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

29 Table 8: U.S. Venture Capital Returns by Quartiles (percent) Vintage Year Number of funds Bottom quartile IRR Median IRR Top quartile IRR TVE Preqin CA TVE Preqin CA TVE Preqin CA TVE Preqin CA Total 1, ,283 This table shows Internal Rates of Return (IRRs) for U.S. venture capital funds with vintage years from 1981 to 2008 for Thomson (TVE), Preqin and Cambridge Associates (CA) reported as of December Bottom quartile IRR, Median IRR and Top quartile IRR refer to the values that separate the four quartiles, i.e. the 25th, 50th and 75th percentile. 29

30 Table 9: U.S. Venture Capital Returns by Averages (percent) Vintage Year Number of Funds TVE Preqin CA TVE Preqin CA Avg Wtd Avg Pooled Avg Wtd Avg Pooled Pooled Eql-Pld n.a n.a n.a n.a n.a n.a n.a n.a n.a n.a n.a (9) (8) (13) (14) (15) (25) (31) (49) (78) (48) (31) (18) (32) (35) (53) (37) n.a Total (496) 1283 This table shows Internal Rates of Return (IRRs) for U.S. venture capital funds with vintage years from 1981 to 2008 for Thomson (TVE), Preqin and Cambridge Associates (CA) reported as of December Numbers in brackets for Preqin refers to the number of funds that were part of Preqin's pooled average calculation, as Preqin does not hold detailed cash flows for all funds covered. Average (Avg) refers to equally weighted average IRR results per vintage year. Weighted average (Wtd Avg) refers to capital weighted average IRR results per vintage year. Pooled average (Pooled) combines all cash flows for funds from each vintage year and calculates the IRR based on this stream of cash flows. Equally weighted pooled average (Eql-Pld) of CA equal-weights all cash flows and ending NAVs based on paid in capital. 30

31 Table 10: U.S. Venture Capital Weighted Multiples Vintage Year # of funds Paid-in to CC Distribution to paid-in Residual value to paid-in Total value to paid-in TVE Preqin CA TVE Preqin CA TVE Preqin CA TVE Preqin CA TVE Preqin CA Total 1, ,283 This table shows capital and value ratios for U.S. venture capital funds with vintage years from 1981 to 2008 for TVE, Preqin and CA as reported for December Paid-in capital to committed capital (Paid-in to CC) refers to the fraction of drawn down capital by all funds in each sample. Distribution to paid-in capital shows the ratio of cash distributions back to LPs relative to drawn down capital. Residual value to paid-in capital is the ratio of current net asset values (NAV) to drawn down capital. Total value to paid-in capital is the sum of cash distributions and current NAVs relative to paid-in capital, also known as money multiple. All numbers are value weighted. 31

32 Table 11: U.S. Buyout Returns by Quartile (percent) Vintage Year Number of Funds Bottom quartile IRR Median IRR Top quartile IRR TVE Preqin CA TVE Preqin CA TVE Preqin CA TVE Preqin CA n.a n.a n.a n.a n.a n.a n.a n.a Total This table shows Internal Rates of Return (IRRs) for U.S. buyout funds with vintage years from 1986 to 2008 for Thomson (TVE), Preqin and Cambridge Associates (CA) reported as of December Bottom quartile IRR, Median IRR and Top quartile IRR refer to the values that separate the four quartiles, i.e. the 25th, 50th and 75th percentile. 32

33 Table 12: U.S. Buyout Returns by Averages (percent) Vintage Year # of funds TVE Preqin CA TVE Preqin CA Avg Wtd Avg Pooled Avg Wtd Avg Pooled Avg Pooled Eql-Pld n/m n/m n/m (4) (7) (4) (10) (12) (17) (11) (22) (25) (34) (28) (40) (16) (24) (23) (25) (47) (44) (43) (30) n/m Total (466) 561 This table shows Internal Rates of Return (IRRs) for U.S. buyout funds with vintage years from 1986 to 2008 for Thomson (TVE), Preqin and Cambridge Associates (CA) reported as of December Numbers in brackets for Preqin refers to the number of funds that were part of Preqin's pooled average calculation, as Preqin does not hold detailed cash flows for all funds covered. Average (Avg) refers to equally weighted average IRR results per vintage year. Weighted average (Wtd Avg) refers to capital weighted average IRR results per vintage year. Pooled average (Pooled Avg) combines all cash flows for funds from each vintage year and calculates the IRR based on this stream of cash flows. Equally weighted pooled average (Eql-pld) of CA equal-weights all cash flows and ending NAVs based on paid in capital. 33

