Non-fixed and asymmetrical margin approach to stock market prediction using Support Vector Regression

ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age

Volumn 3, Issue , 2002, Pages 1398-1402

  Yang, Haiqin ^a   King, I ^a   Chan, Laiwan ^a  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; FINANCIAL DATA PROCESSING; FORECASTING; INFORMATION SCIENCE; TIME SERIES;

DOW JONES INDUSTRIAL AVERAGES; DOWNSIDE RISKS; FINANCIAL TIME SERIES; FINANCIAL TIME SERIES PREDICTIONS; NONSTATIONARY; STOCK MARKET PREDICTION; SUPPORT VECTOR REGRESSION (SVR); TIME VARYING;

FINANCIAL MARKETS;

EID: 84968611317     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICONIP.2002.1202850     Document Type: Conference Paper

References (10)

1. Chih-Chung Chang and Chih-Jen Lin. Libsvm: a library for support vector machines (version 2.31), 2001.
2. S. Mukherjee, E. Osuna, and F. Girosi. Nonlinear prediction of chaotic time series using support vector machines. In J. Principe, L. Giles, N. Morgan, and E. Wilson, editors, IEEE Workshop on Neural Networks for Signal Processing VII, page 511. IEEE Press, 1997.
3. K. R. Müller, A. Smola, G. Rätsch, B. Schölkopf, J. Kohlmorgen, and V. Vapnik. Predicting time series with support vector machines. In ICANN, pages 999-1004. In W. Gerstner, A. Germond, M. Hasler, and J. D. Nicoud, editors,Artificial Neural Networks - ICANN'97, Springer, 1997.
4. Ian T. Nabney. Netlab: Algorithms for Pattern Recognition. Springer, London; New York, 2002.
5. A. Smola and B. Schölkopf. A tutorial on support vector regression. Technical Report NC2-TR-1998-030, NeuroCOLT2, 1998.
6. E. H. Tay and L. J. Cao. Application of support vector machines to financial time series forecasting. Omega, 29:309-317, 2001.
7. T.B. Trafalis and H. Ince. Support vector machine for regression and applications to financial forecasting. In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN2000), volume 6, pages 348-353. IEEE, 2000.
8. V. N. Vapnik. The Nature of Statistical Learning Theory. Springer, New York, 1995.
9. V. N. Vapnik. Statistical Learning Theory. Wiley, New York, 1998.
10. Haiqin Yang, Laiwan Chan, and Irwin King. Support vector machine regression for volatile stock market prediction. In Third International Conference on Intelligent Data Engineering and Automated Learning(IDEAL'02), pages 391-396. Springer, 2002.