Analysis of the Effect of Credit Fluctuation on Capital Structure of Listed Companies: A Research Direction Based on Industry Perspective
Bibliographic record
Abstract
Based on the industry heterogeneity, this paper uses the data of Listed Companies in the A stock market of China for 2003-2015 years to study the impact of credit volatility on the capital structure. The results show that both the credit fluctuation and the capital structure are positively related to the listed companies in different industries, but the significant level is different. During the different policy period, this paper studies the effect of credit fluctuation on the capital structure of listed companies under the heterogeneity of policy environment. The empirical results show that after considering the heterogeneity of industries, the relationship between credit volatility and capital structure of listed companies is different under different policy environments. In the environment of loose credit, there is a significant positive correlation between the credit fluctuation and the capital structure of the listed companies in different industries. But under the tight and steady credit environment, the relationship between the credit fluctuation and the capital structure of the listed companies in different industries is not significant. Based on this, this paper puts forward the policy suggestions to optimize the capital structure of the listed companies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".