With investors having an increased appetite for property in recent years, this has resulted in significant growth in assets under management for the major property fund managers and global property securities funds. These significant capital inflows and increased competition for property assets have seen yield compression in the major mature property markets in 2005-2007, with a resulting increased focus on the potential strategic role for international property investment. Major factors also contributing to this increased international property exposure have included the need for portfolio diversification, potential for higher returns, lower cost of capital and favourable exchange rates. Recent studies have also highlighted the portfolio diversification benefits of international direct and indirect property in a mixed-asset portfolio.
Since the property market recovery in 1988-89, the number of listed property companies has doubled in numbers on the Property Sector of the Kuala Lumpur Stock Exchange (KLSE). Despite the increase in the number of property companies, the market capitalisation of the Property Sector has increased by a mere 13.8% over 1989 .This is in contrast with the pre-currency crisis of RM57.8 billion market capitalisation of the Property Sector in 1996. Among the property asset intensive sectors, the market capitalisation of the Property Sector is about the same size as the Plantation Sector as at end 2000. It is interesting to note that the market capitalisation of the Property Sector moves in tandem closely to that of the Plantation Sector. Currently, there are 86 property companies listed on the Main Market. Most of these companies are active as property development companies carrying out housing development. Property shares are popular among investors particular small investors.
Property stocks is real estate investments that characterized as illiquid, expensive, having long market cycles, and having values that are hard to appraise. We raise the question of whether investors react differently to securitized real estate investments. In particular, we choose two aspects of investors' behavior, namely herding and positive feedback trading. From the market microstructure point of view, we empirically study how investors invest on securitized real estate investments, and compare the herd behavior and positive feedback trading with those in other securities.
In 1998, Malaysia once again facing a very bad financial crisis. At that time, a lot of business need to be closed and people are more saving rather than spending their money. What happens to the property market at that time? Is it property market also affected or not? Head of the International Monetary Funds (IMF), Michel Camdessus publicly stated in a speech on 17th June 1997 that "Malaysia is a good example of a country where the authorities are well aware of the challenges of managing the pressures that result from high growth and of maintaining a sound financial system amidst substantial capital flows and the booming property market. When the economy is down, people take a careful look at their investments. Prior to choosing to invest further, so too must they consider their alternatives. Two popular investment alternatives are leveraged investments in either properties or shares. All leveraged investments are not made alike, though.
1.1 BACKGROUND OF STUDY
As we know many investors predict the market outlook for property sector in 2011 is expected to be risen from 2010 as their confidence towards Malaysia's strong fundamental will make property sector more attractive. Besides, government planned to encourage property companies by injected huge investment as to increase the employment opportunity and for further economy expansion. Therefore, the main objective for this study is to get the best portfolio selection for properties sector investment by determining the risk and return of each property company listed in the main market.
Using six methods of study which are Treynor index, Jensen Alpha index, Sharpe index, Adjusted Jensen Alpha, Adjusted Sharpe and Information Ratio to find the return of risk and return for each property stock and rating from outperform to underperform stock. Then we will select the best three stocks to include in the portfolio selection. This study will help the investors to make the right and accurate decisions towards their investment in this sector. The closing price of 86 property companies will be collected and analyzed to solve the objectives of study.
1.2 PROBLEM STATEMENT
Investment in property sectors can be classified as the fundamental investment decision where normally they put their money in long term and hope for the price to boost up later. The adjustment of property investment structure in Malaysia is a part of the overall change in the economic and industrial structure in Malaysia. Owing to enormous restructuring of global economics as well as constraints from land supply, funding, raw materials and water sources in Malaysia, it becomes a focus of world attention as to how to make full use of rare sources in Malaysia. The property industry has been considered in Malaysia as backbone industry in its present economic development and therefore the importance of adjusting the property investment structure is obvious. Through this study we are interested to know which property stocks are underperform and outperform as well as which stocks has the highest ranking for six years time. In addition, the study will help us to determine the best portfolio selection for property sector by using correlational study of six methods.
1.3 RESEARCH QUESTION
In this study, there are several research questions that has been developed regarding the problem statement occurred. The research questions for this project attempts to answer are as follows:
1. Whether the performance of the stock is outperform or underperform?
2. Which stock is the highest ranking?
3. Which stock to be selected?
1.4 OBJECTIVE OF STUDY
Through this study, there a few objectives of this research. They are:
1. To identify the performance of properties stock in Malaysia.
2. To find the properties stock that most stable.
1.5 SIGNIFICANT OF THE STUDY
A few numbers of studies in property stocks in Malaysia have been conducted. Most of the study is identify on property management, valuation and also constructing and updating property price index series. Only a small number of international studies that focusing on the determinants of property portfolio selection in Malaysia. This study will give significant to the other researcher consecutively to get the some idea for further research on this sector. Other than that, it hopes that the researcher will get a better understanding about the determinants of property stocks performance and researcher also hopes to get some additional knowledge in gathering the information about the Malaysian properties stocks. Beside this study will help investors to make the right investment decision and track market opportunities as it provides the best portfolio selection based on the stocks performance by tracking their risk and return for the investment. For students, the study can help to increase their knowledge towards the equities market movement especially on property stocks. This will facilitate the property companies itself to improve themselves as well as for further research and design.
1.6 SCOPE OF STUDY
The scope of this study would cover the stock prices of the properties company listed in Bursa Malaysia. There are 86 property companies that listed in Main Market. Most of these companies are active as property development companies carrying out housing development.
Kuala Lumpur Composite Index (KLCI) and Treasury Bills acts as benchmark for each company that will determine their return and also with the risk. Return and risk for each property stock will determine the rank for each company and the stock to be selected.
While, this study will cover from year 2005-2010. All the data which is from January until December for each years get from Bursa Malaysia to ensure their accuracy. Inaccuracy of data will influence to the misleading of this research.
1.7 LIMITATION OF STUDY
In this research there are several variables may empirical to the research and also can considered but beyond the control of the researcher. There are:
1.7.1 Data accuracy
Most of the information in this study is mainly gathered from various secondary data such as websites, published newspapers and journals. Their accuracy and reliability heavily depends on the accuracy of the published materials.
Range of Period
Since the study focus on financial crises period, the researcher faced difficulty in choosing the range of period because financial crises sometimes occurred more than one year. The researcher use one year daily market index in this study, and the researcher feel ambiguity whether to choose the first year or the one year before financial crises recover as range of the period for this study.
1.8 DEFINITION OF TERMS
1.8.1 Return
The gain or loss of a security in a particular period. The return consists of the income and the capital gains relative on an investment. It is usually quoted as a percentage.
