A Deep Dive into Private Fund Performance
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A new Dimensional paper studies the performance of 6,000 private funds from 1980 to 2022, covering buyout, venture, credit, and real estate.
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We study the dispersion in absolute performance, the sensitivity of relative performance to the choice of benchmark, and correlations with public markets.
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Private funds may have a place in public investors’ asset allocations, though there are important caveats to consider.
As private investing has grown in popularity, so has the amount of content about it by academics, asset managers, and journalists. Why, then, did Dimensional choose to write another paper about private investing? We believe there’s room for a comprehensive, data-driven, dispassionate deep dive into the topic with a focus on the questions most relevant to investors.
In our paper, we shed new light on the performance of private funds using data on more than 6,000 funds from 1980 to 2022, covering the four major asset classes: buyout (BO), venture capital (VC), private credit, and private real estate. This data set is sourced directly from outside investors (i.e., limited partners) and considered more comprehensive and of higher quality than other data sets.1 We study absolute performance, performance relative to public benchmarks, and correlations between public and private asset classes. Our results can help investors understand the historical range of outcomes, contextualize relative performance, and quantify the potential diversification benefits of combining public and private investments in an asset allocation.
What Range of Outcomes Have Investors Seen from Private Funds?
A common measure of a private fund’s lifetime absolute performance is its total value to paid-in capital (TVPI), or distributions plus any remaining net asset value (NAV) relative to contributions. TVPI is a “money multiple,” with multiples above one meaning distributions plus NAV exceed contributions. It ignores the time value of money and is not straightforward to annualize but is simple to calculate and avoids the many issues associated with the other popular measure of absolute lifetime performance: the internal rate of return (IRR).2
Exhibit 1 shows the range of outcomes for TVPI, net of fees, by asset class. While the average fund had a TVPI above one in all asset classes, there’s wide dispersion around that average in every asset class. The 95th percentile is between 2.08 (for private credit) and 4.24 (for VC), while the 5th percentile is between 0.81 (for private credit) and 0.36 (for VCs). That is, the worst-performing funds delivered a loss on the contributed capital. We find similar dispersion in IRRs.
Dispersion in Lifetime Total Value to Paid-In Capital (TVPI) by Asset Class
This dispersion exacerbates the manager selection problem. In public markets, investors can make their allocations less manager dependent by “holding the market.” This is much less feasible in the private space, especially for smaller allocations, forcing investors to choose among private fund managers, which can be costly. Since there is limited evidence in the academic literature of performance persistence across managers, due diligence in the manager selection process is key for any investor considering a private allocation.3
How Have Private Funds Performed Relative to Comparable Public Investments?
With regard to the opportunity costs of private funds, a major milestone in the academic literature is Kaplan and Schoar’s (2005) public market equivalent (KS-PME), which is a TVPI adjusted for the performance of a chosen benchmark (typically, a public market index). A KS-PME above one indicates the fund outperformed the benchmark when matched on the timing and magnitude of cash flows.
Average Kaplan-Schoar Public Market Equivalent (KS-PME) by Asset Class and Benchmark
Exhibit 2 shows, for the average fund by asset class, KS-PME relative to several public benchmarks. Not surprisingly, the average money multiples suggested by TVPI (Exhibit 1) drop substantially when adjusted for the performance of public benchmarks. More interestingly, relative performance appears highly dependent on the choice of benchmark. BOs and VCs have on average outperformed the S&P 500 Index (KS-PMEs of 1.19 and 1.15) but have performed in line with small cap value stocks (1.02 and 1.04) and slightly underperformed small cap growth stocks (0.96 and 0.97). Private credit has on average outperformed investment-grade corporate bonds but slightly underperformed high-yield ones (KS-PMEs of 1.09 and 0.97). Lastly, private real estate has on average slightly underperformed the Fama/French Real Estate Industry Research Portfolio but substantially underperformed real estate investment trusts (KS-PMEs of 0.97 and 0.81).
The sensitivity of relative performance to the choice of benchmark means any single comparator will likely give an incomplete assessment of opportunity cost. Instead, our results suggest investors should compare private fund performance to multiple benchmarks, including style benchmarks that deviate from broad market exposure, to get a more complete view.
What are the Diversification Benefits of Private Funds?
Because unlisted assets expand the opportunity set for public investors, private funds may offer diversification benefits provided their returns are not perfectly correlated with listed assets’ returns. Gauging any such correlation is notoriously difficult because a private fund’s interim performance (i.e., before all deals are exited) must be estimated from periodic cash flows and changes in the fund’s NAV. Due to the lack of market prices for ongoing investments, periodic fund returns often exhibit delayed reactions (lagging) and lower volatility (smoothing) compared to listed assets’ returns. This can severely understate the correlation between private and public return.
