Valuing algorithms over experts: Evidence from a stock price forecasting experiment - Nobuyuki Hanaki; Bolin Mao; Tiffany Tsz Kwan Tse; Wenxin Zhou (Osaka University, Institute of Social and Economic Research (ISER), 2024)
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 suciently enhance performance. These findings underscore the need to develop tools and policies that enable individuals to better assess algorithm performance.
Keywords: C90; G1; G4; G17; 330; algorithms; Becker-DeGroot-Marschak mechanism; experts; financial market; forecasting