Predicting Financial Failure: Empirical Evidence from Publicly – Quoted Firms in Developed and Developing Countries
This paper analyzes the data of 570 firms from developed and developing countries between 2010 and 2019 in an attempt to create high–accuracy financial failure prediction models. In this sense, we utilize three different methods, namely logistic regression (LR), artificial neural networks (ANN), and...
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| Language: | English |
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Ekonomi ve Finansal Araştırmalar Derneği
2025-03-01
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| Series: | Ekonomi, Politika & Finans Araştırmaları Dergisi |
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| Online Access: | https://dergipark.org.tr/tr/download/article-file/4415035 |
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| author | Serpil Altınırmak Yavuz Gül |
| author_facet | Serpil Altınırmak Yavuz Gül |
| author_sort | Serpil Altınırmak |
| collection | DOAJ |
| description | This paper analyzes the data of 570 firms from developed and developing countries between 2010 and 2019 in an attempt to create high–accuracy financial failure prediction models. In this sense, we utilize three different methods, namely logistic regression (LR), artificial neural networks (ANN), and decision trees (DT), and compare the classification accuracy performances of these techniques. Using 16 financial ratios as independent variables, ANN is able to generate the most accurate prediction and outperforms the other methods in predicting failure. Otherwise said, ANN yields a correct classification accuracy of 98.1% one year prior to failure while LR and DT achieve accuracy rates of 94.7% and 96.1%, respectively. Furthermore, the empirical results demonstrate that the classification accuracy rate reaches 92.5% by ANN, 91.1% by DT, and 84.4% by logistic regression two years in advance. The findings of current research provide valuable insights into financial failure prediction and may entice practical implications for stakeholders, especially investors and regulatory bodies, by indicating that the use of the ANN approach may be more effective. |
| format | Article |
| id | doaj-art-472d5db8b5d24b289487a1e4cf19dd77 |
| institution | OA Journals |
| issn | 2587-151X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Ekonomi ve Finansal Araştırmalar Derneği |
| record_format | Article |
| series | Ekonomi, Politika & Finans Araştırmaları Dergisi |
| spelling | doaj-art-472d5db8b5d24b289487a1e4cf19dd772025-08-20T02:26:45ZengEkonomi ve Finansal Araştırmalar DerneğiEkonomi, Politika & Finans Araştırmaları Dergisi2587-151X2025-03-0110110712610.30784/epfad.1595915957Predicting Financial Failure: Empirical Evidence from Publicly – Quoted Firms in Developed and Developing CountriesSerpil Altınırmak0https://orcid.org/0000-0003-2879-9902Yavuz Gül1https://orcid.org/0000-0002-0208-6798ANADOLU UNIVERSITY, ESKİŞEHİR VOCATIONAL SCHOOLBEYKENT ÜNİVERSİTESİThis paper analyzes the data of 570 firms from developed and developing countries between 2010 and 2019 in an attempt to create high–accuracy financial failure prediction models. In this sense, we utilize three different methods, namely logistic regression (LR), artificial neural networks (ANN), and decision trees (DT), and compare the classification accuracy performances of these techniques. Using 16 financial ratios as independent variables, ANN is able to generate the most accurate prediction and outperforms the other methods in predicting failure. Otherwise said, ANN yields a correct classification accuracy of 98.1% one year prior to failure while LR and DT achieve accuracy rates of 94.7% and 96.1%, respectively. Furthermore, the empirical results demonstrate that the classification accuracy rate reaches 92.5% by ANN, 91.1% by DT, and 84.4% by logistic regression two years in advance. The findings of current research provide valuable insights into financial failure prediction and may entice practical implications for stakeholders, especially investors and regulatory bodies, by indicating that the use of the ANN approach may be more effective.https://dergipark.org.tr/tr/download/article-file/4415035finansal başarısızlıklojistik regresyonyapay sinir ağlarıkarar ağaçlarıfinancial failurelogistic regressionartificial neural networksdecision trees |
| spellingShingle | Serpil Altınırmak Yavuz Gül Predicting Financial Failure: Empirical Evidence from Publicly – Quoted Firms in Developed and Developing Countries Ekonomi, Politika & Finans Araştırmaları Dergisi finansal başarısızlık lojistik regresyon yapay sinir ağları karar ağaçları financial failure logistic regression artificial neural networks decision trees |
| title | Predicting Financial Failure: Empirical Evidence from Publicly – Quoted Firms in Developed and Developing Countries |
| title_full | Predicting Financial Failure: Empirical Evidence from Publicly – Quoted Firms in Developed and Developing Countries |
| title_fullStr | Predicting Financial Failure: Empirical Evidence from Publicly – Quoted Firms in Developed and Developing Countries |
| title_full_unstemmed | Predicting Financial Failure: Empirical Evidence from Publicly – Quoted Firms in Developed and Developing Countries |
| title_short | Predicting Financial Failure: Empirical Evidence from Publicly – Quoted Firms in Developed and Developing Countries |
| title_sort | predicting financial failure empirical evidence from publicly quoted firms in developed and developing countries |
| topic | finansal başarısızlık lojistik regresyon yapay sinir ağları karar ağaçları financial failure logistic regression artificial neural networks decision trees |
| url | https://dergipark.org.tr/tr/download/article-file/4415035 |
| work_keys_str_mv | AT serpilaltınırmak predictingfinancialfailureempiricalevidencefrompubliclyquotedfirmsindevelopedanddevelopingcountries AT yavuzgul predictingfinancialfailureempiricalevidencefrompubliclyquotedfirmsindevelopedanddevelopingcountries |