Ex Ante Predictability of Rapid Growth: A Design Science Approach

We examine how machine learning (ML) predictions of high-growth enterprises (HGEs) help a budget-constrained venture capitalist source investments for a fixed size portfolio. Applying a design science approach, we predict HGEs 3 years ahead and focus on decision (not statistical) errors, using an accuracy measure relevant to the decision-making context. We find that when the ML procedure adheres to the budget constraint and maximizes the accuracy measure, nearly 40% of the HGE predictions are correct. Moreover, ML performs particularly well where it matters in practice-in the upper tail of the distribution of the predicted HGE probabilities. JEL Classification: C53, D22, L25

Automated summary

This study examines how machine learning predictions can assist budget-constrained venture capitalists in making investment decisions for high-growth enterprises. The findings suggest that accurate machine learning predictions are particularly valuable in the decision-making process for investment strategies.