Deep Reinforcement Learning for Long-Short Portfolio Optimization

• URL: http://arxiv.org/abs/2012.13773v8
• Date: Sat, 15 Mar 2025 17:27:19 GMT
• Title: Deep Reinforcement Learning for Long-Short Portfolio Optimization
• Authors: Gang Huang, Xiaohua Zhou, Qingyang Song,
• Abstract summary: This paper constructs a Deep Reinforcement Learning (DRL) portfolio management framework with short-selling mechanisms conforming to actual trading rules.<n>Key innovations include development of a comprehensive short-selling mechanism in continuous trading that accounts for dynamic evolution of transactions across time periods.<n>Compared to traditional approaches, this model delivers superior risk-adjusted returns while reducing maximum drawdown.
• Score: 7.131902599861306
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the rapid development of artificial intelligence, data-driven methods effectively overcome limitations in traditional portfolio optimization. Conventional models primarily employ long-only mechanisms, excluding highly correlated assets to diversify risk. However, incorporating short-selling enables low-risk arbitrage through hedging correlated assets. This paper constructs a Deep Reinforcement Learning (DRL) portfolio management framework with short-selling mechanisms conforming to actual trading rules, exploring strategies for excess returns in China's A-share market. Key innovations include: (1) Development of a comprehensive short-selling mechanism in continuous trading that accounts for dynamic evolution of transactions across time periods; (2) Design of a long-short optimization framework integrating deep neural networks for processing multi-dimensional financial time series with mean Sharpe ratio reward functions. Empirical results show the DRL model with short-selling demonstrates significant optimization capabilities, achieving consistent positive returns during backtesting periods. Compared to traditional approaches, this model delivers superior risk-adjusted returns while reducing maximum drawdown. From an allocation perspective, the DRL model establishes a robust investment style, enhancing defensive capabilities through strategic avoidance of underperforming assets and balanced capital allocation. This research contributes to portfolio theory while providing novel methodologies for quantitative investment practice.

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