34 Table 13: U.S. Buyout Weighted Multiples Vintage Year # of funds Paid-in to CC Distribution to paid-in Residual value to paid-in Total value to paid-in TVE Preqin CA TVE Preqin CA TVE Preqin CA TVE Preqin CA TVE Preqin CA Total This table shows capital and value ratios for U.S. buyout funds with vintage years from 1986 to 2008 for TVE, Preqin and CA as reported for December Paid-in capital to committed capital (Paid-in to CC) refers to the fraction of drawn down capital by all funds in each sample. Distribution to paid-in capital shows the ratio of cash distributions back to LPs relative to drawn down capital. Residual value to paid-in capital is the ratio of current net asset values (NAV) to drawn down capital. Total value to paid-in capital is the sum of cash distributions and current NAVs relative to paid-in capital, also known as money multiple. All numbers are value weighted. 34

35 Appendix 2: Review of research on private equity investment performance This appendix overviews research on private equity returns. Here we use the term private equity broadly to include venture capital and buyout funds. We focus on investments by limited partnerships in private equity funds paralleling our choice of data sets for this paper Private Equity Fund Structure A private equity fund is controlled by the general partner (the private equity firm) who obtains commitments from limited partners who are qualified investors such as financial institutions, endowments, pension funds and wealthy individuals. When the general partner identifies investments, it calls the required capital which is supplied by limited partners on a pro rata basis. These investments create the private equity fund s portfolio of companies. The limited partners receive a return which is net of management fees and carried interest to the general partner. Carried interest is typically 20% of profits. Management fees are stated as a percent of assets with 2% a common figure. These fees are, however, effectively much larger since in early years they are charged on committed capital even if only a fraction of the commitment has been called and invested. For instance, Phalippou and Gottschalg (2009) estimate that fees to general partners make net returns about 6 percent per year less than gross returns. While funds generally have a finite life (approximately 10 years with possible extensions), general partners often start additional funds. General partners normally take an active interest in the governance and management of portfolio companies, often providing management expertise Academic Research on Private Equity Fund Performance We start with what appear, at least to us, key messages from academic research. While many endowments experienced spectacular returns (Lerner et al (2007)), average returns have been much lower. Depending on the study examined, average private equity returns may or may not be competitive with those in public equity markets (e.g. see Kaplan and Schoar (2005) compared to Phalipou and Gottschalg (2009)). Once one adds issues of risk, leverage and illiquidity, private equity returns may be even less attractive. 35

36 The spread between the top and bottom quartile of performance is much more pronounced for private equity funds than for bond or public equity funds. (Kaplan and Schoar (2005)). There is much more importance to picking the right manager than simply getting the right allocation across asset classes (Lerner et al (2007)). The ability to identify and invest in high performing funds may be limited to a small subset of investors such as endowments (Lerner et al 2007). There is high likelihood that next fund of a given general partner will show similar performance to its last fund. (Kaplan and Schoar (2005)). Unlike public equity funds there is less regression to the mean. How the long term performance of venture capital compares to that of buyout funds is uncertain (Kaplan and Schoar (2005) versus Phalippou and Gottschalg (2009)). How risk adjustments affect the relative performance is not well understood. Fund growth leads to lower returns (Kaplan and Schoar (2005)) suggesting a limit on the scalability of the private equity model. The Table below documents the many studies that have been performed. A striking feature is the variety of samples, data sources and methods employed. Thomson is the most commonly used source of returns to limited partners in research to date. Undoubtedly, however, much of the motive for using different data approaches rests in there being no well accepted standard for authoritative returns. Moreover, the samples used are often tiny portions of the universe of funds we show in the body of this paper. Three studies stand out and illustrate many of the research issues. Kaplan and Schoar (2005): In a highly cited study, Kaplan and Schoar (2005) use Thomson Venture Economics data (for the period ) and focus on liquidated funds to eliminate potential contamination created by general partners estimates of remaining value. Median returns are 12% across the funds with buyout firms experiencing a slightly better performance (median return of 13% vs. 11% for venture funds). Notably, mean returns far outstrip median returns, displaying the very high performance of top funds. Mean returns are 17% for venture funds and 18 % for buyout funds. To provide comparison to public equities, Kaplan and Schoar calculate a performance ratio (PME for Public Market Equivalent), assuming private and public equity (proxied by the 36

