We develop a machine learning (ML) procedure that enables a venture capitalist to optimize deal sourcing and to enhance portfolio returns. Specifically, we examine how machine learning 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.
Entrepreneurship Theory and Practice, forthcoming (First published online November 6, 2022).