Private Equity Indices Based on Secondary Market Transactions

Michael S. Weisbach is Ralph W. Kurtz Chair in Finance at The Ohio State University Fisher College of Business, and Research Associate at the National Bureau of Economic Research. This post is based on a recent paper by Professor Weisbash; Brian Boyer, Associate Professor at the Brigham Young University Marriott School of Management; Taylor Nadauld, Associate Professor at the Brigham Young University Marriott School of Management; and Keith VorkinkAssociate Dean and Driggs Professor of Finance at the Brigham Young University Marriott School of Management.

In recent decades, private equity has become an important asset class for institutional investors. A 2017 survey of institutional investors finds that 88% are invested in private equity, with nearly a third having an allocation greater than 10%. A typical private equity investment begins with capital commitments at the fund’s creation and ends with the final distribution, which is often 12 to 15 years after the initial capital commitment. The return on the fund is determined by the returns on the individual portfolio companies in which the fund invests, and is therefore only fully observable following the fund’s final distribution. The underlying value of these portfolio companies fluctuates with firm-specific and economy-wide news in a manner similar to that of public equities, but these fluctuations are usually not fully reflected in the valuations that funds report to their investors. Moreover, since returns are measured at such irregular, infrequent intervals, it can be quite challenging to estimate standard performance parameters such as factor alphas and betas.

While active markets for trading investments in private equity funds did not exist prior to 2000, in the early 2000s, a secondary market developed on which limited partners (LPs) could transact their stakes in private equity funds. In this paper, we use data obtained from a large intermediary in this market to evaluate the risk and return of private equity funds in a similar manner to the way in which investors regularly use public equity markets to understand the risk and return of publicly traded companies. Using these data we construct transaction-based indices for both buyout and venture capital funds, and use these indices to address a number of questions about the private equity market. These indices provide new insights into the performance of private equity as an asset class. In contrast to the existing literature, we find that neither buyout nor venture funds outperform public markets on a risk-adjusted basis. We also find that NAV-based indices, such as the Burgiss index, tend to significantly understate the volatility of private equity as well as its covariance with other asset classes.

The primary difficulty in constructing an index from secondary market data for private equity is accounting for the fact that not every fund trades in every period, and many funds in our sample do not trade at all. In the subsample of funds that could be matched with cash flow data from the Preqin database, there are 1,119 fund transactions for 630 funds (372 buyout and 258 venture) from 2006 through 2017, implying that the average fund in our data trades 1.8 times in our sample. Moreover, the funds that do trade are not random draws, and it is possible that sample selection could affect our estimates. We employ two approaches to construct our indices in light of these challenges.

First, we show that if funds transact at random, we can construct an index that tracks the price of a broad portfolio of funds, even if funds within the portfolio do not transact in our sample every period. Second, we account for the possibility that fund transactions are not random, and that the decision to transact in the secondary market could be related to fund market values or other characteristics. To account for such possible sample selection, we create a hedonic index using the approach of Heckman (1979). Using a broad universe of funds, we estimate the parameters of an econometric model using observed transaction prices and each period create an inferred price for every fund, including those that do not transact. We then use these inferred prices to construct our index. We account for measurement error when estimating performance parameters by applying bias adjustments.

A striking observation about the transactions-based indices is that they are much more cyclical and exposed to market-wide risk than other indices based on reported NAVs. We estimate the beta of the transactions-based buyout index to be greater than two. As emphasized by Axelson, Sorensen and Strömberg (2014), the return on a buyout fund is essentially the return on a portfolio of highly levered firms. Even if the portfolio firms prior to the buyout have unlevered betas slightly less than 1.0, tripling their leverage, as is typically done in a buyout, should lead to a portfolio beta larger than two. For comparison, we estimate the beta of a buyout index that we construct based on the NAVs reported by Preqin using the same set of firms that make up the transaction-based indices, and the beta of a buyout index produced by Burgiss. In contrast to the estimates for the transactions-based indices, the estimated betas for both of these more traditional indices are less than 0.5. These indices based on NAVs have much lower betas than the transactions-based indices because NAVs are smoothed over time and do not reflect the information contemporaneously incorporated into market prices.

An implication of the high estimated betas is that, even though the hedonic buyout index averages a 20% return over the sample period, its estimated CAPM alpha is not significantly different from zero. The relatively high average return, therefore, is just enough to compensate investors for their exposure to market-wide risk. This finding is in contrast to our positive estimates of alpha for the two NAV based indices, which mirror the results from the existing literature.

The betas of transaction-based venture capital indices are also higher than that of the corresponding NAV-based indices. We estimate betas for the transactions-based indices to be about 1.0, and for the NAV-based indices to be about 0.3. Since venture funds usually do not lever up their positions in portfolio companies, there is not a Modigliani-Miller ratcheting of betas as in buyouts, which is why venture capital betas tend to be lower than buyout betas. Nonetheless, the estimated betas of the transactions-based indices are large enough to affect inferences about performance. While NAV-based indices of venture funds have estimated alphas that are positive and statistically significant, the transactions-based indices have estimated alphas that are actually negative, although not statistically significantly different from zero.

Besides altering perceptions of private equity performance, the lack of information from secondary markets can also distort investors’ portfolio decisions. For example, following the Financial Crisis of 2008 a number of investors believed that their portfolio weight in private equity had substantially increased, since NAVs of their private equity positions dropped much less in value than the market values of their stock holdings. Our analysis suggests that at the time of the Crisis, private equity funds’ values had fallen by at least as much as public equities. Therefore, properly measured, the fraction of institutional portfolios made up by private equity did not increase during the Financial Crisis, as was commonly believed.

Finally, our indices also allow us to value individual funds at any given point in time and to estimate the extent to which a NAV for a given fund differs from its value in any particular year. These market-to-book estimates could potentially be used by investors to value stakes of private equity funds in their portfolios. They suggest that the values of private equity stakes sometimes differ substantially from their NAVs. For example, in 2017 NAVs were 44% lower on average than our estimate of market values for some fund vintages. Therefore, investors using NAVs to assess their portfolios are likely to understate the value of their private equity holdings considerably. These understatements could materially affect investors’ portfolio decisions as well as their spending decisions, especially if the organizations set spending levels at a fixed fraction of portfolio values.

The complete paper is available here.

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