Does the Timing of Auditor Changes Affect Audit Quality? Evidence From the Initial Year of the Audit Engagement
Bibliographic record
Abstract
We focus on the first year of the auditor–client relationship and investigate whether audit quality varies with the timing of the new auditor’s appointment. We find that audit quality is not lower for companies that engage new auditors before the end of the third fiscal quarter than for companies that do not change auditors. However, companies that engage new auditors during or after the fourth fiscal quarter are more likely to misstate their audited financial statements than companies that engage new auditors earlier in the year and companies that do not change auditors. In additional tests, we find that the decrease in audit quality associated with late auditor changes is more pronounced for companies with complex operations (i.e., more operating segments). These results suggest that the extent to which audit quality suffers in the first year of audit engagements is affected by both the amount of time required to understand the client’s business, assess risks, and perform the audit (all of which are driven by client complexity), as well as the amount of time available for auditors to perform these tasks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".