Doğan, SeyyideKoçak, DenizAtan, Murat2022-02-152022-02-152022Doğan, S., Koçak, D., & Atan, M. (2022). Financial distress prediction using support vector machines and logistic regression. Contributions to Economics. 429-452.doi:10.1007/978-3-030-85254-2_261431-1933https://doi.org/10.1007/978-3-030-85254-2_26https://hdl.handle.net/11492/5965Financial distress and bankruptcies are highly costly and devastating processes for all parts of the economy. Prediction of distress is notable both for the functioning of the general economy and for the firm’s partners, investors, and lenders at the micro-level. This study aims to develop an effective prediction model with Support Vector Machine and Logistic Regression Analysis. As the field of the study, 172 firms that are traded in Borsa İstanbul, have been chosen. Besides, two basic prediction methods, LRA was also used as a feature selection method and the results of this model were compared. The empirical results show us, both methods achieve a good prediction model. However, the SVM model in which the feature selection phase is applied shows the best performance.enFinancial DistressLogistic Regression AnalysisSupport Vector MachineBook Part429452info:eu-repo/semantics/closedAccess2-s2.0-8512340716010.1007/978-3-030-85254-2_26Q4