from zero, this

Ri - Rf

Ri - Rf = αi + βi [ Rm - Rf ]

Ri - Rf = βi [ Rm - Rf ]

Ri = Rf + βi [ Rm - Rf ]


15

evidence that the portfolio manager matched the market on a risk-adjusted basis. Now to better

demonstrate what αi (alpha) is, the above equation can be rearrange like bellow:

So finally the equation becomes as bellow:

A computable advantage of the Jensen measure is that it permits the performance measure to

be estimated simultaneously with the beta for a portfolio. That is by estimating a characteristic

line in risk premium form, estimates of both alpha and beta are obtained at same time (Jones,

2004). A positive alpha of 1.0 means the fund has outperformed its benchmark index by 1%.

Correspondingly, a similar negative alpha would indicate an underperformance of 1%. However,

unlike the Sharpe and Treynor measures, each period’s returns must be used in estimating process

rather than an average return for the entire period. Thus, if performance is being measured on an

annual return on Rf, Rm and Ri must be obtained.

2.7 Systematic risk

Systematic risk is a risk that cannot be diversified away, as opposed to "idiosyncratic risk”,

which is specific to individual stocks (http://en.wikipedia.org/wiki/Systemic_risk). It also called

market risk or undiversified risk. It refers to the movements of the whole economy. Even if we

have a perfectly diversified portfolio there is some risk that we cannot avoid and this is the

systematic risk. However, the systematic risk is not the same for all securities or portfolios.

Different companies respond differently to a recession or a booming economy. For an example

think of the automobile industry compared to the food industry in case of a recession. Both of

them will be affected negatively but food industry not as much as automobile industry.

αi = (Ri - Rf )– {βi [ Rm - Rf ]}

αi = Ri - [ Rf + βi ( Rm - Rf )]


16

2.8 Geometric Mean

Measuring the investment return generally arithmetic average is used but the pervious study

used geometric average. Though, the geometric average always gives lower value than arithmetic

average. The geometric average is used because the effect of the negative returns of a stock is

fully offsets by its calculation, which is not true for the arithmetic average. In general, the bad

returns have the grater influences on the averaging process in the geometric technique. So

geometric average will be appropriate for this study and finally the measure tools will be as

follows:

Sharpe Measure,

Where,

RiG


Rf G = Geometric average return on risk free Security

σi = Standard deviation of monthly/yearly rates of return

Treynor Measure,

Where,

RiG


RfG



Jensen Measure,

Where,

RiG


RfG


RiG

- RfG

σSi =

αi = RiG

- [ RfG + βi ( Rm

G - Rf

G )]

Ti = βi

RiG

- RfG


17


RmG = Geometric average market return

2.9 Calculation of geometric mean

The geometric mean, in mathematics, is a type of mean or average, which indicates the central

tendency or typical value of a set of numbers. It is similar to the arithmetic mean, which is what

most people think of with the word "average," except that instead of adding the set of numbers

and then dividing the sum by the count of numbers in the set, n, the numbers are multiplied and

then the nth root of the resulting product is taken (http://en.wikipedia.org/wiki/Geometric_mean).

But it is very obvious that returns of stock can be negative or zero. So to calculate the geometric

mean along with the negative return, it requires that the negative values be converted or

transformed to a meaningful positive equivalent value. For example, to calculate the geometric

mean of the values +12%, -8%, and +2%, instead calculate the geometric mean of their decimal

multiplier equivalents of 1.12, 0.92, and 1.02, to compute a geometric mean of 1.0167.

Subtracting 1 from this value gives the geometric mean of +1.67% as a net rate of population

growth (or financial return).

2.10 Portfolio

Portfolio means a combination of different securities with different returns and standard

deviations, which actually minimize the risk and maximize the return. Holding a portfolio is a

part of an investment and risk-limiting strategy called diversification (Investmentpedia.com). The

assets in the portfolio could include stocks, bonds, options, warrants, gold certificates, future

contracts or any other that is expected to retain its value.


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2.11 The risk-free rate

The risk-free rate is the current interest rate on a default-free bond in the absence of inflation.

The risk-free interest rate is the interest rate that it is assumed can be obtained by investing in

financial instruments with no default risk. However, the financial instrument can carry other types

of risk, e.g. market risk (the risk of changes in market interest rates), liquidity risk (the risk of

being unable to sell the instrument for cash at short notice without significant costs) etc. Though a

truly risk-free asset exists only in theory, in practice most professionals and academics use short-

dated government bonds of the currency in question. Usually government Treasury bills are used

for investment. The risk-free interest rate is thus of significant importance to modern portfolio

theory in general, and is an important assumption for rational pricing. It is also a required input in

financial calculations, such as the Sharpe, Treynor, and Jensen formula for measuring volatility of

portfolio. Note that some finance and economic theory assumes that market participants can

borrow at the risk free rate; in practice, of course, very few borrowers have access to finance at

the risk free rate.

