Kernel principal component analysis and support vector machines for stock price prediction

IEEE International Conference on Neural Networks - Conference Proceedings

Volumn 3, Issue , 2004, Pages 2053-2058

  Ince, Huseyin ^a   Trafalis, Theodore B ^b  

^a GEBZE TECHNICAL UNIVERSITY   (Turkey)

^b University of Oklahoma   (United States)

Author keywords

Financial Time Series; Forecasting; Kernel Principal Component Analysis; Support Vector Regression

Indexed keywords

FINANCIAL TIME SERIES; KERNEL PRINCIPAL COMPONENT ANALYSIS; SUPPORT VECTOR MACHINES (SVM); SUPPORT VECTOR REGRESSION;

FORECASTING; GENETIC ALGORITHMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS; TIME SERIES ANALYSIS;

FINANCIAL DATA PROCESSING;

EID: 10844223635     PISSN: 10987576     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2004.1380933     Document Type: Conference Paper

References (25)

1. N. Ancona. Classification properties of support vector machines for regression. Technical report, 1999. RIIESI/CNR- Nr. 02/99.
2. M. Ausloos, and K. Ivanova, Mechanistic approach to generalized technical analysis of share prices and stock market indices, The European Physical Journal B (27): 177-187, 2002.
3. A. Chen, M. T. Leung, and H. Daouk, Application of neural networks to an emerging market: forecasting and trading the Taiwan Stock Index, Computers & Operation Research (30): 901-923, 2003.
4. R. Collobert and S. Bengio. Svmtorch: Support vector machines for large-scale regression problems. Journal of Machine Learning Research, 1:143-160, 2001. 87
5. C. Cortes and V. Vapnik. Support vector networks. Machine Learning, 20:273-297, 1995.
6. J. Galindo. A framework for comperative analysis of statistical and machine learning methods: An application to the black scholes option pricing equations. Technical report, Banco dc Mexico, Mexico, DF, 04930, 1998.
7. A. Hill. Indicator Analysis, http://stockcharts.com/education/IndicatorAnalysis/index.html.
8. J. M. Hutchinson, A. W. Lo, and T. Poggio. A nonparametic approach to pricing and hedging derivative securities via learning networks. The Journal of Finance, XLIX(1):851-889, 1994.
9. T. Joachims. Making large-scale svm learning practical. In B. Scholkopf, C.J.C. Burges, and A.J. Smola, editors, Advances in Kernel Methods: Support Vector Learning, pages 169-184. MIT Press, 1999.
10. Y.-J. Lee and O.L. Mangasarian. Rsvm: Reduced support vector machines. CD Proceedings of the First SIAM International Conference on Data Mining, 2001. 89
11. W. Leigh, M. Paz, and R. Purvis, An analysis of a hybrid neural network and pattern recognition technique for predicting short-term increases in the NYSE, The International Journal of Management Science (30): 69-76, 2002.
12. M. Leus, D. Deugo, F. Oppacher, and R. Cattral, GA and financial analysis, in L. Monostori, J. Vancza, and M. Ali (Eds), IEA/AIE 2001, LNAI 2070, pp. 868-873,2001.
13. B. Scholkopf, A. Smola, and K. R. Muller, Kernel Principal Component Analysis, in: B. Scholkopf, C.J.C. Burgers, A.J. Smola (Eds), Advances in Kernel Methods- Support Vector Learning, MIT Press, Cambridge, MA, 1999, pp. 293-306
14. E. Osuna, R. Freund, and F. Girosi. Training support vector machines: An application to face detection. Proc. Computer Vision and Pattern Recognition'97, pages 130-136, 1997.
15. M. Pontil and A. Verri. Properties of support vector machines. Technical report, Massachusetts Institute of Technology, Artificial Intelligence Laboratory, 1997.
16. R. Rifkin. Svmfu a support vector machine package, 2000.http://fivepercentnation.mit.edu/PersonalPages/rif/SvmFu/index.html.
17. R. Rosipal, M. Girolami, L.J. Trejo, A. Cichocki, Kernel PCA for Feature Extraction and De-Noising in Non-linear Regression. Neural Computing & Applications, 10(3): 231-243, 2001.
18. A. Smola and B. Schölkopf. A tutorial on support vector regression. Statistics and Computing, 1998. Invited paper, in press.
19. F. E. H. Tay and L.J. Cao, Modified support vector machines in financial time series forecasting, Neurocomputing(48): 847-861,2002.
20. C.J. Twining and C.J. Taylor, The use of kernel principal component analysis to model data distribution, Pattern Recognition 36 (2003), 217-227.
21. T.B. Trafalis, H. Ince, and T. Mishina, Support vector regression in option pricing, Procedings of Conference on Computational Intelligence and Financial Engineering (CIFer 2003), March 20-23, 2003, Hong Kong.
22. T.B. Trafalis and H. Ince. Benders decomposition technique for support vector regression. In Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on, volume 3, pages 2767-2772. IEEE, 2002.
23. T.B. Trafalis, "Artificial Neural Networks Applied to Financial Forecasting", Smart Engineering System Design: Neural Networks, Fuzzy Logic, Evolutionary Programming, data Mining and Complex Systems, (Dagli, Buczak, Ghosh, Embrechts and Ersoy, eds.), ASME Press, 1049-1054, 1999.
24. R. Tsaih. Sensitivity analysis, neural networks and, the finance. pages 3830-3835, 1999.
25. V. Vapnik. The Nature of Statistical Learning Theory. Springer Verlag, 1995.