Algorithms Are Coming for Private Equity Allocators
- 4 days ago
- 3 min read
The big picture: Oliver Gottschalg, HEC Paris professor and founder of Gottschalg Analytics, made a striking case on Fund Shack that machine learning models he has been building for eight years can materially outperform human decision making in private equity fund selection and secondaries pricing.
Why it matters:
Gottschalg ran a conservative simulation across 17 large US public pension plans that collectively committed $96 billion to PE buyout funds. By simply reweighting each plan's existing fund commitments based on algorithmic predictions (no new manager access, no strategy changes), those pensions would collectively be $6 billion richer today.
A more aggressive version of the test, where pensions followed the algorithm's top 10 picks per vintage, produced $15 billion in outperformance. Not a single pension plan would have been worse off.
The algorithm trains on two decades of data using 99 features and 60 separate machine learning agents per prediction. It is retrained quarterly, which means it adapts to regime shifts (like the post COVID interest rate environment) faster and with less bias than human experience allows.
The secondaries angle is even bigger:
Gottschalg argues that bottom up NAV valuations in the LP secondary market are fundamentally broken. Academic research confirms pricing does not correlate well with go forward value.
His model instead evaluates fund manager capacity constraints, team stability, strategy drift, social media sentiment, and dozens of other features, then lets machine learning find the interaction effects humans cannot process.
Algorithmic pricing of 865 fund stakes takes hours on a standard PC versus weeks of expensive analyst time for traditional bottom up work.
One large pension fund told Gottschalg they would anchor his fund not for the returns alone, but because widespread algorithmic pricing could compress the cost of secondary market liquidity from 10 to 12 cents on the dollar down to 3 to 5 cents.
Memorable Quotes
On the core finding from his back tests across US public pensions: "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."
On why rigorous model design matters more than raw computational power: "If you don't pay careful attention how to design the training and validation, artificial intelligence turns into very powerful artificial stupidity."
On what happens if the PE industry fails to adopt algorithmic tools on its own terms: "If we private equity are not going to be able to do this, you know, we're going to have the Googles and Metas of the world come in. They understand data. They understand the algorithm. You're just going to have a product that blows us out of the market."
On the long term vision for democratizing private markets access through science driven allocation: "It would be fantastic to kind of build out the Vanguard of private equity by bringing science to secondaries and designing products that have widespread appeal for investors in private equity, institutional and mass affluent, at dramatically lower cost."
The bottom line: Gottschalg envisions building "the Vanguard of private equity" through science driven secondaries. Lower cost, scalable, and tailored to different investor objectives (absolute return, loss avoidance, quick payback). The disruption is not about replacing humans entirely. It is about relegating them to a "downside governor" role: able to veto but not override the algorithm upward. The biggest barrier is not technology. It is incumbent inertia.