Variable Selection for Artificial Neural Networks with Applications for Stock Price Prediction
Gang-Hoo Kim, Sung‐Ho Kim · Applied Artificial Intelligence · 2018
We propose a new Artificial neural network (ANN) method where we select a set of variables as input variables to the ANN. The selection is made so that the input variables may be informative for a target variable as much as possible. The proposed method compared favorably with the existing ANN methods when their performances were evaluated based on 488 stocks in S&P500 in terms of prediction accuracy.