Modelling and Forecasting of EUR/USD Exchange Rate Using Ensemble Learning Approach

Abstract

The aim of the study is to obtain an accurate result from forecasting the EUR/USD exchange rate. To this end, high-performance machine learning models using CART Ensembles and Bagging method have been developed. Key macroeconomic indicators have been also examined including inflation in Europe and the United States, the index of unemployment in Europe and the United States, and more. Official monthly data in the period from December 1998 to December 2021 have been studied. A careful analysis of the macroeconomic time series has shown that their lagged variables are suitable for model’s predictors. CART Ensembles and Bagging predictive models having been built, explaining up to 98.8% of the data with MAPE of 1%. The degree of influence of the considered macroeconomic indicators on the EUR/USD rate has been established. The models have been used for forecasting one-month-ahead. The proposed approach could find a practical application in professional trading, budgeting and currency risk hedging.

Keywords

• cart-ensembles and bagging
• machine learning
• predictive model
• autoregressive integrated moving average
• key macroeconomic indicators