Are the Reputations of the Large Accounting Firms Really International? Evidence from the Andersen-Enron Affair
Bibliographic record
Abstract
SUMMARY: This paper investigates the stock price reaction of Andersen's non-U.S. clients around two key dates leading up to Andersen's demise, i.e., January 10, 2002, when Andersen announced it had shredded documents related to the Enron audit, and February 4, 2002, when Enron's board released a report (the Powers report) that was critical of Andersen and when Andersen established an Independent Oversight Board to examine the firm's audit practice. We find that the cumulative abnormal return for the two dates is negative and significant which suggests that concerns about Andersen's reputation and audit quality spilled over to other countries outside the U.S. We find that the market reaction is more significant when there is a greater demand for assurance, e.g., in common law countries and firms with large changes in total accruals or with new debt or equity issues. In further analyses, we use Andersen's non-U.S. clients that are cross-listed in the U.S. to separate out possible assurance and insurance effects. When Andersen's non-U.S., cross-listed clients are compared with Andersen's U.S. clients, we find similar cumulative abnormal returns. Since this test controls for insurance exposure in the U.S. market, our results suggest a similar assurance effect whether the client is audited by Andersen's U.S. unit or one of its non-U.S. units.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".