Bibliographic record
Abstract
SYNOPSIS The controversy over Chinese reverse mergers has led to concerns about the audit quality of all U.S.-listed Chinese companies. Because a sizeable number of foreign firms cross-list their shares as American Depositary Receipts (ADRs) issued by U.S. depositary banks (as opposed to direct listings), we study how auditors have managed their audits of Chinese ADRs. Our motivation for examining Chinese ADRs is based on the findings that cross-listing via the ADR process is beneficial for U.S. shareholders. We find that relative to ADRs from countries other than China, and relative to directly listed Chinese companies, Chinese ADRs are more likely to be associated with a Big 4 auditor and are less likely to restate prior-period financial statements. We also find that Chinese ADRs pay significantly higher fees than other emerging market ADRs and Chinese direct-listings. Collectively, these results suggest high audit quality for Chinese ADRs, which is in sharp contrast to the Chinese direct-listing results. Using Tobin's Q as a measure of market value, we find that the stock market rewards Chinese ADRs, indicating that investors incorporate the benefits of higher audit quality when evaluating Chinese ADRs.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".