Machine learning private equity returns

Abstract

In this paper, we use two machine learning techniques to learn the aggregated return time series of complete private capital fund segments. First, we propose Stochastic Discount Factor (SDF) model combination to determine the public factor exposure of private equity. Here, we describe our theoretical motivation to favor model combination over model selection. This entails that we apply simple coefficient averaging to obtain multivariate SDF models that mimic the factor exposure of all major private capital fund types. As a second step, we suggest componentwise L2 boosting to estimate the error-term time series associated with our factor models. The simple addition of the public factor model returns and the error terms then yields the total return time series. These return time series can be applied for proper integrated public and private risk management or benchmarking.