A multiple-kernel support vector regression approach for stock market price forecasting

Expert Systems with Applications

Volumn 38, Issue 3, 2011, Pages 2177-2186

  Yeh, Chi Yuan ^a   Huang, Chi Wei ^a   Lee, Shie Jue ^a  

^a NATIONAL SUN YAT SEN UNIVERSITY   (Taiwan)

Author keywords

Gradient projection; Multiple kernel learning; SMO; Stock market forecasting; Support vector regression

Indexed keywords

GRADIENT PROJECTIONS; KERNEL LEARNING; SMO; STOCK MARKET FORECASTING; SUPPORT VECTOR REGRESSION;

FINANCE; FORECASTING; LAGRANGE MULTIPLIERS; LEARNING ALGORITHMS; MATHEMATICAL PROGRAMMING; REGRESSION ANALYSIS; VECTORS;

COMMERCE;

EID: 78049527969     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.08.004     Document Type: Article

References (42)

1. Bach, F. R.; Lanckriet, G. R. G.; & Jordan, M. I. (2004). Multiple kernel learning, conic duality, and the SMO algorithm. In: Proceedings of the 21th international conference on machine learning (pp. 6-13).
2. Bennett, K. P.; Momma, M.; & Embrechts, M. J. (2002). MARK: A boosting algorithm for heterogeneous kernel models. In Proceedings of the eigth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 24-31).
3. D.P. Bertsekas Nonlinear programming 2nd ed. 1999 Athena Scientific Massachusetts, USA
4. T. Bollerslev Generalized autoregressive conditional heteroscedasticity Journal of Econometrics 31 3 1986 307 327
5. G.E.P. Box, and G.M. Jenkins Time series analysis: Forecasting and control 3rd ed. 1994 Prentice Hall Englewood Cliffs
6. L. Cao, and F.E.H. Tay Financial forecasting using support vector machines Neural Computing & Applications 10 2 2001 184 192
7. L. Cao, and F.E.H. Tay Support vector machine with adaptive parameters in financial time series forecasting IEEE Transactions on Neural Networks 14 6 2003 1506 1518
8. D.G. Champernowne Sampling theory applied to autoregressive schemes Journal of the Royal Statistical Society: Series B 10 1948 204 231
9. P.-C. Chang, and C.-H. Liu A TSK type fuzzy rule based system for stock price prediction Expert Systems with Application 34 1 2008 135 144
10. O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee Choosing multiple parameters for support vector machines Machine Learning 46 1-3 2002 131 159 (Pubitemid 34129966)
11. K. Crammer, J. Keshet, and Y. Singer Kernel design using boosting S. Becker, S. Thrun, K. Obermayer, Advances in neural information processing systems Vol. 15 2003 MIT Press Cambridge, MA, USA 537 544
12. N. Cristianini, and J. Shawe-Taylor An introduction to support vector machines and other kernel-based learning methods 2000 Cambridge University Press Cambridge, UK
13. K. Duan, S. Keerthi, and A.N. Poo Evaluation of simple performance measures for tuning SVM hyperparameters Neurocomputing 51 2003 41 59
14. R.F. Engle Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation Econometrica 50 4 1982 987 1008
15. P.-C. Fernando, A.A.-R. Julio, and G. Javier Estimating GARCH models using support vector machines Quantitative Finance 3 3 2003 163 172
16. T.V. Gestel, J.A.K. Suykens, D.E. Baestaens, A. Lambrechts, G. Lanckriet, and B. Vandaele Financial time series prediction using least squares support vector machines within the evidence framework IEEE Transactions on Neural Networks 12 4 2001 809 821
17. Gönen, M.; & Alpaydin, E. (2008). Localized multiple kernel learning. In Proceedings of the 25th international conference on machine learning (pp. 352-359).
18. J.V. Hansen, and R.D. Nelson Neural networks and traditional time series methods: A synergistic combination in state economic forecasts IEEE Transactions on Neural Networks 8 4 1997 863 873
