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The study examines the effects of internal factors, made up of firm specific variables, and the external factors, consisting of industry and macroeconomic variables, on the financial performance of insurance companies in Mauritius. In particular, the study investigates the impact of firm size, leverage, gross written premiums, reinsurance, combined ratio, market concentration, foreign exchange, inflation and GDP growth on the profitability of insurers in both the life and the nonlife markets. Profitability was proxied by return on assets (ROA) and the underwriting profit ratio (UWPR). The study employs an unbalanced panel data sample of twenty non-life and seventeen life insurers from 2008 to 2016, with one hundred and twenty-two (122) non-life and ninety-eight (98) life firm-year panel observations obtained from the Financial Services Commission (FSC) of Mauritius. The models were estimated using the sandwich estimator by White, (1980) and Eicker, (1963) within pooled OLS, fixed and random effects panel estimation techniques. The findings show that, a unit increase in the combined ratio and leverage of life insurers impact negatively on the return on assets (ROA), while an increase in reinsurance dependence and firm size impact underwriting profit ratio positively. In the non-life sector, the findings show that insurance companies' profitability is positively impacted by increases in the combined ratio and gross written premium, while market concentration and foreign exchange negatively impacted non-life insurers' profitability. The findings of the study help us to understand firm pricing behaviour within the insurance industry and help to protect consumer interests in the pricing of policies. The findings also have implications on the growth strategies and risk management activities of insurance companies in Mauritius.

The relationship between unexpected earnings and security returns subsequent to earnings announcements is widely documented in international studies (e.g., Ball and Brown, 1968; Beaver, 1968; Beaver, 1974; Foster, Olsen and Shevlin, 1984). However, much of this research has been conducted in developed stock markets, with only a handful of studies focused on the JSE (e.g., Knight, 1983; Kornik, 2005; Murie, 2014). By drawing lessons from prior international and local evidence, and for the first time on the JSE, an investigation is conducted focusing on the entire price formation process from trading statements releases to the announcements of actual earnings. Adopting the returns based unexpected earnings measures of Foster, Olsen and Shevlin (1984) and van Rensburg's (2002) two factor APT specification to account for systemic risk, this study finds trading statements to contain new and significant information as evidenced by the presence of significant abnormal returns on their publication date. In addition, and consistent with semi-strong form market efficiency, no relationship is found between the sign and magnitude of unexpected earnings and the cumulative abnormal returns in the period subsequent to trading statement releases and preceding earnings announcement. Examining returns in the post-trading statement release period, the study found no evidence of statistically significant abnormal returns drift for good and bad news portfolios classified according to the (-1, 0), (-1, 1) and (0, 1) unexpected earnings models and that classified according to the trading statement sign. Consistent with prior South African studies, the publication of earnings is found to be a noteworthy market event to which investors react. In addition, the sign and magnitude of the initial response to unexpected earnings was found to exhibit a significantly positive relationship with cumulative abnormal returns over the (2, 60) day period subsequent to earnings announcements, representing a stark violation of semi-strong form market efficiency. Furthermore, the negative relationship between CARs in the (-1, 1) day period surrounding earnings and the post-trading statement drift postulated by Das, Kim and Patro (2007) does not appear to apply on the JSE. Examining returns in the (2, 60) day post earnings announcement period, the study found evidence of predictable returns drift but that the magnitudes of the CARs were not statistically significant over this period.

