Thematic Investing With Big Data: The Case of Private Equity
15 Pages Posted: 7 Feb 2022 Last revised: 13 Mar 2023
Date Written: December 21, 2021
Abstract
Using natural language processing, we score companies based on the frequency with which news articles contain both their names and terms Private Equity and Leveraged Buy-Out. An index is then created and can be updated seamlessly at high frequency, with the weights set as a function of a company exposure to this theme. We add several liquidity constraints to ensure minimal transaction costs. Even though the algorithm does not optimize on either return or correlation, this listed private equity index is highly correlated to commonly used private equity fund market indices: nearly 90% correlation with Burgiss LBO fund index. In addition, our index has similar returns as non-tradable LBO fund indices. Our approach can be generalized to many other investment themes.
Keywords: Private Equity, Thematic Investing, Big Data, Natural Langage Processing
JEL Classification: G24
Suggested Citation: Suggested Citation