1.8.2 Risk
The chance that an investment's actual return will be different than expected. Risk includes the possibility of losing some or all of the original investment.
1.8.3 Property
The rights that one individual has in lands or goods to the exclusion of all others; rights gained from the ownership of wealth.
1.8.4 Stock
A type of security that signifies ownership in a corporation and represents a claim on part of the corporation's assets and earnings.
1.8.5 Volatility
A statistical measure of the dispersion of returns for a given security or market index. Volatility can either be measured by using the standard deviation or variance between returns from that same security or market index
1.8.6 Capitalisation
In finance, a process whereby anticipated future income is converted to one lump sum capital value.
1.9 SUMMARY
In price discovery process, price movements of stocks reflect the information flows of
direct investment by companies. In addition, market efficiency is a pivot in determining whether the stock prices correctly reflect the fundamental prices of the underlying assets. This however is under the assumption that the market is affected by rational market participants who form rational expectations of future perspectives and discount all market information into expected prices simultaneously. On the other hand, it is widely known that herding and positive feedback trading are potential causes of some phenomena in financial markets such as asset price momentum, excess volatility and bubbles.
Then, researcher was contributed the statement of the research problem, objective of the study has specific, identify the element scope of the study, describe the significant of the study can contribute, and the key terms that used in this study. Few economic variables are taken to test whether there are any significant correlation between the dependent and independent variables. The dependent variable used in this study is stock price and the independent variables are recession, interest rate and inflation rate.
CHAPTER 2
LITERATURE REVIEW
INTRODUCTION
In this chapter, the researcher has searched some of research that is relevant to the topic study. More studies on the portfolio selection strategies using other method such as robust, correlational method and etc. Less studied has be done for Sharpe, Jensen, Treynor and more over no studies pertaining property selection strategies has been conducted recently. As we know selecting the property stock need an analytical strategies because of huge investment will be injected to buy the property. The risk of the property investment is depends on the market growth among other sectors as well as the economic sector. By measuring the property stocks performance, it will help investors to make an accurate investment decision which companies in Malaysia will be selected as the best portfolio strategies. Other than that, it will give investors some clear picture of the stocks risk level to measure their risk tolerance. By making comparison of the studies that had been conducted by some researchers, it will help to solve the research problem as well as meet the objectives of the studies.
.2.1 PREVIOUS STUDY
There are limited numbers of literature available on properties stock selection in Malaysia using Treynor, Alpha Jensen, Adjusted Alpha, Adjusted Jensen, Information Ratio and Simple Sharpe Portfolio Optimization. As we know investors will only take the risk based on their objectives and risk tolerance. The property stock is said to more risky when the risk is higher and the return is lower. The more risk-averse individuals should have selected lower-volatility stocks. The predictions of the preferred risk habitat hypothesis are consistent with the observations by Dorn and Huberman (2009) which include: (1) These portfolios contain highly similar stocks in terms of volatility. (2) When these stocks are sold they are replaced by stocks of similar volatility. Since portfolio volatility remains about the same after investors rearrange their.
Some of the studies for properties made by other researchers is focusing on the portfolio design for investment companies through scenario where conducted by Payam Hanafizadeh*, Abolfazl Kazazi and Azam Jalili Bolhasani (2008). They found that The dynamicity of today's environment in which organizations operate gives rise to many uncertainties. Accordingly, the portfolio strategy performance of the country is impacted by the dynamicity of the environment. The main objective of portfolio design is to determine the right combination of profitable industries which enable the organization to achieve its expectations of the investment strategy. The present financial methods for selecting portfolio have lost their efficiency in today's uncertain and agitated environment. The concern for better decision making in portfolio diversification has received worldwide attention, especially in the developed countries. This is in realization of the fact that investment scene (property investment inclusive) throughout the world is characterized by risk and uncertainty and ignoring them may bring peril. Arising from the need to address the problem of risk and uncertainty, the pattern of investment has changed substantially and investors have seen the safety aspect of diversification as risk may be reduced by a trade-off with return.
In this study we calculate the average return, market return, beta and standard deviation. This risk is derived from downside frequency, mean downside deviation and downside magnitude, and DRO is to prevent the portfolio from frequently performing too poorly (Swisher and Kasten, 2005). The investor's tolerance for such risk may be assessed based on how often and how much he/she might lose, and should such losses occur, how long it will take the portfolio to recover. Following the DRO principle, a preferred strategy by investors in a downside market would be to trade frequently, in order to avoid a constant losing streak. This could also produce a steady stream of cash flow even if gains are insignificant. It is normal for investors to be wary of the unknown.
In like manner, the drive towards the integration of quantitative strategies, as developed under Modern Portfolio Theory (MPT), into property portfolio diversification and management has increased. In the United States, United Kingdom, and Hong Kong, for example, studies such as Adair, McGreal, and Webb (2006), Lee (2005), Liow, Ooi, and Gong (2005) Bond, Karol3à, and Sanders (2003), Steinert and Crowe (2001), Conover, Friday, and Sirmans (2002) Brown, Li, and Lusht (2000), Cheng and Liang (2000), Viezer (2000) Kuhle, and Walther (1987), Mueller (1993), Wilhams (1996)Miles and McCue (1982), have evaluated and determined the benefits of various diversification strategies to their investors. These studies have shown that different diversification strategies come with different portfolio benefits. Therefore, the question of how best to allocate investment funds within real estate portfolio to achieve optimal return/risk performance is not ambiguous to investors in these countries.
SUMMARY
In this chapter, show that the keys of literature review based on study. There are includes determinants of properties stock selection by previous study. There are only a small number of international studies about forecasting on optimization strategy that prove helpful as a guideline to this study. The literature review in this study was referred, researcher was used a secondary data. Secondary data for this research was gathered from textbooks, journals, and other references in the library. Based on the review of key studies, on portfolio of properties stock was positively related with Treynor, Jenson, Sharpe, Alpha, Information ration and adjusted Sharpe and Jensen Alpha it will help to give clear idea for this study.
CHAPTER 3
METHODOLOGY AND DATA
3.0 INTRODUCTION
In this chapter, it explains about the aspects in the process and design of this study. This involves the data collection method and sample data used in this study as well as the development of the theoretical framework. Besides that, type of study, the time horizon, and the unit of analysis will be described. As mentioned in chapter one, the main objective of this study was to identify the performance of the properties stock in Malaysia whether outperform or underperform and to find the properties stock that most stable. To achieve the above objectives, this study will use six performance test which is Treynor, Jensen, Alpha, Adjusted Sharpe, Adjusted Jensen Alpha and Information ratio.For each test, there are 3 best stocks that will be selected and each company that their regression is below than 0.25 will not be chosen because of the significancy of the data. While, methodology that will be use in this research is Simple Sharp Portfolio Optimization.
