Predicting High Frequency Exchange Rates using Machine Learning

Abstract: This thesis applies a committee of Artificial Neural Networks and Support Vector Machines on high-dimensional, high-frequency EUR/USD exchange rate data in an effort to predict directional market movements on up to a 60 second prediction horizon. The study shows that combining multiple classifiers into a committee produces improved precision relative to the best individual committee members and outperforms previously reported results. A trading simulation implementing the committee classifier yields promising results and highlights the possibility of developing a profitable trading strategy based on the limit order book and historical transactions alone.