Regret-Driven Portfolios: LLM-Guided Smart Clustering for Optimal Allocation

• URL: http://arxiv.org/abs/2601.17021v1
• Date: Fri, 16 Jan 2026 05:11:12 GMT
• Title: Regret-Driven Portfolios: LLM-Guided Smart Clustering for Optimal Allocation
• Abstract summary: We propose a novel LLM-guided no-regret portfolio allocation framework.<n>Our approach builds on a follow-the-leader approach, enriched with sentiment-based trade filtering.<n> Empirical results demonstrate that our method outperforms a SPY buy-and-hold baseline by 69% in annualized returns and 119% in Sharpe ratio.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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