Risk & Return

December 10

2013The higher the risk, the higher the potential return of an investment. We are comparing the performance of 10 chosen companies associated with an amount of risk in 5 different sectors.

Modern Portfolio Theory

Table of ContentsCHAPTER 1: INTRODUCTION.......................................................................................................2

CHAPTER 2: THEORETICAL REVIEW............................................................................................4

CHAPTER 3: METHODOLOGY....................................................................................................11

CHAPTER 4: FINDINGS AND RESULTS........................................................................................36

CHAPTER 5: CONCLUSION.........................................................................................................56

References....................................................................................................................................58

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Prepared by:

Sin May Yee 212535Chan Chai Kuan 212607Tan Wei Kian 212543Yap Ping Way 212576

CHAPTER 1: INTRODUCTION

When there was risk, there will be the opportunity. As the well knows financial theory

describe, if there was high risk, there will be high return. For the investors who want to

gain more return, they need to take more risk. However, risk can be divided into two

types which are diversified and undiversified risk. Diversified risks are the risks that can

be eliminate through the combination of asset in a portfolio. Undiversified risk as the

name, it cannot be eliminate through the combination of asset in a portfolio. Although

diversified risks could be eliminate through the asset combination of the portfolio,

however, the total risk only will reduce as the assets in a portfolio are not perfectly

correlated with each other or it will be eliminate as the assets in a portfolio are perfect

negative correlated with each other. Hence, we conduct this study to understand the risk

diversification through portfolio from Modern Portfolio view. Modern Portfolio Theory

(MPT) was focus on statistical measurement to develop a portfolio plan. It focuses on the

measurement of expected return, standard deviation of return and correlation between

return.

RISK-RETURN SPECTRUM: Also known as Risk and Return Tradeoff, is the

relationship between the amount of return gained on an investment and the amount of risk

undertaken in that investment. The more return sought, the more risk that must be

undertaken. (Wikipedia, 2013) In other word, low levels of uncertainty or risk are

associated with low potential returns, whereas high levels of uncertainty or risk are

associated with high potential returns. According to the principle, invested money can

only render higher profits when it is subject to the possibility of being lost. Thus,

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investors must be aware of the personal risk tolerance when making decision on

investment portfolio choices.

COMPANIES CHOSEN FOR STUDY: We have chosen 10 companies from different

sectors in the main board of Bursa Malaysia to conduct the statistical measurement of the

risk and return on a portfolio. These companies including:

Sectors Companies

Technology - Malaysian Pacific Industries Bhd (MPI)

- Unisem (M) Berhad

Plantations - Kuala Lumpur Kepong Bhd (KLK)

- Kulim Malaysia Bhd (KULIM)

Consumer

Products

- Hup Seng Industries Bhd (HUPSENG)

- Oriental Holdings Bhd (ORIENTAL)

Construction - Muhibbah Engineering (M) Bhd

(MUHIBBAH)

- Ekovest Bhd (EKOVEST)

Trading/

Services

- Yinson Holdings Berhad (YINSON)

- Borneo Oil Berhad (BORNOIL)

The detail of the measurement result will be presented in Excel form later

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CHAPTER 2: THEORETICAL REVIEW

Traditional portfolio analysis can be conveyed by the statement of Benjamin Graham that

commitment to a single security is neither investment nor rational speculation.

Traditional portfolio theory is a portfolio management practice which two parameters of

investment avenues are considered, i.e. (a) returns and (b) risk. It has some characteristics

as below:

Low or reasonable returns can be achieved when risk is low.

High returns can be achieved only when risk is high.

The co-relation between securities is not considered.

Risk is considered in totality, it is not subdivided into systematic and non-

systematic.

Selection of securities is done by matching risk and returns.

Diversification under the portfolio is generally done on the basis of class of

securities – equity or debt, maturity of bonds, selecting different industries.

Both investment and speculation have to be done at portfolio level. Harry

Markowitz proposed modern portfolio analysis. During the 1950s, Harry Markowitz, a

trained mathematician, first developed the theories that form the basis of modern

portfolio theory. Modern portfolio theory (MPT) approaches investing by examining the

entire market and the whole economy to make each investment opportunity unique in

term of the expected long-term return rate and their expected short-term volatility. MPT

is a theory that attempts to maximize portfolio expected return for a given amount of

4

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

portfolio risk, or equivalently minimize risk for a given level of expected return by

carefully choosing the proportions of various assets.

The founders of MPT received a Nobel Prize for revealing these four tenets.

Markets process information so rapidly when determining security prices, that it is

extremely difficult to gain a competitive edge by taking advantage of market

anomalies or inefficiencies.

Over time, riskier investments provide higher returns as compensation to

investors for accepting greater risk.

Adding high-risk, low correlating asset classes to a portfolio can actually reduce

volatility and increase expected rates of return.

Passive asset class fund portfolios can be designed to deliver over time the highest

expected returns for a chosen level of risk.

MPT is a mathematical formulation of the concept of diversification in investing

with the aim of selecting a collection of investment assets that has collectively lower risk

than any individual asset. MPT models an asset's return as a normally

distributed function. The probability distribution of return on security can be described by

expected value and variance (standard deviation). Expected value represents return;

variance represents the risk, a portfolio as a weighted combination of assets, so that the

return of a portfolio is the weighted combination of the assets' returns. By combining

different assets whose returns are not perfectly positively correlated, MPT seeks to

reduce the total variance of the portfolio return.

5

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

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

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

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

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

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

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

Two important aspects of MPT are the efficient frontier and portfolio betas.

Markowitz developed mathematical procedures for finding the set of portfolios that have

increasing expected return or increasing risk levels. This set of portfolios from which an

investor can choose a portfolio is called efficient frontier. In order to compare investment

options, Markowitz developed a system to describe each investment or each asset class

with math, using unsystematic risk statistics. Then he further applied to the portfolios that

contain the investment options. He looked at the expected rate of return and the expected

volatility for each investment.

EFFICIENT FRONTIER: A set of optimal portfolios that offers the highest expected

return for a defined level of risk or the lowest risk for a given level of expected return.

The portfolio with the lowest possible variance is called minimum variance portfolio.

This mean the portfolio must have the lowest possible standard deviation and thus lowest

risk. For a given amount of risk, MPT describes how to select a portfolio with the highest

possible expected return. Or, for a given expected return, MPT explains how to select a

portfolio with the lowest possible risk. The degree of curvature or bend of the efficient set

for portfolio reflects the diversification effect. The lower the correlation between the

securities the more the curve bend. In other word, the diversification effect rises as

correlation declines.

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CAPITAL ASSET PRICING MODEL (CAPM): The CAPM was introduced by Jack

Treynor (1961, 1962), William Sharpe (1964), John Lintner (1965a,b) and Jan

Mossin (1966) independently, building on the earlier work of Harry

Markowitzon diversification and modern portfolio theory. It is a model that describes the

relationship between risk and expected return and that is used in the pricing of risky

securities. A risky investment should offer a return that exceeds what investors can earn

on a risk-free investment. The CAPM says that the expected return on a risky asset equals

the risk-free rate plus a risk premium and the risk premium depends on how much of the

asset’s risk is un-diversifiable.

