Forecasting stock market movement direction with support vector machine

Support vector machine (SVM) is a very specific type of learning algorithms characterized by the capacity control of the decision function, the use of the kernel functions and the sparsity of the solution. In this paper, we investigate the predictability of financial movement direction with SVM by forecasting the weekly movement direction of NIKKEI 225 index. To evaluate the forecasting ability of SVM, we compare its performance with those of Linear Discriminant Analysis, Quadratic Discriminant Analysis and Elman Backpropagation Neural Networks. The experiment results show that SVM outperforms the other classification methods. Further, we propose a combining model by integrating SVM with the other classification methods. The combining model performs best among all the forecasting methods.

[1]  Lijuan Cao,et al.  Application of support vector machines in !nancial time series forecasting , 2001 .

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

[3]  J. Campbell.  Stock Returns and the Term Structure , 1985 .

[4]  F. Tay,et al.  Application of support vector machines in financial time series forecasting , 2001 .

[5]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[6]  S. Ross,et al.  Economic Forces and the Stock Market , 1986 .

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

[8]  Josef Lakonishok,et al.  Contrarian Investment, Extrapolation, and Risk , 1993 .

[9]  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..

[10]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[11]  G. William Schwert,et al.  Asset returns and inflation , 1977 .

[12]  E. Fama,et al.  Permanent and Temporary Components of Stock Prices , 1988, Journal of Political Economy.

[13]  Bernhard Schölkopf,et al.  Learning with kernels , 2001 .

[14]  R. Fletcher.  Practical Methods of Optimization , 1988 .

[15]  Murray Z. Frank,et al.  Measuring the Strangeness of Gold and Silver Rates of Return , 1989 .

[16]  E. Fama,et al.  Dividend yields and expected stock returns , 1988 .

[17]  D. M. Deighton,et al.  Computers in Operations Research , 1977, Aust. Comput. J..

[18]  Francis E. H. Tay,et al.  Modi ed support vector machines in nancial time series forecasting , 2002 .

[19]  S. Ross.  The arbitrage theory of capital asset pricing , 1976 .

[20]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[21]  Walter C. Labys,et al.  Evidence of chaos in commodity futures prices , 1992 .

[22]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

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

[25]  Francis Eng Hock Tay,et al.  Financial Forecasting Using Support Vector Machines , 2001, Neural Computing & Applications.

[26]  Rolph E. Anderson,et al.  Multivariate data analysis (4th ed.): with readings , 1995 .

[27]  Tomaso A. Poggio,et al.  Regularization Networks and Support Vector Machines , 2000, Adv. Comput. Math..

[28]  Francis Eng Hock Tay,et al.  A comparative study of saliency analysis and genetic algorithm for feature selection in support vector machines , 2001, Intell. Data Anal..

[29]  Steven c. Blank.  "Chaos" In Futures Markets? A Nonlinear Dynamical Analysis , 1991 .

[30]  Michael J. Watts,et al.  Spatial-temporal adaptation in evolving fuzzy neural networks for on-line adaptive phoneme recognition , 1999 .

[31]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

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