Discussion paper

DP15305 Exchange Rate Prediction with Machine Learning and a Smart Carry Trade Portfolio

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 overfi tting, 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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Citation

Filippou, I, D Rapach, M Taylor and G Zhou (eds) (2020), “DP15305 Exchange Rate Prediction with Machine Learning and a Smart Carry Trade Portfolio”, CEPR Press Discussion Paper No. 15305. https://cepr.org/publications/dp15305