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dc.contributor.authorChikolwa, B.
dc.contributor.authorChan, Felix
dc.date.accessioned2017-01-30T15:37:49Z
dc.date.available2017-01-30T15:37:49Z
dc.date.created2015-03-03T20:13:47Z
dc.date.issued2008
dc.identifier.citationChikolwa, B. and Chan, F. 2008. Determinants of commercial mortgage-backed securities credit ratings: Australian evidence. International Journal Of Strategic Property Management. 12 (2): pp. 69-94.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/48145
dc.identifier.doi10.3846/1648-715X.2008.12.69-94
dc.description.abstract

Using artificial neural networks (ANN) and ordinal regression (OR) as alternative methods to predict Commercial Mortgage-backed Securities (CMBS) credit ratings, we examine the role that various financial and industry-based variables have on CMBS credit ratings issued by Standard and Poor’s from 1999–2005. Our OR results show that rating agencies use only a subset of variables they describe or indicate as important to CMBS credit rating as some of the variables they use were statistically insignificant. Overall, ANN show superior results to OR in predicting CMBS credit ratings.

dc.publisherVilnius Jediminas Technical University, Lithuanian Academy of Sciences and Napier University
dc.subjectCredit rating prediction
dc.subjectOrdinal regression
dc.subjectArtificial neural networks
dc.subjectCommercial mortgage-backed securities
dc.titleDeterminants of commercial mortgage-backed securities credit ratings: Australian evidence
dc.typeJournal Article
dcterms.source.volume12
dcterms.source.number2
dcterms.source.startPage69
dcterms.source.endPage94
dcterms.source.issn1648-715X
dcterms.source.titleInternational Journal Of Strategic Property Management
curtin.departmentSchool of Economics and Finance
curtin.accessStatusOpen access via publisher


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