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Nonlinear relationships in soybean commodities Pairs trading-test by deep reinforcement learning

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  • Liu, Jianhe
  • Lu, Luze
  • Zong, Xiangyu
  • Xie, Baao

Abstract

The pairs trading strategy involves selecting two highly correlated securities to profit from mean reversion. However, the traditional simple threshold method is subjective, random, and ignores nonlinear relationships. This paper proposes a new cointegration deep reinforcement learning (DRL) pairs trading model applied to Dalian Commodity Exchange futures to capture nonlinear relationships and gain profits. The CA-DRL model outperforms other models in terms of efficiency and performance.

Suggested Citation

  • Liu, Jianhe & Lu, Luze & Zong, Xiangyu & Xie, Baao, 2023. "Nonlinear relationships in soybean commodities Pairs trading-test by deep reinforcement learning," Finance Research Letters, Elsevier, vol. 58(PC).
    • Handle: RePEc:eee:finlet:v:58:y:2023:i:pc:s1544612323008498
    DOI: 10.1016/j.frl.2023.104477
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    References listed on IDEAS

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    Cited by:

    1. Ma, Cong & Nan, Shijing, 2024. "Dynamic graph reinforcement learning algorithm for portfolio management: A novel time–frequency correlated model," Finance Research Letters, Elsevier, vol. 63(C).
    2. Bo Yan & Mengru Liang & Yinxin Zhao, 2024. "Market sentiment and price dynamics in weak markets: A comprehensive empirical analysis of the soybean meal option market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(5), pages 744-766, May.
    3. Guo, Minjia & Liu, Jianhe & Luo, Ziping & Han, Xiao, 2024. "Deep reinforcement learning for pairs trading: Evidence from China black series futures," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 981-993.

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