Robots Pick Better Private Equity Funds? | Oliver Gottschalg
Oliver Gottschalg, a veteran private‑equity researcher, argues that machine‑learning algorithms now outperform traditional human allocators in selecting funds. Drawing on 25 years of empirical work, he explains how persistent manager‑skill signals can be quantified and assembled into a “DNA” profile of value‑creation capabilities. Back‑testing of models built between 2018 and 2020 shows algorithmic decisions delivering markedly higher returns than the majority of choices made by conventional private‑equity teams. The analysis isolates performance‑driving factors—such as deal‑execution consistency and portfolio‑level operational improvements—and feeds them into predictive models that consistently identify top‑quartile managers. Gottschalg highlights a striking quote: “the investment outcomes of those algorithmic investment decisions were vastly superior to at least a majority of investment decisions that I observe in the market done by normal private equity teams.” This empirical evidence underscores the predictive power of the machine‑learning approach. If investors adopt these tools, capital allocation could shift toward data‑driven selections, pressuring traditional fund‑of‑funds and placement agents to integrate AI or risk losing capital to algorithmic competitors.
Private Equity Evergreens–NAV Squeezing, Secondaries, Fee Alignment | with the AltView
The conversation between host Funshack and Tim McGillin of The AltView centers on the growing scrutiny of private‑equity evergreen vehicles, NAV‑squeezing tactics, secondary‑market dynamics, and fee alignment. McGillin, a former equity‑research analyst turned investment consultant, argues that the industry’s long‑held...
