Abstract
In this paper, we propose a two-step kernel learning method based on the support vector regression (SVR) for financial time series forecasting. Given a number of candidate kernels, our method learns a sparse linear combination of these kernels so that the resulting kernel can be used to predict well on future data. The L 1-norm regularization approach is used to achieve kernel learning. Since the regularization parameter must be carefully selected, to facilitate parameter tuning, we develop an efficient solution path algorithm that solves the optimal solutions for all possible values of the regularization parameter. Our kernel learning method has been applied to forecast the S&P500 and the NASDAQ market indices and showed promising results.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bach, F., Lanckriet, G., & Jordan, M. (2004). Multiple kernel learning, conic duality, and the SMO algorithm. In Proceedings of the 21st international conference on machine learning (p. 6).
Bradley, P., & Mangasarian, O. (1998). Feature selection via concave minimization and support vector machines. In Machine learning proceedings of the fifteenth international conference (pp. 82–90).
Breiman, L. (1995). Better subset regression using the nonnegative garrote. Technometrics, 37, 373–384.
Cao, L., & Tay, F. (2001). Financial forecasting using support vector machines. Neural Computing and Applications, 10, 184–192.
Cao, L., & Tay, F. (2003). Support vector machine with adaptive parameters in financial time series forecasting. IEEE Transactions on Neural Networks, 14, 1506–1518.
Chapelle, O., Vapnik, V., Bousquet, O., & Mukherjee, S. (2002). Choosing multiple parameters for support vector machines. Machine Learning, 46, 131–159.
Cristianini, N., Kandola, J., Elisseeff, A., & Taylor, J. (2006). On kernel target alignment. Innovations in machine learning: theory and applications, Berlin: Springer.
Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004). Least angle regression. The Annals of Statistics, 32, 407–499.
Fama, E. (1970). Efficient capital markets: a review of theory and empirical work. Journal of Finance, 25, 383–417.
Fama, E. (1991). Efficient capital markets: II. Journal of Finance, 46, 1575–1617.
Fan, R., Chen, P., & Lin, C. (2005). Working set selection using second order information for training support vector machines. Journal of Machine Learning Research, 6, 1889–1918.
Friedrichs, F., & Igel, C. (2005). Evolutionary tuning of multiple SVM parameters. Neurocomputing, 64, 107–117.
Fung, G., Dundar, M., Bi, J., & Rao, B. (2004). A fast iterative algorithm for fisher discriminant using heterogeneous kernels. In Proceedings of the 21st international conference on machine learning (p. 40).
Gestel, T., Suykens, J., Baestaens, D., Lambrechts, A., Lanckriet, G., Vandaele, B., Moor, B., & Vandewalle, J. (2001). Financial time series prediction using least squares support vector machines within the evidence framework. IEEE Transactions on Neural Networks, 12, 809–821.
Gunter, L., & Zhu, J. (2006). Computing the solution path for the regularized support vector regression. In Advances in neural information processing systems (pp. 483–490).
Hastie, T., Rosset, S., Tibshirani, R., & Zhu, J. (2004). The entire regularization path for the support vector machine. Journal of Machine Learning Research, 5, 1391–1415.
Hirshleifer, D. (2001). Investor psychology and asset pricing. The Journal of Finance, 4, 1533.
Huang, W., Nakamori, Y., & Wang, S. (2005). Forecasting stock market movement direction with using support vector machine. Computers and Operations Research, 32, 2513–2522.
Keerthi, S., Sindhwani, V., & Chapelle, O. (2007). An efficient method for gradient-based adaptation of hyperparameters in SVM models. In Advances in neural information processing systems (pp. 217–224).
Kim, K. (2003). Financial time series forecasting using support vector machines. Neurocomputing, 55, 307–319.
Kim, S., Magnani, A., & Boyd, S. (2006). Optimal kernel selection in kernel fisher discriminant analysis. In Proceedings of the 23rd international conference on machine learning (pp. 465–472).
Lanckriet, G., Cristianini, N., Bartlett, P., Ghaoui, L., & Jordan, M. (2004). Learning the kernel matrix with semidefinite programming. Journal of Machine Learning Research, 5, 27–72.
Lo, A., & MacKinlay, C. (2001). A non-random walk down wall street. Princeton: Princeton University Press.
Lo, A., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: computational algorithms, statistical inference, and empirical implementation. Journal of Finance, 55, 1705–1765.
Malkiel, J. (1973). A random walk down wall street. New York: W.W. Norton & Company.
Nguyen, H., Ohn, S., & Choi, W. (2007). Combined kernel function for support vector machine and learning method based on evolutionary algorithm. In Proceedings of the 11th international conference on neural information processing (pp. 1273–1278).
Ong, C., Smola, A., & Williamson, R. (2005). Learning the kernel with hyperkernels. Journal of Machine Learning Research, 6, 1043–1071.
Platt, J. (1999). Fast training of support vector machines using sequential minimal optimization. In Advances in kernel methods—support vector learning. Cambridge: MIT Press.
Rosset, S., & Zhu, J. (2007). Piecewise linear regularized solution paths. The Annals of Statistics, 35, 1012–1030.
Smola, A., & Schoelkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14, 199–222.
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of Royal Statistical Society, Series B, 58, 267–288.
Vapnik, V. (1995). The nature of statistical learning theory. Berlin: Springer.
Wang, G., Yeung, D., & Lochovsky, F. (2007). A kernel path algorithm for support vector machines. In Proceedings of the 24th international conference on machine learning (pp. 951–958).
Wang, X., Chen, S., Lowe, D., & Harris, C. (2006). Sparse support vector regression based on orthogonal forward selection for the generalised kernel model. Neurocomputing, 70, 462–474.
Author information
Authors and Affiliations
Corresponding author
Additional information
L. Wang will join Barclays Global Investors, San Francisco, CA 94105, USA.
Rights and permissions
About this article
Cite this article
Wang, L., Zhu, J. Financial market forecasting using a two-step kernel learning method for the support vector regression. Ann Oper Res 174, 103–120 (2010). https://doi.org/10.1007/s10479-008-0357-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10479-008-0357-7