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

Muhammad Abro; Hassan Jaleel

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arXiv · 2601.17021

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

Muhammad Abro·Hassan Jaleel

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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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BibTeX RIS

Muhammad Abro, Hassan Jaleel. 2026-01-16. Regret-Driven Portfolios: LLM-Guided Smart Clustering for Optimal Allocation. https://arxiv.org/abs/2601.17021

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