48 Pages Posted: 30 Apr 2008 Last revised: 10 Jan 2017
Date Written: January 6, 2017
This paper uses a simple Bayesian model to highlight the distinction between the coefficient relating security returns to observable unexpected earnings (the earnings response coefficient, or ERC) versus the coefficient relating changes in firm value to unobservable revisions in expected earnings (the earnings capitalization factor, or ECF). In this model, the ERC is the product of a Bayesian weight and the ECF, where the weight is determined by the relative precision of the earnings signal. Empirical results show that proxies for precision explain a large range of relations between earnings and returns in a sample of widely followed firms from 1992-2014. For the pooled sample, ERCs are consistent with typical previously reported ERCs in the range of 1 to 3 (Kothari 2001). However, we predict and find that ERCs are close to zero for observations with low precision whereas ERCs are far higher for observations with high precision. In fact, we find that higher precision subsets comprising the majority of observations have ERCs an order of magnitude larger than previously reported ERCs, in the range of 10 to 30. Thus, our model and results reconcile the large gap between typical empirical estimates of ERCs versus plausible values of the (unobservable) ECFs. Further, results showing large ERCs for the majority of observations suggest that the information in annual earnings announcements is, in the majority of cases, much more important than the low ERCs in the prior literature would seem to imply.
Keywords: Earnings precision, earnings response coefficients, analyst forecasts, uncertainty
JEL Classification: G14, M40
Suggested Citation: Suggested Citation
Burgstahler, David and Chuk, Elizabeth, Earnings Precision and the Relations between Earnings and Returns (January 6, 2017). Available at SSRN: https://ssrn.com/abstract=1119400 or http://dx.doi.org/10.2139/ssrn.1119400
By Ron Kasznik