Investor risk profiles of large language models

2026-03-10

AI Summary

This study systematically investigates how large language models (LLMs) form and express investor risk preferences in the context of retail investment advice. Using standardized financial risk questionnaires combined with prompt engineering and persona-based role assignments—such as age, wealth, and investment experience—the authors conduct multi-round evaluations of GPT, Gemini, and Llama. The work reveals, for the first time, that LLMs exhibit identifiable and adjustable default risk preferences, and quantifies their heterogeneous responsiveness to persona-induced interventions. Results show that Gemini displays a neutral and highly consistent risk profile, Llama leans conservative, and GPT tends toward aggressiveness but with notable volatility. All three models adapt their risk profiles according to assigned personas, yet the magnitude of adjustment varies significantly across models.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Humans and AI: Learning Human Values and Preferences

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

This paper investigates how large language models (LLMs) form and express investor risk profiles, a critical component of retail investment advising. We examine three LLMs (GPT, Gemini, and Llama) and assess their responses to a standardized risk questionnaire under varying prompts. In particular, we establish each model's default investment profile by analyzing repeated responses per model. We observe that LLMs are generally longterm investors but exhibit different tendencies in risk tolerance: Gemini has a moderate risk level with highly consistent responses, Llama skews more conservative, and GPT appears moderately aggressive with the greatest variation in answers. Moreover, we find that assigning specific personas such as age, wealth, and investment experience leads each LLM to adjust its risk profile, although the extent of these adjustments differs across the models.

Problem

• large language models
• investor risk profiles
• risk tolerance
• retail investment advising
• prompt variation

Innovation

• large language models
• investor risk profiling
• personalized financial advice
• prompt engineering
• behavioral finance