Exchange Rate Prediction with Machine Learning and a Smart Carry Portfolio
61 Pages Posted: 27 Sep 2019 Last revised: 6 Jul 2021
Date Written: July 2, 2021
We establish the out-of-sample predictability of monthly exchange rates via machine learning techniques based on 70 predictors capturing country characteristics, global variables, and their interactions. To better guard against overfitting in our high-dimensional and noisy data environment, we make additional adjustments to “off-the-shelf” implementations of machine learning techniques, including imposing economic constraints. The resulting forecasts consistently outperform the no-change benchmark, which has proven difficult to beat. Country characteristics are important for forecasting, once they interact with global variables. Machine learning forecasts also markedly improve the performance of a carry trade portfolio, especially since the global financial crisis.
Keywords: Short-horizon exchange rate predictability, Panel predictive regression, Elastic net, Deep neural network, Carry trade
JEL Classification: C45, F31, F37, G11, G12, G15
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