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The Cross-market Effects of Stock Market System Risk Factors on the Corporate Bond Pricing: Empirical Study Based on the Panel Data Model

2011· article· en· W2135683496 on OpenAlexvenueno aff
Chenggang Li, Yixiang Tian, Cong Luo

Bibliographic record

VenueCanadian social science · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCredit Risk and Financial Regulations
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate bondBusinessStock exchangeStock marketCapital asset pricing modelBondFinancial economicsEconomicsFinance

Abstract

fetched live from OpenAlex

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

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.152
GPT teacher head0.274
Teacher spread0.122 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2011
Admission routes1
Has abstractyes

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