A White Paper on Private Equity Data and Research

Transcription

1 A White Paper on Private Equity Data and Research Robert Harris University of Virginia Tim Jenkinson University of Oxford Rüdiger Stucke University of Oxford UAI FOUNDATION CONSORTIUM December 2010 Corresponding author: Robert S. Harris, The UAI Foundation Consortium comprises (in alphabetical order): Gregory Brown, Jennifer Conrad, Robert Harris, Tim Jenkinson, Steve Kaplan, and Rüdiger Stucke. We thank Oleg Credil and Jeremiah Green for excellent research assistance.

2 A White Paper on Private Equity Data and Research Executive Summary This paper contrasts private equity data from three leading providers and draws implications for research and practice. If each data provider s sample were a random draw from the same underlying universe, we d expect similar messages to emerge across all data sources. This is not the case. Different providers have different mixes of funds (e.g. venture capital versus buyout), especially in periods studied by prior research. Turning to performance data, the picture is even more troubling. Performance data cover only a small fraction of funds started. Moreover, return data from the three sources often signal different performance to limited partner investors. Some of these differences are not readily explained by random variation and suggest systematic effects related to data methods and sample selection. We also review findings of past research on private equity with particular attention to the data used. Our findings highlight the need for better private equity data to improve investment decisions and enable research. We conclude with thoughts on steps toward such improved data. 2

3 A White Paper on Private Equity Data and Research A familiar theme in the private equity world is the lack of good data for understanding the asset class. As an illustration of the immensity of the problem, consider benchmark returns to limited partners reported by two prominent suppliers of private equity data. As of the first quarter of 2008, Thomson Reuters reported that the 10-year IRR for venture capital funds was 17.20%. At the same time, Cambridge Associates reported a return of 32.83% for the same time period and investment class. 1 Lack of comprehensive and trusted data leaves the industry, including active and potential investors, underserved and thwarts our ability to learn more about private equity through research. This paper assesses currently available sources of private equity data and research on private equity, focusing on several key questions. What is a reasonable proxy for the private equity universe? What do we know about private equity performance from historical data? Are existing data sources immune from sample selection biases? What explains the divergent results produced by major providers of data? What would characterize good data for private equity research? As an initial step, we look across key sources of widely used fund level data to understand differences in samples and biases that might creep into a particular sample. We also summarize existing research on private equity returns, highlighting possible links between the findings and the data used in the study. We focus on net returns to private equity investors. A host of additional questions about the asset class also await better data. A partial listing includes gross returns (prior to fees), the structure of partnership arrangements and fees, and the effects on capital formation. 1 See page 112 of Kocis et al., Inside Private Equity, Wiley,

4 1. The Unsettled State of Conclusions on Private Equity Performance There is no doubt that some investors have made spectacular returns in private equity. Individual endowments such as Yale s are often singled out, and Lerner et al. (2007) find endowments earned 44 percent annually on investments in private equity funds raised between 1991 and Moreover, industry sources regularly report average private equity returns suggesting good historic performance. For the 20 year period through December 2009, Cambridge Associates reports that investors in U.S. venture capital averaged an annual return of 23.5% (after all management fees), compared to 8.2% for the S&P 500 and 8.8% for the NASDAQ. For the same period, Cambridge reports a 12.1% annual return to U.S. private equity funds (exclusive of venture capital). Understandably, some observers are wary that future performance may not match the past, especially given the scale of inflows to funds. More germane to the present paper, closer examination of history spurs questions about what private equity returns have actually been. For instance, one can draw quite different conclusions from different widely-used data sources. As one illustration, we compared results reported by three leading suppliers of private equity returns. For funds classified as U.S. buyout with a vintage year of 1995, the pooled IRRs are as follows: Cambridge Associates (13.7%), Thomson Reuters (7.3%) and Preqin (16.6%). And these figures are as of yearend 2009 when the 1995 vintage funds are essentially (if not completely) liquidated. Unfortunately, such differences are all too common across sources of private equity data. And these differences raise severe questions about the reliability of conclusions drawn from research and benchmarking using available data. Given this state of affairs, it is no surprise that experts views differ. In a survey of academic research, Phalippou (2008) concludes that the average investor has obtained poor returns from investments in private equity funds (p. 1) compared to those available in public markets. In contrast, Kaplan and Lerner (2010) conclude venture capital returns net of fees have been competitive with the return from public markets but there is a great deal of variation over time in whether VC returns outperform or underperform public markets (p. 40). And, as cited earlier, Cambridge Associates report both VC and other private equity returns outstripping public market equity indices for the last two decades. The contrast between the spectacular returns earned by some private equity investors and doubts about the level of average returns is linked to the wide gap between top and bottom 4