37 S&P500) carry equivalent risk. Median PME s are below one for both venture capital funds (median of.66) and buyout funds (median of.80). As a result, if a fund falls in the middle of the pack among its peers, it appears to provide returns worse than those available in the public markets. In contrast, mean PME s, especially on a size weighted basis, cast private equity in a more positive light. On a size weighted basis, the mean PME for all funds is 1.05, suggesting performance at least on par with the S&P500. Using this metric, venture capital funds look reasonably attractive (a PME of 1.21) while buyout funds do not (PME of.93). Overall, Kaplan and Schoar s work would suggest that net returns to private equity are competitive with those from public markets. This stems from fees to general partners capturing the spread between gross private equity returns and public market rates. More recently, Kaplan and Lerner (2010) come to the same conclusions focusing on Venture Capital in recent years. Phalippou and Gottschlag (2009): In stark contrast to Kaplan and Schoar, Phalippou and Gottschlag (2009) use the same Thomson data and come to dramatically different conclusions. They contend that the TVE data suffer from sample selection bias such Thomson s reliance only on funds that voluntarily report. The authors also address issues in measuring performance, such as risk adjustment and weighting of funds. Based on procedures to adjust for these biases, Phalippou and Gottschalg conclude that the average private equity fund underperforms the S&P 500 Index net-of-fees by about 3% per year and overperforms that index gross-of-fees by 3% per year (p. 1747). Lacking return data other than for the TVE funds; however, these conclusions draw on proxy variables from performance (e.g. exit by IPO or sale) rather than from actual calculation of returns for a more representative sample. Thus, the differences between their conclusions and those of Kaplan and Schoar (2005) are not directly testable using data in the study. Lerner, Schoar and Wongsunwai (2007): Another complexity of private equity returns is surfaced by Lerner, Schoar and Wongsunwai (2007) who segment returns based on the form of institutional investor. Their primary data source is Private Equity Intelligence s 2004 Private Equity Performance Monitor. These data have been collected from public sources and underlie Preqin s business. The authors 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). The differences among institutions are robust to controls for type and year of investment. The authors 37

38 also conclude that endowments are better at forecasting the performance of follow-on funds and use this ability to make wise reinvestment decisions Insights from studies that do not focus on Limited Partner returns For private equity broadly defined, Moskowitz and Vissing-Jorgensen (2002) harness an array of aggregate U. S. data (e.g. Survey of Consumer Finance, the Flow of Funds) to estimate annual private equity returns of 13.5% for the period 1990 through These estimates fall short of public equity market returns (16.8%) and returns provided by small public firms (24.3%) over the same time span. Moreover, the authors find no evidence that private equity is safer than public equity. The finding of subpar investment performance may be explained by non-pecuniary benefits to entrepreneurs or unsophisticated investment by first timers since, as the authors note, their data primarily cover entrepreneurial investments in small business. Cochrane (2005) examines companies in which private equity funds invested. The returns on these investments do not reflect the fees or diversification inherent in the fund. Using the Thomson VentureOne database (from 1987 to June 2000), Cochrane tracks 7765 companies for financing rounds or whether they went public, were acquired or went out of business. Returns are estimated using the valuation at the time of the transaction (e.g., comparing the offering price in an IPO to the value in an earlier financing round). Cochrane notes a positive selection bias since he can only observe a valuation when the firm goes public, receives new financing or is acquired and these events are more likely when returns are good. Prior to a correction for selection bias, mean arithmetic returns exceed 600% with a standard deviation of over 3000%. While maximum likelihood techniques substantially reduce estimated returns, Cochrane still finds high returns, extreme volatility and strongly skewed return distributions. Corrected for selection bias, average log returns (annual) are 15% with a standard deviation of 89%. The average arithmetic return is 59% with a standard deviation of 109%. Returns appear lower in later rounds of financing as would be expected as a firm becomes more established over time. Cochrane likens these investments to options which have a small potential for huge payoffs but often see all investment lost. 11 Cochrane notes that these puzzles are not special to venture 11 Since a portfolio of options on specific assets is more valuable than an option on the portfolio, option like payoffs on venture capital investments would provide incentive for the holding company like structure of a private equity 38

