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Analysts’ role in shaping non-GAAP reporting: evidence from a natural experiment

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Abstract

We examine how exogenous changes in analyst coverage influence (1) the likelihood that managers will voluntarily disclose customized (non-GAAP) performance metrics and (2) the relative quality of their non-GAAP disclosures. Specifically, we use a quasi-natural-experimental setting in which brokerage firms terminate analyst coverage and find that, following an unanticipated decrease in analyst coverage, managers are more likely to disclose non-GAAP earnings per share (EPS) numbers. We also find that managers become more aggressive in their disclosure choices and that the quality of their non-GAAP exclusions decreases after analysts terminate coverage. These effects are more pronounced among firms losing an analyst with greater ability and firms with weaker corporate governance. Overall, our evidence suggests that analysts’ monitoring deters aggressive non-GAAP reporting.

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  1. Many prior studies have examined incentives for non-GAAP reporting (e.g., Doyle et al. 2003; Gu and Chen 2004; Bowen et al. 2005; Graham et al. 2005; Kolev et al. 2008; Black and Christensen 2009; Marques 2010; Zhang and Zheng 2011; Jennings and Marques 2011; Huang and Skantz 2016; Heflin et al. 2016).

  2. Investors’ information demand could affect both of our dependent variables (i.e., the likelihood and quality of non-GAAP reporting) as well as our primary explanatory variable, analyst coverage. On the one hand, a few studies find that analysts respond to investors’ information demands (e.g., Frankel et al. 2006; Lang et al. 2003). In particular, analysts tend to cover firms for which there is more investor demand for information (i.e., larger and more visible firms). On the other hand, prior evidence indicates that investor demand for information influences managers’ disclosure practices (Healy and Palepu 2001). As a particular form of voluntary disclosure, managers’ non-GAAP EPS numbers are likely influenced by investor demand.

  3. Hong and Kacperczyk (2010) and Kelly and Ljungqvist (2012) find that when brokerages merge, the merged firms typically fire some analysts primarily because of redundancy in research capacity.

  4. This second approach for measuring non-GAAP reporting quality, introduced by Doyle et al. (2003), assumes that recurring exclusions and those with cash implications are low in quality, while it is justifiable to exclude non-recurring (“one-time”) or noncash items. Thus regressing future cash flows on current exclusions would result in an insignificant coefficient if the exclusions are completely transitory or noncash in nature. A coefficient that is statistically significant and higher would indicate lower exclusion quality. Kolev et al. (2009) extend this line of thinking by regressing future operating earnings on non-GAAP exclusions, again focusing on the notion that nonrecurring items should not influence future earnings.

  5. While both the monitoring and the information hypotheses posit an increase in managers’ likelihood of reporting non-GAAP EPS numbers, they lead to opposite predictions for non-GAAP reporting quality. Thus our quality tests differentiate between the two hypotheses.

  6. While prior studies have examined the effect of analyst coverage on accrual-based earnings management (Yu 2008; Irani and Oesch 2013) and real earnings management (Irani and Oesch 2016), their evidence does not necessarily illuminate the interplay between managers and analysts with respect to non-GAAP reporting choices. First, prior research suggests that the association between aggressive non-GAAP reporting and real/accrual-based earnings management varies and is context-specific, due to differences in their timing and relative costs (Black et al. 2017). Second, while real and accrual-based earnings management are used primarily for perception management, non-GAAP reporting can be used either as (1) a type of voluntary disclosure or (2) a perception management tool, leading to different outcomes.

  7. Accrual-based and real earnings management have real costs influencing future performance. Specifically, accruals reverse in subsequent periods, and real decisions result in real performance tradeoffs. For example, if managers cut advertising expense, the firm will likely experience lower future sales. In contrast, non-GAAP EPS numbers have no effect on future performance, because managers simply exclude current-period income statement components (usually expenses) to report an alternative metric that may or may not accurately reflect core performance.

  8. Specifically, they find that when strong operating performance alone allows firms to meet expectations, managers do not employ earnings management or non-GAAP reporting. However, when managers meet expectations using real and accrual-based management, they are significantly less likely to report a non-GAAP EPS number. Next, they explore scenarios where companies fall short of expectations. They find that when managers just miss expectations after managing GAAP earnings, they are significantly more likely to employ non-GAAP reporting, suggesting that the timing and relatively costless nature of non-GAAP reporting allows managers to appear to meet expectations on a non-GAAP basis when managed GAAP earnings fall short. Moreover, they find that companies are more likely to report non-GAAP EPS (and to do so aggressively) when (i) they cannot use real or accruals earnings management, (ii) are constrained by prior-period accrual-based earnings management, and (iii) their operating performance is poor.