This research investigates whether artificial neural networks which make use of firm specific fundamental and technical factors can accurately predict the returns of a sample of several large-cap stocks from various markets across the globe. This study also explores which hidden layer configuration leads to the best network predictive performance. Furthermore, this research identifies which firm-specific factors predominantly influence the predictions made by the artificial neural networks. Five artificial neural networks are designed, trained and tested on a sample of 161 stocks from the Russell 1000 and the S&P International 700 stock indices. The investigation period extends over a 166-month period from January 2001 to October 2014 with a 70:30 split for training and testing subsamples respectively. Eighteen firm-specific factors, based on prior research about the presence of style effects or anomalies on the cross-section of global equity returns, are used as the input variables of the artificial neural networks to forecast one-month forward returns of all the stocks in the sample. The five artificial neural networks investigated in this research differed in hidden layer size. Specifically, the number of hidden neurons examined were three, nine, 13, 18 and 30. All five networks train significantly well, with each network's training error indicating a good model fit. Each network also achieves the desirable information coefficient of 0.1 between its predicted returns and the actual returns in the training sample. It is interestingly discovered that network performance generally improves as the number of hidden neurons in the hidden layer increases until a specific point, after which network performance weakens. In the context of avoiding overfitting, the best-trained network in this research is that with 13 neurons in its hidden layer. This is the primary network used for the out-of sample testing analysis. This network achieves an average prediction error magnitude of approximately 7% and an information coefficient of 0.05 during out-of-sample testing. These results underperform their respective benchmarks moderately. However, further analyses of the network's performance suggest an overall poor out-of-sample predictive ability. This is illustrated by a significant bias and a considerably weak relationship between the network's predicted returns and the actual returns in the testing sample. Global sensitivity analysis reveals that growth style effects, particularly, the capital expenditure ratio, return on equity, sales growth, 12-month percentage change in non-current assets and six-month percentage change in asset turnover were the most persistent factors across all the ANN models. Other significant factors include the 12-month percentage change in monthly volume traded, three-month cumulative prior return and one-month prior return. An unconventional result of this analysis is the relative insignificance of the size and value style effects.

This study examines how mutual fund size affects performance. Academic literature on this topic is extensive but has yielded conflicting results. Some studies find a distinct relationship between fund size and risk-adjusted returns while others do not; some studies also posit that an optimal fund size exists where risk-adjusted returns are maximised. The size of equity mutual funds in South Africa and the market dynamics of the Johannesburg Stock Exchange provide an interesting context within which to analyse the relationship between size and performance. In this study, hypothetical portfolios are created, and an allocation procedure is used to distribute capital to these hypothetical portfolios. The allocation procedure distributes capital to the portfolio stocks by controlling for each stock's yearly volume traded. This method works to distribute capital up until a certain fund size; beyond that size, the hypothetical portfolio might no longer be fully invested in the random portfolio. To control for this, the simulation model engages in a routine to discard the stock with the lowest volume-traded level from the portfolio and reselect another stock from the investable universe with a higher volume-traded level. This process is repeated until the portfolio is fully invested. Stock selection and investment dates are randomised and variance reduction techniques are used to improve the efficiency of the simulation, and 10 000 simulation runs are performed. The results of the simulation found a non-monotonic relationship between mutual fund size and performance over a one-year holding period, consistent with some research internationally and in South Africa. Over a two- and three-year holding period, mutual fund size and returns, however, seem to be negatively correlated. Over the three holding periods, the study suggests that the optimal equity mutual fund size in South Africa is approximately ZAR 2bn. Portfolios with assets under management greater than ZAR 2bn see their returns decrease noticeably as fund size continues to increase. These findings are supported by comparing simulated returns to actual benchmark returns over the same random periods. The results of this study suggest that mutual funds should be aware that consistent increases in assets under management could negatively affect performance and that all funds should ensure that total assets under management do not exceed ZAR 2bn.

