Benjamin C. Whipple

The Disclosure of Non-GAAP Earnings Information in the Presence of Transitory Gains

We examine the disclosure of non-GAAP earnings information in quarters containing transitory gains to investigatewhether the primarymotivation for thesemanagers to disclose non-GAAP earnings is to inform or mislead. In this setting, non-GAAP earnings aremore informative thanGAAPearnings, even though they are lower thanGAAPearnings. Thus, managers motivated to inform stakeholders about sustainable earnings will disclose non-GAAP earnings information excluding the gain, whereas managers motivated to report higher earningswill obscure the transitory nature of the gain by focusing onGAAPearnings. We find evidence that managers’ disclosure choices vary widely across firms, and these choices affect investors’ perceptions of core operating earnings. We then contrast how the same firm discloses non-GAAP earnings in the presence of transitory losses to provide additional evidenceon themotivesof individual firms’disclosures.Weconclude that themost pervasivemotivation to disclose non-GAAP earnings in the presence of transitory gains is to inform. An economically significant proportion of firms, however, appear opportunistic in that they only disclose non-GAAP earnings information when it increases investors’ perceptions of core operating earnings.Our evidence is important becausewespeak to the influence that eachmotive has on the choice to disclose non-GAAP earnings and we provide evidence on the underlying motives behind specific firms’ disclosures.

Investor Preference and Manager Motivation for Non-GAAP Earnings: Correcting for Measurement Error in Empirical Research

We examine analysts’ GAAP earnings forecasts and illustrate their usefulness in two prominent research settings. First, we find that the availability of GAAP forecasts has increased dramatically since 2003, and they are now available for most I/B/E/S-covered firms. Next, we utilize GAAP forecasts to solve important measurement error problems in prior research that attempts to examine GAAP forecast errors without an explicit GAAP forecast. We begin with research exploring investors’ preferences for GAAP versus non-GAAP earnings. We find that traditionally-identified GAAP forecast errors are subject to 37% measurement error. Nevertheless, in contrast to the pervasive caveats in the non-GAAP literature, evidence of an investor preference for non-GAAP earnings relative to GAAP earnings is robust after correcting for this measurement error. Second, we revisit the literature identifying firms that use non-GAAP exclusions to meet or beat analysts’ forecasts when GAAP earnings fall short of expectations. We find that 34% of the traditionally-identified meet-or-beat firms are misidentified due to measurement error and this misidentification masks the inference that firms more frequently exclude transitory expenses rather than recurring expenses for meet-or-beat purposes.This study examines how measurement error in earnings expectations affects prior evidence regarding (1) investors’ preferences for GAAP versus non-GAAP earnings and (2) the quality of non-GAAP reporting in meet-or-beat settings. Prior research on non-GAAP reporting computes earnings surprises for both GAAP and non-GAAP earnings relative to analysts’ non-GAAP earnings forecasts. As a result, GAAP earnings surprises are subject to measurement error due to the use of a misaligned earnings expectation. Many studies highlight this measurement error problem and caution that evidence of an investor preference for non-GAAP earnings might simply be due to the mechanical error in GAAP surprises, which creates statistical bias in favor of non-GAAP earnings. Utilizing newly available GAAP forecasts, we find that the traditional GAAP earnings surprise is comprised of 60% measurement error, on average. Nevertheless, we find that the impact of this form of measurement error on inferences regarding investors’ preferred earnings metric is small, and provide evidence that the reason the impact is small is that the components measured with error have low persistence. Next, we examine how measurement error influences inferences regarding the use of non-GAAP exclusions to meet-or-beat analysts’ forecasts. Contrary to prior evidence, after we correct for measurement error, we find that the non-GAAP disclosures of benchmark-beating firms are of lower quality than those of other non-GAAP reporters.