3.1 DATA COLLECTION
Secondary data is data gathered through such existing sources. This study was conducted based on secondary data obtained from the Emerald, Yahoo Finance and Bursa Malaysia website over five year period from 2005 to 2010. Data concerning on the Ordinary Least Square Regression that will involving alpha, beta and regression. Companies' closing stock prices from year 2005-2010 have been collected to find companies return and also with the market return. While Treasury Bills used to calculate companies' risk.
3.2 SAMPLING FRAME
86 companies that will be sampling frame. They are:
A & M REALTY BHD
AMCORP PROPERTIES BHD
ASAS DUNIA BHD
ASIAN PAC HOLDING BHD
ASIAN PACIFIC LAND BHD
BANDAR RAYA DEVELOPMENT BHD
BCB BHD
BERJAYA ASSETS BERHAD
BERTAM ALLIANCE BHD
BINA DARULAMAN BHD
BOLTON BHD
COUNTRY HEIGHT HOLDING BHD
COUNTRY VIEW BHD
CRESCENDO CORPORATION BHD
DAIMAN DEVELOPMENT BHD
DAMANSARA REALTY BHD
DIJAYA CORPORATION BHD
EASTERN & ORIENTAL BHD
ENCORP BHD
EQUINE CAPITAL BHD
EUPE CORPORATION BHD
FARLIM GROUP (M) BHD
FOCAL AIMS HOLDING BHD
GLOMAC BHD
GOLDEN PLUS HOLDING BHD
GROMUTUAL BHD
GUOCOLAND (MALAYSIA) BHD
HUA YANG BHD
HUNZA PROPERTIES BHD
I-BHD
IBRACO BHD
IGB CORPORATION
IJM LAND BHD
IVORY PROPERTIES GROUP BHD
KARAMBUNAI CORP BHD
KELADI MAJU BHD
KLCC PROPERTY HOLDINGS BHD
KRISASSETS HOLDINGS BHD
KSL HOLDINGS BHD
KUMPULAN HARTANAH SELANGOR BHD
LAND & GENERAL BHD
LBI CAPITAL BHD
LBS BINA GROUP BHD
LIEN HOE CORPORATION BHD
MAGNA PRIMA BHD
MAH SING GROUP BHD
MAHAJAYA BHD
MAJUPERAK HOLDINGS BHD
MALAYSIA PACIFIC CORP BHD
MALTON BHD
MEDA INC. BHD
MENANG CORPORATION
MERGE HOUSING BHD
METRO KAJANG HOLDINGS BHD
MK LAND HOLDINGS BHD
MUI PROPERTIES BHD
MULPHA LAND BHD
MUTIARA GOODYEAR DEVELOPMENT BHD
NAIM HOLDINGS BHD
NILAI RESOURCES GROUP BHD
ORIENTAL INTEREST BHD
OSK PROPERTY HOLDINGS BHD
PARAMOUNT CORPORATION BHD
PASDEC HOLDINGS BHD
PERDUREN (M) BHD
PETALING TIN BHD
PJ DEVELOPMENTS HOLDINGS BHD
PLENITUDE BHD
SAPURA RESOURCES BHD
SELANGOR DREDGING BHD
SELANGOR PROPERTIES BHD
SHL CONSOLIDATED BHD
SOUTH MALAYSIA INDUSTRIES BHD
SP SETIA BHD
SUNWAY CITY BHD
TAHPS GROUP BHD
TALAM CORPORATION BHD
TANCO HOLDINGS BHD
TEBRAU TEGUH BHD
TRIPLC BHD
UEM LAND HOLDING BHD
UNITED MALAYAN LAND BHD
WING TAI MALAYSIA BHD
Y&G CORPORATION BHD
YNH PROPERTY BHD
YTL LAND & DEVELOPMENT B
3.3 SOURCES OF DATA
The data on monthly stock price of property companies in Malaysia was collected from Bursa Malaysia website which provided information about the historical data of stock price like opening price, closing price and volume of stock. Besides that, data for Treasury Bills and KLCI also collected from Bursa Malaysia.
RESEARCH DESIGN
3.5.1 Purpose of the Study
The purpose of this study is to identify the performance of the properties stock in Malaysia. Descriptive study will be able to describe the characteristics of the variable of the situation. Beside that, this study will help others to make decision making in financing and offer the ideas for future study and research.
3.5.2 Types of Investigation
This study involved the correlational study types of investigation. It is used to determine the important variable related with the objective of the study. The important variables are closing stock price, companies' return and market return. Field studies and field experiment will be conducted in this study. Field studies are correlational studies that being done in this study. Meanwhile, field experiments are studies that will be conducted to establish cause and affect relationship by using the same measurement in the market. The experiment done to establish the cause and affect of the studies so that can make corrective action to make any decision in the investment.
3.5.3 Unit of Analysis
In this study, the stock price will be based on the money values. The stock price for property companies has been selected from Bursa Malaysia website as a dependent variable. The stock price will be taken from 2005 until 2010 data. Meanwhile Treasury Bills determined by percentage and KLCI will be determined by indexes.
3.5.4 Time Horizon
Cross-sectional studies will be used in these studies. Where's monthly basis data of properties company stock price from 2005-2010 is used to find out the performance of properties stock in Malaysia. Meanwhile for KLCI and Treasury bills data also taken for same years to make sure the accuracy of the result.
3.6 THEORETICAL FRAMEWORK
PROPERTIES
STOCK SELECTION
3.7 DATA ANALYSIS AND TREATMENT
After all the data and information had been gathered through the data collection method, the process of analyzes a data and finding analysis is begin. Ordinary Least Square (OLS) Method is a method originated by Legendre, which refers to the process of estimating the unknown parameters of a model by minimizing the sum of squared differences between the observed values of a random variable and the values predicted by the model. If every observation is given equal weight then this is ordinary least squares.
Ordinary least square method is the regression that are run to get alpha and beta value.
Represent market return
Y= α + βx
Represent companies stock
There are six performance test that will be used, which is:
Treynor Performance Measurement
-This theory explain and analyse the return in relation to market risk, it measuring the return adjusted with systematic risk represent by Beta (β). The higher the ratio obtained, the better the performance of the fund.
(R i - R f) / ß i
Where : Ri is the fund return
Rf is Risk free rate of return
ßi is the Beta
Jensen Alpha
α = Ri - [Rf + β . (Rm − Rf )]
- Jensen Alpha is used to determine the abnormal return of a security or portfolio of securities over the theoretical expected return. The theoretical return is predicted by a market model, most commonly the Capital Asset Pricing Model (CAPM) model.
where : Ri is the fund return
Rf is the risk free rate
Rm is the market return
β is the fund Beta
Sharpe Performance Measurement
Ri-Rf /σi
-Sharpe measure is the most widely used method to measure performance of stocks or funds. It measures the excess return per unit of risk in a trading of funds.
