Regret-Driven Portfolios: LLM-Guided Smart Clustering for Optimal Allocation
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866914276723130368 |
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| author | Abro, Muhammad Jaleel, Hassan |
| author_facet | Abro, Muhammad Jaleel, Hassan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17021 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Regret-Driven Portfolios: LLM-Guided Smart Clustering for Optimal Allocation Abro, Muhammad Jaleel, Hassan Portfolio Management Machine Learning Multiagent Systems 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. |
| title | Regret-Driven Portfolios: LLM-Guided Smart Clustering for Optimal Allocation |
| topic | Portfolio Management Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2601.17021 |