Exchange Rate Prediction with Machine Learning and a Smart Carry Portfolio

61 Pages Posted: 27 Sep 2019 Last revised: 6 Jul 2021

See all articles by Ilias Filippou

Ilias Filippou

Washington University in St. Louis - John M. Olin Business School

David Rapach

Saint Louis University; Washington University in St. Louis

Mark P. Taylor

Washington University in St. Louis - John M. Olin Business School; Centre for Economic Policy Research (CEPR); Brookings Institution

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School

Date Written: July 2, 2021

Abstract

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

Suggested Citation

Filippou, Ilias and Rapach, David and Taylor, Mark P. and Zhou, Guofu, Exchange Rate Prediction with Machine Learning and a Smart Carry Portfolio (July 2, 2021). Available at SSRN: https://ssrn.com/abstract=3455713 or http://dx.doi.org/10.2139/ssrn.3455713

Ilias Filippou

Washington University in St. Louis - John M. Olin Business School ( email )

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

David Rapach (Contact Author)

Saint Louis University ( email )

3674 Lindell Blvd
St. Louis, MO 63108-3397
United States

HOME PAGE: http://https://sites.google.com/slu.edu/daverapach

Washington University in St. Louis

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

HOME PAGE: http://https://sites.google.com/slu.edu/daverapach

Mark P. Taylor

Washington University in St. Louis - John M. Olin Business School ( email )

One Brookings Drive
Campus Box 1156
St. Louis, MO 63130-4899
United States

Centre for Economic Policy Research (CEPR)

London
United Kingdom

Brookings Institution ( email )

1775 Massachusetts Ave, NW
Washington, DC 20036
United States

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School ( email )

Washington University
Campus Box 1133
St. Louis, MO 63130-4899
United States
314-935-6384 (Phone)
314-658-6359 (Fax)

HOME PAGE: http://apps.olin.wustl.edu/faculty/zhou/

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