Ex Ante Predictability of Rapid Growth: A Design Science Approach

Ari Hyytinen*, Petri Rouvinen, Mika Pajarinen, Joosua Virtanen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)

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
Peer-reviewed scientific journalEntrepreneurship Theory and Practice
ISSN1042-2587
DOIs
Publication statusPublished - 06.11.2022
MoE publication typeA1 Journal article - refereed

Keywords

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

Areas of Strength and Areas of High Potential (AoS and AoHP)

  • AoS: Competition economics and service strategy - Quantitative consumer behaviour and competition economics

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