Event studies, which have significantly advanced mergers and acquisitions (M&A) research, obtain excess returns based on a theory linking a firm's shareholder returns to those of the market. For outcomes lacking such a theory, we propose an empirical approach using a synthetic control method with machine learning to link outcomes for the acquirer or target to those for a group of comparison firms. We discuss the method's assumptions, its close parallel to event studies, and its difference in weighting comparison firms (based on data versus derived from theory). We provide an illustration of Dollar Tree's acquisition of Family Dollar, by analyzing shareholder returns (to demonstrate consistent results with an event study), realized cost and sales synergies, and customer sentiment (derived from more than 52 million Twitter messages). We highlight this method's potential—for M&A and other areas of strategy research—to open up new lines of inquiry.

事件研究极大地推进了并购（M&A）研究，它基于将公司股东回报与市场回报联系起来的理论来获得超额回报。对于缺乏这种理论的结果，我们提出了一种实证方法使用机器学习的综合控制方法将收购方或目标公司的结果与一组比较公司的结果联系起来。我们讨论了该方法的假设、它与事件研究的密切相似性，以及它在加权比较公司方面的差异（基于数据与理论推导）。我们通过分析股东回报（以证明与事件研究的结果一致）、实现的成本和销售协同效应以及客户情绪（来自超过 5200 万条 Twitter 消息）来说明 Dollar Tree 收购 Family Dollar 的情况。我们强调这种方法在并购和其他战略研究领域开辟新的研究方向的潜力。