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Applying Independent Component Analysis and Predictive Systems for Algorithmic Trading

Author

Listed:
  • Attila Ceffer

    (Budapest University of Technology and Economics)

  • Janos Levendovszky

    (Budapest University of Technology and Economics)

  • Norbert Fogarasi

    (Budapest University of Technology and Economics)

Abstract

In this paper, a Nonlinear AutoRegressive network with eXogenous inputs and a support vector machine are proposed for algorithmic trading by predicting the future value of financial time series. These architectures are capable of modeling and predicting vector autoregressive VAR(p) time series. In order to avoid overfitting, the input is pre-processed by independent component analysis to filter out the most noise like component. In this way, the accuracy of the prediction and the trading performance is increased. The proposed algorithms have a small number of free parameters which makes fast learning and trading possible. The method is not only tested on single asset price series, but also on predicting the value of mean reverting portfolios obtained by maximizing the predictability parameter of VAR(1) processes. The tests were first performed on artificially generated data and then on real data selected from exchange traded fund time series including bid–ask spread. In both cases the proposed method could achieve positive returns.

Suggested Citation

  • Attila Ceffer & Janos Levendovszky & Norbert Fogarasi, 2019. "Applying Independent Component Analysis and Predictive Systems for Algorithmic Trading," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 281-303, June.
    • Handle: RePEc:kap:compec:v:54:y:2019:i:1:d:10.1007_s10614-017-9719-z
    DOI: 10.1007/s10614-017-9719-z
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    References listed on IDEAS

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    1. Alexei Chekhlov & Stanislav Uryasev & Michael Zabarankin, 2005. "Drawdown Measure In Portfolio Optimization," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 8(01), pages 13-58.
    2. Fogarasi, Norbert & Levendovszky, Janos, 2013. "Sparse, mean reverting portfolio selection using simulated annealing," Algorithmic Finance, IOS Press, vol. 2(3-4), pages 197-211.
    3. Sipos, I. Róbert & Levendovszky, János, 2013. "Optimizing sparse mean reverting portfolios," Algorithmic Finance, IOS Press, vol. 2(2), pages 127-139.
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    Cited by:

    1. Abbas Haider & Hui Wang & Bryan Scotney & Glenn Hawe, 2022. "Predictive Market Making via Machine Learning," SN Operations Research Forum, Springer, vol. 3(1), pages 1-21, March.

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