Capital Asset Pricing Model (CAPM) was introduced by William Sharpe (1964), Jan Mossin (1966) and John Lintner (1969) separately. This model has been regarded as a great contribution to financial economics. According to Fama and French (1992), “the model has long shaped the way academics and practitioners think about average return and risk”. In practice, it is the most used investment model by fund managers and analysts to determine risk and return of assets. Because of CAPM’s importance in the world of finance, it has been largely tested and its validity has become debatable. Earlier testing of CAPM by Lintner(1965), Black(1972) and Fama and Macbeth(1973) found a strong positive relationship between return and risk (measured by beta). But not all empirical studies have achieved the same results. For instance, Corhay et al (1987) found no relationship between beta and returns for US, UK, France and Belgium for the period of 1969-1983. Similarly, Fama and French (1992) found no relationship between US beta and returns over the period of 1963-1990 and only a weak one from 1941-1990(Fabozzi, 2002). Fama and French claimed that other factors other than beta explained the returns contrary to CAPM. Their research showed that size and book-to-market equity were a more appropriate measure of risk, since they significantly explained cross-sectional changes in average returns. The importance of size and book-to-market equity has been extensively tested in Developed markets as well as emerging markets such as Hong Kong and Japanese. Moreover, further studies on the relationship of beta and returns have also been tested in US, UK, French, Hong Kong, Swiss, Australian, German, Argentina, Brazil, Chile and Mexican markets (Morelli 2007). But none of such research has been tested using Africa’s data. This research aims to apply a similar empirical test on South Africa’s market data to explore the relationship between average share returns and these variables; beta, size and book-to-market equity. Since, this will be the first comprehensive study of this kind on an African emerging market, it looks promising. The finding will give an insight into the behavior share returns in Africa emerging capital markets which could aid investment and financial decision-making. Moreover, since I have a keen interest in stock market investments, a discovery of the factors that truly affect returns in my part of the world, will not only place me at an advantage of managing portfolios constructed in such markets that could yield high return, but also keep me motivated to start and finish the research.
The two most important attributes of every investment is return and risk (Rutterford and Davison 2007: 40). The goal of every investor, who is usually assumed to be risk-averse, is to maximize the return on investment while minimizing the likelihood that the expected return will be achieved, that is, risk. There is a lot of the existing literature on the effects of risk on stock investment returns and models have been development to explore this relationship. CAPM is one of such models and is widely used by investment analyst and fund managers as it serves as a useful benchmark (Haugen 1997:239,254); despite its several empirical contradictions. CAPM offered a theoretical framework that explained the relationship between risk and expected return (Rutterford and Davison 2007:222,250). CAPM also introduced beta as a measure of systematic risk. “A central idea of CAPM is that only one risk, beta, affects long-term average return on an investment” (Redhead 2003:160-161,167). Where expected return is directly proportionate to beta relative to the market portfolio (Burton 1998). CAPM stipulate that a risk-free rate of return and a risk premium, which is a function of beta is the only explanatory factor for cross-sectional variations in average returns and that it significantly measures risk. It also implied that a proportional relationship exists between beta and returns. Early empirical studies of Black (1972) and Fama and Macbeth (1973) using US market data supported this preposition. And more recently, Pettengill and Sundaram(1995) also came up with similar conclusion using US data, where they found significantly high relation between beta and average returns. But, other empirical tests have revealed otherwise. Some discovered no significant relationship between beta and returns while others discovered other explanatory factors that could explain returns and measure risk other than beta alone. Banz(1981) and Keim(1983) discovered the firm “size effect” while Fama and French(1992) discovered “book-to-market equity”. These factors amongst others were found to significantly explain cross-sectional variations of average returns contrary to CAPM’s preposition. Due its importance in the finance world, series of empirical studies have been carried out to test these explanatory factors (beta, size and book-to-market equity) on average stock return using different markets’ data. Such studies were primarily focused on US, but over the years the focus has shifted to other developed nations like UK as well as emerging markets. Corhay et al(1987), Levis and Liodakis(2001) and Fletcher (2002) studied the UK market; Hawawini et al(1983) studied French data and also studied Belgium data(Hawawini, Michel and Corhay 1985); Elsas et al (2003) studied Germany data; Hong Kong and Singapore(Yue-Cheong 1997), Korea and Taiwan(Yan-Leung, Kie-Ann and Yan- Ki 1993) and Istanbul(Akdeniz, Altay-Salih and Aydogan 2000) market data have also being tested for the effects of beta, size and book-to-market equity effects on average returns. More recent studies on beta, size and book-to-equity and returns were carried out by Ho et al(2006) on Hong Kong data; Morelli (2007) on UK data; and Wang and DI Lorio(2007) on Chinese data. The findings of these studies are varied; some found a significant relation of these variables with stock returns while others did not. From the above analysis, it is evident that such an analysis is lacking using data from an African market. I feel further