Where…

rj = the required return on investment j, given its risk as measured by beta

rrf = the risk-free rate of return; the return that can be earned on a risk-free investment

bj = beta coefficient, or index of non-diversifiable risk for investment j

rm =the expected market return; the average return on all securities.

Total risk = non-diversifiable risk + diversifiable risk

The risk of an investment consists of two components: diversifiable and non-

diversifiable risk. Diversifiable risk sometimes called unsystematic risk, results from

factors that are firm-specific. Unsystematic risk is the portion of an investment’s risk that

can be eliminated through diversification. Non-diversifiable risk, also called systematic

7

E( R i)=R f +β i( E( Rm )−R f ）

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

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

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

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

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

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

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

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

http://en.wikipedia.org/wiki/Jack_L._Treynor

http://en.wikipedia.org/wiki/Jack_L._Treynor

risk or market risk, is the risk that remains even if a portfolio is well-diversified. A

careful investor can reduce or virtually eliminate diversifiable risk by holding a

diversified portfolio of securities. Investors can eliminate most diversifiable risk by

selecting a portfolio of as few 8 to 15 securities.

CAPM links an investment’s beta to its return. A security’s beta indicated how the

securities return responds to fluctuations in market returns. The more sensitive the return

of a security is to changes in market returns, the higher that security’s beta. For stocks

with positive betas, increase in market returns result in increase in security returns.

Stocks that have betas less than 1.0 are, of course, less responsive to changing returns in

the market and therefore less risky.

Beta Comment Interpretation

2.0 Twice as responsive as the market

1.0 Move in same direction as the market Same response as the market

0.5 One half as responsive as the market

0.0 Unaffected by market movement

-0.5 One-half as responsive as the market

-1.0 Move in opposite direction of the market Same response as the market

-2.0 Twice as responsive as the market

When CAPM depicted graphically, it is called security market line (SML). For

each level of non-diversifiable risk (beta), SML reflects the required return the investor

should earn in the marketplace.

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The x-axis represents the risk (beta), and the y-axis represents the expected return.

The market risk premium is determined from the slope of the SML. The intercept is the

nominal risk-free rate available for the market, while the slope is the market premium,

E(Rm)− Rf. The securities market line can be regarded as representing a single-factor

model of the asset price, where Beta is exposure to changes in value of the Market. The

equation of the SML is thus:

For individual securities, we make use of the security market line (SML) and its

relation to expected return and systematic risk (beta) to show how the market must price

individual securities in relation to their security risk class. The SML enables us to

calculate the reward-to-risk ratio for any security in relation to that of the overall market.

Therefore, when the expected rate of return for any security is deflated by its beta

coefficient, the reward-to-risk ratio for any individual security in the market is equal to

the market reward-to-risk ratio, thus:

9

E( R i)=R f +β i( E( Rm )−R f ）

E( Ri)−Rf

β i=E (Rm )−Rf

http://en.wikipedia.org/wiki/Risk-return_spectrum

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

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

http://en.wikipedia.org/wiki/File:SecMktLine.png

It is a useful tool in determining if an asset being considered for a portfolio offers

a reasonable expected return for risk. If the security's expected return versus risk is

plotted above the SML, it is undervalued since the investor can expect a greater return for

the inherent risk. And a security plotted below the SML is overvalued since the investor

would be accepting less return for the amount of risk assumed.

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CHAPTER 3: METHODOLOGY

MODERN PORTFOLIO THEORY (MPT): A theory explained on how risk-adverse

investors can construct portfolios to optimize or maximize their expected return based on

a given level of market risk, emphasizing that risk is an inherent part of higher reward.

According to the theory, it’s possible to construct an “efficient frontier” of optimal

portfolios offering the maximum possible expected return for a given level of risk.

(Investopedia, 2013)

DATA COLLECTION: Firstly, we choose 10 companies from different sectors from

the main board of Kuala Lumpur Stock Exchange. Next, we gather relevant information

such as weekly stock prices and weekly KLCI price for year 2007 till year 2011 from

datastream and exported the information to Microsoft Excel Worksheet.

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FORMULA USED IN THIS ARTICLE:

(i) Expected Return - It is the amount that one would anticipate receiving on an

investment that has various known or expected rates of return. The formula is

as shown below:

E ( RP )=∑i

wi(E) Ri

Where…

- Rp is the return of portfolio

- w i is weight of the asset i in the portfolio and

- Ri is the return of an asset i in the portfolio.

(ii) Variance - It is represents by σ 2 which is the measure of dispersion.

σ 2=∑i=1

n

(k t−k t)2

n−1

Where…

- k t is for the past rate of return of time t

- k̄ t is for the average rate of return

- n is for number of year.

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To determine the riskiness of a portfolio which consists of two assets:

Variance of portfolioAB:

σ p=x2σ A2 +(1−x )2σ B

2 +2 x(1−x )r AB σ A σ B

Where…

- x is the fraction of portfolio invested in asset A

To determine the riskiness of a portfolio which consists of three assets:

Variance of portfolioABC:

σ2

p=wA2 σ A

2 +wB2 σ B

2 +2 wA wB Cov ( AB )+wC2 σC

2 +2 wA wC Cov ( AC )

+2 wB wC Cov( BC )

Or in Matrix Form: σ 2p=w t vw

Where…

- W = Weight

- V = Covariance Matrix

To make things easier to understand in three-asset case, x is converted to w,

which denotes weight or proportion of the portfolio allocated to each asset.

See also: r AB σ A σ ⇒COV ( A , B )

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(iii) Standard Deviation - It is represents by σ which is used to measure the

investment’s volatility. It is also known as historical volatility and is used by

investors as a gauge for the amount of expected volatility.

σ=√∑i=1

n

(k t−k t)2

n−1@√σ2

Where…

- k t is for the past rate of return of time t

- k̄ t is for the average rate of return

- n is for number of year.

(iv) Covariance, Cov - It is refers to the measure of the degree to which returns

on two risky assets move in tandem. A positive covariance means that asset

returns move together. A negative covariance means returns move inversely.

