<scp>Non‐GAAP</scp> Earnings: A Consistency and Comparability Crisis?*
Bibliographic record
Abstract
ABSTRACT 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".