Predicting business failure using forward ranking-order case-based reasoning

With the rapid development of business computing for Chinese listed companies, it is focused on to use case-based reasoning (CBR) in business failure prediction (BFP). Ranking-order case-based reasoning (RCBR) uses ranking-order information among cases to calculate similarity in the framework of k-nearest neighbor. RCBR is sensitive to the choice of features, meaning that optimal features can help it produce better performance. In this research, we attempt to use wrapper approach to find the optimal feature subset for RCBR in BFP. Forward feature selection method and RCBR are combined to construct a new method, namely forward RCBR (FRCBR). The combination is implemented by combining forward feature selection with RCBR as a wrapper module. Hold out method is used to assessing the performance of the classifier. Empirical data were collected from Chinese listed companies in the Shenzhen Stock Exchange and Shanghai Stock Exchange. We employed the standalone RCBR, the classical CBR with Euclidean metric as its heart, the inductive CBR, the two statistical methods of logistic regression and multivariate discriminate analysis (MDA), and support vector machines to make comparisons. For comparative methods, stepwise MDA was employed to select optimal feature subset. Empirical results indicated that FRCBR can produce dominating performance in short-term BFP of Chinese listed companies.

Research highlights
► This research improves performance of ranking-order case-based reasoning by combining it with forward feature selection.
► The new method is applied to predict business failure of Chinese listed companies.
► Experiential results show that the new method produced superior performance to classical algorithms of case-based reasoning, statistical methods, and a support vector machine.