Simulating Systemic Market Crashes and Behavioral Cascades Using Generative AI Multi-Agent Systems

Introduction: Traditional agent-based models (ABMs) in finance rely on rigid, rule-based heuristics that fail to capture the complex, bounded rationality and psychological biases of human traders. This study introduces a novel framework utilizing Generative AI Multi-Agent Systems to simulate systemic market crashes and behavioral cascades in financial networks.

Methods: We construct a simulated financial market environment populated by multiple heterogeneous Large Language Model (LLM)-based agents. Each agent is initialized with unique trading personas (e.g., risk-averse institutional investor, noise trader, aggressive speculator) and programmed with cognitive biases, including herding behavior and loss aversion. The agents interact dynamically, executing trades based on real-time simulated financial news feeds and macroeconomic shocks.

Results: Our simulations demonstrate that LLM-based agents exhibit realistic emergent behaviors under stress. When subjected to negative macroeconomic shocks, generative agents initiate self-reinforcing panic-selling loops, generating endogenous market crashes that closely mimic historical "flash crashes." Unlike traditional ABMs, these generative agents dynamically adapt their strategies based on contextual textual information, leading to complex, non-linear behavioral cascades.

Conclusions: This research highlights the paradigm-shifting potential of generative multi-agent systems in financial risk management. By providing a more realistic simulation of market dynamics, our framework offers regulators and financial institutions a robust, proactive tool for stress-testing and predicting systemic risk propagation.