Application of support vector machines on the basis of the first Hungarian bankruptcy model

Miklós Virág and Tamás Nyitrai

Society and Economy, 2013, vol. 35, issue 2, 227-248

Abstract: In our study we rely on a data mining procedure known as support vector machine (SVM) on the database of the first Hungarian bankruptcy model. The models constructed are then contrasted with the results of earlier bankruptcy models with the use of classification accuracy and the area under the ROC curve. In using the SVM technique, in addition to conventional kernel functions, we also examine the possibilities of applying the ANOVA kernel function and take a detailed look at data preparation tasks recommended in using the SVM method (handling of outliers). The results of the models assembled suggest that a significant improvement of classification accuracy can be achieved on the database of the first Hungarian bankruptcy model when using the SVM method as opposed to neural networks.

Keywords: bankruptcy prediction; classification; data preparation; outliers; support vector machines (SVM); ROC curve analysis (search for similar items in EconPapers)
JEL-codes: C33 C45 C51 C52 C53 G33 (search for similar items in EconPapers)
Date: 2013
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.akademiai.com/content/w459170887q183u5/fulltext.pdf (application/pdf)
subscription

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aka:soceco:v:35:y:2013:i:2:p:227-248

Ordering information: This journal article can be ordered from
Akadémiai Kiadó Zrt., P. O. Box 245, H-1519 Budapest, Hungary
https://akjournals.com/

Access Statistics for this article

Society and Economy is currently edited by Szent-Iványi, Balázs

More articles in Society and Economy from Akadémiai Kiadó, Hungary
Bibliographic data for series maintained by Kriston, Orsolya ().