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Forex Exchange Rate Forecasting Using Deep Recurrent Neural Networks

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  • Dautel, Alexander J.
  • Härdle, Wolfgang Karl
  • Lessmann, Stefan
  • Seow, Hsin-Vonn

Abstract

Deep learning has substantially advanced the state-of-the-art in computer vision, natural language processing and other elds. The paper examines the potential of contemporary recurrent deep learning architectures for nancial time series forecasting. Considering the foreign exchange market as testbed, we systematically compare long short-term memory networks and gated recurrent units to traditional recurrent 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 diculty of implementing and tuning corresponding architectures. Especially with regard to trading pro t, a simpler neural network may perform as well as if not better than a more complex deep neural network.

Suggested Citation

  • Dautel, Alexander J. & Härdle, Wolfgang Karl & Lessmann, Stefan & Seow, Hsin-Vonn, 2019. "Forex Exchange Rate Forecasting Using Deep Recurrent Neural Networks," IRTG 1792 Discussion Papers 2019-008, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    • Handle: RePEc:zbw:irtgdp:2019008
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    Cited by:

    1. Zimeng Lyu & Amulya Saxena & Rohaan Nadeem & Hao Zhang & Travis Desell, 2024. "Neuroevolution Neural Architecture Search for Evolving RNNs in Stock Return Prediction and Portfolio Trading," Papers 2410.17212, arXiv.org.
    2. 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.
    3. Sylvain Barthélémy & Virginie Gautier & Fabien Rondeau, 2024. "Early warning system for currency crises using long short‐term memory and gated recurrent unit neural networks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1235-1262, August.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.

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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:

    • C00 - Mathematical and Quantitative Methods - - General - - - General

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