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Cited by 199

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Trading Volume and Different Aspects of Disagreement Coincident with Earnings Announcements.

Linda Smith Bamber, Ode E. Barron, Thomas Stöber·1997·The Accounting Review

Abstract

Abstract This paper investigates the association between aspects of investors' disagreement around earnings announcements and investors' trading decisions. Theory suggests that trading volume arises because of investor disagreement, but disagreement is a multi-faceted construct. We find that three distinctly different aspects of disagreement each play an incremental role in explaining trading volume around earnings announcements, even after controlling for the magnitude of the contemporaneous price change. These aspects of disagreement are: dispersion in prior beliefs, change in dispersion, and belief jumbling. Dispersion in prior beliefs is the level of variation in expectations before the earnings announcement, change in dispersion is the difference in the level of dispersion in beliefs after vs. before the earnings announcement, and belief jumbling occurs when investors' beliefs change positions relative to each other around the earnings announcement. Our results indicate that each of these three aspects of disagreement is associated with investors' real economic (i.e., trading) decisions around earnings announcements.

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Metadata

Authors
Bamber, L. S., Barron, O. E., & Stöber, T.
Venue
The Accounting Review
Publisher
American Accounting Association
Published
1997-10-01
Volume
72
Issue
4
Pages
575-597
ISSN
0001-4826 (ISSN-L)
OpenAlex
W2280949288
Cited by
199
References
0
FWCI
5.65
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Trading Volume and Different Aspects of Disagreement Coincident with Earnings Announcements. https://app.cochat.ai/papers/10.2308/tar-9712114166
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Bamber, L. S., Barron, O. E., & Stöber, T. (1997). Trading Volume and Different Aspects of Disagreement Coincident with Earnings Announcements. The Accounting Review. https://doi.org/10.2308/tar-9712114166

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.