As mentioned above, Malaysia will be chosen for analysis purposes as there is no literature investigating the validity of Fama-French Three Factor Model in the Malaysian context. However, not all companies listed in Malaysia will be included in this study, due to time and resources constraints. As such, the research will be conducted on all of the feasible companies included under the most popular index in Malaysia, namely the FTSE Bursa Malaysia Top 100 KLCI. In Malaysia, the Kuala Lumpur Stock Exchange is also known as Bursa Malaysia. Although Malaysia is still an emerging country, the capital market is a pretty famous one in the international arena. In this study, the stock market index to be used is FTSE Bursa Malaysia Top 100 KLCI (Kuala Lumpur Composite Index). In the FTSE Bursa Malaysia Top 100 KLCI Index, there are a total of 100 Malaysian listed companies included in the index. According to Bursa Malaysia (with official corporate web portal at www.klse.com.my), at April 2009, these 100 companies actually cover approximately 81% of the market capitalization of companies listed in Main Board on Bursa Malaysia. In Table below, the 100 listed companies comprising the FTSE Bursa Malaysia Top 100 KLCI are presented (www.klse.com.my). The data pertaining to the feasible companies, necessary for the study presented in this section, will be employed in this study.
It is crucial to mentioned that not all companies listed in FTSE Bursa Malaysia Top 100 KLCI will be included in this study. Only those feasible companies will be included. Firstly, those firms that had undergone merger or acquisition activities, and thus, had already merged, acquired by other corporations or being delisted will be excluded from this study. This is because through merger, acquisition or the delisting process, the full set of data pertaining to these companies in the period under research is not available. Among the companies being excluded from this study for such reason include the following: Axiata Group, Resorts World, Tanjong, Parkson Holdings, Astro All Asia Networks Plc, Malaysia Airport Holdings, Sarawak Energy, and Titan Chemical Com. Then, the finance related companies in FTSE Bursa Malaysia Top 100 KLCI will also be excluded. This is consistent with Drew et. al. (2005), and Naughton (2005). The reasons these researchers exclude financial firm is because in the original tests conducted by Fama and French (1992), financial firms are excluded. Thus, among the financial related firms that are excluded from the list include the following: Public Bank, Bumiputra-Commerce, Malayan Banking, AMMB Holdings, Hong Leong Bank, RHB Capital, Bursa Malaysia, Alliance Financial Group, Affin Holdings, EON Capital, TA Enterprise, Bandar Raya Developments, and OSK Holdings. Thirdly, consistent with Drew et. al. (2005), those firms with negative book equity should be excluded from the analysis because they do not have meaningful explanation. However, as none of the firm in the sample has negative book equity, none of the stocks are excluded from the analysis for this reason.
Then, the data pertaining to the remaining companies to be studies are obtained from database of Kenanga Investment Bank Berhad. Kenanga Investment Bank Berhad is a brokerage house in Malaysia, and data on each of the listed companies are provided to the clients whom trade stocks using the company platform and services. The corporate website of Kenanga Investment Bank Berhad is at http://www.kenwealth.com/bin/home.asp. For this study, data from year 2006 to 2010 will be employed to investigate validity of Fama-French Three Factor Model in Malaysian context. Quarterly data for the following will be obtained and analyzed: (a) stock index returns of FTSE Bursa Malaysia Top 100 KLCI, (b) stock returns for the respective stocks, (c) quarter-end market capitalization of the respective stocks in Malaysia, (d) quarter-end book value of equity of the respective stocks, and (e) the relevant risk free rate (i.e., as represented by KLIBOR) in Malaysia. A total of five years has been selected as the research period because the stock market database provided by Kenanga Investment Bank Berhad only allow the users to retrieve market related historical data for a maximum of five years.
In the discussion presented in Chapter 2, beta is found to not able to explain securities’ risk factor satisfactorily. Then it is also discussed that there are other risk factors that are affecting a particular common stock expected returns beyond the beta calculation. Then, it is articulated that Fama and French (1992) found that risk factors capable of explaining variation in cross sectional average stock returns include: (a) the size of the firm, and (b) the market value to book value ratio. By combining the two additional factors with beta in the CAPM equation, Fama and French (1992) developed a three factor model as follow:
Thus, from the equation stated above, the three factors used to explain cross section average stock returns are: (a) beta (i.e., the tendency of a particular stock to move more or less than the market), (b) the size effect, and lastly, (c) the book to market ratio. Aside from beta, the other two factors were added to explain cross section average stock returns because it is observed that the average returns from smaller stocks tend to outperformed those of the larger stocks historically. Secondly, it is also observed that stocks traded with higher ratio of book value to market value ratio tend to outperform those stocks with lower book value to market value ratio historically. As Fama and French three factors models are popular among scholars, it will be employed as the key model used in this study. In the following section, the research methods, specific steps and procedures, adopted from Fama and French (1992) will be discussed and articulated.
