Deep Reinforcement Learning for Long-Short Portfolio Optimization

Understand

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.