Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/258726 
Erscheinungsjahr: 
2022
Quellenangabe: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 15 [Issue:] 1 [Article No.:] 2 [Publisher:] MDPI [Place:] Basel [Year:] 2022 [Pages:] 1-17
Verlag: 
MDPI, Basel
Zusammenfassung: 
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.
Schlagwörter: 
machine learning
exchange rate forecasting
fundamentals
JEL: 
E00
E04
E05
E06
F00
F04
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