Ri-Rf /σi
Where : R is the fund return,
Rf is the risk free rate of return,
σi is the standard deviation of the asset excess return.
Information Ratio
-The Information ratio is a measure of the risk-adjusted return of a financial security. It is defined as expected active return divided by tracking error, where active return is the difference between the return of the security and the return of a selected benchmark index, and tracking error is the standard deviation of the active return. The information ratio IR is:
,
Where: R is the fund return
Rb is the benchmark return
α = E[R − Rb] is the expected value of the active return,
ω = σ is the standard deviation of the active return, which is an alternate definition of the aforementioned tracking error.
Adjusted Jensen Alpha
Adjusted Jensen Alpha: αp / βp
-the portfolio is said to have a fair performance when alpha is zero or not significantly different from zero. However this index cannot be used to compare the performance of different portfolios with different level of systematic risk and therefore the Adjusted Jensen Alpha was computed as follows:
Adjusted Sharpe
-The Sharp index uses standard deviation and a small number of observations can create a bias. Jacobson and Korkie (1983) modified the Sharpe's index to eliminate the bias. The Adjusted Sharpe Index is expressed as follows:
ASi = Si Ã- no of observation / no of observation + 0.75
Sharpe optimization uses a single index model to generate variance covariance structures.This model not only allows one to determine which securities are included in an optimal portfolio but also how much to invest in each. Furthermore, the technique allows the definition of a cut-off rate defined solely in terms of the characteristics of the individual security, such that the impact on the optimal portfolio of the introduction of any new security into the manager's decision set can quickly and easily be seen.
3.8 SUMMARY
In this chapter, it will provide the research design that will be used in this study, reviews all parts of theoretical framework and research methodology will be using in this study. With theoretical framework provides properties company stock price, 6 performance test and the stock selection. Although in part of methodology was describes how research approach is adopted in the study, the data that was collected for this study and statistical methods to be use in analyzing the data. The study aims to to identify the performance of the properties stock in Malaysia. This research will be done in accordance to the objective where to know the properties stock that most stable and also the performance of Malaysia properties stock. This information will perhaps can be used by the future researcher and also customer for them to make some financing and investment decisions. Since study focuses on the data from 2005 until 2010, if would give a better picture on the decision of the customer to make the correct choice on their financing planning.
CHAPTER 4
FINDINGS AND ANALYSIS
INTRODUCTION
The previous chapters have discussed the objective of the study, reviewed prior literature related to the study and specify the research design that being used in this study. As mentioned in Chapter One, the main objective of this study is to identify the performance of the properties stock in Malaysia. Then in Chapter Two, discusses the previous study by another researchers that related to this study. Meanwhile in previous chapter, it highlights the research design that being used in this study. Therefore, this chapter will be discuss and analysis the finding from research done.
DESCRIPTIVE ANALYSIS
Property companies that listed in Main Market is 86 companies. From these 86 companies, 7 of them is rejected. The reason of rejected is because of all of them is do not meet the requirement which is in terms of period of years. For this study, it used 6 years data which is from 2005-2010. 6 companies that rejected is:
BERTAM ALLIANCE BHD 2010
ENCORP BHD 2006-2010
GOLDEN PLUS HOLDING BHD 2005-2009
IGB CORPORATION 2006-2010
IVORY PROPERTIES GROUP BHD 2010
TRIPLC BHD 2006-2010
UEM LAND HOLDING BHD 2008-2010
After reject that 7 companies, there are 79 companies left. Another 79 companies is:
A & M REALTY BHD
AMCORP PROPERTIES BHD
ASAS DUNIA BHD
ASIAN PAC HOLDING BHD
ASIAN PACIFIC LAND BHD
BANDAR RAYA DEVELOPMENT BHD
BCB BHD
BERJAYA ASSETS BERHAD
BINA DARULAMAN BHD
BOLTON BHD
COUNTRY HEIGHT HOLDING BHD
COUNTRY VIEW BHD
CRESCENDO CORPORATION BHD
DAIMAN DEVELOPMENT BHD
DAMANSARA REALTY BHD
DIJAYA CORPORATION BHD
EASTERN & ORIENTAL BHD
EQUINE CAPITAL BHD
EUPE CORPORATION BHD
FARLIM GROUP (M) BHD
FOCAL AIMS HOLDING BHD
GLOMAC BHD
GROMUTUAL BHD
GUOCOLAND (MALAYSIA) BHD
HUA YANG BHD
HUNZA PROPERTIES BHD
I-BHD
IBRACO BHD
IJM LAND BHD
KARAMBUNAI CORP BHD
KELADI MAJU BHD
KLCC PROPERTY HOLDINGS BHD
KRISASSETS HOLDINGS BHD
KSL HOLDINGS BHD
KUMPULAN HARTANAH SELANGOR BHD
LAND & GENERAL BHD
LBI CAPITAL BHD
LBS BINA GROUP BHD
LIEN HOE CORPORATION BHD
MAGNA PRIMA BHD
MAH SING GROUP BHD
MAHAJAYA BHD
MAJUPERAK HOLDINGS BHD
MALAYSIA PACIFIC CORP BHD
MALTON BHD
MEDA INC. BHD
MENANG CORPORATION
MERGE HOUSING BHD
METRO KAJANG HOLDINGS BHD
MK LAND HOLDINGS BHD
MUI PROPERTIES BHD
MULPHA LAND BHD
MUTIARA GOODYEAR DEVELOPMENT BHD
NAIM HOLDINGS BHD
NILAI RESOURCES GROUP BHD
ORIENTAL INTEREST BHD
OSK PROPERTY HOLDINGS BHD
PARAMOUNT CORPORATION BHD
PASDEC HOLDINGS BHD
PERDUREN (M) BHD
PETALING TIN BHD
PJ DEVELOPMENTS HOLDINGS BHD
PLENITUDE BHD
SAPURA RESOURCES BHD
SELANGOR DREDGING BHD
SELANGOR PROPERTIES BHD
SHL CONSOLIDATED BHD
SOUTH MALAYSIA INDUSTRIES BHD
SP SETIA BHD
SUNWAY CITY BHD
TAHPS GROUP BHD
TALAM CORPORATION BHD
TANCO HOLDINGS BHD
TEBRAU TEGUH BHD
UNITED MALAYAN LAND BHD
WING TAI MALAYSIA BHD
Y&G CORPORATION BHD
YNH PROPERTY BHD
YTL LAND & DEVELOPMENT BHD
REGRESSION
R2= 0.25
Coefficient of determination (R2) is a measure of how much the variability in the outcome is accounted for by the variables. The higher the value R², the higher explanatory power of the estimated equation and it is more accurate for forecasting purpose. The value of R-square (R2) will indicates the proportion of the total variation in the independent variable. The higher the value of R2, the better the explanation will be. For this study, (R2) is standardized at 0.25. This means there is about 25% of performance of the property companies is explained by variables and another 75% indicates that values of (R2) are explained by other variables that are needed to fit the data.
