dc.contributor.author | Novotná, Martina | |
dc.date.accessioned | 2014-02-10T10:54:44Z | |
dc.date.available | 2014-02-10T10:54:44Z | |
dc.date.issued | 2013 | |
dc.identifier.citation | Economic Computation and Economic Cybernetics Studies and Research. 2013, vol. 47, no. 2, p. 67-83. | cs |
dc.identifier.issn | 0424-267X | |
dc.identifier.issn | 1842–3264 | |
dc.identifier.uri | http://hdl.handle.net/10084/101682 | |
dc.description.abstract | The analysis of factors which have the strongest influence on rating can contribute to the higher information availability of market participants, and it enables to react on changes and new information sooner and independently from rating agencies. The paper presents an estimation of corporate bond rating models based on both financial and market-based indicators. Multivariate discriminant analysis and logistic regression were used to identify variables with a significant impact on corporate bond rating in oil and gas industry. In addition to common financial variables, the following market-based indicators such as earnings per share, enterprise value, market capitalization and beta are considered in this paper. Among all the variables used in this study, the enterprise value is the most significant variable for bond rating prediction. The practical use of models lies in the area of management decision process and managing credit risk. | cs |
dc.format.extent | 358742 bytes | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | cs |
dc.publisher | Academy of Economic Studies, Department of Economic Cybernetics | cs |
dc.relation.ispartofseries | Economic Computation and Economic Cybernetics Studies and Research | cs |
dc.relation.uri | http://www.ecocyb.ase.ro/20132/Martina Novotn%C3%A1.pdf | cs |
dc.subject | credit risk | cs |
dc.subject | discriminant analysis | cs |
dc.subject | logistic regression | cs |
dc.subject | prediction | cs |
dc.subject | rating model | cs |
dc.title | A multivariate analysis of financial and market-based variables for bond rating prediction | cs |
dc.type | article | cs |
dc.rights.access | openAccess | |
dc.type.version | publishedVersion | cs |
dc.type.status | Peer-reviewed | cs |
dc.description.source | Web of Science | cs |
dc.description.volume | 47 | cs |
dc.description.issue | 2 | cs |
dc.description.lastpage | 83 | cs |
dc.description.firstpage | 67 | cs |
dc.identifier.wos | 000328587300005 | |