39 capital as the smallest NASDAQ stocks have similar large means and volatilities in this time period. Woodward and Hall (2003) also use valuations of companies financed with venture capital and deploy them to construct an overall private equity index. They note that valuations revealed in episodic transactions (such as an initial public offering or a company liquidation) create problems similar to those in constructing a real estate index from individual property prices. They find that general partner estimates of companies remaining values lag market conditions by six to nine months. As a consequence, returns constructed using these remaining value estimates are too high in falling markets and too low in rising markets. To address this issue, Woodward and Hall use an iterative procedure that updates remaining value estimates based on transactions that have recently occurred. Unlike Cochrane (2005), they reject taking a parametric approach to dealing with selection bias 12 and instead report tracking down data that are typically missed in constructing private equity benchmarks. These are instances in which no valuation is reported and often coincide with bad investment outcomes (such as going out of business). While Woodward and Hall (2003) only display graphs of the index, Woodward (2005, p. 834) reports an annual return on private equity investment of about 20 percent before paying the venture capitalist general partner and after, 16 percent for the period Moreover, she cites that the beta of private equity investment is about two (2.0) and the standard deviation of return on an annual basis is 45 percent, (nearly triple the stock market). Given the definition of beta, these figures imply a significant positive correlation of about.67 between private and public equity returns. Woodward concludes that given its risk, the return on venture capital is just about where we would expect it to be implying that the market for venture capital, despite its opacity and hyperbole, is efficient. fund as opposed to common corporate ownership of assets such as a conglomerate firm. The motivation would be to keep limited liability on each underlying asset. 12 Woodward and Hall report that their return data are not log-normally distributed and as a consequence they do not use the repeat-sales regression approach or parametric specifications (such as used in Cochrane (2005) and Hwang, Quigley and Woodward (2005)). Using essentially the same data as in Woodward and Hall (2003) but alternate techniques, Wang, Quigley and Woodward (2005) report quite different results. They find a beta of private equity investment to be only.6 with the S&P500 and much lower standard deviations. 39

40 A natural research extension is to exploit prices of traded securities with private equity characteristics. Zimmerman et al (2005) study listed private equity (LPE) securities where the firm s underlying business is private equity investing and the firm is quoted on an exchange. They classify LPE s into one of three categories: public companies whose core business is private equity (such as 3i), quoted investment funds who co-invest with specific private equity funds and specially structured vehicles that invest directly in private companies and/or indirectly through various private equity funds. Zimmerman et al find 287 LPE s over the period , including the range of financing stages and approaches: early stage, later stage, expansion, buyouts and turnarounds. Sixty percent of the funds were listed since 1997 and only eight LPE s existed at the beginning of the sample period. Over half of the funds are listed in Europe, almost 30 percent in North America and over 10 percent in Asia. The UK has a large share of the funds (40 percent) due to tax alleviation provisions leading to Investment and Venture Capital trusts. While liquidity is one motive for LPE s, Zimmerman et al show that many of these vehicles provide quite limited liquidity compared to other public stocks. Over 40 percent of the LPE s have average bid ask spreads exceeding 20%. Risk and return estimates for LPE s are highly sensitive to liquidity issues and to the time period studied. They are also confounded by concerns of survivorship bias that Zimmerman et al can only partially address. The average annual return estimates are substantially reduced once one adjusts for transactions cost implied by the large bid-ask spreads. Risk estimates increase dramatically once one makes corrections for the downward biases associated with thin trading. For instance beta goes from.6 to.99. Jagadeesh et al (2009) study another sample of listed private equity funds and compare results to those of unlisted funds. They conclude that the market expects unlisted PE funds to outperform public market by around 1% per year while listed funds of funds match or slightly underperform public returns. They find that both listed and unlisted PE funds have market betas close to one and positive loadings on the Fama-French SMB factor. Other market proxies are sometimes used as surrogates for private equity. For instance, Ennis and Sebastian (2005) use the Post-Venture Capital Index (PVCI), which is available through Venture Economics, to examine private equity s place in portfolio allocation decisions. PVCI tracks companies after they leave private equity funds and then are publicly traded. It is valued daily and firms remain in the index for a decade after going public. As such, it is a market 40

