A Multi-Agent Approach to Investor Profiling Using Large Language Models

Abstract

Investor profiling is essential in financial advising, allowing advisors to tailor investment strategies based on individual risk preferences, experience, and financial goals. This research aims to automate and enhance the investor profiling process using large language models (LLMs) through interactive multi-agent conversations. Our approach involves designing an investor agent, which represents a pre-defined investor derived from a narrative generated by a large language model (LLM) with given attributes, and an advisor agent that engages in conversation to infer the hidden attributes of the investor. The advisor-agent dynamically adjusts its questions based on previous conversation context to maximize the accuracy of its attribute predictions. The advisor agent makes predictions once it acquires sufficient information and compares them against the ground truth. We conducted extensive simulations across thousands of investor attribute sets and evaluated the effectiveness of the advisor-agent’s predictions based on key metrics. Our results demonstrate that LLM can effectively approximate investor characteristics. This research contributes to the field of AI-driven financial advising and unveils the potential of conversational agents in refining investor assessment methodologies.