2.12 Capitalization

Capitalization is a measure of a corporation's reliance on long-term debt. These ratios compare

debt to shareholders' equity and thus reflect the extent to which a corporation is trading on its

equity. This ratio is calculated by dividing debt by shareholders' equity. It also called debt to

equity ratio (www.investmentpedia.com/finance/cap_ratio).


19

3.0 Analysis of Performance

3.1 Financial Performance

The measures of financial performance are exhibited by three tables for the both MNC and

DMC portfolios. Several observations are promptly noticeable.

3.1.1 Sharpe Measure

Table 1 Sharpe Measure of MNC and DMC

Sharpe Measure 2000-2002 2001-2003 2002-2004 2003-2005 2004-2006

MNCs 3.92 3.20 4.93 4.11 1.81

DMCs 10.64 3.33 2.45 32.79 9.14

The table 1 is presenting Sharpe measure of MNC and DMC portfolio, where DMC portfolio

is higher than MNC portfolio almost each of the five time periods. Therefore DMC portfolio is

obtaining higher return than MNC portfolio. In other word, DMC portfolio is compensating in a

good way of the risk taken by the investors.

3.1.2 Treynor Measure

Table 2 Treynor Measure of MNC and DMC Treynor Measure 2000-2002 2001-2003 2002-2004 2003-2005 2004-2006

MNCs 0.76 0.89 2.74 2.5 4.38

DMCs -2.76 -1.02 -2.16 12.10 17.21

Treynor measure is demonstrated by Table 2. All the risk-averse investors would prefer to

maximize the Treynor value. But the denominator (β) of the Treynor equation measures the

volatility of the portfolio in relation to the rest of the market. The T value of the DMC portfolio is

greater than the MNC portfolio almost over each of the five time periods. Though, the T values of


20

the DMC portfolio are greater than the MNC, the DMC portfolio is inversely correlated to the rest

of the market. The beta of the DMC portfolio is negative over 2000 to 2004 time periods.

Therefore, the DMC portfolio is much more volatile than the MNC portfolio, hence they are

considered more risky than the MNC portfolio. Some of that high return can be explained by their

higher volatility.

3.1.3 Jensen Measure

In table 3 Jensen measure of MNC and DMC portfolio is displayed. Here in Jensen measure

the value of DMC portfolio is superior to MNC portfolio almost over the each time periods. As

the Jensen model adjust the level of risk for the level of beta and that’s why the riskier portfolios

are expected to have higher excess returns. Therefore, DMC portfolio is riskier as well as giving

higher excess returns.

Table 3 Jensen Measure of MNC and DMC Jensen Measure 2000-2002 2001-2003 2002-2004 2003-2005 2004-2006

MNCs 2.94 2.71 2.55 2.50 3.54

DMCs 6.04 5.26 4.81 3.35 3.55

The previous researchers Michel and Shaked (1986) conclude that domestic corporations’

(DMC) performances have a superior risk-adjusted performance. While Hughes, Logue and

Sweeney (1975) showed that multinational corporations’ (MNC) performances are more risk-

adjusted with higher return than domestic corporations (DMC). Interestingly, this study reveals

that domestic corporations (DMC) are less risk-adjusted with higher returns whereas

multinational corporations (MNC) are more risk-adjusted with lower returns. T-test is performed

separately for each performance measure. The result does not show any significant differences


21

between the groups for both Sharpe and Jensen measure. The result of t-test of Treynor

measurement of the two samples significantly differs at a level of p < 0.001.

3.2 Financial Characteristic

The Financial characteristic of two groups is presented by four tables. The selected variables

for the financial characteristic are debt equity ratio as a capitalization ratio, average standard

deviation of equity, frequency distribution of beta and average total asset.

3.2.1 Debt equity ratio

Table 4 Debt equity ratio of MNC and DMC 2006 2005 2004 2003 2002 2001 2000

MNCs 0.115 0.085 0.095 0.115 0.115 0.120 0.095

DMCs 0.221 0.303 0.323 0.254 0.095 0.125 0.135

The table 4 is demonstrating debt equity ratio of MNC and DMC portfolios. The previous

study demonstrates that MNCs are highly leveraged than DMCs. But the present study finds out

that DMCs are more leveraged rather than MNCs. The higher debt equity ratio of DMCs reflects

the higher borrowed fund in the capital structure. This might be an explanation of the higher

volatility of DMCs portfolio than MNCs. Further more, MNC can reduce the total risk:

operational and financial risk, by diversifying internationally at the corporate level. The range of

the debt equity ratio of MNC is 0.085 to 0.12, whereas the range of DMC is 0.095 to 0.303.

Result of the t-test indicates that the debt equity ratio of the two samples significantly differs at a

level of p < 0.001.


