Murekachiro, Dennis2024-10-172024-10-172020Murekachiro, Dennis. (2020). Stock Price Prediction in Sub-Saharan Africa [PhD thesis, University of the Witwatersrand, Johannesburg]. WireDSpace.https://hdl.handle.net/10539/41669A research report submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Finance Wits Business School to the Faculty of Commerce, Law and Management, School of Accountancy, University of the Witwatersrand, Johannesburg, 2020Investors, 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 softwareen© 2020 University of the Witwatersrand, Johannesburg. All rights reserved. The copyright in this work vests in the University of the Witwatersrand, Johannesburg. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of University of the Witwatersrand, Johannesburg.Stock pricesAfrican stock marketsPredictionDeep neural networksMacro-economic variablesDecision makingGAMUCTDSDG-8: Decent work and economic growthThesisUniversity of the Witwatersrand, Johannesburg