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

BA4602 Corporate foresight and strategy

Research output: Contribution to journal › Article › Scientific › peer-review

3 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 language	English
Journal	Entrepreneurship Theory and Practice
DOIs	https://doi.org/10.1177/10422587221128268
Publication status	E-pub ahead of print - 6 Dec 2022
MoE publication type	A1 Journal article-refereed

Keywords

design research
high-growth enterprises
machine learning
prediction
relevance

Access to Document

10.1177/10422587221128268Licence: CC BY

Cite this

@article{78e2444905cd467db2e6d46b1ebb3cd3,

title = "Ex Ante Predictability of Rapid Growth: A Design Science Approach",

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.",

keywords = "design research, high-growth enterprises, machine learning, prediction, relevance",

author = "Ari Hyytinen and Petri Rouvinen and Mika Pajarinen and Joosua Virtanen",

note = "Publisher Copyright: {\textcopyright} {\textcopyright}The Author(s) 2022.",

year = "2022",

month = dec,

day = "6",

doi = "10.1177/10422587221128268",

language = "English",

journal = "Entrepreneurship Theory and Practice",

issn = "1042-2587",

publisher = "Wiley-Blackwell",

}

TY - JOUR

T1 - Ex Ante Predictability of Rapid Growth

T2 - A Design Science Approach

AU - Hyytinen, Ari

AU - Rouvinen, Petri

AU - Pajarinen, Mika

AU - Virtanen, Joosua

N1 - Publisher Copyright: © ©The Author(s) 2022.

PY - 2022/12/6

Y1 - 2022/12/6

N2 - 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.

AB - 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.

KW - design research

KW - high-growth enterprises

KW - machine learning

KW - prediction

KW - relevance

UR - http://www.scopus.com/inward/record.url?scp=85141622981&partnerID=8YFLogxK

U2 - 10.1177/10422587221128268

DO - 10.1177/10422587221128268

M3 - Article

AN - SCOPUS:85141622981

SN - 1042-2587

JO - Entrepreneurship Theory and Practice

JF - Entrepreneurship Theory and Practice

ER -

Ex Ante Predictability of Rapid Growth: A Design Science Approach

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this