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Forecasting Exchange Rates with Artificial Intelligence: A Study of European Currencies

Author

Listed:
  • Chikashi Tsuji

Abstract

With the increasing interest in the application of artificial intelligence in various fields, this paper aims to predict exchange rates using artificial intelligence. Specifically, we focus on European currencies such as the euro, British pound, Swiss franc, and Swedish krona. We apply the random forest method and gradient boosting approach of the XGBoost model to the four exchange rates for the period from January 2010 to January 2025. Through our meticulous analysis using daily data, we find that overall, the random forest is more effective than the gradient boosting approach using the XGBoost model for predicting the exchange rates of the four European currencies.

Suggested Citation

  • Chikashi Tsuji, 2025. "Forecasting Exchange Rates with Artificial Intelligence: A Study of European Currencies," International Business Research, Canadian Center of Science and Education, vol. 18(5), pages 1-24, October.
    • Handle: RePEc:ibn:ibrjnl:v:18:y:2025:i:5:p:24
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    References listed on IDEAS

    as
    1. Justin Sirignano & Rama Cont, 2019. "Universal features of price formation in financial markets: perspectives from deep learning," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1449-1459, September.
    2. Le Quoc Tuan & Chih-Yung Lin & Huei-Wen Teng, 2024. "Machine learning methods for predicting failures of US commercial bank," Applied Economics Letters, Taylor & Francis Journals, vol. 31(15), pages 1353-1359, September.
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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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