studies need to be carried out also using Africa data in order to have a holistic view to aid in accepting/rejecting these factors as explanatory factors of stock returns. Research has proven that stock market returns in emerging markets has been characterised by high risk as well as high returns (Akdeniz, Altay-Salih and Aydogan 2000). These high returns have attracted a lot of capital inflow into such markets creating the need to study the nature of stock returns in such markets more apparent. According to Organisation for Economic Co-operation and Development (2008), a common concern for investors about African countries is “lack of consistent, reliable and timely information…” which this study will try to addredd by making information in respect to stock returns and risks available. Johannesburg Stock Exchange (JSE) of South Africa could be seen as an example of an emerging market in terms of market capitalization, trade volume and listed companies. A study done on JSE revealed it as a semi-efficient market (Okeahalam and Jefferis 1999) which is one of the forms of efficient market hypothesis. Fioramonti and Poletti (2008) described South Africa as not only an emerging market but also one of the “leading nations of the global south”. This “stock market started operations in 1887(Okeahalam and Jefferis, 1999) A detailed analysis of its stock market will no doubt illuminate on other African emerging markets and also give an idea about JSE market microstructure in comparison to other international markets.
The objective of this research is to…
The research plan proposed is based on the choice of topic which centres on testing a popular asset pricing model in an African emerging market. Since this research aims to test Fama and French model (1992, 1995) on the explanatory factors that effect average stock returns using another scenario (South Africa data), it lies more towards theory testing and thus, a deductive study (Bryman and Bell c2007) would be more suitable. The research philosophy to be adopted for this research will embrace both the principles of positivist and interpretivist approach. These approaches are widely related to social sciences, business and management research. A positivist approach observes social reality in order to make an inference or generalisation based on findings to establish or explain underlying relationships between variables. In such an approach the researcher can do little to alter the substance of data collected and so the process of data collection is external (Saunders, Lewis and Thornhill c2007: 103). According to Quinton and Smallbone (2006: 18), the positivist approach seeks to provide an explanation for an event in an organisation while the interpretivist approach tries to understand the event. The interpretivist approach involves interpreting the social world in which we live in to order and make adjustments where necessary (Bryman and Bell c2007: 107). This research will be associated mainly with the positivist approach since the variables under consideration such as stock returns, book value and market value which can be observed and quantified. Moreover, such an approach not only allows for the establishment of relationship between the variables based on the findings from their analysis, but is also objective. On the other hand, since these variables are economic factors which are influenced by man to an extent, it is also associated with the interpretivist approach. Moreover, the research aims to investigate the behaviour of stock return as to understand its relation with the other variables under study. There are a number of research designs or strategies that could be used for a research e.g. experimental design, cross-sectional design, longitudinal design, case study design and comparative design(Bryman and Bell c2007: 44). For this study, two research designs will be employed; longitudinal and case study designs. A longitudinal research design involves the study of a particular phenomenon over an extended period of time (Saunders, Lewis and Thornhill c2007). This research design describe this research which will involve a sample of period of 20 years ( from June 1987 to July 2007) that will be divided into sub-periods, in order to discover patterns that will aid in valid judgements. Usually, such a design has more external validity than experimental design since it relies on empirical data. But care has to be taken to avoid errors in the selection process of the companies listed on JSE whose stock return will be analysed, so that the sample chosen is a clear representation of the entire population (Smith 2003). A case study design on the other hand, is a research strategy that “entails the detailed and intensive analysis of a single case” where emphasises is upon the examination of a setting (Bryman and Bell c2007: 62). In such an instance, the researcher usually seeks to illuminate the unique future of a case (Jewell 2008: 74). In the case of this research, the analysis is on Johannesburg Stock Market in South Africa. According to Yin (1994: 38), such a research could be described as a single-case study which is appropriate for theory testing, a unique and revelatory case. One of the limitations of case study lies in proving that findings can be generalised to a wider universe and are not only unique to the case under study. As a result of this, Yin argues that a multi-case study is more preferable. Both longitudinal and case study design will be relevant for this research which could be considered a case study research with a longitudinal element of analysing archival or historical stock and company information to make inference. Major limitations of such a research are associated with the data collected for studies. Historical data of stock returns in some databases, which this research will rely on for data collection, may exclude some current companies, nonsurviving companies (merged, acquired or bankrupt), data may not be up-to-date and complete (Smith 2003). Thus, care has to be taken in order to capture reliable and comprehensive sources of data. The