Using Volume Weighted Support Vector Machines with walk forward testing and feature selection for the purpose of creating stock trading strategy

Forecasting short-term stocks trends using Volume Weighted SVM.Robust techniques of feature selection used to enhance classifier accuracy.Experiments show that presented approach performs better than basic classifier.Significant improvement of the rate of return and maximum drawdown achieved.We designed and built system for walk-forward testing of proposed strategy. This study aims to verify whether modified Support Vector Machine classifier can be successfully applied for the purpose of forecasting short-term trends on the stock market. As the input, several technical indicators and statistical measures are selected. In order to conduct appropriate verification dedicated system with the ability to proceed walk-forward testing was designed and developed. In conjunction with modified SVM classifier, we use Fishers method for feature selection. The outcome shows that using the example weighting combined with feature selection significantly improves sample trading strategy results in terms of the overall rate of return, as well as maximum drawdown during a trading period.

[1]  Chien-Feng Huang,et al.  A hybrid stock selection model using genetic algorithms and support vector regression , 2012, Appl. Soft Comput..

[2]  Francis Eng Hock Tay,et al.  Modified support vector machines in financial time series forecasting , 2002, Neurocomputing.

[3]  F. S. Wong,et al.  Time series forecasting using backpropagation neural networks , 1991, Neurocomputing.

[4]  Patrick P. K. Chan,et al.  LG-Trader: Stock trading decision support based on feature selection by weighted localized generalization error model , 2014, Neurocomputing.

[5]  Sergios Theodoridis,et al.  Pattern Recognition, Fourth Edition , 2008 .

[6]  David de la Fuente,et al.  The effectiveness of the combined use of VIX and Support Vector Machines on the prediction of S&P 500 , 2013, Neural Computing and Applications.

[7]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[8]  Ömer Kaan Baykan,et al.  Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange , 2011, Expert Syst. Appl..

[9]  Chih-Jen Lin,et al.  Combining SVMs with Various Feature Selection Strategies , 2006, Feature Extraction.

[10]  David Enke,et al.  Intelligent technical analysis based equivolume charting for stock trading using neural networks , 2008, Expert Syst. Appl..

[11]  Amir F. Atiya,et al.  The maximum drawdown of the Brownian motion , 2003, 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings..

[12]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[13]  Qinghua Wen,et al.  Automatic stock decision support system based on box theory and SVM algorithm , 2010, Expert Syst. Appl..

[14]  Kamil Zbikowski.  Time Series Forecasting with Volume Weighted Support Vector Machines , 2014, BDAS.

[15]  Przemyslaw Grzegorzewski,et al.  Stock Trading with Random Forests, Trend Detection Tests and Force Index Volume Indicators , 2013, ICAISC.

[16]  Ching-Hsue Cheng,et al.  Fuzzy dual-factor time-series for stock index forecasting , 2009, Expert Syst. Appl..

[17]  Adriano Lorena Inácio de Oliveira,et al.  A method for automatic stock trading combining technical analysis and nearest neighbor classification , 2010, Expert Syst. Appl..

[18]  Chih-Ming Hsu.  A hybrid procedure with feature selection for resolving stock/futures price forecasting problems , 2011, Neural Computing and Applications.

[19]  Derek W. Bunn,et al.  Financial time series modelling with discounted least squares backpropagation , 1997, Neurocomputing.

[20]  Ming-Chi Lee,et al.  Using support vector machine with a hybrid feature selection method to the stock trend prediction , 2009, Expert Syst. Appl..

[21]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[22]  Yuehjen E. Shao,et al.  Incorporating feature selection method into support vector regression for stock index forecasting , 2012, Neural Computing and Applications.

[23]  Ronald R. Yager,et al.  The personalization of security selection: An application of fuzzy set theory , 1981 .

[24]  Shouyang Wang,et al.  A causal feature selection algorithm for stock prediction modeling , 2014, Neurocomputing.