Fund2Persona: A Framework for Building and Refining Financial Advisor Personas from Fund Disclosure Data

Abstract:Demand for personalized financial advice is growing, yet current LLM-based advisors often fail to provide consistent and specialized guidance.\ Simple persona prompts rarely specify how a financial advisor should reason and often drift toward generic recommendations. We propose Fund2Persona, a framework that builds financial-advisor personas from real-world fund disclosures and refines them through an actor--scorer--patcher loop. We test whether the resulting personas can predict held-out portfolio changes and produce commentary consistent with fund managers' own explanations. They outperform generic baselines on both tasks. We further study two downstream diagnostics: market-scenario generation, where persona retrieval broadens plausible investment views, and multi-turn investor--advisor conversations, where matched personas give more specific and useful advice than a generic advisor. These results suggest that real fund data can bring manager-specific investment expertise to LLM advisors rather than merely changing an LLM's surface style.

18 pages, 4 figures, 18 tables. EMNLP 2026 Industry Track

Computation and Language (cs.CL); General Finance (q-fin.GN)

arXiv:2606.29793 [cs.CL]

(or arXiv:2606.29793v3 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.29793 Focus to learn more arXiv-issued DOI via DataCite

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