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

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  • Muhammad Abro
  • Hassan Jaleel

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.

Suggested Citation

  • Muhammad Abro & Hassan Jaleel, 2026. "Regret-Driven Portfolios: LLM-Guided Smart Clustering for Optimal Allocation," Papers 2601.17021, arXiv.org.
    • Handle: RePEc:arx:papers:2601.17021
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    File URL: http://arxiv.org/pdf/2601.17021
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