AN INVESTIGATION OF THE HYBRID FORECASTING MODELS FOR STOCK PRICE VARIATION IN TAIWAN

Abstract

ABSTRACT The investigation to the stock market index in the literature could be divided into two aspects. The first one is the technical analysis by using the historical trend of the stock market index to predict the future fluctuation. The other one is the basic analysis; it analyzes the factors that affect the macroeconomics to forecast the stock market index. In this research, the technical analysis and the basic analysis were applied to investigate the trend of the weighted stock market index in Taiwan. Stepwise regression analysis was first used to identify the key variables that affect the trend of the stock market index significantly. According to the identified variables, three models, i.e. a multiple regression analysis model, a backpropagation neural network, and an autoregressive integrated moving average model were built. A hybrid model that integrates the technical and basic analyses was developed and expected to forecast the stock market index more accurately. The result has showed that the average error of the hybrid model is 0.493%. The best performance of the other three models is 0.703%. The experimental results shows that the improvement in the hybrid model attains 29.875%. The result indicates that the hybrid model is the best one in forecasting the future fluctuation of the stock market index in this research.