Auditing Goodwill in the Post‐Amortization Era: Challenges for Auditors
Bibliographic record
Abstract
ABSTRACT The elimination of goodwill amortization in 2001 brought about significant change in how companies are required to account for goodwill. This change in accounting also brought with it new challenges for auditors, namely evaluating the reasonableness of management's assumptions related to goodwill valuation. In addition to introducing technical challenges, this task is particularly difficult given the misalignment in incentives it creates between managers who likely prefer to avoid recording an impairment and auditors who seek to minimize the bias in management's impairment testing. This study focuses on the consequences of the misaligned incentives that auditors face under the current goodwill assessment process. We find that the decision to record a goodwill impairment is associated with an increase in the probability of auditor dismissal. Consistent with the presence of significant friction with clients, our results also indicate that the likelihood of auditor dismissals is negatively related to the favorability of the impairment decision. Furthermore, we find that companies impairing goodwill prior to dismissing auditors subsequently employ auditors that are, on average, more favorable to clients in their impairment decisions.
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 | 0.071 | 0.198 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".