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

In: Alternative Data and Artificial Intelligence Techniques

Author

Listed:
  • Qingquan Tony Zhang

    (University of Illinois Urbana-Champaign)

  • Beibei Li

    (Carnegie Mellon University)

  • Danxia Xie

    (Tsinghua University)

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.

Suggested Citation

  • Qingquan Tony Zhang & Beibei Li & Danxia Xie, 2022. "Machine Learning Technique in Trading: A Case Study in the EURUSD Market," Palgrave Studies in Risk and Insurance, in: Alternative Data and Artificial Intelligence Techniques, chapter 0, pages 199-215, Palgrave Macmillan.
    • Handle: RePEc:pal:psircp:978-3-031-11612-4_11
    DOI: 10.1007/978-3-031-11612-4_11
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