Measuring the Market for Startup Acquisitions
Team Information
Team Members
Jorge Guzman, Assistant Professor, Management Division, Columbia Business School
Max Wang, PhD Candidate, Management Division, Columbia Business School
Abstract
We measure the market for startup acquisitions by developing a detailed machine learning model that predicts startup 'acquirability' based on founding characteristics. Using a sample of highly innovative firms (SBIR), we implement a random forest using all grant characteristics at the time of founding and intellectual property records. We find our model performs well, and we are able to account for 54% of variation (AUC) in outcomes. We then aggregate this measure at the city level and compare the acquisition potential of firms, to the the incidence of true acquisition events in that region. We then study what determines differences between the supply of acquirable startups, and the demand for their outcomes at the regional level.
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Team Contact: Jorge Guzman (use form to send email)
