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

Document Type : Research Paper

Authors

1 Tehran University

2 Tehran university

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

References

-      سینایی حسنعلی، مرتضوی سعید الله، تیموری اصل یاسر."پیش­بینی شاخصبورس اوراق بهادار تهران با استفاده از شبکه­های عصبی"، ­بررسی­های حسابداری و حسابرسی، شماره 41، دوره 12، صص. 59 - 83.
-      عبادی،ا."پیش­بینی قیمت شاخص کل سهام در بازار بورس تهران با استفاده از شبکه­های عصبی مصنوعی". پایان­نامه کارشناسی ارشد، دانشکده اقتصاد و علوم اجتماعی دانشگاه بوعلی سینا، همدان.
-      فلاحپور، سعید، گل ارضی، حسین، فتوره چیان، ناصر."پیش­بینی روند حرکتی قیمت سهام با استفاده از ماشین بردار پشتیبان بر پایه ژنتیک در بورس اوراق بهادار تهران"، تحقیقات مالی، شماره 2، دوره 15، صص. 269-288.
-      فلاحپور، سعید، طبسی، ملیحه، "برآورد ارزش در معرض ریسک با استفاده از مدل ترکیبی ماشین بردار پشتیبان و گارچ"، راهبرد مدیریت مالی الزهرا، شماره 1، دوره 1، صص 177-195.
-      منجمی، سید امیر حسین، ابزاری، مهدی، رعیتی شوازی، علیرضا."پیش­بینی قیمت سهام در بازار بورس اوراق بهادار با استفاده از شبکه عصبی مصنوعی". فصلنامه اقتصاد مالی، شماره 3، دوره 6، صص.1-26.
-      هاشمی احمد. "تاثیر فاکتورهای رفتاری بر پیش­بینی قیمت سهام با استفاده از مدل شبکه­های عصبی رگرسیونی جلوسو"، پایان نامه کارشناسی ارشد. دانشکده صنایع دانشگاه علم و فرهنگ، تهران.
-      Abraham, A., Nath, B., & Mahanti, P. K. (2001) "Hybrid intelligent systems for stock market analysis." In International Conference on Computational Science. Springer Berlin Heidelberg, pp.337-345.
-      Bao, D. Yang, Z. (2008). Intelligent stock trading system by turning point confirming and probabilistic reasoning, Expert Systems with Applications, 34(1), pp. 620–627.
-      Blum AL, Langley P. (1997). Selection of relevant features and examples in machine learning. Artif Intell, 97(1), pp.245–70.
-      Botes, E., & Siepman, D. (2010). The Vortex Indicator. Technical Analysis of Stocks & Commodities, 28(1), p.21.
-      Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering ,40(1), pp. 16-28.
-      Chavarnakul, T., & Enke, D. (2008) Intelligent technical analysis based equivolume charting for stock trading using neural networks. Expert Systems with Applications 34(2), pp.1004-1017.
-            Chen, Y.-W., & Lin, C.-J. (2006). Combining SVMs with various feature selection strategies. In Feature extraction , Springer Berlin Heidelberg, pp. 315-324.
-      Chong, E. K., & Zak, S. H. (2013). An Introduction to Optimization. Vol.76. John Wiley & Sons.
-      Choudry, R. & Grag, K. (2008). A Hybrid Machine Learning System for Stock Market Forecasting. World Academy of Science, Engineering and Technology, 39(3), pp.315-318.
-      Fodor, I. K. (2002). A survey of dimension reduction techniques.
-      Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine learning; 46(1-3), pp.389–422.
-      Granville, J. E. (1960). A strategy of daily stock market timing for maximum profit". Prentice-Hall.
-      Gustafson, G. (2001). Which Volatility Measure? Is average true range, an approximation, superior to standard deviation, the most beloved of quants, as a measure of volatility?. TECHNICAL ANALYSIS OF STOCKS AND COMMODITIES-MAGAZINE EDITION-, 19(6), pp. 46-50.
-      Huang, S. C., & Wu, T. K. (2008). Integrating GA-based time-scale feature extractions with SVMs for stock index forecasting. Expert Systems with Applications, 35(4), pp. 2080-2088.
-      Kohavi, R., & John, G. H. (1997). Wrappers for feature subset selection. Artificial intelligence, 97(1), pp.273-324.
-       Langley P. (1994, November). Selection of relevant features in machine learning. In Proceedings of the AAAI Fall symposium on relevance, Vol. 184, pp. 245-271
-      Lee, M. C. (2009). Using support vector machine with a hybrid feature selection method to the stock trend prediction. Expert Systems with Applications, 36(8), pp. 10896-10904.
-      Leung, M. T., Chen, A. S., & Daouk, H. (2000). Forecasting exchange rates using general regression neural networks. Computers & Operations Research, 27(11), pp.1093-1110
-      Liu, H., & Setiono, R. (1996, July). A probabilistic approach to feature selection-a filter solution. In ICML , Vol. 96, pp. 319-327.
-      Liu, Y., & Zheng, Y. F. (2006). (2006). FS_SFS: A novel feature selection method for support vector machines. Pattern recognition, 39(7), pp.1333-1345.
-      Malkiel, B. G., & Fama, E. F. (1970), Efficient capital markets: A review of theory and empirical work, The  Journal of Finance, 25(2), pp.383–417.
-      Min, J. H., & Lee, Y. C. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters, Expert Systems with Applications, 28(4), pp. 603-614.
-      Nair, B.B., Mohandas, V.P. & Sakthivel, N.R. (2010). A Genetic Algorithm Optimized Decision Tree-SVM based Stock Market Trend Prediction System. International Journal on Computer Science and Engineering. 2 (9), pp. 2981-2988.
-      Narendra P, Fukunaga K. (1977). A branch and bound algorithm for feature subset selection. IEEE Transactions on Computers, 100(9), pp.917–22.
-      Pudil, P., Novovičová, J., & Kittler, J. (1994), Floating search methods in feature selection. Pattern Recog Letters; 15(11), pp.1119–25.
-      Saeys, Y., Inza, I., & Larrañaga, P. (2007). A review of feature selection techniques in bioinformatics. bioinformatics, 23(19), pp.2507-2517
-      Teixeira, L. A., & De Oliveira, A. L. I. (2010). A method for automatic stock trading combining technical analysis and nearest neighbor classification. Expert Systems with Applications, 37(10), pp.6885–6890.
-      Theodoridis, S., & Koutroumbas, K. (1999). Pattern Recognition. Academic Press. New York.
-      Wang, L. (Ed.). (2005). Support vector machines: theory and applications. Springer Science & Business Media,Vol. 177.
-      Żbikowski, K. (2015). Using volume weighted support vector machines with walk forward testing and feature selection for the purpose of creating stock trading strategy. Expert Systems with Applications. 42(4), pp.1797-1805
-      Zhang, X., Hu, Y., Xie, K., Wang, S., Ngai, E. W. T., & Liu, M. (2014). A causal feature selection algorithm for stock prediction modeling. Neurocomputing, 142, pp. 48-59.
Financial Management Strategy
Volume 4, Issue 3 - Serial Number 14
December 2016
Pages 121-148

Files

History

  • Receive Date: 08 February 2016
  • Revise Date: 08 December 2016
  • Accept Date: 20 December 2016

Share

How to cite

Statistics