Volume 41, Issue 5 (September 2013)

    CBR-Based Fuzzy Support Vector Machine for Financial Distress Prediction

    (Received 17 September 2012; accepted 11 February 2013)

    Published Online: 2013

    CODEN: JTEOAD

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    Abstract

    Financial distress prediction has attracted wide attention since the 1960s. With the development of quantitative methods and computing tools, the models of financial distress prediction are constantly innovated. Commonly used models in this area include multiple discriminant analysis (MDA), logit, decision trees (DT), neural networks (NN), support vector machine (SVM), and case-based reasoning (CBR), etc. Support vector machine (SVM), because of its excellent generalization ability, has been a hot subject nowadays. Especially, fuzzy SVM (FSVM) has achieved great development in recent years. As choosing an appropriate fuzzy membership is an important issue in FSVM, in this paper we propose a new fuzzy membership for FSVM combined with CBR. Generally, our basic idea is to detect the outliers by their k-nearest neighbors, all or the majority of which are enclosed with the other class, and then assign them a lower fuzzy membership by the output of CBR. By adopting the hold-out method 30 times to generate 30 hold-out data, the empirical experiment shows the feasibility and validity of the proposed CBR-based fuzzy SVM for Chinese listed companies' financial distress prediction.


    Author Information:

    Cao, Yu
    Dept. of Information Systems, School of Computing, National Univ. of Singapore, Singapore,

    School of Business, Central South Univ., Changsha,

    Chen, Xiaohong
    School of Business, Central South Univ., Changsha,


    Stock #: JTE20120282

    ISSN: 0090-3973

    DOI: 10.1520/JTE20120282

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    Author
    Title CBR-Based Fuzzy Support Vector Machine for Financial Distress Prediction
    Symposium , 0000-00-00
    Committee F40