From humans to algorithms: How financial advice differs across professionals, peers, and LLMs

This study compares belief-driven financial advice from professionals, peers, and LLM with vignettes, eliminating matching problems and enabling belief elicitation without incentive confounds. Repeated identical LLM prompts yield varied risky portfolio recommendations from shifting implicit rules. A Bayesian hierarchical Tobit model captures observed and unobserved heterogeneity. Professionals and peers respond to vignettes consistently with theory but reflect their risk preferences and characteristics. Professional advice differs in responding to client characteristics. The LLM shows smaller variance and great sensitivity to declared risk tolerance. Peers discourage stock participation among younger, lower-income investors with limited professional-advice access; AI can mitigate or reverse this.