A Fuzzy-Neural Intelligent Trading Model for Stock Price Prediction

In this paper, Fuzzy logic and Neural Network approaches for predicting financial stock price are investigated. A study of a knowledge based system for stock price prediction is carried out. We explore Trapezoidal membership function method and Sugeno-type fuzzy inference engine to optimize the estimated result. Our model utilizes the performance of artificial neural networks trained using back propagation and supervised learning methods respectively. The system is developed based on the selection of stock data history obtained from Nigerian Stock Exchange in Nigeria, which are studied and used in training the system. A computer simulation is designed to assist the experimental decision for the best control action. The system is developed using MySQL, NetBeans, Java, and MatLab. The experimental result shows that the model has such properties as fast convergence, high precision and strong function approximation ability. It has shown to perform well in the context of various trading strategies involving stocks.