Beware the Performance of an Algorithm Before Relying on it: Evidence from a Stock Price Forecasting Experiment

Material type: 文書・図像類

Author: Tse, Tiffany Tsz Kwanほか

Publisher: The Institute of Social and Economic Research, Osaka University

Publication date: 2022-10

Material Format: Paper

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Note (General)：: We experimentally investigated the relationship between participants' reliance on algorithms, their familiarity with the task, and the performance lev...

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Paper

Material Type: 文書・図像類
Title: Beware the Performance of an Algorithm Before Relying on it: Evidence from a Stock Price Forecasting Experiment
Author/Editor: Tse, Tiffany Tsz Kwan
Hanaki, Nobuyuki
Mao, Bolin
Author Heading: Tse, Tiffany Tsz Kwan
Hanaki, Nobuyuki
https://nrid.nii.ac.jp/nrid/1000070400611
Mao, Bolin
Publication, Distribution, etc.: The Institute of Social and Economic Research, Osaka University
Publication Date: 2022-10
Publication Date (W3CDTF): 2022-10
Periodical title: Institute of Social and Economic Research Discussion Papers
No. or year of volume/issue: 1194
Volume: 1194
Pages: 1-53
ISSN (Periodical Title): PISSN : 0473453X
EISSN : 24350982
Text Language Code: eng
Subject Heading: algorithms
financial market
forecasting
modification
technology adoption
Target Audience: 一般
Note (General): 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 could freely decide on 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 signicantly differ for participants with little experience in the task. Experienced participants relied less on the algorithm than inexperienced participants, 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.
References (URI): https://www.iser.osaka-u.ac.jp/library/dp/2022/DP1194.pdf
URI
https://www.iser.osaka-u.ac.jp/library/dp/2022/DP1194.pdf
Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）
https://irdb.nii.ac.jp
Original Data Provider (Database): 大阪大学 : 大阪大学学術情報庫　桜華