Mandatory Audit Partner Rotation, Audit Quality, and Market Perception: Evidence from Taiwan*
Bibliographic record
Abstract
We examine the effectiveness of mandatory audit-partner rotation in promoting audit quality using audit data in Taiwan where a five-year audit-partner rotation became de facto mandatory in 2004. Using both absolute and signed abnormal accruals and abnormal working capital accruals as proxies for audit quality, we find some evidence that audit quality of companies subject to mandatory audit-partner rotation in 2004 is higher than audit quality of companies not subject to rotation in 2004. However, audit quality of companies subject to mandatory rotation in 2004 is lower than audit quality of these same companies in 2003 under the old audit partners. Furthermore, audit quality of companies subject to mandatory rotation in 2004 is indistinguishable from audit quality of companies whose audit partners were voluntarily rotated before 2003. Therefore, the effect of mandatory audit-partner rotation on audit quality, in terms of auditors constraining management's extreme income-increasing or extreme income-decreasing accruals, is mixed. In contrast, using earnings response coefficients as a proxy for investor perceptions of audit quality, we consistently find that investors perceive mandatory audit-partner rotation as enhancing audit quality, suggesting that mandatory audit-partner rotation enhances auditor independence in appearance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".