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

Suyuan Luo, Xudong Lin and Zunxin Zheng

Transportation Research Part E: Logistics and Transportation Review, 2019, vol. 131, issue C, 68-79

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.

Keywords: Finance and operations; Forecasting; Reinforcement learning; Deep deterministic policy gradient; Convolutional neural network (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (16)

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DOI: 10.1016/j.tre.2019.09.013

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