Forecasting KOSPI using a neural network with weighted fuzzy membership functions and technical indicators

Abstract

This paper presents a methodology to forecast the direction of change in the daily Korea composite stock price index (KOSPI) by extracting fuzzy rules based on the neural network with weighted fuzzy membership functions (NEWFM) and thirteen numbers of input features that are derived by overbought conditions and oversold conditions of three numbers of technical indicators. This paper consists of three steps for forecasting the direction of change in the daily KOSPI. In the first step, three numbers of technical indicators are selected to preprocess the daily KOSPI. In the second step, thirteen numbers of input features are derived by overbought conditions and oversold conditions of three numbers of technical indicators. In the final step, NEWFM classifies the next day's direction of change in the daily KOSPI using thirteen numbers of input features that are produced in the second step. The total number of samples is 2928 trading days, from January 1989 to December 1998. About 80% of the whole trading days is used for training and 20% for testing. The performance result of NEWFM for the direction of change in the daily KOSPI is 58.86%. © 2009 Springer-Verlag Berlin Heidelberg.