Did Houston Clients of Arthur Andersen Recognize Publicly Available Bad News in a Timely Fashion?*
Bibliographic record
Abstract
Abstract Despite the allegations of audit failure and the enormous publicity surrounding Arthur Andersen's indictment, there is no systematic empirical evidence on characteristics of accounting information of clients of Arthur Andersen vis‐à‐vis other Big 6 auditors. I examine whether earnings of Andersen's Houston‐based clients are timely in reporting bad news about future cash flows. I find that relative to a control group consisting of Houston‐based clients audited by other Big 6 auditors, earnings of Andersen clients are less timely in reporting bad news. Further, it appears that operating accruals of Andersen clients are less effective in accelerating the timely recognition of bad news than operating accruals of non‐Andersen clients. The findings suggest that the clients of Andersen's Houston office engaged in aggressive accounting practices, including delayed recognition of publicly available bad news.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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; both teacher heads agree on what is shown here.
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".