Journal article 590 views
Credit default prediction modeling: an application of support vector machine
Risk Management, Volume: 19, Issue: 2, Pages: 158 - 187
Swansea University Author:
Mohammad Abedin
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DOI (Published version): 10.1057/s41283-017-0016-x
Abstract
Credit default prediction (CDP) modeling is a fundamental and critical issue for financial institutions. However, the previous studies indicate that the classifier’s performances in CDP analysis differ using different performance criterions on different databases under different circumstances. The p...
Published in: | Risk Management |
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ISSN: | 1460-3799 1743-4637 |
Published: |
Springer Science and Business Media LLC
2017
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URI: | https://cronfa.swan.ac.uk/Record/cronfa64269 |
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2024-11-25T14:13:45Z |
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2023-09-19T16:16:35.0772662 v2 64269 2023-08-31 Credit default prediction modeling: an application of support vector machine 4ed8c020eae0c9bec4f5d9495d86d415 0000-0002-4688-0619 Mohammad Abedin Mohammad Abedin true false 2023-08-31 CBAE Credit default prediction (CDP) modeling is a fundamental and critical issue for financial institutions. However, the previous studies indicate that the classifier’s performances in CDP analysis differ using different performance criterions on different databases under different circumstances. The performance assessment exercise under a set of criteria remains understudied in nature, on the one hand, and the real–scenario is not taken into account in that a single/very limited number of measure only are used, on the other hand. These problems affect the ability to make a consistent conclusion. Therefore, the aim of this study is to address this methodological issue by applying support vector machine (SVM)-based CDP algorithm by means of a set of representative performance criterions, with enclosing some novel performance measures, its performance compare with the results gained by statistical and intelligent approaches using six different types of databases from the credit prediction domains. Experimental results show that SVM model is marginally superior to CART with DA, being more robust than its other counterparts. In consequence, this study recommends that the supremacy of a classifier is linked to the way in which evaluations are measured. Journal Article Risk Management 19 2 158 187 Springer Science and Business Media LLC 1460-3799 1743-4637 Credit default prediction, Support vector machine, Performance measures 31 5 2017 2017-05-31 10.1057/s41283-017-0016-x http://dx.doi.org/10.1057/s41283-017-0016-x COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University 2023-09-19T16:16:35.0772662 2023-08-31T19:03:07.8899874 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Fahmida E. Moula 1 Chi Guotai 2 Mohammad Abedin 0000-0002-4688-0619 3 |
title |
Credit default prediction modeling: an application of support vector machine |
spellingShingle |
Credit default prediction modeling: an application of support vector machine Mohammad Abedin |
title_short |
Credit default prediction modeling: an application of support vector machine |
title_full |
Credit default prediction modeling: an application of support vector machine |
title_fullStr |
Credit default prediction modeling: an application of support vector machine |
title_full_unstemmed |
Credit default prediction modeling: an application of support vector machine |
title_sort |
Credit default prediction modeling: an application of support vector machine |
author_id_str_mv |
4ed8c020eae0c9bec4f5d9495d86d415 |
author_id_fullname_str_mv |
4ed8c020eae0c9bec4f5d9495d86d415_***_Mohammad Abedin |
author |
Mohammad Abedin |
author2 |
Fahmida E. Moula Chi Guotai Mohammad Abedin |
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Journal article |
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Risk Management |
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19 |
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158 |
publishDate |
2017 |
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Swansea University |
issn |
1460-3799 1743-4637 |
doi_str_mv |
10.1057/s41283-017-0016-x |
publisher |
Springer Science and Business Media LLC |
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Faculty of Humanities and Social Sciences |
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facultyofhumanitiesandsocialsciences |
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Faculty of Humanities and Social Sciences |
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Faculty of Humanities and Social Sciences |
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School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance |
url |
http://dx.doi.org/10.1057/s41283-017-0016-x |
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description |
Credit default prediction (CDP) modeling is a fundamental and critical issue for financial institutions. However, the previous studies indicate that the classifier’s performances in CDP analysis differ using different performance criterions on different databases under different circumstances. The performance assessment exercise under a set of criteria remains understudied in nature, on the one hand, and the real–scenario is not taken into account in that a single/very limited number of measure only are used, on the other hand. These problems affect the ability to make a consistent conclusion. Therefore, the aim of this study is to address this methodological issue by applying support vector machine (SVM)-based CDP algorithm by means of a set of representative performance criterions, with enclosing some novel performance measures, its performance compare with the results gained by statistical and intelligent approaches using six different types of databases from the credit prediction domains. Experimental results show that SVM model is marginally superior to CART with DA, being more robust than its other counterparts. In consequence, this study recommends that the supremacy of a classifier is linked to the way in which evaluations are measured. |
published_date |
2017-05-31T17:57:28Z |
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1831844079385706496 |
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11.058631 |