The Stock Trend Prediction Using Volume Weighted Support Vector Machine with a Hybrid Feature Selection Method to Predict the Stock Price Trend in Tehran Stock Exchange Saeed Bajalan 1 Saeed Falahpour 2 Nahid Dana 3 Abstract In this study, a prediction model based on support vector machines (SVM) improved by introducing a volume weighted penalty function to the model was introduced to increase the accuracy of forecasting short term trends on the stock market to develop the optimal trading strategy. Along with VW-SVM classifier, a hybrid feature selection method was used that consisted of F-score as the filter part and supported Sequential forward selection as the wrapper part, to select the optimal feature subset. In order to verify the capability of the proposed model in successfully predicting short term trends, a trading strategy was developed. The model input included several technical indicators and statistical measures that were calculated for chosen 10 stocks from Tehran Stock Exchange. The results show that the VW-SVM, combined with the hybrid feature selection method, significantly increases the profitability of the proposed strategy compared to rival strategies, in terms of both overall rate of return and the maximum draw down during trading period. Keywords: Support vector machines, feature selection, trend forecasting, trading strategy. JEL: C38 ،C53،G11 ،G12 ،G17 1. Assistant professor, Faculty of Management, Tehran University, Email: saeedbajalan@ut.ac.ir 2. Assistant professor, Faculty of Management, Tehran University, Email: Falahpor@ut.ac.ir 3. M.A. Student in Financial Engineering, Tehran University, Corresponding Author, Email: nahiddana@gmail.com Received: 2016/07/05 Accepted: 2016/08/27 Journal of Financial Management Strategy Vol. 4, No. 14 Autumn 2016 Alzahra University- Faculty of Social Sciences and Economics http://jfm.alzahra.ac.ir/