Learning to Trade with Incremental Support Vector Regression Experts

  • Giovanni Montana
  • Francesco Parrella
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5271)

Abstract

Support vector regression (SVR) is an established non-linear regression technique that has been applied successfully to a variety of predictive problems arising in computational finance, such as forecasting asset returns and volatilities. In real-time applications with streaming data two major issues that need particular care are the inefficiency of batch-mode learning, and the arduous task of training the learning machine in presence of non-stationary behavior. We tackle these issues in the context of algorithmic trading, where sequential decisions need to be made quickly as new data points arrive, and where the data generating process may change continuously with time. We propose a master algorithm that evolves a pool of on-line SVR experts and learns to trade by dynamically weighting the experts’ opinions. We report on risk-adjusted returns generated by the hybrid algorithm for two large exchange-traded funds, the iShare S&P 500 and Dow Jones EuroStoxx 50.

Keywords

Incremental support vector regression subspace tracking ensemble learning computational finance algorithmic trading 

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References

  1. 1.
    Bao, Y., Liu, Z., Wang, W.: Forecasting stock composite index by fuzzy support vector machine regression. In: Proceedings of the Fourth International Conference on Machine Learning and Cybernetics (2005)Google Scholar
  2. 2.
    Burgess, A.N.: Applied quantitative methods for trading and investment, chapter Using Cointegration to Hedge and Trade International EquitiesFinance, pp. 41–69. Wiley, Chichester (2003)CrossRefGoogle Scholar
  3. 3.
    Cao, D., Pang, S., Bai, Y.: Forecasting exchange rate using support vector machines. In: Fourth International Conference on Machine Learning and Cybernetics (2005)Google Scholar
  4. 4.
    Cauwenberghs, G., Poggio, T.: Incremental and decremental support vector machine learning, Cambridge, vol. 13, pp. 409–123 (2001)Google Scholar
  5. 5.
    Cesa-Bianchi, N., Lugosi, G.: Prediction, learning, and games. Cambridge University Press, Cambridge (2006)MATHGoogle Scholar
  6. 6.
    Chang, B.R., Tsai, H.F.: Forecast approach using neural network adaptation to support vector regression grey model and generalized auto-regressive conditional heteroscedasticity. Expert Systems with Applications: An International Journal 34, 925–934 (2008)CrossRefGoogle Scholar
  7. 7.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)Google Scholar
  8. 8.
    Elliott, R.J., van der Hoek, J., Malcolm, W.P.: Pairs trading. Quantitative Finance, 271–276 (2005)Google Scholar
  9. 9.
    Gavrishchaka, V.V., Banerjee, S.: Support vector machine as an efficient framework for stock market volatility forecasting. Computational Management Science 3(2), 147–160 (2006)MATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    He, Y., Zhu, Y., Duan, D.: Research on hybrid arima and support vector machine model in short term load forecasting. In: Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications (2006)Google Scholar
  11. 11.
    Huang, W., Nakamori, Y., Wang, S.: Forecasting stock market movement direction with support vector machine. Computers & Operations Research (2004)Google Scholar
  12. 12.
    Ince, H., Trafalis, T.B.: A hybrid model for exchange rate prediction. Decision Support Systems 42, 1054–1062 (2006)CrossRefGoogle Scholar
  13. 13.
    Laskov, P., Gehl, C., Kruger, S.: Incremental support vector learning: analysis, implementation and applications. Journal of machine learning research 7, 1909–1936 (2006)MathSciNetGoogle Scholar
  14. 14.
    Littlestone, N., Warmuth, M.K.: The weighted majority algorithm. Information and Computation 108, 212–226 (1994)MATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Ma, J., Theiler, J., Perkins, S.: Accurate on-line support vector regression. Neural Computation 15 (2003)CrossRefGoogle Scholar
  16. 16.
    Martin, M.: On-line support vector machine regression. In: 13th European Conference on Machine Learning (2002)Google Scholar
  17. 17.
    Montana, G., Triantafyllopoulos, K., Tsagaris, T.: Data stream mining for market-neutral algorithmic trading. In: Proceedings of the ACM Symposium on Applied Computing, pp. 966–970 (2008)Google Scholar
  18. 18.
    Montana, G., Triantafyllopoulos, K., Tsagaris, T.: Flexible least squares for temporal data mining and statistical arbitrage. Expert Systems with Applications (2008) doi:10.1016/j.eswa.2008.01.062Google Scholar
  19. 19.
    Nalbantov, G., Bauer, R., Sprinkhuizen-Kuyper, I.: Equity style timing using support vector regressions. Applied Financial Economics 16, 1095–1111 (2006)CrossRefGoogle Scholar
  20. 20.
    Nicholas, J.G.: Market-Neutral Investing: Long/Short Hedge Fund Strategies. Bloomberg Professional Library (2000)Google Scholar
  21. 21.
    Parrella, F., Montana, G.: A note on incremental support vector regression. Technical report, Imperial College London (2008)Google Scholar
  22. 22.
    Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)Google Scholar
  23. 23.
    Tay, F., Cao, L.: ε-descending support vector machines for financial time series forecasting. Neural Processing Letters 15, 179–195 (2002)MATHCrossRefGoogle Scholar
  24. 24.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)MATHGoogle Scholar
  25. 25.
    Wabha, G.: Spline models for observational data. CBMS-NSF Regional Conference Series in Applied Mathematics, vol. 59. SIAM, Philadelphia (1990)Google Scholar
  26. 26.
    Wang, W.: An incremental learning strategy for support vector regression. Neural Processing Letters 21, 175–188 (2005)CrossRefGoogle Scholar
  27. 27.
    Wen, Y., Lu, B.: Advances in Knowledge Discovery and Data Mining, chapter Incremental Learning of Support Vector Machines by Classifier Combining, pp. 904–911. Springer, Heidelberg (2007)Google Scholar
  28. 28.
    Weng, J., Zhang, Y., Hwang, W.S.: Candid covariance-free incremental principal component analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(8), 1034–1040 (2003)CrossRefGoogle Scholar
  29. 29.
    Yaroshinsky, R., El-Yaniv, R., Seiden, S.: How to better use expert advice. Machine Learning 55(3), 271–309 (2004)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Giovanni Montana
    • 1
  • Francesco Parrella
    • 1
  1. 1.Department of MathematicsImperial College London 

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