Cov ( AB )=∑i=1

n

(k A,i−k A ) ( kB ,i−k B )

n−1

Or in Matrix Form:

Cov ( A , B )= 1n−1

XrT Xr

Where…

- Xr is refers to excess return

- n is refers to number of sample

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(v) Correlation Coefficient, r - It is a statistical measure of how two securities

move in relation to each other which are used in advanced portfolio

management and ranges between -1 and +1. Perfect positive correlation, a

correlation coefficient of +1 implies that as one security moves, either up or

down, the other security will move in lockstep, in the same direction. In

contrast, perfect negative correlation, a correlation coefficient of -1 implies

that if one security moves, either up or down, the other security that is

perfectly negatively correlated will move in the opposite direction. If the

correlation coefficient is 0, they are completely moves randomly. In real life,

perfectly correlated securities are rare where most securities have some degree

of correlation. The formula for correlation coefficient is as below:

r AB=Cov ( AB )

σ A ∙σ B

(vi) Beta, β - Beta of a stock or portfolio is to describe how the return of the stock

or portfolio is predicted by a benchmark. Generally, the benchmark is refers to

the overall financial market and is often estimated via the use of representative

indices, such as KLCI indices. In Capital Asset Pricing Model (CAPM), Beta

is used to measure the volatility or systematic risk of a security or a portfolio

in comparison to the market as a whole.

#1 β i=Cov ( i , M )

σ M2 or #2 β i=

ri , M σ i σ M

σ M2 or #3 β i=

ri ,M σ i

σ M

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Table below shows the summary of interpretation of Beta. As we mentioned before,

we have to compute Beta for 10 assets, thus we utilize the covariance matrix to

compute Beta where formula #1 is used in the article.

Value of Beta Interpretation

β < 0 Asset generally moves in the opposite direction as compared to the index

β = 0 Movement of the asset is uncorrelated with the movement of the benchmark

0 < β < 1Movement of the asset is generally in the same direction as, but less than the

movement of the benchmark

β = 1Movement of the asset is generally in the same direction as, and about the same

amount as the movement of the benchmark

β > 1Movement of the asset is generally in the same direction as, but more than the

movement of the benchmark

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DATA COMPUTATION: First, we copy the entire stock price for the 10 chosen

companies to a worksheet namely KLCI. Then, we rename the date of each stock price

taken to number from 0 to 521. Next, we compute the stock return for every company by

usingRCS=log

P1

P0 , where P1

= Stock Price at current term and P0

= Stock Price at

previous term. However, in Excel, the natural logarithm function is used. For example, to

compute the first term of stock return for Malaysia Pacific, we used the formula

=LN(C5/C4), where C5 is the current stock price and C4 is the previous stock price. We

did the same for the other 9 assets and also for KLCI price index. Next, we compute the

Stock Excess Return by using a simple formula where Excess Return=r−r , where r =

Actual Stock Return and r = Expected Stock Return). For example, the stock excess

return of Malaysia Pacific is represented by =N5:N525-AVERAGE(N5:N525) where

N5:N525 represents the values of Stock Return and AVERAGE(N5:N525) represents the

average values of Stock Return (which is the expected return of a stock). The same

formula applies to the rest too. Then, we named the entire Stock Excess Return as E_R

for the further computation use.

Now, we are going to compute a Covariance Matrix. To compute the Covariance Matrix,

we simply use the Matrix Multiplication function in Excel, where the function will be

=(MMULT(TRANSPOSE(RET),RET))/521. * Note: we have to press Ctrl + Shift +

Enter for matrix form.

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COVARIANCE MATRIX

FTSE BURSA

MALAYSIA KLCI - PRICE INDEX (~M$)

MALAYSIA PACIFIC (~M$)

UNISEM (M)

KUALA LUMPUR KEPONG

(~M$)

KULIM (M’SIA) (~M$)

HUP SENG IND

(~M$)

ORIENTAL HOLD. (~M$)

MUHIBBAH ENG. (M)

(~M$)

EKOVEST (~M$)

YINSON HOLDIN

GS (~M$)

BORNEO OIL (~M$)

FTSE BURSA MALAYSIA

KLCI - PRICE INDEX (~M$)

=(MMULT(TRANSPOSE(RET),RET))/521

MALAYSIA PACIFIC (~M$)

UNISEM (M)

KUALA LUMPUR KEPONG

(~M$)

KULIM (MALAYSIA)

(~M$)

HUP SENG INDUSTRIES

(~M$)

ORIENTAL HOLDINGS

(~M$)

MUHIBBAH ENGINEERING

(M) (~M$)

EKOVEST (~M$)

YINSON HOLDINGS

(~M$)

BORNEO OIL (~M$)

Table 3.1: Draft of Covariance Matrix

19

To compute Beta by using the formula, β=Cov( i , m)

σ m2 . Next, we formed another

table with the relevant information such as, Beta, Variance and Standard Deviation,

Covariance between stock and market, and also Correlation Coefficient. Standard

Deviation is the square root product of Variance of the respective stock and Correlation

Coefficient (r AB=

Cov( AB)σ A . σ B ) is the product of Covariance between stock and market

divided by the multiplication of Standard Deviation of stock and Standard Deviation of

market.

To proceed, we have to form a table concluding weightage of the stock, Variance,

Stock Annual Return, and also the Portfolio Return as shown in Table 3.1. To form an

investment portfolio, there are two (2) conditions: (a) Short Selling is allowed, (b) Short

Selling is not allowed. Besides, we need to use the Solver Function in Excel. Solver

Function is part of a suite of commands sometimes called what-if-analysis which helps to

find an optimal value for a formula in one cell (called the target cell) on a worksheet.

Solver works with a group of cells that are related, either directly or indirectly, to the

formula in the target cell. It will adjusts the values in the changing cells that we specified

(called the adjustable cell) to produce the result that we specify from the target cell

formula. Thus, to use the Solver Function, we need to generate Table 3.2 first.

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ASSET WEIGHTAGE VARIANCE RETURN RETURNPF

MALAYSIA PACIFIC 0.10 0.00707476 -0.05572494 -0.0053572494

UNISEM (M) 0.10 0.00627582 -0.08490298 -0.0084950298

KUALA LUMPUR KEPONG 0.10 0.00129728 0.18507246 0.0185047246

KULIM (MALAYSIA) 0.10 0.00250902 0.20096830 0.0250096830

HUP SENG INDUSTRIES 0.10 0.00090901 0.06688487 0.0066288487

ORIENTAL HOLDINGS 0.10 0.00757104 0.05176927 0.0051769297

MUHIBBAH ENGINEERING (M) 0.10 0.04610 9261 0.011582610

EKOVEST 0.100.00379487

40.006461940 0.0006461940

YINSON HOLDINGS 0.100.00295766

10.127550033 0.0127550033

BORNEO OIL 0.100.01107301

7-0.13478562 -0.0134785620

TOTAL 1.00

RETURNPF

VARIANCEPF

STANDARD DEVIATIONPF

Table 3.2: Format of Individual Stock and Portfolio’s Weightage, Variance, Return

for 10 Stocks

21

Solver Function: We named the Weightage of Stock as W_PF and Covariance Matrix as

CV_PF. Next, copy the values of Variance from the Covariance Matrix. Return of

individual stock is computed by =N5:N525-AVERAGE(N5:N525)*52, where N5:N525

represents the values of Stock Annual Return.

