Credit analysis on the biotech industry using structural models
This paper looks at the relative rankings of biotech firms using expected default probabilities (EDPs) of structural credit risk models and analyzes the sensitivities of default probabilities to model inputs.
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2008
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Online Access: | http://hdl.handle.net/10356/10043 |
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sg-ntu-dr.10356-100432023-05-19T03:30:06Z Credit analysis on the biotech industry using structural models Leong, Jun Hao Ng, Zhiguang Yang, Zhaoqiang Lee, Hon Sing Nanyang Business School DRNTU::Business::Finance::Corporate finance This paper looks at the relative rankings of biotech firms using expected default probabilities (EDPs) of structural credit risk models and analyzes the sensitivities of default probabilities to model inputs. 2008-09-24T07:39:11Z 2008-09-24T07:39:11Z 2006 2006 Final Year Project (FYP) http://hdl.handle.net/10356/10043 Nanyang Technological University application/pdf |
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DRNTU::Business::Finance::Corporate finance Leong, Jun Hao Ng, Zhiguang Yang, Zhaoqiang Credit analysis on the biotech industry using structural models |
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This paper looks at the relative rankings of biotech firms using expected default probabilities (EDPs) of structural credit risk models and analyzes the sensitivities of default probabilities to model inputs. |
author2 |
Lee, Hon Sing |
author_facet |
Lee, Hon Sing Leong, Jun Hao Ng, Zhiguang Yang, Zhaoqiang |
format |
Final Year Project |
author |
Leong, Jun Hao Ng, Zhiguang Yang, Zhaoqiang |
author_sort |
Leong, Jun Hao |
title |
Credit analysis on the biotech industry using structural models |
title_short |
Credit analysis on the biotech industry using structural models |
title_full |
Credit analysis on the biotech industry using structural models |
title_fullStr |
Credit analysis on the biotech industry using structural models |
title_full_unstemmed |
Credit analysis on the biotech industry using structural models |
title_sort |
credit analysis on the biotech industry using structural models |
publishDate |
2008 |
url |
http://hdl.handle.net/10356/10043 |
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1770564318336647168 |