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Estimation Capability of Financial Failures and Successes of Enterprises Using Data Mining and Logistic Regression Analysis

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dc.contributor.author Tazegul, Alper
dc.contributor.author Yazarkan, Hakan
dc.contributor.author Yerdelen Kaygin, Ceyda
dc.date.accessioned 2022-08-19T11:07:07Z
dc.date.available 2022-08-19T11:07:07Z
dc.date.issued 2016
dc.identifier.uri http://doi.org/10.21121/eab.2016116590
dc.identifier.uri https://dergipark.org.tr/tr/pub/eab/issue/39985/475267
dc.identifier.uri http://earsiv.odu.edu.tr:8080/xmlui/handle/11489/2884
dc.description.abstract Given today's conditions, enterprises should be financially powerful in order to achieve their goals. Therefore, estimating financial failures is quite important in terms of determining possible future financial risks of enterprises and taking the required steps. In this study, annual balance sheets and income statements of 143 manufacturing firms, which were publicly traded in Borsa Istanbul between 2010 and 2013 and which displayed continuity, were used to estimate their financial failures and successes. Data Mining and Logistic Regression Analysis methods were used in this research. Models were developed to make an estimation prior to the first, second and third years taking year 2013 as basis, and classification accuracy, in other words estimation power of these models were compared. Among all models that were suggested to estimate failures and successes of enterprises, 2012 was determined as the year with the most successful estimation power as a result of our analysis. en_US
dc.language.iso tur en_US
dc.publisher EGE UNIV, FAC ECONOMICS & ADMIN SCIENCES, DEPT BUSINESS ADMIN, BORNOVA, 35100, TURKEY en_US
dc.relation.isversionof 10.21121/eab.2016116590 en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Financial Failure/Success; Data Mining; Logistic Regression Analysis en_US
dc.subject NEURAL-NETWORKS; PREDICTION; RATIOS en_US
dc.title Estimation Capability of Financial Failures and Successes of Enterprises Using Data Mining and Logistic Regression Analysis en_US
dc.type article en_US
dc.relation.journal EGE ACADEMIC REVIEW en_US
dc.contributor.department Ordu Üniversitesi en_US
dc.contributor.authorID 0000-0001-6167-0559 en_US
dc.identifier.volume 16 en_US
dc.identifier.issue 1 en_US
dc.identifier.startpage 147 en_US
dc.identifier.endpage 159 en_US


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