5 quartile performance which seems (at least in the past) to persist for fund families (Kaplan and Schoar (2005)). Some conclude that private equity investing is about picking good managers not selecting the right asset class. David Swensen, Yale s Chief Investment Officer, writes: No sensible investor manages private assets passively. Even if participation in a broadly diversified market alternative were available, investors would face nearly certain disappointment [ ] Investors justify the inclusion of private equity in portfolios only by selecting top-quality managers pursuing value-added strategies with appropriate deal structures (2000, p. 239). We view the unsettled state of conclusions about private equity performance as directly linked to the inadequacy of data available for analysis and research. This paper s objective is to highlight key features and weaknesses of existing data sources as a step towards creating improved measures. 2. Data Sources A primary difficulty in arriving at any set of conclusions about private equity returns is obtaining a sample representative of the universe of private equity investments. We use three prominent private equity data sources for returns to investing in private equity funds. The first two, Thomson Reuters and Preqin, supply aggregate figures for the industry as well as performance data on a fraction of funds. For a fee, one can subscribe and use their data and drill down to the fund level. In contrast, Cambridge Associates, our third source, is primarily an advisory firm with a large number of institutional clients who invest in private equity. While Cambridge supplies public reports and partners with the NVCA, customized fund level data are available only to its advisory clients. Cambridge reports aggregate data for the sector only across the funds it also follows for performance. All three sources claim industry leadership as shown in Table 1. Each company has a different approach to create its sample. As noted by Phalippou and Gottschalg (2009), Thomson obtains data mostly from fund investors as most fund managers refrain from giving out information (p. 13). Thomson s performance data (TVE) have been the most widely used in academic research (see Appendix 2) and we access it using Thomson One Banker. Preqin (originally Private Equity Intelligence ) obtains its data from various sources including public filings and reports, general partners (GPs) and by requesting information from 5

6 public institutional investors. 2 Access to its data is available to us by subscription. Preqin data have been used only rarely in academic research as it is a more recent entrant to the market and consistent time series data on cash flows is only available for a small number of funds prior to Cambridge gathers data from the funds and indicates that 60% to 70% of their sample funds (higher by dollar value) are in client portfolios; the remaining funds supply financial statements voluntarily and Cambridge actively encourages funds to join and backfills its database with their returns. While we cannot examine Cambridge as closely the other sources, we include it due to its prominence in the industry and appreciate Cambridge s assistance in supplying data. 3 One issue pointed out early on is the general difficulty of collecting data directly from GPs when creating a performance benchmark representative for investor returns. GPs are not nessecarily incentivized to report net-to-lp performance figures. Furthermore, the occurrence of a backfill bias (as detailed later) is not unlikely. Table 1: Descriptions of Data Sources (from company websites) Thomson Reuters: The most extensive global coverage of the venture and buyout market providing all the critical information you need to analyze everything from investee companies and industry sectors to public market comparables, potential partners and fund performance. Cambridge Associates: We compile the performance results for more than three-fourths of institutional-quality venture capital assets and nearly two-thirds of leveraged buyouts, subordinated debt, and special situations partnerships to publish Cambridge Associates U.S. Venture Capital Index and the Cambridge Associates Private Equity Index. Widely considered the industry standard, these indices report preliminary returns in Barron's Market Laboratory section and quarterly returns approximately weeks following the close of each quarter. We developed the Private Equity Benchmark Calculator to enable investors to calculate internal rates of return (IRRs) and end-to-end returns for a customized universe of private equity and venture capital assets. Preqin: The industry s leading source of intelligence on private equity fundraising. This constantly updated resource includes details for all funds of all types being raised worldwide, with key information on target sizes, interim closes, placement agents, lawyers, investors, plus much more all included. The industry s most extensive source of net to LP private equity fund performance, with full metrics for over 5,100 named vehicles. In terms of capital raised, Performance Analyst contains data for over 70% of all funds raised historically. 2 According to direct information from Preqin, 60% of their performance data is directly provided by GPs. 3 We thank Cambridge for their assistance in providing summary data shown in the paper. VC and PE indices are available on the Cambridge website. In addition, Cambridge kindly shared some reports available to clients. 6

7 Each data source has its own scheme for categorizing funds, running to over 20 categories in the case of Preqin. To provide a basis of comparison, we collapse the Thomson and Preqin schemes to four broad categories: Venture Capital, Private Equity (mainly buyout), Second Tier (e.g. Fund of Funds and LP-Secondaries) and Other (e.g. Mezzanine, Energy, Real Estate). While it would be ideal to start at the fund level and examine overlaps across data sources and categories, that work is beyond this paper s scope. 3. Estimates of the Private Equity Universe What do the sources tell us about the size and scope of private equity? Since Thomson and Preqin provide broad aggregate statistics on a global basis, we focus on their reports of funds started and capital raised Number of funds Table 2 compares estimates of funds started globally. We start with 1980 since prior data are sparse or do not exist. Appendix 1 provides detail by vintage year. Both data providers report well over 10,000 funds with an upper estimate of over 15,000. The vast majority of funds started in the last decade; a full 79% of Preqin s funds launched since Though, Thomson has more funds in early years, the gap is largely eliminated in the last decade. Moreover, as detailed in Appendix 1, Preqin s sample is larger than Thomson s in the last five years. 7

