Differential Evaluation Learning of Fuzzy Wavelet Neural Networks for Stock Price Prediction

Abstract

Prediction of a stock price movement becomes very difficult problem in finance because of the presence of financial instability and crisis. The time series describing the movement of stock price are complex and non stationary. This paper presents the development of fuzzy wavelet neural networks that combines the advantages of fuzzy systems and wavelet neural networks for prediction of stock prices. The structure of Fuzzy Wavelet Neural Networks (FWNN) is proposed and its learning algorithm is derived. The proposed network is constructed on the base of a set of TSK fuzzy rules that includes a wavelet function in the consequent part of each rules. The proposed FWNN structure is trained with differential evaluation (DE) algorithm. The use of DE allows quickly train the FWNN system than traditional genetic algorithm (GA). FWNN is used for modelling and prediction of stock prices. Stock prices are changed every day and have high-order nonlinearity. The statistical data for the last three years are used for the development of FWNN prediction model. Effectiveness of the proposed system is evaluated with the results obtained from the simulation of FWNN based systems and with the comparative simulation results of other related models.