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.
- Sprache
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Englisch
- Erschienen in
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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
- Klassifikation
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Wirtschaft
Macroeconomics and Monetary Economics: General
International Economics: General
- Thema
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machine learning
exchange rate forecasting
fundamentals
- Ereignis
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Geistige Schöpfung
- (wer)
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Pfahler, Jonathan Felix
- Ereignis
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Veröffentlichung
- (wer)
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MDPI
- (wo)
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Basel
- (wann)
-
2022
- DOI
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doi:10.3390/jrfm15010002
- Handle
- Letzte Aktualisierung
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10.03.2025, 11:44 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
- Pfahler, Jonathan Felix
- MDPI
Entstanden
- 2022
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