22

Figure 3: Debt equity ratio

3.2.2 Average Standard Deviation of Equity Table 5 Average Standard Deviation of Equity of MNC and DMC 2000-2002 2001-2003 2002-2004 2003-2005 2004-2006

MNCs 0.005 0.010 0.070 0.070 0.02

DMCs 0.986 0.290 0.531 1.214 1.625

The above table provides the average standard deviation of equity for the two groups. As

indicated by the results, the average standard deviation of equity of the DMCs is consistently

higher than MNCs. A t-test was performed which indicate the average standard deviation of the

two samples are significantly differs at a level of p < 0.05. The lower equity-variability reported

for the MNC portfolio is consistent with the theoretical hypothesis on total risk reduction. The

result of the standard deviation of equity of the present study similar with the empirical findings

reported by Hughes et al. (1975) as well as with the previous research reported by Michel and

Shaked (1986).


23

3.2.3 Frequency Distribution of Beta

Table 6 Frequency Distribution of Beta of MNC and DMC

Range MNC DMC

-4 - -3 - 0.20

-2 - -1 - 0.40

-1 - 0 0.20 0.40

1 - 2 0.40 -

3 - 4 0.40 -

Mean beta 2.05 -1.69

The table 6 is presenting the frequency distribution of beta. After frequency distribution of

betas of the two portfolios, it reveals that 60 percent of DMC portfolio’s beta is under negative

range and 80 percent of MNC portfolio’s beta is under positive range. Furthermore the mean of

betas of MNC and DMC portfolios are respectively 2.05 and -1.69. The results are clearly

suggestive. As most of the values of beta of MNC portfolio falls under positive range and close to

market’s beta, they should have lower systematic risk. On the other hand most of the value of

beta of DMC portfolio falls under negative range; they should have higher systematic risk.

This might be one of the explanations of low returns for MNCs and high returns for DMCs.

The result of lower systematic risk of MNC is also supported by Hughes et al (1975), Rugman

(1977) and Agmon and Lessard (1977).


24

3.2.4 Average Total Assets

Table 7 Average Total Assets of MNC and DMC 2000-2002 2001-2003 2002-2004 2003-2005 2004-2006

MNCs 6131.33 6562.16 6871.00 7448.87 7853.94

DMCs 4661.64 4254.13 4828.00 5807.56 6827.74

*million in TK

By using asset as a variable the size of the portfolios is measured. The above table indicates

the average size of multinational corporations (MNC) is higher compared to the domestic

corporations (DMC). As a consequence it is necessary to test whether average performance

measures of the two groups differ because of the size effect. Miller and Pras (1979) reported

through regression result that size is a significant explanatory variable for performances. The

pervious study conducted by Michel and Shaked (1986) reported that size is not a significant

variable in any case as well as for observed differences in the two groups’ performance.

Similarly, the present study reveals the size can not explain observed differences in the two

groups’ performance.

A correlation analysis has been conducted on all the performance tools and size as variables

to explore the relationship among variables. For interpreting the strength of relationships among

variables, the guideline suggested by Rowntree (1981) has been followed; and the classification

of the correlation coefficient (r) is as follows:


25

0.0 to 0.2 Very weak, negligible

0.2 to 0.4 Weak, low

0.4 to 0.7 Moderate

0.7 to 0.9 Strong, high marked

0.9 to 1.0 Very strong, very high

The bi-variate correlation procedure was a subject to a two tailed test of statistical significance

at two different levels highly significant (p<. 001) and significant (p<. 01) or (p<.05). The results

of the correlational analysis are shown in Table 8. The result shows that size has a moderate

correlation but not significantly.

Table 8 Correlational Matrix for Sharpe, Treynor, Jensen Measure and Size of MNC and DMC

Correlations

Sharpe Treynor Jensen size Sharpe Pearson Correlation 1 .536 -.220 .537 Sig. (2-tailed) . .110 .541 .110 N 10 10 10 10Treynor Pearson Correlation .536 1 .046 .437 Sig. (2-tailed) .110 . .899 .207 N 10 10 10 10Jensen Pearson Correlation -.220 .046 1 -.306 Sig. (2-tailed) .541 .899 . .390 N 10 10 10 10size Pearson Correlation .537 .437 -.306 1 Sig. (2-tailed) .110 .207 .390 . N 10 10 10 10


26

4.0 Summary and Discussion

For better understanding of MNC and DMC portfolios performances the researcher have to

compare the return of measures. The comparison is given bellow:

Table 9 Financial Performance Comparison of MNC and DMC

Measurement Tool MNC (GOOD)

DMC (GOOD)

Sharpe Measure - √

Treynor Measure - √

Jensen Measure - √

The above table shows that returns of all measure of DMC portfolio are better than MNC

portfolio. Therefore DMC portfolio could be considered as attractive option if we disregard of

risk factor.

The following table illustrates the riskiness of the portfolios. As the result of frequency

distribution of beta shows that the DMC portfolio is more risky than MNC portfolio.

Table 10 Systematic Risk (β) Comparison of MNC and DMC

Less Risky More Risky

MNC √ -

DMC - √

It is obvious that the results and findings of the study could be momentary and even an

incident. According to Hassan, Islam and Basher (2000), the Dhaka Stock exchange is an

inefficient capital market. The present study collected the trade prices of stocks from DSE.


