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Erschienen in: Schmalenbach Business Review 2/2019

07.03.2019 | Original Article

Non-GAAP Reporting and Debt Market Outcomes: Evidence from Regulation G

verfasst von: Felix Thielemann, Tami Dinh, Helen Kang

Erschienen in: Schmalenbach Business Review | Ausgabe 2/2019

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Abstract

We exploit the introduction of Regulation G (RegG) by the U.S. Securities and Exchange Commission (SEC) in 2003 to study the association between non-GAAP earnings disclosure and debt market outcomes. Specifically, we focus on a subset of 199 S&P 500 firms to analyse the association between non-GAAP earnings disclosure and long-term issuer credit ratings as well as bond spreads before and after the regulatory intervention. We find that post-RegG, correlation between non-GAAP earnings disclosure and bond spreads changes from positive to negative but do not document any change in its correlation with respect to credit ratings. Our study provides first evidence that post-RegG, bond investors, but not rating agencies, seem to view non-GAAP earnings disclosure favourably as part of their credit risk assessment. In practical terms, our results document a specific benefit of RegG and may inform the SEC that its goal of more accurate security pricing extends to the bond markets, which is an under-researched area in the context of non-GAAP regulation.

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Fußnoten
1
While RegG applies to all different kinds of non-GAAP measures, we focus only on non-GAAP earnings as a specific subset of the non-GAAP universe (refer to Sect. 2 for further discussions).
 
2
Specifically, firms’ choices relate to: (a) whether or not to disclose non-GAAP earnings in a given quarter; (b) whether to start or stop non-GAAP reporting after the adoption of RegG; and, finally (c) how to report them (i. e., the choice among a plethora of different non-GAAP reporting attributes).
 
3
In general, furnished information is subject to less strict requirements than filed information (see SEC 2002a).
 
4
By contrast, information filed with the SEC (i. e., not published in press releases) is subject to further restrictions. For instance, Item 10 contains additional requirements on the emphasis with which non-GAAP earnings are presented as well as how they are labelled. Similarly, Item 12 of Form 8‑K augments the requirements of RegG to the extent that if a furnished press release happens to contain a non-GAAP measure, management needs to articulate how this measure is useful to investors and—if applicable—disclose how it uses the respective measure for internal purposes. Taken together, it could be that the stricter requirements for filed and furnished information also impact firms’ disclosure choices via press releases but there is no explicit regulatory reasoning for it; i. e., RegG defines the boundaries for non-GAAP reporting in press releases.
 
5
The C&DIs are provided in a question-and-answer format and cover a whole range of issues in relation to the regulatory framework applicable to non-GAAP financial measures. Examples include the clarification of what exactly constitutes a misleading non-GAAP measure, and to which GAAP measure a given non-GAAP measure should be reconciled to.
 
6
Beyond the increased litigation costs discussed here, other potential costs of non-GAAP disclosure might be of proprietary nature. For instance, Isidro and Marques (2016) argue that reporting strong non-GAAP profitability is constrained by the fact that it might attract competitors to enter the market or overproduce, thereby ultimately competing away profits. As for the benefits, there is a broad literature stream on the link between voluntary disclosure and cost of capital (e. g., Botosan 1997, 2006; Lambert et al. 2007, 2012; Barth et al. 2013), which we draw on here by associating non-GAAP earnings disclosure with bond spreads and credit ratings as proxies for the cost of debt.
 
7
Under the assumption that special items are uninformative to investors, this amounts to a reduction in the willingness to disclose non-GAAP earnings when they are informative.
 
8
Note that Continuers report non-GAAP information during both pre-RegG and post-RegG periods, but post-RegG, they may still decide whether to disclose non-GAAP figures or not per each quarter.
 
9
This approach ensures that our results are not confounded, e. g., by more creditworthy firms entering the S&P 500 after the enactment of RegG. We acknowledge that it may introduce survivorship bias into our analysis though.
 
10
We retain only the most recent analyst forecasts from IBES. In cases where IBES shows two equally recent forecasts, we select the one with higher analyst coverage.
 
11
For some firms, Compustat and IBES tickers did not match. In these cases, we manually searched IBES via firm name and collected the corresponding IBES ticker. In some rare cases where more than one IBES ticker was available, all corresponding observations were deleted as a precautionary measure.
 
12
Other “potentially cleaner” proxies for the cost of debt would be offering yields or credit default swap (CDS) spreads. For both proxies, however, we do not have sufficient data since the CDS market was still in its infancy during our sample period. Until 2004, CDS represented only a minor share of the global derivatives market (Bank for International Settlements (BIS) 2016) and data for academic research was typically obtained from private sources (e. g. Norden and Weber 2004).
 
13
Specifically, we select all bonds that have an offering date between January 1950, which is the earliest possible, and December 2005 to ensure that we take into account all bonds outstanding during our sample period.
 
14
For illustration of Excel’s Yield function, refer to Bodie et al. (2011, p. 480). Note that by calculating yield to maturity, we assume that bonds are not being called prior to maturity.
 
15
We drop observations with negative spread, which mostly result from spikes in the risk-free Treasury rate in the third and fourth quarters of 2000.
 
