Machine learning in FX trading with long short-term memory recurrent neural networks - Mäenpää, Eero (My University, 2019)
Advanced machine learning has outperformed previous benchmarks in numerous fields and applications, e.g. speech and image recognition, in an ever accelerating way during the past two decades. Furthermore, early evidence indicates its massive potential to improve over traditional techniques applied to financial market prediction tasks. In this study, long short-term memory (LSTM) networks are applied to G10 currency market prediction task in a sample period from the beginning of 1999 to the end of 2018. The predictive factor set consists of past excess returns, forward premiums and seven factors capturing risk-aversion, price uncertainty, commodity returns and funding liquidity. The LSTM model reaches a total accuracy of more than 55% outperforming simple recurrent neural networks (RNN), which indicates that currency excess returns are partly driven by signals with more than a two-month temporal distance. The model predictions are used in US dollar-neutral currency trading strategy in order to exploit the attractiveness of this approach in terms of performance metrics and to analyse its sources of profitability. The LSTM model is able to predict the profitability of carry and momentum strategies. In particular, the LSTM portfolio utilizes carry and long-term momentum signals during calm market conditions and short-term momentum signals in market turmoil. Consequently, the best performing LSTM portfolio delivers a Sharpe ratio of 0.32 with less tail risk than the carry portfolio that has a Sharpe ratio of 0.23. Furthermore, it is not exposed to the common risk factors driving currency carry and momentum trading strategies. Attractive risk-return profile and low correlation with equity markets make the LSTM portfolio extremely suitable to the FX risk management of an international equity portfolio. When the LSTM portfolio is used in the modified portfolio mean-variance optimization routine that is introduced by Boudoukh et al. (2018), the Sharpe ratio of an unhedged international equity portfolio is almost doubled as the Sharpe ratio increases from 0.29 to 0.51.
Keywords: Foreign exchange market, machine learning, forecasting