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

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

Institute of Social and Economic Research Discussion Papers, 2022, 1194, 1-53

Number of Access:412（2025-11-03 10:25 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/89379

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Title	Beware the Performance of an Algorithm Before Relying on it: Evidence from a Stock Price Forecasting Experiment
Creator	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
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 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.
Publisher	The Institute of Social and Economic Research, Osaka University
Source Title	Institute of Social and Economic Research Discussion Papers
Volume (Issue)	1194
Page	1-53
Date of Issued	2022-10
Language	English
Handle URL	https://hdl.handle.net/11094/89379
PISSN	0473453X
eISSN	24350982
Relation.references	URI https://www.iser.osaka-u.ac.jp/static/resources/docs/dp/2022/DP1194.pdf
Access Rights	metadata only access
Category	ディスカッション／ワーキング・ペーパー DP/WP
Category	Institute of Social and Economic Research Discussion Papers

Resource Type	technical report
Local Type	DP/WP
DCMI Type	Text
DCTERMS.bibliographicCitation	Institute of Social and Economic Research Discussion Papers. 2022, 1194, p. 1-53
DC.title	Beware the Performance of an Algorithm Before Relying on it: Evidence from a Stock Price Forecasting Experiment
DC.creator	Tse, Tiffany Tsz Kwan
	Hanaki, Nobuyuki
	Mao, Bolin
DC.publisher	The Institute of Social and Economic Research, Osaka University
DC.language' scheme='DCTERMS.RFC1766	English
DC.type' scheme='DCTERMS.DCMIType	Text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2022-10
DC.relation' scheme='DCTERMS.URI	https://www.iser.osaka-u.ac.jp/static/resources/docs/dp/2022/DP1194.pdf
DC.subject	algorithms
	financial market
	forecasting
	modification
	technology adoption
DCTERMS.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 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.
DC.format	89379
citation_title	Beware the Performance of an Algorithm Before Relying on it: Evidence from a Stock Price Forecasting Experiment
citation_author	Tse, Tiffany Tsz Kwan
	Hanaki, Nobuyuki
	Mao, Bolin
citation_publisher	The Institute of Social and Economic Research, Osaka University
citation_language	English
citation_date	2022-10
citation_journal_title	Institute of Social and Economic Research Discussion Papers
citation_volume	1194
citation_firstpage	1
citation_lastpage	53
citation_public_url	https://hdl.handle.net/11094/89379
citation_abstract_html_url	https://www.iser.osaka-u.ac.jp/static/resources/docs/dp/2022/DP1194.pdf

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

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