Faculty of Commerce, Law and Management (ETDs)

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    Reconsidering the debate relating to the proposals for the regulation of cryptocurrencies in South Africa
    (University of the Witwatersrand, Johannesburg, 2023) De Sousa, Michaella Alexandrine; Kawadza, Hebert
    Words cannot express my gratitude to my supervisor, Professor Herbert Kawadza for his vital patience and feedback. His guidance and vast knowledge in commercial and banking law was the main reason I hoped to have him as my supervisor for the completion of my LLM Research Report, without him I would still be lost in my drafting. I am extremely grateful for the guidance and encouragement of my two very close colleagues and mentors, Daven Dass and Alicia Raymond, their encouragement and conversation pushed me through writer’s block and self-doubt. I certainly could not have undertaken this journey without my strong support system and the most important people in my life, my father, Rui de Sousa; my sister, Claudia de Sousa; and partner, Nicholas Elliott. Their belief in me and ongoing patience in reading and rereading every iteration of this LLM Research Report will always be appreciated. This degree would never have been completed without them. As the adage goes, I am because they are, and I will always be indebted to them. Lastly, this LLM degree is for my late mother, Carla de Sousa, without her guidance and belief I would not be where I am now, and I will always be grateful to her
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    Predicting Systematic Risk Using Artificial Neural Networks
    (University of the Witswatersrand, Johannesburg, 2023) Setloboko, Thabiso; Alovokpinhou, Sedjro Aaron
    Financial institutions and investors are always investigating mathematical models that can enable them to make accurate predictions of time varying variables. For the longest time, statistical and autoregressive models have been at the forefront of forecasting. However, these are only accurate in short horizons; that is, these models are more accurate in daily, weekly, and monthly forecasts. This paper seeks to investigate long-horizon (yearly) forecasts using machine learning models called Artificial Neural Networks. The network uses neurons similar to biological neurons in living things, allowing them to study complex data patterns and retain pattern behaviors that allow them to make accurate predictions. The paper is based on the novel discovery that in forecasting long-horizon time series data, neural networks outperform statistical models significantly. The paper uses market data from the Johannesburg Stock Exchange and the New York Stock Exchange to represent the emerging and advanced markets, respectively. The forecasted data involves pre and post COVID-19. The shock introduced by the coronavirus is investigated to check if the forecasting ability of the model is affected. The empirical results demonstrate that the models accurately forecast systematic risk in the South African market more than in the American market. The accuracy of the model is measured by using root mean square error and mean absolute error. The model produced low error values for both markets, indicating their effectiveness in forecasting. It was expected that the error measures would consistently get lower as the time horizon increased; however, there were inconsistencies. For a portfolio manager, the results obtained in this research are interesting because the model handles large quantities of data and forecasts long-horizon systematic risk with little error. However, further investigation on this model still needs to be done
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    Impact of exchange controls on South African public equity investment performance
    (2021) Mathews, Marc John
    The study investigates the impact of changes in regulation which allowed for South African investors to diversify equity portfolios internationally. Since 1995 exchange control regulations have been relaxed from completely prohibiting international equity investment to allowing up to 40% exposure for institutional investors. Since 2010, in effect no limits are seton the ability of an individual to diversify internationally. No previous study has considered the impact that structural barriers such as exchange control have had on the ability of South African investors to diversify internationally. The mean-variance spanning test was utilised to determine the scope for diversification benefits each time regulations changed and the magnitude of the benefit was measured as a change in the Sharpe ratio. Overall, the study found that the relaxation of regulations would have allowed for improved investment performance. However, regulatory restrictions limited some of the benefit for institutional investors while individual investors could have been able to benefit slightly more
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    Does Sub-Saharan Africa require a new private equity model: a case study of South Africa and Nigeria
    (2021) Moloto, Thabo
    Private Equity in Sub-Saharan Africa has grown significantly over the last decade. This growth was supported by an improved middle-class, macroeconomic factors, infrastructure and foreign direct investments. The case study provides an overview of Private Equity in Sub-Saharan Africa by focusing on South Africa and Nigeria. Where lessons from developed nations underpin the opportunity for Sub-Saharan Africa government policy makers to provide progress in the depth of capital markets, regulatory and governance of firms, investor protection and access to public information. Though my study is an empirical paper, the need for further research is required for the Private Equity market in Sub-Saharan Africa to be come robust, enable massive inflow of capital and create an optimistic business environment for institutional investors
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    Effects of the COVID-19 pandemic on the financial markets: a comparative analysis
    (2021) Kapalu, Njamba
    This paper investigates the effects of the COVID-19 pandemic on bond yields and stock returns. The paper examines how the coronavirus outbreak affected the performance of capital assets as well as what role financial contagion played in the evolution of asset prices over time. This paper employs an event study, a regression analysis, using both Ordinary Least Squares (OLS) and Generalised Method of Moments (GMM) estimations as well as a BEKK GARCH model to test for contagion. The research found that the flight-to-safety phenomenon was more prominent in emerging markets, whereas, in developed markets, bonds were not seen as the safe-haven assets and investors opted to invest in assets such as gold. The second event study showed that investors began reacting in anticipation of the of the Fed announcement in March to slash interest rates, showing herding behaviour rather that market efficiency was driving market behaviour during the pandemic. With regards to the effect of financial contagion being exhibited during the COVID-19 pandemic, the research had different findings for stock returns and bond yields. Using an MVGARCH BEKK model for the estimations, the research found that cross-market effects in the stock returns showed that the USA exhibited high unidirectional linkages with the other markets, thereby confirming significant effect of financial contagion in stock returns during the pandemic. With bond yields, however, no single country was found to be the source of the volatility