Earnings Management and Earnings Quality: Theory and Evidence
45 Pages Posted: 31 Oct 2014 Last revised: 18 Oct 2018
Date Written: August 13, 2018
We study a dynamic model of earnings quality and earnings management in which firms take into account long- and short-term considerations when reporting earnings. In addition to providing predictions about the time series properties of earnings quality and reporting bias, the model offers a distinction between two components of investor uncertainty: (i) fundamental economic uncertainty and (ii) information asymmetry between the manager and investors due to reporting or accounting distortions. We also structurally estimate the parameters of the model to empirically separate these two components of investor uncertainty. This allows us to address existing concerns about archival studies of earnings quality, such as the concerns raised by Dechow et al. (2010) and Dichev et al. (2013) that empirical archival research cannot satisfactorily distinguish managed earnings from fundamental or economic earnings. We find that: (i) our results strongly reject the null hypothesis of zero reporting noise; (ii) the ratio of the variance of the noise introduced by the reporting process to the variance of earnings shocks is on average 45%, suggesting that the noise added by the reporting process significantly contributes to investor uncertainty; (iii) however, from a valuation perspective, reporting noise plays a significantly lesser role due to the persistence of shocks to economic earnings; (iv) the investor uncertainty created by reporting noise about firms’ assets-in-place and future economic earnings is similar in magnitude; and (v) ignoring the possibility of reporting distortions would bias the estimates of variance and persistence of economic earnings.
Keywords: Earnings Management, Dynamics, Structural Estimation
JEL Classification: D82
Suggested Citation: Suggested Citation