Institutional ownership and bond pricing: Evidence from China
Bibliographic record
Abstract
This paper examines the impact of institutional ownership on the bond yield spreads of publicly traded Chinese firms. Our research results show the presence of a U-shaped, non-linear relationship between the shareholdings of institutional investors and bond yield spreads. Heterogeneity tests reveal differences in the impact of institutional ownership on yield spreads among different types of institutional investors and for firms in which members of the central government stabilization fund, commonly referred to as “national team” institutions, hold shares. Further tests indicate that corporate governance levels and firm performance serve as channels through which institutional shareholders affect bond yield spreads. • This study finds a significant U-shaped relationship between institutional ownership and secondary market bond yield spreads in the Chinese market, suggesting that an initial increase in institutional ownership leads to a decline in bond yield spreads, but beyond a certain threshold, an increase in institutional ownership causes yield spreads to rise. • The U-shaped relationship between institutional ownership and bond spreads significantly weakens when the main institutional investors are long-term-oriented, or include central government funds aiming to stabilize the stock market, commonly referred to as the “national team”. • Mechanism tests indicate that corporate governance and firm performance serve as channels through which institutional shareholders affect bond yield spreads.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".