41 proxy for returns on new public firms. Small stock indices (e.g. NASDAQ) are also sometimes by used by practitioners to help gauge success in private equity Overall Summary Studying primarily entrepreneurial investment, Moskowitz and Vissing-Jorgensen (2002) conclude that private equity is not competitive with public equity in terms of pecuniary returns and risks. When we turn to evidence more germane to private equity funds, recent research displays a wide array of estimates for the returns and risk of private equity investing. Kaplan and Schoar (2005) find that liquidated funds have essentially neutral performance relative to the S&P500. Philappou and Gottschalg (2009) find private equity underperforms the S&P 500 by 3% annually. The difference in conclusions comes from Philappou and Gottschalg s (2009) adjustments for sample biases and methods used to summarize performance. If anything, these studies point to disappointing results for private equity overall. On the other hand, Lerner, Schoar and Wongsunwai (2007) demonstrate that some investors, such as endowments have done especially well in private equity. Research looking at valuations of companies in private equity portfolios emphasizes problems with general partners remaining value estimates. Woodward and Hall (2003) conclude that remaining value estimates are smoothed and lag market conditions by six to nine months. Their procedures suggest that private equity is riskier than public equity and much higher than the risk captured by reported limited partner returns. They still find private equity returns are higher than those on public equity. Woodward (2005) concludes that private equity s extra return and extra risk bring it into line with public equity on a risk adjusted basis. While Cochrane (2005) does not focus on investor returns, his findings on selection bias related to assets within funds have implications for estimating returns at the fund level. Return estimates for individual assets are much lower when Cochrane adjusts for the pattern that good returns are the ones most likely observed. If general partners assume that all the outcomes in the portfolio will be as good as those that have already been realized, remaining value estimates will be biased upward leading to overstatement of investor returns during the life of the fund. Selection bias also complicates research at the fund level as shown in Phallipou and Gottschalg (2009). Work using market prices on LPE s and other private equity proxies (e.g. PVCI) faces issues of how well these instruments reflect the mainstream of private equity investment. There 41

42 are notable differences between firms that are already public and enter PVCI and many companies in private equity fund portfolios. These include (depending on the fund) stages in the company life cycle and corporate reorganizations executed by buyout funds. Many LPE s are quite illiquid and, as a result, are themselves subject to severe measurement problems (Zimmerman et al (2003). Overall, the recent research suggests that private equity s attractiveness is likely overstated by looking at reported returns. The level of returns is subject to debate and risk measures are often understated. Startling disparities in returns among large institutions speak to the difficulty of investing in private equity arena (Lerner, Schoar and Wongsunwai (2007). 42

43 Citation Data Source Data Type Metric Period Findings Cochrane, J The Risk and Return of Venture Capital. Journal of Financial Economics 75:3 52. Thomson Venture One, SDC, Market Watch, other online sources, Portfolio Companies / Financing rounds IRRs with alpha estimate / maximum likehood method for missing data When corrected for selection bias, venture capital investments have mean returns, volatilities and alphas similar to that of smallest Nasdaq stocks. Cumming, D Venture Capital: Investment Strategies, Structures, and Policies, John Wiley & Sons. EVCA, Thomson Venture Economics Various Various Various PE literature overview. Elaborates on fertile PE/VC research directions. Driessen, J., T. Lin, and L. Phalippou A New Method to Estimate Risk and Return of Non-Traded Assets from Cash Flows: The Case of Private Equity Funds. NBER Working paper No Thomson Venture Economics Cash flows to Limited Partners Public Market Equivalent / GMMstyle methodology to estimate risk and return of a non-traded asset with cash flows 958 mature (= at least 10 years old) PE funds Large betas and underperformance even before fees for VC funds and relatively low beta and mixed performance evidence for BO funds. Net Asset Values are significantly overstated. Groh, A.P. and O.Gottschalg The Risk-Adjusted Performance of US Buyouts. Working paper, Darmstadt University of Technology, HEC. Data collected privately and anonymously from General Partners and Limited Partners, primary source is 122 Private Placement memorandums which contain track records as reported by General Partners. Portfolio Companies / Financing rounds Annual IRRs' regression of returns for buyout deals and for comparable public companies 199 U.S. buyout funds' investments Significant outperformance of U.S. buyouts compared to a mimicking portfolio of equally risky levered investments in the S&P 500. Hall, R.E. and S.E. Woodward The Incentives to Start New Companies: Evidence from Venture Capital. Working paper, Stanford University Sand Hill Econometrics Portfolio Companies & Cash flows to Limited Partners (quarterly) Public Market Equivalent/ Regression of eventually realized returns of VC investments per quarter against similarly timed investments in public equity 55,307 funding rounds Entrepreneurs receive no help from venture capital in avoiding the huge idiosyncratic risk of the typical venture-backed startup. Excess return for outside investors measured by the CAPM is positive but may reflect only random variation. Hwang, M., J. Quigley, and S. E. Woodward. An Index for Venture Capital, Contributions to Economic Analysis and Policy 2005 Vol 4, Article 13. Sand Hill Econometrics, Thomson Venture One Portfolio Companies / Financing rounds IRRs with alpha estimate / Repeat Valuation model 19,208 valuations of 9,092 firms An index for venture capital returns using with correction for selection bias allowing for covariance analysis with publicly traded assets that supports the asset class inclusion in an optimal portfolio. 43

