Prediction of Foreign Exchange Best Rates by Collective Knowledge of Counter Party Banks

Abstract

In foreign-exchange (FX) dealing, FX brokers basically cancel out the orders from their customers to prevent the price fluctuation risk by cover transactions with global megabanks called Counter Party (CP). Each CP has huge amount of money to play a role of market reader, and might have proprietary know-how to foresee future price movements. From this viewpoint, we try to extract their knowledge by a machine learning approach, and therefore we apply the stacking method that aggregates some predictors to extract the ensemble knowledge. If CP's price quotations are decided by foreseeing future price possibilities, their quotations can be considered as predictors. From this concept, we apply the stacking method to their quotations and obtain the ensemble knowledge from them. Through some simulations using real price data, we could confirm that the given ensemble knowledge improves the prediction accuracy of FX price movements compared to the machine learning using a single CP's price quotation.