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      • Theses - Graduate School of Economics and Social Sciences
      • Graduate School of Economics and Social Sciences - Master's degree
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      Predicting business failures in non-financial turkish companies

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      Author
      Okay, Kaan
      Advisor
      Esmer, Burcu
      Date
      2015
      Publisher
      Bilkent University
      Language
      English
      Type
      Thesis
      Item Usage Stats
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      Abstract
      The prediction of corporate bankruptcies has been widely studied in the finance literature. This paper investigates business failures in non-financial Turkish companies between the years 2000 and 2015. I compare the accuracies of different prediction models such as multivariate linear discriminant, quadratic discriminant, logit, probit, decision tree, neural networks and support vector machine models. This study shows that accounting variables are powerful predictors of business failures one to two years prior to the bankruptcy. The results show that three financial ratios: working capital to total assets, net income to total assets, net income to total liabilities are significant in predicting business failures in non-financial Turkish companies. When the whole sample is used, all five models predict the business failures with at least 75% total accuracy, where the decision tree model has the best accuracy. When the hold-out samples are used, neural networks model has the best prediction power among all models used in this study.
      Keywords
      multivariate linear discriminant model
      quadratic discriminant model
      logit model
      probit model
      decision tree model
      neural networks model
      support vector machines
      business failures
      bankruptcy prediction
      financial ratios
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      http://hdl.handle.net/11693/30046
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      • Graduate School of Economics and Social Sciences - Master's degree 152
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