The Effect of Mandatory Audit Firm Rotation on Earnings Management and Audit Fees: Evidence from Iran
Bibliographic record
Abstract
The present study aims to investigate the effects of mandatory requirements of audit firm rotation on earnings management among companies listed on the Tehran Stock Exchange (TSE). The study population consists of 1030 observations and 103 companies listed on the TSE during the years 2003–2012; moreover, the statistical technique used to test the hypotheses is panel data and pooled data. The results showed that the rule of mandatory audit firm rotation increased accruals-based earnings management (AEM) significantly. In addition, outcomes demonstrated that mandatory requirements of audit firm rotation did not have a significant influence on real earnings management (REM) and audit fees. Overall, our findings proved that the mandatory requirements of audit firm rotation in Iran have not been able to prevent the opportunistic actions of management at a time when they were faced with severe financial problems because of economic sanctions and auditors taking standardized systems-based auditing approaches. This research will make investors and others aware of the fact that mandatory audit firm rotation might be not effective in stopping managers wishing to manipulate the accounting figures. This paper actually suggests that when firms have financial distress, regulatory mechanisms such as audit firm rotation may not have a deterrent role. Our findings give lawgivers a stark warning that the length of an audit firm’s tenure should be based on the features of the audit market structure of each country.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".