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23-03-2019

Non-GAAP reporting following debt covenant violations

Authors: Theodore E. Christensen, Hang Pei, Spencer R. Pierce, Liang Tan

Published in: Review of Accounting Studies | Issue 2/2019

Abstract

We investigate whether firms change their non-GAAP reporting practices after debt covenant violations. We find that the likelihood that a firm will disclose non-GAAP earnings decreases and (for those that continue to disclose) the quality of non-GAAP reporting improves following covenant violations, consistent with stronger shareholder monitoring during this period of scrutiny. Consistent with increased monitoring following a debt covenant violation, cross-sectional analyses indicate that these changes in non-GAAP reporting are concentrated among firms with strong governance. Moreover, we find that investor demand for disclosure (proxied by analyst-provided non-GAAP performance metrics and EDGAR search volume) increases following a covenant violation. Collectively, our evidence is consistent with heightened investor scrutiny following covenant violations, and it casts doubt on the competing explanation that shareholders delegate monitoring to creditors following a covenant violation. Overall, our evidence provides new insights on the determinants of firms’ non-GAAP reporting practices and an alternative view about how debt covenant violations influence voluntary disclosure.

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While research has generally focused on managers’ disclosure of non-GAAP performance metrics, we note that concurrent research also examines other sources of non-GAAP performance metrics, such as equity and credit analysts’ adjustments to GAAP earnings (e.g., Bentley et al. 2018; Batta and Muslu 2017).

The SEC issued “cautionary advice” in December of 2001, alerting investors regarding the impending dangers associated with reliance on non-GAAP disclosures. It then issued Regulation G in March of 2003 to specifically regulate non-GAAP reporting. Recently, the former SEC chairperson, Mary Jo White, has suggested that non-GAAP reporting needs to be “reined in,” possibly through regulation (Lahart 2016). The growing concern about non-GAAP reporting led to SEC staff interpretations in 2010 and 2016. The SEC has also significantly increased the number of comment letters regarding the disclosure of non-GAAP performance metrics (Heflin and Hsu 2008; Rapoport 2013; Ernst and Young 2017).

It is not uncommon for firms to receive waivers from creditors following covenant violations. However, waivers themselves do not indicate the absence of creditor control. On the contrary, in practice, a waiver is often the result of a borrower concession granted through behind-the-scenes creditor intervention.

In robustness tests, we also consider two alternative measures of non-GAAP reporting quality. Specifically, we investigate the likelihood of aggressiveness in non-GAAP disclosures as proxied by (1) managers’ use of non-GAAP exclusions to meet analysts’ expectations (i.e., when the non-GAAP number meets or beats the consensus street (EPS) forecast while GAAP earnings fall short of the consensus GAAP (GPS) forecast) and (2) the magnitude of managers’ incremental exclusions beyond analysts’ exclusions. Prior research uses both measures to capture aggressive non-GAAP reporting. We find that after a covenant violation, non-GAAP disclosures becomes less aggressive (i.e., the likelihood that managers use exclusions to meet or beat expectations and the magnitude of incremental exclusions both decline, consistent with our conjecture of an overall improvement in non-GAAP disclosure quality).

In other words, had the persistent items not been excluded in calculating non-GAAP earnings, the non-GAAP earnings would have been more predictive of future earnings.

Otherwise, there would be no need for managers to disclose non-GAAP performance metrics based on the delegation of monitoring hypothesis. Notice that, unlike creditors, shareholders rely on public channels to obtain information.

For SEC enforcement actions, see for example http://www.sec.gov/news/headlines/trumphotels.htm. For shareholder lawsuits based on non-GAAP disclosures, see for example: In re Segue Software, Inc. Sec. Litig., 106 F. Supp. 2d 161 (2000); In re Mercator Software, Inc. 161 F. Supp. 2d 143 (2001); In re Humphrey Hospitality Trust, Inc. Secs. Litig., 219 F. Supp. 2d 675 (2002); In re Vivendi Universal, S.A. Sec. Litig., 381 F. Supp. 2d 158, 166 (2003); In re Qwest Communs. Int’l, Inc. Secs. Litig., 396 F. Supp. 2d 1178, 1189-90 (D.C.Co. 2004); In re Stellent, Inc. Secs. Litig., 326 F. Supp. 2d 970 (2004); In re Ravisent Techs., Inc. Sec. Litig., 2005 U.S. Dist. LEXIS 6680 (2005); Kuehbeck v. Genesis Microchip, Inc., 2005 U.S. Dist. LEXIS 20213 (2005); Golesorkhi v. Green Mt. Coffee Roasters, Inc., 973 F. Supp. 2d 541 (2013). Also see Cazier et al. (2018) for a more detailed discussion about these lawsuits.

The dataset is available at http://faculty.chicagobooth.edu/amir.sufi/data.html. Nini et al.'s (2012) data appendix describes how the violation incidences are identified from SEC filings and the specific text-search algorithm

Our covenant violation sample omits the financial crisis period (2008 and the first two quarters of 2009, as identified by the NBER), since the lending market, corporate borrowing and the design of debt contracts are all drastically different from other periods (Ivashina and Scharfstein 2010; Christensen and Nikolaev 2012).

We first use a text-search algorithm to scan 10-K and 10-Q filings in EDGAR for the same search terms used by Nini et al. (2012). This process identifies 9650 potential violation observations for further screening. We then manually read filings of each identified observation to determine whether it contains an actual covenant violation. We take several precautions to ensure our data quality. First, each research assistant was trained and practiced on a subsample of data from Nini et al. (2012), to ensure they correctly understood the coding rules and could exercise appropriate judgment. Second, each observation and its associated filing were independently inspected by two research assistants to minimize human errors. Third, when the two assistants disagreed with a particular coding, which rarely happened, one of the authors acted as a third reader of the filing and examined the coding again to make a final determination.

