KOSPI Time Series Analysis Using Neural Network with Weighted Fuzzy Membership Functions

Abstract

Fuzzy neural networks have been successfully applied to generate predictive rules for stock forecasting. This paper presents a methodology to forecast the daily Korea composite stock price index (KOSPI) by extracting fuzzy rules based on the neural network with weighted fuzzy membership functions (NEWFM) and the minimized number of input features using the distributed non-overlap area measurement method. NEWFM supports the KOSPI time series analysis based on the defuzzyfication of weighted average method which is the fuzzy model suggested by Takagi and Sugeno. NEWFM classifies upward and downward cases of next day's KOSPI using the recent 32 days of CPPn,m (Current Price Position of day n : a percentage of the difference between the price of day n and the moving average of the past m days from day n-1) of KOSPI. In this paper, the Haar wavelet function is used as a mother wavelet. The most important five input features among CPPn,m and 38 numbers of wavelet transformed coefficients produced by the recent 32 days of CPPn,m are selected by the non-overlap area distribution measurement method. For the data sets, from 1991 to 1998, the proposed method shows that the average of accuracy rate is 67.62%.