Ex Ante Predictability of Rapid Growth: A Design Science Approach

Ari Hyytinen (Corresponding Author), Petri Rouvinen, Mika Pajarinen, Joosua Virtanen

Research output: Contribution to journalArticleScientificpeer-review

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.
Original languageEnglish
JournalEntrepreneurship Theory and Practice
DOIs
Publication statusE-pub ahead of print - 6 Dec 2022
MoE publication typeA1 Journal article-refereed

Keywords

  • design research
  • high-growth enterprises
  • machine learning
  • prediction
  • relevance

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