Active vs passive management is a central debate within asset management, with active managers promising superior market beating performance after fees through their superior knowledge and stock selection. This study investigates the performance of 34 South African property unit trusts over multiple periods between 2005 and 2018. Fund performance was evaluated using three risk-adjusted measures, namely the Sharpe ratio, information ratio and Jensen's alpha, in order to determine whether there is significant outperformance amongst the funds. The benchmark used to compare performance was the South African Listed Property index (SAPY), which is the most common and well established proxy for the South African property market. The sample was divided into three periods, long term 2005-2018, medium term 2008-2018 and short term 2015-2018. In all periods, outperformance of active funds were shown to be inconclusive, with only a small number of funds showing significant positive alphas and significantly high Sharpe and information ratios. A small number of funds achieved outperformance across multiple periods. On average significant outperformance was uncommon and inconsistent. Furthermore, a number of funds achieved significant underperformance over multiple periods, with inferior risk-adjusted returns and alphas compared to the benchmark. However, the volatility of fund returns were shown to be less than the benchmark on average in all periods, indicating that active managers were able to reduce volatility compared to the benchmark. In the more recent short term period, performance of the active funds were especially low with many negative alphas' present. The best performing fund across multiple periods was shown to be a risk parity portfolio of property stocks, which achieved significantly higher returns whilst having lower volatility than the benchmark and other funds. Ultimately the results suggest that active managers in the sector do not provide sufficient evidence for outperformance. Hence investors are better of making use of passive indices or a risk parity portfolio if they are looking for exposure to South African listed property. This is in line with other international studies which have also found that active management in the property industry does not provide significant and consistent outperformance. These results provide useful insight to property investors in South Africa and contribute to the debate between active vs passive management within the financial literature.

The risk management functions of most financial institutions occupy themselves with the estimation of the value at risk (VaR) of their portfolios as a measure of market risk. Various methods are available to calculate the VaR measure, and this can be done at various degrees of confidence. This study evaluates and analyses the performance of five popular VaR forecasting methods in the South African context, using the closing values of three of the major indices available on the Johannesburg Stock Exchange (JSE), namely the All Share Index (ALSI), the Financials-Industrials Index (FINDI), and the Resources Index (RESI). These three indices are considered based on the findings of prior studies that indicate that not only does decomposing the ALSI into its constituent (the FINDI and the RESI) indices provide a better measurement of market risk on the JSE, but these sub-indices also have different systematic risk exposures which may necessitate different treatments in measuring their risks appropriately. The periods examined surrounded the 2008 global financial crisis in order to allow an evaluation of the impact of varying levels of volatility on the analysis. Overall, the study concludes that the performance of the VaR models examined is similar when assessing the risk of the ALSI and the RESI returns, while they are very different for the FINDI. This conclusion provides crucial insight into the risk management and investment decisions concerning portfolios which are more heavily invested in the FINDI as opposed to the other two, as this study suggests that a blanket treatment to the South African market is incorrect.

This dissertation highlights the importance of considering higher moments and partial moments of the distribution when conducting portfolio optimisation and selection. This is due partly to the weaknesses of mean-variance optimisation, as discussed throughout the dissertation, and the appropriateness of considering higher moments to better meet the investors utility functions. This dissertation investigates the usage of two bi-objective optimisation frameworks, a Skewness/Semivariance framework previously suggested by Brito et al (2016), and a proposed upside and downside semivariance framework (referred to as Semivariance/Semivariance), developed from Cumova and Nawrocki’s (2014) general upper partial and lower partial moment framework. It solves the endogeneity issue present in the co-semivariance matrices, through the usage of a direct multi-search algorithm. The two frameworks were tested across multiple datasets, including one of pure stocks and one of asset classes, to test the ability to both allocate assets and select stocks. The performance was measured through nominal returns, statistical tests, Sharpe ratios, Sortino ratios, and Skewness/Semivariance ratios. The results reveal the Semivariance/Semivariance optimisation process to outperform the Skewness/Semivariance optimisation in the majority of the cases investigated. This suggests it may be a superior selection optimisation process. Furthermore, the Semivariance/Semivariance portfolios remain competitive with the benchmark portfolios selected in this dissertation, often outperforming them on an absolute return and ratio basis; however, this outperformance has not consistently proven to be statistically significant.

