Electronic Theses and Dissertations (PhDs)

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Browsing Electronic Theses and Dissertations (PhDs) by Keyword "African stock markets"

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    Stock Price Prediction in Sub-Saharan Africa
    (University of the Witwatersrand, Johannesburg, 2020) Murekachiro, Dennis; Mokoaleli, Thabang
    Investors, researchers and practitioners are continuously exploring various ways to understanding stock market price movements and the development of techniques that can assist them in accurately predicting the stock markets and improve on in- vestment decision making and policy making. This study sought out to develop a prediction model for stock markets, determine which factors move stock prices and investigate the inefficiency of 11 selected stock markets. In order to predict the stock markets, this study made use of deep learning prediction models (LSTM, RNN, GRU, BLSTM, BRNN, BGRU) and statistical GAM in ten sub-Saharan African coun- tries (Botswana, Egypt, Kenya, Mauritius, Morocco, Nigeria, South Africa, Tunisia, Zambia, Zimbabwe) and the S&P500 (USA). Stock markets are predictable with inef- ficiencies found for the African stock markets as evidenced through calendar anoma-lies and high prediction accuracies whilst the lower prediction results for the S&P500 indicate market efficiency. The prediction model greatly improved prediction accuracy. However, there is no remarkable difference between unidirectional and bidirectional prediction models accuracy results for the eleven countries concerned. GAM statistical approach outperformed compared to all deep neural networks architectures in this study. The varying results for each country point to the uniqueness of each market confirming the varying market ecologies. In addition, this study also investigated the effect of macroeconomic variables (inflation, money supply, interest rates, exchange rates) on stock prices. Time series analyses were implemented through Johansen cointegration and Granger causality tests for short and long run relationships between macroeconomic variables and each stock market. Overall, empirical results for the African stock markets reveal a negative association between closing price and exchange rates, a positive relationship between money supply and closing stock prices for all countries. Mixed results for the other variables for each country attest to the fact that stock markets are unique and are influenced differently by these macroeconomic variables. Notably, African stock markets relate differently to macroeconomic variables as compared to developed stock markets. Stock market predictions were run on a python 3.5 environment using deep learning libraries Theano, Tensorflow, and Keras and Scikit learn and the time series analysis was analyzed using stata13 and R 3.6 software