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.

翻译：本文旨在缓解中长期投资组合管理中风险与收益间的固有权衡。我们提出一种新颖的LLM引导的无遗憾投资组合配置框架，该框架融合在线学习动态、市场情绪指标以及基于大语言模型（LLM）的风险对冲机制，从而构建适用于风险厌恶型投资者与机构基金管理人的高夏普比率投资组合。本方法以跟随领先者策略为基础，通过情绪驱动的交易筛选与LLM引导的下行保护机制进行增强。实证结果表明，我们的策略相较SPY买入持有基准策略，年化收益率提升69%，夏普比率提高119%。