Machine learning of optimal timing in currency forward trading

Abstract

In the rollover of forward foreign exchange contracts, FX brokers generally select tomorrow-next transactions because of higher liquidity. However, it might be possible to obtain larger swap points by selecting longer forward transactions such as one-week or three-week forward in terms of the term premium. Therefore, we detect optimal timings to select longer forward transactions by machine learning techniques, and propose a mixed strategy that combines longer and shorter forward transactions. These timings might be affected by various factors such as global stocks, bonds, commodities, etc., and we could obtain larger swap points by the mixed strategy using the machine learning with these global factors.