FORECASTING DEFERRED TAXES IN INTERNATIONAL ACCOUNTING WITH MACHINE LEARNING
The aim of this study is to estimate the possible deferred tax values and the TAS-TFRS profit/loss of 31 companies in three different sectors- the wholesale trade, retail trade and hospitality industry- whose shares are traded on Borsa Istanbul (BIST). This estimation is based on the companies'...
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Mehmet Akif Ersoy University
2022-07-01
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| Series: | Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/2124192 |
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| author | Osman Bayri Ahmet Çağdaş Seçkin Feden Koç |
| author_facet | Osman Bayri Ahmet Çağdaş Seçkin Feden Koç |
| author_sort | Osman Bayri |
| collection | DOAJ |
| description | The aim of this study is to estimate the possible deferred tax values and the TAS-TFRS profit/loss of 31 companies in three different sectors- the wholesale trade, retail trade and hospitality industry- whose shares are traded on Borsa Istanbul (BIST). This estimation is based on the companies' deferred tax values for the years 2015-2019 as well as twelve main economic parameters. Within the context of the study, the deferred tax output parameters, which companies will present in their annual financial reports in 2020, have been estimated using the following methods: the DTA value using the random forest method with an accuracy rate of 0,823, the net DTA value using the artificial neural networks method with an accuracy rate of 0,790, the DTL value using the random forest method with an accuracy rate of 0,823 and the net DTL value using the random forest method with an accuracy rate of 0,887. In addition, it has been discovered that the TAS-TFRS profit/loss, which is one of the output parameters, can be estimated using the random forest method with an accuracy rate of 0,629. |
| format | Article |
| id | doaj-art-8713e2a785a84a048a707639edc9e208 |
| institution | Kabale University |
| issn | 2149-1658 |
| language | English |
| publishDate | 2022-07-01 |
| publisher | Mehmet Akif Ersoy University |
| record_format | Article |
| series | Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi |
| spelling | doaj-art-8713e2a785a84a048a707639edc9e2082025-01-27T14:02:41ZengMehmet Akif Ersoy UniversityMehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi2149-16582022-07-01921303132610.30798/makuiibf.1034685273FORECASTING DEFERRED TAXES IN INTERNATIONAL ACCOUNTING WITH MACHINE LEARNINGOsman Bayri0https://orcid.org/0000-0003-2837-0778Ahmet Çağdaş Seçkin1https://orcid.org/0000-0002-9849-3338Feden Koç2https://orcid.org/0000-0003-4413-5188SULEYMAN DEMIREL UNIVERSITYADNAN MENDERES UNIVERSITYUşak ÜniversitesiThe aim of this study is to estimate the possible deferred tax values and the TAS-TFRS profit/loss of 31 companies in three different sectors- the wholesale trade, retail trade and hospitality industry- whose shares are traded on Borsa Istanbul (BIST). This estimation is based on the companies' deferred tax values for the years 2015-2019 as well as twelve main economic parameters. Within the context of the study, the deferred tax output parameters, which companies will present in their annual financial reports in 2020, have been estimated using the following methods: the DTA value using the random forest method with an accuracy rate of 0,823, the net DTA value using the artificial neural networks method with an accuracy rate of 0,790, the DTL value using the random forest method with an accuracy rate of 0,823 and the net DTL value using the random forest method with an accuracy rate of 0,887. In addition, it has been discovered that the TAS-TFRS profit/loss, which is one of the output parameters, can be estimated using the random forest method with an accuracy rate of 0,629.https://dergipark.org.tr/en/download/article-file/2124192international accounting standards-international financial reporting standards (ias-ifrs)turkish accounting standards- turkish financial reporting standards (tas-tfrs)valuationdeffered taxesmachine learningartifical neural networks.international accounting standards-international financial reporting standards (ias-ifrs)turkish accounting standards- turkish financial reporting standards (tas-tfrs)valuationdeffered taxesmachine learningartifical neural networks. |
| spellingShingle | Osman Bayri Ahmet Çağdaş Seçkin Feden Koç FORECASTING DEFERRED TAXES IN INTERNATIONAL ACCOUNTING WITH MACHINE LEARNING Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi international accounting standards-international financial reporting standards (ias-ifrs) turkish accounting standards- turkish financial reporting standards (tas-tfrs) valuation deffered taxes machine learning artifical neural networks. international accounting standards-international financial reporting standards (ias-ifrs) turkish accounting standards- turkish financial reporting standards (tas-tfrs) valuation deffered taxes machine learning artifical neural networks. |
| title | FORECASTING DEFERRED TAXES IN INTERNATIONAL ACCOUNTING WITH MACHINE LEARNING |
| title_full | FORECASTING DEFERRED TAXES IN INTERNATIONAL ACCOUNTING WITH MACHINE LEARNING |
| title_fullStr | FORECASTING DEFERRED TAXES IN INTERNATIONAL ACCOUNTING WITH MACHINE LEARNING |
| title_full_unstemmed | FORECASTING DEFERRED TAXES IN INTERNATIONAL ACCOUNTING WITH MACHINE LEARNING |
| title_short | FORECASTING DEFERRED TAXES IN INTERNATIONAL ACCOUNTING WITH MACHINE LEARNING |
| title_sort | forecasting deferred taxes in international accounting with machine learning |
| topic | international accounting standards-international financial reporting standards (ias-ifrs) turkish accounting standards- turkish financial reporting standards (tas-tfrs) valuation deffered taxes machine learning artifical neural networks. international accounting standards-international financial reporting standards (ias-ifrs) turkish accounting standards- turkish financial reporting standards (tas-tfrs) valuation deffered taxes machine learning artifical neural networks. |
| url | https://dergipark.org.tr/en/download/article-file/2124192 |
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