A New Computational Method of Input Selection for Stock Market Forecasting with Neural Networks
We propose a new computational method of input selection for stock market forecasting with neural networks. The method results from synthetically considering the special feature of input variables of neural networks and the special feature of stock market time series. We conduct the experiments to compare the prediction performance of the neural networks based on the different input variables by using the different input selection methods for forecasting S&P 500 and NIKKEI 225. The experiment results show that our method performs best in selecting the appropriate input variables of neural networks.
KeywordsNeural Network Bayesian Information Criterion Prediction Performance Radial Basis Function Network Stock Index
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