The evolution and dynamics of stocks on the Johannesburg Securities Exchange and their implications for equity investment management

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dc.contributor.advisorMlambo, Chipoen_ZA
dc.contributor.authorChimanga, Artwell Shingiraien_ZA
dc.date.accessioned2015-12-10T09:31:51Z
dc.date.available2015-12-10T09:31:51Z
dc.date.issued2015en_ZA
dc.description.abstract[No subject] This thesis explores the dynamics of the Johannesburg Stock Exchange returns to understand how they impact stock prices. The introductory chapter renders a brief overview of financial markets in general and the Johannesburg Securities Exchange (JSE) in particular. The second chapter employs the fractal analysis technique, a method for estimating the Hurst exponent, to examine the JSE indices. The results suggest that the JSE is fractal in nature, implying a long-term predictability property. The results also indicate a logical system of variation of the Hurst exponent by firm size, market characteristics and sector grouping. The third chapter investigates the economic and political events that affect different market sectors and how they are implicated in the structural dynamics of the JSE. It provides some insights into the degree of sensitivity of different market sectors to positive and negative news. The findings demonstrate transient episodes of nonlinearity that can be attributed to economic events and the state of the market. Chapter 4 looks at the evolution of risk measurement and the distribution of returns on the JSE. There is evidence of fat tails and that the Student t-distribution is a better fit for the JSE returns than the Normal distribution. The Gaussian based Value-at-Risk model also proved to be an ineffective risk measurement tool under high market volatility. In Chapter 5 simulations are used to investigate how different agent interactions affect market dynamics. The results show that it is possible for traders to switch between trading strategies and this evolutionary switching of strategies is dependent on the state of the market. Chapter 6 shows the extent to which endogeneity affects price formation. To explore this relationship, the Poisson Hawkes model, which combines exogenous influences with self-excited dynamics, is employed. Evidence suggests that the level of endogeneity has been increasing rapidly over the past decade. This implies that there is an increasing influence of internal dynamics on price formation. The findings also demonstrate that market crashes are caused by endogenous dynamics and exogenous shocks merely act as catalysts. Chapter 7 presents the hybrid adaptive intelligent model for financial time series prediction. Given evidence of non-linearity, heterogeneous agents and the fractal nature of the JSE market, neural networks, fuzzy logic and fractal theory are combined, to obtain a hybrid adaptive intelligent model. The proposed system outperformed traditional models.en_ZA
dc.identifier.apacitationChimanga, A. S. (2015). <i>The evolution and dynamics of stocks on the Johannesburg Securities Exchange and their implications for equity investment management</i>. (Thesis). University of Cape Town ,Unknown ,GSB: Faculty. Retrieved from http://hdl.handle.net/11427/15758en_ZA
dc.identifier.chicagocitationChimanga, Artwell Shingirai. <i>"The evolution and dynamics of stocks on the Johannesburg Securities Exchange and their implications for equity investment management."</i> Thesis., University of Cape Town ,Unknown ,GSB: Faculty, 2015. http://hdl.handle.net/11427/15758en_ZA
dc.identifier.citationChimanga, A. 2015. The evolution and dynamics of stocks on the Johannesburg Securities Exchange and their implications for equity investment management. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Chimanga, Artwell Shingirai AB - [No subject] This thesis explores the dynamics of the Johannesburg Stock Exchange returns to understand how they impact stock prices. The introductory chapter renders a brief overview of financial markets in general and the Johannesburg Securities Exchange (JSE) in particular. The second chapter employs the fractal analysis technique, a method for estimating the Hurst exponent, to examine the JSE indices. The results suggest that the JSE is fractal in nature, implying a long-term predictability property. The results also indicate a logical system of variation of the Hurst exponent by firm size, market characteristics and sector grouping. The third chapter investigates the economic and political events that affect different market sectors and how they are implicated in the structural dynamics of the JSE. It provides some insights into the degree of sensitivity of different market sectors to positive and negative news. The findings demonstrate transient episodes of nonlinearity that can be attributed to economic events and the state of the market. Chapter 4 looks at the evolution of risk measurement and the distribution of returns on the JSE. There is evidence of fat tails and that the Student t-distribution is a better fit for the JSE returns than the Normal distribution. The Gaussian based Value-at-Risk model also proved to be an ineffective risk measurement tool under high market volatility. In Chapter 5 simulations are used to investigate how different agent interactions affect market dynamics. The results show that it is possible for traders to switch between trading strategies and this evolutionary switching of strategies is dependent on the state of the market. Chapter 6 shows the extent to which endogeneity affects price formation. To explore this relationship, the Poisson Hawkes model, which combines exogenous influences with self-excited dynamics, is employed. Evidence suggests that the level of endogeneity has been increasing rapidly over the past decade. This implies that there is an increasing influence of internal dynamics on price formation. The findings also demonstrate that market crashes are caused by endogenous dynamics and exogenous shocks merely act as catalysts. Chapter 7 presents the hybrid adaptive intelligent model for financial time series prediction. Given evidence of non-linearity, heterogeneous agents and the fractal nature of the JSE market, neural networks, fuzzy logic and fractal theory are combined, to obtain a hybrid adaptive intelligent model. The proposed system outperformed traditional models. DA - 2015 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2015 T1 - The evolution and dynamics of stocks on the Johannesburg Securities Exchange and their implications for equity investment management TI - The evolution and dynamics of stocks on the Johannesburg Securities Exchange and their implications for equity investment management UR - http://hdl.handle.net/11427/15758 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/15758
dc.identifier.vancouvercitationChimanga AS. The evolution and dynamics of stocks on the Johannesburg Securities Exchange and their implications for equity investment management. [Thesis]. University of Cape Town ,Unknown ,GSB: Faculty, 2015 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/15758en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentGSB: Facultyen_ZA
dc.publisher.facultyUnknownen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.titleThe evolution and dynamics of stocks on the Johannesburg Securities Exchange and their implications for equity investment managementen_ZA
dc.typeDoctoral Thesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnamePhDen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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