Machine Learning Technique in Trading: A Case Study in the EURUSD Market

Qingquan Tony Zhang (), Beibei Li () and Danxia Xie
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Qingquan Tony Zhang: University of Illinois Urbana-Champaign
Beibei Li: Carnegie Mellon University

Chapter Chapter 11 in Alternative Data and Artificial Intelligence Techniques, 2022, pp 199-215 from Palgrave Macmillan

Abstract: Abstract We intend to illustrate profitable trading strategies in the EURUSD exchange rate market using machine learning techniques. Consequently, we applied three supervised learning classification techniques (K-Nearest Neighbors, Support Vector Machines, and Random Forests) in the problem of one day ahead directional prediction of the EURUSD exchange rate with autoregressive terms as inputs. The performance of said machine learning models was benchmarked against two traditional techniques (Naive Strategy and Moving Average Convergence/Divergence). The Random Forest and K-Nearest Neighbors models produced superior results compared to the other models in terms of Net Annualized Returns and Sharpe Ratio.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:pal:psircp:978-3-031-11612-4_11

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DOI: 10.1007/978-3-031-11612-4_11

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