Ex Ante Predictability of Rapid Growth: A Design Science Approach
研究了机器学习如何帮助预算有限的风险投资家提前三年预测高增长企业，在遵守预算约束下近40%的预测正确，且在高概率区间表现尤佳。
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.