An Examination of Auditor Choice using Evidence from Andersen's Demise
Bibliographic record
Abstract
The Enron/Andersen scandal provides a unique opportunity to examine the role of signaling in auditor choice. Not surprisingly, many clients dismissed Andersen quickly after Enron declared bankruptcy – in some cases even before a replacement auditor was engaged – and lawsuits against the audit firm were mounted. However, many clients did not dismiss Andersen until its auditing practice was shut down by the court. This study investigates why some clients did not make a quick auditor switch, that is: was the timing of the switch a signal? Our predictions are based on the theory that those that switched early (compared to those that switched late) were sending a signal that they were high‐quality financial reporters. We test a sample of 711 companies from the final portfolio of Andersen auditees. Consistent with our hypotheses, we find that subsequent to the change of auditors, management of those companies that dismissed earlier was more likely to initiate the restatement of their financial statements than those that dismissed later. It appears as though the early switchers were attempting to distance themselves from Andersen and the financial reporting used with Andersen. In contrast, those clients that dismissed Andersen later had more restatements imposed on them than those that dismissed earlier, suggesting that their financial statements were of lower quality.
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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 | 0.015 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".