8 Table 2: Number of Private Equity funds started globally from Thomson and Preqin Thomson VentureXpert Preqin Vintage years VC PE Other 2nd tier Total VC PE Other 2nd tier Total Panel A: Number of Funds , , ,229 1, ,957 1, , ,963 1, ,576 3,108 1,653 2,321 1,230 8,312 All years 9,495 3,517 1,159 1,106 15,277 4,394 2,310 2,728 1,416 10,848 Panel B: % of Sample % 20% 4% 2% 100% 67% 25% 6% 3% 100% % 24% 6% 5% 100% 48% 26% 17% 8% 100% % 23% 9% 10% 100% 37% 20% 28% 15% 100% All years 62% 23% 8% 7% 100% 41% 21% 25% 13% 100% Table 2 surfaces differences in sample composition. Over time, the growth of VC funds has not kept pace with funds started in other areas. In the 1980s two-thirds or more of all private equity funds were in venture capital but VC s share drops each decade. The sample composition also differs across the two data sources. A full 62% of the Thomson funds are VC versus only 41% for Preqin. This reflects two factors. First, Thomson has heavier weighting in early years when VC funds predominated. Second, Preqin captures many more funds classified as other, including a large number of real estate and natural resources funds. In the remainder of this paper we focus on the VC and PE and categories. We view these as at the core of discussions of private equity. VC funds represent the roots of the private equity industry and are of great interest in understanding how economies form new enterprises. The PE category is dominated by buyout funds that involve issues of effective governance, managing and financing of businesses at later stages. Figure 1 below charts the number of funds in these categories that Preqin and Thomson include in their capital raising figures. In most years, Thomson records more funds than Preqin, for both VC and PE. However, as we detail later, both providers report performance data for only a small proportion of funds started. 8

9 Figure 1: Number of Funds Started Globally 1,500 Venture capital 400 Private Equity 1, , TVE Preqin TVE Preqin 3.2. Capital raised Perhaps more relevant than funds started are the dollars raised. Table 3 displays, not surprisingly, that VC funds account for a smaller fraction of dollars raised than fund numbers would suggest. Using Thomson data, only 24% of capital committed 4 went to VC funds even though they represent over sixty percent of funds. Private equity funds (largely buyout) raised over twice as much as VC funds using either data set. Both data sets give similar dollar values for VC and PE capital committed over the last decade as the two data sets undoubtedly have significant overlap. A striking difference across the sets is Preqin s reporting of much larger figures for other (e.g. real estate) and 2nd tier funds. 4 Figures in the total column have not accounted for overlap in that second tier funds such as funds of funds themselves invest in other types of funds. 9

10 Table 3: Capital raised by Private Equity funds globally from Thomson and Preqin ($bn) Thomson VentureXpert Preqin Vintage years VC PE Other 2nd tier Total VC PE Other 2nd tier Total Panel A: Capital raised , , , , ,565.6 All years , , , , ,186.0 Panel B: % of Sample % 52% 8% 1% 100% 29% 59% 8% 4% 100% % 48% 12% 8% 100% 24% 48% 20% 8% 100% % 52% 17% 11% 100% 17% 38% 34% 12% 100% All years 23% 51% 16% 10% 100% 18% 39% 32% 11% 100% One major opportunity for improving research would be to understand the overlap and intersection among the data sets. With unique fund identifiers and full data access, one could create a better proxy for the private equity universe as a super set of independent sources. One could examine consistency in fund classifications (e.g. category and vintage year) and conduct detailed checks to understand potential biases. While the challenges and complexity are considerable, such an effort offers a path to a much better understanding of private equity Current estimates of capital invested and undrawn Another means to scale the volume of private equity is to measure the stock and flow of investment, which, given the buy-to-sell nature of the asset class, will be quite different from similar measures from public markets. Considering first the stock, we provide rough estimates of the value of positions in funds portfolio companies, as reported by the funds as of year-end Table 4 below illustrates our method using Preqin data. The stock of VC invested is estimated at $285 billion with $509 billion in PE. TVE data produces slightly higher estimates of $291 billion and $679 billion respectively (see Appendix 1). Independently, Cambridge estimates a year-end 2009 value of $716 billion 5 We estimate global values based on globally raised funds matched against U.S. ratios of uncalled and invested capital. 10

