Performance Predictability

The private equity secondaries market has evolved from a niche segment into a crucial $100+ billion marketplace, representing approximately 10% of global private equity transactions. Yet, despite this growth, significant challenges remain in asset selection and pricing. This article demonstrates how artificial intelligence (AI) - particularly machine learning and natural language processing - is revolutionizing secondaries investing by uncovering predictive signals hidden in qualitative data that traditional metrics miss. Groundbreaking research examining thousands of GP reports across multiple funds reveals how the “tone” of management communications can predict future performance more accurately than conventional metrics alone. For investors navigating the complex secondaries market, embracing AI-driven approaches to augment human judgement is rapidly becoming not just an advantage but a necessity for sustainable competitive edge.

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.