Private Equity Fund Performance: A Time-Series Approach

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 DGP indicate that our estimator exhibits lower mean-squared-error (bias plus variance) than competing time-series 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 over time.

Keywords: JEL Classifications: C01, C22, G12, G17 Private Equity, Buyout Funds, Time-Series, Machine Learning, Cross-Validation, Factor Modeling, Long Horizons, Overlapping

JEL Classification: C01, C22, G12, G17