27

Therefore it would not be sensible to recommend which portfolio would be attractive or risky

between MNC and DMC portfolios. Moreover, the study observed a few negative betas of DMC

portfolio, which is unusual for the pharmaceutical and chemical industry. May be over short

periods luck can over shadowed all else, but luck can not be expected to continue.

Table 11 T-bill Rate Comparison of Bangladesh and U.S Government

Month Bangladesh T-bill Rate U.S T-bill Rate

November 07 7.30 4.48

December 07 7.33 3.98

January 08 7.29 3.74

February 08 7.35 2.70

March 08 7.33 2.23

Source: http://www.federalreserve.gov/releases/H15/data/Business_day/H15_NFCP_M1.txt http://www.bangladesh-bank.org/selectedecooind/magecoind.pdf

T-bill rates of Bangladesh are much higher than any other country. It is almost double than

U.S. Hence is Bangladesh government discouraging investment? Or is there no strong

coordination between policy-makers and other related department? Most of the multinational

corporations as well as better performed domestic corporations are not listed under Dhaka Stock

Exchange. But those companies are also key players in the market. Therefore are MNCs making

profit here and drain it outside of the country? Is Bangladesh government thoughtless about the

unlisted companies on encouraging and imposing rules to be listed under DSE? How much DSE

is inefficient and trustworthy of DSE data? Will the investment decision be reliable and

profitable based on DSE data?


28

5.0 Conclusion

A serious study is necessary on the issue of higher interest payment for T-bill by Bangladesh

government. It is also necessary to check, whether any substantial diminish in investment for the

higher T-bill rate. Bangladesh Bank, Board of Investment, Security Exchange Commission (SEC)

and other related department should have strong coordination for both policy-making and

imposing. Without proper coordination among the departments it is not possible to come up with

the best solution. Bangladesh government should be caring for the growing domestic

pharmaceutical and chemical companies. In addition, government is liable to limit the

multinational companies’ freedom to repatriate of its profits. Furthermore, government should

encourage and facilitate those unlisted companies to be listed under DSE. SEC should take bold

steps to make an efficient capital market in Bangladesh as soon as possible. However, Rome was

not built in a day.


29

References

Annual report (2000-2006): Advance Chemical Industry. Annual report (2000-2006): Beximco Pharmaceutical Ltd. Annual report (2000-2006): GlaxoSmithKline Bd. Ltd. Annual report (2000-2006): Reckitt Benckiser Bd. Ltd. Annual report (2000-2006): Square Pharmaceutical Ltd. Alexander, G.J., Bailey, J.V. & Sharpe, W.F. (2003). Investment (6th ed.). Prentice Hall India:

India. Beta coefficient

Retrieved from http://en.wikipedia.org/wiki/Beta_%finince%29 Bodie, Z., Kane,A. & Marcus, A.J. (2003). Investments (5th ed.). McGraw-Hill: New Delhi. Dhaka stock exchange’s industry wise company list. Retrieved from: http://www.dsebd.org/industrylisting.php Jensen’s Alpha

Retrieved from http://en.wikipedia.org/wiki/Jensen_ratio Jones, C.P. (2004). Investment analysis and management. (9th ed.) John Wiley & Sons Inc. Major Economic Indicator (March, 2008).

Retrieved from: http://www.bangladesh-bank.org/selectedecooind/magecoind.pdf

Rahman, H. The Growth of the Pharmaceutical Sector. Retrieved from: http://www.ais-dhaka.net/School_Library/Senior%20Projects/

06_rahman_pharmaceuticals.pdf Sharp ratio

Retrieved from http://en.wikipedia.org/wiki/Sharpe_ratio Standard deviation Retrieved from http://en.wikipedia.org/wiki/Standard_deviation Systemic risk

Retrieved from http://en.wikipedia.org/wiki/Systemic_risk Treynor ratio

Retrieved from http://en.wikipedia.org/wiki/Treynor_ratio


30

Bibliography Ahmed, M. (2004). Effects of regulation on Pharmaceutical Market in Bangladesh.

Retrieved from http://www.pharmadu.net/articlesdetail.php?art=2&pg=3 Bangla Pharma Waiting in the wings. (Thursday, January 27, 2005).

Retrieved from http://www.pharmabiz.com/ Chronicle Specials /article/ detnews.asp? articleid=25947%sectionoid=50.

Davis, J. L. (2001). Explaining Stock Returns: A Literature Survey. Retrieve from:

http://www.dfaus.com/library/articles/explaining_stock_returns/ Grinblatt, M., & Titman, S. (1994). A Study of Monthly Mutual Fund Returns and Performance

Evaluation Techniques. The Journal of Financial and Quantitative Analysis, 29 (3), 419-444.