There are 2 steps needed to compute Portfolio’s Return. First, use the formula =

AK32*AM32 where AK32 is the Weightage of the Stock and AM32 is the Return of the

respective Stock. Second, sum up all the values computed. Variance of Portfolio will be

computed by using the formula

=(MMULT(MMULT(TRANSPOSE(WD_MIN),CV_PF),WD_MIN)), where MMULT

refers to Matrix Multiplication, Transpose refers to returns a vertical range of cells as a

horizontal range, or vice versa, WD_MIN is the Name of all weightage for 10 assets, and

lastly CV_PF is Covariance of the 10 individual assets respondent to the market which is

KLCI, whereas Standard Deviation of Portfolio will be the product of square root of

Variance of Portfolio.

22

SHORT SELLING ALLOWED

VARIANCE RETURN

0.000401356 Optimal +0.002

0.000400994 Optimal +0.001

Minimum Value

0.000400993 Optimal -0.001

0.000401356 Optimal -0.002

Table 3.3: Format of Data to compute Investment Portfolio Table

[Short Selling is allowed] To complete the Investment Portfolio Table with Optimum

-0.002, Optimum -0.001, Minimum, Optimum +0.001, and Optimum +0.002, we need to

use the Excel Solver function as shown in Figure 3.1. To proceed, we have to compute

for the minimum value as below.

i. Set Objective - Set Variance of Portfolio as the target cell to the Min. Value.

ii. By Changing Variable Cells - Variable Cells will be WD_PF (the weightage of

each stock).

iii. Subject to the Constraints - Set the Total Weightage always equal to 1. (Note:

For short selling is allowed, uncheck the column for Make Unconstrained

Variables Non-Negative)

iv. Solve and copy the value to the Short Selling Allowed table (as shown in Table

3.4) and also the Investment Portfolio Table (as shown in Table 3.6).

23

Figure 3.1: Print Screen of Solver Function (MIN Value) where Short Selling is

Allowed

24

[Short Selling is Not Allowed] To complete the Investment Portfolio Table with

Optimum -0.002, Optimum -0.001, Minimum, Optimum +0.001, and Optimum +0.002,

we need to use the Excel Solver function as shown in Figure 3.2. To proceed, we have to

compute for the minimum value as below.

i. Set Objective - Set Variance of Portfolio as the target cell to the Min. Value.


each stock).


For short selling is not allowed, check the column for Make Unconstrained


iv. Solve and copy the value to the Short Selling is Not Allowed table (as shown in

Table 3.5) and also the Investment Portfolio Table (as shown in Table 3.7).

25

Figure 3.2: Print Screen of Solver Function (MIN Value) where Short Selling is not

Allowed

26


VARIANCE RETURN

-0.0645982630 = AU57+0.002

-0.0655982630 = AU57+0.001

AT57 0.0004039307 -0.0665982630 AU57

-0.0675982630 = AU57-0.001

-0.0685982630 = AU57-0.002

Table 3.4: Partial Summary of Variance and Return of Portfolio where Short

Selling is Allowed

Table 3.4 shows the partial summary of Variance and return of investment portfolio

which consists of 10 companies where short selling is allowed. The cell AT57 and AU57

is the minimum value obtained from Solver solution. It is the minimum return will gain

from the portfolio associated with the lowest risk.

27

SHORT SELLING N/A

VARIANCE RETURN

-0.057725455 = BE57+0.002

-0.058725455 = BE57+0.001

BD57 0.000414638 -0.059725455 BE57

-0.060725455 = BE57-0.001

-0.061725455 = BE57-0.002

Table 3.5: Partial Summary of Variance and Return of Portfolio where Short

Selling is not Allowed

Table 3.5 shows the partial summary of Variance and return of investment portfolio

which consists of 10 companies where short selling is not allowed. The cell BD57 and

BE57 is the minimum value obtained from Solver solution for the case Short Selling is

not allowed. It is the minimum return will gain from the portfolio associated with the

lowest risk when there is no short selling

.

28

INVESTMENT PORTFOLIO WHERE SHORT SELLING IS ALLOWED

ASSETOPTIMUM

-0.002

OPTIMU

M -0.001MINIMUM

OPTIMUM

+0.001

OPTIMUM

+0.002

MALAYSIA PACIFIC 0.010

UNISEM (M) -0.037

KUALA LUMPUR KEPONG 0.154

KULIM (MALAYSIA) 0.020

HUP SENG INDUSTRIES 0.379

ORIENTAL HOLDINGS 0.387

MUHIBBAH ENGINEERING (M) -0.031

EKOVEST 0.044

YINSON HOLDINGS 0.085

BORNEO OIL -0.010

TOTAL 1.000

RETURNPF -0.0665982630

VARIANCEPF 0.0004039307

STANDARD DEVIATIONPF 0.0200980265

Table 3.6: Partial of Investment Portfolio Table where Short Selling is Allowed

29

INVESTMENT PORTFOLIO WHERE SHORT SELLING IS NOT ALLOWED

ASSETOPTIMUM

-0.002

OPTIMU

M -0.001MINIMUM

OPTIMUM

+0.001

OPTIMUM

+0.002

MALAYSIA PACIFIC 0.002

UNISEM (M) 0.000

KUALA LUMPUR KEPONG 0.140

KULIM (MALAYSIA) 0.012

HUP SENG INDUSTRIES 0.366

ORIENTAL HOLDINGS 0.375

MUHIBBAH ENGINEERING (M) 0.000

EKOVEST 0.026

YINSON HOLDINGS 0.079

BORNEO OIL 0.000

TOTAL 1.000

RETURNPF -0.0587263340



Table 3.7: Partial of Investment Portfolio Table where Short Selling is not Allowed

30

[Short Selling is Allowed] To complete Table 3.4, Table 3.5, Table 3.6 and Table 3.7,

we are also using the Solver Function with different target cell as shown in Figure 3.3.

The steps are as shown below:

i. Set Objective - Set Return of Portfolio as the target cell to the Value of

*.

- * refers to the value of return from the respective cell in Table 3.4.


each stock).




iv. Solve and copy the value to the Short Selling Allowed table (as shown in Table


v. Repeat Steps (i) to (iv) for the column of Optimal +0.002, Optimal +0.001,

Optimal -0.001, and also Optimal -0.002.

31

Figure 3.3: Print Screen of Solver Function (OPTIMAL Value) where Short Selling

is Allowed

32


VARIANCE RETURN

-0.0645982630

-0.0655982630

0.0004039307 -0.0665982630

-0.0675982630

-0.0685982630

Table 3.8: Summary of Variance and Return of Portfolio where Short Selling is

Allowed

33

[Short Selling is Not Allowed] To complete Table 3.4, Table 3.5, Table 3.6 and Table 3.7, we are also using the Solver Function with different target cell as shown in Figure 3.4. The steps are as shown below:

vi. Set Objective - Set Return of Portfolio as the target cell to the Value of

*.

