Application of Wavelet Neuro-Fuzzy System (WNFS) method for stock forecasting

Abstract

Forecasting is a very important element in decision making, because the effectiveness of a decision, generally depends on several factors that we cannot see when the decision was taken. In this study, the Wavelet Neuro-Fuzzy System (WNFS) model that combines wavelet transformation and neuro-fuzzy techniques is applied to forecast daily closing stock price data of BMRI.JK. The observed daily stock price data are decomposed into some sub-series components by maximal overlap discrete wavelet transform (MODWT), then the appropriate sub-series that have higher correlation to the real data are used as inputs of the neuro-fuzzy model for daily forecasting stock price for three days in advance. The neuro-fuzzy model is begun with determining the membership value of each data using Fuzzy C-Means, followed by fuzzy inference procedure of the Sugeno model. The result shows that the presence of wavelet input in Neuro-Fuzzy System, can provide optimal prediction in daily stock price data, with small error value of predicted result. This would be helped investors or economists to produce meaningful information in either buy or sell a stock.