This study investigates the relationship between the environmental, social and governance (ESG) performance of South African firms and their corresponding financial performance over the period 2012 to 2019. Operating with an ESG-based mindset has become of increasing importance for firms over the past two decades, as a result of increasing regulation as well heightened public scrutiny and pressure. There exists evidence in support of the theory that ESG-conscious firms that practice sustainably tend to financially outperform their peers. This study employs a quantitative research methodology, using variations of panel regression models to investigate the ESG-corporate financial performance (CFP) relationship. Privately held proprietary ESG scores are used as a proxy for ESG performance and financial data is obtained from Bloomberg in order to assess financial performance. The study does not find statistically significant evidence of a relationship between firm ESG performance and financial performance. Contrasting results emerge from the study, with positive relationships and correlation coefficients found between both the ESG performance of firms and their annual stock return, as well as the ESG performance and return on assets (ROA) ratio. A negative relationship and correlation were found to exist between firm ESG performance and their price earnings ratio. These contradicting results lead to a conclusion that no relationship exists between ESG performance and CFP. Significant evidence was however found regarding certain firm characteristics leading to firms having higher ESG performance. Results show that the larger firms with greater financial leverage are higher ESG performers relative to their peers. This may imply that in order for ESG practices to be effective, firms themselves should be of a sufficient size and have access to a large amount of debt to fund relevant activities. It is recommended that further research be performed on the driving forces behind a firm's ESG performance, and the various factors that contribute most towards it, including varying levels of access to debt.

Includes bibliographical references. / Style anomalies comprise patterns and relationships found in the cross-section of stock returns data, which contradict the existing asset-pricing models. They have proven to be reasonably effective at explaining the return-genera ting process of ordinary shares, and have bro ad uses within modern finance. Empirically, style anomalies are found to have statistically significant rewards in individual markets and s mall market groupings, and are found to be significant at a sector level on a global scale, but have not been tested at a firm level on a global scale. The aim of this study is to explain the cross-section of returns of the 1468 largest global firms by market capitalisation. The worldwide study considers stocks from 53 different countries and 112 industries, and investigates the end of month return forecasting power of 44 different firm-specific attributes over the period August 2003 to August 2013. A univariate analysis is performed through a cross-sectional regression of the forward stock re turns on the firm-specific attributes in a similar method to Fama and MacBeth (1973). A ‘Full Data’ regression is also conducted, and results are presented both before and after a beta-adjustment for market risk. Following this, a multivariate analysis is conducted and a forward stepwise procedure is used to construct a multi-factor model. According to the results of this study, style anomalies exist and have a statistically significant reward at a firm level on a global scale. In a univariate setting there are 25 firm-specific style factors that have a significant return payoff at a 5% level of significance. The specific style groups containing significant firm-specific attributes are the Value, Growth, Momentum, Size and Liquidity, Leverage, and Emerging Market groupings. Ten attributes within these style groupings are found to be robust as they are highly significant both before and after beta-adjustment, and within both a univariate and multivariate setting, namely: EBITDA to Share Price (EBP), Emerging Market (EM), CAPEX to Sales (CXS), Sales to Total Assets (STA), Payout Ratio (PR), 24-month growth in Turnover by Volume (TVO24), Sales to Share Price (SP), 6-month growth in Earnings (E6), 1-month prior return (MOM1), and 3-month prior return (MOM3). This confirms that style effects exist both independently, in a univariate setting, and in a multi-factor model. The results of this study show that the Value and Emerging Market styles have the highest cumulative payoffs over the 10-year period, and the evidence of strong correlation between attributes within specific styles gives further validation to the traditional style groupings. The behaviour of, and relationships between the firm-specific style factors give great insight into the payoffs to investing in different style factors over time, and are key to the construction of a multi-factor model. The fifteen firm-specific style factors that are significant in a multivariate setting form the core of a multi-factor style model, which can potentially be used to explain a degree of unexplained returns, predict returns, give insight into global market behaviour, and price global assets for use within a global portfolio. These firm-specific attributes include: EBITDA to Share Price (EBP), Emerging Market (EM), CAPEX to Sales (CXS), Sales to Total Assets (STA), Payout Ratio (PR), 24-month growth in Turnover by Volume (TVO24), Sales to Share Price (SP), 6-month growth in Earnings (E6), 1-month prior return (MOM1), 3-month prior return (MOM3), the natural log of Enterprise Value (LNEV), Interest Cover before Tax (ITBT), 6-month prior return (MOM6), Price-to-Book value (PTB), and Cash Flow-to-Price (CFP).