Conference Agenda

Large language models can trade: they execute coherent strategies, generate realistic market dynamics, and can be weaponized for manipulation. Using an open-source simulated stock market with persistent order books, short selling, dividends, and social feeds, I document three findings. First, LLMs consistently adhere to their assigned strategies, functioning as value investors, momentum traders, or market makers per their instructions. Second, their markets exhibit features of real financial markets, including price discovery and bubble formation. Third, when instructed to manipulate, they generate persuasive messages that create feedback loops, driving prices far from fundamentals. The framework enables testing financial theories that lack closed-form solutions and running experiments that would be too costly or unethical with human participants.

AI agents can emulate the beliefs of human survey respondents. We leverage this idea at scale to examine how investor disagreement emerges in response to firm news and to measure such disagreement at high frequency. We endow a local large language model (Llama 3) with over 200 demographically representative investor personas and elicit their sentiment toward S&P 500 firm-specific news headlines from 2010–2025. LLM personas disagree in economically meaningful ways: disagreement is largest for social and governance-related news, and smallest for hard news tied to firm fundamentals. The measure aligns with human survey evidence and traditional uncertainty measures, yet it is distinct from social-media disagreement. Turning to market outcomes, LLM-derived disagreement is strongly associated with same-day and next-day abnormal trading volume. Our main findings are stable across the model’s pre- and post-training windows.