Exchange rates forecasting and trend analysis after the COVID-19 outbreak: new evidence from interpretable machine learning
Applied Economics Letters
; : 1-8, 2022.
Article
in English
| Web of Science | ID: covidwho-1908571
ABSTRACT
We investigate the predictability of 12 exchange rates with machine learning, Deep Learning and interpretable machine learning (IML) models, based on a daily dataset from December 2019 to August 2021. We find that the appreciation and depreciation of exchange rates can be partly captured by Light Gradient Boosting Machine (LightGBM) and Long Short-Term Memory, especially for the developed currencies. Inconsistent with general perception, the LightGBM model performs the best in exchange rates forecasting since its short-term information extracting mode and great robustness on small datasets. Furthermore, by employing a representative global IML method, the Accumulated Local Effect algorithm, we find that the 1 similar to 3 lags of exchange rates provide more useful information for forecasting, which can help investors improve their models' predictive ability.
Full text:
Available
Collection:
Databases of international organizations
Database:
Web of Science
Language:
English
Journal:
Applied Economics Letters
Year:
2022
Document Type:
Article
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