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dc.contributor.authorChou, HI
dc.contributor.authorLi, M
dc.contributor.authorYin, X
dc.contributor.authorZhao, J
dc.date.accessioned2020-08-18T06:03:07Z
dc.date.available2020-08-18T06:03:07Z
dc.date.issued2020
dc.identifier.issn0022-1090
dc.identifier.doi10.1017/S002210902000037X
dc.identifier.urihttp://hdl.handle.net/10072/396529
dc.description.abstractInstitutional demand for a stock prior to its earnings announcement is negatively related to subsequent returns. The relationship is not attributable to the price pressure of institutional demand and is stronger for stocks with higher information asymmetry and/or greater valuation difficulty. These findings support the notion of overconfident institutions mispricing stocks. Following announcements, institutions' behavior exhibits the outcome-dependent feature of self-attribution bias. Whether they become more overly confident and delay their mispricing correction depends on whether earnings news confirms their pre-announcement trades or not. This behavioral bias also offers a new explanation for the well-known anomaly of post-earnings-announcement drift.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherCambridge University Press (CUP)
dc.relation.ispartofjournalJournal of Financial and Quantitative Analysis
dc.subject.fieldofresearchAccounting, Auditing and Accountability
dc.subject.fieldofresearchBanking, Finance and Investment
dc.subject.fieldofresearchcode1501
dc.subject.fieldofresearchcode1502
dc.titleOverconfident Institutions and Their Self-Attribution Bias: Evidence from Earnings Announcements
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationChou, HI; Li, M; Yin, X; Zhao, J, Overconfident Institutions and Their Self-Attribution Bias: Evidence from Earnings Announcements, Journal of Financial and Quantitative Analysis, 2020
dc.date.updated2020-08-18T00:53:06Z
dc.description.versionAccepted Manuscript (AM)
gro.description.notepublicThis publication has been entered in Griffith Research Online as an advanced online version.
gro.rights.copyright© 2020 Cambridge University Press. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
gro.hasfulltextFull Text
gro.griffith.authorLi, Mingyi


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