Deep reinforcement learning for portfolio management based on the empirical study of chinese stock market

• URL: http://arxiv.org/abs/2012.13773v4
• Date: Mon, 19 Apr 2021 16:25:09 GMT
• Title: Deep reinforcement learning for portfolio management based on the empirical study of chinese stock market
• Authors: Gang Huang, Xiaohua Zhou, Qingyang Song
• Abstract summary: This paper is to verify that current cutting-edge technology, deep reinforcement learning, can be applied to portfolio management. In experiments, we use our model in several randomly selected portfolios which include CSI300 that represents the market's rate of return and the randomly selected constituents of CSI500.
• Score: 3.5952664589125916
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: According to documents, there has not been a completely artificial intelligence framework with shorting mechanism in continuous action space for portfolio optimization. The objective of this paper is to verify that current cutting-edge technology, deep reinforcement learning, can be applied to portfolio management, and help us get artificial intelligence. We improve on the existing Deep Reinforcement Learning Portfolio model and make many innovations. Unlike many previous studies on discrete trading signals, we make the agent to short in a continuous action space for portfolio optimization. In addition, we design an arbitrage mechanism based on Arbitrage Pricing Theory, and redesign the activation function for acquiring action vectors. Furthermore, we redesign neural networks for reinforcement learning with reference to deep neural networks that process image data. In experiments, we use our model in several randomly selected portfolios which include CSI300 that represents the market's rate of return and the randomly selected constituents of CSI500. The experimental results show that no matter what stocks we select in our portfolios, we can always get a higher return than the market itself, namely we can get artificial intelligence through deep reinforcement learning to defeat market.

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