A Machine Learning Approach to Forecasting Carry Trade Returns

Abstract

Carry trade refers to a risky arbitrage in interest rate differentials between two currencies. Persistent excess carry trade returns pose a challenge to foreign exchange market efficiency. Using a data set of ten currencies between 1990 and 2017, we find: (i) a machine learning model, long short-term memory (LSTM) networks, forecast carry trade returns better than linear and threshold regressions based on economic fundamentals; and (ii) excess carry trade returns deteriorate after the 2007--2008 global financial crisis in LSTM networks and other model forecasts, indicating that the uncovered interest rate parity may still hold in the long run.

Keywords: carry trade, uncovered interest rate parity, machine learning, long short-term memory networks

JEL Classification: C45, F21, F31, F37