Mergers and acquisitions are mainly due to financial and technological innovations but could also be due to changes in the structure of the economy, which alters the optimal production functions of banks. Banks that seek to be operationally efficient would focus more on expanding their asset size, in the face of bad loans, leading to the acquisition of less efficient banks. This paper develops two-stage inverse data envelopment analysis (DEA) models for estimating potential gains from bank mergers for the top US commercial banks. The results show additional intermediate and final outputs at different predefined target levels of technical efficiencies.

中文翻译：

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用于估计美国银行业潜在合并收益的两阶段逆数据包络分析方法

并购主要是由于金融和技术创新，但也可能是由于经济结构的变化，改变了银行的最优生产函数。寻求运营效率的银行将更多地关注扩大其资产规模，面对不良贷款，导致收购效率较低的银行。本文开发了两阶段逆向数据包络分析 (DEA) 模型，用于估计美国顶级商业银行从银行合并中获得的潜在收益。结果显示了在不同的预定义技术效率目标水平下的额外中间和最终输出。