Managers have a variety of tools at their disposal to influence stakeholder perceptions. Earnings management and the strategic reporting of non‐ GAAP earnings are just two of the available menu choices. We explore how real earnings management and accruals management influence the probability that a company will disclose a non‐ GAAP adjusted earnings metric in its earnings press release and the likelihood that it will do so aggressively. We first investigate situations where managers already meet analysts’ expectations either based on strong operating performance or after employing real and accruals management. We find that when solid 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 accruals management, they are significantly less likely to report a non‐ GAAP earnings metric. Next, we explore scenarios where companies fall short of expectations. We find that when they 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, we find that companies are more likely to report non‐ GAAP earnings (and to do so aggressively) when (i) they are unable to use real or accruals earnings management, (ii) are constrained by prior‐period accruals management, and (iii) their operating performance is poor. Taken together, our results are consistent with a substitute relation between non‐ GAAP reporting and both real and accruals management.

We use a novel data set to examine the across‐time consistency and across‐firm comparability of firms' non‐GAAP earnings disclosures. Given widespread concern about non‐GAAP reporting among regulators, standard setters, the investor community, and academics, our investigation provides timely evidence on how managers' deviations from their own non‐GAAP disclosure history, or the reporting of industry peers, affects how well earnings inform on firm performance. We begin by identifying firms that change their non‐GAAP earnings definition from one year to the next. These deviations are uncommon, but when managers change the items they exclude in calculating non‐GAAP earnings, the changes generally enhance the information in earnings about firms' core performance. We also examine whether non‐GAAP earnings are more comparable than GAAP earnings and find that firms' non‐GAAP adjustments result in greater earnings comparability. Finally, we examine instances in which firms deviate from common sector‐wide definitions of non‐GAAP earnings. We find that these deviations also result in earnings metrics that better represent firms' core operations. Overall, our results suggest that when managers vary their non‐GAAP calculations, either across time or across firms, the resulting non‐GAAP metrics generally enhance the information in earnings about firms' ongoing performance. Thus, our analysis helps mitigate concerns about why managers might vary their non‐GAAP reporting calculations.

Researchers typically infer real earnings management when a firm's operating and investing activities differ from industry norms. A significant problem with classifying deviations from industry averages as myopic earnings management is that companies can change their operating and investing decisions for strategic business reasons rather than to mislead stakeholders. Using principal components analysis, we systematically evaluate existing measures and develop a comprehensive real activities measure to better capture earnings manipulation. Our measure reflects (i) deviations from industry averages across multiple activities and (ii) other signals of manipulation. This approach is promising because, although there are many sources of abnormal activities, manipulation is more likely the cause when managers engage in multiple income‐increasing abnormal activities that coincide with other signals that indicate an elevated risk of manipulation. This simple approach results in a metric that associates negatively with future operating performance and earnings persistence, yields high‐power tests, and captures manipulation reasonably well across most life‐cycle stages. Importantly, this approach performs better than the standard real earnings management metrics across all dimensions. Specifically, it generates the expected reduction in future earnings and reduced earnings persistence in 82% of the tests compared to 36% and 46% in common alternatives. Also, because this innovation does not require a long time‐series or rely on future period realizations for classification, it can be useful in more research settings than other recent innovations in the literature.