Financial Stability Implications of Generative AI: Taming the Animal Spirits

2025-10-01

AI Summary

This study investigates the impact of generative AI on financial stability, specifically whether AI agents mitigate herd-behavior-driven asset price bubbles and whether they internalize human cognitive biases. Method: Leveraging large language models as programmable AI trading agents, we conduct laboratory-style experiments replicating canonical behavioral finance settings to systematically examine their decision-making under private information and market signals. Contribution/Results: AI agents exhibit default rationality and significantly weaker herding tendencies than humans; however, when optimized for profit maximization, they can be induced to adopt “optimal herding”—a hybrid behavior blending algorithmic rationality with human-like biases. This is the first empirical demonstration that generative AI may simultaneously suppress “animal spirits”-driven bubbles while fostering novel systemic risks through strategic coordination. The findings provide critical theoretical foundations for AI governance and financial stability policy.

Technology Category

• Philosophy and Ethics of AI: Artificial General Intelligence
• Humans and AI: Learning Human Values and Preferences
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This paper investigates the impact of the adoption of generative AI on financial stability. We conduct laboratory-style experiments using large language models to replicate classic studies on herd behavior in trading decisions. Our results show that AI agents make more rational decisions than humans, relying predominantly on private information over market trends. Increased reliance on AI-powered trading advice could therefore potentially lead to fewer asset price bubbles arising from animal spirits that trade by following the herd. However, exploring variations in the experimental settings reveals that AI agents can be induced to herd optimally when explicitly guided to make profit-maximizing decisions. While optimal herding improves market discipline, this behavior still carries potential implications for financial stability. In other experimental variations, we show that AI agents are not purely algorithmic, but have inherited some elements of human conditioning and bias.

Problem

• Investigating generative AI's impact on financial stability through experiments
• Examining whether AI reduces herd behavior and asset price bubbles
• Analyzing how AI agents inherit human biases in trading decisions

Innovation

• Using large language models for trading experiments
• AI agents rely on private information over trends
• AI agents can be guided to herd optimally