Adjusted R2 Along with 95% Confidence Interval by Asset Class and Time Period
In the paper, we adopt a regression approach that allows us to estimate correlations in a parsimonious manner while correcting for lagging and smoothing. Specifically, we regress aggregate quarterly private fund returns on the quarterly returns to factors that proxy for the comparable public asset class or differences in average returns within said public asset class. As an example, for BOs and VCs, we use the five factors proposed by Fama and French (2015), i.e., the market, size, value, profitability, and investment factors. We “unsmooth” aggregate private fund returns by undoing their serial correlation, and we correct for lagging by including lagged factor returns as explanatory variables.4
Exhibit 3 shows the fraction of variation in aggregate private fund returns explained by the public factors, i.e., the R2, along with 95% confidence intervals.5 Higher R2 values translate to higher public-private correlations. We show two sets of results for each asset class: one for the period through 2007Q4 (the “early” period), and one for the period since 2008Q1 (the “recent” period). The recent period coincides with the adoption of accounting standards on fair value measurement in the US, which include specific mark-to-market provisions.6
In the early period, point estimates for R2 are between 14.4% (for real estate) and 43.5% (for VC). The confidence intervals for R2 range from below zero (for BOs, credit, and real estate) to 48.2% (for VC). That is, we find no evidence of perfect correlation between the private and public asset classes in the early period. In the recent period, the point estimates for R2 are noticeably higher in every asset class: between 34.7% (for real estate) and 81.5% (for credit). The confidence intervals range from below zero (for real estate) to 88.3% (for credit), so, despite the higher point estimates, there is still meaningful return variation unexplained by public factors. In sum, while correlations have gone up substantially post-2007, private funds have continued to provide nonnegligible diversification.
Do Private Funds Have a Place in Your Asset Allocation?
Democratization of private markets has given a broader set of investors access to the asset class through various vehicles. Still, private investing is inherently associated with special fund structures, minimum capital commitments, illiquidity, and relatively high fees. Like other alternatives, private funds are not for everyone.
If you’re undeterred, our paper suggests private funds may have a place in your asset allocation, though with important caveats. First, the wide dispersion in lifetime performance suggests outcomes may be highly manager dependent, with clear upside potential but also substantial downside risk. Second, while private funds have on average performed in line with listed style investments, the latter are often available in broadly diversified, low-cost, liquid options. Lastly, our findings are generally supportive of diversification benefits. Nonetheless, the greater correlations we see in more recent decades suggest public investors should temper their expectations around such benefits, especially if we continue to see more transparent and comparable valuation techniques.
The paper is available here.
Footnotes
- 1. See, for example, Harris, Jenkinson, Kaplan, and Stucke (2023) and Korteweg and Nagel (2023).
- 2. The IRR is the breakeven discount rate on net cash flows (distributions minus contributions) and any remaining NAV. As we discuss in the paper, there are several caveats with interpreting IRR as a “return on capital,” chief among them that it assumes distributions are reinvested at a rate equal to the IRR, whether positive or negative. Moreover, IRR can be inflated by large, early distributions, possibly through the use of leverage, because its calculation entails that later cash flows are discounted more heavily.
- 3. For example, Harris, Jenkinson, Kaplan, and Stucke (2023) find that, conditional on the interim performance of an existing fund at the time a new fund is raised, there is “little evidence of persistence for buyouts, especially post-2000” but some “persistence for VC funds though it declines post-2000” (abstract).
- 4. We estimate unsmoothed returns as the residuals from an autoregressive model fitted to the “raw” returns by private asset class. To adjust for lagging, we calculate the exposure to a given factor as the sum of the regression coefficients on that factor’s contemporaneous and lagged returns. The details around how we choose the order of the autoregressive models and the number of lags in the regressions are in the paper.
- 5. The R2 values are adjusted for the regressions’ degrees of freedom to avoid them being mechanically inflated by a greater number of explanatory variables. The adjusted R2 can become negative when the explanatory power is sufficiently low, as can be seen from the confidence intervals in Exhibit 3.
- 6. The US accounting standard on fair value measurement is FASB GAAP ASC 820, formerly SFAS 157, effective November 15, 2007. The corresponding international standards are the amended IAS 39, effective November 15, 2005 (now replaced by IFRS 9), and IFRS 13, effective January 1, 2013. The International Private Equity and Venture Capital Valuation (IPEV) guidelines were updated in November 2006 to be consistent with both IAS 39 and SFAS 157. The most recent update was in December 2022 (see www.privateequityvaluation.com).
