Ex Ante Predictability of Rapid Growth: A Design Science Approach

Ari Hyytinen; Petri Rouvinen; Mika Pajarinen; Joosua Virtanen

doi:10.1177/10422587221128268

Ex Ante Predictability of Rapid Growth: A Design Science Approach

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

^*Motsvarande författare för detta arbete

Forskningsoutput: Tidskriftsbidrag › Artikel › Vetenskaplig › Peer review

3 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.

Originalspråk	Engelska
Referentgranskad vetenskaplig tidskrift	Entrepreneurship Theory and Practice
ISSN	1042-2587
DOI	https://doi.org/10.1177/10422587221128268
Status	Publicerad - 06.11.2022
MoE-publikationstyp	A1 Originalartikel i en vetenskaplig tidskrift

Nyckelord

511 Nationalekonomi

Styrkeområden och områden med hög potential (AoS och AoHP)

AoS: Konkurrensanalys och servicestrategi - Kvantitativt konsumentbeteende och konkurrensekonomi

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10.1177/10422587221128268Licens: CC BY

10422587221128268Publicerad version, 575 KBLicens: CC BY

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Ex Ante Predictability of Rapid Growth: A Design Science Approach
Hyytinen, A., Rouvinen, P., Pajarinen, M. & Virtanen, J.
01.05.2018 → 06.11.2022
Projekt: Projekt finanserat av Hanken / Hankens fonder

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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.",

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Ex Ante Predictability of Rapid Growth: A Design Science Approach

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Styrkeområden och områden med hög potential (AoS och AoHP)

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Ex Ante Predictability of Rapid Growth: A Design Science Approach

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