The Relation Between Earnings Management and Non‐<scp>GAAP</scp> Reporting
Bibliographic record
Abstract
Abstract 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".