Browsing by Subject "support vector machines"

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    Predicting business failures in non-financial turkish companies
    (2015) Okay, Kaan
    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.