Support Vector Machines for Prediction of Futures Prices in Indian Stock Market

Machine learning methods are being used by several researchers for successfully predicting prices of financial instruments from the financial time series data of different markets. As the nature of markets in different regions are different, in this paper two machine learning techniques: Back Propagation Technique (BP) and Support Vector Machine Technique (SVM) have been used to predict futures prices traded in Indian stock market. The performances of these techniques are compared and it is observed that SVM provides better performance results as compared to BP technique. The implementation is carried out using MATLAB and SVM Tools (LS-SVM Tool Box). General Terms Machine Learning, Forecasting

[1]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[2]  Soushan Wu,et al.  Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets , 2006 .

[3]  Hans-Georg Wittkemper,et al.  Using neural networks to forecast the systematic risk of stocks , 1996 .

[4]  Francis Eng Hock Tay,et al.  Improved financial time series forecasting by combining Support Vector Machines with self-organizing feature map , 2001, Intell. Data Anal..

[5]  Johan A. K. Suykens,et al.  Financial time series prediction using least squares support vector machines within the evidence framework , 2001, IEEE Trans. Neural Networks.

[6]  Mona R. El Shazly,et al.  Forecasting currency prices using a genetically evolved neural network architecture , 1999 .

[7]  Amir F. Atiya,et al.  Introduction to financial forecasting , 1996, Applied Intelligence.

[8]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[9]  A. Refenes.  Neural Networks in the Capital Markets , 1994 .

[10]  Chih-Fong Tsai.  Financial decision support using neural networks and support vector machines , 2008, Expert Syst. J. Knowl. Eng..

[11]  Yansheng Lu,et al.  Forecasting Stock Price by SVMs Regression , 2004, AIMSA.

[12]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[13]  Guido Deboeck,et al.  Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets , 1994 .

[14]  Wu Meng,et al.  Application of Support Vector Machines in Financial Time Series Forecasting , 2007 .