Abstract
Regulators and standard setters strive to enhance the transparency of corporate disclosures. To address concerns regarding the improper use of non-GAAP financial measures, the US Securities and Exchange Commission (SEC) implemented Regulation G and related amendments in 2003. US firms that report non-GAAP earnings must now reconcile them with the most directly comparable GAAP earnings, which are presented with equal or greater prominence than non-GAAP earnings. This paper examines the effect of Regulation G on analysts’ information environment for non-GAAP reporting firms. Before Regulation G, non-GAAP earnings reporting is associated with less accurate, more positively biased, and more dispersed analysts’ earnings forecasts. By contrast, the quality of analysts’ earnings forecasts for non-GAAP reporting firms is enhanced by higher accuracy, less bias, and lower dispersion after Regulation G. The case for Regulation G making non-GAAP disclosures more transparent may be relevant for regulators and standard setters when considering future disclosure regulations.
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Notes
Based on the US setting, this paper calls earnings measures that are derived from GAAP earnings, but deviate from them, non-GAAP earnings, also known as pro forma earnings, street earnings, or core earnings. Non-GAAP earnings can be derived from GAAP earnings by adjusting certain profit or loss items. Typically, either firm managers or analysts often exclude non-recurring, transitory items, such as impairment losses or restructuring costs, from GAAP earnings to deliver non-GAAP earnings, which are believed to better reflect firms’ earnings power. In particular, pro forma earnings usually refer to non-GAAP earnings reported by firm managers, and street earnings usually refer to non-GAAP earnings discussed by analysts and reported by forecast database providers like IBES (Bentley et al. 2018). This study uses non-GAAP earnings to refer to any earnings metrics that deviate from earnings reported under GAAP.
Regulation G requires public companies that disclose or release non-GAAP financial measures to include a presentation of the most directly comparable GAAP financial measure and a reconciliation of the disclosed non-GAAP financial measure to the most directly comparable GAAP financial measure in that disclosure or release. The amendments to Item 10 of Regulation S-K and Item 10 of Regulation S-B require the most directly comparable GAAP financial measure to be presented with equal or greater prominence as the non-GAAP financial measures. Unless the emphasis is on specific regulations, such as Regulation S-K, this study uses Regulation G to indicate broadly the regulatory reform of non-GAAP reporting practice in 2003. Furthermore, this study focuses on non-GAAP earnings and does not cover other non-GAAP financial measures.
Multi-period earnings forecasts can be important when not assuming the earnings dynamics among future earnings, current earnings, and dividends, or when assuming valuation by abnormal earnings growth (Cheng et al. 2021).
Some studies suggest that unreconciled or unregulated non-GAAP earnings information does not affect analysts’ judgments or behaviors. Experimental studies by Elliott (2006) and Frederickson and Miller (2004) find no evidence that the presence of unreconciled non-GAAP earnings affects analysts’ judgments in either earnings forecasting or stock pricing. Allee et al. (2007) provide archival evidence and validate these experimental results. Also, less sophisticated investors, rather than sophisticated investors, dominate trading activities based on non-GAAP earnings information, suggesting little effect of non-GAAP disclosures on analysts’ trading behaviors (Bhattacharya et al. 2007). Both Allee et al. (2007) and Bhattacharya et al. (2007) use US data from 1998 to 2003, which provides inferences mainly for the setting before Regulation G. However, other studies find that unregulated non-GAAP earnings information can affect analysts’ judgments. Bhattacharya et al. (2003) show that analysts’ earnings forecast revisions are associated with non-GAAP earnings surprises. Hirshleifer and Teoh (2003) predict that non-GAAP earnings disclosures bias investors’ perceptions of firm performance upwards. Andersson and Hellman (2007) provide experimental evidence that analysts’ judgments are biased by non-GAAP profits that overturn GAAP losses. Chen (2010) finds that analysts do not fully understand the persistence of non-GAAP excluded items before Regulation G.
