Wits Business School (ETDs)

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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
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    Exploring the mindsets and behaviours necessary for cultivating data-driven decision making within an organisation
    (University of the Witwatersrand, Johannesburg, 2021) Jacobs, Jef Andreas; Ngubane, Samukelo; Wotela, Kambidima
    The advancement of data storage and processing technologies and the exponential growth in data generated by online activity and smart devices has stimulated a desire by organisations to be more data-driven in their decision making. Adopting a data-driven approach to decision making is associated with improved organisation performance and innovation. However, most organisations are struggling to realise these benefits because crafting clear data use strategies and cultivating a culture of data-driven decision making appears to be more challenging than investing in relevant technologies or implementing organisational processes. Given this situation, the purpose of this study is to investigate the mindsets and associated behaviours of leaders and their teams who are successfully leveraging data to improve market competitiveness or impact. Using a qualitative research strategy and semi-structured interview processes with six experienced professionals, this research paper identifies six mindsets and associated behaviours that senior decision makers should adopt to help overcome the common people related challenges that hinder effective data-driven decision making in organisations. Prime examples include senior leaders as data advocates who communicate and reflect of data-driven decisions and leaders who encourage quick experimentation with an openness to failure. Based on these findings the study recommends that senior decision makers, working in organisations that have invested in data related technologies and skills, acknowledge that their attitudes and behaviours have a direct impact on how successful any data strategy and investment will be. These influential leaders or managers need to understand and believe in the data- driven decision making process and they need to ensure the implementation of key activities that ensure informed actions are eventually taken on the back of data collected. Research in this field mostly predominantly discusses issues related to numerical techniques, technological innovations and studies around impact. This study contributesto the current body of knowledge by investigating leadership and managerial aspects of data use or Big Data in organisational decision making