Artikel
Exchange rate forecasting with advanced machine learning methods
Historically, exchange rate forecasting models have exhibited poor out-of-sample performances and were inferior to the random walk model. Monthly panel data from 1973 to 2014 for ten currency pairs of OECD countries are used to make out-of sample forecasts with artificial neural networks and XGBoost models. Most approaches show significant and substantial predictive power in directional forecasts. Moreover, the evidence suggests that information regarding prediction timing is a key component in the forecasting performance.
- Language
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Englisch
- Bibliographic citation
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Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 15 ; Year: 2022 ; Issue: 1 ; Pages: 1-17 ; Basel: MDPI
- Classification
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Wirtschaft
Macroeconomics and Monetary Economics: General
International Economics: General
- Subject
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machine learning
exchange rate forecasting
fundamentals
- Event
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Geistige Schöpfung
- (who)
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Pfahler, Jonathan Felix
- Event
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Veröffentlichung
- (who)
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MDPI
- (where)
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Basel
- (when)
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2022
- DOI
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doi:10.3390/jrfm15010002
- Handle
- Last update
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10.03.2025, 11:44 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Pfahler, Jonathan Felix
- MDPI
Time of origin
- 2022
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