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

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.


Reiner Braun

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

Borja Fernández Tamayo

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

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