Examining auditors’ ability to evaluate the reasonableness of fair value estimates
Bibliographic record
Abstract
Abstract One of the most difficult challenges facing contemporary auditors is evaluating the reasonableness of fair value estimates (FVEs) made by management. Both practitioners and academic studies have shown auditors to be deficient when tasked with assessing FVEs. However, it is not well understood whether the root cause of this deficiency lies in auditors’ lack of knowledge to appropriately evaluate estimates or auditors’ lack of willingness to challenge management. Using the setting of common auditors in M&A transactions, this study empirically examines whether the audit deficiency can be resolved by providing auditors with additional knowledge or willingness. Our results show that common auditors significantly outperform their peers when tasked with assessing the reasonableness of FVEs in purchase price allocations and reducing overallocation to goodwill when managers have incentives to do so. Further, the evidence is consistent with common auditors demonstrating improved performance in challenging information environments, but not in scenarios where risks to auditors may be perceived to be higher. The results suggest that it is their greater asset‐specific knowledge that drives mitigation of the audit deficiency and that targeting improvements to knowledge rather than willingness is likely to be more effective in improving auditors’ ability to evaluate FVEs.
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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 | 0.061 | 0.289 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".