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

2026-06-29

AI Summary

This work addresses the growing demand for personalized financial advisory services by proposing a novel role-playing framework grounded in large language models. Existing approaches struggle to encode consistent, scalable investment reasoning capabilities, often yielding overly generic advice through standard prompting. In contrast, the proposed method uniquely integrates fund disclosure documents, portfolio holdings changes, market context, and fund manager commentary within a multi-agent loop comprising actor, evaluator, and refiner components that iteratively refine the advisor’s performance. This enables the system to develop transferable, manager-specific investment reasoning—not merely stylistic adaptation. Experimental results demonstrate superior performance over baselines in portfolio reconstruction and commentary alignment tasks, while generating more concrete and actionable investment advice in dialogues involving market scenario generation and investor profile matching.

Abstract

Demand for personalized financial advising is growing, but consistent advisor expertise is difficult to obtain, scale, and encode in LLM systems. Simple persona prompts rarely specify how a financial advisor should reason and often drift toward generic recommendations. We propose Fund2Persona, a framework that grounds financial-advisor personas in fund disclosures, holdings transitions, market context, and manager commentary, then refines them through an agentic actor--scorer--patcher loop. We evaluate the resulting personas on held-out holdings-transition reconstruction and manager-commentary alignment, where they better recover portfolio decisions and grounded manager interpretation than generic baselines. We further study two downstream diagnostics: market-scenario generation, where persona retrieval broadens plausible investment views beyond repeated generic rollouts, and advisory dialogues grounded in investor profiles, where matched personas give more specific and useful advice than a generic advisor. These results suggest that fund-data-grounded financial-advisor personas can make manager-specific investment expertise portable rather than merely changing an LLM's surface style.

Problem

• financial advisor personas
• personalized financial advising
• fund disclosure data
• LLM grounding
• investment expertise

Innovation

• Fund2Persona
• financial advisor personas
• agent-based refinement
• fund disclosure grounding
• personalized investment advice