16
We realise that our spreads are slightly lower than the ones reported by Barth et al. (2012), albeit average ratings are similar, and the sample period partly overlaps. We find that this is mainly attributable to the fact that we match annualised yield to maturity with annualised risk-free rates, while Barth et al. (2012) deduct a three-month risk-free rate from an annualised yield to maturity. When we discount our annual rate to proxy for the three-month rate and use this proxy instead of our annualised three-month rate, our average spread across the whole sample period increases to 4.5%. This comes relatively close to the 5.16% by Barth et al. (2012). Using this approach, our results presented in Table 4 remain significant but only for Continuers.
 
17
Black et al. (2017) delete all firms that report non-GAAP earnings only once in either the pre-RegG or post-RegG time-period. This would greatly reduce our sample size. Instead, we keep firms that report non-GAAP earnings only once. However, in order for firms to be counted as a non-GAAP “reporter” for the respective sub-period, we require them to report non-GAAP earnings at least twice.
 
18
Note that Non-Reporters did engage in non-GAAP reporting in some quarters. This is because we define firms as Non-Reporters for each time-period if they report non-GAAP earnings once or less.
 
19
Our cut-off point of 2.5 relates to the argument by Zhang and Zheng (2011) that the most pronounced difference in reconciliation quality is between reconciliation scores of 2 and below vs. reconciliation scores of 3 or 4.
 
20
In addition, there are 38 firms, which had good reconciliation quality in both time-periods and 6 firms whose reconciliation quality worsened from the pre-RegG to the post-RegG time-period.
 
21
To mitigate the effect of potential outliers, we winsorise Rating and Spread as well as all other continuous variables (relevant for the supplementary analyses in Sect. 6) at the 1st and 99th percentile.
 
22
For instance, Jorion et al. (2005) argue that rating agencies have access to internal information such as budgets or forecasts, as well as advance notification of major corporate events.
 
23
In particular, for the entire sample, the negative sign on bond spreads during the post-RegG time-period turns insignificant whereas for Continuers it remains significant at the 1%-level.
 
24
Specifically, the IMR serves as an instrumental variable that mitigates endogeneity associated with the subset of variables that jointly determine non-GAAP disclosure and credit ratings or bond spreads, respectively.
 
25
Unlike Isidro and Marques (2015), we do not include a variable proxying for the ownership of inside investors because we do not have access to the data. Further, our specification does not include dummies on U.S. stock market listing as well as the use of IFRS, which we do not need due to our U.S. setting. In addition, we do not include a proxy for institutional ownership due to severe loss of observations. However, in unreported robustness checks, we include institutional ownership and find that NonGAAP_Post_RegG always remains negative and significant at the 1% level.
 
26
Isidro and Marques (2015) use intangible assets because these are known to be associated with increased non-GAAP disclosure (see also Lougee and Marquardt 2004). However, we use tangibility instead and expect an opposite sign for the variable, since the coverage for net PPE is much larger than for intangible assets in the Compustat database.
 
27
Again, we caution that, although the coefficient resembles the difference-in-difference estimator, the described self-selection issues implicit in our setting impede any causal identification. Hence, results should be interpreted with caution.
 
28
Note that only the 3398 observations displayed for the contemporaneous association with credit ratings in column 2 reconcile with the number of observations shown in Table 5; i. e., 2237 (Continuers) + 1161 (Non-Reporters). The reasons are that: (a) in the lead specifications, we always lose more than the number of first quarter observations because we do not have observations for every firm-quarter; and, (b) not all 3398 observations pertaining to Continuers and Non-Reporters have data on bond spreads.
 
29
For instance, in another context, Barth et al. (2012) also show that rating agencies process accounting information differently than bond investors do. Specifically, they document that rating agencies distinguish between credit risk from the retained and non-retained portions of asset securitisations while the bond market does not.
 
30
As for (c), the interaction term of NonGAAP_Post_RegG remains negative and highly significant but the combined effect of NonGAAP and NonGAAP_Post_RegG weakens to significance at the 10% level for the lead specification and to insignificance with respect to the contemporaneous association.
 
31
Additionally, we also replace our year-fixed effects with quarter-fixed effects and our results change as follows: when applying the standard two-tailed significance tests, the coefficient on NonGAAP_Post_RegG only remains significant at the 10% level in the contemporaneous association whereas it becomes insignificant in the lead-specification. Yet, given our directional hypothesis and when using one-tail tests, the coefficient is significantly negative at the 10% and 5%-levels for the lead- and contemporaneous specifications, respectively.
 
32
A common example of an adjustment that would not qualify as non-GAAP reporting in our paper is adjusted sales (growth rate). For instance, in Q2/1999, Avery Dennison adjusts its sales for currency effects but does not present an adjusted net income or EPS figure. Hence, it does not qualify as a non-GAAP earnings disclosure for the purpose of our paper.
 
33
While Curtis et al. (2013) also require a prominent presentation, they apply slightly stricter rules since they only count firms as non-GAAP reporters if non-GAAP measures are stated within the first ten lines of any press release. An analysis of our sample shows that if firms state non-GAAP earnings on the first page of a press release, they also typically do this within the first ten lines.
 
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Metadaten
Titel
Non-GAAP Reporting and Debt Market Outcomes: Evidence from Regulation G
verfasst von
Felix Thielemann
Tami Dinh
Helen Kang
Publikationsdatum
07.03.2019
Verlag
Springer International Publishing
Erschienen in
Schmalenbach Business Review / Ausgabe 2/2019
Print ISSN: 1439-2917
Elektronische ISSN: 2194-072X
DOI
https://doi.org/10.1007/s41464-019-00074-x

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