A comparative study of different survival analysis models for bankruptcy prediction
Survival analysis is one of the most advanced techniques in bankruptcy prediction. However, to date, only few nonlinear techniques in survival analysis have been implemented in financial applications. This study introduces four nonlinear survival analysis, namely, partial logistic artificial neural...
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| Other Authors: | |
| Format: | Thesis |
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2008
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| Online Access: | https://hdl.handle.net/10356/2181 |
| _version_ | 1811676638237163520 |
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| author | Li, Ting |
| author2 | School of Humanities and Social Sciences |
| author_facet | School of Humanities and Social Sciences Li, Ting |
| author_sort | Li, Ting |
| collection | NTU |
| description | Survival analysis is one of the most advanced techniques in bankruptcy prediction. However, to date, only few nonlinear techniques in survival analysis have been implemented in financial applications. This study introduces four nonlinear survival analysis, namely, partial logistic artificial neural networks (“PLANNs”) (Biganzoli et al., 1998), the Cox’s survival artificial neural networks (“Cox’s ANNs”) (Faraggi, 1995), the Weibull parametric survival artificial neural networks (“Weibull ANNs”) (Ripley, 1998) and the log-logistic parametric survival artificial neural networks (“log-logistic ANNs”) (Ripley, 1998) into bankruptcy prediction. Based on the data of about 1,000 US corporations in consumer goods/services industries, estimation and prediction results of linear regression and neural networks are presented. A comprehensive comparison among the outputs from different models is conducted. Relevant topics such as misclassification costs and the optimal structure of neural networks are also discussed. The results of this study show that survival artificial neural networks (“ANNs”) are superior to linear survival approaches in terms of prediction performance. |
| first_indexed | 2024-10-01T02:24:39Z |
| format | Thesis |
| id | ntu-10356/2181 |
| institution | Nanyang Technological University |
| last_indexed | 2024-10-01T02:24:39Z |
| publishDate | 2008 |
| record_format | dspace |
| spelling | ntu-10356/21812020-10-29T18:01:36Z A comparative study of different survival analysis models for bankruptcy prediction Li, Ting School of Humanities and Social Sciences DRNTU::Business::Law::Bankruptcy DRNTU::Business::Finance Survival analysis is one of the most advanced techniques in bankruptcy prediction. However, to date, only few nonlinear techniques in survival analysis have been implemented in financial applications. This study introduces four nonlinear survival analysis, namely, partial logistic artificial neural networks (“PLANNs”) (Biganzoli et al., 1998), the Cox’s survival artificial neural networks (“Cox’s ANNs”) (Faraggi, 1995), the Weibull parametric survival artificial neural networks (“Weibull ANNs”) (Ripley, 1998) and the log-logistic parametric survival artificial neural networks (“log-logistic ANNs”) (Ripley, 1998) into bankruptcy prediction. Based on the data of about 1,000 US corporations in consumer goods/services industries, estimation and prediction results of linear regression and neural networks are presented. A comprehensive comparison among the outputs from different models is conducted. Relevant topics such as misclassification costs and the optimal structure of neural networks are also discussed. The results of this study show that survival artificial neural networks (“ANNs”) are superior to linear survival approaches in terms of prediction performance. DOCTOR OF PHILOSOPHY (HSS) 2008-09-16T06:37:12Z 2008-09-16T06:37:12Z 2008 2008 Thesis Li, T. (2008). Comparative study of different survival analysis models for bankruptcy prediction. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/2181 10.32657/10356/2181 Nanyang Technological University application/pdf |
| spellingShingle | DRNTU::Business::Law::Bankruptcy DRNTU::Business::Finance Li, Ting A comparative study of different survival analysis models for bankruptcy prediction |
| title | A comparative study of different survival analysis models for bankruptcy prediction |
| title_full | A comparative study of different survival analysis models for bankruptcy prediction |
| title_fullStr | A comparative study of different survival analysis models for bankruptcy prediction |
| title_full_unstemmed | A comparative study of different survival analysis models for bankruptcy prediction |
| title_short | A comparative study of different survival analysis models for bankruptcy prediction |
| title_sort | comparative study of different survival analysis models for bankruptcy prediction |
| topic | DRNTU::Business::Law::Bankruptcy DRNTU::Business::Finance |
| url | https://hdl.handle.net/10356/2181 |
| work_keys_str_mv | AT liting acomparativestudyofdifferentsurvivalanalysismodelsforbankruptcyprediction AT liting comparativestudyofdifferentsurvivalanalysismodelsforbankruptcyprediction |