Market Consequences of Earnings Management in Response to Security Regulations in China*
Bibliographic record
Abstract
Abstract Under the 1996‐98 security regulations in China, the accounting rate of return on equity (ROE) has to be greater than 10 percent for three "consecutive" years for a firm to qualify for stock rights offers. Despite declining economic conditions during this period, the percentage of firms reporting ROE between 10 and 11 percent is about "three" times that for 1994‐95. This unique regulatory environment provides a natural experimental setting for the empirical assessment of earnings‐management behavior and its consequences. This study examines whether listed Chinese firms manage earnings to meet regulatory benchmarks and whether regulators and investors consider the quality of earnings in their respective regulatory and investment decisions. On the basis of a sample of listed Chinese firms from 1996 to 1998, we observe that managers execute transactions involving below‐the‐line items and use income‐increasing accounting accruals to meet regulatory ROE targets for stock rights offerings. The firms that apply for, but fail to receive, regulatory approval manage earnings more significantly than do firms that receive approval and pair‐matched control firms. Our market study also suggests that investors differentiate the quality of earnings and put less value on earnings suspected of a greater degree of management. Overall, our results imply that the regulatory bodies and investors to some extent make rational adjustments for the quality of earnings.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".