After get all the data, the next step is to find alpha (α), beta (β) and coefficient of determination (R2). Coefficient of determination (R2) of each companies will determined whether that companies to be calculated on performance test or not.
Company
Alpha
Beta
R2
A & M REALTY BHD
-0.011709
1.6165071
0.1806471
AMCORP PROPERTIES BHD
0.024185
1.832337
0.076163
ASAS DUNIA BHD
-0.004193
1.4013229
0.2249215
ASIAN PAC HOLDING BHD
-0.00846
1.615374
0.181198
ASIAN PACIFIC LAND BHD
0.003693
1.313661
0.142521
BANDAR RAYA DEVELOPMENT BHD
-0.00008494
1.75872436
0.256958329
BCB BHD
-0.00656
0.576415
0.079681
BERJAYA ASSETS BERHAD
-0.008
1.738797
0.215104
BINA DARULAMAN BHD
-0.00663
1.362501
0.327855
BOLTON BHD
-0.00205
0.916983
0.148341
COUNTRY HEIGHT HOLDING BHD
-0.01177
1.310265
0.268677
COUNTRY VIEW BHD
0.001944
-0.42086
0.009374
CRESCENDO CORPORATION BHD
0.00008707
0.805557732
0.149644214
DAIMAN DEVELOPMENT BHD
0.000331
0.710572
0.218304
DAMANSARA REALTY BHD
0.030109
2.849969
0.152127
DIJAYA CORPORATION BHD
-0.00257
0.976458
0.168312
EASTERN & ORIENTAL BHD
0.005258
1.608968
0.17898
EQUINE CAPITAL BHD
0.000836
2.376771
0.134916
EUPE CORPORATION BHD
-0.00868
1.751167
0.270357
FARLIM GROUP (M) BHD
0.0004
0.376905
0.017571
FOCAL AIMS HOLDING BHD
-0.00183
1.030926
0.066354
GLOMAC BHD
-0.01062
1.670084
0.396996
GROMUTUAL BHD
-0.01254
1.080923
0.10847
GUOCOLAND (MALAYSIA) BHD
0.007635
1.566786
0.195309
HUA YANG BHD
0.00161
1.209232
0.105827
HUNZA PROPERTIES BHD
-0.00215
0.868497
0.205319
I-BHD
0.001236
0.575194
0.051236
IBRACO BHD
-0.000011602
0.844474657
0.07037675
IJM LAND BHD
0.011394
2.736317
0.275488
KARAMBUNAI CORP BHD
0.023599
2.012129
0.095853
KELADI MAJU BHD
-0.00348
1.020085
0.13542
KLCC PROPERTY HOLDINGS BHD
0.003477
0.638317
0.305341
KRISASSETS HOLDINGS BHD
0.001442
0.367409
0.083746
KSL HOLDINGS BHD
-0.01052
1.628745
0.430357
KUMPULAN HARTANAH SELANGOR BHD
0.010837
2.086687
0.08993
LAND & GENERAL BHD
0.007119
1.730359
0.198247
LBI CAPITAL BHD
-0.00256
0.858395
0.170316
LBS BINA GROUP BHD
-0.0123
1.298137
0.190835
LIEN HOE CORPORATION BHD
-0.00176
1.291239
0.19107
MAGNA PRIMA BHD
-0.00659
1.649206
0.132672
MAH SING GROUP BHD
0.007049
0.853462
0.153573
MAHAJAYA BHD
-0.00904
0.681616
0.048793
MAJUPERAK HOLDINGS BHD
0.056646
1.932909
0.029682
MALAYSIA PACIFIC CORP BHD
0.004835
1.382703
0.105187
MALTON BHD
0.005431
0.056238
0.00043
MEDA INC. BHD
0.008527
1.367817
0.117397
MENANG CORPORATION
0.013151
1.385608
0.040159
MERGE HOUSING BHD
-0.01673
0.55159
0.074499
METRO KAJANG HOLDINGS BHD
-0.00295
1.20647
0.347225
MK LAND HOLDINGS BHD
-0.02667
2.377639
0.328176
MUI PROPERTIES BHD
-0.00994
1.136432
0.157573
MULPHA LAND BHD
0.0032
1.656726
0.125541
MUTIARA GOODYEAR DEVELOPMENT BHD
0.002593
0.917132
0.0902
NAIM HOLDINGS BHD
-0.00301
1.131317
0.21343
NILAI RESOURCES GROUP BHD
0.000752
1.013022
0.157063
ORIENTAL INTEREST BHD
-0.00257
0.670229
0.146989
OSK PROPERTY HOLDINGS BHD
-0.00844
1.729397
0.265635
PARAMOUNT CORPORATION BHD
0.005832
0.666891
0.234105
PASDEC HOLDINGS BHD
-0.00533
1.464054
0.292114
PERDUREN (M) BHD
0.005934
0.3657
0.008715
PETALING TIN BHD
0.013373
2.02623
0.103769
PJ DEVELOPMENTS HOLDINGS BHD
0.002505
1.271719
0.298144
PLENITUDE BHD
0.004396
1.239212
0.221082
SAPURA RESOURCES BHD
0.008774
2.225153
0.232492
SELANGOR DREDGING BHD
0.003872
1.38801
0.27392
SELANGOR PROPERTIES BHD
-0.0014
1.901514
0.344756
SHL CONSOLIDATED BHD
-0.00345
0.626721
0.05596
SOUTH MALAYSIA INDUSTRIES BHD
-0.00992
1.757348
0.333131
SP SETIA BHD
-0.00093
1.394309
0.310626
SUNWAY CITY BHD
0.007169
1.711271
0.311874
TAHPS GROUP BHD
0.006229
0.728796
0.175492
TALAM CORPORATION BHD
-0.02278
1.954538
0.120382
TANCO HOLDINGS BHD
0.011051
3.69554
0.294081
TEBRAU TEGUH BHD
0.023772
1.829497
0.111851
UNITED MALAYAN LAND BHD
0.003287
1.26701
0.243409
WING TAI MALAYSIA BHD
0.009585
1.740851
0.259671
Y&G CORPORATION BHD
-0.01115
0.689969
0.029925
YNH PROPERTY BHD
-0.00279
1.537911
0.319422
YTL LAND & DEVELOPMENT BHD
-0.0014
1.901514
0.344756
As the result, company that have (R2) above 0.25 is 22 companies. There are:
BANDAR RAYA DEVELOPMENT BHD
BINA DARULAMAN BHD
COUNTRY HEIGHT HOLDING BHD