  9. We thank Novia Chen for the use of the Chen et al. (2017) sample on broker mergers after 2008.

  10. We rank the differences in the level of each of these three variables between dropped coverage firms and candidate control firms, and we compute the total rank across all three variables. We then retain control firms with the lowest total rank.

  11. In untabulated analyses, we find that, relative to control firms, dropped coverage firms experience a decrease in analyst coverage following broker closures and mergers, similar to Irani and Oesch’s (2016) evidence.

  12. We follow Bradshaw et al. (2018) in using the I/B/E/S GPS forecast to compare to actual GAAP EPS and the I/B/E/S EPS forecast to compare to the non-GAAP EPS number.

  13. We code non-GAAP exclusions as positive amounts. To the extent that these exclusions are recurring (not one-time) in nature, they should be negatively associated with future GAAP EPS and future cash flows For example, assume a company excludes stock-based compensation expense from current GAAP EPS (positive non-GAAP exclusion) to increase current non-GAAP EPS. If stock-based compensation also occurs in the next period, it would result in lower GAAP EPS next period. Current non-GAAP exclusions would be negatively associated with GAAP EPS next period. Assume that managers exclude some recurring expenses. These expenses recur and consume cash in the next period, resulting in a negative relation between non-GAAP exclusions and future cash flows.

  14. We do not have an expectation for the sign of the coefficients on the BELOWLINEit × DROPCOVit × POSTit (β6) and the coefficient on the SPECIALEXCLit × DROPCOVit × POSTit (β7) interaction terms in equation 6. Both below-the-line items (BELOWLINE) and special items (SPECIALEXCL) are one-time or nonrecurring in nature, so we do not expect these exclusions to be significantly related to future GAAP EPS or future cash flows (Doyle et al. 2003; Kolev et al. 2008). We do not expect a difference in the relation for dropped coverage and control firms following a decrease in analyst coverage.

  15. The effect of brokerage closures and mergers on non-GAAP reporting can be relatively short-lived for two reasons. First, non-GAAP reporting and analyst coverage are very different. The costs associated with initiating coverage could be higher than those to initiate non-GAAP reporting. Specifically, it likely takes longer for an analyst to find employment and begin coverage after a brokerage closure than it does for managers to begin non-GAAP reporting. Second, analyst coverage is not the only monitoring mechanism affecting non-GAAP reporting. As long as other monitoring mechanisms are functioning after the shock, they can gradually push non-GAAP reporting practices back to the mean. For example, Bhattacharya et al. (2019) find that short sellers can constrain overly optimistic non-GAAP reporting. As a result, following brokerage closures and mergers, the shift in non-GAAP reporting may be short-lived.

  16. We follow Bradshaw et al. (2018) in comparing the GAAP and non-GAAP realizations to the appropriate GAAP and street forecasts available on I/B/E/S.

  17. Whipple (2018) hand-collects a large sample of exclusions and finds that a significant portion of items coded as special items by Compustat are actually recurring items. Hence the accuracy of Compustat’s classification cannot always be relied upon 100%.

  18. In an untabulated test, we follow Chen et al. (2019) in directly examining how the persistence of non-GAAP earnings changes following an exogenous drop in coverage by adding an additional interaction term (NONGAAP×DROPCOV×POST) to models 1 and 3 of Table 4. The coefficient on this term is significantly negative in both the future EPS and cash flow regressions, suggesting that non-GAAP earnings are less persistent following exogenous drops in coverage.

  19. We perform the chi-squared tests on the OLS specifications since the tests are not available for the Probit model.

  20. Our results are robust to the use of Gompers et al.’s (2003) governance index as the measure of internal governance.

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Acknowledgements

We appreciate helpful comments from Jeremy Bentley, Marc Caylor, Paul Demere, Kurt Gee, Michael Shen (AAA discussant), Steven Savoy (FARS discussant), Ben Whipple, and participants at the 2016 Brigham Young University Accounting Research Symposium, the 2017 AAA Annual Meeting, the 2019 FARS Mid-year Meeting and the 2019 University of Toronto PAC Conference, University of Bristol, Chinese University of Hong Kong, University of Exeter, University of Georgia, Hong Kong University of Science and Technology, Idaho State University, Kennesaw State University, and University of Manchester workshops. We thank Novia Chen for sharing the Chen et al. (2017) sample on broker mergers after 2008. We also thank Kurt Gee for providing the Bentley et al. (2018) non-GAAP EPS dataset.

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Appendix 1

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Table 10 Variable Definitions

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Christensen, T.E., Gomez, E., Ma, M. et al. Analysts’ role in shaping non-GAAP reporting: evidence from a natural experiment. Rev Account Stud 26, 172–217 (2021). https://doi.org/10.1007/s11142-020-09564-7

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