The Cross-market Effects of Stock Market System Risk Factors on the Corporate Bond Pricing: Empirical Study Based on the Panel Data Model
Bibliographic record
Abstract
The research in capital asset pricing focuses on the pricing within the market, and the research on cross-market pricing are relatively small. Using corporate bonds in Shanghai and Shenzhen Stock Exchange from January 1st, 2001 to March 31st, 2010 as the sample, this paper investigates the cross-market effects of stock market system risk factors on the corporate bond pricing in China. The results shows that in the longer term and the lower the credit rating of corporate bonds, the stock market system risk factors receive higher risk compensation; system risk factors of stock market have strong cross-market effects on corporate bond yields; bond pricing structure model variables and target firm characteristics variables significantly affects the bond yield spreads. Key words: Systematic risk factor; Cross-market; Pricing; Corporate bonds Resume: La recherche en matiere de tarification des immobilisations se concentre sur les prix dans le marche, et la recherche sur la croisee du marche de prix sont relativement faibles. Utiliser des obligations d'entreprises a Shanghai et a Shenzhen Stock Exchange du 1er Janvier 2001 au 31 Mars 2010, comme l'echantillon, cette etude examine les effets croises de marche des facteurs de risque de marche d'actions sur le systeme de fixation des prix des obligations d'entreprises en Chine. Les resultats montrent que dans le long terme et la baisse la cote de credit des obligations de societes, les facteurs de risque du marche actions du systeme recevoir une indemnisation plus elevee de risque, les facteurs de risque du systeme des marches boursiers ont fortement effets croises sur les rendements des obligations d'entreprises; modele de la structure des prix obligataires variables et les variables caracteristiques de l'entreprise cible affecte de maniere significative les ecarts de rendement obligataire. Mots cles: Facteur de risque systematique; Croix¬ marche; Tarification; Obligations de societes
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".