Prediction Performance Improvement of KOSDAQ Business Bankruptcy using SVM Preprocessor (SVM 전처리기를 활용한 코스닥기업 도산예측 성능 향상)

Kang, BooSik (강부식); Cho, Jun Hee (조준희)

Prediction Performance Improvement of KOSDAQ Business Bankruptcy using SVM Preprocessor

Journal of Knowledge Information Technology and Systems
Abbr : JKITS
2012, 7(4), pp.21-27
Publisher : Korea Knowledge Information Technology Society
Research Area : Interdisciplinary Studies > Interdisciplinary Research
Published : August 31, 2012

Kang, BooSik ¹, Cho, Jun Hee ¹

¹목원대학교

Accredited

ABSTRACT

Business bankruptcy has had a lot of losses to various stakeholders. If we are able to predict bankruptcy or insolvency in advance, we can defend enterprises against bankruptcy or insolvency and minimize the loss. Various types of statistical and data mining models have been proposed for prediction of business bankruptcy. One of the main issues of the bankruptcy prediction models is to increase predictability. This research deals with logit, decision tree, neural networks, and SVM models those are mainly used to predict corporate bankruptcy. Sample companies were consisted of 49 bankrupt firms and 49 normal firms in KOSDAQ. Nine financial variables were selected by statistical test and five years of data were collected. In case of using SVM for preprocessing data, experimental results showed that classification models could improve the performance of the bankruptcy prediction.

KEYWORDS

Bankruptcy Prediction, Support Vector Machine, Logit, Decision Tree, Neural Networks.

Citation status

* References for papers published after 2023 are currently being built.

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