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Tsuji, C. (2022). The Meaning of Structural Breaks for Risk Management: New Evidence, Mechanisms, and Innovative Views for the Post-COVID-19 Era. Quantitative Finance and Economics, 6, 270-302.
https://doi.org/10.3934/QFE.2022012

has been cited by the following article:

• TITLE: Exchange Rate Forecasting via a Machine Learning Approach

  AUTHORS: Chikashi Tsuji

  KEYWORDS: Artificial Intelligence, Exchange Rate, Machine Learning, Random Forest Algorithm

  JOURNAL NAME: iBusiness, Vol.14 No.3, July 12, 2022

  ABSTRACT: This paper attempts to forecast exchange rates by applying a machine learning approach. More specifically, in this study, we attempt to forecast the dynamic evolutions of the four exchange rates of Canadian dollars, Australian dollars, Great Britain pounds, and euros, which are all against the US dollar, by using random forest methodology. Evaluating the effectiveness, we find that the predictive performance of random forest approach in exchange rate forecasting is rather high.