Highlights Proposing and validating a novel CNN-DDPG based AI-trader application. Can be generalized for other business operations such as demand forecasting in logistics and risk hedging. This paper lays the foundation for further studies with AI applications.

    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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    Title :

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


    Contributors:
    Luo, Suyuan (author) / Lin, Xudong (author) / Zheng, Zunxin (author)


    Publication date :

    2019-09-26


    Size :

    12 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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