Stock market trend prediction using ARIMA-based neural networks
Jung-Hua Wang, Jia-Yann Leu · 2002
We develop a prediction system useful in forecasting mid-term price trend in Taiwan stock market (Taiwan stock exchange weighted stock index, abbreviated as TSEWSI). The system is based on a recurrent neural network trained by using features extracted from ARIMA analyses. By differencing the raw data of the TSEWSI series and then examining the autocorrelation and partial autocorrelation function plots, the series can be identified as a nonlinear version of ARIMA(1,2,1). Neural networks trained by using second difference data are shown to give better predictions than otherwise trained by using raw data. During backpropagation training, in addition to the traditional error modification term, we also feedback the difference of two successive predictions in order to adjust the connection weights. Empirical results shows that the networks trained using 4-year weekly data is capable of predicting up to 6 weeks market trend with acceptable accuracy.