Why Better Models Can Create Riskier Systems: Evidence from LLM Agents in Financial Markets
J. Ross, E. So, Z. De Simone, C. Pozniak, A. W. Lo · 2026
What this means for traders
Several analysts running on the same model and reading the same news are closer to a single opinion than to many: real diversity comes from independent information, not just from different personas.
Abstract
Argues that as language models grow more capable, their errors become more correlated with one another, so a market populated by many LLM agents can carry a risk floor that adding more agents never diversifies away. Across the models studied, the correlation of residual decisions between pairs of models rises with capability, while sharing a provider shows no significant effect. In a simulated single-asset market, more LLM traders improve price discovery under normal conditions, but when every agent reads the same misleading commentary, tracking error rises well above a noise-trader baseline for two of the three model families tested. The authors also show that mixing model families removes only the family-specific part of the correlation; the part that comes from a shared information environment remains.