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Estimation of potential gains from bank mergers: A novel two-stage cost efficiency DEA model

Xiao Shi (), Yongjun Li (), Ali Emrouznejad (), Jianhui Xie () and Liang Liang ()
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Xiao Shi: University of Science and Technology of China
Yongjun Li: University of Science and Technology of China
Ali Emrouznejad: Aston University
Jianhui Xie: University of Science and Technology of China
Liang Liang: University of Science and Technology of China

Journal of the Operational Research Society, 2017, vol. 68, issue 9, 1045-1055

Abstract: Abstract This paper develops a novel two-stage cost efficiency model to estimate and decompose the potential gains from Mergers and Acquisitions (M&As). In this model, a hypothetical DMU is defined as a combination of two or more candidate DMUs. The hypothetical DMU would surpass the traditional Production Possibility Set (PPS). In order to solve the problem, a Merger Production Possibility Set (PPSM) is constructed. The model minimizes the total cost of the hypothetical DMU while maintaining its outputs at the current level, and estimates the overall merger efficiency by comparing its minimal total cost with its actual cost. Moreover, the overall merger efficiency could be decomposed into technical efficiency, harmony efficiency, and scale efficiency. We show that the model can be extended to a two-stage structure and these efficiencies can be decomposed to both sub-systems. To show the usefulness of the proposed approach, we applied it to a real dataset of top 20 most competitive Chinese City Commercial Banks (CCBs). We concluded that (1) there exist considerably potential gains for the proposed merged banks. (2) It is also shown that the main impact on potential merger gains are from technical and harmony efficiency. (3) As an interesting result we found that the scale effect works against the merger, indicating that it is not favorable for a full-scale merger.

Keywords: DEA; mergers and acquisitions; potential cost saving; two-stage network (search for similar items in EconPapers)
Date: 2017
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