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

Tse, Tiffany Tsz Kwan; Hanaki, Nobuyuki; Mao, Bolin

Journal of Economic Psychology, 2024, 102, 102727

Number of Access:213（2025-09-04 10:57 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/97848

Link to primary information 

File Format Terms of use Size Views Date.Available Description information
JEconPsychol_102_102727 pdf None 1.58 MB 56 2024.09.10  
Creative Commons : Attribution

Item Information

Output File Export EndNote Basic Export Mendeley

Title
Beware the performance of an algorithm before relying on it: Evidence from a stock price forecasting experiment
Creator
NRID
1000050881348 1000050881348
ORCID
0000-0003-1614-2428 0000-0003-1614-2428
researchmap permalink
tse tse
CreatorName
Tse, Tiffany Tsz Kwan
NRID
1000070400611 1000070400611
ORCID
0000-0003-3127-690X 0000-0003-3127-690X
researchmap permalink
nhanaki nhanaki
Scopus Author ID
16233221700 16233221700
CreatorName
Hanaki, Nobuyuki
CreatorName
Mao, Bolin
Subject
Algorithms
Financial market
Forecasting
Modification
Technology adoption
Description
Tse T.T.K., Hanaki N., Mao B.. Beware the performance of an algorithm before relying on it: Evidence from a stock price forecasting experiment. Journal of Economic Psychology 102, 102727 (2024); https://doi.org/10.1016/j.joep.2024.102727.
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.
Publisher
Elsevier B.V.
Source Title
Journal of Economic Psychology
Volume (Issue)
102
Page
102727
Date of Issued
2024-06-01
Language
English
Handle URL
https://hdl.handle.net/11094/97848
PISSN
01674870
Relation.isIdenticalTo
DOI (Publisher Version)
https://doi.org/10.1016/j.joep.2024.102727
Access Rights
open access
CopyRight
This article is licensed under a Creative Commons Attribution 4.0 International License.
oaire:version
Version of Record
Category
学術雑誌論文 Journal Article