COVID-19-Related Audit Report Disclosures: Determinants and Consequences
Bibliographic record
Abstract
In this study, we identified firms receiving COVID-19-related audit report disclosures through critical audit matter (CAM) mentions of COVID-19 in their audit reports. Through OLS regressions, we then investigated the fundamental accounting and auditing determinants that predict the likelihood of firms to receive these COVID-19-related disclosures, and found that firms with intangibles and goodwill were more likely to have these mentioned in their audit reports. Next, we examined the content of these disclosures and found that auditors’ COVID-19 disclosures focused on significant accounting estimates (e.g., fair value accounting and asset impairment considerations). These results are consistent with the idea that COVID-19-related uncertainty represented a triggering event to firms, who then reassessed the carrying value of these long-term assets. After exploring the spillover effects to outsiders, we found that investors obtained a 7.3 basis point for abnormal returns following COVID-19 report disclosures and that auditors were able to charge these firms USD 452,000 higher audit fees relative to benchmark firms. The results are also consistent in entropy-balanced estimations and two-stage analyses that address endogeneity.
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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 | 0.006 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".