Business failure prediction using hybrid2 case-based reasoning (H2CBR)

We have investigated business failure prediction (BFP) by a combination of decision-aid, statistical, and artificial intelligence techniques. The goal is to construct a hybrid forecasting method for BFP by combining various outranking preference functions with case-based reasoning (CBR), whose heart is the k-nearest neighbor (k-NN) algorithm, and to empirically test the predictive performance of its modules. The hybrid2 CBR (H2CBR) forecasting method was constructed by integrating six hybrid CBR modules. These hybrid CBR modules were built up by combining and modifying six outranking preference functions with the algorithm of k-NN inside CBR. A trial-and-error iterative process was employed to identify the optimal hybrid CBR module of the H2CBR forecasting system. The prediction of the optimal module is the final output of the H2CBR forecasting method. We have compared the predictive performance of the six hybrid CBR modules in BFP of Chinese listed companies. In this empirical study, the classical CBR algorithm based on the Euclidean metric, and the two classical statistical methods of logistic regression (Logit) and multivariate discriminant analysis (MDA) were used as baseline models for comparison. Feature subsets were selected with the stepwise method of MDA. The predictive performance of the H2CBR system is promising; the most preferred hybrid CBR for short-term BFP of Chinese listed companies is based on the ranking-order preference function.