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.

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