Determinants of commercial mortgage-backed securities credit ratings: Australian evidence
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 199...
| Main Authors: | , |
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| Format: | Journal Article |
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Vilnius Jediminas Technical University, Lithuanian Academy of Sciences and Napier University
2008
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| Online Access: | http://hdl.handle.net/20.500.11937/48145 |
| _version_ | 1848758029994950656 |
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| author | Chikolwa, B. Chan, Felix |
| author_facet | Chikolwa, B. Chan, Felix |
| author_sort | Chikolwa, B. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T09:37:30Z |
| format | Journal Article |
| id | curtin-20.500.11937-48145 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:37:30Z |
| publishDate | 2008 |
| publisher | Vilnius Jediminas Technical University, Lithuanian Academy of Sciences and Napier University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-481452023-02-22T06:24:19Z Determinants of commercial mortgage-backed securities credit ratings: Australian evidence Chikolwa, B. Chan, Felix Credit rating prediction Ordinal regression Artificial neural networks Commercial mortgage-backed securities 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. 2008 Journal Article http://hdl.handle.net/20.500.11937/48145 10.3846/1648-715X.2008.12.69-94 Vilnius Jediminas Technical University, Lithuanian Academy of Sciences and Napier University unknown |
| spellingShingle | Credit rating prediction Ordinal regression Artificial neural networks Commercial mortgage-backed securities Chikolwa, B. Chan, Felix Determinants of commercial mortgage-backed securities credit ratings: Australian evidence |
| title | Determinants of commercial mortgage-backed securities credit ratings: Australian evidence |
| title_full | Determinants of commercial mortgage-backed securities credit ratings: Australian evidence |
| title_fullStr | Determinants of commercial mortgage-backed securities credit ratings: Australian evidence |
| title_full_unstemmed | Determinants of commercial mortgage-backed securities credit ratings: Australian evidence |
| title_short | Determinants of commercial mortgage-backed securities credit ratings: Australian evidence |
| title_sort | determinants of commercial mortgage-backed securities credit ratings: australian evidence |
| topic | Credit rating prediction Ordinal regression Artificial neural networks Commercial mortgage-backed securities |
| url | http://hdl.handle.net/20.500.11937/48145 |