Limited Partners versus Unlimited Machines; Artificial Intelligence and the Performance of Private Equity Funds

62 Pages Posted: 26 Jun 2023 Last revised: 8 Jan 2024

See all articles by Borja Fernández Tamayo

Borja Fernández Tamayo

Université Côte d'Azur - SKEMA Business School; Unigestion SA

Reiner Braun

Technische Universität München (TUM) - TUM School of Management; Center for Entrepreneurial and Financial Studies

Florencio Lopez-de-Silanes

SKEMA Business School; National Bureau of Economic Research (NBER)

Ludovic Phalippou

University of Oxford - Said Business School

Natalia Sigrist

Unigestion SA

Date Written: December 15, 2023

Abstract

We assemble a proprietary dataset of 395 private equity (PE) fund prospectuses to analyze fund performance and fundraising success. We analyze both quantitative and qualitative information contained in these documents using econometric methods and machine learning techniques. PE fund performance is unrelated to quantitative information, such as prior performance, and measures of document readability. Measures of fundraising success, in contrast, are correlated to most fund characteristics but are not related to future performance. Meanwhile, machine learning tools can use qualitative information to predict future fund performance: the performance spread between the funds within the top and bottom terciles of predicted probability of success is about 25%. Our findings support the view that in opaque and non-standardized markets, investors fail to incorporate qualitative information in their asset manager selection process, but do incorporate salient quantitative information.

Keywords: Private equity, performance predictability, natural language processing, machine learning

JEL Classification: C10, C38, C60, G11, G20

Suggested Citation

Fernández Tamayo, Borja and Braun, Reiner and Lopez-de-Silanes, Florencio and Phalippou, Ludovic and Sigrist, Natalia, Limited Partners versus Unlimited Machines; Artificial Intelligence and the Performance of Private Equity Funds (December 15, 2023). Available at SSRN: https://ssrn.com/abstract=4490991 or http://dx.doi.org/10.2139/ssrn.4490991

Borja Fernández Tamayo

Université Côte d'Azur - SKEMA Business School ( email )

Campus Sophia Antipolis
Valbonne, 06902

Unigestion SA ( email )

8c, avenue de Champel CP 387
CP 387
Genève 12, CH 1211
Switzerland

Reiner Braun

Technische Universität München (TUM) - TUM School of Management ( email )

Arcisstr. 21
Munich, Deutschland 80333
Germany
+498928925181 (Phone)
+498928925188 (Fax)

HOME PAGE: http://www.ef.wi.tum.de/

Center for Entrepreneurial and Financial Studies ( email )

Arcisstrasse 21
Munich, DE 80333
Germany

Florencio Lopez-de-Silanes (Contact Author)

SKEMA Business School ( email )

Avenue Willy Brandt, Euralille
Lille, 59777
France

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Ludovic Phalippou

University of Oxford - Said Business School ( email )

Park End Street
Oxford, OX1 1HP
Great Britain

Natalia Sigrist

Unigestion SA ( email )

8c, avenue de Champel CP 387
CP 387
Genève 12, CH 1211
Switzerland

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
3,115
Abstract Views
8,287
Rank
7,662
PlumX Metrics