11 across the VC and PE categories and $1,006 billion across all funds. 6 These figures are clearly a tiny fraction of the value of public equity. For instance, the World Federation of Exchanges (WFE) estimates the market value of public equity at approximately $47 trillion at year-end Using the average across the three data bases, the current value of VC and PE investments is only around 1.8% of the size of public equity markets. We caution that these estimates reflect only the private equity investments in funds covered by the data bases. Table 4: Current Estimates of Uncalled and Invested Capital, using Preqin data Panel A: Venture Capital Vintage year # of VC funds Committed capital # of funds Paid-in to CC Uncalled RVPI / invested to all VC in $ US VC US VC VC in % VC in $ VC in % VC in $ , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,045 Total 3, , , ,955 Panel B: Private Equity (PE) Vintage year # of PE funds Committed capital # of funds Paid-in to CC Uncalled RVPI / invested to all PE in $ US PE US PE PE in % PE in $ PE in % PE in $ , , , , , , , , , , , , , , , , , , , , , , , , , , , , Total 1,880 1,481, , ,442 6 These figures are from Cambridge Associates LLC, Non-Marketable Alternative Asset Benchmarks as of December 31, 2009 supplied to the authors. 11

12 In terms of investment flows, the picture is different. For instance, WFE reports that over the five-year period ending 2009, approximately $4 trillion was raised in global public equity markets through initial and seasoned equity offerings. For that same period, the capital committed to VC and buyouts was $1.4 trillion using Preqin figures. Finally, it is interesting to estimate the extent of committed but uncalled capital. We perform this calculation as of year-end 2009, and show (again in Table 4) that Preqin data produce an estimate of $479 billion to be spent on PE and $184 billion on VC so $663 billion in total. The corresponding figures using TVE are $531 billion and $142 billion respectively, therefore $673 billion in total. It is striking how similar the invested and uncalled stocks of capital are, especially for buyouts. If we believe the Preqin figures, as of year-end 2009 PE funds needed to invest an amount over the next few years roughly equivalent to the totality of their current holdings. 4. Private Equity Performance 4.1. Challenges of measurement and common metrics Measuring private equity performance is not straightforward. Partnership claims are generally not traded 7 nor are the underlying assets in the fund. Whatever market pricing information exists for the fund s underlying assets is typically stale, as years may have elapsed since the purchase of a company or a funding round. 8 Moreover, even if one gauges an individual fund s returns adequately, aggregating across funds requires careful attention. Portfolio measures such as internal rates of return (often used in private equity reporting) can t be derived by simply averaging IRRs of the underlying funds. Additionally, private equity does not afford the luxury of many data points to develop risk measures frequently used for traded assets. Industry practice is to report internal rates of return to limited partners based on cash inflows and outflows of a private equity fund. Cash distributions come from the proceeds as the 7 There is a limited secondary market for limited partners to sell their interest in a private equity fund prior to its final liquidation. Traditionally, such transactions have involved substantial discounts to net asset value. 8 Kocis et al (2009) discuss a wide range of performance metrics. Over and above the challenges in measuring returns, the illiquidity of partnership claims complicates comparisons to other assets which are liquid (such as publicly traded stocks). To the extent that liquidity is valuable, investors demand higher returns on illiquid assets. For instance, Dimson and Hanke (2004) estimate that investors demand an annual premium of 100 basis points or more to hold illiquid equity index-linked bonds that have the same payoffs as the liquid equity index. 12

13 fund sheds portfolio companies and all cash flows are net of the fund s management fees and carried interest. After the fund is completely liquidated, the internal rate of return reflects cash flows actually realized. Prior to the end of the fund s life, however, practice is to report an internal rate of return based on the distributions to date plus whatever remaining value resides in the fund (net asset values). This remaining value is estimated by the general partner. As a result, returns reported prior to a fund s liquidation reflect general partner estimates and may be affected by stale price information, infrequent updating or any general partner biases. Remaining values are a large component for private equity funds during most of their lifetime. 9 Two other widelyused metrics are the ratios of remaining value to paid-in capital (RVPI) and total value (the sum of distributions and remaining value) to paid-in capital (TVPI) Performance samples and data To provide a measure of typical private equity performance requires that the sample used reflects the underlying universe. The literatures on mutual fund and hedge fund performance (e.g. Aggarwal and Jorion (2010)) show that practices often used to construct datasets can introduce large biases. For instance reliance on voluntary reporting may create both a survivorship bias and a backfill bias. A positive survivorship bias will result if poor performing funds cease to report and are then not included in return calculations. A positive backfill bias can result if firms only volunteer their information after experiencing good returns. Another type of bias may infect private equity samples if the data is gleaned from investors who do not invest in a representative sample of funds. While public markets generally offer securities to the highest bidder, general partners allocate funds to private equity investors on a number of attributes. 10 In addition, limited partners may have differential skills in selecting private equity funds. Some research using single existing data sets hints that such biases cloud conclusions about private equity. Phallipou and Gottschalg (2009)) suggest that Thomson contains funds that perform better than average (p. 1749) when compared to funds not captured by Thomson. Using 9 As an example, as of year-end 2004, the European Venture Capital Association estimates that, on average, the remaining value contributes over 40 percent of the value expected to be delivered to investors in European private equity funds. Source: EVCA and Thomson Financial June 16, 2005 press release obtained from the EVCA website. 10 There are exceptions in public markets such as the allocation of shares in Initial Public Offerings which are often significantly underpriced. Lerner and Schoar (2004) argue that a potential limited partner s ability to withstand a liquidity shock makes it more attractive to the general partner. Another possibility is that the limited partner contributes to the general partner s ability to attract other funds (e.g. reputation of a smart investor ) or to monitor and manage the companies in the fund. Also see Lerner, Schoar and Wong (2005). 13

