Paper: arXiv 2604.18373

Authors: Shumiao Ouyang, Pengfei Sui

Abstract

We study how AI agents form expectations and trade in experimental asset markets. Using a simulated open-call auction populated by autonomous Large Language Model (LLM) agents, we document three main findings. First, AI agents exhibit classic behavioral patterns: a pronounced disposition effect and recency-weighted extrapolative beliefs. Second, these individual-level patterns aggregate into equilibrium dynamics that replicate classic experimental findings (Smith et al., 1988), including the predictive power of excess demand for future prices and the positive relationship between disagreement and trading volume. Third, by analyzing the agents’ reasoning text through a twenty-mechanism scoring framework, we show that targeted prompt interventions causally amplify or suppress specific behavioral mechanisms, significantly altering the magnitude of market bubbles.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: This paper offers a highly novel experimental design that uses LLM agents to study behavioral finance and market bubble dynamics. While advanced mathematical proofs are absent, the empirical structure using simulated auctions and a detailed 20-mechanism prompt scoring framework provides strong quantitative rigor.

Research Flowchart

  flowchart TD
    A[Research Goal: Analyze AI Agent Trading Behavior & Market Bubbles] --> B[Data/Input: LLM Agents & Simulated Open-Call Auction Environment]
    B --> C[Methodology: Agent-Based Market Experiments & Prompt Interventions]
    C --> D[Analysis: 20-Mechanism Scoring Framework on Agent Reasoning Text]
    D --> E1[Finding 1: AI exhibits behavioral traits - Disposition Effect & Recency Bias]
    D --> E2[Finding 2: Market aggregates to classic bubble dynamics & volume-disagreement links]
    D --> E3[Finding 3: Targeted prompts causally alter mechanism strength & bubble magnitude]

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