Audit Office Experience with SOX 404(b) Filers and SOX 404 Audit Quality
Bibliographic record
Abstract
ABSTRACT We measure two dimensions of SOX 404 audit quality: (1) whether auditors identify and report material weaknesses (MWs) in a timely fashion, and (2) on identifying MWs, whether auditors identify misstatements arising from MWs in a timely fashion. We find that audit practice-offices with a large base of SOX 404(b) clients and those with a long history of conducting control evaluations for that client are more likely (1) to identify and report MWs in a timely manner (i.e., before resulting restatements come to light), and conditional on identifying MWs, (2) to detect MW-related misstatements in a timely manner (i.e., before the misstatements become restatements). Audit office industry expertise also matters, but only to timely MW reporting. Our results inform on the drivers of variation in SOX 404 audit quality, and highlight the key role that auditors play in identifying internal control weaknesses and assessing their impact on financial statement reliability.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".