An application of support vector machines in bankruptcy prediction model

Kyung Shik Shin, Talk Soo Lee, Hyun Jung Kim

Research output: Contribution to journalArticlepeer-review

531 Scopus citations

Abstract

This study investigates the efficacy of applying support vector machines (SVM) to bankruptcy prediction problem. Although it is a well-known fact that the back-propagation neural network (BPN) performs well in pattern recognition tasks, the method has some limitations in that it is an art to find an appropriate model structure and optimal solution. Furthermore, loading as many of the training set as possible into the network is needed to search the weights of the network. On the other hand, since SVM captures geometric characteristics of feature space without deriving weights of networks from the training data, it is capable of extracting the optimal solution with the small training set size. In this study, we show that the proposed classifier of SVM approach outperforms BPN to the problem of corporate bankruptcy prediction. The results demonstrate that the accuracy and generalization performance of SVM is better than that of BPN as the training set size gets smaller. We also examine the effect of the variability in performance with respect to various values of parameters in SVM. In addition, we investigate and summarize the several superior points of the SVM algorithm compared with BPN.

Original languageEnglish
Pages (from-to)127-135
Number of pages9
JournalExpert Systems with Applications
Volume28
Issue number1
DOIs
StatePublished - Jan 2005

Keywords

  • Bankruptcy prediction
  • Support vector machines

Fingerprint

Dive into the research topics of 'An application of support vector machines in bankruptcy prediction model'. Together they form a unique fingerprint.

Cite this