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ARXIV:2604.18373 · AI AGENTS IN FINANCE · SUBMITTED 21 APR · 04:19 UTC · FRESHNESS STALE
ARXIV:2604.18373AI AGENTS IN FINANCESUBMITTED 21 APR · 04:19 UTCFRESHNESS STALEShumiao Ouyang · Pengfei Sui · arXiv
AI agents trained on LLMs exhibit behavioral finance patterns and can be manipulated via prompt engineering to influence market bubbles.
Opportunity summary
Pain AI agents trained on LLMs exhibit behavioral finance patterns and can be manipulated via prompt engineering to influence market bubbles.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
AI agents trained on LLMs exhibit behavioral finance patterns and can be manipulated via prompt engineering to influence market bubbles. Using a simulated open-call auction populated by autonomous Large Language Model (LLM) agents, we…
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.
ScienceToStartup currently rates this 3.0/10 on the public viability pass. 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…
AI Agents in Finance moved forward this cycle; last verified April 2026. Public score 3.0/10.
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AI agents trained on LLMs exhibit behavioral finance patterns and can be manipulated via prompt engineering to influence market bubbles.
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10.48550/arXiv.2604.18373AI agents trained on LLMs exhibit behavioral finance patterns and can be manipulated via prompt engineering to influence market bubbles.
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.
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Proof status
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What was readable
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Dimensions overall score 3.0
PROBLEM
AI agents trained on LLMs exhibit behavioral finance patterns and can be manipulated via prompt engineering to influence market bubbles. Using a simulated open-call auction populated by autonomous Large Language Model (LLM) agents, we document three main findings.
METHOD
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.
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. 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, signifi...
WHY NOW
AI Agents in Finance moved forward this cycle; last verified April 2026. Public score 3.0/10.
- 10. overconfidence Labels: overconfident | well_calibrated | underconfident | unobserved Score: 1.0=excessive certainty, 0.5=moderate, 0=well-calibrated Definition: Excessive certainty about one’s own judgments
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AI agents trained on LLMs exhibit behavioral finance patterns and can be manipulated via prompt engineering to influence market bubbles.
Segment
AI Agents in Finance
Adoption evidence
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Commercial read
3.0/10 public viability
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