CapitalVX: A machine learning model for startup selection and exit prediction

Abstract

Using a big data set of venture capital financing and related startup firms from Crunchbase, this paper develops a machine-learning model called CapitalVX (for “Capital Venture eXchange”) to predict the outcomes for startups, i.e., whether they will exit successfully through an IPO or acquisition, fail, or remain private. Using a large feature set, the out-of-sample accuracy of predictions on startup outcomes and follow-on funding is 80–89%. This research suggests that VC/PE firms may be able to benefit from using machine learning to screen potential investments using publicly available information, diverting this time instead into mentoring and monitoring the investments they make.