No Cover Image

Journal article 969 views 199 downloads

The role of feature importance in predicting corporate financial distress in pre and post COVID periods: Evidence from China

Shusheng Ding Orcid Logo, Tianxiang Cui Orcid Logo, Anthony Graham Bellotti, Mohammad Abedin Orcid Logo, Brian Lucey

International Review of Financial Analysis, Volume: 90, Start page: 102851

Swansea University Author: Mohammad Abedin Orcid Logo

  • 64218.AAM.pdf

    PDF | Accepted Manuscript

    Distributed under the terms of a Creative Commons CC-BY-NC-ND licence.

    Download (289.18KB)

Check full text

DOI (Published version): 10.1016/j.irfa.2023.102851

Abstract

The prediction of firm financial distress during the COVID-19 crisis episode attracted massive academic attention since economic uncertainty was exacerbated. In this paper, we propose a firm financial distress prediction model based on the Extreme Gradient Boosting-Genetic Programming (XGB-GP) frame...

Full description

Published in: International Review of Financial Analysis
ISSN: 1057-5219 1873-8079
Published: Elsevier BV 2023
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa64218
first_indexed 2023-09-25T13:13:44Z
last_indexed 2025-06-19T10:23:39Z
id cronfa64218
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2025-06-18T15:21:06.7557499</datestamp><bib-version>v2</bib-version><id>64218</id><entry>2023-08-31</entry><title>The role of feature importance in predicting corporate financial distress in pre and post COVID periods: Evidence from China</title><swanseaauthors><author><sid>4ed8c020eae0c9bec4f5d9495d86d415</sid><ORCID>0000-0002-4688-0619</ORCID><firstname>Mohammad</firstname><surname>Abedin</surname><name>Mohammad Abedin</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2023-08-31</date><deptcode>CBAE</deptcode><abstract>The prediction of firm financial distress during the COVID-19 crisis episode attracted massive academic attention since economic uncertainty was exacerbated. In this paper, we propose a firm financial distress prediction model based on the Extreme Gradient Boosting-Genetic Programming (XGB-GP) framework by investigating subsamples of pre-COVID and post-COVID periods. The key contribution of our paper is that we explore time-varying prediction features for pre-COVID and post-COVID periods. We illuminate that the earning financial indicator is the dominant feature for financial distress prediction during the pre-COVID period, whereas total financial leverage is the most important factor during the post-COVID period. On this basis, our XGB-GP financial distress prediction model exhibits higher prediction accuracy than the traditional models. As a result, managers can modify the financial leverage level to improve the financial situation of the firm by reducing the debt burden and increasing profitability during the post-COVID period.</abstract><type>Journal Article</type><journal>International Review of Financial Analysis</journal><volume>90</volume><journalNumber/><paginationStart>102851</paginationStart><paginationEnd/><publisher>Elsevier BV</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>1057-5219</issnPrint><issnElectronic>1873-8079</issnElectronic><keywords>Financial distress prediction, Time-varying feature selection, Extreme gradient boosting, Genetic programming, COVID-19 crisis</keywords><publishedDay>30</publishedDay><publishedMonth>11</publishedMonth><publishedYear>2023</publishedYear><publishedDate>2023-11-30</publishedDate><doi>10.1016/j.irfa.2023.102851</doi><url/><notes/><college>COLLEGE NANME</college><department>Management School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>CBAE</DepartmentCode><institution>Swansea University</institution><apcterm>Not Required</apcterm><funders/><projectreference/><lastEdited>2025-06-18T15:21:06.7557499</lastEdited><Created>2023-08-31T17:15:33.4475312</Created><path><level id="1">Faculty of Humanities and Social Sciences</level><level id="2">School of Management - Accounting and Finance</level></path><authors><author><firstname>Shusheng</firstname><surname>Ding</surname><orcid>0000-0001-5745-7552</orcid><order>1</order></author><author><firstname>Tianxiang</firstname><surname>Cui</surname><orcid>0000-0002-0102-2581</orcid><order>2</order></author><author><firstname>Anthony Graham</firstname><surname>Bellotti</surname><order>3</order></author><author><firstname>Mohammad</firstname><surname>Abedin</surname><orcid>0000-0002-4688-0619</orcid><order>4</order></author><author><firstname>Brian</firstname><surname>Lucey</surname><order>5</order></author></authors><documents><document><filename>64218__28792__75866a6a45524177b8df3db6328394d6.pdf</filename><originalFilename>64218.AAM.pdf</originalFilename><uploaded>2023-10-16T10:22:07.9652345</uploaded><type>Output</type><contentLength>296123</contentLength><contentType>application/pdf</contentType><version>Accepted Manuscript</version><cronfaStatus>true</cronfaStatus><embargoDate>2025-08-05T00:00:00.0000000</embargoDate><documentNotes>Distributed under the terms of a Creative Commons CC-BY-NC-ND licence.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en</licence></document></documents><OutputDurs/></rfc1807>