- * refers to the value of return from the respective cell in Table 3.5.

vii. By Changing Variable Cells - Variable Cells will be WD_PF (the weightage of

each stock).

viii. Subject to the Constraints - Set the Total Weightage always equal to 1. (Note:



ix. Solve and copy the value to the Short Selling Allowed table (as shown in Table


x. Repeat Steps (i) to (iv) for the column of Optimal +0.002, Optimal +0.001,

Optimal -0.001, and also Optimal -0.002.

34

Figure 3.4: Print Screen of Solver Function (OPTIMAL Value) where Short Selling

is not Allowed

35

SHORT SELLING N/A

VARIANCE RETURN

0.0004164181 -0.0577254551

0.0004158947 -0.0587254551

0.0004146381 -0.0597254551

0.0004150221 -0.0607254551

0.0004148999 -0.0617254551

Table 3.9: Summary of Variance and Return of Portfolio where Short Selling is not

Allowed

36

PEARSON CORRELATION COEFFICIENT: Refers to the measurement of how

well the variables are related.

Pearson's Correlation Coefficient, r Interpretation

r = +0.70 or higher Very Strong Positive Relationship

+0.40 ≤ r ≤ +0.69 Strong Positive Relationship

+0.30 ≤ r ≤ +0.39 Moderate Positive Relationship

+0.20 ≤ r ≤ +0.29 Weak Positive Relationship

+0.01 ≤ r ≤ +0.19 No or Negligible Relationship

-0.01 ≤ r ≤ -0.19 No or Negligible Relationship

-0.20 ≤ r ≤ -0.29 Weak Negative Relationship

-0.30 ≤ r ≤ -0.39 Moderate Negative Relationship

-0.40 ≤ r ≤ -0.69 Strong Negative Relationship

r = -0.70 or higher Very Strong Negative Relationship

To compute, we simply used the Excel built-in function, CORREL, which is to returns the

correlation coefficient of the array1 and array2 cell ranges. For example, to compute the

correlation coefficient of stock return between KLCI and Malaysia Pacific, we used

=CORREL(A5:A525,B5:B525), where A5:A525 is the values of stock return of KLCI

and B5:B525 is the values of stock return of Malaysia Pacific.

37

38

Table 3.9.1: Draft of Pearson’s Correlation Coefficient

CHAPTER 4: FINDINGS AND RESULTS

39

40

Table 4.1: Covariance Matrix

Beta of market is always equals to 1 and individual stocks are ranked according to

how much they deviate from the market. A stock that is more volatile than the market

over time has a beta above 1.0 whereas a stock that is less volatile than the market has a

beta which is less than 1.0. High beta stocks are supposed to be riskier buy provide a

potential for higher return. In contrast, stocks with low beta exposed to lower risk but

also lower returns.

Technology sector, plantation sector and construction sector have positive beta

and the value of beta is more than 1. This shows that the companies in these sectors are

expected to change by more than 1 percent in the same direction by market. The

companies are Malaysian Pacific Industries Bhd, Unisem (M) Bhd, Kuala Lumpur

Kepong Bhd, Kulim (Malaysia) Bhd, Muhibbah Engineering (M) Bhd and Ekovest Bhd.

Among these companies, Unisem (M) Bhd and Muhibbah Engineering (M) Bhd have the

highest beta which is 1.59 in value. This indicates that both companies are 59% more

volatile than the market.

Next, let’s proceed to the consumer products companies - Hup Seng Industries

Bhd and Oriental Holdings Berhad. Both companies has beta which greater than zero but

less than 1. This indicates that returns of companies are generally in the same direction

with market, but less than the movement of the benchmark. Consumer products

companies mostly less volatile than the market. This is because demand for consumer

product such as clothing, food, and automobiles are stable and less affected by the

economic condition. Thus, even during economic downturn, consumer will still demand

for these products as products such as food and clothing are basic need of human-being.

41

Lastly, trading or services sectors - Yinson Holdings Berhad and Borneo Oil

Berhad. Yinson Holdings has beta which greater than zero but less than 1. Unlike Borneo

Oil, Yinson is not too much affected by the market. Borneo Oil has highest beta, which is

1.97. In other words, Borneo Oil is double as responsive the market.

From the Table 4.1, we are also found that all the variables move together in same

direction. FTSE Bursa Malaysia KLCI comprises the 30 largest companies listed on the

Malaysian Main Market by full market capitalization that meet the eligibility

requirements of the FTSE Bursa Malaysia Index Ground Rules. Thus, the data in the

table shows that the 10 assets in our portfolio have a positive relationship with the largest

30 companies which is represented by the price index. Among 10 companies we have

been invested, Borneo Oil Berhad is the company which is able to react quickly with

market and can earn more return when the return of market increases. However, it is also

brings greater loss if the market become worst. Hup Seng Industries Bhd is more

independent with the trend of market, it is only has covariance matrix of 0.0001400767.

Normally companies in same sector will face same market risk and challenges.

For Malaysian Pacific, return of the asset move positive with market which has

covariance matrix is 0.0005532323. It is also generate high return whenever return of

other assets is high. The highest covariance matrix of Malaysian Pacific is 0.0018557480.

This is shows Malaysian Pacific is able to get higher return if return of Borneo Oil

increases. For Unisem, it is also shows positive relationship with market return and other

asset return. When the market return increases 1%, return of Unisem will increases by

0.07%. It is also has highest covariance matrix with Borneo Oil, 0.0021290632. Both of

42

business of Malaysian Pacific and Unisem shows weak relationship with Hup Seng

Industries.

For companies in plantation sector; Kuala Lumpur Kepong and Kulim, both

companies have nearly covariance matrix with market, these are 0.0004556918 and

0.0005354185. For Kuala Lumpur Kepong has highest covariance matrix with Borneo

Oil, that is 0.0009785125. Kulim has highest positive relationship with Muhibbah

Engineering, which is 0.0010413557. Both of companies from sector plantation have

lowest covariance matrix with Hup Seng Industries.

Hup Seng Industries and Oriental Holdings are based on consumer products

sector. Hup Seng Industries is produce daily foodstuff. This is the reason it is slightly

affect by the unfavorable market situation. It is only has covariance matrix of

0.0001400767 with market. It is has lower covariance matrix with others companies, such

as Muhibbah Engineering and Ekovest. For Oriental Holdings, it is has slightly positive

relationship with market, covariance matrix of 0.0003113934.

Muhibbah Engineering and Ekovest both have slight relationship with market,

these are 0.0006985672 and 0.0005372219. This is due to it is based on construction

sector, and this sector will always affected by government’s policies. Both of it has

highest covariance matrix with each other, that is 0.0015642546. This is due to it is from

same sector.

For trading and services sector, Yinson Holdings has slightly relationship with

market, it is only has covariance matrix of 0.0002249608. It is also has slightly

relationship with others company. Covariance matrix of Yinson Holdings with others

43

companies are not more than 0.0006. For Borneo Oil, it is more affected by sector of

construction because it has some investment that related to construction sector.

