The evolution and dynamics of stocks on the Johannesburg Securities Exchange and their implications for equity investment management - Chimanga, Artwell Shingirai (GSB: Faculty, 2015)
[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.