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Forex exchange rate forecasting using deep recurrent neural networks

Author

Listed:
  • Alexander Jakob Dautel

    (Humboldt-Universiät zu Berlin)

  • Wolfgang Karl Härdle

    (Humboldt-Universiät zu Berlin
    Singapore Management University)

  • Stefan Lessmann

    (Humboldt-Universiät zu Berlin)

  • Hsin-Vonn Seow

    (Nottingham University Business School)

Abstract

Deep learning has substantially advanced the state of the art in computer vision, natural language processing, and other fields. The paper examines the potential of deep learning for exchange rate forecasting. We systematically compare long short-term memory networks and gated recurrent units to traditional recurrent network architectures as well as feedforward networks in terms of their directional forecasting accuracy and the profitability of trading model predictions. Empirical results indicate the suitability of deep networks for exchange rate forecasting in general but also evidence the difficulty of implementing and tuning corresponding architectures. Especially with regard to trading profit, a simpler neural network may perform as well as if not better than a more complex deep neural network.

Suggested Citation

  • Alexander Jakob Dautel & Wolfgang Karl Härdle & Stefan Lessmann & Hsin-Vonn Seow, 2020. "Forex exchange rate forecasting using deep recurrent neural networks," Digital Finance, Springer, vol. 2(1), pages 69-96, September.
    • Handle: RePEc:spr:digfin:v:2:y:2020:i:1:d:10.1007_s42521-020-00019-x
    DOI: 10.1007/s42521-020-00019-x
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    Cited by:

    1. Krystian Jaworski, 2021. "Forecasting exchange rates for Central and Eastern European currencies using country‐specific factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 977-999, September.
    2. Davood Pirayesh Neghab & Mucahit Cevik & M. I. M. Wahab, 2023. "Explaining Exchange Rate Forecasts with Macroeconomic Fundamentals Using Interpretive Machine Learning," Papers 2303.16149, arXiv.org.
    3. Fengmin Xu & Jieao Ma, 2023. "Intelligent option portfolio model with perspective of shadow price and risk-free profit," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-28, December.
    4. J. C. Garza Sepúlveda & F. Lopez-Irarragorri & S. E. Schaeffer, 2023. "Forecasting Forex Trend Indicators with Fuzzy Rough Sets," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 229-287, June.
    5. Mehmet Sahiner, 2024. "Volatility Spillovers and Contagion During Major Crises: An Early Warning Approach Based on a Deep Learning Model," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2435-2499, June.
    6. Daniel Poh & Bryan Lim & Stefan Zohren & Stephen Roberts, 2021. "Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention," Papers 2105.10019, arXiv.org, revised Jan 2022.

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    More about this item

    Keywords

    Deep learning; Financial time series forecasting; Recurrent neural networks; Foreign exchange rates;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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