Can a Not-for-Profit Minority Institutional Shareholder Make a Big Difference in Corporate Governance? A Quasi-Natural Experiment on Its Effect on Earnings Management
Bibliographic record
Abstract
In this study, we examine the effectiveness of the China Securities Investor Service Center (CSISC), a new minority shareholder protection mechanism promoted by the China Securities Regulatory Commission, in constraining earnings management. Employing a difference-in-differences analysis for a sample of Chinese listed companies during 2015-2017, we find that CSISC shareholding reduces earnings management. We also find that this effect exists when the internal and external corporate governance mechanisms of listed companies are weaker. Furthermore, our empirical evidence indicates that restraining tunneling is a channel through which the CSISC affects earnings management. The additional analyses show that the CSISC-holding firms (i.e., treatment firms) exhibit higher cumulative abnormal returns around the announcement of the CSISC shareholding pilot program than the control firms, and the difference in earnings management between the treatment and control firms is diminishing after the pilot program was promoted nationwide. Our findings have important policy implications for emerging markets that attempt to improve minority shareholder protection.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".