Auditing <scp>Non‐GAAP</scp> Measures: Signaling More Than Intended*
Bibliographic record
Abstract
ABSTRACT Many companies regularly disclose non‐GAAP performance measures to communicate firm‐specific information that does not fit within the mold of GAAP reporting. However, these non‐GAAP measures may have low information content or even be misleading to investors. Thus, the question arises of whether auditors should play a larger role in the reporting of non‐GAAP measures, which currently are not audited. We run an experiment to provide ex ante evidence on the effect of auditing non‐GAAP measures. Specifically, we present investor‐participants with a non‐GAAP measure that should be used when making investment judgments (more informative) or should not be used when making investment judgments (less informative) and is either audited or is not audited. As predicted, we find that, when participants view a non‐GAAP measure that is more informative, they appropriately use the non‐GAAP measure in their investment‐related judgments, regardless of whether the measure is audited. However, also as predicted, we find that, while participants appropriately do not use a less informative non‐GAAP measure when it is not audited, participants inappropriately do use the less informative non‐GAAP measure in their investment‐related judgments when it is audited. Mediation results provide evidence consistent with audits affecting investors' reliance on non‐GAAP measures. Specifically, our results are consistent with audits of non‐GAAP measures signaling more than is intended, evidenced by investors perceiving an audited non‐GAAP measure as being useful in their investment decisions when the measure is less informative to them. Our findings suggest that regulators should exercise caution when it comes to prescribing assurance over non‐GAAP measures.
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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 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".