Abstract

http://ssrn.com/abstract=1119400
 
 

References (38)



 
 

Citations (2)



 


 



Earnings Precision and the Relations between Earnings and Returns


David Burgstahler


University of Washington - Department of Accounting

Elizabeth Chuk


University of Southern California

October 7, 2010


Abstract:     
Formal and heuristic valuation models suggest that changes in firm value associated with a revision in expected earnings should be a multiple on the order of typical earnings capitalization factors between 10 and 30. However, empirical estimates of the earnings response coefficient (ERC) have usually been in a range an order of magnitude lower, between 1 and 3 (Kothari 2001). This paper uses a simple Bayesian model to integrate previous theoretical and empirical results and bridge this large gap. In the Bayesian model, low precision implies a low weight on new earnings information. As a result, the observable earnings surprise is much larger than the unobservable earnings revision, which in turn implies a smaller coefficient on earnings surprise than on the revision in expected earnings. Although the model uses statistical measures, the Bayesian concept of precision closely parallels the accounting concept of earnings quality, i.e., the extent to which current earnings is informative about expected future earnings. Precision summarizes the effects of multiple determinants of earnings quality.

Consistent with the model, two proxies for precision, forecast dispersion and absolute magnitude of earnings surprise, explain a broad empirical range of coefficients on earnings surprise ranging from near 0 up to 30. We reconcile the surprisingly large proportion of observations falling in the upper end of the range with the surprisingly low estimated ERCs typically reported in previous research. Finally, we demonstrate in the appendix how the precision proxies can be used to design tests that focus on the majority of observations with larger surprise coefficients and thereby allow researchers to detect the effect of determinants of surprise coefficients that would otherwise be empirically undetectable.

Number of Pages in PDF File: 56

Keywords: Earnings precision, earnings response coefficients, analyst forecasts, uncertainty, adaptation value

JEL Classification: G14, M40

working papers series


Download This Paper

Date posted: April 30, 2008 ; Last revised: October 11, 2010

Suggested Citation

Burgstahler, David and Chuk, Elizabeth, Earnings Precision and the Relations between Earnings and Returns (October 7, 2010). Available at SSRN: http://ssrn.com/abstract=1119400 or http://dx.doi.org/10.2139/ssrn.1119400

Contact Information

David C. Burgstahler (Contact Author)
University of Washington - Department of Accounting ( email )
224 Mackenzie Hall, Box 353200
Seattle, WA 98195-3200
United States
206-543-6316 (Phone)
206-685-9392 (Fax)
Elizabeth Chuk
University of Southern California ( email )
Los Angeles, CA 90089
United States
Feedback to SSRN


Paper statistics
Abstract Views: 1,756
Downloads: 556
Download Rank: 26,021
References:  38
Citations:  2

© 2014 Social Science Electronic Publishing, Inc. All Rights Reserved.  FAQ   Terms of Use   Privacy Policy   Copyright   Contact Us
This page was processed by apollo4 in 0.250 seconds