EUPE CORPORATION BHD
GLOMAC BHD
IJM LAND BHD
KLCC PROPERTY HOLDINGS BHD
KSL HOLDINGS BHD
METRO KAJANG HOLDINGS BHD
MK LAND HOLDINGS BHD
OSK PROPERTY HOLDINGS BHD
PASDEC HOLDINGS BHD
PJ DEVELOPMENTS HOLDINGS BHD
SELANGOR DREDGING BHD
SELANGOR PROPERTIES BHD
SOUTH MALAYSIA INDUSTRIES BHD
SP SETIA BHD
SUNWAY CITY BHD
TANCO HOLDINGS BHD
WING TAI MALAYSIA BHD
YNH PROPERTY BHD
YTL LAND & DEVELOPMENT BHD
4.3 PERFORMANCE TEST
4.3.1 SHARPE RATIO
4.3.2 TREYNOR RATIO
4.3.3 JENSEN ALPHA RATIO
4.3.4 ADJUSTED SHARPE
4.3.5 ADJUSTED JENSEN ALPHA
4.3.6 INFORMATION RATIO
TABLE 4.3.1 SHARPE RATIO
RANKING
COMPANY
SHARPE RATIO
%
Rm (%)
Performance
1
IJM LAND BHD
0.112801636
11.28
0.17795
Outperform
2
TANCO HOLDINGS BHD
0.111405496
11.14
0.17795
Outperform
3
WING TAI MALAYSIA BHD
0.105509433
10.55
0.17795
Outperform
4
SUNWAY CITY BHD
0.09761121
9.76
0.17795
Outperform
5
SELANGOR DREDGING BHD
0.061294609
6.12
0.17795
Outperform
6
PJ DEVELOPMENTS HOLDINGS BHD
0.046912674
4.69
0.17795
Outperform
7
SELANGOR PROPERTIES BHD
0.042138932
4.21
0.17795
Outperform
8
YTL LAND & DEVELOPMENT BHD
0.042138932
4.21
0.17795
Outperform
9
BANDAR RAYA DEVELOPMENT BHD
0.040441883
4.04
0.17795
Outperform
10
SP SETIA BHD
0.021051018
2.1
0.17795
Outperform
11
YNH PROPERTY BHD
0.013428726
1.34
0.17795
Outperform
12
KLCC PROPERTY HOLDINGS BHD
0.011679316
1.16
0.17795
Outperform
13
PASDEC HOLDINGS BHD
-0.01324075
-1.32
0.17795
Underperform
14
METRO KAJANG HOLDINGS BHD
-0.01413409
-1.41
0.17795
Underperform
15
OSK PROPERTY HOLDINGS BHD
-0.01736321
-1.73
0.17795
Underperform
16
EUPE CORPORATION BHD
-0.01775183
-1.77
0.17795
Underperform
17
SOUTH MALAYSIA INDUSTRIES BHD
-0.02865683
-2.86
0.17795
Underperform
18
BINA DARULAMAN BHD
-0.03563885
-3.56
0.17795
Underperform
19
GLOMAC BHD
-0.04516417
-4.51
0.17795
Underperform
20
KSL HOLDINGS BHD
-0.05034928
-5.03
0.17795
Underperform
21
COUNTRY HEIGHT HOLDING BHD
-0.08441884
-8.44
0.17795
Underperform
22
MK LAND HOLDINGS BHD
-0.0863866
-8.63
0.17795
Underperform
To calculate Rm= rp-rfr /
=0.008077-0.008 / 0.04327
=0.0017795 0.17795%
TABLE 4.3.2 TREYNOR RATIO
RANKING
COMPANY
TREYNOR RATIO
%
Rm (%)
PERFORMANCE
1
IJM LAND BHD
0.009299237
0.929
0.0077
OUTPERFORM
2
WING TAI MALAYSIA BHD
0.008959066
0.895
0.0077
OUTPERFORM
3
TANCO HOLDINGS BHD
0.008889065
0.888
0.0077
OUTPERFORM
4
SUNWAY CITY BHD
0.007562981
0.756
0.0077
OUTPERFORM
5
SELANGOR DREDGING BHD
0.005067495
0.506
0.0077
OUTPERFORM
6
PJ DEVELOPMENTS HOLDINGS BHD
0.003717578
0.371
0.0077
OUTPERFORM
7
BANDAR RAYA DEVELOPMENT BHD
0.003452098
0.345
0.0077
OUTPERFORM
8
SELANGOR PROPERTIES BHD
0.003105353
0.31
0.0077
OUTPERFORM
9
YTL LAND & DEVELOPMENT BHD
0.003105353
0.31
0.0077
OUTPERFORM
10
SP SETIA BHD
0.00163432
0.163
0.0077
OUTPERFORM
11
YNH PROPERTY BHD
0.001028101
0.102
0.0077
OUTPERFORM
12
KLCC PROPERTY HOLDINGS BHD
0.000914551
0.091
0.0077
OUTPERFORM
13
METRO KAJANG HOLDINGS BHD
-0.001037876
-0.103
0.0077
UNDERPERFORM
14
PASDEC HOLDINGS BHD
-0.001060033
-0.106
0.0077
UNDERPERFORM
15
OSK PROPERTY HOLDINGS BHD
-0.001457708
-0.145
0.0077
UNDERPERFORM
16
EUPE CORPORATION BHD
-0.001477261
-0.147
0.0077
UNDERPERFORM
17
SOUTH MALAYSIA INDUSTRIES BHD
-0.002148344
-0.214
0.0077
UNDERPERFORM
18
BINA DARULAMAN BHD
-0.002693185
-0.269
0.0077
UNDERPERFORM
19
GLOMAC BHD
-0.003101589
-0.31
0.0077
UNDERPERFORM
20
KSL HOLDINGS BHD
-0.003320946
-0.332
0.0077
UNDERPERFORM
21
MK LAND HOLDINGS BHD
-0.006524936
-0.652
0.0077
UNDERPERFORM
22
COUNTRY HEIGHT HOLDING BHD
-0.007047048
-0.704
0.0077
UNDERPERFORM
To calculate Rm= rp-rfr/βp
=0.008077-0.008/1
=0.000077 0.0077%
TABLE 4.3.3 JENSEN ALPHA RATIO
RANKING
COMPANY
JENSEN ALPHA
%
Rm(%)
PERFORMANCE
1
TANCO HOLDINGS BHD
0.033
3.3
0
OUTPERFORM
2
IJM LAND BHD
0.025
2.5
0
OUTPERFORM
3
WING TAI MALAYSIA BHD
0.016
1.6
0
OUTPERFORM
4
SUNWAY CITY BHD
0.013
1.3
0
OUTPERFORM
5
SELANGOR DREDGING BHD
0.007
0.7
0
OUTPERFORM
6
BANDAR RAYA DEVELOPMENT BHD
0.006
0.6
0
OUTPERFORM
7
SELANGOR PROPERTIES BHD
0.006
0.6
0
OUTPERFORM
8
YTL LAND & DEVELOPMENT
BHD
0.006
0.6
0
OUTPERFORM
9
PJ DEVELOPMENTS HOLDINGS BHD
0.005
0.5
0
OUTPERFORM
10
SP SETIA BHD
0.002
0.2
0
OUTPERFORM
11
YNH PROPERTY BHD
0.002
0.2
0
OUTPERFORM
