Journal article 198 views
Feature Transformation for Corporate Tax Default Prediction: Application of Machine Learning Approaches
Abedin Abedin,
M. Kabir Hassan,
Imran Khan,
Ivan F. Julio
Asia-Pacific Journal of Operational Research, Volume: 39, Issue: 04
Swansea University Author: Abedin Abedin
Full text not available from this repository: check for access using links below.
DOI (Published version): 10.1142/s0217595921400170
Abstract
Applications of machine learning (ML) and data science have extended significantly into contemporary accounting and finance. Yet, the prediction and analysis of taxpayers’ status are relatively untapped to date. Moreover, this paper focuses on the combination of feature transformation as a novel dom...
Published in: | Asia-Pacific Journal of Operational Research |
---|---|
ISSN: | 0217-5959 1793-7019 |
Published: |
World Scientific Pub Co Pte Ltd
2022
|
Online Access: |
Check full text
|
URI: | https://cronfa.swan.ac.uk/Record/cronfa64276 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
first_indexed |
2023-09-18T13:04:07Z |
---|---|
last_indexed |
2023-09-18T13:04:07Z |
id |
cronfa64276 |
recordtype |
SURis |
fullrecord |
<?xml version="1.0" encoding="utf-8"?><rfc1807 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"><bib-version>v2</bib-version><id>64276</id><entry>2023-08-31</entry><title>Feature Transformation for Corporate Tax Default Prediction: Application of Machine Learning Approaches</title><swanseaauthors><author><sid>4ed8c020eae0c9bec4f5d9495d86d415</sid><firstname>Abedin</firstname><surname>Abedin</surname><name>Abedin Abedin</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2023-08-31</date><deptcode>BAF</deptcode><abstract>Applications of machine learning (ML) and data science have extended significantly into contemporary accounting and finance. Yet, the prediction and analysis of taxpayers’ status are relatively untapped to date. Moreover, this paper focuses on the combination of feature transformation as a novel domain of research for corporate firms’ tax status prediction with the applicability of ML approaches. The paper also applies a tax payment dataset of Finish limited liability firms with failed and non-failed tax information. Seven different ML approaches train across four datasets, transformed to non-transformed, that effectively discriminate the non-default tax firms from their default counterparts. The findings advocate tax administration to choose the single best ML approach and feature transformation method for the execution purpose.</abstract><type>Journal Article</type><journal>Asia-Pacific Journal of Operational Research</journal><volume>39</volume><journalNumber>04</journalNumber><paginationStart/><paginationEnd/><publisher>World Scientific Pub Co Pte Ltd</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0217-5959</issnPrint><issnElectronic>1793-7019</issnElectronic><keywords>Data mining, machine learning, default prediction, corporate tax</keywords><publishedDay>1</publishedDay><publishedMonth>8</publishedMonth><publishedYear>2022</publishedYear><publishedDate>2022-08-01</publishedDate><doi>10.1142/s0217595921400170</doi><url>http://dx.doi.org/10.1142/s0217595921400170</url><notes/><college>COLLEGE NANME</college><department>Accounting and Finance</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>BAF</DepartmentCode><institution>Swansea University</institution><apcterm/><funders/><projectreference/><lastEdited>2023-09-19T16:08:17.0862963</lastEdited><Created>2023-08-31T19:09:18.4863021</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>Abedin</firstname><surname>Abedin</surname><order>1</order></author><author><firstname>M. Kabir</firstname><surname>Hassan</surname><order>2</order></author><author><firstname>Imran</firstname><surname>Khan</surname><order>3</order></author><author><firstname>Ivan F.</firstname><surname>Julio</surname><order>4</order></author></authors><documents/><OutputDurs/></rfc1807> |
spelling |
v2 64276 2023-08-31 Feature Transformation for Corporate Tax Default Prediction: Application of Machine Learning Approaches 4ed8c020eae0c9bec4f5d9495d86d415 Abedin Abedin Abedin Abedin true false 2023-08-31 BAF Applications of machine learning (ML) and data science have extended significantly into contemporary accounting and finance. Yet, the prediction and analysis of taxpayers’ status are relatively untapped to date. Moreover, this paper focuses on the combination of feature transformation as a novel domain of research for corporate firms’ tax status prediction with the applicability of ML approaches. The paper also applies a tax payment dataset of Finish limited liability firms with failed and non-failed tax information. Seven different ML approaches train across four datasets, transformed to non-transformed, that effectively discriminate the non-default tax firms from their default counterparts. The findings advocate tax administration to choose the single best ML approach and feature transformation method for the execution purpose. Journal Article Asia-Pacific Journal of Operational Research 39 04 World Scientific Pub Co Pte Ltd 0217-5959 1793-7019 Data mining, machine learning, default prediction, corporate tax 1 8 2022 2022-08-01 10.1142/s0217595921400170 http://dx.doi.org/10.1142/s0217595921400170 COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University 2023-09-19T16:08:17.0862963 2023-08-31T19:09:18.4863021 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Abedin Abedin 1 M. Kabir Hassan 2 Imran Khan 3 Ivan F. Julio 4 |
title |
Feature Transformation for Corporate Tax Default Prediction: Application of Machine Learning Approaches |
spellingShingle |
Feature Transformation for Corporate Tax Default Prediction: Application of Machine Learning Approaches Abedin Abedin |
title_short |
Feature Transformation for Corporate Tax Default Prediction: Application of Machine Learning Approaches |
title_full |
Feature Transformation for Corporate Tax Default Prediction: Application of Machine Learning Approaches |
title_fullStr |
Feature Transformation for Corporate Tax Default Prediction: Application of Machine Learning Approaches |
title_full_unstemmed |
Feature Transformation for Corporate Tax Default Prediction: Application of Machine Learning Approaches |
title_sort |
Feature Transformation for Corporate Tax Default Prediction: Application of Machine Learning Approaches |
author_id_str_mv |
4ed8c020eae0c9bec4f5d9495d86d415 |
author_id_fullname_str_mv |
4ed8c020eae0c9bec4f5d9495d86d415_***_Abedin Abedin |
author |
Abedin Abedin |
author2 |
Abedin Abedin M. Kabir Hassan Imran Khan Ivan F. Julio |
format |
Journal article |
container_title |
Asia-Pacific Journal of Operational Research |
container_volume |
39 |
container_issue |
04 |
publishDate |
2022 |
institution |
Swansea University |
issn |
0217-5959 1793-7019 |
doi_str_mv |
10.1142/s0217595921400170 |
publisher |
World Scientific Pub Co Pte Ltd |
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 |
url |
http://dx.doi.org/10.1142/s0217595921400170 |
document_store_str |
0 |
active_str |
0 |
description |
Applications of machine learning (ML) and data science have extended significantly into contemporary accounting and finance. Yet, the prediction and analysis of taxpayers’ status are relatively untapped to date. Moreover, this paper focuses on the combination of feature transformation as a novel domain of research for corporate firms’ tax status prediction with the applicability of ML approaches. The paper also applies a tax payment dataset of Finish limited liability firms with failed and non-failed tax information. Seven different ML approaches train across four datasets, transformed to non-transformed, that effectively discriminate the non-default tax firms from their default counterparts. The findings advocate tax administration to choose the single best ML approach and feature transformation method for the execution purpose. |
published_date |
2022-08-01T16:08:20Z |
_version_ |
1777479031281680384 |
score |
11.017731 |