機械学習による為替フォワード取引期間の判別モデル

抄録

In the rollover of forward foreign exchange contracts, FX brokers generally selects tomorrow-next transaction because of higher liquidity and lower risk. 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 tomorrow-next and longer forward transactions. This timing 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.