A novel CNN-DDPG based AI-trader: Performance and roles in business operations. (November 2019) - Luo, Suyuan; Lin, Xudong; Zheng, Zunxin
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.