12
KLCC PROPERTY HOLDINGS BHD
0.001
0.1
0
OUTPERFORM
13
METRO KAJANG HOLDINGS BHD
-0.001
-0.1
0
UNDERPERFORM
14
PASDEC HOLDINGS BHD
-0.002
-0.2
0
UNDERPERFORM
15
EUPE CORPORATION BHD
-0.003
-0.3
0
UNDERPERFORM
16
OSK PROPERTY HOLDINGS BHD
-0.003
-0.3
0
UNDERPERFORM
17
BINA DARULAMAN BHD
-0.004
-0.4
0
UNDERPERFORM
18
SOUTH MALAYSIA INDUSTRIES BHD
-0.004
-0.4
0
UNDERPERFORM
19
GLOMAC BHD
-0.005
-0.5
0
UNDERPERFORM
20
KSL HOLDINGS BHD
-0.005
-0.5
0
UNDERPERFORM
21
COUNTRY HEIGHT HOLDING BHD
-0.009
-0.9
0
UNDERPERFORM
22
MK LAND HOLDINGS BHD
-0.016
-1.6
0
UNDERPERFORM
To calculate Rm=
Rp-rfr =αp+βp (rm-rfr)
TABLE 4.3.4 ADJUSTED SHARPE
RANKING
COMPANY
ADJUSTED SHARPE
%
1
IJM LAND BHD
0.111638733
11.16
2
TANCO HOLDINGS BHD
0.110256985
11.02
3
WING TAI MALAYSIA BHD
0.104421707
10.44
4
SUNWAY CITY BHD
0.096604908
9.66
5
SELANGOR DREDGING BHD
0.060662706
6.06
6
PJ DEVELOPMENTS HOLDINGS BHD
0.046429038
4.64
7
SELANGOR PROPERTIES BHD
0.04170451
4.17
8
YTL LAND & DEVELOPMENT BHD
0.04170451
4.17
9
BANDAR RAYA DEVELOPMENT BHD
0.040024956
4
10
SP SETIA BHD
0.020833997
2.08
11
YNH PROPERTY BHD
0.013290286
1.32
12
KLCC PROPERTY HOLDINGS BHD
0.011558911
1.15
13
PASDEC HOLDINGS BHD
-0.013104247
-1.31
14
METRO KAJANG HOLDINGS BHD
-0.01398838
-1.39
15
OSK PROPERTY HOLDINGS BHD
-0.017184207
-1.71
16
EUPE CORPORATION BHD
-0.017568819
-1.75
17
SOUTH MALAYSIA INDUSTRIES BHD
-0.0283614
-2.83
18
BINA DARULAMAN BHD
-0.035271438
-3.52
19
GLOMAC BHD
-0.044698559
-4.46
20
KSL HOLDINGS BHD
-0.049830218
-4.98
21
COUNTRY HEIGHT HOLDING BHD
-0.083548541
-8.35
22
MK LAND HOLDINGS BHD
-0.085496019
-8.54
TABLE 4.3.5 ADJUSTED JENSEN ALPHA
RANKING
COMPANY
ADJUSTED JENSEN ALPHA
%
1
WING TAI MALAYSIA BHD
0.005505658
0.55
2
KLCC PROPERTY HOLDINGS BHD
0.005446524
0.544
3
SUNWAY CITY BHD
0.004189487
0.418
4
IJM LAND BHD
0.00416393
0.416
5
TANCO HOLDINGS BHD
0.002990316
0.299
6
SELANGOR DREDGING BHD
0.002789325
0.278
7
PJ DEVELOPMENTS HOLDINGS BHD
0.001969636
0.196
8
SP SETIA BHD
-0.000670044
-0.07
9
SELANGOR PROPERTIES BHD
-0.000738677
-0.07
10
YTL LAND & DEVELOPMENT BHD
-0.000738677
-0.07
11
YNH PROPERTY BHD
-0.001815243
-0.18
12
METRO KAJANG HOLDINGS BHD
-0.00244355
-0.24
13
PASDEC HOLDINGS BHD
-0.003639374
-0.36
14
BINA DARULAMAN BHD
-0.004862794
-0.49
15
OSK PROPERTY HOLDINGS BHD
-0.004880497
-0.49
16
EUPE CORPORATION BHD
-0.004957904
-0.5
17
SOUTH MALAYSIA INDUSTRIES BHD
-0.005645151
-0.56
18
GLOMAC BHD
-0.006359097
-0.64
19
KSL HOLDINGS BHD
-0.006456144
-0.65
20
COUNTRY HEIGHT HOLDING BHD
-0.008981166
-0.9
21
MK LAND HOLDINGS BHD
-0.011216538
-1.12
22
BANDAR RAYA DEVELOPMENT BHD
-4.83E-05
-483
TABLE 4.3.6 INFORMATION RATIO
RANKING
COMPANY
INFORMATION RATIO
%
1
KLCC PROPERTY HOLDINGS BHD
20.00661526
20
2
METRO KAJANG HOLDINGS BHD
11.28770778
11.28
3
PJ DEVELOPMENTS HOLDINGS BHD
9.922905917
9.92
4
BINA DARULAMAN BHD
9.712271341
9.71
5
KSL HOLDINGS BHD
9.308468731
9.3
6
SP SETIA BHD
9.237980095
9.23
7
COUNTRY HEIGHT HOLDING BHD
9.142670116
9.14
8
GLOMAC BHD
8.719098351
8.719
9
SELANGOR DREDGING BHD
8.714375187
8.714
10
PASDEC HOLDINGS BHD
8.531715084
8.53
11
YNH PROPERTY BHD
8.493133911
8.49
12
SOUTH MALAYSIA INDUSTRIES BHD
7.590433081
7.59
13
SUNWAY CITY BHD
7.542023023
7.54
14
SELANGOR PROPERTIES BHD
7.136296825
7.13
15
YTL LAND & DEVELOPMENT BHD
7.136296825
7.13
16
OSK PROPERTY HOLDINGS BHD
6.887549781
6.88
17
EUPE CORPORATION BHD
6.862119304
6.86
18
WING TAI MALAYSIA BHD
6.764987563
6.76
19
BANDAR RAYA DEVELOPMENT BHD
6.661169023
6.66
20
MK LAND HOLDINGS BHD
5.568321143
5.56
21
IJM LAND BHD
4.433040226
4.43
22
TANCO HOLDINGS BHD
3.391349623
3.39
After calculate all the ratio, 3 best stock from each method that be chosen to be use in Simple Sharpe Portfolio Optimization. The same stock that appeared in top three of 6 ratio calculated as one. Company that be chosen is:
IJM Land Bhd
Tanco Holdings Bhd
Wing Tai Malaysia Bhd
KLCC Property Holdings Bhd
Metro Kajang Holdings Bhd
PJ Developments Holdings Bhd
Sunway City Bhd
4.4 SIMPLE SHARPE PORTFOLIO OPTIMIZATION
Steps in Simple Sharpe Portfolio Optimization:
Calculate the excess return-to-beta ratio for each stock and rank from the highest to lowest.