data collection method to be employed in this research will be mainly from secondary data sources. The advantage of using this type of data is that it is easier to analyse; less time consuming; and more economical and cost-efficient; high quality data; and provides an opportunity for longitudinal study; amongst others than using primary data (Bryman and Bell c2007: 331-334). Saunders et al (c2007:248-262) has classified the types of secondary data and their uses in research into “three categories; Documentary, Survey-based and Multi-source secondary data. My secondary data collection will be based on these sources. Documentary sources are divided into written and un-written materials. Written materials will be gotten from investment and stock market textbooks, academic journals in found electronic database consisting mainly of Business Source Premier and ScienceDirect (assess through Coventry University Library) to build more on the theoretical framework and investigate other previous studies for the critical review of literature. Newspapers such as Reuters, Bloomberg and Financial Times and their web-sites will also be a source for the written material so that recent statistical information can be captured as well. These sources, especially the journals, are peer-reviewed for quality and suitability and hence reliable. Survey-based data sources to be employed would be African Economic Research Corporation; Organisation for economic co-operation and development; international financial corporation and world federation of exchanges websites which are renowned. Multiple sources include times-series and area-based sources. Times-series source as well as area-based source for this research to obtain historical share information on all JSE shares will be collected from ShareNet Limited (The Sekunjalo Investment Group South Africa), I-Net Bridge (a stock broking firm) and Johannesburg Stock Exchange (JSE). ISIN data as well as annual company reports will also be obtained from the databases managed by them of which I have gained access. These three sources will allow for comparison of data so important companies are not omitted and data errors are minimised. Despite the advantages of using secondary data collection methods, there are also inherent disadvantages as outlined by Bryman and Bell (c2007: 334-335). These data sources where not built with this type of research in mind, so I might not be total familiar with structure and contours of the data which may also contain some complexities. The quality of data from some of the sources also cannot be totally guaranteed. The sampling plan to be use will be non-probability sampling. This type of sampling may not be used to make statistical inference about the population but will make it possible to draw generalisations (Jewell 2008). It will also allow me to choose companies that will fit the norm of the research. The non-probability technique to be employed will be a purposive sampling since financial companies according to Fama and French (1992) have to be excluded since their book-to – market equity ratios are interpreted differently from non-financial companies. A sample of 80 companies will be randomly selected from all the securities listed in JSE with available data covering the period under study. The companies will be selected from different industry groups so that it can be a fair representation of the entire population and not industry specific. This sample is restricted to companies on both i-net bridge and sharenet databases. The data collected will be mainly quantitative and will involve both bivariate and multivariate analysis (Jewell 2008: 89). A measure of central tendency, the median will be adopted to allocate the company stocks into big and small groups for the size effect. Pettengill’s (2002) cross-sectional regression analysis model of will be used to test the effects of beta, size and boo-to-market equity on average returns. Results will be represented mainly in tables and interpreted to make generalisations and conclusion. The research will be carried out in such a way as to have relevance and originality as to meet research criteria of validity, reliability, generalisability and transparency(Jewell 2008: 68-69). It will be valid as the research plan discussed so far has been carefully chosen as that the research questions are being addressed. Since this research is a test of model, the same approach and measurements embarked by Fama and French will be replicated to avoid errors. It will also be conducted in such a way that other researcher can replicate not only the study but also the results. But it should also be pointed out that since this research involves real time data and organisational structures that a prone to change, getting the exactly similar results may not be likely. But every step of the research will be properly documented so that the research is as transparent as possible. I hope the findings of this research can be applied to other emerging African countries. But it should be noted that since I can’t control the variables investigated, it might be difficult to generalise in the end, unless these markets have the same microstructure. The limitations of this research as mentioned earlier will be centred mostly on data collection errors, and the results of the study may apply only to the period and market under study, making it difficult to generalise.
The research will abide to regulations stated in the Faculty of Business, Environment and Society (BES) ethics handbook 2006-2007 of Coventry University which identifies its ethics procedure policies. I will complete the ethics checklist and compliance form at the end of this research which will be duly signed by my supervisor and I. Since my research involves mainly secondary data, it is exempt from any ethical issues inherent in primary data collection. But I will abide to the regulations of data protection act that may be applicable to some companies’ information that will be part of the sample collected for the study.
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