Bond covenants and the speed of corporate capital structure adjustment: Evidence from China
Bibliographic record
Abstract
Abstract We investigate the effect of bond covenants on the speed of corporate capital structure adjustment. Based on manually collected bond covenant information from publicly listed Chinese firms between 2007 and 2019, we construct an index that measures the intensity of corporate bond covenants. Our results show that the greater the covenant intensity index of a firm's debt covenants, the faster the capital structure adjustment. Option covenants, restrictive asset transfer covenants, restrictive investment covenants, and event‐driven covenants all have a positive and significant association with the speed of capital structure adjustment, whereas no such effect is observed for financing covenants and repayment arrangement covenants. Furthermore, we examine the direction of adjustment and adjustment method, and demonstrate that bond covenants promote an upward adjustment in a firm's capital structure by increasing debt financing. An analysis of heterogeneity effects reveals that the positive relation between the intensity of bond covenants and speed of capital structure adjustment is more pronounced in state‐owned companies and companies headquartered in areas with higher legal standards. Finally, we show that information transparency, internal control, and environmental, social, and governance (ESG) performance are channels through which bond covenants affect the speed of capital structure adjustment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".