Beware the performance of an algorithm before relying on it: Evidence from a stock price forecasting experiment

Tiffany Tsz Kwan Tse, Nobuyuki Hanaki and Bolin Mao

Journal of Economic Psychology, 2024, vol. 102, issue C

Abstract: We experimentally investigated the relationship between participants’ reliance on algorithms, their familiarity with the task, and the performance level of the algorithm. We found that when participants were given the freedom to submit any number as their final forecast after observing the one produced by the algorithm (a condition found to mitigate algorithm aversion), the average degree of reliance on high and low performing algorithms did not significantly differ when there was no practice stage. Participants relied less on the algorithm when there was practice stage, regardless of its performance level. The reliance on the low performing algorithm was positive even when participants could infer that they outperformed the algorithm. Indeed, participants would have done better without relying on the low performing algorithm at all. Our results suggest that, at least in some domains, excessive reliance on algorithms, rather than algorithm aversion, should be a concern.

Keywords: Algorithms; Financial market; Forecasting; Modification; Technology adoption (search for similar items in EconPapers)
JEL-codes: C90 G1 G17 G4 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:joepsy:v:102:y:2024:i:c:s0167487024000357

DOI: 10.1016/j.joep.2024.102727

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