Stock market prediction using artificial neural networks with optimal feature transformation

This paper compares a feature transformation method using a genetic algorithm (GA) with two conventional methods for artificial neural networks (ANNs). In this study, the GA is incorporated to improve the learning and generalizability of ANNs for stock market prediction. Daily predictions are conducted and prediction accuracy is measured. In this study, three feature transformation methods for ANNs are compared. Comparison of the results achieved by a feature transformation method using the GA to the other two feature transformation methods shows that the performance of the proposed model is better. Experimental results show that the proposed approach reduces the dimensionality of the feature space and decreases irrelevant factors for stock market prediction.