Earnings Precision and the Relations Between Earnings and Returns

The low estimates of earnings response coefficients (hereafter, ERCs) reported in the literature have sometimes been interpreted as indicating that earnings information is relatively unimportant (Beaver, Lambert, and Morse 1980; Lev 1989). Prior literature typically documents ERCs in the range of 1 to 3 (Kothari 2001), which is an order of magnitude lower than theoretically plausible annual earnings capitalization factors (hereafter, ECFs) in the range of 10 to 30. This paper uses a simple Bayesian model to highlight and explain differences between the coefficient relating security returns to observable unexpected earnings (i.e., the ERC) versus the coefficient relating changes in firm value to unobservable revisions in expected earnings (i.e., the ECF). In this model, the ERC is the product of a Bayesian weight and the ECF, which implies that the ERC is lower than the ECF and that variation in the relative precision of the earnings signal translates directly into variation in the ERCs. Results for a large sample of widely followed firms from 1992-2014 show that proxies for precision explain a broad empirical range of relations between earnings and returns. In fact, we find that the highest precision subsamples have 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 for ECFs, and suggest that earnings information is much more important than the low ERCs in the prior literature would seem to imply.