Regret-Driven Portfolios: LLM-Guided Smart Clustering for Optimal Allocation
This addresses portfolio optimization for risk-averse investors and institutional fund managers, though it appears incremental as it builds on existing follow-the-leader methods.
The paper tackles the risk-return tradeoff in portfolio management by proposing an LLM-guided no-regret allocation framework, which outperforms a SPY buy-and-hold baseline by 69% in annualized returns and 119% in Sharpe ratio.
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.