Forecasting stock market performance using hybrid intelligent system

Abstract

Predicting the future has always been one of mankind’s desires. In recent years, artificial intelligent techniques such as Neural Networks, Fuzzy Logic, and Genetic Algorithms have gained popularity for this kind of applications. Much research effort has been made to improve the prediction accuracy and computational efficiency. In this paper, a hybridized neural networks and fuzzy logic system, namely the FeedForward NeuroFuzzy (FFNF) model, is proposed to tackle a financial forecasting problem. It is found that, by breaking down a large problem into manageable "chunks", the proposed FFNF model yields better performance in terms of computational efficiency, prediction accuracy and generalization ability. It also overcomes the black art approach in conventional NNs by incorporating "transparency" into the system.