COMPARISON OF CLASSIFICATION PERFORMANCE OF MACHINE LEARNING METHODS IN PREDICTION FINANCIAL FAILURE: EVIDENCE FROM BORSA İSTANBUL
This study aimed to predict the 1 to 2 year future time of the financial failure of 86 manufacturing companies that are operating in Borsa İstanbul. The data comprised of 2010-2012 period, and it depends on 8 quantitative financial variables. Beside 6 variables come from non financial statements. In...
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| Format: | Article |
| Language: | English |
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Hitit University
2021-06-01
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| Series: | Hitit Sosyal Bilimler Dergisi |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/tr/download/article-file/1578734 |
| _version_ | 1797315241360490496 |
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| author | Derviş Boztosun Barış Aksoy |
| author_facet | Derviş Boztosun Barış Aksoy |
| author_sort | Derviş Boztosun |
| collection | DOAJ |
| description | This study aimed to predict the 1 to 2 year future time of the financial failure of 86 manufacturing companies that are operating in Borsa İstanbul. The data comprised of 2010-2012 period, and it depends on 8 quantitative financial variables. Beside 6 variables come from non financial statements. In the study, Artificial Neural Network (NN), Classification and Regression Trees (CART), Support Vector Machine (SVM) and k-Nearest Neighbors (KNN) were used to compare classification performances of related methods. ROC Curve was used to compare the classification performance of the methods. As a result of the analyseis, the overall classification accuracy from the highest to the lowest was SVM (92,31%), CART (88,46%), ANN (84,62%) and KNN (80,77%) 2 years before the financial failure. The overall classification accuracy from the highest to the lowest was CART (96,15%), ANN (92,31%), SVM (80,77%) and KNN (84,62%) 1 year before the financial failure. Return on Equity (ROE) and Return on Assets Ratio (ROA) were found as important variables in the creation of the CART decision tree. The fact that the four models obtained in thise study predicted financial success/failure at a higher rate, and it shows that the models obtained in this study can be included in the models used by relevant people. |
| first_indexed | 2024-03-08T02:57:59Z |
| format | Article |
| id | doaj.art-486f42bfeecf4cd09ebf2f245e7443d3 |
| institution | Directory Open Access Journal |
| issn | 2757-7449 |
| language | English |
| last_indexed | 2024-03-08T02:57:59Z |
| publishDate | 2021-06-01 |
| publisher | Hitit University |
| record_format | Article |
| series | Hitit Sosyal Bilimler Dergisi |
| spelling | doaj.art-486f42bfeecf4cd09ebf2f245e7443d32024-02-13T12:01:41ZengHitit UniversityHitit Sosyal Bilimler Dergisi2757-74492021-06-01141568610.17218/hititsbd.880658150COMPARISON OF CLASSIFICATION PERFORMANCE OF MACHINE LEARNING METHODS IN PREDICTION FINANCIAL FAILURE: EVIDENCE FROM BORSA İSTANBULDerviş Boztosun0Barış Aksoy1MESLEK YÜKSEKOKULUSivas Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Fakültesi Bankacılık ve Finans BölümüThis study aimed to predict the 1 to 2 year future time of the financial failure of 86 manufacturing companies that are operating in Borsa İstanbul. The data comprised of 2010-2012 period, and it depends on 8 quantitative financial variables. Beside 6 variables come from non financial statements. In the study, Artificial Neural Network (NN), Classification and Regression Trees (CART), Support Vector Machine (SVM) and k-Nearest Neighbors (KNN) were used to compare classification performances of related methods. ROC Curve was used to compare the classification performance of the methods. As a result of the analyseis, the overall classification accuracy from the highest to the lowest was SVM (92,31%), CART (88,46%), ANN (84,62%) and KNN (80,77%) 2 years before the financial failure. The overall classification accuracy from the highest to the lowest was CART (96,15%), ANN (92,31%), SVM (80,77%) and KNN (84,62%) 1 year before the financial failure. Return on Equity (ROE) and Return on Assets Ratio (ROA) were found as important variables in the creation of the CART decision tree. The fact that the four models obtained in thise study predicted financial success/failure at a higher rate, and it shows that the models obtained in this study can be included in the models used by relevant people.https://dergipark.org.tr/tr/download/article-file/1578734financial failure predictionborsa i̇stanbulartificial neural networksclassification and regression treessupport vector machineknn algorithmfinansal başarısızlık tahminiborsa istanbulyapay sinir ağlarısınıflandırma ve regresyon ağaçlarıdestek vektör makinesi,knn algoritması |
| spellingShingle | Derviş Boztosun Barış Aksoy COMPARISON OF CLASSIFICATION PERFORMANCE OF MACHINE LEARNING METHODS IN PREDICTION FINANCIAL FAILURE: EVIDENCE FROM BORSA İSTANBUL Hitit Sosyal Bilimler Dergisi financial failure prediction borsa i̇stanbul artificial neural networks classification and regression trees support vector machine knn algorithm finansal başarısızlık tahmini borsa istanbul yapay sinir ağları sınıflandırma ve regresyon ağaçları destek vektör makinesi, knn algoritması |
| title | COMPARISON OF CLASSIFICATION PERFORMANCE OF MACHINE LEARNING METHODS IN PREDICTION FINANCIAL FAILURE: EVIDENCE FROM BORSA İSTANBUL |
| title_full | COMPARISON OF CLASSIFICATION PERFORMANCE OF MACHINE LEARNING METHODS IN PREDICTION FINANCIAL FAILURE: EVIDENCE FROM BORSA İSTANBUL |
| title_fullStr | COMPARISON OF CLASSIFICATION PERFORMANCE OF MACHINE LEARNING METHODS IN PREDICTION FINANCIAL FAILURE: EVIDENCE FROM BORSA İSTANBUL |
| title_full_unstemmed | COMPARISON OF CLASSIFICATION PERFORMANCE OF MACHINE LEARNING METHODS IN PREDICTION FINANCIAL FAILURE: EVIDENCE FROM BORSA İSTANBUL |
| title_short | COMPARISON OF CLASSIFICATION PERFORMANCE OF MACHINE LEARNING METHODS IN PREDICTION FINANCIAL FAILURE: EVIDENCE FROM BORSA İSTANBUL |
| title_sort | comparison of classification performance of machine learning methods in prediction financial failure evidence from borsa istanbul |
| topic | financial failure prediction borsa i̇stanbul artificial neural networks classification and regression trees support vector machine knn algorithm finansal başarısızlık tahmini borsa istanbul yapay sinir ağları sınıflandırma ve regresyon ağaçları destek vektör makinesi, knn algoritması |
| url | https://dergipark.org.tr/tr/download/article-file/1578734 |
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