In order to perform the research, the Fama and French (1992) methods in establishing portfolios for analysis purposes using the Three Factor Model will be employed. Portfolios will be constructed by referring to two different variables or factors. The two factors are: firm size and book-to-market equity. The portfolios construction process will be outlined as follow.
Ranking the stocks based on firm size. Each quarter under the research period, all of the relevant stocks will be ranked based on size. Similar to Fama and French (1992), the size of a firm in this study is defined as the respective market equity of these stocks. Technically speaking, a firm’s market equity (ME) can be defined as the closing price at the period under investigation times the total amount of shares outstanding at the closing period. For certain researchers, a firm’s market equity is also referred to as the market capitalization of the firm (Drew et. al., 2005). After the firms are ranked accordingly based on their respective market equity, the median of the market equity for the sample will be computed. The median of the market equity will be used to split the stocks into two categories. Those stocks with market equity smaller than the median market equity will be allocated to the ‘small group’. These stocks are considered as the small firms in the sample, as their respective market equity is smaller than the market equity of other stocks in the sample. Similarly, those stocks with market equity bigger than the median market equity will be allocated to the ‘big group’. These stocks are considered as the big firms in the sample, as their respective market equity is bigger than the market equity of other stocks in the sample. Overall, through this process, the total amount of stocks will be assigned to two different portfolios, based on the size of the stock (i.e., either big or small).
Ranking the stocks based on book-to-market equity ratio. In a similar way, all of the stocks under the samples will be divided into three groups, based on their respective book-to-market equity ratio. Mathematically speaking, the book-to-market equity ratio can be subsequently computed by dividing the book value of stockholders’ equity to market equity, as stipulated in formula below.
Similar to Fama and French (1992), all of the stocks in the sample will be separated into three groups, based on their respective ranking of book-to-market equity ratio in the list. Those stocks with book-to-market equity ratio rank under the bottom 33.33% in the sample will be assigned to the ‘low group’; while those stocks with book-to-market equity ratio rank above the top 66.67% in the sample will be assigned to the ‘high group’. The others are allocated into the ‘medium group’. Thus, three groups, namely the low, medium and high groups can be created.
Overall, a total of six intersection portfolios can be formed. For naming convenience purposes, the two groups of portfolios separated from firm size variables (i.e., market equity, or more commonly known as market capitalization) will be named Small (S) and Big (B). In a similar manner, the three groups of portfolios separated from book-to-market equity ratio will be named high (H), medium (M) and low (L). As such, the six portfolios that are formulated based on firm size and book-to-market equity ratio are: S/L, S/M, S/H, B/L, B/M, and B/H. After the portfolios are constructed successfully, the subsequent step of analysis can be readily performed. In the following section, the methods whereby these data can be fitted into the Fama-French Three Factor model/ equations will be further articulated.
Upon dividing the stocks into different portfolios, the variables in Fama-French Three Factor equations can be defined. The specific equations are presented as follow for convenience purposes.
In the equation above, the dependent variable is [Rpt – Rft]. To explain, Rpt is the returns of a particular portfolio at a specific year. Then, Rft is the risk free rate pertaining to the Malaysia stock market at the particular point of time. As mentioned before, the risk free rates pertaining to Malaysia market is KLIBOR (Kuala Lumpur Inter-Bank Offer Rate). On the other hand, there are three independent variables, namely, (Rmt-Rft), SMBt, and HMLt. The conventional definitions, firstly popularized by Fama and French (1992), and then by other researchers such as Drew et. al. (2005), Naughton et. al. (2005), and Howton and Peterson (1999) and others, for these independent variables will be adopted in this dissertation. The definition for these independent variables will be presented in Table 3.2 below.
Then, the respective factor sensitivities for each of these variables, namely, bp, sp, and hp are actually the slope coefficient in the multiple regressions. In this study, the specific risk factors, namely, (Rmt-Rft), SMBt, and HMLt will be proven to be able to explain cross sectional average stock returns on the respective factors sensitivities (bp, sp, and hp) is shown to be statistically significant different than zero. In event that the factor sensitivities are shown to be statistically significant different than zero, then the respective variables can be proven as the priced risk factors in the Malaysian stock market.