Predicting Stock Price trend, using Volume Weighted Support Vector Machine and Hybrid Feature Selection Method, in Tehran Stock Exchange

In this study we focus on developing a stock trend prediction model based on a modified version of support vector machine, named volume weighted support vector machine, along with a hybrid feature selection method named FSSFS method. In order to evaluate the prediction accuracy of this model we compare the VW-SVM classifier with plain support vector machine along with three commonly used feature selection methods including Information gain, Symmetrical uncertainty and correlation-based feature selection, via paired t-test. As the model input, we use several technical indicators and statistical measures, calculated for 10 stocks. The results show that the VW-SVM, combined with the hybrid feature selection method, significantly outperforms plain SVM model to the problem of stock trend prediction. In addition our experimental result show that VW-SVM combined with F-SSFS has the highest level of accuracies and generalization performance in comparison with the other three feature selection methods. With these results, we claim that VW-SVM combined with F-SSFS can serve as a promising addition to the existing stock trend prediction.