Improving the Performance of Stock Trend Prediction by Applying GA to Feature Selection

Abstract

Predicting stock trend by using machining learning is a hot research issue today. However, due to the non linearity and instability of the stock data, it is still very difficult to predict the stock trend with high accuracy. In order to improve the accuracy, most researchers focus on the models selection and features construction. A variety of feature construction methods have been proposed. However, not all features constructed in those paper are equally useful. Further more, many features of significant importance may not be selected in prediction. In order to improve the accuracy of stock trend prediction, this paper will focus on the features selection problem. Most feature selection methods employed in the stock trend prediction are based on filtration methods. Wrapper methods are rarely used. Compared with filtration methods, wrapper methods have better stability and accuracy. In this paper, we propose a feature selection algorithm by extending genetic algorithm (GA). Experiments are conducted on real-world stock price data set. The experiment results show that our GA-based feature selection algorithm is better in both stability and performance.