In conclusion, the 10 assets have positive relationship with each other and also

market. Thus, if one asset drops or increases, other assets will also being affected with

different degree of positive effect.

44

ASSET BETA VARIANCESTANDARD

DEVIATION

COVARIANCE

(STOCK VS

MARKET)

CORRELATION

COEFFICIENT

(STOCK VS

MARKET)

FTSE BURSA

MALAYSIA KLCI -

PRICE INDEX (~M$)

1 0.000438468 0.020939622 0.000438468 1

MALAYSIA PACIFIC

(~M$)1.261739886 0.007075848 0.084118057 0.000553232 0.31408662

UNISEM (M) 1.594774102 0.003630250 0.060251560 0.000699257 0.55424236

KUALA LUMPUR

KEPONG (~M$)1.039282392 0.001309912 0.036192704 0.000455692 0.60128639

KULIM (MALAYSIA)

(~M$)1.221112592 0.002531034 0.050309380 0.000535419 0.508247885

HUP SENG

INDUSTRIES (~M$)0.319468546 0.000910642 0.030176847 0.000140077 0.221678249

ORIENTAL

HOLDINGS (~M$)0.710185352 0.000758096 0.027533534 0.000311393 0.54010548

MUHIBBAH

ENGINEERING (M)

(~M$)

1.593200896 0.004624776 0.068005706 0.000698567 0.490562136

EKOVEST (~M$) 1.225225464 0.003794890 0.061602676 0.000537222 0.416471484

YINSON HOLDINGS

(~M$)0.513061189 0.002963677 0.054439668 0.000224961 0.197343366

BORNEO OIL (~M$) 1.973321375 0.011079736 0.105260324 0.000865238 0.392556304

Table 4.2: Summary of Beta, Variance, Standard Deviation, Covariance between

Assets and Market, and Correlation Coefficient

45

For individual stock, Malaysian Pacific and Unisem have a good performance in

these 10 years. The variance of Malaysian Pacific and Unisem are high; these are 0.071%

and 0.36%. The standard deviation of Malaysian Pacific and Unisem are high: 8.41% and

6.02%. This is due to both of the company are based technology sector. Technology

sector always faced volatile. New technology is develops rapid in recent years. It is a

challenge for company and price of stock company will fluctuate. Income of company

also always affected if company is unable to react quickly on the trend of market. Both of

the companies enhance its’ management and able to compete with others. It is able to get

high return to investor. We find that return of Malaysian Pacific and Unisem are 12.11%

and 15.6%.

In recent decade, plantation sector has faced challenges. The community price

always fluctuated. Climate is also always changed. Kuala Lumpur Kepong and Kulim are

companies based on plantation. We find that variance for Kuala Lumpur Kepong and

Kulim are 0.13% and 0.25%, standard deviation for Kuala Lumpur Kepong and Kulim

are 3.61% and 5%. Although the risk is high, return for both companies are negative;

these are -19.4% and -20.4%. This phenomenon is unmatched with theory Risk Return

Trade Off, high risk associated with potentially high return. This is normally due to

geopolitical event happen such as economic factor. As mention before, company need

face many challenges. This is the reason why the companies’ performance also volatiles.

Companies which product consumer product normally face lower risk. Hup Seng

Industries has 0.09% of variance and 3% of standard deviation. Oriental Holdings has

0.08% of variance and 2.75% of standard deviation. Both of the companies have lowest

risk in this portfolio. This is due to people will buy the consumer products although the

46

economic recession happens. According to the theory Risk Return Trade Off, we are able

to expect the returns of both companies are low. However, both of companies’

performances are unsatisfied, because it is generate negative return to us. Hup Seng

Industries generated -2.7% of return. Oriental Holdings has -5.7% of return. Hup Seng

Industries produces biscuit product. It faced challenges because people have many

choices instead of biscuits. They are preferred bread or instant noodles, because it is more

convenient. Therefore, sales of the Hup Seng Industries have affected. For Oriental

Holdings, it is do diversified investment. It is expand the business segments to 7 parts;

these are automotive, hotels and resorts, plastic products, plantation, investment holding

and financial services, property development and healthcare. Because of expanded

business, the performance of the company is unstable and the price of stock becomes

volatile.

For construction sector, it is develop in this decade. This is due to markets in this

industry are increasing according to the development at Malaysia. Moreover, construction

companies in Malaysia are also looking forward to the world market. We know that the

risk of company in construction sector is high. This is due to the project of company

normally is costing. For Ekovest, it is reacts quickly in trend of market. It is faced high

risk with 0.37% of variance and 6.16% of standard deviation. But, it is generated 17% of

return. We also can say the successful of Ekovest is not only depends of the trend of

market, it is also due to the good internal control of company. This is on account of

Ekovest do well than others construction companies, such as Muhibbah Engineering.

Muhibbah Engineering is a high risk investment too (0.46% of variance and 6.8% of

47

standard deviation), but it is only generate -4.8% of return. This is due to the price of

materials increases sharply and the competition becomes more intense.

For trading or services sector, it is also high risk investment. For Yinson

Holdings, it is has 0.3% of variance and 6.16% of standard deviation. Although risk is

high, it is unable to generate satisfy return. It is generate -4% of return. This is due to this

sector easy influenced by technologies and research and development efforts. Yinson

Holdings is unable to face highly competitive in this sector. However, for Borneo Oil, it

is shows it talent in company’s management. It is able to integrate its core activities with

advances in technology in order to remain relevant and competitive. It is also operates in

four segments, such as restaurant, franchising and head office operations segment,

general trading segment, management and operations of properties segment, and oil, gas

and energy related business segment. It is achieve excellent result in these four segments

and bring 10.5% of return.

48

ASSET WEIGHTAGE VARIANCEANNUAL

RETURNRETURNPF

MALAYSIA PACIFIC 0.100.00707584

80.121051714 0.012105171

UNISEM (M) 0.100.00363025

00.155857045 0.015585705

KUALA LUMPUR

KEPONG0.10

0.00130991

2-0.194431646 -0.019443165

KULIM (MALAYSIA) 0.100.00253103

4-0.204840239 -0.020484024

HUP SENG INDUSTRIES 0.100.00091064

2-0.027067773 -0.002706777

ORIENTAL HOLDINGS 0.100.00075809

6-0.057151612 -0.005715161

MUHIBBAH

ENGINEERING (M)0.10

0.00462477

6-0.048197099 -0.004819710

EKOVEST 0.100.00379489

00.170579665 0.017057966

YINSON HOLDINGS 0.100.00296367

7-0.042027860 -0.004202786

BORNEO OIL 0.100.01107973

60.105153822 0.010515382

TOTAL 1.00

RETURNPF -0.0021073983

49



Table 4.3: Summary of Individual Stock and Portfolio’s Weightage, Variance,

Return for 10 Assets (If every stocks is invested equally)

50

If we equally invested in the 10 assets, we can find that the return of the portfolio

is -0.21% where 0.11% of portfolio variance and 3.28% of standard deviation. Through

the diversification, we can balance out each asset and can get a well-rounded portfolio

with ability of recovering from market setbacks and limit the losses. We can say the loss

from Kuala Lumpur Kepong, Kulim, Hup Seng Industries, Oriental Holdings, Muhibbah

Engineering and Yinson Holdings can be offset with the high return asset. It is also

stabilize the risks of companies which are always faced volatile.

