An Emerging Market's Reaction to Initial Modified Audit Opinions: Evidence from the Shanghai Stock Exchange*
Bibliographic record
Abstract
Abstract This study investigates the valuation effect of modified audit opinions (MAOs) on the emerging Chinese stock market. Here, the term MAO refers to both qualified opinions and unqualified opinions with explanatory notes. The latter can be considered an alternative form of a qualified opinion in China. The institutional setting in China enables us to find compelling evidence in support of the monitoring role of independent auditing as an institution. First, we find a significantly negative association between MAOs and cumulative abnormal returns after controlling for effects of other concurrent announcements. Further, results from a by‐year analysis suggest that investors did not reach negative consensus about MAOs' valuation effect until the second year, exhibiting the learning process of a market without prior exposure to MAOs. Second, we do not observe significant differences between market reaction to non‐GAAP‐ and GAAP‐violation‐related MAOs. Third, no significant difference is found between market reaction to qualified opinions and market reaction to unqualified opinions with explanatory notes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".