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Discussion Paper Details

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Title: Exchange Rate Prediction with Machine Learning and a Smart Carry Trade Portfolio

Author(s): Ilias Filippou, David Rapach, Mark P Taylor and Guofu Zhou

Publication Date: September 2020

Keyword(s): carry trade, deep neural network, Elastic Net and exchange rate predictability

Programme Area(s): International Macroeconomics and Finance

Abstract: We establish the out-of-sample predictability of monthly exchange rate changes via machine learning techniques based on 70 predictors capturing country characteristics, global variables, and their interactions. To guard against overfitting, we use the elastic net to estimate a high-dimensional panel predictive regression and find that the resulting forecast consistently outperforms the naive no-change benchmark, which has proven difficult to beat in the literature. The forecast also markedly improves the performance of a carry trade portfolio, especially during and after the global financial crisis. When we allow for more complex deep learning models, nonlinearities do not appear substantial in the data.

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Bibliographic Reference

Filippou, I, Rapach, D, Taylor, M and Zhou, G. 2020. 'Exchange Rate Prediction with Machine Learning and a Smart Carry Trade Portfolio'. London, Centre for Economic Policy Research. https://cepr.org/active/publications/discussion_papers/dp.php?dpno=15305