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, The University of Osaka

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
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Persistent link: https://EconPapers.repec.org/RePEc:dpr:wpaper:1268

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