Ex Ante Predictability of Rapid Growth: A Design Science Approach

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

*Huvudförfattare för detta arbete

Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review

6 Citeringar (Scopus)

Sammanfattning

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.
OriginalspråkEngelska
Referentgranskad vetenskaplig tidskriftEntrepreneurship Theory and Practice
ISSN1042-2587
DOI
StatusPublicerad - 06.11.2022
MoE-publikationstypA1 Originalartikel i en vetenskaplig tidskrift

Nyckelord

  • 511 Nationalekonomi

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