Private Equity Fund Performance: A Time-Series Approach
45 Pages Posted: 6 May 2019 Last revised: 22 Jun 2022
Date Written: June 21, 2022
Abstract
We introduce an estimator that measures factor exposures and alphas of individual private equity funds, with minimal assumptions about the fund return data-generating process (DGP). Simulations using varying assumptions about the autoregressive and moving-average properties of the DGP indicate that our estimator exhibits lower mean-squared-error (bias plus variance) than competing estimators. Applying our model to a newly available commercial dataset, PitchBook, we undercover new findings of economic importance: buyout managers have higher average skill levels than claimed by past studies; portfolios are marked with forward-looking and lagged multiples of factors; and skill and systematic exposures vary significantly across vintages.
Keywords: Private Equity, Buyout Funds, Time-Series, Supervised Machine Learning, Jackknife, Cross-Validation, Factor Modeling, Long Horizons, Overlapping Observations
JEL Classification: C01, C22, G12, G17
Suggested Citation: Suggested Citation