A Neural Networks Adoption Framework for Predicting Stock Market Trends: Case of the Zimbabwe Stock Exchange

The Zimbabwe Stock Exchange (ZSE) is a key institution in the country which falls under the Ministry of Finance.This institution feeds important information into the National Budgets for forecasting and planning. The degree of bias or uncertainty in the information provided will, in turn, distort the planning of critical department or even other arms of the Government. This dissertation seeks to rationalise and advocate for the use of Artificial Neural Networks (ANN) by this key department in predicting, yearly turnovers, as well as daily stock market, counters price's by the stock brokers. The Zimbabwe Stock Exchange currently uses trend analysis based on historical data to compute their predictions. The absence of machine learning in the prediction in the current method being used by the exchange creates a gap and increases the level of bias. The Stock Exchange is mandated by the Government to provide markets and economic forecasts yearly.The forecasted data includes Foreign Direct Investments (FDI), Annual Turnovers and the market Outlook in terms of Listed Counters.The figures provided by the exchange have to be factual and accurate. The research included the set objectives that are detailed in the first chapter. The second chapter is made up of the literature review of Stock Exchanges that has walked the path before and the computational methods using ANN that are the ZSE can consider, benefits and demerits of each type of neural network model are assessed. The third chapter analysed the methodology used in data collection and how the information was gathered. A detailed framework is developed in the fourth chapter and the summary and recommendations are in the last chapter.