14 Preqin data, Lerner, Schoar and Wongsunwai (2007) find dramatic differences in the performance of investments by different institutions: Endowments realize an annual return that is approximately 21% better than that of other institutions, while funds selected by banks perform particularly poorly (p. 760). To shed light on these issues we compare results across data sources. Figure 2: Performance Sample as a Proportion of the Total, Preqin and TVE Panel A: Venture Capital Funds 1,400 90% 1,400 90% 1,200 80% 1,200 80% 1,000 70% 60% 1,000 70% 60% % % % % % 20% 10% % 20% 10% 0 0% 0 0% # of funds in TVE universe Fraction of universe with performance data # of funds in Preqin universe Fraction of universe with performance data Panel B: PE Funds % % % 80% % 80% % 60% % 60% % % % 30% % 30% 50 20% 10% 50 20% 10% 0 0% 0 0% # of funds in TVE universe Fraction of universe with performance data # of funds in Preqin universe Fraction of universe with performance data 14

15 Figure 2 displays that Thomson and Preqin typically manage to source performance data for a small fraction funds they record as started. A host of factors contribute to this gap. GPs tend to be secretive about performance and are often unwilling to provide such information to anyone other than their limited partners (LPs). Moreover, GPs may restrict LPs from providing such information to third parties. Nonetheless, such information does partly surface either by GPs or LPs voluntarily providing information to data providers, or by public entities responding to Freedom of Information requirements Figure 2 covers funds on a global basis. Since geographic and currency issues introduce additional complexity in understanding fund performance, we focus on U.S. funds in the remainder of the paper. Moreover, we restrict our focus to VC and buyout funds. Much of the industry s history and hence past data is in the U.S. At this stage, we also introduce data from Cambridge Associates (CA). While Cambridge is a widely used data provider regarding performance, they do not publish capital raising data for funds not in their performance samples as do Thomson and Preqin. Figure 3 shows the number of U.S. funds with performance data reported by Preqin, TVE and CA. Until the vintages of the late 1990s, TVE generally have more funds in their performance samples than either Preqin or CA. However, TVE then switches to having by far the lowest coverage of both VC and buyouts, with CA having the best coverage of VC funds, and Preqin and CA vying for best coverage for buyouts. Although the coverage varies broadly in line with fundraising trends, as would be expected, in most years the performance samples for VC and buyouts each consist of between 30 and 50 funds. Obviously there are exceptional years, most noticeably the tech bubble years of 1999 and 2000 for VC funds. 15

16 Figure 3: U.S. Funds with Performance Data Panel A: Venture Capital Funds TVE Preqin CA Panel B: Buyout Funds TVE Preqin CA 4.3. Evidence on performance of U.S. Venture Capital funds We now turn our attention to the various performance metrics, and how these differ according to data provider. We start with VC returns, before performing a similar analysis on buyouts. There are clearly many ways to summarize returns; we first compare Median IRRs by vintage year for the three data providers. The median has the advantage of minimizing the impact of outliers which can significantly affect other measures of average performance. Since return 16

17 data is less meaningful for the first few years after a fund is raised, we limit our attention to vintage years before Figure 4: U.S. Venture Capital returns, Median IRRs (percent) TVE Preqin CA Figure 4 depicts the range for Median IRRs. If the three samples were all drawn from the same universe, we would expect the median values to trend together over time and to be approximately equal across the sources. While the series do trend together (simple pairwise correlations exceed 0.8) two features immediately stand out to indicate different signals about performance. First, the spread of the medians is substantial, especially in the mid-1990s. However, the deviations in the median returns are very small from 1999 onwards despite the significant differences in the sample sizes, in particular for the tech bubble period, noted earlier. Second, there are some strange patterns in the rankings of the three data providers. In particular, TVE consistently reports the lowest returns for every vintage from inclusive, and Preqin consistently reports the highest returns for these vintages. Since Phalippou and Gottschalg (2009) suggest that TVE contains funds that perform better than the average, one might conclude that this is even more the case for Preqin and CA. By 1996 the ranking by providers becomes more in line with the random pattern we might expect, and the medians converge dramatically. We return to this issue later, as we believe there may be factors that are biasing TVE returns. 17