Hassan, M.K., Islam, M.A. and Basher, S.A., (2000). Market efficiency, time-varying volatility

and equity returns in Bangladesh stock market. Retrieve from: http://129.3.20.41/eps/fin/papers/0310/0310015.pdf Horowitz, I. (1966). The "Reward-to-Variability" Ratio and Mutual Fund Performance. The

Journal of Business, 39 (4), 485-488. Hughes, J. S., D. E. Logue, & R. J. Sweeney. (1975). Corporate International Diversification and

Market Assigned Measures of Risk and Diversification. Journal of Financial and Quantitative Analysis. 32 (1), 39 – 46

IMS Global Insights- An Eye on The World of Pharma. (2005). Retrieve from: http://www.ims-global.com/globalinsights.htm. Jensen, G. M. (1968). The performance of Mutual Funds in the Period 1945 -1964. Journal of

Finance. 23 (2), 389 – 416. Litzenberger, R. H. (1991). William F. Sharpe's Contributions to Financial Economics. The

Scandinavian Journal of Economics, 93 (1), 37-46. Miller, J. & Pras, R. (1979). The Effecls of Multinational and Lxport Diversification on the Profit

Stability of U.S. Corporations. Southern Economic Review. 46 (1), 49-75.

Rahman, A. (2005). Pharmaceutical Sector In A Crucial Phase. Retrieve from: http://www.pharmabiz.com/article/detnews.asp?articleid=25948&sectionoid=50

Reeb, D. M., Kwok, C. C. Y., & Baek, H. Y. (1998). Systematic Risk of the Multinational

Corporation. Journal of International Business Studies, 29 (2), 263-279.


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Reilly, F.K. (2004). Investment analysis and portfolio management. (4th ed.). McGraw-Hill: San Francisco.

Roll R. (1980). Performance Evaluation and Benchmark Errors (I). Journal of portfolio

Management. 6 (4), 5-12. Shaked, I. (1986). Are Multinational Corporations Safer? Journal of International Business

Studies, 17 (1), 83-106. Sharpe, W.F. (1966, January). Mutual fund performance. Journal of Business. 39 (1), 119-138. Treynor, J.L. (1965). How to rate management of investment funds. Journal of portfolio

management. 43, 63-75.


32

Appendix


33

Table 12 Excess Market Return

Year

Market Return

(General Index Return) T-bill return Excess Return (Rm – Rf )

2000 0.184248835 0.037398374 0.146850461

2001 0.272468414 0.064220183 0.20824823

2002 0.005563776 0.170087977 -0.164524201

2003 0.176982757 0.017925736 0.15905702

2004 1.03672976 0.0125 1.02422976

2005 -0.149119114 0.722772277 -0.871891391

2006 -0.040444749 0.047142857 -0.087587606

Table 13 Excess Return of DMC Portfolio

Year DMC Portfolio Return T-bill return Excess Return(Rp – Rf )

2000 6.089387971 0.037398374 6.051989597

2001 5.434761136 0.064220183 5.370540953

2002 6.540355521 0.170087977 6.370267545

2003 3.595000944 0.017925736 3.577075208

2004 3.469296702 0.0125 3.456796702

2005 3.672785864 0.722772277 2.950013587

2006 4.219940493 0.047142857 4.172797636


34

Table 14 Excess Return of MNC Portfolio

Year MNC Portfolio Return T-bill return Excess Return(Rp – Rf )

2000 3.358652766 0.037398374 3.321254392

2001 4.149126249 0.064220183 4.084906065

2002 2.524016865 0.170087977 2.353928889

2003 2.677421892 0.017925736 2.659496155

2004 3.585044134 0.0125 3.572544134

2005 2.359289362 0.722772277 1.636517085

2006 6.155215181 0.047142857 6.108072324


35

Table 15 Geometric Mean calculation and Standard deviation of T-bill, Market return, MNC and DMC Portfolios return

Year Portfolio return Of MNC

Portfolio return of DMC T-bill Return

Market Return

2000-2002 Geometric Mean 3.276458486 6.004153972 1.089093561 1.148593971

Standard Deviation 0.812654695 0.555914707 0.089093561 0.148593971

2001-2003 Geometric Mean 3.038005605 5.036864589 1.082236144 1.146240045


2002-2004 Geometric Mean 2.893573531 4.336911928 1.064412292 1.34082174


2003-2005 Geometric Mean 2.829203426 3.578041335 1.210913164 1.268208791


2004-2006 Geometric Mean 3.73398858 3.774398864 1.222389508 1.184742736



36

Table 16 Beta of MNC Portfolio

Excess Return of MNC Excess Return of Market Beta Of MNC Year (Rp - Rf) (Rm - Rf) (Slope)

