Dynamic Portfolio Management Based on Pair Trading and Deep Reinforcement Learning

F. Xu, S. Tan
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引用次数: 4

Abstract

Existing portfolio management methods have made great progress in diversifying non-systematic risks, but they have ignored systemic risks. In response to this issue, we proposed a dynamic, market-neutral, risk-diversified portfolio management model by combining the ideas of pair trading strategy, deep reinforcement learning with traditional portfolio management model. We conduct an experiment on the Chinese A-share market by selecting 32 pairs of stocks. The experiment results showed that the proposed pair-based deep portfolio model has superiority for dynamic portfolio management problem in trade-off investment returns and risks.
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基于配对交易和深度强化学习的动态投资组合管理
现有的投资组合管理方法在分散非系统性风险方面取得了很大进展,但却忽视了系统性风险。针对这一问题,我们将配对交易策略、深度强化学习的思想与传统的投资组合管理模型相结合,提出了一个动态的、市场中性的、风险多元化的投资组合管理模型。我们选取了32对股票,对中国a股市场进行了实验。实验结果表明,本文提出的基于对的深度投资组合模型对于投资收益与风险权衡的动态投资组合管理问题具有优越性。
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