Bibliographic record
Abstract
Because the PCAOB was unable to inspect audits of Chinese accounting firms until recently, regulators introduced legislation (HFCAA) potentially forcing Chinese companies to delist for non-compliance with PCAOB audit requirements. To understand the equity markets’ response to this legislation, we analyze the short-term (event study) and long-term stock performance of U.S.-listed Chinese firms relative to the stock performance of other foreign companies. We find that Chinese companies outperform other Asian firms for the Pre-HFCAA Period, but they underperform other Asian firms from the time the HFCAA was introduced (28 March 2019) until an agreement was reached (26 August 2022). For the post-agreement period (26 August 2022 to 31 December 2022), the performance of Chinese and other Asian stocks is similar. Between 28 March 2019 and 31 December 2022, a typical shareholder lost 76% of wealth, and, compared to other Asian companies, the losses were around 87%. The findings highlight the importance of regulatory compliance and transparency in maintaining investor confidence and protecting shareholders’ interests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".