The Consequences of Audit‐Related Earnings Revisions
Bibliographic record
Abstract
Abstract In this study, we investigate the consequences that auditors and their clients face when earnings announced in an unaudited earnings release are subsequently revised, presumably as a result of year‐end audit procedures, so that earnings as reported in the 10‐K differ from earnings as previously announced. Specifically, we examine whether the likelihood of an auditor “losing the client” is greater following such revisions, and whether the likelihood of dismissal is influenced by revisions that more negatively impact earnings, that cause the client to miss important earnings benchmarks, by greater local auditor competition, or by auditor characteristics. We also examine audit pricing subsequent to audit‐related earnings revisions for evidence of pricing concessions to retain the client. Finally, we examine whether client executives experience a greater likelihood of turnover following an audit‐related earnings revision. Consistent with expectations, we find that auditor dismissals are more likely following audit‐related earnings revisions. We also find that dismissals are more likely when revisions cause clients to miss important benchmarks and when there is greater local auditor competition. Among nondismissing clients, we find that future audit fees are lower when the effect of the revision on earnings is more negative, consistent with auditors offering price concessions to retain clients when revisions are more displeasing. We also find a greater likelihood of future chief financial officer ( CFO ) turnover as the effect of the revision worsens. Our findings offer important insights into the consequences that auditors face when balancing their responsibility for high audit quality and client satisfaction, as well as into the consequences that CFO s face when releasing inflated but not fully audited earnings.
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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 | 0.007 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".