The article, ‘Australian Stock Market’ is an experiment that observes the function of size, book-to-market and momentum factors. The objective of the study is to explain stock proceeds in the Australian stock market. As a result of this, the Capital Asset Pricing Model (CAPM) of Sharpe and Lintner act like a linear relationship between an asset’s excess returns and the excess return of the market portfolio (Kassimatis, 2008, p. 146). This study indicates that CAPM is not effective in clarifying the realized returns. Previously, experimental data confirmed the reliability of the CAPM although this changed when the model started performing poorly. As a result of this, another model known as Fama and French factor model was introduced to replace the CAPM. Experiments were carried out and it was realized that Fama and French factor models were more efficient than the earlier pricing model. Primarily, the Fama and French model is an inclusion of factors like size and book-to market (BM).
Following this new study, other studies were also carried out to examine the role of these factors. Some of the studies conducted include the one by Halliwell, Heaney and Sawicki whose results were neutral (Kassimatis, 2008, p.151). In other words, the three authors were only able to find verification of a size effect with some unstable evidence of a BM effect. Another study conducted by Gaunt showed that there was a strong premium and a weak BM premium though the size and BM factors fail to account for realized returns. Studies do not stop with Gaunt since there were many other people like Anderson, Lynch and Marsh who found similar evidence about the factors (Kassimatis, 2008, p.150).
The new findings therefore replaced earlier experimental evidence which supported the model. Basically, the objective of this new study is to address the gap in the literature by combining two empirical findings. The findings include time variations in Australia market risk and the essence of the Fama and French factors.
Data and Methodology
In his methodology, Kassimatis has used the available data from Data stream obtained within the period July 1992 to June 2005. Kassimatis has also applied quantitative method of research whereby total returns were obtained by distributing the annual dividends evenly throughout the year. Thus the process eases monthly returns although it does not affect the means of returns. Normally in related studies, from the financial sector as well as preferred shares and warrants are exempted from the database. Kassimatis has used end of month prices, dividends, market capitalization and price to book ratios. According to the new study, the highest returns are those for the small stock quintile which reflect on Haliwell, Heaney and Sawicki‘s findings (Kassimatis, 2008, p.157).This means that the portfolio are comparable to those in other Australian studies. Furthermore, any stocks that are not included in the database should not result in negative effects in the evaluation of the size, BM and momentum portfolio returns.
In order to construct the factor returns, the new study used a procedure conducted by Liew and Vassalou (Kassimatis, 2008, p.157). Through this procedure, factor portfolios are created by sorting the sample by BM and creating tertile portfolios. The portfolios are then sorted one after the other in terms of size hence tertile portfolios are created from each. The last step involves sorting every portfolio by momentum returns and creating tertile portfolios from each. After constructing the stock portfolios and the factor returns, the next procedure is to compare the CAPM to the four factor model. The database includes both currently traded and invalid securities. As a result of this, there are some portfolios based on size and BM. As a way of creating the portfolios, all stocks are sorted by BM into quintile portfolios. The portfolios are the crossroads of each BM quintile portfolio with each of the size quintile portfolio. Averagely, the number of stock per quintile is 102 which are enough to construct well diversified portfolios.
The research findings suggest that the CAPM is not effective as it does not function properly in explaining realized returns. Contrary to this finding, the Fama and French factors have proved reliable and have attracted the attention of many researchers who do not believe in CAPM (Kassimatis, 2008, p.148). This confirms the earlier findings of the first study. Indeed, CAPM could not be relied on because of its inefficiency. Clearly, the French and factor model proves more dependable than the CAPM pricing model (Kassimatis, 2008, p.155). Even though the factor models are not reliable, results on the Australian market show that the returns of these portfolios can explain realized stock returns which are not satisfactorily explained by the CAPM. This new study observes whether the CAMP is rejected in favor of a four factor model. The factor model consists of excess return of the market portfolio, the SMB, HML and WML factor portfolio returns. Because of space consideration, deterioration of the CAPM is not reported. Meanwhile the stagnant CAPM is rejected for Australian stock market since the intercept for most regressions is relevant (Kassimatis, 2008, p.154).
Research conducted yearly on the factors of returns indicates that they produced positive statistical returns for every year. Apart from this, the findings show that the returns of SMB, HTML and WML arbitrage portfolios are relevant in explaining realized returns. Observations shows that time variation in factor loadings is an important consideration when testing asset pricing models. More results suggest that the Fama and French factors may account for time variation in market risk which is related to the business. These results have serious implications for asset pricing in two ways (Kassimatis, 2008, p.151). Foremost, the significant progression in explanatory power when moving from the statistic CAPM to the time-varying CAPM suggests that studies on the Australian market should focus on models of time-varying returns. The second implication of the results is that the Fama- French factors as well as the momentum factor do not seem to work for the Australian market. The findings compared with the results of other researchers suggest that the Australian market is an exception to this rule. Thus, if the Fama-French model indeed works for the US market, then the return-generating process may differ from country to country (Kassimatis, 2008, p.148). Through the results of the new study, researchers are able to compare their findings and rectify any shortcoming. Fundamentally, most results proved that the Fama and French factors were more reliable than the CAPM pricing model.
Kassimatis, K. 2008. Size and book to market and Momentum Effects in the Australian Stock market. Australian Journal of Management, Vol. 33, No. 1, pp, 145-168.