18 Figure 5 presents a similar analysis for the top quartile IRRs for U.S. VC funds and illustrates some of the same patterns found for median returns. Preqin normally produce the highest value, and TVE normally the lowest, although this is spectacularly reversed in 1996 when TVE reports a top quartile IRR for U.S. VC funds of around 115%. The drop in top quartile VC returns after this vintage year is truly striking with the data providers all reporting returns in single figures for each year from 1999 onwards. The spreads in top quartile returns across data providers highlight the difficulties faced in deciding what truly constitutes a top quartile fund Figure 5: U.S. Venture Capital Returns, Top Quartile IRRs (percent) TVE Preqin CA So far we have only looked at median and top quartile returns. However, performance data for samples of funds can be averaged and presented in many different ways. The pros and cons of the various averaging techniques are not the focus of this paper, although interested readers are referred to Appendix 1 where we present returns on an un-weighted, weighted and pooled average basis. There are other ways of measuring performance, the most important of which for private equity is the money multiple, calculated as the ratio of distributions and remaining value to capital paid in. Figure 6 presents money multiple estimates (weighted by capital) from each data 18

19 source by vintage year. We focus on this weighted measure as it is the most widely reported, and is available from the CA reports to which we currently have access. 7.0 Figure 6: U.S. VC Returns, Weighted-Average Money Multiples TVE Preqin CA The estimates of money multiples echo many patterns already seen in IRRs. The deviations in the estimates may appear smaller than for the median IRRs, but scale of Figure 6 is somewhat distorted by the 1994 and 1995 vintages. Differences in median money multiples of 0.5 loom significant in economic terms Evidence on the performance of U.S. Buyout funds Until recent years data sources have performance information on far fewer buyout funds than for VC funds. This is to be expected given the longer heritage of VC, and the relatively recent rise to prominence of buyout funds. Figure 7 presents the range of Median IRRs reported by Preqin, TVE and CA. 19

20 Figure 7: U.S. Buyout Returns, Median IRRs (percent) TVE Preqin CA In some respects our findings mirror those found earlier for VC. In particular, TVE reports lower median returns than the other providers, not just for the vintages up until 1996, but for most years over the entire 20 year period. Preqin and CA split the honors in terms of reporting the highest returns. It is worth reiterating, however, that many of the sample sizes before the mid-1990s are extremely small. For the vintages with reasonably large sample sizes, such as and , the median returns are reasonably tightly clustered. One explanation for the lower median returns of TVE might be a different definition of what constitutes their U.S. buyout (and venture capital) sample. Whereas the samples of Preqin and CA contain U.S. buyout funds that actually invest in the U.S., TVE s definition refers to the location of the GP s headquarter and the fund s currency. As a result, the U.S. buyout sample of TVE also includes buyout funds of U.S. GPs that invest in Latin America and Asia, and are raised in US-dollar. As an analysis of Preqin s RoW (Rest of World) funds confirms, median Asia and Latin America funds have delivered rather poor performance compared to true U.S. buyout funds. We repeat the analysis for the top quartile of U.S. buyout fund returns in Figure 8 below. In most vintages TVE again reports the lowest returns, and the range of estimates is again closely related to the vintage sample sizes, as would be expected. To take a specific example, the 20 percentage point difference in the estimate of the top quartile IRRs for buyouts in 2001 is derived 20

21 from samples of 27 (TVE), 18 (Preqin) and 12 (CA) funds. Therefore, it is not hard to see how such significant variance in the estimates can be produced: the figures refer to the performance of the 3 rd best fund in the CA sample, the 5 th best in the Preqin sample and the 7 th best in the TVE sample. Figure 8: U.S. Buyout Returns, Top Quartile (percent) TVE Preqin CA Finally, in Figure 9 we present the range of estimates for the value-weighted Money Multiples for buyouts. The variance in the late 1980s and early 1990s is again driven by sample sizes CA and Preqin, in particular, have very limited samples until the mid-1990s, which calls into question whether vintage-year comparisons are reliable before that point for buyouts. 21

