Abstract
In this paper we develop classification models for the identification of acquisition targets in the EU banking industry, incorporating financial variables that are mostly unique to the banking industry and originate from the CAMEL approach. A sample of 168 non-acquired banks matched with 168 acquired banks is used over the period 1998-2002, covering 15 EU countries. We compare and evaluate the relative efficiency of three multicriteria approaches, namely MHDIS, PAIRCLAS, and UTADIS, with all models developed and tested using a 10-fold cross validation approach. We find that the importance of the variables differs across the models. However, on the basis of univariate test and the results of the models we could state that in general after adjusting for the country where banks operate, acquired banks are less well capitalized and less cost and profit efficient. The results show that the developed models can achieve higher classification accuracies than a naïve model based on random assignments. Nevertheless, there is fair amount of misclassification that is hard to avoid given the nature of the problem, showing that as in previous studies for non-financial firms, the identification of acquisitions targets in banking is a difficult task. © 2006 Elsevier Inc. All rights reserved.