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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Main Authors: Osman Bayri, Ahmet Çağdaş Seçkin, Feden Koç
Format: Article
Language:English
Published: Mehmet Akif Ersoy University 2022-07-01
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.
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publisher Mehmet Akif Ersoy University
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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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