Forecasting movement of the Nigerian Stock Exchange All Share index using artificial neural and Bayesian networks

This paper presents a study of Artificial Neural Network (ANN) and Bayesian Network (BN) for use in stock index prediction. The data from Nigerian Stock Exchange (NSE) market are applied as a case study. Based on the rescaled range analysis, the neural network was used to capture the relationship in terms of weights between the technical indicators derived from the NSE data and levels of the index. The BayesNet Classifier was based on discretizing the numeric attributes into distinct ranges from where the conditional probability was calculated, stored in the Conditional Probability Table (CPT) and the new instance were classified. The performance evaluation carried out showed results of 59.38% for ANN and 78.13% for BN in terms of predictive power of the networks. The result also showed that Bayesian Network has better performance than ANN when it comes to predicting short period of time; and that useful prediction can be made for All Share index of NSE stock market without the use of extensive market data.