The purpose of the present study is the development of classification models for 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. 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 the 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. (C) 2009 Elsevier B.V. All rights reserved.
Pasiouras, F., Gaganis, C., & Zopounidis, C. (2010). Multicriteria classification models for the identification of targets and acquirers in the Asian banking sector. European Journal of Operational Research, 204(2), 328-335. https://doi.org/10.1016/j.ejor.2009.10.026