A closely related paper by Chen (2010) studies whether analysts and investors fully understand the persistence of non-GAAP excluded items and how the results change after Regulation G. By contrast, this paper focuses only on analysts and aims to infer the policy effect of Regulation G on analysts’ information environment for non-GAAP reporting firms. While the research design adopted by Chen (2010) implies his research emphasis on evaluating the effectiveness of analysts’ earnings forecast processes, the focus in this paper is on understanding the associations between non-GAAP earnings reporting and attributes of analysts’ earnings forecast outputs, and how these associations are affected by the SEC’s regulatory efforts. Despite the similarity in research topics regarding analysts’ earnings forecasts, non-GAAP earnings information, and Regulation G, the two studies approach these topics differently. Besides providing evidence on forecast bias that triangulates with both Hirshleifer and Teoh (2003) and Andersson and Hellman (2007), this paper incrementally contributes to the literature by offering results that strengthen and complement Chen (2010). The authors thank the anonymous reviewer(s) for the comment about clarifying the differences between the two papers.
The common belief that analysts are sophisticated investors with little cognitive bias or with enough expertise to de-bias biased information would argue against this prediction.
Using data from 2004 to 2014 after Regulation G and the consensus measure by Barron, Kim, Lim, and Stevens (1998), Bradshaw et al. (2018) find that non-GAAP disclosures are associated with higher (lower) consensus for revenues (expenses). Our paper differs from Bradshaw et al. (2018) by using different measures for analysts’ information environment and by focusing on the effect of non-GAAP reporting and its regulations on analysts’ earnings forecast accuracy, bias, and dispersion, instead of their consensus on earnings components.
Heflin and Hsu (2008), Kolev et al. (2008), and Chen (2010) all use IBES actual earnings as a proxy for non-GAAP earnings. Furthermore, since Bentley et al. (2018) find that 73.3%, of IBES street earnings agree with managers’ pro forma earnings, they advise studies on non-GAAP earnings before Regulation G to rely on IBES street earnings as valid proxies. In addition, using IBES street earnings before and after Regulation G in this study ensures consisitency across our sample periods.
The study constructs control variables for the 13,408 firm-quarter observations. However, the observations deployed in regression tests can be fewer than the selected 13,408 firm-quarters and vary with different dependent variables. This is because analysts may not offer earnings forecasts for the earnings quarter(s) ahead or the future actual earnings are not available. In particular, this study has 11,929, 11,499, 10,848, and 9,987 firm-quarter observations in the analysis of forecast accuracy and forecast bias for the earnings 1 to 4 quarter(s) ahead, respectively. Furthermore, this study has 10,176, 9,460, 8,522, and 7,359 firm-quarter observations in the analysis of forecast dispersion, which further requires at least 2 forecasts on the same firm’s quarterly earnings, for the earnings 1 to 4 quarter(s) ahead, respectively. The numbers of observations used in regression tests may be slightly lower than the numbers discussed above if there are singleton observations to drop under fixed effects. The authors thank the anonymous reviewer(s) for suggesting clarifying the varying numbers of observations used in different regressions.
This study follows Pan et al. (2019) to estimate unexpected core earnings and unexpected changes in core earnings for firms that may engage in income classification shifting. Appendix B shows the results of these estimations, and the signs of the coefficients are consistent with theoretical predictions by Pan et al. (2019). For Appendix B, the sample period ends in 2005 due to the control for classification shifting in the forecasted periods. Except for \(NG\_ROA\_VOL\), which is constructed by firm-year to alleviate the concern about multicollinearity between GAAP and non-GAAP earnings volatility, all other control variables are constructed and measured on a quarterly basis.
In untabulated results, the inferences are robust to using industry fixed effects and clustering by industry (Lougee and Marquardt 2004). This paper also drops certain quarterly dummies to ensure no multicollinearity issue between the quarterly dummies and some control variables for regulations (\(POST\_01\), \(SOX\), and \(REG\)). In addition, the use of firm fixed effects subsumes the independent variable \(TREAT\). Therefore, this paper presents and discusses the results of the interaction effect \(TREAT\times REG\) for testing H1b through H3b under the fixed effect model.
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Acknowledgments
The authors are thankful for insightful comments and discussions from Kari Lee, Carolyn Levine, Bharat Sarath, Zhiwei Xu, and Ari Yezegel (alphabetically by last name). All errors are our own.
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Chen, YA., Medinets, A.F. & Palmon, D. Disclosure regulations work: The case of regulation G. Rev Quant Finan Acc 58, 1037–1062 (2022). https://doi.org/10.1007/s11156-021-01017-9
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DOI: https://doi.org/10.1007/s11156-021-01017-9