44 Citation Data Source Data Type Metric Period Findings Jegadeesh, N., R. Kraussl, and J. Pollet Risk and Expected Returns of Private Equity Investments: Evidence Based on Market Prices. Journal of Finance, forthcoming. SDC, Dow Jones Private Equity Funds of Funds database, S&P Listed private equity index, PowerShares listed private equity indices. Listed PE vehicles, one of the samples is comprised of fund of funds only IRRs with alpha estimates / regressions on various factor sets 26 Fund of Funds and 129 listed PEs 1994 to 2008 Market expects unlisted PE funds to earn annualized alpha around 1% while listed PE Fund of Funds have zero or marginally negative alphas. Both listed and unlisted PE funds have market betas close to one and positive loadings on the Fama-French SMB factor. Jones, C. and M. Rhodes-Kropf The Price of Diversifiable Risk in Venture Capital and Private Equity. Working Paper, Columbia University Thomson Venture Economics Cash flows to Limited Partners, Net Asset Values Quarterly IRRs regressed on contemporaneous and lagged public market returns 1,245 funds Positive alphas occur because financing is unavoidably wrapped with VC compensation. Funds that have more idiosyncratic risk ex post earn higher returns. Kaplan, S. N., and A. Schoar Private Equity Performance: Returns, Persistence, and Capital Flows. Journal of Finance 60: Thomson Venture Economics ( ), publicly reported performance: CALPERS, Univ. Cal, UTIMCO, Univ. Michigan Cash flows to Limited Partners (quarterly) Public Market Equivalent/ Present value of cash flows discounted at public equity rates of return 1090 funds in large sample, 746 in the one with stricter criteria; Average net of fees returns are approximately equal to the S&P500 returns. Gross returns outperform public market returns. PE returns are persistent across funds by the same general partner. Several findings differ markedly from those about mutual funds industry. Kaplan, S.N., B.A. Sensoy, and P. Stromberg How Well Do Venture Capital Databases Reflect Actual Investments? Working paper, University of Chicago. Thomson Venture Economics, Venture One, actual VC financings contracts data Portfolio Companies / Financing rounds Other: Financings amounts and dates, valuations 143 VC financings The databases exclude roughly 15% of the financing rounds; provide unbiased, but noisy measures of amounts and valuations. VE oversamples larger rounds and California. 44

45 Citation Data Source Data Type Metric Period Findings Lerner, J., A. Schoar, and W.Wongsunwai Smart Institutions, Foolish Choices? The Limited Partner Performance Puzzle. Journal of Finance 62: Novel Asset Alternatives compilation, Limited Partners' public and private disclosure, Galante s Venture Capital and Private Equity Directory, Venture Economics, Private Equity Intelligence s Private Equity Performance Monitor Net Returns to Limited Partners, Distributions to Limited Partners (money multiples) IRRs Funds raised , returns through 2003 The returns that institutional investors realize from private equity differ dramatically across institutions. Endowments annual returns are nearly 21% greater than average not primarily because of better access. Ljungqvist, A. and M. Richardson The Cash Flow, Return, and Risk Characteristics of Private Equity. Working Paper No. 9495, NBER. Large institutional investor, Thomson Venture Economics Cash flows to Limited Partners and Portfolio Companies with accurate amounts and timing Excess IRRs and Profitability Indices based on average drawdown and exit duration assumptions 73 funds raised PE generates excess returns on the order of five to eight percent p.a. relative to the aggregate public equity market as a compensation for a 10-year illiquid investment. Ljungqvist, A., M. Richardson, and D. Wolfenzon The Investment Behavior of Buyout Funds: Theory and Evidence. Working Paper No. 174/2007, NBER. Large institutional investor, Thomson Venture Economics Cash flows to Limited Partners and Portfolio Companies with accurate amounts and timing IRRs 207 buyout funds that invested in 2,274 buyout targets (35% of estimated PE universe): 1981 and 2000 Established funds accelerate their investment flows and earn higher returns when investment opportunities improve. The investment behavior of first-time funds is less sensitive to market conditions. Younger funds invest in riskier buyouts, in an effort to establish a track record. Following periods of good performance, funds become more conservative, and this effect is stronger for younger funds. Metrick, A., and A.Yasuda Economics of Private Equity Funds. Journal of Finance, Forthcoming Large investor, Galante s Venture Capital and Private Equity Directory Contractual terms of LP agreements, Net returns to Limited Partners Other: fee schedules, General Partners' characteristics, IRRs 238 funds raised Model for expected revenue for General Partners. Buyout General Partners build on their prior experience by increasing the size of funds faster than VC managers do leading to higher revenue per partner and per professional in later funds. Buyout business is more scalable and past success has a differential impact on the terms of their future funds. Moskowitz, T., and A.Vissing-Jorgensen The Private Equity Premium Puzzle. American Economic Review Survey of Consumer Finances, Flow of Funds Accounts, National Income and Product Accounts, National Survey of Portfolio Companies Returns based on U.S. household estimates of the market value and profits of the private firms Returns to private equity are no higher than the returns to public equity despite poor diversification. Nonpecuniary benefits, a preference for skewness, or overestimates of 45

