LLM Agents Do Not Replicate Human Market Traders: Evidence From Experimental Finance

TL;DR

This study tests whether Large Language Models can replicate human market behaviors in experimental finance settings, finding that LLMs tend to act rationally and do not produce typical human-like bubbles or crashes.

Contribution

It provides empirical evidence that LLMs do not naturally exhibit complex human market behaviors, challenging their use as models for human financial decision-making.

Findings

LLMs price assets near fundamental value

LLMs show less trading strategy variance than humans

LLMs do not produce large emergent bubbles

Abstract

This paper explores how Large Language Models (LLMs) behave in a classic experimental finance paradigm widely known for eliciting bubbles and crashes in human participants. We adapt an established trading design, where traders buy and sell a risky asset with a known fundamental value, and introduce several LLM-based agents, both in single-model markets (all traders are instances of the same LLM) and in mixed-model "battle royale" settings (multiple LLMs competing in the same market). Our findings reveal that LLMs generally exhibit a "textbook-rational" approach, pricing the asset near its fundamental value, and show only a muted tendency toward bubble formation. Further analyses indicate that LLM-based agents display less trading strategy variance in contrast to humans. Taken together, these results highlight the risk of relying on LLM-only data to replicate human-driven market phenomena, as key behavioral features, such as large emergent bubbles, were not robustly reproduced. While LLMs clearly possess the capacity for strategic decision-making, their relative consistency and rationality suggest that they do not accurately mimic human market dynamics.