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 fixed size portfolio. Applying 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. find that when ML procedure adheres budget constraint maximizes measure, nearly 40% HGE are correct. Moreover, performs particularly well where it matters in practice—in upper tail distribution predicted probabilities. JEL Classification: C53, D22, L25