Intelligent stock trading system based on SVM algorithm and oscillation box prediction

The stock market is considered as a high complex and dynamic system. Many machine learning and data mining technologies are used for stock analysis, but it still leaves an open question about how to integrate these methods with the plentiful knowledge and techniques accumulated in stock investment which are critical to the successful stock analysis. In this paper, we propose an intelligent stock trading system by combining support vector machine (SVM) algorithm and box theory of stock. The box theory believes a successful stock buying/selling generally occurs when the price effectivley breaks out the original oscillation box into another new box. In the system, support vector machine algorithm is utilized to make forecasts of the top and bottom of the oscillation box. Then a trading strategy based on the box theory is constructed to make trading decisions. The different stock movement patterns, i.e. bull, bear and fluctuant market, are used to test the feasibility of the system. The experiments on S&P500 components show a promising performance is achieved.

[1]  Norio Baba,et al.  Utilization of soft computing techniques for constructing reliable decision support systems for dealing stocks , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[2]  G. Grudnitski,et al.  Forecasting S&P and gold futures prices: An application of neural networks , 1993 .

[3]  Paolo Tenti,et al.  Forecasting Foreign Exchange Rates Using Recurrent Neural Networks , 1996, Appl. Artif. Intell..

[4]  Gunnar Rätsch,et al.  Predicting Time Series with Support Vector Machines , 1997, ICANN.

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

[6]  Francis Eng Hock Tay,et al.  Support vector machine with adaptive parameters in financial time series forecasting , 2003, IEEE Trans. Neural Networks.

[7]  Lei Guo,et al.  Forecasting stock composite index by fuzzy support vector machines regression , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[8]  Zehong Yang,et al.  Intelligent stock trading system by turning point confirming and probabilistic reasoning , 2008, Expert Syst. Appl..

[9]  Zehong Yang,et al.  The Application of Echo State Network in Stock Data Mining , 2008, PAKDD.

[10]  An-Sing Chen,et al.  Regression neural network for error correction in foreign exchange forecasting and trading , 2004, Comput. Oper. Res..

[11]  An-Pin Chen,et al.  Refined Group Learning Based on XCS and Neural Network in Intelligent Financial Decision Support System , 2006, Sixth International Conference on Intelligent Systems Design and Applications.

[12]  Chung-Ming Kuan,et al.  Forecasting exchange rates using feedforward and recurrent neural networks , 1992 .

[13]  Jae Won Lee,et al.  Stock price prediction using reinforcement learning , 2001, ISIE 2001. 2001 IEEE International Symposium on Industrial Electronics Proceedings (Cat. No.01TH8570).