Institutional investors who allocated to buyouts for diversification hold something closer to a 1.5x leveraged equity position, and they recorded it at about two-thirds of its true volatility. That is the positioning consequence of Ennis and Rasmussen's 2026 working paper, and it matters more than the paper's headline finding of no alpha. The no-alpha result settles an argument about manager skill. The beta result describes a structural exposure sitting on pension and endowment balance sheets. When public markets break, that exposure shows up in a specific sequence: the denominator effect, discounted secondary sales, and a cut in new commitments.
The paper's design is what gives the claim weight. The authors avoid inferring risk from fund cash flows or sponsor-reported NAVs. They study portfolios of buyout fund interests that trade on European stock exchanges, which lets them put a market price and a reported NAV side by side for essentially the same underlying companies. Over the 20 years ending June 2025, market-priced listed private equity ran at roughly 29% annualized volatility. Measured on NAVs, the same assets showed 19%. Global equities came in near 16%.
Two Prices for the Same Companies
The year ending June 2009 shows the gap most clearly. MSCI ACWI fell 29%. Listed private equity NAVs fell 46%. The market value of those same interests fell 69%.
Three numbers, three readings of one portfolio. Even the sponsor marks, the slowest of the three, fell further than global stocks. That fits the leverage these companies carry. The market price fell further still, and that 23-point gap between NAV and market value is the most useful figure in the paper for anyone thinking about flows. It measures how far stale marks can drift from the price at which a holder can actually exit while public markets are under stress.
Correlation shows the same pattern. Market-priced listed private equity has a 0.94 correlation with global equities, compared with 0.81 on NAVs. At 0.81, an asset still looks like a partial diversifier and earns a separate line in a strategic allocation. At 0.94, it is the same risk factor as public equity with more leverage applied. The CAPM fit confirms this. Against the MSCI World Index, beta is about 1.5 and the regression explains 88% of return variation. That leaves little room for anything resembling an independent return source.
Why the Smoothing Survives in Allocation Models
The mechanism that turns a measurement problem into a positioning problem is the optimizer. A mean-variance model receiving buyout returns at 19% volatility and 0.81 correlation will find an asset with equity-like or better returns, moderate risk, and imperfect co-movement with stocks. It will recommend more of it. Each increase in the allocation adds hidden beta to a portfolio whose risk report says it is diversifying.
The paper's horizon results show why this is hard to correct from inside the model. Beta estimated on daily returns is only 0.40. Monthly returns raise it to 0.93, quarterly to 1.28, annual to 1.47, and biennial to 1.55. R² rises in step, from 22% on daily data to 91% on annual and 96% on biennial observations.
Even exchange-traded prices for these vehicles absorb public market moves with a lag, so short-interval statistics understate the exposure. The true co-movement only appears over horizons of a year or more. Most institutional risk systems are calibrated on monthly or quarterly data, which in this paper's numbers produces a beta between roughly 0.9 and 1.3. That range happens to sit near the "about market beta" figure many cash-flow studies report. The puzzle the paper addresses is how leveraged buyouts could have a beta at or below 1.0 when basic finance says leverage should raise equity risk. The horizon table answers it: much of that low-beta evidence reflects how often the returns are sampled.
The rolling analysis backs this up. Ten-year windows give betas between about 1.5 and 1.9. None of those windows produces positive alpha. The intercepts are consistently negative and generally statistically indistinguishable from zero. A three-factor model that adds size and value produces a beta near 1.5 and alpha of -3.0%, also statistically insignificant.
The Flow That Arrives in a Drawdown
If buyout portfolios carry around 1.5x equity beta while reporting something milder, the gap between economic and reported exposure is not neutral. It gets settled at specific moments, and those moments are equity drawdowns.
The sequence runs like this. Public equities fall. Buyout NAVs, based on lagged sponsor marks, fall less in the first quarters. The private equity weight in the portfolio jumps above target because the denominator has shrunk while the private numerator has not yet been marked down. This is the familiar denominator effect, and the paper makes clear what it really is: the allocator is not overweight in economic terms. It is holding the same levered exposure at a stale price. The real loss has already happened. It has not yet been booked.
