Fund Performance
Sponsor & Deal Intelligence
GP-Level
Market Level
Learn
Connect
About
Fund Performance
Sponsor & Deal Intelligence
Fund-Level
GP-Level
Market Level
Learn
Connect
About
Four metrics show up on nearly every private fund report. Here's what each one actually measures, how they work together, and where they can mislead you if read in isolation.
Alex deMarco, Investment Research Analyst · September 21, 2026
Data sourced from Dakota Private Markets, the private fund performance platform powered by Dakota. Learn More | Request Access
A fund can be top-quartile in one benchmark and second-quartile in another using the same cash flows and the same strategy label. The difference isn't performance. It's how the peer group was built. Dakota's own benchmark data shows how much a single classification choice can move the numbers: top-quartile net IRR for US private equity funds has ranged from 11.5% for the 2005 vintage to 27.3% for the 2023 vintage (Dakota Marketplace Performance Benchmarks, data as of June 30, 2026). Vintage year alone produces that spread. Add inconsistent strategy filters, sector mixing, and peer groups too thin to be reliable, and "top quartile" starts to mean whatever the peer group was built to show. This post breaks down where peer group construction goes wrong and what to check before citing a benchmark, whether you're the one being ranked or the one doing the ranking.
Vintage year is the variable every peer group anchors to, and the swings it produces are the starting point for everything else. But vintage alone isn't a finished peer group, it's a first filter. A properly constructed peer group layers several more:
|
Classification Layer |
What It Controls For |
What Happens If Skipped |
|
Vintage year |
Market timing and entry conditions |
Funds deployed in different cycles get compared as if conditions were equal |
|
Strategy (buyout, growth, VC) |
Return profile and risk structure |
Growth-stage multiples get benchmarked against buyout leverage-driven returns |
|
Sector of underlying portfolio companies |
Exit multiples and holding-period dynamics |
A software-focused fund and an industrials fund from the same vintage get pooled despite facing different exit cycles |
|
Fund size |
Deal sourcing and portfolio construction |
A $200M fund and a $2B fund are compared as if they access the same deal flow |
|
Geography |
Regulatory, currency, and market maturity effects |
Developed-market and emerging-market PE get blended into one number |
Source: Dakota Private Markets, How to Benchmark Private Equity Fund Performance, 2026.
When a peer group is too thin, filtered by strategy, vintage, and geography, the fastest fix is to widen the vintage window until enough funds show up. A 2019 buyout fund gets benchmarked against a "2018-2020" cohort instead of its true vintage peers.
Combining vintages erases the market-timing effects that made each fund's environment different in the first place (Dakota Marketplace, 2026). A tough capital-deployment year and a strong one get averaged into a single, misleading midpoint. For fund managers, this means a "beat the vintage benchmark" claim is only as good as whether the vintage was defined narrowly. Ask what window was used before citing the number.
A benchmark built on net IRR alone misses whether returns have actually been realized (DPI) or are still sitting on paper (RVPI). Two funds can show identical IRR while one has returned real cash to allocators and the other hasn't returned a dollar. Citing IRR in isolation is the fastest way to make an unrealized fund look identical to a realized one.
A quartile ranking built from five comparable funds is far less reliable than one built from fifty, even when the filter criteria are identical. A single outlier fund can swing the top-quartile threshold by several points of IRR when the sample is that thin. As the TVPI data above shows, this isn't hypothetical: benchmark providers routinely publish quartile rankings on samples an order of magnitude smaller than the PE median.
The instinct to widen the vintage window (Error 1) is often a direct response to this problem: the peer group was too small, so the fix was to make it bigger by making it less precise. Neither fixes the underlying issue. The right fix is a larger underlying dataset, not a looser filter.
Stopping at "middle market buyout" without accounting for what those portfolio companies actually do produces a peer group that includes fundamentally different return profiles under one label. A software-focused middle market buyout fund and an industrials-focused middle market buyout fund from the same vintage face different multiple-expansion environments and different exit cycles, even though they'd be pooled together under most default peer group definitions.
Allocators running diligence on a "top-quartile" claim increasingly ask about construction before accepting the ranking.
What makes a benchmark claim credible:
What makes allocators discount a claim:
The practical result: a GP who can name the exact filter criteria behind a benchmark claim has a stronger position in diligence than one who cites a headline quartile number without explaining how the peer group was built.
Dakota Private Markets tracks over 18,000 funds with performance data filterable by vintage, strategy, geography, and the sector of underlying portfolio companies, not just headline strategy labels. Build a peer group and see net IRR, DPI, TVPI, and quartile rankings for funds that actually belong together.
If you're preparing to defend a benchmark claim in your next LP conversation, request access to see how the peer group behind your number was actually built.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis at ante dui. Duis euismod quam sed lectus ornare tempus. Morbi rhoncus urna et ante interdum imperdiet. Cras sit amet sodales arcu, ac rutrum turpis. Aliquam et tempus ligula, at eleifend diam.
©2026 All Rights Reserved Dakota Private Markets Privacy Policy | Terms of Use