Optimization of the prediction performance in the future exchange rate

Abstract

International trade is one of the most important elements in both national and global economies. It involves transactions in different currencies, requiring exchange rate conversions. Fluctuations in exchange rates can have significant implications for international trade. Currency risk arises from these fluctuations and can impact the profitability and stability of international trade. Currency risk management strategies, such as hedging and diversification, are employed by businesses and investors to mitigate the adverse effects of currency fluctuations on trade and financial transactions. To cover that risk, machine learning techniques offer valuable tools for addressing currency risk. By analyzing historical exchange rate data, macroeconomic indicators, and other relevant factors, machine learning algorithms can uncover patterns and relationships that influence currency movements. In this article, we’re going to use the random forest algorithm that is a suitable choice for addressing currency risk due to its strengths in handling complex and non-linear relationships. And we’re going to use Bayesian optimization algorithm to optimize the parameters and configurations of this machine learning algorithm and minimize the cross-validation value to manage the risk of the currency.