著者

矢野 和洞 鈴木 丈裕 鈴木 智也

出版者

Research Institute of Signal Processing, Japan

雑誌

Journal of Signal Processing (ISSN:13426230)

巻号頁・発行日

vol.24, no.3, pp.113-122, 2020-05-15 (Released:2020-05-15)

参考文献数

10

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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].