Ex Ante Predictability of Rapid Growth: A Design Science Approach

Hyytinen AriRouvinen PetriPajarinen MikaVirtanen Joosua

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).

Information om publikationen

Forskningsgrupp
Förnyelse av företag
Datum
06.11.2022
Nyckelord
High-growth enterprises, Relevance, Design research, Design science, Machine learning, Prediction, Venture capital
JEL
C53, D22, L25
Utgivare / serie
Entrepreneurship Theory and Practice
Språk
Engelska
Ladda ner publikationen
doi.org