Valuing Algorithms Over Experts: Evidence from a Stock Price Forecasting Experiment
Nobuyuki Hanaki,
Bolin Mao,
Tiffany Tsz Kwan Tse and
Wenxin Zhou
ISER Discussion Paper from Institute of Social and Economic Research, Osaka University
Abstract:
This study examined participants’ willingness to pay for stock price forecasts provided by an algorithm, financial experts, and peers. Participants valued algorithmic advice more highly and relied on it as much as expert advice. This preference for algorithms – despite their similar or even lower performance – suggests a shift in perception, particularly among students, toward viewing AI as a reliable and valuable source. However, this “algorithm appreciation” reduced participants’ payoffs, as they overpaid for advice that did not sufficiently enhance performance. These findings underscore the need to develop tools and policies that enable individuals to better assess algorithm performance.
Date: 2024-12
New Economics Papers: this item is included in nep-ain, nep-dcm and nep-exp
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://www.iser.osaka-u.ac.jp/library/dp/2024/DP1268.pdf
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:dpr:wpaper:1268
Access Statistics for this paper
More papers in ISER Discussion Paper from Institute of Social and Economic Research, Osaka University Contact information at EDIRC.
Bibliographic data for series maintained by Librarian ().