2000 3.321254392 0.146850461 4.144420355

2001 4.084906065 0.20824823

2002 2.353928889 -0.164524201

2001 4.084906065 0.20824823 3.308847697

2002 2.353928889 -0.164524201

2003 2.659496155 0.15905702

2002 2.353928889 -0.164524201 1.031367015

2003 2.659496155 0.15905702

2004 3.572544134 1.02422976

2003 2.659496155 0.15905702 1.020136446

2004 3.572544134 1.02422976

2005 1.636517085 -0.871891391

2004 3.572544134 1.02422976 0.800315558

2005 1.636517085 -0.871891391

2006 6.108072324 -0.087587606


37

Table 17 Betas of DMC Portfolio

Excess Return of DMC Excess Return of Market Beta Of DMC Year (Rp - Rf) (Rm - Rf) (Slope)

2000 6.051989597 0.146850461 -2.142885823

2001 5.370540953 0.20824823

2002 6.370267545 -0.164524201

2001 5.370540953 0.20824823 -4.828729178

2002 6.370267545 -0.164524201

2003 3.577075208 0.15905702

2002 6.370267545 -0.164524201 -1.972889166

2003 3.577075208 0.15905702

2004 3.456796702 1.02422976

2003 3.577075208 0.15905702 0.278051583

2004 3.456796702 1.02422976

2005 2.950013587 -0.871891391

2004 3.456796702 1.02422976 0.206349883

2005 2.950013587 -0.871891391

2006 4.172797636 -0.087587606


38

Table 18 Result of the Measurement of MNC Portfolio Measurement Tool 2000-2002 2001-2003 2002-2004 2003-2005 2004-2006

Sharpe Measure 3.92216392 3.294226121 4.933553766 4.116397131 1.812684946

Treynor Measure 0.769073755 0.89329269 2.74311782 2.566607902 4.387768096

Jensen Measure 2.940770216 2.7439903 2.544081651 2.559840905 3.541728369

Table 19 Result of the Measurement of DMC Portfolio Measurement Tool 2000-2002 2001-2003 2002-2004 2003-2005 2004-2006

Sharpe measure 10.6402301 3.33006107 2.458390071 32.79223821 9.149660006

Treynor Measure -2.7603246 -1.02607296 -2.165605504 12.10972486 17.21352734

Jensen measure 6.042563 5.263685949 4.817824843 3.351197032 3.559777763

Table 20 Debt equity ratio Of MNC and DMC portfolios

Year DMC MNC

2006 0.221 0.115

2005 0.303 0.085

2004 0.323 0.095

2003 0.254 0.115

2002 0.095 0.115

2001 0.125 0.120

2000 0.135 0.095


39

Table 21 Total assets of Companies

2006 2005 2004 2003 2002 2001 2000

ACI 2915 2674 1978 1675 1334 1028 9913

Beximco 11913 10945 8560 8013 6763 6360 5411

Square 9299 7908 5877 5164 4526 3811 3234

DMC Portfolio 7962 7104 5417 4901 4166 3696 6124

GSK 11096 11728 11038 11119 8955 8460 7987

Reckitt Benckiser 5587 3787 3887 3134 3093 4612 3681

MNC Portfolio 8342 7758 7463 7127 6024 6536 5834

Table 22 Average Total Assets of MNC and DMC Portfolios

Year MNC DMC

2000-2002 6131.333333 4661.644705

2001-2003 6562.166667 4254.139508

2002-2004 6871.000 4828.002874

2003-2005 7448.876667 5807.560938

2004-2006 7853.943333 6827.747111


40

T-Test

Group Statistics

Type N Mean Std. Deviation Std. Error Mean

Sharpe MNC 5 3.5960 1.17496 .52546

DMC 5 11.6700 12.32964 5.51398

Treynor MNC 5 2.2640 1.49463 .66842

DMC 5 4.6560 9.29188 4.15545

Jensen MNC 5 72.9480 157.11293 70.26304

DMC 5 4.6020 1.14218 .51080

Independent Samples Test

Levene's Test for Equality of

Variances t-test for Equality of Means

F Sig. t df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower Upper Sharpe Equal

variances assumed

4.537 .066 -1.458 8 .183

-8.0740

05.53896

-20.846

87

4.69887

Equal variances not assumed

-1.458 4.073 .217

-8.0740

05.53896

-23.345

05

7.19705

Treynor Equal variances assumed

30.326 .001 -.568 8 .585-

2.39200

4.20887 -

12.09767

7.31367


-.568 4.207 .599-

2.39200

4.20887 -

13.85454

9.07054

Jensen Equal variances assumed

6.995 .029 .973 8 .359 68.34600 70.26489

-93.685

13

230.37713


.973 4.000 .386 68.34600 70.26489

-126.73

249

263.42449


41

Group Statistics

type N Mean Std. Deviation Std. Error Mean

Debt to Equity Ratio

MNC 7 .1058 .01510 .00571 DMC 7 .2081 .09070 .03428


Levene's Test for Equality of Variances t-test for Equality of Means

F Sig. t df Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower Upper Debt Equity Ratio

