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.
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| Author: | Jonathan Felix PfahlerORCiDGND |
|---|---|
| URN: | urn:nbn:de:bvb:384-opus4-916310 |
| Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/91631 |
| ISSN: | 1911-8074OPAC |
| Parent Title (English): | Journal of Risk and Financial Management |
| Publisher: | MDPI |
| Type: | Article |
| Language: | English |
| Date of first Publication: | 2021/12/21 |
| Publishing Institution: | Universität Augsburg |
| Release Date: | 2022/01/10 |
| Tag: | machine learning; exchange rate forecasting; fundamentals; E0; E4; E5; E6; F0; F4 |
| Volume: | 15 |
| Issue: | 1 |
| First Page: | 2 |
| DOI: | https://doi.org/10.3390/jrfm15010002 |
| Institutes: | Wirtschaftswissenschaftliche Fakultät |
| Wirtschaftswissenschaftliche Fakultät / Institut für Statistik und mathematische Wirtschaftstheorie | |
| Wirtschaftswissenschaftliche Fakultät / Institut für Statistik und mathematische Wirtschaftstheorie / Lehrstuhl für Statistik | |
| Dewey Decimal Classification: | 3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft |
| Licence (German): |  CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand) |
