Ari Hyytinen, Petri Rouvinen, Mika Pajarinen, Joosua Virtanen
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引用次数: 2
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. JEL Classification: C53, D22, L25
期刊介绍：
Entrepreneurship Theory and Practice (ETP) is an interdisciplinary scholarly journal dedicated to conceptual and empirical research that advances, tests, or extends theory relating to entrepreneurship in its broadest sense.
Article Topics:
Topics covered in ETP include, but are not limited to:
New Venture Creation, Development, Growth, and Performance
Characteristics, Behaviors, and Types of Entrepreneurs
Small Business Management
Family-Owned Businesses
Corporate, Social, and Sustainable Entrepreneurship
National and International Studies of Enterprise Creation
Research Methods in Entrepreneurship
Venture Financing
Content:
The journal publishes articles that explore these topics through rigorous theoretical development, empirical analysis, and methodological innovation. ETP serves as a platform for advancing our understanding of entrepreneurship and its implications for individuals, organizations, and society.
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