Appendix: index Definitions
Dimensional US Large Cap Value Index
January 1975–present: Compiled by Dimensional from CRSP and Compustat data. The index composition consists of large cap companies in the eligible market whose relative price is in the bottom 30% of the large cap market after the exclusion of utilities, companies lacking financial data, and companies with negative relative price. The index emphasizes securities with higher profitability, lower relative price, and lower market capitalization. Profitability is defined as operating income before depreciation and amortization minus interest expense divided by book equity. The eligible market is composed of securities of US companies traded on the NYSE, NYSE MKT (formerly AMEX), and Nasdaq Global Market. Exclusions: non-US companies, REITs, UITs, and investment companies. The index has been retrospectively calculated by Dimensional and did not exist prior to March 2007. Accordingly, the results shown during the periods prior to March 2007 do not represent actual returns of the index. Other periods selected may have different results, including losses. The calculation methodology for the index was amended in January 2014 to include profitability as a factor in selecting securities for inclusion in the index. Prior to January 1975: Compiled by Dimensional from CRSP and Compustat data. The index composition consists of large cap companies in the eligible market whose relative price is in the bottom 25% of the US Large Cap Index after the exclusion of utilities, companies lacking financial data, and companies with negative relative price. The eligible market is composed of securities of US companies traded on the NYSE, NYSE MKT (formerly AMEX), and Nasdaq Global Market. Exclusions: non-US companies, REITs, UITs, and investment companies.
Dimensional US Large Cap High Profitability Index
Compiled by Dimensional from CRSP and Compustat data. Consists of companies with market capitalizations above the 1,000th largest company whose profitability is in the top 35% of all large cap companies after the exclusion of utilities, companies lacking financial data, and companies with negative relative price. The index emphasizes companies with lower relative price, higher profitability, and lower market capitalization. Profitability is defined as operating income before depreciation and amortization minus interest expense divided by book equity. The eligible market is composed of securities of US companies traded on the NYSE, NYSE MKT (formerly AMEX), and Nasdaq Global Market. Exclusions: non-US companies, REITs, UITs, and investment companies. The index has been retroactively calculated by Dimensional and did not exist prior to December 2016. Accordingly, the results shown during the periods prior to December 2016 do not represent actual returns of the index. Other periods selected may have different results, including losses.
Dimensional US Small Cap Value Index
January 1975–present: Compiled by Dimensional from CRSP and Compustat data. The index composition is a subset of the US Small Cap Index. The subset is defined as companies whose relative price is in the bottom 35% of the US Small Cap Index after the exclusion of utilities, companies lacking financial data, and companies with negative relative price. The eligible market is composed of securities of US companies traded on the NYSE, NYSE MKT (formerly AMEX), and Nasdaq Global Market. Exclusions: non-US companies, REITs, UITs, investment companies, and companies with the lowest profitability within the small cap value universe. The index also excludes those companies with the highest asset growth within the small cap universe. Profitability is defined as operating income before depreciation and amortization minus interest expense divided by book equity. Asset growth is defined as change in total assets from the prior fiscal year to current fiscal year. The index has been retrospectively calculated by Dimensional and did not exist prior to March 2007. Accordingly, the results shown during the periods prior to March 2007 do not represent actual returns of the index. Other periods selected may have different results, including losses. The calculation methodology for the index was amended in January 2014 to include profitability as a factor in selecting securities for inclusion in the index. The calculation methodology for the index was amended in December 2019 to include asset growth as a factor in selecting securities for inclusion in the index. Prior to January 1975: Compiled by Dimensional from CRSP and Compustat data. The index composition is a subset of the US Small Cap Index. The subset is defined as companies whose relative price is in the bottom 25% of the US Small Cap Index after the exclusion of utilities, companies lacking financial data, and companies with negative relative price. The eligible market is composed of securities of US companies traded on the NYSE, NYSE MKT (formerly AMEX), and Nasdaq Global Market. Exclusions: non-US companies, REITs, UITs, and investment companies.
Dimensional US Small Cap Growth Index
Compiled by Dimensional from CRSP and Compustat data. Consists of companies with market capitalizations in the lowest 8% of the total market capitalization of the eligible market whose relative price is in the top 50% of all small cap companies after the exclusion of utilities, companies lacking financial data, and companies with negative relative price. The index excludes companies with the lowest profitability within the small cap growth universe. The index also excludes those companies with the highest asset growth within the small cap universe. Profitability is defined as operating income before depreciation and amortization minus interest expense divided by book equity. Asset growth is defined as change in total assets from the prior fiscal year to current fiscal year. The eligible market is composed of securities of US companies traded on the NYSE, NYSE MKT (formerly AMEX), and Nasdaq Global Market. Exclusions: non-US companies, REITs, UITs, and investment companies. The index has been retrospectively calculated by Dimensional and did not exist prior to December 2012. Accordingly, the results shown during the periods prior to December 2012 do not represent actual returns of the index. Other periods selected may have different results, including losses. The calculation methodology for the index was amended in December 2019 to include asset growth as a factor in selecting securities for inclusion in the index.
