Genetic-Based Algorithms Towards Effective Stock Price Prediction: Case Study of Dar es Salaam Stock Exchange

Predicting the movement of stock prices in financial markets has become a challenging task. This is due to the fact that stock prices are dynamic and volatile, exhibiting a stochastic behaviour. Hence, it requires a well-constructed predictive model capable of determining the probable cause of the price of a stock in the future. In an attempt to address the challenge, this study has explored prediction parameters and proposed a Genetic Algorithm (GA) approach. The proposed predictive model is capable of determining future stock price trends. The proposed approach involves identifying patterns using statistical concepts and predicting stock prices. The approach has been deployed and evaluated using the dataset (2014–2018) collected from the Dar es Salaam Stock Exchange (DSE). The evaluation results proved that the proposed Genetic Algorithm (GA) approach achieved a prediction accuracy of 94.86% when used as a natural selection algorithm to find the optimal stock price. Hence, the effectiveness of the proposed algorithm has been confirmed, and this study is a milestone towards the improvement of stock price prediction in the Stock Exchange financial market