Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/230812
Authors: 
Dautel, Alexander Jakob
Härdle, Wolfgang Karl
Lessmann, Stefan
Seow, Hsin-Vonn
Year of Publication: 
2020
Series/Report no.: 
IRTG 1792 Discussion Paper No. 2020-006
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.
Subjects: 
Deep learning
Financial time series forecasting
Recurrent neural networks
Foreign exchange rates
JEL: 
C14
C22
C45
Document Type: 
Working Paper

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