Financial Distress Prediction Using Support Vector Machines and Logistic Regression

Seyyide Doğan (), Deniz Koçak () and Murat Atan ()
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Seyyide Doğan: Karamanoğlu Mehmetbey University
Deniz Koçak: Osmaniye Korkut Ata University
Murat Atan: Ankara Hacı Bayram Veli University

A chapter in Advances in Econometrics, Operational Research, Data Science and Actuarial Studies, 2022, pp 429-452 from Springer

Abstract: Abstract Financial 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.

Keywords: Financial distress; Support vector machine; Logistic regression analysis (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:conchp:978-3-030-85254-2_26

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DOI: 10.1007/978-3-030-85254-2_26

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