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 investigates participants’ willingness to pay for stock forecasting advice from algorithms, financial experts, and peers. Contrary to prior findings on “algorithm aversion,” participants valued algorithmic advice as much as expert advice and relied on it heavily, even though its performance was not superior. This algorithm appreciation reflects a shift in perceived reliability among students. However, it led to lower payoffs, as participants overpaid for advice that failed to significantly improve outcomes. These findings highlight the importance of developing tools and policies that help individuals better evaluate the actual value of algorithmic advice.

Date: 2024-12, Revised 2025-07
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