Forecasting stock composite index by fuzzy support vector machines regression

Financial time series forecasting methods such as exponential smoothing are commonly used for prediction on stock composition index (SCI) and have made great contribution in practice, but efforts on looking for superior forecasting method are still made by practitioners and academia. This paper deals with the application of a novel neural network technique, fuzzy support vector machines regression (FSVMR), in SCI forecasting. The objective of this paper is not only to examine the feasibility of FSVMR in SCI forecasting but presents our efforts on improving the accuracy of FSVMR in terms of data pre-processing, kernel function selection and parameters selection. A data set from Shanghai Stock Exchange is used for the experiment to test the validity of FSVMR. The experiment shows FSVMR a better method in SCI forecasting.

[1]  Alexander J. Smola,et al.  Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.

[2]  Ian Witten,et al.  Data Mining , 2000 .

[3]  Lutgarde M. C. Buydens,et al.  Using support vector machines for time series prediction , 2003 .

[4]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

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

[6]  Chih-Jen Lin,et al.  Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.

[7]  Ingoo Han,et al.  Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index , 2000 .

[8]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[9]  Ramesh Sharda,et al.  Connectionist approach to time series prediction: an empirical test , 1992, J. Intell. Manuf..

[10]  Kyoung-jae Kim,et al.  Financial time series forecasting using support vector machines , 2003, Neurocomputing.

[11]  Michael S. Schmidt,et al.  Identifying Speakers With Support Vector Networks , 1996 .

[12]  H. Ahmadi.  Testability of the arbitrage pricing theory by neural network , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

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

[14]  F. Girosi,et al.  Nonlinear prediction of chaotic time series using support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

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

[16]  Yukihiro Nakamura,et al.  Stock Price Prediction Using Prior Knowledge and Neural Networks , 1997, Intell. Syst. Account. Finance Manag..

[17]  Hsuan-Tien Lin.  A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods , 2005 .

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

[19]  Tong-Seng Quah,et al.  Improving returns on stock investment through neural network selection , 1999 .

[20]  Shigeo Abe,et al.  Fuzzy support vector machines for multiclass problems , 2002, ESANN.

[21]  Wei-Guo Cheng,et al.  "Forecasting the 30-year U.S. Treasury Bond with a System of Neural Networks" , 2000 .

[22]  Ray Tsaih,et al.  Forecasting S&P 500 stock index futures with a hybrid AI system , 1998, Decis. Support Syst..

[23]  Sheng-De Wang,et al.  Fuzzy support vector machines , 2002, IEEE Trans. Neural Networks.

[24]  Milton S. Boyd,et al.  Forecasting futures trading volume using neural networks , 1995 .

[25]  Dug Hun Hong,et al.  Support vector fuzzy regression machines , 2003, Fuzzy Sets Syst..

[26]  Michael Y. Hu,et al.  Neural network forecasting of the British pound/US dol-lar exchange rate , 1998 .

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

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