Establishing a C-value. The optimum portfolio consists of stocks which a c-value is lower than a particular cutoff point. The cutoff point is determined by looking at the highest c.
Once we know which securities are to be included in the optimum potfolio, we must calculate the percent invested in each security.
Company
Return
Beta
Residual
Error
Rf
Excess
return (Ri-Rf)
Ri-rf/ beta
(Excess return to beta ratio)
IJM Land Bhd
0.033493906
2.736317146
0.0509
0.008
0.32693906
0.12
Tanco Holdings Bhd
0.040898146
3.695540475
0.0869
0.008
0.32898146
0.0890212
Wing Tai Malaysia Bhd
0.023644643
1.740850818
0.0219
0.008
0.015644643
0.00898678
KLCC Property Holdings Bhd
0.008632019
0.638316551
0.0025
0.008
0.000632019
0.00099013
Metro Kajang Holdings Bhd
0.006796079
1.206470492
0.0078
0.008
-0.001203921
-0.00099789
PJ Developments Holdings Bhd
0.012775961
1.271719305
0.0102
0.008
0.004775961
0.37382401
Sunway City Bhd
0.020990557
1.711271054
0.0176
0.008
0.012990557
0.00759117
TABLE 4.4.1 TO FIND RANKING BY CALCULATING EXCESS RETURN TO BETA RATIO
RANKING
PJ Developments Holdings Bhd
IJM Land Bhd
Tanco Holdings Bhd
Wing Tai Malaysia Bhd
Sunway City Bhd
KLCC Property Holdings Bhd
Metro Kajang Holdings Bhd
After rank
Company
Excess Return,
Beta, β
Residual Error,
excess*beta/ stderror
accumulate
PJ Developments Holdings Bhd
0.004775961
1.271719305
0.0102
0.595459
0.595459
IJM Land Bhd
0.32693906
2.736317146
0.0509
17.57581445
18.17127345
Tanco Holdings Bhd
0.32898146
3.695540475
0.0869
13.99038321
32.16165666
Wing Tai Malaysia Bhd
0.015644643
1.740850818
0.0219
1.24360683
33.40526349
Sunway City Bhd
0.012990557
1.711271054
0.0176
1.263088873
34.66835236
KLCC Property Holdings Bhd
0.000632019
0.638316551
0.0025
0.161371275
34.82972364
Metro Kajang Holdings Bhd
-0.001203921
1.206470492
0.0078
-0.186217328
34.64350631
accumulate
numerator
denumerator1
1+j
c-value
124.678363
124.6783632
0.001114874
0.233434427
1.233434427
0.000903878
147.100816
271.7791787
0.034021948
0.508850254
1.508850254
0.02254826
157.157876
428.9370544
0.060216045
0.803095844
1.803095844
0.03339592
138.381807
567.3188612
0.062544441
1.062187133
2.062187133
0.030329178
166.389126
733.7079873
0.064909313
1.373716329
2.373716329
0.027345017
162.979208
896.687195
0.067930657
1.678861158
2.678861158
0.025358036
186.611673
1083.298867
0.067582003
2.028252886
3.028252886
0.02231716
TABLE 4.4.2 TO FIND C-VALUE
C-value is 0.03339592 which is from Tanco Holdings Bhd
Company above than Tanco Holdings Bhd in their excess return to beta ratio will be chosen
To be included under portfolio:
Company
Beta
Residual
ratio
Error
PJ Developments Holdings Bhd
1.271719305
0.0102
0.373824012
IJM Land Bhd
2.736317146
0.0509
0.12
Tanco Holdings Bhd
3.695540475
0.0869
0.089021203
ratio-
error*f
Zi
Wi
Percentage
cutoffpoint
0.340428092
0.00347237
366.2399378
0.208655786
20.86557864
0.0861
0.00438175
624.48016
0.35578151
35.57815103
0.0556
0.00483384
764.51490
0.435562703
43.55627033
Z
1755.23499
1
100
TABLE 4.4.3 TO FIND PERCENTAGE
From the above table, by comparing percent invested in three stocks, we can conclude that Tanco Holdings Bhd is the best stock followed by IJM Land Bhd and PJ Development Holdings Bhd.
CHAPTER 5
CONCLUSION AND RECOMMENDATION
5.0 CONCLUSION
As a conclusion, we can conclude that only 12 from 86 property companies in Malaysia was outperform from period 2005-2010. By referring to the statistic, it shows that probability to make a profit from property stock is only 13.95%. Investor need to make a deep research before they make investment in property sector because the chances for them to lose their money is high.
But, for year 2011 there must be some changes. Government encourage property sector by injected huge investment. Commitment from government to enhance employment opportunity and for further economy expansion shows that property stock will become a good investment for an investor.
In addition, due to Sarawak Election, property sector is said to be most beneficiary sector because it included in one of government's Economy Transformation Plan (ETP). More buildings will be built up to improve the standard living as well as to create more job opportunity to the citizen. Maybe in next few years, Malaysian economy will be as good as other well developed country and one of big contributor is from property sector.
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