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

Author

Listed:
  • Christian L Dunis
  • Spiros D Likothanassis
  • Andreas S Karathanasopoulos
  • Georgios S Sermpinis
  • Konstantinos A Theofilatos

    (Pattern Recognition Laboratory, University of Patras)

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.

Suggested Citation

  • Christian L Dunis & Spiros D Likothanassis & Andreas S Karathanasopoulos & Georgios S Sermpinis & Konstantinos A Theofilatos, 2013. "A hybrid genetic algorithm–support vector machine approach in the task of forecasting and trading," Journal of Asset Management, Palgrave Macmillan, vol. 14(1), pages 52-71, February.
    • Handle: RePEc:pal:assmgt:v:14:y:2013:i:1:d:10.1057_jam.2013.2
    DOI: 10.1057/jam.2013.2
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    References listed on IDEAS

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    Cited by:

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    2. Kang, Haijun & Zong, Xiangyu & Wang, Jianyong & Chen, Haonan, 2023. "Binary gravity search algorithm and support vector machine for forecasting and trading stock indices," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 507-526.

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