22 Figure 9: U.S. Buyout Returns, Weighted-Average Money Multiples TVE Preqin CA 4.5. Residual (Remaining) Values and Return Estimates We return at this point to an issue alluded to earlier. Internal rates of return are driven by both distributions and any remaining net asset value. We would expect these remaining values to gradually become less and less important and eventually reach zero for liquidated funds. Our analysis, however, surfaces a notable difference among data sources. We focus on RVPI (Remaining Value to Paid-In Capital) and TVPI (Total Value to Paid-In) for funds started in 1993 and before. We pick these vintage years since they would be expected to be largely, if not entirely, liquidated as of the date (December 2009) for which we sourced data. This is what we find for Preqin and CA. For instance, if we calculate the average of the VC RVPI figures for each vintage from we produce a value of The corresponding figure for TVE is The same pattern is found for buyouts: the average RVPI figures for the vintages are 0.01, whereas TVE reports The precise reason for these patterns and the impact on conclusions about private equity performance remain unsettled. Prior research on the underlying TVE cash flow dataset has paid careful attention to residual values. Phalippou and Gottschalg (2009) base most of their study on residual values within TVE and provide evidence that these mainly represent living dead investments. The authors then use an econometric approach to partly write these off. Kaplan and Schoar s (2005) 22

23 primary sample screens required a fund s remaining value to be less that 10 percent of committed capital. Consequently, their sample results capture funds that have already realized all or essentially all of their investment. Our analysis, however, suggests that the issue may not only be one of valuation per se, i.e. whether residual values are entirely comprised of living dead investments, but that some funds within the TVE sample may not have up-to-date performance figures. We should stress that this is a conjecture at this stage, as we have not yet discussed the issue directly with Thomson. If correct, this pattern would have (at least) three consequences. First, the sample size for which TVE presents up-to-date return data may be smaller than reported. Second, the estimates of IRRs would tend to go down for these funds over time (which would be consistent with the pattern of results for performance noted earlier) as the RVPI fossilizes. Third, by not excluding funds without up-to-date performance numbers from the samples, the estimates of returns would be biased. However, this remains a subject demanding further research. 23

24 5. Conclusions and Future Directions At the outset of this paper, we noted the unsettled state of conclusions about private equity performance. Our view entering this project was that a major culprit was the existing lack of comprehensive, high quality data for research and benchmarking in this asset class. Our comparison of results from currently available data sources strengthens that view. If each data provider s sample were a random draw from the underlying universe of net-to-lp returns, we d expect similar messages to emerge across all data sources. This is not the case. For instance, different providers have different mixes of funds (e.g. venture capital versus buyout), especially in periods studied by prior research. For performance data, the picture is even more troubling. Currently available performance data cover only a small fraction of funds started. Moreover, return data from leading sources often signal different performance to limited partner investors. Some of these differences are not readily explained by random variation and suggest systematic effects related to data methods and sample selection. The current state of private equity data clouds answers to basic practical questions. For instance, what is good performance by a fund? Looking at VC funds with 2003 vintage years, here are the top-quartile IRRs: Thomson 5.4%, Preqin 8.4% and CA 4.2%. For 1998 the top quartile cutoffs are respectively 10.6%, 20.1% and 18.5%. With these variations across data sources, consistent performance judgments are fraught with difficulty. The data issues for research are equally troublesome. For instance, are returns from performance samples representative of the universe? While most research to date has relied on Thomson data, we show large differences in returns across data sources. Moreover, Thomson is often the outlier among the three data sets. This may be due to differences in samples across data sources, net-to- LP versus gross returns, or data integrity issues. What is clear is the need for better data. To date, many obstacles have blocked the development of comprehensive private equity data, including legal issues, public policy debates, commercial incentives and limited partner coordination costs. In the end, however, such data offers large benefits to many parties. The question is how to move forward recognizing the complexities and challenges. We sketch elements of a path forward. We see fund data as the most practical level for initial focus. An initial step would be to combine archival data from various sources to create a super set of private equity funds. Such a super set can be used as a closer approximation to the private equity universe. It would help in understanding the unique features, quality and reliability 24

25 of results from individual commercial data sets that are available on a real time basis. A common framework for classifying private equity funds could be developed, one that could potentially be adopted broadly to aide benchmarking and research. Data integrity could be improved by cross checking reports for the same fund from different sources. Potential biases from backfill and survivor effects could be studied. Such archival data would be a boon to academic and practitioner research. Such an undertaking is complex and we realize significant hurdles. Unique fund identifiers and a number of fund characteristics are needed to track overlaps and intersections across data sources and make the data useful for research. Time stamped cash flows are required, not just fund level returns. The work requires careful attention to confidentiality and legal obligations of a number of parties. We believe the time is right for such an undertaking. The sources studied here and others hold a wealth of data. A combination of resources from the academy and industry could create an objective process and benefit from charitable funding dedicated to support such an effort. Once created, such comprehensive data has the potential to vastly improve our understanding of the features, risks and rewards of investments in private equity. 25

