Investigating Insensitivity to Prior Probabilities in Merger and Acquisition (M&A) Decision Making
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Abstract
In this paper we investigate the high failure rates of Mergers and Acquisitions (M&As) over the last several decades, despite greater access to data, sophisticated business intelligence (BI) and data analytics (DA) tools, and work by industry professionals and academics to improve outcomes. We explore the possibility that the representativeness heuristic could play a role, and specifically, if prior probabilities are being ignored or discounted in M&A evaluations. We confirm our hypothesis using a regression discontinuity in time (RDiT) model and a two-way fixed effects model. By highlighting the negative consequences of this heuristic on management decisions, we promote the use of data-driven decision making and the role of analytics in formulating business strategy.