What Do We Actually Know About Private Fund Performance? A Major New Study Has Answers

For decades, institutional investors have poured capital into private markets—buyout funds, venture capital, private credit, real estate, and infrastructure—with the belief that these asset classes offer superior returns over public markets. But are those returns superior once you account for the risk involved? A new academic study from the Institute for Private Capital takes a comprehensive crack at answering the question.

Here’s what the researchers found—and what it means for investors.

What Did the Researchers Examine?

Gregory Brown, Christian Lundblad, and William Volckmann, authors of the March 2025 study “Risk-Adjusted Performance of Private Funds: What Do We Know?” analyzed 7,816 private funds spanning vintage years 1988 to 2019, drawing on cash flow and net asset value data from the MSCI-Burgiss Private Capital database through the end of 2023. The dataset covers roughly $6.1 trillion in committed capital—making this one of the largest and most exhaustive studies of private fund performance ever conducted.

The fund types covered include private equity (buyout, venture capital, and growth/generalist funds), private debt (senior lending, mezzanine, distressed, and generalist), real estate, and infrastructure. They examined funds across all geographies, with a focus on North America and the “rest of world” (ROW).

Critically, the researchers didn’t just measure raw returns. They tested a spectrum of performance metrics—from the simplest (multiples on invested capital and IRR) to more sophisticated risk-adjusted tools like the Public Market Equivalent (PME), Direct Alpha, the Korteweg-Nagel Generalized PME (KN-GPME), and the Brown-Ghysels-Gredil “NowCasting” model. They also paid careful attention to benchmark selection, testing everything from broad equity indices to custom benchmarks matched to fund-level sector and geographic exposures.

The Key Findings

Private Equity: Buyouts Win on a Risk-Adjusted Basis

On raw, unadjusted performance, venture capital funds look like the winners. They posted higher MOICs (often exceeding 2x) and higher IRRs than buyout funds. But once you properly account for risk, the picture flips.

North American buyout funds have historically delivered reliable, positive excess returns regardless of the benchmark or risk model used. Their estimated market beta sits consistently around 1.0, meaning they carry roughly the same systematic risk as public equity markets—and they outperformed.

North American venture capital funds, by contrast, carry substantially more risk—estimated market betas ranging from 1.45 to 2.33 depending on the method used. After adjusting for that risk, the excess returns for U.S. venture capital essentially evaporate, and in many specifications are zero or negative. Strong headline numbers driven by a few spectacular dotcom-era funds have masked weak risk-adjusted performance over the long run.

Outside North America, the story improves. Rest-of-world buyout and venture capital funds show consistently positive risk-adjusted excess returns. When measured against a rest-of-world public equity benchmark (like MSCI-EAFE) rather than U.S. indices, international private equity looks quite strong.

Private Credit: Positive Excess Returns, But Benchmark Choice Matters Enormously

Private credit has received less academic attention than private equity, and the findings here represent some of the paper’s more novel contributions. The authors find that the choice of benchmark has an outsized effect on private debt analysis—far more so than for equity funds.

Using broad fixed income indices (like the Bloomberg Aggregate) produces essentially no correlation with private debt fund performance. High-yield bond indices do better. But leveraged loan indices — composed of floating-rate, below-investment-grade loans, much like the assets inside most private debt funds—turn out to be the most appropriate benchmark, and the one the authors use as their primary metric.

Using leveraged loan benchmarks, private credit funds have generated consistently positive excess returns across strategies and geographies, with direct alphas typically in the 1%–4% range. Senior debt and mezzanine funds tend to outperform, while distressed debt and generalist funds show more modest —sometimes negligible—risk-adjusted excess returns.

One important caveat: the private credit dataset largely covers a period without a serious full credit cycle since the Global Financial Crisis. The authors note that the risk of private credit funds is likely underestimated as a result, meaning risk-adjusted performance figures may be somewhat overstated relative to what a full cycle would reveal.

Real Estate: Disappointing. Infrastructure: Impressive.

Real assets are where the findings diverge most starkly between sub-asset classes.

Private real estate funds have, on a risk-adjusted basis, largely failed to generate meaningful excess returns. This holds across geographies, fund strategies (core, value-add, opportunistic), and most risk models. In fact, rest-of-world real estate funds showed outright negative risk-adjusted returns using most methods. North American funds fared slightly better, roughly matching their public REIT benchmarks after risk adjustment. One modeling approach (the BGG NowCasting model) did generate modestly positive alphas of 2–4%, but the authors acknowledged they don’t have a clean explanation for why results vary across methods for real estate.

Private infrastructure funds tell a very different story. They have delivered reliably positive risk-adjusted excess returns across all geographies, sub-strategies, and risk models—a remarkably consistent finding. Part of the explanation is that infrastructure funds appear to carry genuinely lower systematic risk than public infrastructure benchmarks (estimated betas around 0.5–0.7), yet they still outperform. Direct alphas for infrastructure funds are typically in the 3%–6% range.

A Practical Insight: Do You Need Complex Models?

One of the study’s most useful contributions is its honest assessment of methodological complexity. Advanced models like the Korteweg-Nagel GPME require large datasets, complete fund cash flows, and sophisticated statistical estimation—and even then, they couldn’t always generate reliable estimates in this analysis, particularly outside U.S. equities.

So do investors actually need these complex models? The researchers’ answer is largely no—for the purpose of identifying top-performing funds. They show that simpler metrics (PME and Direct Alpha) combined with well-chosen benchmarks produce results that are highly correlated with those from far more complex models. For top-quartile identification specifically, the agreement rate between simple and complex methods averages 85% across equity fund subsamples.

The implication is clear: investing time and resources in selecting the right benchmark matters far more than building ever-more-elaborate econometric machinery. A practitioner with a spreadsheet, good benchmark data, and Dimson beta estimates can reach conclusions very close to those generated by cutting-edge academic models.

Key Investor Takeaways