Algorithmic Mindset for Selecting Private Market Managers

This document is a research brief that outlines an algorithmic approach to selecting private market managers. It emphasizes the importance of systematic, data-driven selection processes to enhance investment performance in private markets, where performance differentials can be significant. The brief discusses how many investors rely on unsystematic, heuristic-driven methods, which can hinder their ability to select the most suitable managers. By adopting an algorithmic mindset, investors can focus on the design elements of their selection processes, ensuring they are efficient, objective, and backed by empirical evidence. Key components of this approach include formulating hypotheses based on data, assigning objective scores to managers, applying filters to narrow down candidates, and conducting deep diligence. The document also highlights the necessity of using high-quality data to support these processes, ultimately aiming to maximize the likelihood of selecting the best-fit managers in private markets.