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Machine Learning and Algorithmic Pairs Trading in Futures Markets

Author

Listed:
  • Seungho Baek

    (Department of Finance, Brooklyn College, City University of New York, 2900 Bedford Ave, Brooklyn, NY 11210, USA)

  • Mina Glambosky

    (Department of Finance, Brooklyn College, City University of New York, 2900 Bedford Ave, Brooklyn, NY 11210, USA)

  • Seok Hee Oh

    (Department of Computer Engineering, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do 461-701, Korea)

  • Jeong Lee

    (Department of Economics and Finance, University of North Dakota, Gamble Hall Room 110, 293 Centennial Dr Stop 8098, Grand Forks, ND 58202, USA)

Abstract

This study applies machine learning methods to develop a sustainable pairs trading market-neutral investment strategy across multiple futures markets. Cointegrated pairs with similar price trends are identified, and a hedge ratio is determined using an Error Correction Model (ECM) framework and support vector machine algorithm based upon the two-step Engle–Granger method. The study shows that normal backwardation and contango do not consistently characterize futures markets, and an algorithmic pairs trading strategy is effective, given the unique predominant price trends of each futures market. Across multiple futures markets, the pairs trading strategy results in larger risk-adjusted returns and lower exposure to market risk, relative to an appropriate benchmark. Backtesting is employed and results show that the pairs trading strategy may hedge against unexpected negative systemic events, specifically the COVID-19 pandemic, remaining profitable over the period examined.

Suggested Citation

  • Seungho Baek & Mina Glambosky & Seok Hee Oh & Jeong Lee, 2020. "Machine Learning and Algorithmic Pairs Trading in Futures Markets," Sustainability, MDPI, vol. 12(17), pages 1-24, August.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:17:p:6791-:d:402176
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    References listed on IDEAS

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