51

Investing with equal weightage of asset might not a best portfolio. Therefore, we

look at the other alternatives; these are with short selling and without short selling.


VARIANCE RETURN

0.0004043 -0.0645983

0.0004040 -0.0655983

0.0004039 -0.0665983

0.0004040 -0.0675983

0.0004043 -0.0685983

Table 4.4: Summary of Data to graph Scatter Bar if Short Selling is Allowed

52

0.0004039 0.0004040 0.0004041 0.0004042 0.0004043 0.0004044

-0.0690000

-0.0680000

-0.0670000

-0.0660000

-0.0650000

-0.0640000

-0.0630000

-0.0620000

EFFICIENT FRONTIER WHERE SHORT SELLING IS ALLOWED

VARIANCE (RISK ASSOCIATED)

RETU

RN

Figure 4.1: Efficient Frontier where Short Selling is Allowed

In the case where short selling is allowed, we found that the portfolio variance has

decreased compared to the equally invested portfolio. The portfolio variances become

0.04039%, 0.04040% and 0.04043%. Since the variance is decrease, this is means the

risk an investor might take is also decrease. A smaller variance indicates the numbers in

the set are close from the mean. The standard deviation is also decrease if compared to

the portfolio which consists of 10 equally invested stocks. The standard deviation has

decreased to 2.0% compared with 3.28% for the equally invested portfolio. We can

conclude that the risk of investment portfolio where short selling is allowed is lower than

the portfolio which gives 0.1 of weightage to each asset. Since the risk is lower, the

return also decreases. The return for the investment portfolio where short selling is

allowed is around -6.6%.

Through the graph, we can found that a set represents the risk-return

combinations attainable with all possible portfolios. The portfolio which has 0.04039% of

variance, 2.009% of standard deviation and -6.66% of return is the optimal portfolio. It

represents the highest level of satisfaction we can achieve given the available set of

portfolio. The portfolio above the optimal portfolio is the efficient portfolio. However the

portfolio below the optimal portfolio is below the optimal portfolio is unsatisfactory, it is

due to higher risk we need accept, but lower potential return that we can get. We found

that the return of portfolio becomes lower. This is due to we are selling assets that are

borrowed in expectation of a fall in the assets’ price, such as Unisem and Muhibbah

Engineering. After that, we will buy an equivalent number of assets at the new lower

price and returns to the lender of the assets that were borrowed. However, this kind of the

53

stock actually generated high return to us. This caused portfolio returns to decrease

because of we cannot take advantages of the potential of stock in future. Thus, in the

optimal portfolio, Excel advised us to short sell some of the stocks, which are Unisem

(M), Muhibbah Engineering, and Borneo Oil to minimize the loss.

54

SHORT SELLING N/A

VARIANCE RETURN

0.0004164 -0.05773

0.0004159 -0.05873

0.0004146 -0.05973

0.0004150 -0.06073

0.0004149 -0.06173

Table 4.5: Summary of Data to graph Scatter Bar if Short Selling is not Allowed

Figure 4.2: Efficient Frontier where Short Selling is Allowed

55

0.0004145 0.0004150 0.0004155 0.0004160 0.0004165 0.0004170

-0.06300

-0.06200

-0.06100

-0.06000

-0.05900

-0.05800

-0.05700

-0.05600

-0.05500

EFFICIENT FRONTIER WHERE SHORT SELLING IS NOT ALLOWED

VARIANCE (RISK ASSOCIATED)

RETU

RN

In the case where short selling is not allowed, the shape of efficient frontier for

case where short selling is not allowed is not that smooth compared to the case where

short selling is allowed. We found that the portfolio variance is lower than the equally

weightage of portfolio, but it is higher than the portfolio with short selling. This indicates

that investment portfolio where short selling is not allowed is more risky than the

investment portfolio where short selling is allowed. The return of the portfolio without

short selling is higher than the equally weightage of portfolio, but it is lower than the

portfolio with short selling. This happens as we did not sell the stock even the price has

raised to the peak where other investors are selling their stocks. The aggressive selling of

stock will cause the stock price to drop and lastly leads to loss for us. The standard

deviation also lower than the equally weightage of portfolio, but it is higher than the

portfolio with short selling. This is due to it is hasn’t an opportunity to selling assets

which is expected fall in price.

