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Optimized Bankruptcy Prediction through Combining SVM with Fuzzy Theory

퍼지이론과 SVM 결합을 통한 기업부도예측 최적화

  • Choi, So-Yun (Graduate School of Business IT, Kookmin University) ;
  • Ahn, Hyun-Chul (Graduate School of Business IT, Kookmin University)
  • 최소윤 (국민대학교 비즈니스IT전문대학원) ;
  • 안현철 (국민대학교 비즈니스IT전문대학원)
  • Received : 2015.01.18
  • Accepted : 2015.03.20
  • Published : 2015.03.28

Abstract

Bankruptcy prediction has been one of the important research topics in finance since 1960s. In Korea, it has gotten attention from researchers since IMF crisis in 1998. This study aims at proposing a novel model for better bankruptcy prediction by converging three techniques - support vector machine(SVM), fuzzy theory, and genetic algorithm(GA). Our convergence model is basically based on SVM, a classification algorithm enables to predict accurately and to avoid overfitting. It also incorporates fuzzy theory to extend the dimensions of the input variables, and GA to optimize the controlling parameters and feature subset selection. To validate the usefulness of the proposed model, we applied it to H Bank's non-external auditing companies' data. We also experimented six comparative models to validate the superiority of the proposed model. As a result, our model was found to show the best prediction accuracy among the models. Our study is expected to contribute to the relevant literature and practitioners on bankruptcy prediction.

Keywords

Bankruptcy prediction;Support vector machine;Fuzzy theory;Genetic algorithm;Convergence model

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