USING PRIOR PROBABILITIES FOR THE EVALUATION OF MERGERS AND ACQUISITIONS

AbstractAbstract In this paper we look at the high failure rates of Mergers and Acquisitions (M&As) and specifically, why there has been no discernible change in behavior or outcomes over the last few decades, even with new evidence that these high failure rates exist. Although we have foundational rational models like expected utility, greater access to data, greater volume of data, sophisticated business intelligence (BI) and data analytics (DA) tools, and work by industry professional and academics to improve process, high failure rates have persisted. We use data from the Institute of Mergers, Acquisitions, and Alliances (IMAA) and apply a regression discontinuity in time (RDiT) model to investigate behavior regarding M&As when presented with new and important prior information. We find that a major KPMG (1999) study that showed 83% failure rates across M&As had no discernible impact on M&A evaluations going forward, which would be considered non-rational.


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