Do Investors Respond to Explanatory Language Included in Unqualified Audit Reports?
Bibliographic record
Abstract
ABSTRACT This article investigates whether investors respond to explanatory language (EL) added to unqualified audit reports. Although prior research finds an association between auditor EL and lower financial reporting quality, surveys suggest that many investors limit their attention to the unqualified nature of the opinion. We use three‐day abnormal returns and abnormal trading volume to measure investor response to EL in unqualified audit reports issued from 2000 to 2014. We find little evidence to indicate that investors respond to auditor EL at the audit report release date. In further analyses, we find that the lack of investor response is attributable both to incomplete investor reactions (55 percent of EL occurrences) and previous incorporation of EL (40 percent of EL occurrences). Overall, the results support policymakers’ initiatives to improve the usefulness of unqualified audit reports.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".