A two‐stage Bayesian network model for corporate bankruptcy prediction
We develop a Bayesian network (LASSO-BN) model for firm bankruptcy prediction. We select fnancial ratios via the Least Absolute Shrinkage Selection Operator (LASSO), establish the BN topology, and estimate model parameters. Our empirical results, based on 32,344 US firms from 1961-2018, show that th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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John Wiley and Sons Ltd
2020
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| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/61457/ |
| _version_ | 1848799878993412096 |
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| author | Cao, Yi Liu, Xiaoquan Zhai, Jia Hua, Shan |
| author_facet | Cao, Yi Liu, Xiaoquan Zhai, Jia Hua, Shan |
| author_sort | Cao, Yi |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | We develop a Bayesian network (LASSO-BN) model for firm bankruptcy prediction. We select fnancial ratios via the Least Absolute Shrinkage Selection Operator (LASSO), establish the BN topology, and estimate model parameters. Our empirical results, based on 32,344 US firms from 1961-2018, show that the LASSO-BN model outperforms most alternative methods except the deep neural network. Crucially, the model provides a clear interpretation of its internal functionality by describing the logic of how conditional default probabilities are obtained from selected variables. Thus our model represents a major step towards interpretable machine learning models with strong performance and is relevant to investors and policymakers. |
| first_indexed | 2025-11-14T20:42:40Z |
| format | Article |
| id | nottingham-61457 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:42:40Z |
| publishDate | 2020 |
| publisher | John Wiley and Sons Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-614572020-08-28T01:19:25Z https://eprints.nottingham.ac.uk/61457/ A two‐stage Bayesian network model for corporate bankruptcy prediction Cao, Yi Liu, Xiaoquan Zhai, Jia Hua, Shan We develop a Bayesian network (LASSO-BN) model for firm bankruptcy prediction. We select fnancial ratios via the Least Absolute Shrinkage Selection Operator (LASSO), establish the BN topology, and estimate model parameters. Our empirical results, based on 32,344 US firms from 1961-2018, show that the LASSO-BN model outperforms most alternative methods except the deep neural network. Crucially, the model provides a clear interpretation of its internal functionality by describing the logic of how conditional default probabilities are obtained from selected variables. Thus our model represents a major step towards interpretable machine learning models with strong performance and is relevant to investors and policymakers. John Wiley and Sons Ltd 2020-08-10 Article PeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/61457/1/A%20two-stage%20Bayesian%20network%20model%20for%20corporate%20bankruptcy%20prediction.pdf Cao, Yi, Liu, Xiaoquan, Zhai, Jia and Hua, Shan (2020) A two‐stage Bayesian network model for corporate bankruptcy prediction. International Journal of Finance & Economics . ISSN 1076-9307 Bayesian network; LASSO; Accounting ratios; Sensitivity analysis; Interpretability analysis http://dx.doi.org/10.1002/ijfe.2162 doi:10.1002/ijfe.2162 doi:10.1002/ijfe.2162 |
| spellingShingle | Bayesian network; LASSO; Accounting ratios; Sensitivity analysis; Interpretability analysis Cao, Yi Liu, Xiaoquan Zhai, Jia Hua, Shan A two‐stage Bayesian network model for corporate bankruptcy prediction |
| title | A two‐stage Bayesian network model for corporate bankruptcy prediction |
| title_full | A two‐stage Bayesian network model for corporate bankruptcy prediction |
| title_fullStr | A two‐stage Bayesian network model for corporate bankruptcy prediction |
| title_full_unstemmed | A two‐stage Bayesian network model for corporate bankruptcy prediction |
| title_short | A two‐stage Bayesian network model for corporate bankruptcy prediction |
| title_sort | two‐stage bayesian network model for corporate bankruptcy prediction |
| topic | Bayesian network; LASSO; Accounting ratios; Sensitivity analysis; Interpretability analysis |
| url | https://eprints.nottingham.ac.uk/61457/ https://eprints.nottingham.ac.uk/61457/ https://eprints.nottingham.ac.uk/61457/ |