Earnings Management? Erroneous Inferences Based on Earnings Frequency Distributions

Abstract

A vast literature following Hayn [1995] and Burgstahler and Dichev [1997] attributed the so‐called “discontinuities” in earnings distributions around zero to earnings management. Despite recent evidence that these discontinuities are likely caused by other factors, researchers and teachers continue to point to the shapes of these distributions as evidence of earnings management. We provide three sets of further evidence that these discontinuities are likely caused by factors other than earnings management: (1) we provide, as an example, a detailed analysis of the severe effects of sample selection in a recent study; this study erroneously concludes that the shape of an earnings distribution is evidence of earnings management, (2) we provide a simple explanation for the shape of the earnings distribution that is most often cited as evidence of earnings management; the relation between earnings and prices differs with the magnitude and the sign of earnings, and (3) we provide further examples that support the main point of our paper; evidence beyond the mere shape of a distribution must be brought to bear before researchers can draw conclusions regarding the presence/absence of earnings management.

Small-sized companies' earnings management: evidence from Italy

“…Durtschi and Easton, 2005;Durtschi and Easton, 2009;Holland, 2004;McNichols, 2003). One of the more critical and debated issues refers to the deflator.…”

Overvalued equity, benchmark beating and unexpected accruals

“…Given the recurring debate about the extent to which apparent benchmark beating is indicative of some degree of earnings management (compare Jacob and Jorgensen, 2007;and Durtschi and Easton, 2009), our results suggest an opportunity to at least partially resolve this debate. Absent any explicit incentive to care about the consequences of (not) beating a pertinent earnings benchmark, we expect it is more likely that benchmark beating may be an artefact of sample selection and scaling issues previously highlighted Easton, 2005, 2009).…”

The Power of Numbers: Base-Ten Threshold Effects in Reported Revenue

“…Prior literature has discussed the need to scale when examining discontinuities in earnings distributions (see Burgstahler andDichev 1997, Burgstahler andChuk 2015) because larger firms are able to manage larger absolute amounts near thresholds. The log approach effectively allows the revenue numbers themselves to serve as the scaling variable for the bin widths, avoiding the problems associated with other scalars such as price (see Durtschi and Easton 2005, 2009, and Burgstahler and Chuk 2015. The changes in logged revenue can then easily be converted back to percentage changes in unlogged revenue; for example, a 0.0100 change in logged revenue translates to a change of 2.33%.…”