用大模型指导投资组合，实现低风险高回报。
Regret-Driven Portfolios: LLM-Guided Smart Clustering for Optimal Allocation
- 结合大模型与在线学习，动态调整投资策略。
- 年化收益比基准高69%，夏普比率提升119%。
- 适合风险敏感的机构投资者和资管经理。
本文旨在缓解中长期投资组合管理中风险与收益间的持续权衡。提出一种新型基于大语言模型（LLM）的无后悔投资组合配置框架，融合在线学习机制、市场情绪指标与LLM驱动的对冲策略，为风险厌恶型投资者及机构基金经理构建高夏普比率的投资组合。该方法基于跟随领先者（follow-the-leader）原则，引入基于情绪的交易过滤与LLM驱动的下行保护机制。实证结果表明，本方法相比SPY买入持有基准，年化收益率提升69%，夏普比率提高119%。
原文摘要 · Abstract (English)
We attempt to mitigate the persistent tradeoff between risk and return in medium- to long-term portfolio management. This paper proposes a novel LLM-guided no-regret portfolio allocation framework that integrates online learning dynamics, market sentiment indicators, and large language model (LLM)-based hedging to construct high-Sharpe ratio portfolios tailored for risk-averse investors and institutional fund managers. Our approach builds on a follow-the-leader approach, enriched with sentiment-based trade filtering and LLM-driven downside protection. Empirical results demonstrate that our method outperforms a SPY buy-and-hold baseline by 69% in annualized returns and 119% in Sharpe ratio.
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