Multicriteria Classification Models for the Identification of Targets and Acquirers in the Asian Banking Sector

The purpose of the present study is the development of classification models that could be used in the identification of acquirers and targets in the Asian banking sector. We use a sample of 52 targets and 47 acquirers that were involved in acquisitions in 9 Asian banking markets during 1998-2004 and match them by country and time with an equal number of non-involved banks. The models are developed and validated through a tenfold cross-validation approach using two multicriteria decision aid techniques. These techniques are based on mathematical programming allowing them to avoid the limitations and assumptions of statistical and econometric techniques. In each case three versions of the model are developed. The first one distinguishes between acquired and non-involved banks. The second one distinguishes between acquirers and non-involved banks. The last one, is a three outcome model that simultaneously distinguishes between targets, acquirers and non-involved banks. For comparison purposes we also develop models through discriminant analysis. The results indicate that the multicriteria decision aid models are more efficient that the ones developed through discriminant analysis. Furthermore, in all cases the models are more efficient in distinguishing between acquirers and non-involved banks than between targets and non-involved banks. Finally, the models with a binary outcome achieve higher accuracies than the ones which simultaneously distinguish between acquirers, targets and non-involved banks