Ex Ante Predictability of Rapid Growth: A Design Science Approach

Ari Hyytinen, Petri Rouvinen, Mika Pajarinen and Joosua Virtanen

Entrepreneurship Theory and Practice, 2023, vol. 47, issue 6, 2465-2493

Abstract: 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

Keywords: high-growth enterprises; relevance; prediction; design research; machine learning (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:entthe:v:47:y:2023:i:6:p:2465-2493

DOI: 10.1177/10422587221128268

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