Journal of Financial and Quantitative Analysis· 2026Q1
Economic Fundamentals and Short-Run Exchange Rate Prediction: A Machine-Learning Perspective
- 3citations
- Q1SCImago
- 2026year
Short summary
Machine learning models incorporating interactions between country characteristics and foreign exchange market volatility significantly improve short-run exchange rate prediction, outperforming traditional methods.
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Key points
- Economic fundamentals can predict short-run exchange rate movements when interacted with global financial conditions.
- Machine learning techniques are effective in handling high-dimensional data for exchange rate forecasting.
- Interactions between country characteristics and foreign exchange market volatility are key predictors, particularly during financial stress ('flight to fundamentals').
- The developed forecasting models deliver significant economic value and offer new insights into the carry trade.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract The exchange rate predictability puzzle—that fundamentals fail to forecast short-horizon exchange rate movements out of sample—has long resisted resolution. We show that fundamentals do matter, but their effects vary with global financial conditions. Using a rich set of country characteristics interacted with global state variables and machine-learning techniques to handle the high-dimensional data, we document that interactions between country characteristics and foreign exchange market volatility are particularly important, consistent with a “flight to fundamentals” during periods of financial stress. Our forecasts deliver substantial economic value and offer new insight into the carry trade.
The authors' abstract, as published at the source. Journal of Financial and Quantitative Analysis, 2026 · DOI ↗
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Field: Finance
FinanceEconomics, Econometrics and Finance