Forecasting stock/futures prices by using neural networks with feature selection

Stock/futures price forecasting is an important financial topic for individual investors, stock fund managers and financial analysts, and is currently receiving considerable attention from both researchers and practitioners. However, the inherent characteristics of stock/futures prices, namely, high volatility, complexity, and turbulence, make forecasting a challenging endeavor. In the past, various approaches have been proposed to deal with the problems of stock/futures price forecasting, that are difficult to resolve by using only a single soft computing technique. In this study, a systematic procedure based on a backpropagation (BP) neural network and a feature selection technique is proposed to tackle stock/futures price forecasting problems with the use of technical indicators. The feasibility and effectiveness of this procedure are evaluated through a case study on forecasting the closing prices of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) futures of the spot month. Experimental results show that the proposed forecasting procedure is a feasible and effective tool for forecasting stock/futures prices. Furthermore, the statistical hypothesis testing indicates that the forecasting performance of a BP model with feature selection is better than that obtained through a simple BP model.