Equal variances assumed

20.482 .001 -2.944 12 .012 -.10231 .03476 -.17804 -.02659


-2.944 6.333 .024 -.10231 .03476 -.18629 -.01834

Group Statistics

type N Mean Std. Deviation Std. Error

Mean STDEV MNC 5 .0350 .03240 .01449

DMC 5 .9292 .53283 .23829


42


Levene's Test for Equality of Variances t-test for Equality of Means

F Sig. t df Sig. (2-tailed)

Mean Differen

ce

Std. Error

Difference

95% Confidence Interval of the

Difference

Lower Upper STDEV Equal

variances assumed

10.893 .011 -3.746 8 .006 -.89420 .23873 -

1.44471

-.34369


-3.746 4.030 .020 -.89420 .23873 -

1.55510

-.23330

Regression

Variables Entered/Removed(b)

Model

Variables

Entered

Variables

Removed Method

1 size(a) . Enter

a All requested variables entered.

b Dependent Variable: Sharpe

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of

the Estimate

1 .537(a) .288 .199 8.31421

a Predictors: (Constant), size


43

ANOVA(b)

Model

Sum of

Squares df Mean Square F Sig.

1 Regression 223.567 1 223.567 3.234 .110(a)

Residual 553.009 8 69.126

Total 776.576 9


b Dependent Variable: Sharpe

Coefficients(a)

Model Unstandardized

Coefficients Standardized Coefficients t Sig.

B Std. Error Beta 1 (Constant) 1.916 4.126 .464 .655 size .002 .001 .537 1.798 .110

a Dependent Variable: Sharpe

Regression


Model

Variables

Entered

Variables

Removed Method

1 size(a) . Enter


b Dependent Variable: Treynor


44

Model Summary

Model R R Square Adjusted R Square

Std. Error of the Estimate

1 .437(a) .191 .090 6.10538


ANOVA(b)

Model Sum of Squares df Mean Square F Sig.

1 Regression 70.390 1 70.390 1.888 .207(a) Residual 298.206 8 37.276 Total 368.596 9


b Dependent Variable: Treynor

Coefficients(a)

Model

Unstandardized Coefficients

Standardized Coefficients

t Sig. B Std. Error Beta 1 (Constant) .252 3.030 .083 .936

size .001 .001 .437 1.374 .207

a Dependent Variable: Treynor


45

Regression


Model

Variables

Entered

Variables

Removed Method

1 size(a) . Enter


b Dependent Variable: Jensen

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .306(a) .094 -.020 111.85465


ANOVA(b)

Model Sum of Squares df Mean Square F Sig.

1 Regression 10329.349 1 10329.349 .826 .390(a)

Residual 100091.694 8 12511.462

Total 110421.043 9


b Dependent Variable: Jensen


46

Coefficients(a)

Model

Unstandardized Coefficients

Standardized Coefficients

t Sig. B Std. Error Beta 1 (Constant) 77.638 55.502 1.399 .199

size -.013 .015 -.306 -.909 .390

a Dependent Variable: Jensen

Correlations

Correlations

Sharpe Treynor Jensen Size

Sharpe Pearson Correlation 1 .536 -.220 .537

Sig. (2-tailed) . .110 .541 .110

N 10 10 10 10

Treynor Pearson Correlation .536 1 .046 .437

Sig. (2-tailed) .110 . .899 .207

N 10 10 10 10

Jensen Pearson Correlation -.220 .046 1 -.306

Sig. (2-tailed) .541 .899 . .390

N 10 10 10 10

size Pearson Correlation .537 .437 -.306 1

Sig. (2-tailed) .110 .207 .390 .

N 10 10 10 10


47

Company Introduction

GlaxoSmithKline Bangladesh Limited is a subsidiary of GlaxoSmithKline plc, one of the

world’s leading research based pharmaceutical companies with a powerful combination of skills

and resources that provides a platform for delivering strong growth in today’s rapidly changing

healthcare environment. The Company was incorporated on 25 February 1974 as a Public

Limited Company and is listed with Dhaka Stock Exchange Limited. The principal activities of

the Company through out the year continued to be manufacturing and marketing of

pharmaceuticals, vaccines and healthcare products. Name of the Company has been changed

from “Glaxo Wellcome Bangladesh Limited” to “GlaxoSmithKline Bangladesh Limited”,

effective from 4th September 2002. This change of name took place following global merger of

Glaxo Wellcome and SmithKline Beecham in December 2000.

Figure1: History of GSK. Source: GSK website

As multinational company, GSK has an excellent internal work environment, outstanding

corporate culture, well organized and well-structured work-process, highly effective workforce

who works with a common corporate vision and mission. The registered office and factory of the

company are situated in Chittagong while its corporate office is located in Dhaka. Currently

GlaxoSmithKline owns 82.58% of the share, Investment Corporation of Bangladesh has 15.50%,

general public has got 0.98% and the rest 94% has gone to others.

SmithKli Glaxo

SmithKline Beecham (1989) GlaxoWellcome (1995)

Beecham PLC Wellcome

GlaxoSmithKline (2000)


48

Company Profile

Corporate Headquarter: GlaxoSmithKline Bangladesh Limited

House No 2A, Road no 138, Gulshan-1

Dhaka 1212, Bangladesh.

