Asymmetric Determinants of Trading Volume at Earnings Announcements

63 Pages Posted: 8 Jan 2019 Last revised: 14 Jun 2021

See all articles by Alina Lerman

Alina Lerman

University of Connecticut - Department of Accounting

Qin Tan

City University of Hong Kong (CityUHK)

Date Written: May 26, 2021


Accounting literature offers three possible determinants of informationally driven trading volume at earnings announcements: differential interpretation of public news, pre-announcement difference in beliefs, and signal strength. We empirically test, conditional on the level of earnings news, which determinant best explains earnings announcement volume. First, consistent with the notion that differential interpretation by itself without a change in the mean of investor valuations (a typical metric of signal strength) is unlikely to drive volume, we document a strong association between volume and signed contemporaneous stock returns. We also show that, at all levels of earnings news, trading volume is most consistently associated with proxies of signal strength. However, we predict and find that volume also reflects differential interpretation for bad news but not for good news due to short sale dynamics. We confirm this asymmetry by observing a decrease in trading volume only for bad news firms after an exogenous reduction in investor disagreement, the staggered EDGAR implementation. Lastly, we find that proxies for the third determinant, pre-announcement belief difference, are the least significant in explaining trading volume. Overall, our results suggest that trading volume at earnings announcements is most reflective of the quantity and quality of information released, but its dynamics vary considerably with the nature of the disclosed news.

Keywords: Trading Volume, Earnings Announcements, ERC, short sale

JEL Classification: G12, G14

Suggested Citation

Lerman, Alina and Tan, Qin, Asymmetric Determinants of Trading Volume at Earnings Announcements (May 26, 2021). University of Connecticut School of Business Research Paper No. 19-05, Available at SSRN: or

Alina Lerman (Contact Author)

University of Connecticut - Department of Accounting ( email )

School of Business
Storrs, CT 06269-2041
United States

Qin Tan

City University of Hong Kong (CityUHK) ( email )

83 Tat Chee Avenue
Hong Kong

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