A multi-industry default prediction model using logistic regression and decision tree
The accurate prediction of corporate bankruptcy for the firms in different industries is of a great concern to investors and creditors, as the reduction of creditors' risk and a considerable amount of saving for an industry economy can be possible. Financial statements vary between industries....
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| Format: | Article |
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Maxwell Science Publications
2015
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| _version_ | 1796860205195067392 |
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| author | Ramakrishnan, Suresh Mirzaei, Maryam Bekri, Mahmoud |
| author_facet | Ramakrishnan, Suresh Mirzaei, Maryam Bekri, Mahmoud |
| author_sort | Ramakrishnan, Suresh |
| collection | ePrints |
| description | The accurate prediction of corporate bankruptcy for the firms in different industries is of a great concern to investors and creditors, as the reduction of creditors' risk and a considerable amount of saving for an industry economy can be possible. Financial statements vary between industries. Therefore, economic intuition suggests that industry effects should be an important component in bankruptcy prediction. This study attempts to detail the characteristics of each industry using sector indicators. The results show significant relationship between probability of default and sector indicators. The results of this study may improve the default prediction models performance and reduce the costs of risk management. |
| first_indexed | 2024-03-05T19:38:28Z |
| format | Article |
| id | utm.eprints-55704 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T19:38:28Z |
| publishDate | 2015 |
| publisher | Maxwell Science Publications |
| record_format | dspace |
| spelling | utm.eprints-557042017-08-22T00:40:31Z http://eprints.utm.my/55704/ A multi-industry default prediction model using logistic regression and decision tree Ramakrishnan, Suresh Mirzaei, Maryam Bekri, Mahmoud HD Industries. Land use. Labor HD28 Management. Industrial Management The accurate prediction of corporate bankruptcy for the firms in different industries is of a great concern to investors and creditors, as the reduction of creditors' risk and a considerable amount of saving for an industry economy can be possible. Financial statements vary between industries. Therefore, economic intuition suggests that industry effects should be an important component in bankruptcy prediction. This study attempts to detail the characteristics of each industry using sector indicators. The results show significant relationship between probability of default and sector indicators. The results of this study may improve the default prediction models performance and reduce the costs of risk management. Maxwell Science Publications 2015 Article PeerReviewed Ramakrishnan, Suresh and Mirzaei, Maryam and Bekri, Mahmoud (2015) A multi-industry default prediction model using logistic regression and decision tree. Research Journal of Applied Sciences, Engineering and Technology, 9 (10). pp. 856-861. ISSN 2040-7459 http://www.maxwellsci.com/jp/abstract.php?jid=RJASET&no=535&abs=10 |
| spellingShingle | HD Industries. Land use. Labor HD28 Management. Industrial Management Ramakrishnan, Suresh Mirzaei, Maryam Bekri, Mahmoud A multi-industry default prediction model using logistic regression and decision tree |
| title | A multi-industry default prediction model using logistic regression and decision tree |
| title_full | A multi-industry default prediction model using logistic regression and decision tree |
| title_fullStr | A multi-industry default prediction model using logistic regression and decision tree |
| title_full_unstemmed | A multi-industry default prediction model using logistic regression and decision tree |
| title_short | A multi-industry default prediction model using logistic regression and decision tree |
| title_sort | multi industry default prediction model using logistic regression and decision tree |
| topic | HD Industries. Land use. Labor HD28 Management. Industrial Management |
| work_keys_str_mv | AT ramakrishnansuresh amultiindustrydefaultpredictionmodelusinglogisticregressionanddecisiontree AT mirzaeimaryam amultiindustrydefaultpredictionmodelusinglogisticregressionanddecisiontree AT bekrimahmoud amultiindustrydefaultpredictionmodelusinglogisticregressionanddecisiontree AT ramakrishnansuresh multiindustrydefaultpredictionmodelusinglogisticregressionanddecisiontree AT mirzaeimaryam multiindustrydefaultpredictionmodelusinglogisticregressionanddecisiontree AT bekrimahmoud multiindustrydefaultpredictionmodelusinglogisticregressionanddecisiontree |