Common auditors in mergers and acquisitions: Post‐acquisition financial reporting quality and audit fees
Bibliographic record
Abstract
Abstract Prior research documents that mergers and acquisitions result in significant financial reporting risks. In this article, we examine whether acquirers that share a common auditor with the target experience higher post‐acquisition financial reporting quality (FRQ) and reduced audit fees. We find that same‐office, but not different‐office, common auditors are associated with improved post‐acquisition FRQ, as evidenced by a decreased likelihood of misstatement, lower F ‐score, and a lower likelihood of meeting or just beating analyst forecasts. We also find that same‐office common auditors are associated with a lower percentage change in audit fees. In additional tests, we find that these inferences are robust to limiting the sample to acquirers with multiple acquisitions or to acquirers and targets with no auditor switches in the prior 3 years. Together, our findings suggest that same‐office common auditors facilitate knowledge transfer about the target and provide important post‐acquisition benefits.
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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 | 0.004 | 0.037 |
| 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 | 0.002 | 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".