spelling 2025-06-18T15:21:06.7557499 v2 64218 2023-08-31 The role of feature importance in predicting corporate financial distress in pre and post COVID periods: Evidence from China 4ed8c020eae0c9bec4f5d9495d86d415 0000-0002-4688-0619 Mohammad Abedin Mohammad Abedin true false 2023-08-31 CBAE The prediction of firm financial distress during the COVID-19 crisis episode attracted massive academic attention since economic uncertainty was exacerbated. In this paper, we propose a firm financial distress prediction model based on the Extreme Gradient Boosting-Genetic Programming (XGB-GP) framework by investigating subsamples of pre-COVID and post-COVID periods. The key contribution of our paper is that we explore time-varying prediction features for pre-COVID and post-COVID periods. We illuminate that the earning financial indicator is the dominant feature for financial distress prediction during the pre-COVID period, whereas total financial leverage is the most important factor during the post-COVID period. On this basis, our XGB-GP financial distress prediction model exhibits higher prediction accuracy than the traditional models. As a result, managers can modify the financial leverage level to improve the financial situation of the firm by reducing the debt burden and increasing profitability during the post-COVID period. Journal Article International Review of Financial Analysis 90 102851 Elsevier BV 1057-5219 1873-8079 Financial distress prediction, Time-varying feature selection, Extreme gradient boosting, Genetic programming, COVID-19 crisis 30 11 2023 2023-11-30 10.1016/j.irfa.2023.102851 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University Not Required 2025-06-18T15:21:06.7557499 2023-08-31T17:15:33.4475312 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Shusheng Ding 0000-0001-5745-7552 1 Tianxiang Cui 0000-0002-0102-2581 2 Anthony Graham Bellotti 3 Mohammad Abedin 0000-0002-4688-0619 4 Brian Lucey 5 64218__28792__75866a6a45524177b8df3db6328394d6.pdf 64218.AAM.pdf 2023-10-16T10:22:07.9652345 Output 296123 application/pdf Accepted Manuscript true 2025-08-05T00:00:00.0000000 Distributed under the terms of a Creative Commons CC-BY-NC-ND licence. true eng https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en
title The role of feature importance in predicting corporate financial distress in pre and post COVID periods: Evidence from China
spellingShingle The role of feature importance in predicting corporate financial distress in pre and post COVID periods: Evidence from China
Mohammad Abedin
title_short The role of feature importance in predicting corporate financial distress in pre and post COVID periods: Evidence from China
title_full The role of feature importance in predicting corporate financial distress in pre and post COVID periods: Evidence from China
title_fullStr The role of feature importance in predicting corporate financial distress in pre and post COVID periods: Evidence from China
title_full_unstemmed The role of feature importance in predicting corporate financial distress in pre and post COVID periods: Evidence from China
title_sort The role of feature importance in predicting corporate financial distress in pre and post COVID periods: Evidence from China
author_id_str_mv 4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv 4ed8c020eae0c9bec4f5d9495d86d415_***_Mohammad Abedin
author Mohammad Abedin
author2 Shusheng Ding
Tianxiang Cui
Anthony Graham Bellotti
Mohammad Abedin
Brian Lucey
format Journal article
container_title International Review of Financial Analysis
container_volume 90
container_start_page 102851
publishDate 2023
institution Swansea University
issn 1057-5219
1873-8079
doi_str_mv 10.1016/j.irfa.2023.102851
publisher Elsevier BV
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
document_store_str 1
active_str 0
description The prediction of firm financial distress during the COVID-19 crisis episode attracted massive academic attention since economic uncertainty was exacerbated. In this paper, we propose a firm financial distress prediction model based on the Extreme Gradient Boosting-Genetic Programming (XGB-GP) framework by investigating subsamples of pre-COVID and post-COVID periods. The key contribution of our paper is that we explore time-varying prediction features for pre-COVID and post-COVID periods. We illuminate that the earning financial indicator is the dominant feature for financial distress prediction during the pre-COVID period, whereas total financial leverage is the most important factor during the post-COVID period. On this basis, our XGB-GP financial distress prediction model exhibits higher prediction accuracy than the traditional models. As a result, managers can modify the financial leverage level to improve the financial situation of the firm by reducing the debt burden and increasing profitability during the post-COVID period.
published_date 2023-11-30T07:10:01Z
_version_ 1850741884634267648
score 11.088929

Similar Items