Deep Reinforcement Learning for Long-Short Portfolio Optimization
This research provides a novel methodology for quantitative investment practice by improving portfolio optimization and risk management for investors in the China A-share market.
This paper develops a Deep Reinforcement Learning (DRL) framework for long-short portfolio optimization, incorporating short-selling mechanisms that conform to real-world trading rules in China's A-share market. The DRL model achieved consistent positive returns, superior risk-adjusted returns, and reduced maximum drawdown compared to traditional approaches during backtesting.
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.