How does AI affect price discovery and liquidity in asset market experiments?

Carol Luengo (), Steven Tucker (), Yilong Xu () and Frank Scrimgeour ()
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Carol Luengo: University of Waikato, https://www.waikato.ac.nz/about/faculties-schools/management/
Steven Tucker: University of Waikato, https://www.waikato.ac.nz/about/faculties-schools/management/
Yilong Xu: Utrecht School of Economics, Utrecht University
Frank Scrimgeour: University of Waikato, https://www.waikato.ac.nz/about/faculties-schools/management/

Working Papers in Economics from University of Waikato

Abstract: This paper investigates the causal impact of artificial intelligence (AI) advice on price discovery in a controlled asset-market experiment. Using a call market setting in which the asset's value follows a stochastic geometric random walk, we compare a baseline "No AI" condition against two treatments: "Good AI" (advice aligned with long-term fundamentals) and "Bad AI" (myopic advice anchored to current buyout prices). Our results show that access to AI advice significantly reduces mispricing relative to the baseline. In particular, Good AI market prices started low, then converged and closely followed the fundamentals in the second half of the market. Interestingly, we find no meaningful difference in aggregate mispricing between the Good and Bad AI treatments. Analysis of trader behavior reveals that while over half of the participants follow AI recommendations, a significant portion actively filters out erroneous advice, particularly in the Bad AI treatment. This selective adherence explains why market prices remain resilient to poor advice.

Keywords: artificial intelligence; financial advice; assest market experiment; mispricing; Human-AI interaction (search for similar items in EconPapers)
JEL-codes: C90 C91 G12 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2026-09-15
New Economics Papers: this item is included in nep-ain and nep-exp
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Persistent link: https://EconPapers.repec.org/RePEc:wai:econwp:26/05

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