56

INVESTMENT PORTFOLIO WHERE SHORT SELLING IS ALLOWED

ASSET OPTIMUM -0.002 OPTIMUM -0.001 MINIMUM OPTIMUM +0.001 OPTIMUM +0.002

MALAYSIA PACIFIC 0.002 0.006 0.010 0.014 0.018

UNISEM (M) -0.038 -0.037 -0.037 -0.037 -0.037

KUALA LUMPUR KEPONG 0.156 0.155 0.154 0.153 0.152

KULIM (MALAYSIA) 0.022 0.021 0.020 0.019 0.018

HUP SENG INDUSTRIES 0.380 0.379 0.379 0.378 0.378

ORIENTAL HOLDINGS 0.388 0.387 0.387 0.386 0.386

MUHIBBAH ENGINEERING (M) -0.030 -0.030 -0.031 -0.032 -0.032

EKOVEST 0.044 0.044 0.044 0.044 0.045

YINSON HOLDINGS 0.086 0.086 0.085 0.085 0.084

BORNEO OIL -0.010 -0.010 -0.010 -0.010 -0.010

TOTAL 1.000 1.000 1.000 1.000 1.000

RETURNPF -0.0685982630 -0.0675992630 -0.0665982630 -0.0655982630 -0.0645972630

VARIANCEPF 0.0004043157 0.0004040272 0.0004039307 0.0004040268 0.0004043159

STANDARD DEVIATIONPF 0.0201076032 0.0201004266 0.0200980265 0.0201004189 0.0201076070

Table 4.6: Investment Portfolio where Short Selling is Allowed

57

INVESTMENT PORTFOLIO WHERE SHORT SELLING IS NOT ALLOWED

ASSET OPTIMUM -0.002 OPTIMUM -0.001 MINIMUM OPTIMUM +0.001 OPTIMUM +0.002

MALAYSIA PACIFIC 0.00 0.000 0.002 0.000 0.00

UNISEM (M) 0.00 0.000 0.000 0.000 0.00

KUALA LUMPUR KEPONG 0.14 0.140 0.140 0.133 0.13

KULIM (MALAYSIA) 0.01 0.014 0.012 0.013 0.01

HUP SENG INDUSTRIES 0.37 0.366 0.366 0.367 0.37

ORIENTAL HOLDINGS 0.37 0.375 0.375 0.376 0.38

MUHIBBAH ENGINEERING (M) 0.00 0.001 0.000 0.002 0.00

EKOVEST 0.02 0.024 0.026 0.027 0.03

YINSON HOLDINGS 0.08 0.079 0.079 0.080 0.08

BORNEO OIL 0.00 0.000 0.000 0.002 0.00

TOTAL 1.00 1.000 1.000 1.000 1.00

RETURNPF -0.0617254551 -0.0607264551 -0.0597254551 -0.0587263340 -0.0577244551

VARIANCEPF 0.0004148999 0.0004150221 0.0004146381 0.0004158953 0.0004164181

STANDARD DEVIATIONPF 0.0203690930 0.0203720922 0.0203626636 0.0203935103 0.0204063243

Table 4.7: Investment Portfolio where Short Selling is not Allowed

Table 4.6 and Table 4.7 have shown the deviation of weightage for each stock

when the portfolio return changed by 0.001 positively or negatively.

58

59

Table 4.8: Pearson’s Correlation Table

We found that the correlation coefficient of FTSE Bursa Malaysia KLCI has

fairly positive with many sectors, these are plantation and construction. Unisem from

technology, Oriental Holdings from consumer product and Borneo Oil have fairly

positive relationship with FTSE Bursa Malaysia KLCI. It is has returns that move

together in the same direction and magnitude.

Hup Seng Industries and Yinson Holdings have very weak and positive

relationship with other companies. This is means it only increases or decreases a bit of

return when return of other companies is increase.

Malaysian Pacific has slightly positive relationship with some companies; these

are Unisem, Kuala Lumpur Kepong, Kulim, Muhibbah Engeering, Ekovest and Borneo

Oil. The return of Malaysian Pacific will increase mildly when the companies earn more

return to us.

Oriental Holdings and Muhibbah Engineering have the slightly positive with

Unisem, Kuala Lumpur Kepong and Kulim. Its relationship is stronger than relationship

between Malaysia Pacific and Unisem, Kuala Lumpur Kepong and Kulim. This is means

it is more affected by the performances of Unisem, Kuala Lumpur Kepong and Kulim.

60

CHAPTER 5: CONCLUSION

Throughout our analysis, we are facing loss from our investment. Although we

have invested in 10 companies from five different sectors, we are unable to get a greater

return from the portfolio. To prevent this situation becomes worst, we should diversify

the investment portfolio again.

We should not only invest in the asset which move positive with market. This is

due to when bear market happens, we will loss. We should invest in the asset which is

move opposite with market, such as defensive stock. This is due to they tend to be less

susceptible to downswings in the business cycle. Furthermore, we also need to add blue

chip stocks as our investment asset. Blue chips stocks are less risk than other assets and it

is able to get high return. Blue chip stocks are not immune from bear market. With this,

we are able to sustain in unfavorable market which occurs in this recent year.

Moreover, we should not only focus in stock investment. We should diversify our

portfolio with some fixed-income securities such as bonds. Bonds are long-term debt

instruments where a bondholder has a contractual right to receive periodic interest

payments plus return of the bond’s face value at maturity. Bond is less risky compared to

common stock as bond offer contractually guaranteed returns. Thus, by adding bonds

into a portfolio may protect the value of the portfolio.

Besides, invest in mutual funds may also hedge some market risk. A mutual fund

is a portfolio of stocks, bonds, or other assets that were purchased with a pool of funds

contributed by various investors and are managed by an investment company on behalf of

61

its clients. Mutual funds allow investors to construct a well-diversified portfolio without

having to invest a large sum of money.

To construct a well-diversified portfolio, we must establish a clear investment

goal. Whether we want to accumulate retirement funds or we want to enhance our

income. If we want to accumulate retirement funds, we should adopt a long term

investment plan which can provide a stable return over a period of time. In contrast, if we

want to enhance our income, we should go for short term investment which is more

volatile. As the more volatile the investment, the risky the investment; the risky the

investment, the higher the potential of return.

62

REFERENCES

Investopedia. (2013, 11 18). Retrieved from Modern Portfolio Theory - MPT:

http://www.investopedia.com/terms/m/modernportfoliotheory.asp

Wikipedia. (2013). Retrieved from Wikipedia: The Free Encyclopedia:

http://en.wikipedia.org/wiki/Risk-return_spectrum

63

APPENDIX

64

PRINT SCREEN OF SOLVER FUNCTION

CASE 1: SHORT SELLING IS ALLOWED

OPTIMAL +0.001i. Set Objective - Set Return of Portfolio as the target cell to the Value of

Optimal +0.001 which is -.0655982629658807. ii. By Changing Variable Cells - Variable Cells will be WD_PF (the weightage

of each stock).iii. Subject to the Constraints - Set the Total Weightage always equal to 1.

(Note: For short selling is allowed, uncheck the column for Make Unconstrained Variables Non-Negative)

iv. Solve and copy the value to the Short Selling Allowed table (as shown in Table 1.4) and also the Investment Portfolio Table (as shown in Table 3.5).

65

OPTIMAL +0.002

i. Set Objective - Set Return of Portfolio as the target cell to the Value of Optimal +0.002 which is -0.0645982629658807.

ii. By Changing Variable Cells - Variable Cells will be WD_PF (the weightage of each stock).

iii. Subject to the Constraints - Set the Total Weightage always equal to 1. (Note: For short selling is allowed, uncheck the column for Make Unconstrained Variables Non-Negative)


66

OPTIMAL -0.001

i. Set Objective - Set Return of Portfolio as the target cell to the Value of Optimal -0.001 which is -0.0675982629658807.




67

OPTIMAL -0.002

i. Set Objective - Set Return of Portfolio as the target cell to the Value of Optimal +-0.002 which is -.0.0685982629658807.




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CASE 2: SHORT SELLING IS NOT ALLOWED

OPTIMAL +0.001

i. Set Objective - Set Return of Portfolio as the target cell to the Value of Optimal +-0.001 which is -0.0587254550793991.


iii. Subject to the Constraints - Set the Total Weightage always equal to 1. (Note: For short selling is not allowed, check the column for Make Unconstrained Variables Non-Negative)


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OPTIMAL +0.002

i. Set Objective - Set Return of Portfolio as the target cell to the Value of Optimal

+-0.002 which is -0.0577254550793991.


each stock).

iii. Subject to the Constraints - Set the Total Weightage always equal to 1. (Note: For

short selling is not allowed, check the column for Make Unconstrained Variables

Non-Negative)

iv. Solve and copy the value to the Short Selling Allowed table (as shown in Table 1.4)

and also the Investment Portfolio Table (as shown in Table 3.5).

70

OPTIMAL -0.001





71

OPTIMAL -0.002





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Risk & Return