19. J.A. Hartigan, and M.A. Wong A K-means clustering algorithm Applied Statistics 28 1979 100 108
20. V. Kecman Learning and soft computing: Support vector machines Neural networks, and fuzzy logic models 2001 MIT Press Cambridge, MA, USA
21. K.J. Kim, and I. Han Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index Expert Systems with Application 19 2 2008 125 132
22. J.T.-Y. Kwok The evidence framework applied to support vector machines IEEE Transactions on Neural Networks 11 5 2000 1162 1173
23. Y.-K. Kwon, and B.-R. Moon A hybrid neurogenetic approach for stock forecasting IEEE Transactions on Neural Networks 18 3 2007 851 864
24. G.R.G. Lanckriet, N. Cristianini, P. Bartlett, L.E. Ghaoui, and M.I. Jordan Learning the kernel matrix with semidefinite programming Journal of Machine Learning Research 5 2004 27 72
25. S.-K. Oh, W. Pedrycz, and H.-S. Park Genetically optimized fuzzy polynomial neural networks IEEE Transactions on Fuzzy Systems 14 1 2006 125 144
26. C.S. Ong, A.J. Smola, and R.C. Williamson Learning the kernel with hyperkernels Journal of Machine Learning Research 6 2005 1043 1071
27. P.-F. Pai, and C.-S. Lin A hybrid ARIMA and support vector machines model in stock price forecasting Omega: The International Journal of Management Science 33 6 2005 497 505
28. J.C. Platt Fast training of support vector machines using sequential minimal optimization B. Schölkopf, C.J.C. Burges, A.J. Smola, Advances in kernel methods: Support vector learning Vol. 11 1999 MIT Press Cambridge, MA, USA 185 208
29. M. Qi, and G.P. Zhang Trend timevseries modeling and forecasting with neural networks IEEE Transactions on Neural Networks 19 5 2008 808 816
30. A. Rakotomamonjy, F.R. Bach, S. Canu, and Y. Grandvalet Simplemkl Journal of Machine Learning Research 9 2008 2491 2521
31. Rakotomamonjy, A.; Bach, F. R.; Canu, S.; & Grandvalet, Y. (2007) More efficiency in multiple kernel learning. In Proceedings of the 24th international conference on machine learning (pp. 775-782).
32. S. Sonnenburg, G. Ratsch, C. Schäfer, and B. Schölkopf Large scale multiple kernel learning Journal of Machine Learning Research 7 2006 1531 1565
33. Szafranski, M.; Grandvalet, Y.; & Rakotomamonjy, A. (2008). Composite kernel learning. In Proceedings of the 25th international conference on machine learning (pp. 1040-1047).
34. Taiwan Stock Exchange Corporation. .
35. F.E.H. Tay, and L. Cao Application of support vector machines in financial time series forecasting Omega: The International Journal of Management Science 29 4 2001 309 317
36. I.W.-H. Tsang, and J.T.-Y. Kwok Efficient hyperkernel learning using second-order cone programming IEEE Transactions on Neural Networks 17 1 2006 48 58
37. G. Valeriy, and B. Supriya Support vector machine as an efficient framework for stock market volatility forecasting Computational Management Science 3 2 2006 147 160
38. V. Vapnik The nature of statistical learning theory 1995 Springer-Verlag New York, USA
39. Z. Wang, S. Chen, and T. Sun MultiK-MHKS: A novel multiple kernel learning algorithm IEEE Transactions on Pattern Analysis and Machine Intelligence 30 2 2008 348 353
40. Yang, H.; Chan, L.; & King, I. (2002). Support vector machine regression for volatile stock market prediction. In Proceedings of the third international conference on intelligent data engineering and automated learning (pp. 391-396).
41. M.H.F. Zarandi, B. Rezaee, I.B. Turksen, and E. Neshat A type-2 fuzzy rule-based expert system model for stock price analysis Expert Systems with Application 36 1 2009 139 154
42. D. Zhang, and L. Zhou Discovering golden nuggets: Data mining in financial application IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 34 4 2004 513 522