A novel CNN-DDPG based AI-trader: Performance and roles in business operations. (November 2019)

• Record Type: Journal Article
• Authors: Luo, Suyuan; Lin, Xudong; Zheng, Zunxin
• Is Part Of: Transportation research. Volume 131(2019)
• Page Start: 68
• Page End: 79
• Publication Date: 2019-11
• Subjects: Finance and operations -- Forecasting -- Reinforcement learning -- Deep deterministic policy gradient -- Convolutional neural network; Logistics -- Periodicals; Transportation -- Periodicals; 388.011
• DOI: 10.1016/j.tre.2019.09.013
• Languages: English
• ISSNs: 1366-5545

Abstract

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.