46 Citation Data Source Data Type Metric Period Findings 92: Small Business Finances, CRSP, SDC, IPO return data from Jay Ritter the probability of survival could potentially explain investment in private equity. Peng, L Building a Venture Capital Index. Working paper, Yale University OffRoad Capital [from Venture One, SDC, Market Guide, and other sources] Portfolio Companies Appraisal values based on moment repeat sales regression and a re-weighting procedure 12,946 rounds of venture financing with 5,643 venturebacked firms: VC index mitigating missing data, censored data and sample selection. The geometric average return is 55.18% per year in the sample periods with higher volatility than and significant correlation with NASDAQ. Phalippou, L Investing in Private Equity Funds: A Survey. CFA institute monograph. Various Various, including Cash Flows to Limited Partners Various Various PE literature overview. Elaborates on poor average performance and other issues. Phalippou, L Venture Capital Funds: Flow-Performance Relationship and Performance Persistence. Working paper, University of Amsterdam. Thomson Venture Economics Cash flows to Limited Partners, funds self-reported Net Asset Values Public Market Equivalent / Present value of cash flows discounted at public equity rates of return VC funds >$5m raised , performance through 2003 Only skilled investors use all available information to adjust their capital allocation and, as a result, eliminate performance predictability. Kaplan and Schoar (2005) overstate the persistence in fund performance by not using an ex ante measure of the performance of earlier funds. Phalippou, L. and O. Gottschalg The Performance of Private Equity Funds. The Review of Financial Studies 22 v4, Thomson Venture Economics, Venture Xpert Cash flows to Limited Partners, funds self-reported Net Asset Values, Portfolio Companies / Financing rounds Public Market Equivalent / Present value of cash flows discounted at public equity rates of return The performance of PE funds as reported by industry associations and previous research is overstated due to a sample bias toward better performing funds. Private equity net returns underperform public markets. Large average fees to general partners. 46

47 6. References Aggarwal, Rajesh K. and Phillipe Jorion, 2010, Hidden Survivorship in Hedge Fund Returns, Financial Analysts Journal 66, 2, 1-6. Center for International Securities and Derivatives Markets, 2005, The Benefits of Private Equity, CISDM Research Department, Isenberg School of Management, University of Massachusetts. Cochrane, John H., 2005, The Risks and Return of Venture Capital, Journal of Financial Economics 75, Conroy, Robert M. and Robert S. Harris, 2007, How Good are Private Equity Returns? Journal of Applied Corporate Finance 19, 3, Cumming, D., 2010, Venture Capital: Investment Strategies, Structures and Policies, Hoboken, NJ, USA, John Wiley & Sons. Dimson, Elroy, 1979, Risk Measurement When Shares are Subject to Infrequent Trading, Journal of Financial Economics, 7(2), Dimson, Elroy and Bernd Hanke, 2004, The Expected Illiquidity Premium: Evidence from Equity Index-Linked Bonds, Review of Finance, Vol. 8, Iss.1, Driessen, J., T. Lin, and T. Phalippou, 2009, A New Method to Estimate Risk and Return of Non- Traded Assets from Cash Flows: The Case of Private Equity Funds. NBER Working Paper No Ennis, Richard M. and Michael D. Sebastian, 2005, Asset Allocation with Private Equity, The Journal of Private Equity, Summer, European Private Equity and Venture Capital Association, 2004, Performance Measurement and Asset Allocation for European Private Equity Funds, An EVCA Research Paper, March. Fisher, Jeffrey and William N. Goetzmann, 2005, The Performance of Real Estate Portfolios: A Simulation Approach, Journal of Portfolio Management, Special Issue on Real Estate. Groh, A.P. and O.Gottschalg, 2007, The Risk-Adjusted Performance of US Buyouts. Working paper, Darmstadt University of Technology, HEC. Hall, R.E. and S.E. Woodward, 2007, The Incentives to Start New Companies: Evidence from Venture Capital. Working paper, Stanford University 47

