Artikel
Forex exchange rate forecasting using deep recurrent neural networks
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.
- Sprache
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Englisch
- Erschienen in
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Journal: Digital Finance ; ISSN: 2524-6186 ; Volume: 2 ; Year: 2020 ; Issue: 1-2 ; Pages: 69-96 ; Cham: Springer International Publishing
- Klassifikation
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Wirtschaft
Semiparametric and Nonparametric Methods: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Neural Networks and Related Topics
- Thema
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Deep learning
Financial time series forecasting
Recurrent neural networks
Foreign exchange rates
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Dautel, Alexander Jakob
Härdle, Wolfgang Karl
Lessmann, Stefan
Seow, Hsin-Vonn
- Ereignis
-
Veröffentlichung
- (wer)
-
Springer International Publishing
- (wo)
-
Cham
- (wann)
-
2020
- DOI
-
doi:10.1007/s42521-020-00019-x
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Dautel, Alexander Jakob
- Härdle, Wolfgang Karl
- Lessmann, Stefan
- Seow, Hsin-Vonn
- Springer International Publishing
Entstanden
- 2020
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