Bloomberg US Credit Index
The Bloomberg US Credit Index measures the investment grade, US dollar-denominated, fixed-rate, taxable corporate and government-related bond markets. It is composed of the US Corporate Index and a noncorporate component that includes non–US agencies, sovereigns, supranationals, and local authorities. The US Credit Index was called the US Corporate Index until July 2000, when it was renamed to reflect its inclusion of both corporate and noncorporate issuers. The US Credit Index is a subset of the US Government/Credit Index and US Aggregate Index. Index history is available back to 1973. Bloomberg data provided by Bloomberg Finance L.P.
Bloomberg US Corporate High Yield Indices
The Bloomberg US Corporate High Yield Bond Index measures the USD-denominated, high-yield, fixed-rate corporate bond market. Securities are classified as high yield if the middle rating of Moody’s, Fitch, and S&P is Ba1/BB+/BB+ or below. Bonds from issuers with an emerging markets country of risk, based on the indices’ EM country definition, are excluded. The US Corporate High Yield Index is a component of the US Universal and Global High Yield Indices. The index was created in 1998, with history backfilled to July 1, 1983. The “Intermediate” subindex corresponds to the subset of constituents with maturities of 1–10 years. The “Long” subindex corresponds to the subset of constituents with maturities of 10–30 years. Bloomberg data provided by Bloomberg Finance L.P.
Dow Jones US Select REIT Index
Total Return in USD. January 1987–present: Dow Jones US Select REIT Index; source: Dow Jones Indexes. January 1978–January 1987: Dow Jones Wilshire REIT Index; source: Dow Jones Wilshire. Composition: US publicly traded real estate investment trusts weighted by float-adjusted market capitalization. © 2024 S&P Dow Jones Indices LLC, a division of S&P Global. All rights reserved.
Fama and French (2015) Factors
The Market, Size, Value, Profitability, and Investment research factors based on 2x3 sorts for the US. Constructed using the six value-weight portfolios formed on size and book-to-market, the six value-weight portfolios formed on size and operating profitability, and the six value-weight portfolios formed on size and investment. Available from Ken French’s website: https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.
Fama/French US Real Estate Industry Research Portfolio
Based on the Fama/French 49 US Industry Research Portfolios. Each NYSE, AMEX, and NASDAQ stock is assigned to an industry research portfolio at the end of June of year t based on its four-digit SIC code at that time. When possible, this is based on Compustat SIC codes for the fiscal year ending in calendar year t-1. Whenever Compustat SIC codes are not available, it is instead based on CRSP SIC codes for June of year t. Monthly value-weighted returns are computed from July of t to June of t+1. The Real Estate industry corresponds to SIC codes 6500, 6510, 6512-6515, 6517-6519, 6520-6532, 6540-6541, 6550-6553, 6590-6599, 6610-6611. Available from Ken French’s website: https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.
References
Fama, E. F., and K. R. French. 2015. “A Five-Factor Asset Pricing Model.” Journal of Financial Economics 116, no. 1: 1–22.
Gompers, P. A., and S. N. Kaplan. 2022. Advanced Introduction to Private Equity. Cheltenham, UK; Northampton, MA: Edward Elgar Publishing.
Harris, R. S., T. Jenkinson, S. N. Kaplan, and R. Stucke. 2023. “Has Persistence Persisted in Private Equity? Evidence from Buyout and Venture Capital Funds.” Journal of Corporate Finance 81, article 102361: 1–16.
Kaplan, S. N., and A. Schoar. 2005. “Private Equity Performance: Returns, Persistence, and Capital Flows.” Journal of Finance 60, no. 4: 1791–1823.
Kaplan, S. N., and B. A. Sensoy. 2015. “Private Equity Performance: A Survey.” Annual Review of Financial Economics 7, no. 1: 597–614.
Korteweg, A. “Risk and Return in Private Equity.” 2023. Chap. 8 in Handbook of the Economics of Corporate Finance: Private Equity and Entrepreneurial Finance. North Holland: Elsevier.
Korteweg, A., and M. M. Westerfield. 2022. “Asset Allocation with Private Equity.” Foundations and Trends in Finance 13, no. 2: 95–204.
Korteweg, A., and S. Nagel. 2023. “Risk-Adjusting the Returns of Private Equity Funds: A New Approach” (working paper, available at SSRN: 4157952).
Phalippou, L. 2021. Private Equity Laid Bare. Independently published.
Sørensen, M., and R. Jagannathan. 2015. “The Public Market Equivalent and Private Equity Performance.” Financial Analysts Journal 71, no. 4: 43–50.
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