Paper: arXiv 2502.15800

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.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper relies on a well-defined experimental design with clear statistical metrics (MSE, correlation) and visualizations of market behavior, indicating moderate empirical rigor, but its mathematical content is relatively light, focusing on behavioral comparisons rather than dense theoretical derivations.

Research Flowchart

  flowchart TD
  RQ["Research Goal<br>Do LLM agents replicate<br>human market trading<br>and bubble formation?"] --> I["Inputs<br>Experimental Finance Design<br>(Classic Bubble/Crash Paradigm)"]
  
  I --> M["Methodology<br>Agent-Based Modeling<br>LLM Agents as Traders"]
  
  M --> MP["Market Scenarios<br>1. Single-Model Markets<br>2. Mixed-Model Battle Royale"]
  
  MP --> CP["Computational Process<br>Simulated Trading Sessions<br>Asset priced near<br>fundamental value<br>vs. Human trading data"]
  
  CP --> F["Key Findings<br>1. LLMs: 'Textbook-Rational'<br>2. Muted Bubble Formation<br>3. Low Strategy Variance<br>4. Do NOT replicate<br>human market dynamics"]
  
  F --> O["Outcome<br>LLM-only data is insufficient<br>for replicating<br>key human behavioral features"]

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15 of 83 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.