26 Appendix 1: Private Equity Data from Thomson, Preqin and Cambridge by Vintage Year Table 5: Number of Funds in the TVE and Preqin universes Vintage year Thomson Venture Economics Preqin VC PE Other 2nd tier Total VC PE Other 2nd tier Total , , , , , , ,066 Total 9,589 3,544 1,177 1,114 15,424 4,698 2,475 3,203 1,538 11,914 This table shows the total number of all funds recorded by TVE and Preqin that carry a vintage year and investment category. The categorizations for TVE are as follows: VC: Seed, Development, Early, Balanced, Expansion, Later. Buyout: Generalist, Buyout, Recap. Other: Mezzanine, Turnaround, Distressed debt, Real estate, Energy, Other PE. Second tier: Fund of funds, Secondary funds. The categorizations for Preqin are as follows: VC: Early stage, Early stage: seed, Early stage: start-up, Expansion, Balance, Late stage, Venture (General), Venture debt. Buyout: Buyout, Co-investment, Co-investment multi-manager. Other: Mezzanine, Special situations, Turnaround, Distressed debt, Real estate, Infrastructure, Natural resources, Timber. Second tier: Fund of funds, Real estate fund of funds, Secondaries, Real estate secondaries, Direct secondaries. 26

27 Table 6: Capital raised by funds in the TVE and Preqin universes Vintage year Thomson Venture Economics Preqin VC PE Other 2nd tier Total VC PE Other 2nd tier Total , , , , ,344 1, , ,316 3, ,792 1, , ,669 3, ,329 1,422 1, , ,059 3,379 1, ,055 1, , ,238 4, ,260 2,045 1, , ,469 15,022 2, ,948 2,647 8, , ,881 15,864 2, ,946 1,796 7,539 1, , ,634 19,718 2, ,052 2,112 6,567 1, , ,716 9,676 2, ,109 4,846 4,474 2, , ,769 6,949 1, ,672 2,477 2, , ,813 13,458 2, ,127 3,096 5,919 2, , ,395 18,700 2,373 1,026 30,494 3,680 8,937 3,394 1,082 17, ,217 26,053 6,032 1,525 47,827 7,694 25,085 5, , ,583 28,161 5,457 3,057 52,258 6,761 18,230 7,037 2,823 34, ,303 25,888 10,006 2,741 60,938 10,214 22,685 22,571 1,244 56, ,831 61,428 18,039 7, ,379 17,681 51,917 18,700 4,530 92, ,426 89,825 17,760 15, ,961 32,701 68,473 26,594 13, , ,550 70,964 20,166 25, ,238 50,378 64,441 26,660 20, , , ,321 17,270 27, ,846 92, ,712 31,008 22, , ,538 83,070 32,110 24, ,334 62,486 52,554 43,180 24, , ,873 40,482 22,807 17,385 98,547 25,732 62,939 34,429 21, , ,577 56,359 21,159 12, ,101 19,932 47,960 45,134 20, , ,356 87,821 27,863 22, ,167 33,393 69,370 68,966 31, , , ,944 55,607 37, ,323 60, , ,613 48, , , ,592 87,486 46, ,166 73, , ,744 72, , , , ,427 49, ,221 98, , ,095 75, , , ,395 56,755 45, ,403 69, , ,357 70, , ,429 58,918 27,383 12, ,828 54,147 85, ,788 30, , ,338 4,537 5,493 1,543 18,911 58, , ,055 49, ,635 Total 860,635 1,862, , ,860 3,665, ,996 1,762,270 1,559, ,343 4,640,590 This table shows the total value of committed capital of all funds recorded by TVE and Preqin that carry a vintage year, a fund size and an investment category. Categorizations are as detailed in Table 5. 27

28 Table 7: Current Estimates of Uncalled and Invested Capital, using TVE data Panel A: Venture Capital Vintage year # of VC funds Committed capital # of funds Paid-in to CC Uncalled RVPI / invested to all VC in $ US VC US VC VC in % VC in $ VC in % VC in $ , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,655 Total 6, , , ,892 Panel B: Private Equity (PE) Vintage year # of PE funds Committed capital # of funds Paid-in to CC Uncalled RVPI / invested to all PE in $ US PE US PE PE in % PE in $ PE in % PE in $ , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,939 Total 2,388 1,600, , ,191 This table calculates the amounts of undrawn commitments and the value of current investments held for worldwide venture capital (Panel A) and private equity (Panel B) funds based on figures from TVE. "# of VC (PE) funds" is the number of worldwide recorded private equity (venture capital) funds from 1998 to Committed capital to all VC (PE) funds is the worldwide combined value of all fund sizes. We then apply the inverse paid-in to committed capital ratio, i.e. uncalled commitments, of US private equity (venture capital) funds to the worldwide amount of committed capital to arrive at an estimation of capital that needs to be invested in the near to mid future. Funds from 2005 to 2009 are the ones which are still in their investment period in mid We further apply the RVPI ratio of US private equity (venture capital) funds to the amount of worldwide drawn down capital (as calculated by using US VC (PE) ratios of paid-in capital to worldwide commitments) to arrive at an estimation for equity values of currently held investments. Funds from 1998 to 2009 are the ones which are still active. 28