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Directional forecasting in financial time series using support vector machines: The USD/Euro exchange rate

Author

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  • Plakandaras, Vasilios

    (Democritus University of Thrace, Department of International Economic Relations and Development)

  • Papadimitriou, Theophilos

    (Democritus University of Thrace, Department of International Economic Relations and Development)

  • Gogas, Periklis

    (Democritus University of Thrace, Department of International Economic Relations and Development)

Abstract

In this paper, we present a novel machine learning based forecasting system of the EU/USD exchange rate directional changes. Specifically, we feed an overcomplete variable set to a Support Vector Machines (SVM) model and refine it through a Sensitivity Analysis process. The dataset spans from 1/1/1999 to 30/11/2011; the data of the last 7 months are reserved for out-of-sample testing. Results show that the proposed scheme outperforms various other machine learning methods treating similar scenarios.

Suggested Citation

  • Plakandaras, Vasilios & Papadimitriou, Theophilos & Gogas, Periklis, 2012. "Directional forecasting in financial time series using support vector machines: The USD/Euro exchange rate," DUTH Research Papers in Economics 5-2012, Democritus University of Thrace, Department of Economics.
    • Handle: RePEc:ris:duthrp:2012_005
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    References listed on IDEAS

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    Cited by:

    1. Kea BARET & Theophilos PAPADIMITRIOU, 2019. "On the Stability and Growth Pact compliance: what is predictable with machine learning?," Working Papers of BETA 2019-48, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    2. Pragidis, Ioannis & Gogas, Periklis & Plakandaras, Vasilios & Papadimitriou, Theophilos, 2015. "Fiscal shocks and asymmetric effects: A comparative analysis," The Journal of Economic Asymmetries, Elsevier, vol. 12(1), pages 22-33.
    3. Christina Christou & Rangan Gupta & Christis Hassapis & Tahir Suleman, 2018. "The role of economic uncertainty in forecasting exchange rate returns and realized volatility: Evidence from quantile predictive regressions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(7), pages 705-719, November.
    4. Rangan Gupta & Vasilios Plakandaras, 2019. "Efficiency in BRICS Currency Markets Using Long-Spans of Data: Evidence from Model-Free Tests of Directional Predictability," Journal of Economics and Behavioral Studies, AMH International, vol. 11(1), pages 152-165.

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    More about this item

    Keywords

    Machine Learning; Support Vector Machines; Exchange Rates; Forecasting;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C59 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Other
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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