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Journal of Asset Management

, Volume 14, Issue 1, pp 52–71 | Cite as

A hybrid genetic algorithm–support vector machine approach in the task of forecasting and trading

  • Christian L Dunis
  • Spiros D Likothanassis
  • Andreas S Karathanasopoulos
  • Georgios S Sermpinis
  • Konstantinos A TheofilatosEmail author
Original Article

Abstract

The motivation of this article is to introduce a novel hybrid Genetic algorithm–Support Vector Machines method when applied to the task of forecasting and trading the daily and weekly returns of the FTSE 100 and ASE 20 indices. This is done by benchmarking its results with a Higher-Order Neural Network, a Naïve Bayesian Classifier, an autoregressive moving average model, a moving average convergence/divergence model, plus a naïve and a buy and hold strategy. More specifically, the trading performance of all models is investigated in forecast and trading simulations on the FTSE 100 and ASE 20 time series over the period January 2001–May 2010, using the last 18 months for out-of-sample testing. As it turns out, the proposed hybrid model does remarkably well and outperforms its benchmarks in terms of correct directional change and trading performance.

Keywords

ASE 20 FTSE 100 trading simulation genetic algorithms support vector machines 

References

  1. Adeodato, P., Arnaud, A., Vasconcelos, G., Cunha, R. and Monteiro, D. (2011) MLP ensembles improve long term prediction accuracy over single networks. International Journal of Forecasting 27 (3): 661–671.CrossRefGoogle Scholar
  2. Andreou, P., Charalampous, C. and Martzoukos, S. (2008) Pricing and trading European options by combining artificial neural networks and parametric models with implied parameters. European Journal of Operational Research 185 (3): 1415–1433.CrossRefGoogle Scholar
  3. Box, G., Jenkins, G. and Gregory, G. (1994) Time Series Analysis: Forecasting and Control. Hoboken, NJ: Prentice-Hall.Google Scholar
  4. Cao, L. and Tay, F. (2003) Support vector machine with adaptive parameters in financial time series forecasting. IEEE Transactions on Neural Networks 14 (6): 1506–1518.CrossRefGoogle Scholar
  5. Cortes, C. and Vapnik, V.N. (1995) Support vector networks. Machine Learning 20 (1): 1–25.Google Scholar
  6. Dunis, C., Laws, J. and Evans, B. (2008) Trading futures spread portfolios: Applications of higher order and recurrent networks. European Journal of Finance 14 (5–6): 503–521.CrossRefGoogle Scholar
  7. Dunis, C., Laws, J. and Karathanasopoulos, A. (2011) Modeling and trading the Greek stock market with mixed neural network models. Applied Financial Economics 21 (23): 1793–1808.CrossRefGoogle Scholar
  8. Dunis, C., Laws, J. and Sermpinis, G. (2009) The robustness of neural networks for modelling and trading the EUR/USD exchange rate at the ECB fixing. Journal of Derivatives and Hedge Funds 15 (3): 186–205.CrossRefGoogle Scholar
  9. Giles, L.C. and Maxwell, T. (1987) Learning, invariance, and generalization in high-order neural networks. Applied Optics 26 (23): 4972–4978.CrossRefGoogle Scholar
  10. Holland, J. (1995) Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. Cambridge, MA: MIT Press.Google Scholar
  11. Howson, C. and Urbach, P. (1993) Scientific Reasoning The Bayesian Approach, 3rd edn. London: Open Course Publishing Company.Google Scholar
  12. Huang, W., Nakamori, Y. and Wang, S. (2005) Forecasting stock market movement direction with support vector machine. Computers & Operations Research 32 (10): 2513–2522.CrossRefGoogle Scholar
  13. Ince, H. and Trafalis, T. (2008) Short term forecasting with support vector machines and application to stock price prediction. International Journal of General Systems 37 (6): 677–687.CrossRefGoogle Scholar
  14. Kaastra, I. and Boyd, M. (1996) Designing a neural network for forecasting financial and economic time series. Neurocomputing 10 (10): 215–236.CrossRefGoogle Scholar
  15. Kerthi, S. and Lin, C.J. (2003) Asymptotic behaviors of support vector machines with Gaussian Kernel. Neural Computation 15 (7): 1667–1689.CrossRefGoogle Scholar
  16. Kiani, K. and Kastens, T. (2008) Testing forecast accuracy of foreign exchange rates: Predictions from feed forward and various recurrent neural network architectures. Computational Economics 4 (32): 383–406.CrossRefGoogle Scholar
  17. Kim, K. (2003) Financial time series forecasting using support vector machines. Neurocomputing 55 (1–2): 307–319.CrossRefGoogle Scholar
  18. Knowles, A., Hussain, A., El Deredy, W., Lisboa, P.G. and Dunis, C.L. (2011) Higher order neural networks with Bayesian confidence measure for the prediction of the EUR/USD exchange rate. In: M. Zhang (ed.) Artificial Higher Order Neural Networks for Economics and Business. New York: IGI Global, pp. 48–59.Google Scholar
  19. Matias, J.M. and Reboredo, J.C. (2012) Forecasting performance of non-linear models for intraday stock returns. Journal of Forecasting 31 (2): 172–188.CrossRefGoogle Scholar
  20. Min, S., Lee, J. and Han, I. (2006) Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert Systems with Applications 31 (3): 652–660.CrossRefGoogle Scholar
  21. Nguyen, T., Gordon-Brown, L., Wheeler, P. and Peterson, J. (2009) GA-SVM Based Framework for Time Series Forecasting. ICNC 1, Fifth International Conference on Natural Computation, pp. 493–498.Google Scholar
  22. Panda, C. and Narasimhan, V. (2007) Forecasting exchange rate better with artificial neural network. Journal of Policy Modelling 29 (2): 227–236.CrossRefGoogle Scholar
  23. Scholkopf, B. et al (1999) Input space versus feature space in kernel-based methods. IEEE Transactions on Neural Networks 10 (5): 1000–1017.CrossRefGoogle Scholar
  24. Scholkopf, B. and Smola, A.J. (2002) Learning with Kernels: Support Vector Machines, Regularization and Beyond. Cambridge, MA: MIT Press.Google Scholar
  25. Sermpinis, G., Laws, J. and Dunis, C.L. (2013) Modelling and trading the realised volatility of the FTSE100 futures with higher order neural networks. European Journal of Finance, pp. 1–15, doi: 10.1080/1351847X.2011.606990.Google Scholar
  26. Shapiro, A.F. (2000) A hitchhiker's guide to the techniques of adaptive nonlinear models. Insurance, Mathematics and Economics 26 (2–3): 119–132.CrossRefGoogle Scholar
  27. Vapnik, V.N. (2000) The Nature of Statistical Learning Theory. New York: Springer.CrossRefGoogle Scholar
  28. Wu, C., Tzeng, G. and Lin, R. (2009) A novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression. Expert Systems with Applications 36 (3): 4725–4735.CrossRefGoogle Scholar
  29. Zhang, M., Shuxiang, X. and Fulcher, J. (2002) Neuron-adaptive higher order neural-network models for automated financial data modeling. IEEE Transactions on Neural Networks 13 (1): 188–204.CrossRefGoogle Scholar

Copyright information

© Palgrave Macmillan, a division of Macmillan Publishers Ltd 2013

Authors and Affiliations

  • Christian L Dunis
  • Spiros D Likothanassis
  • Andreas S Karathanasopoulos
  • Georgios S Sermpinis
  • Konstantinos A Theofilatos
    • 1
    Email author
  1. 1.Department of Computer Engineering & InformaticsPattern Recognition Laboratory, University of PatrasGreece

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