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

117 Pages Posted: 22 Sep 2020

See all articles by Ilias Filippou

Ilias Filippou

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

David Rapach

Washington University in St. Louis; Saint Louis University

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: September 1, 2020

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 naıve 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.

Keywords: carry trade, deep neural network, Elastic Net, exchange rate predictability

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 Trade Portfolio (September 1, 2020). Available at SSRN: https://ssrn.com/abstract=3696388

Ilias Filippou (Contact Author)

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

Washington University in St. Louis ( email )

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

Saint Louis University ( email )

Lindell Boulevard
Saint Louis, MO 63108
United States

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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