Determinants and consequences of auditor switching during fiscal year‐end audit fieldwork
Bibliographic record
Abstract
We compare a group of firms switching auditors while their annual audit is underway (LateSwitches) with two control groups: firms switching their auditors during the fourth quarter of the fiscal year and firms switching their auditors during the first three quarters. First, we find that LateSwitches tend to be riskier with regard to litigation risk, audit risk and business risk. Second, we find that LateSwitches have a higher chance of announcing restatements and receiving going concern opinions in the first year of audit with the successor auditor. Despite the higher risks, we fail to find that LateSwitches disclose more adverse events in Forms 8‐K than other groups. We also document that stock market returns following an auditor change are more negative for LateSwitches. These results indicate that LateSwitches stand to face significant negative consequences when the relationship with their auditor is terminated abruptly in the final phases of an audit.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".