Data-driven approach to find the best partner for merger and acquisitions in banking industry

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.

Automated summary

This study proposes a method to select the best merger partner for a given company by using historical data to construct its production technology and applying 0-1 integer programming. It aims to comprehensively reflect production changes after M&A and has been tested on 27 commercial banks in China to assist them in selecting their best merger partner.