Regulatory spillovers of inquiry letters on analyst earnings forecasts: evidence from director networks in China
Bibliographic record
Abstract
We investigate the regulatory spillovers of inquiry letters issued by Chinese stock exchanges on analyst earnings forecasts through director networks. We conduct a difference-in-differences test, exploiting the revised Securities Law as an exogenous shock to disclosure liabilities of firms and directors. Our empirical results show that the receipt of inquiry letters by director-interlocked firms reduces analyst earnings forecast errors and dispersion for non-inquired focal firms, revealing the indirect deterrence of regulatory inquiries. Our findings remain robust after applying the propensity score matching approach and conducting a series of additional robustness checks. Furthermore, director diligence and information disclosure are crucial channels through which the regulatory spillovers occur. The indirect deterrence of regulatory inquiries is more pronounced when the directors of focal firms perceive higher risks. Additionally, the spillover effects of inquiry letters are amplified by directors’ social networks, their industry expertise and professional experience. Our findings highlight the positive externalities of public enforcement by documenting how director networks enhance regulatory effectiveness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".