48 Hwang, Min, John M. Quigley and Susan E. Woodward, 2005, An Index for Venture Capital, , Contributions to Economic Analysis & Policy, Journals in Economic Analysis & Policy, Vol. 4, No. 1, Article 13. Jegadeesh, N., R. Kraussl, and J. Pollet, 2009, Risk and Expected Returns of Private Equity Investments: Evidence Based on Market Prices. Journal of Finance. Jones, C. and M. Rhodes-Kropf, 2004, The Price of Diversifiable Risk in Venture Capital and Private Equity. Working Paper, Columbia University Kaplan, Steven N. and Antoinette Schoar, 2005, Private Equity Performance: Returns, Persistence, and Capital Flows, The Journal of Finance, Vol. LX, No. 4, Kaplan, S.N., B.A. Sensoy, and P. Stromberg, 2002, How Well Do Venture Capital Databases Reflect Actual Investments? Working paper, University of Chicago. Kaplan, Steven N. and Josh Lerner, 2010, It Ain t Broke: The Past, Present and Future of Venture Capital, Journal of Applied Corporate Finance, 22,2, Kocis, James M., James C. Bachman, Austin M. Long and Craig J. Nickels, 2009, Inside Private Equity, Hoboken, N.J., USA, John Wiley & Sons, Inc. Lerner, Josh and Antoinette Schoar, 2004, The Illiquidity Puzzle: Theory and Evidence from Private Equity, Journal of Financial Economics 72, Lerner, Josh, Antoinette Schoar and Wan Wongsunwai, 2007, Smart Institutions, Foolish Choices? The Limited Partner Performance Puzzle, Journal of Finance 62: Ljungqvist, A. and M. Richardson, 2003, The Cash Flow, Return, and Risk Characteristics of Private Equity. Working Paper No. 9495, NBER. Ljungqvist, A., M. Richardson, and D. Wolfenzon, 2007, The Investment Behavior of Buyout Funds: Theory and Evidence. Working Paper No. 174/2007, NBER. Metrick, Andrew, and Ayako Yasuda, 2009, Economics of Private Equity Funds. Working paper, Wharton, Yale, forthcoming the Review of Financial Studies. Moskowitz, T. J. and A. Vissing-Jorgensen, 2002, The Returns to Entrepreneurial Investment: A Private Equity Premium Puzzle?, American Economic Review 92, Peng, Liang, 2001, Building a Venture Capital Index, Working Paper, Yale School of Management. 48

49 Phalippou, Ludovic and Oliver Gottschalg, 2009, The Performance of Private Equity Funds, Review of Financial Studies Vol. 22, No. 4, Phalippou, L., 2008, Investing in Private Equity Funds: A Survey. CFA institute monograph. Phalippou, L., 2009, Venture Capital Funds: Flow-Performance Relationship and Performance Persistence. Working paper, University of Amsterdam. Ritter, Jay R. and Ivo Welch, 2002, A Review of IPO Activity, Pricing and Allocations, Journal of Finance, Vol. 57, No. 4, Swensen, David F., 2000, Pioneering Portfolio Management, New York: The Free Press, a Division of Simon and Schuster, Inc. Woodward, Susan E., 2005, Book Review, Journal of Economic Literature, Vol. XLIII, , Venture Capital Contracting and the Valuation of High-Technology Firms, Edited by Joseph McCahery and Luc Renneboog. Woodward, Susan E. and Robert E. Hall, 2003, Benchmarking the Returns to Venture, Working Paper 10202, National Bureau of Economic Research. Zimmerman, Heinz, Stéphanie Bilo, Hans Christophers and Michèl Degosciu, 2005, Risk, returns, and biases of listed private equity portfolios, Working Paper No. 1/05, WWZ/Department of Finance, University of Basel 49