用多智能体强化学习优化期权组合，提升投资组合的风险收益比。
DeltaHedge: A Multi-Agent Framework for Portfolio Options Optimization
- 设计多智能体框架，融合期权对冲与AI选股策略。
- 在多种市场条件下，风险调整后收益优于传统方法。
- 适合量化金融与AI投资研究者参考。
在波动性金融市场上，平衡风险与回报仍是重大挑战。传统方法通常只关注股票配置，忽视了期权交易在动态对冲中的战略优势。本文提出DeltaHedge，一个将期权交易与AI驱动的投资组合管理相结合的多智能体框架。通过结合先进的强化学习技术与集成式期权对冲策略，DeltaHedge提升了风险调整后的收益，并在不同市场条件下稳定了投资组合表现。实验结果表明，该方法优于传统策略和独立模型，凸显其在复杂金融环境中变革实际投资管理的潜力。本工作为量化金融与基于AI的投资组合优化领域贡献了新型多智能体系统，填补了现有文献的空白。
原文摘要 · Abstract (English)
In volatile financial markets, balancing risk and return remains a significant challenge. Traditional approaches often focus solely on equity allocation, overlooking the strategic advantages of options trading for dynamic risk hedging. This work presents DeltaHedge, a multi-agent framework that integrates options trading with AI-driven portfolio management. By combining advanced reinforcement learning techniques with an ensembled options-based hedging strategy, DeltaHedge enhances risk-adjusted returns and stabilizes portfolio performance across varying market conditions. Experimental results demonstrate that DeltaHedge outperforms traditional strategies and standalone models, underscoring its potential to transform practical portfolio management in complex financial environments. Building on these findings, this paper contributes to the fields of quantitative finance and AI-driven portfolio optimization by introducing a novel multi-agent system for integrating options trading strategies, addressing a gap in the existing literature.
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