We construct two alternative full samples and conduct robustness checks, regarding the way to assign zero frequency of non-GAAP disclosure to the data. First, we keep all firms in the entire sample between 1998 and 2012 and assign zero disclosure frequency to all firm-quarters in which we do not observe a non-GAAP disclosure. Second, we keep all firm-quarters that follow the first appearance of a non-GAAP disclosure. Our frequency results are robust to the two alternative samples.

Years in Fig. 1 are based on earnings announcement dates when non-GAAP earnings are release.

We have also considered an augmented regression model that includes the interaction terms between VIOLATION and covenant controls and their higher order terms. Our results remain significant. Due to the concern of potential multicollinearity introduced by those interaction terms, we choose to report results based on Eq. 2.

All analyses conducted in section 4.2 are for firms that actually report non-GAAP earnings following a covenant violation. In an untabulated test, we also examine non-GAAP reporting quality prior to the covenant violation for firms that stop reporting it after the violation. We find a significant negative association between managers’ recurring exclusions and future earnings, indicating that the non-GAAP reporting quality is of low quality and may reflect managers’ potentially aggressive motives prior to the violation.

The Sarbanes-Oxley Act mandates all members of the audit committee to be independent. Listed companies must comply with the rule by their first annual meeting after January 15, 2004 or by October 31, 2004, whichever occurs first (SEC Release No. 33-8220). Therefore, our audit committee test restricts the sample period till 2004.

Bentley et al. (2018) find that managers’ non-GAAP disclosures often vary from those of analysts’ non-GAAP forecasts as to when they are made and what they exclude. For example, they find that managers’ non-GAAP earnings agree with analysts’ non-GAAP earnings for only 63% of their sample and the two types of disclosure differ in systematic ways.

In addition to frequency, we also examine the quality of analysts’ non-GAAP disclosures following a covenant violation. In untabulated results, we do not find that the quality of analysts’ non-GAAP disclosures changes significantly following a covenant violation.

We obtain the EDGAR search data from Jake Thornock and the data start in 2003. We use the approach in Drake et al. (2016) to remove “robot” (i.e., automated) downloads.

Our results are robust to alternative 60-day or 90-day windows.

For example, studies find that, following a covenant violation, firms reduce capital expenditures (Chava and Roberts 2008), decrease debt financing (Roberts and Sufi 2009), decrease acquisitions and payouts and are more likely to have CEO turnover (Nini et al. 2012). These studies attribute their evidence to creditor intervention.

We focus on the post-Reg-G period because firms must disclose a reconciliation between non-GAAP earnings and GAAP earnings after Reg-G. Reconciliations allow us to determine how close public non-GAAP earnings and contractual earnings are to each other.

For example, Mace Security International disclosed its non-GAAP earnings for the quarter ending on Dec. 31, 2003. At the same time, the firm also disclosed a consolidated EBITDA measure, which, as described by the company, “is also used by creditors in assessing debt covenant compliance” (i.e., contractual earnings). We compare the reconciliations of these two non-GAAP numbers and find that the exclusion items of the contractual earnings overlap and include all exclusion items of the public non-GAAP earnings. See reference at: https://www.sec.gov/Archives/edgar/data/912607/000115752304002308/a4593066ex991.txt

Note that this test requires many future lead terms, which significantly decreases our sample size and the power of our tests.

We follow Bradshaw et al. (2018) and take advantage of the newly available analysts’ GAAP earnings forecasts data to construct MEETBEAT. The data start from 2004. Lacking a real GAAP earnings forecast, prior studies calculate GAAP earnings surprise as the difference between actual GAAP earnings and analysts’ street earnings forecasts (non-GAAP earnings). This mismatched comparison creates considerable measurement errors. For example, Bradshaw et al. (2018) find that 34% of the traditionally identified meet-or-beat observations are not actually meet-or-beat firms and that this misclassification can lead to incorrect conclusions in prior meet-or-beat studies. Due to the data restriction on analysts’ GAAP earnings forecasts, which limits the sample period, we present this analysis as a supplemental test.

In untabulated analysis, we conduct another robustness test that further controls for specific changes in firms’ operating environment surrounding a covenant violation. Prior studies find that a covenant violation could result in an increase in CEO turnover (Nini et al. 2012), and a decline in both capital expenditures (Chava and Roberts 2008) and net debt issuances (Roberts and Sufi 2009). Our results are robust to controlling for CEO turnover and lagged, contemporaneous, and future investment and financing activities, surrounding the covenant violation. Due to data restriction on CEO turnover and lead and lagged terms of investment and financing activities, we choose to keep this analysis as a supplemental test. The robustness results are available upon request.

We also note that, as discussed by Shipman et al. (2017), propensity-score matching does not correct for all types of endogeneity. The type of endogeneity it controls for, functional form misspecification, may not be a significant concern in our main analysis because of the higher-order terms included in the regression. As such, we include propensity-score matching as a robustness test.

Specifically, we match violators with nonviolators based on the five nearest neighbors (Nini et al. 2012) with equal weight, replacement and a caliper of 0.2. All covariates are balanced after matching.

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Title: Non-GAAP reporting following debt covenant violations
Authors: Theodore E. Christensen
Hang Pei
Spencer R. Pierce
Liang Tan
Publication date: 23-03-2019
Publisher: Springer US
Published in: Review of Accounting Studies / Issue 2/2019
Print ISSN: 1380-6653
Electronic ISSN: 1573-7136
DOI: https://doi.org/10.1007/s11142-019-09492-1

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