Registered Office: Fouzderhat Industrial Area

Dhaka Trunk Road

P. O. North Kattali

Chittagong - 4217, Bangladesh

Manufacturing Site: Fouzderhat Industrial Area

Dhaka Trunk Road

P. O. North Kattali

Chittagong- 4217, Bangladesh

Status: Public Limited Company.

General Information

Company Secretary Mr. Sarwar Azam Khan, FCA, FCS

Bankers HSBC, Standard Chartered Bank

Citibank NA, Agrani Bank, Sonali Bank

Auditors A Qasem & Co.

Chartered Accountants

Legal Advisors Barrister Abdullah Al Mamun

Advocate S C Lala


49

The Board of Directors

Chairman: S Kalyanasundaram

Managing Director: M. Azizul Haque

Finance Director & Company Secretary: Sarwar Azam Khan FCA, FCS

Commercial Director: Shamim Rabbani

Non-Executive Director: Ziaul H Khondker

Non-Executive Director: Mehernosh Kapadia

Company Executive Committee

Managing Director: M. Azizul Haque

Finance Director & Company Secretary: Sarwar Azam Khan FCA, FCS

Commercial Director: Shamim Rabbani

Site Director: Fariduddin Ahmed, FCMA

Human Resources Director: A.K.M Firoz Alam

GSK Mission Statement

The mission statement explains why GSK in business- “Our global quest to improve the

quality of human life by enabling people to do more, feel better and live longer. Our mission

gives us purpose. Our size gives us opportunity. Our spirit gives us the qualities as individuals

and as an organization that will enable us to turn our opportunities into achievements. Our spirit

will guide us, keep us focused and differentiate us from the competition”.

(http://mygsk.com/mission)


50

GSK Spirit

The Company spirit describes how the company needs to behave if they are to achieve

their goal- “We undertake our quest with the enthusiasm of entrepreneurs, excited by the constant

search for innovation. We value performance achieved with integrity. We will attain success as a

world class global leader with each and every one of our people contributing with passion and an

unmatched sense of urgency”. (http://mygsk.com/sprit)

GSK Vision for the future

“GlaxoSmithKline’s vision is exciting and will give us the opportunity to make a

difference in the health of billions of people. Our value system and operating principles will

provide the necessary guide on how we work at GSK. The key to our success will be our desire

and passion to pursue GSK’s priorities expressed by the business drivers.

We want to become the Indisputable leader in our industry. The work we do bring good

benefit to the society. Becoming the indisputable leader in our industry isn’t up to someone else

in another department or another country. It’s up to all of us working together, to build a better

world for ourselves, our families, and the patients who depend on us. We are now one company,

one team, with resources enough to turn opportunity into the delivery of better healthcare to the

world. By delivering on our mission, our business success will follow. And the best measure of

that success will be an improved quality of life for people around the world who will be able to

do more, feel better and live longer”.

GSK Quality Statement

“Quality is at the heart of everything we do-from the discovery of the molecule, through

product development, manufacture, supply and sale- and is visual to all the service that supports

our business performance”.


51

GSK in Time

Every second, more than 35 doses of vaccines are distributed by GlaxoSmithKline.

Every minute, More than 1100 prescription are written for GlaxoSmithKline products.

Every hour, GlaxoSmithKline spends more than US$ 450,000 to find new medicines.

Every day, more than 200 million people around the world use a GlaxoSmithKline brand

tooth brush or tooth paste.

Every year, GlaxoSmithKline donates more than US$ 148 million in cash and products

to communities around the world.

Recognition & Achievement

The Institute of Chartered Accountants of Bangladesh (ICAB) conducts an evaluation of

the published annual reports and accounts of the listed companies based on stringent

criteria set by the South Asian Federation of Accountants (SAFA). Among the

competition of non-financial listed companies GSK secured the first position for the year

2002.

GSK secured second position for the publishing of annual repot 2004 at ICAB award in

non-financial sector.

Key Products of GSK

GSK Bangladesh covers nearly all drugs like Respiratory, Anti-depressive, Vitamins,

Gastro- intestinal, Cough and Cold preps, Oral steroid, Eye/Ear drops, Anti-bacterial, Anti-viral,

and Non-Pharmaceutical. The top pharmaceutical products of GSK in Bangladesh (as per

December 2006) are:


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Table1: Product index of GSK Product Vaccines Non – Pharmaceutical

Zantac Priorix Dextrose

Piriton Varilrix Horlicks

Zinnat Typherix

Crystapen V Havrix

Parapyrol Engerix B

Betnovates Tritanrix HB

Ventolin Mencevax - ACWY

Ceporex Hiberix

Grisovin FP

Amoxil

Peflon

Glaxipro

Dermovate

Neobacrin

Seretide

Ventolin

Betnelan

Betnesol

Lanoxin

Actifed

Source: GKS Marketing Department

Documents

Sample Report