Private capital & investment governance · CC BY 4.0
Determining Drivers of Private Equity Return with Computational Approaches
Prosper Lamothe-Fernández; Eduardo García-Argüelles; Sergio Manuel Fernández-Miguélez; Omar Hassani-Zerrouk
Computational Economics · 2024-04-03
Helps family offices evaluate computational return models without confusing classification accuracy with investment performance.
Original practical companion
An Accuracy Claim Is Not an Allocation Mandate: A Model-Risk Dossier for Private-Equity Forecasts
Computational models can help investigate private-equity return patterns, but classification performance is not an investment return. Drawing on Lamothe-Fernández and colleagues' analysis of 1,606 funds, this companion proposes a model-risk dossier for family offices evaluating forecasts, manager-selection tools, or AI-supported allocation research. It emphasizes target definitions, information timing, benchmark comparisons, distributional limits, and human approval.