Machine Learning Forecasting of EUR/USD Trends with Qualitative Cross-Validation from Institutional Reports
Abstract
This study presents a comparative methodology for forecasting weekly trends in the EUR/USD exchange rate using daily financial data. A first model is calibrated using a Classification and Regression Tree (CART), followed by an enhanced version based on the Random Forest algorithm. The analysis evaluates statistical performance, model robustness, interpretability, and decision-making quality through systematic backtesting.
The machine learning models are trained to classify weekly currency movements into two categories: +1 for an expected rise and -1 for an expected decline. A prediction is made every 5 days to anticipate the market direction for the subsequent 5-day period, using only information available at the time of the prediction (ensuring strict causality).
To complement and validate the quantitative results, a qualitative layer is introduced using a large language model (LLM) to extract directional sentiment from institutional FX research reports. This dual approach enhances the interpretability and contextual relevance of the forecasting framework.
Keywords: EUR/USD, machine learning, Random Forest, CART, forecasting, large language models, sentiment analysis, time series
DOI: 10.7176/EJBM/17-4-06
Publication date: May 30th 2025

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ISSN (Paper)2222-1905 ISSN (Online)2222-2839
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