# Ex Ante Predictability of Rapid Growth: A Design Science Approach

**Published:** 2022-11-08  
**Categories:** Akateemiset julkaisut, Julkaisut  
**URL:** https://www.etla.fi/julkaisut/ex-ante-predictability-of-rapid-growth-a-design-science-approach/

## Abstract

We develop a machine learning (ML) procedure that enables a venture capitalist to optimize deal sourcing and to enhance portfolio returns. Specifically, we examine how machine learning 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.

*Entrepreneurship Theory and Practice*, *forthcoming (First published online November 6, 2022)*.

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## Additional Information

**Kirjoittajat:** Hyytinen, Ari - Rouvinen, Petri - Pajarinen, Mika - Virtanen, Joosua

**Publication Research:** No

64815

**Kansikuva:** No

**Lataa Pdf:** No

**Key Words:** High-growth enterprises, Relevance, Design research, Design science, Machine learning, Prediction, Venture capital

**Jel:** C53, D22, L25

**Paivays:** 06.11.2022

**Kieli:** en

**Sarja:** Other articles

https://doi.org/10.1177/10422587221128268

**Saatavuus:** 2

Entrepreneurship Theory and Practice