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In recent years, mergers and acquisitions (M&#x0026;A) have been frequent, but the failure rate of M&#x0026;A has remained high in reality. In order to reduce the M&#x0026;A failure rate, a key step is to achieve an effective M&#x0026;A matching which can help integrate enterprise resources and can make companies bigger and stronger. Given the risk of poor M&#x0026;A matching decisions, it is important to consider both the rational and irrational behavior of decision-makers. This study developed a M&#x0026;A matching method that considers irrational behavior from a DEA-based performance perspective. In this method, the M&#x0026;A party evaluates the other party with itself as the reference point. However, the approach also draws on the idea of cross-efficiency modeling. In this approach, we construct a data envelopment analysis (DEA) model that has a prospect value as the secondary optimization goal. This generates index weights to further determine decision weights. Then, the value and decision weights are aggregated into a prospect value, which is used to assess the degree of satisfactory matching to optimize the two-sided M&#x0026;A matching. We then provide an example to illustrate the feasibility and effectiveness of two-side M&#x0026;A matching. This method can be applied to third-party platforms to provide matching recommendation services for participating M&#x0026;A companies.

A Merger and Acquisition Matching Method That Considers Irrational Behavior From a Performance Perspective