カバー先銀行の集合知による外国為替レートの短期予測

抄録

Foreign-exchange (FX) brokers have some risk factors such as price fluctuation risk and latency of data transmission. To reduce these risks in FX brokerage services, we propose a short-term prediction of exchange rates quoted by counter-party banks. We consider that these exchange rates are generated by the knowledge of each counter-party bank, and therefore try to extract the knowledge by using a machine learning method. As a result, we could predict the direction of exchange rates with a prediction accuracy of about 80% if the prediction interval is 100[ms]. Furthermore, by integrating the knowledge of counterparty banks by the ensemble learning, we could improve not only prediction accuracy but also profitability of foreign-exchange brokers. These improvements can be considered as an effect of collective knowledge based on the diversity prediction theorem, but this effect might be limited by extremely short-term prediction of foreign-exchange rates after 100[ms]~200[ms].