Forex exchange rate forecasting using deep recurrent neural networks

Alexander Jakob Dautel (), Wolfgang Härdle, Stefan Lessmann () and Hsin-Vonn Seow ()
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Alexander Jakob Dautel: Humboldt-Universiät zu Berlin
Stefan Lessmann: Humboldt-Universiät zu Berlin
Hsin-Vonn Seow: Nottingham University Business School

Digital Finance, 2020, vol. 2, issue 1, No 4, 69-96

Abstract: 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.

Keywords: Deep learning; Financial time series forecasting; Recurrent neural networks; Foreign exchange rates (search for similar items in EconPapers)
JEL-codes: C14 C22 C45 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (9)

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Working Paper: Forex exchange rate forecasting using deep recurrent neural networks (2020)
Working Paper: Forex Exchange Rate Forecasting Using Deep Recurrent Neural Networks (2019)
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DOI: 10.1007/s42521-020-00019-x

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