Predicting business failure using multiple case-based reasoning combined with support vector machine

  • Authors:

  • Hui Li;Jie Sun

  • Affiliations:

  • School of Business Administration, Zhejiang Normal University, 91# Sub-mailbox in P.O. Box 62#, YingBinDaDao 688#, Jinhua City 321004, Zhejiang Province, PR China;School of Business Administration, Zhejiang Normal University, 91# Sub-mailbox in P.O. Box 62#, YingBinDaDao 688#, Jinhua City 321004, Zhejiang Province, PR China

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2009

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Abstract

Financial distress prediction of business institutions is a long cherished topic concentrating on reducing loss of the society. Case-based reasoning (CBR) is an easily understandable methodology for problem solving. Support vector machine (SVM) is a new technology developed recently with high classification performance. Combining-classifiers system is capable of taking advantages of various single techniques to produce high performance. In this research, we develop a new combining-classifiers system for financial distress prediction, where four independent CBR systems with k-nearest neighbor (KNN) algorithms are employed as classifiers to be combined, and SVM is utilized as the algorithm fulfilling combining-classifiers. The new combining-classifiers system is named as Multiple CBR systems by SVM (Multi-CBR-SVM). The four CBR systems, respectively, are found on similarity measure on the basis of Euclidean distance metric, Manhattan distance metric, Grey coefficient metric, and Outranking relation metric. Outputs of independent CBRs are transferred as inputs of SVM to carry out combination. How to implement the combining-classifiers system with collected data is illustrated in detail. In the experiment, 83 pairs of sample companies in health and distress from Shanghai and Shenzhen Stock Exchange were collected, the technique of grid-search was utilized to get optimal parameters, leave-one-out cross-validation (LOO-CV) was used as assessment in parameter optimization, and predictive performances on 30-times hold-out data were used to make comparisons among Multi-CBR-SVM, its components and statistical models. Empirical results have indicated that Multi-CBR-SVM is feasible and validated for listed companies' business failure prediction in China.