Multifactor Asset Pricing Models in the South African Stock Market

Lenia Mukoyi^*
*Correspondence: Lenia Mukoyi, Department of Microbiology, University of Mpumalanga, Nelspruit 1200, Mpumalanga, South Africa, Email:

Introduction

According to the Efficient Market Hypothesis all facts are accessible in the market; thus, current news is quickly assimilated into the price of securities, which renders attempts to pick individual stocks and outperform the market useless against the backdrop of EMH, investors manage to use the performance of economic sectors to determine where to place assets and diversify their portfolios. Based on and French’s real work, it appears that investors would be better served by employing other factor models until this technique is verified by empirical data. A considerable increase in profitability paired with investment drove the and French proposed a new five-factor model based on their previous three but with significantly different definitions of investment and profitability. The has yet to be shown to outperform previous variants. The classic Fame and French three-factor model is still used by most investors. Nonetheless, in South Africa, there is no consensus on the causes of market irregularities or their impact on expected returns. Identified value; Jegadeesh and Titman identified momentum; and Fame and French identified profitability and investment various empirical studies have also shown crosssectional diversity in average security returns. An assessment of sectors and the relationship between style anomalies and security performance over time in the South African stock market is critical because of limited studies on the subject. Furthermore, the has evolved significantly over the last, with several changes in composition, expansion, improvements and reclassification; as a result, this study focuses on three primary sectors that have been stable throughout. By comparing the five-factor model to other models in explaining the cross-section of asset returns in the market, this research contributes to the growing literature on stock return predictability in the African market. In recent years, one of the most important and remarkable topics for academics, practitioners and analysts in the financial industry has been the factors impacting stock returns and the determination of the explanatory capacity of asset pricing models.

Description

In order to evaluate the connection between investment style risks and industry sectors, this study compares multi-asset pricing models with the French three-factor model, the Car hart four-factor model and the French fivefactor model. The study included both the years before and after the financial crisis of. Two important questions are addressed in this article: Which asset pricing model better captures the expected returns for stocks? How much do investment-style risks affect the choice of assets and sector performance. There is currently no evidence in South Africa that a new paradigm will be accepted by stakeholders; this process takes years. Model parameters are important in asset pricing models and choosing the ones that are suitable for the South African market is crucial. There is still little research that has concentrated on looking at all five factors market risk, value effect, size effect, momentum effect, profitability effect and investment effecting the South African stock market. The research methods and objectives varied across all investment style risks. Furthermore, there is disagreement over the results presented due to the quality of the different research data samples; some have found evidence, while others have not. Graham and Uliana found proof of the "value effect”. Muller and Ward and La Grange and Krieg uncovered evidence of the momentum effect. Van Rensburg and Robertson found evidence of the size and value impact. Most of these writers employed basic ratios, the French and Fame models and cross-section regressions. Bolton and Von Boetticher did not find the momentum impact, while Aurea and Cline could not find any proof of a size or value influence on the JSE. Thus, Ali and Ülkü came to the conclusion that it is crucial to frequently review asset pricing models and discriminate between essential and redundant components in order to maintain models sparse in a world with an increasing number of factors [1,2].

During the 2008 financial crisis, they begin with a difference and make a significant difference. It is essential to determine if there are longterm correlations between the explanatory and dependent variables after demonstrating that all the variables are integrating in the same order at the initial differencing. The log-return series seem to share comparable shocks or disturbances, but they are not identical. Counteraction was used to establish this process. The presence of a long-term link between variables is demonstrated through counteraction. It indicates that while they are integrating in the same order, they are integrating in order when there is a linear combination of at least one or more variables specifies the ideal amount of lag to be used. The serial correlation on the residuals is eliminated by the Johansen test, which identifies the deterministic trend assumption in the model. As a result, the information criteria approach yields the best lag in terms of how many lags to include in the model. The nave assumption that there are no integrating vectors. Although there has been and is being significant research on this topic, there does not seem to be agreement on how market anomalies affect expected returns. Fame and French’s three-factor model is presented along with the market anomalies identified in each of the three sectors. The industrial and financial sectors exhibit the small-cap bias in full force. The financial industry is the only one where value bias exists. Although they were not statistically significant, the modest growth bias and the mild small-cap bias imply some existence. In their investigation of the industrial sector in, Graham and Uliana discovered the presence of the value growth effect in the South African market. In our analysis, the study's industrial sector the industrial sector over the course of the study does not show the same outcome. This could be because the research was conducted during a different time period; Graham and Uliana's study spanned the years and a different technique was used. Value portfolios consistently outperformed growth portfolios from January, according to a study by Bhana Value anomaly, or a positive correlation between security returns and the ratio of accounting-based value estimations to the security's market price, has an impact on the financial industries [3,4].

The three-factor and French model the model and the and French model using the FINI Top 15 index and RESI Top 10 index, respectively, have the highest R-squared values. More so than in the previous era, both and regressions from to seem to reflect the sector return. The risk component supported statistically high probability values for each sector index when the was studied separately. As more components are added, the significant levels are reduced. For multifactor models, it turns into a negative significant factor in the financial industry. The results show that the regression coefficients for most of the style risk factors are statistically negligible when the excess returns of resource, industrial and financial indices are regressed against the risk factors. The coefficients are statistically insignificant the statistical insignificance of the coefficients, some information may be gleaned from the size of the probability value findings. The findings imply that the sector performance has a weak operational profitability bias to some extent, despite the fact that the regression results for the three-sector indexes have negative coefficients and poor sensitivity to the RMW risk component. The same thing of aggressive investing bias in both to be implied by the risk factor on the and the Top . The financial sector exhibits high sensitivity to the risk factor. The WML risk factor generally provides modest sensitivity to each of the sectors. The bulk of coefficients show that for all three sectors and time periods [5].

Conclusion

The other two asset pricing models in this study are outperformed by the when it comes to capturing the effects of market anomalies. Furthermore, when variables are included in the model, the value anomaly loses its predictive power in all industries except the banking sector. In conclusion, the statistics show that there is not yet agreement on the appropriate asset pricing model for valuing assets and assessing risk and returns on the. According to the study, market anomalies differ throughout industries and historical eras as well as with modifications to asset pricing models and their details. The forecasting capacity of the value anomaly may be weakened when profitability and investment are taken into account in the model. The most significant finding in terms of statistics, education and application appears to be that risk factors associated with investing styles are the best predictors of the relationship between risk and return in the South African market. The looks to be out of date as the South African market expands and becomes more globally connected. In addition, because different research data samples were used, there isn't agreement on the results put some have found evidence, while others haven't. There isn't a clear, thorough, or accurate model or understanding of the stock market. As a result, this study advances our knowledge of the connections between stock return and risk.

Acknowledgement

None.

Conflict of Interest

None.

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