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Exchange rates forecasting and trend analysis after the COVID-19 outbreak: new evidence from interpretable machine learning

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  • Zhi Su
  • Xuanye Cai
  • You Wu

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 ~ 3 lags of exchange rates provide more useful information for forecasting, which can help investors improve their models’ predictive ability.

Suggested Citation

  • Zhi Su & Xuanye Cai & You Wu, 2023. "Exchange rates forecasting and trend analysis after the COVID-19 outbreak: new evidence from interpretable machine learning," Applied Economics Letters, Taylor & Francis Journals, vol. 30(15), pages 2052-2059, September.
    • Handle: RePEc:taf:apeclt:v:30:y:2023:i:15:p:2052-2059
    DOI: 10.1080/13504851.2022.2089621
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    Cited by:

    1. Xu, Yingying & Lien, Donald, 2024. "Together in bad times? The effect of COVID-19 on inflation spillovers in China," International Review of Economics & Finance, Elsevier, vol. 91(C), pages 316-331.

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