The allocator can respond in three ways, and each one has a market footprint. It can sell fund interests in the secondary market, accepting a discount that reflects the gap between NAV and market value the 2009 figures captured. It can slow or stop new commitments, which reduces fundraising for the next vintage. Or it can raise liquidity for capital calls by selling public equities and bonds into a falling market. That last route is the pro-cyclical one: it pushes real selling into public markets precisely because the private book's smoothing delays the recognition of losses.
The direction of these flows is an inference. The paper does not measure allocator behavior, and the size of any forced selling depends on governance rules, rebalancing bands, and liquidity reserves that vary widely by institution. The structural point is not an inference. A portfolio that measures 1.5x beta as something near 1.0 has under-reserved for drawdowns, and under-reservation shows up as either a distressed sale or a missed rebalance.
The Countercase: Is the Exchange Price Overstating Risk?
The strongest objection to the paper is that listed vehicles carry their own price noise. A closed-end fund holding buyout interests trades at a discount or premium to NAV, and that discount moves with liquidity, retail sentiment, and investor forced selling. Some of the 29% volatility could belong to the wrapper rather than the underlying companies. A long-horizon pension that never sells would not experience that noise. On this view, NAV is the more accurate measure of economic value and the exchange price is the distorted one.
This objection has real force, and it is why the 0.40 daily beta should be read cautiously in both directions. Two pieces of evidence weaken it. First, the gap between NAV and market risk does not shrink at longer horizons, which is what would happen if it were short-term noise. Beta rises toward 1.55 and R² toward 96% at the biennial interval. Wrapper sentiment does not usually produce a tighter fit with global equities as the horizon extends. Second, the paper notes that studies using secondary market transactions, which involve no listed wrapper, estimate buyout volatility of roughly 30% to 40%, betas between 1.8 and 2.1, and no statistically significant alpha. Two different market-pricing methods arrive at a beta well above 1.0 and no detectable skill premium. That convergence is hard to attribute to closed-end fund discount behavior.
There is also a fair limit on the alpha finding itself. An estimate of -4.2% that is not statistically significant does not show that buyouts destroy value. The paper's own conclusion is narrower: once risk is priced at market, there is no meaningful evidence of positive alpha. The sample covers European listed vehicles, which may differ from the top-quartile funds a well-connected endowment can access. Manager selection could matter. The data simply cannot show that it does after adjusting for beta.
What Reprices If the Benchmark Changes
The implication for allocators is a harder hurdle. If buyout equity has a beta near 1.5, beating the MSCI ACWI is not evidence of skill. It is roughly the return that leverage alone should deliver in a rising market. The appropriate comparison is a levered public equity benchmark, before considering illiquidity, fees, and complexity. On the paper's evidence, the asset class has not cleared that bar.
The rule-based risk budgets that drew institutions into the asset are the channel through which positioning changes. If risk systems begin using market-based or long-horizon return estimates, the reported volatility of the private book rises from around 19% toward 29%. Correlation rises toward 0.94, and the optimizer's case for further allocation weakens. That revision does not require a crisis. It requires one investment committee to replace the NAV series with a de-smoothed or market-priced one. It produces fewer new commitments, slower fundraising, and more supply in the secondary market even without forced selling.
The test comes in the next sustained equity drawdown. Two observations will show whether the paper's beta is the right one for positioning. First, track how far listed private equity vehicles fall relative to global equities during the decline. A ratio near 1.5x, larger than any concurrent NAV markdown, would replicate the 2009 pattern. Second, check whether sponsor NAVs move toward those market prices over the following two to four quarters, as the biennial beta of 1.55 predicts. If NAVs catch up, the smoothing was a delay, not protection, and the allocators who relied on it will have been carrying levered equity at stale prices. If NAVs hold steady while secondary bids and listed prices recover on their own, the wrapper-noise objection gains ground and the reported 19% looks closer to the truth.
