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.

Keywords

• Machine learning
• Ensemble models
• Exit prediction
• Funding prediction
• Private equity
• Venture capital