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A novel CNN-DDPG based AI-trader: Performance and roles in business operations

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  • Luo, Suyuan
  • Lin, Xudong
  • Zheng, Zunxin

Abstract

Artificial Intelligence (AI) is well-developed as a part of human life. In both financial markets and business operations, AI is getting more and more important. In this paper, we build a novel “Reinforcement Learning” (RL) framework based AI-trader. We adopt an actor-critic RL algorithm called “Deep Deterministic Policy Gradient” (DDPG) to find the optimal policy. Our proposed DDPG has two different convolutional neutral networks (CNNs) based function approximators. The proposed AI-trader’s performance is shown to outperform other methods with the use of real stock-index future data. We further discuss the generalization and implications of the proposed method for business operations.

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  • Luo, Suyuan & Lin, Xudong & Zheng, Zunxin, 2019. "A novel CNN-DDPG based AI-trader: Performance and roles in business operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 68-79.
    • Handle: RePEc:eee:transe:v:131:y:2019:i:c:p:68-79
    DOI: 10.1016/j.tre.2019.09.013
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    8. Shrutika Mishra & A. R. Tripathi, 2021. "AI business model: an integrative business approach," Journal of Innovation and Entrepreneurship, Springer, vol. 10(1), pages 1-21, December.
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    13. Gabriel Borrageiro & Nick Firoozye & Paolo Barucca, 2021. "Reinforcement Learning for Systematic FX Trading," Papers 2110.04745, arXiv.org, revised May 2022.
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