Comparing the financial reporting quality of Chinese and US public firms
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate whether US regulatory actions around reverse mergers (RM) have exerted any spillover effects on the Chinese firms listed in China and whether Chinese firms have exhibited lower financial reporting quality than their US counterparts. Design/methodology/approach To test the possible spillover effect, this paper calculates three-day cumulative average abnormal returns (CAAR) and the aggregate CAAR for a series of US regulatory actions in 2010 and 2011. The study then compares the accrual quality, conditional conservatism, and information content of accruals of Chinese firms and US firms. Findings The paper documents a spillover effect of US actions around RM on Chinese stocks listed in China. Overall results do not support the perception that Chinese firms have lower financial reporting quality than their US counterparts. Research limitations/implications While this study provides evidence consistent with investors perceiving poor financial reporting quality among Chinese firms, that perception is not justified by empirical evidence. Practical implications Investors need not be overly concerned about the financial reporting quality among the Chinese firms when they make asset allocation decisions. Social implications A reality check is important given that perceptions may be outdated, biased, misleading, and costly. Originality/value This study puts the financial reporting quality of Chinese firms into perspective helping global investors assess information risk for optimal resource allocation.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".