Data-driven approach to find the best partner for merger and acquisitions in banking industry
Industrial Management & Data Systems
ISSN: 0263-5577
Article publication date: 22 April 2020
Issue publication date: 29 March 2021
Abstract
Purpose
Merger and acquisitions (M&A) is a process of restructuring two or more companies into one, a process that occurs frequently in many companies. Previous studies on M&A mainly paid attention to the potential gains from a merger, while ignored the problem of how to select the partners to merge. This paper aims to select the best partner from different candidates for a given company to merge.
Design/methodology/approach
Each company's historical data are used to identify each company's own production technology. With resources change, each company's new operation is restricted by its own production technology. Then, a 0–1 integer programming is proposed to select the best partner for M&A.
Findings
The banking industry involving 27 China's commercial banks is given to verify the applicability of our proposed model. The study shows the best partner selection for each bank company.
Originality/value
On the theoretical side, the study uses each company's own historical data to construct its own production technology to compressively reflect the production change after M&A. On the practical side, the study uses the proposed model to help the 27 commercial banks in China to select their best merger partner.
Keywords
Acknowledgements
This research was financially supported by the National Natural Science Foundation of China (Nos. 71904084, 71901178, 71834003, 71910107002 and 71573121); the Natural Science Foundation for Jiangsu Province, China (No. BK20190427); the Social Science Foundation of Jiangsu Province, China (No. 19GLC017); the Fundamental Research Funds for the Central Universities by Nanjing University of Aeronautics and Astronautics (No. NR2019003); Southwestern University of Finance and Economics (Nos. JBK2003021 and JBK190504); the Innovation and Entrepreneurship Foundation for Doctor of Jiangsu Province; and the Priority Academic Program Development of Jiangsu Higher Education Institutions, China (No. D10207000001/003).
Citation
Zhu, Q., Li, X., Li, F. and Amirteimoori, A. (2021), "Data-driven approach to find the best partner for merger and acquisitions in banking industry", Industrial Management & Data Systems, Vol. 121 No. 4, pp. 879-893. https://doi.org/10.1108/IMDS-12-2019-0640
Publisher
:Emerald Publishing Limited
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