Does Regulator as a Minority Shareholder Affect Bond Yield Spreads? A Quasi‐Natural Experiment
Bibliographic record
Abstract
ABSTRACT Research Question/Issue To strengthen the protection of minority shareholders, in 2016, the China Securities Regulatory Commission authorized the China Securities Investor Services Center (CSISC), a non‐profit institution with official backing, to buy and hold 100 shares of listed firms in pilot regions. By exercising shareholder rights, the CSISC plays a governance role as a regulatory minority shareholder. This study examines whether CSISC shareholding has a spillover effect in the bond market and whether this effect varies across firms with different levels of information asymmetry, insider expropriation, shareholder–creditor agency conflicts, and trustee reputation. Research Findings/Insights Employing a difference‐in‐differences analysis on bonds issued by listed firms between 2015 and 2017, we find that CSISC shareholding is associated with lower bond yield spreads. Cross‐sectional tests suggest that CSISC shareholding reduces bond yield spreads by mitigating information asymmetry, curbing insider expropriation, and alleviating shareholder–creditor agency conflicts. We also find that trustee reputation moderates the relationship between CSISC shareholding and bond yield spreads. Furthermore, CSISC shareholding influences the nonpricing terms of bonds, and the difference in bond yield spreads between the treatment and control groups diminishes following the nationwide implementation of CSISC shareholding. Theoretical/Academic Implications This study contributes to the growing literature on the economic consequences of CSISC shareholding by uncovering its spillover governance effect on bondholder protection. It also extends the research on the role of government regulation in safeguarding bondholder interests. Practitioner/Policy Implications Our study has important policy implications for investor protection in other emerging markets. Given the unique characteristics of China's bond market, directly replicating this mechanism may not yield similarly favorable outcomes elsewhere. Nevertheless, regulators in other emerging markets could draw on China's experience and consider implementing novel investor protection mechanisms tailored to their specific market conditions.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.002 |
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".