Projects per year
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.
|Peer-reviewed scientific journal||Entrepreneurship Theory and Practice|
|Publication status||Published - 06.11.2022|
|MoE publication type||A1 Journal article - refereed|
- 511 Economics
- high-growth enterprises
- design research
- machine learning
Areas of Strength and Areas of High Potential (AoS and AoHP)
- AoS: Competition economics and service strategy - Quantitative consumer behaviour and competition economics
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- 1 Finished
Ex Ante Predictability of Rapid Growth: A Design Science Approach
Hyytinen, A., Rouvinen, P., Pajarinen, M. & Virtanen, J.
01.05.2018 → 06.11.2022
Project: Project funded by Hanken/Hanken funds