@inproceedings{park-etal-2026-fund2persona,
title = "{F}und2{P}ersona: A Framework for Building and Refining Financial Advisor Personas from Fund Disclosure Data",
author = "Park, Suhwan and
Lee, Hoyoung and
Wang, Zhangyang and
Lopez-Lira, Alejandro and
Cha, Young and
Choi, Chanyeol and
Choi, Jaewon and
Lee, Yongjae",
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.249/",
pages = "4212--4229",
ISBN = "979-8-89176-460-6",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Fund2Persona: A Framework for Building and Refining Financial Advisor Personas from Fund Disclosure Data
%A Park, Suhwan
%A Lee, Hoyoung
%A Wang, Zhangyang
%A Lopez-Lira, Alejandro
%A Cha, Young
%A Choi, Chanyeol
%A Choi, Jaewon
%A Lee, Yongjae
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F park-etal-2026-fund2persona
%X 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.
%U https://aclanthology.org/2026.emnlp-industry.249/
%P 4212-4229
Markdown (Informal)
[Fund2Persona: A Framework for Building and Refining Financial Advisor Personas from Fund Disclosure Data](https://aclanthology.org/2026.emnlp-industry.249/) (Park et al., EMNLP 2026)
ACL
- Suhwan Park, Hoyoung Lee, Zhangyang Wang, Alejandro Lopez-Lira, Young Cha, Chanyeol Choi, Jaewon Choi, and Yongjae Lee. 2026. Fund2Persona: A Framework for Building and Refining Financial Advisor Personas from Fund Disclosure Data. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 4212–4229, Budapest, Hungary. Association for Computational Linguistics.