Predicting Private Equity Fund Returns
Abstract
This thesis investigates the potential of a Private Equity fund performance forecasting model,
to assist Private Equity investors in their investment decision making process. Fund
performance is measured by the fund’s Kaplan Schoar Public Market Equivalent and is
forecasted using a binary classification approach. The top performing Machine Learning
models are able to forecast Buyout fund performance with 63 % accuracy, and Venture Capital
fund performance with 66 % accuracy. Therefore, the features used to train the models and
selected based on the literature on Private Equity performance drivers, possess important
predictive power, which can be integrated in the investment procedure.
Description
Masteroppgave(MSc) in Master of Science in Finance - Handelshøyskolen BI, 2022