Research Research Paper

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Research Paper

Performance Measurement and Asset Allocation

for European Private Equity Funds

Contributors

CDC IXIS Capital Markets

Center for Entrepreneurial and Financial Studies (CEFS-TUM), Technische Universität München

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About EVCA

The European Private Equity and Venture Capital Association (EVCA) exists to represent the European private equity sector. With over 950 members throughout Europe, EVCA’s many roles include working to promote the asset class both within Europe and throughout the world, representing the industry in public affairs and developing professional standards. EVCA’s services and information products’ range includes research and information papers, renowned large-scale conferences and networking opportunities, small-scale but industry-specific workshops and private equity management training courses through the EVCA Institute. EVCA’s activities cover the whole range of private equity, from seed and start-up to development capital, buyouts and buyins, and the flotation of private equity-backed companies.

Please note

This publication does not purport to contain a complete explanation of the private equity asset class and any related securities. No statement in this publication is to be construed as a recommendation to purchase or sell a security or to provide investment advice.

Private equity involves risk and is not suitable for all investors. Prospective private equity investors considering purchase of securities should reach an investment decision only after carefully considering the suitability of these securities in light of their own personal financial condition and objectives.

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Executive summary page 2

1. Introduction page 5

2. Asset allocation and European private equity: A first approach using aggregated data

by Patrick Artus & Jérôme Teïletche, CDC Ixis Capital Markets page 7

2.1 General remarks on private equity returns page 7

2.2 Asset allocation among venture capital,

equities and bonds in the European case page15

2.2.1 A naïve approach of asset allocation page16

2.2.2 The smoothing of venture capital returns page18

2.2.3 The correction of venture capital variance and

of the correlation between venture capital and equities page19

2.2.4 A “corrected“ asset allocation page20

2.3 Introducing buyout funds into the portfolio page21

2.4 Concluding remarks page25

3. European private equity funds – A cash flow based performance analysis by Christoph Kaserer and Christian Diller

Center for Entrepreneurial and Financial Studies (CEFS-TUM),

Technische Universität München page27

3.1 Cash flow characteristics of European private equity funds page30

3.2 Return/risk characteristics of European private equity funds page36

3.2.1 The cash flow based IRR as a return measure page37

3.2.2 The cash flow based PME as a return measure page39

3.2.3 Risk characteristics of cash flow based returns page42

3.2.4 Correlation characteristics of different reinvestment

hypotheses page43

3.2.5 Increasing the data universe page45

3.2.6 Results with respect to IRR page47

3.2.7 Relative Performance Characteristics page53

3.3 Private equity, asset allocation and limited liquidity page60

3.4 Cash flow patterns, liquidity risk, and performance assessment page65

3.5 Concluding remarks page67

4. Conclusion page69

5. Bibliography page72

Appendix I page73

Index of figures page77

Index of tables page78

Contributors page80

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Executive Summary

What should be the share of private equity in the portfolio of European institutional investors? This paper argues that the answer is between 5%-10% when the portfolio consists of private equity (i.e. venture capital and buyout funds), quoted equity and bonds.

The research was conducted on behalf of EVCA and carried out by Patrick Artus and Jérôme Teïletche, CDC Ixis Capital Markets Research Department, and by Christoph Kaserer and Christian Diller, Center for Entrepreneurial and Financial Studies (CEFS-TUM) Technische Universität München. The authors of this paper make a case for the introduction of private equity into the framework of modern portfolio theory. Moreover, due to the characteristics of private equity itself, they were required to consider the following aspects within their analysis:

• The absence of a market providing pricing guidance for the assets in the portfolios of funds

on a continuous basis. As a consequence the value of assets in portfolios is the result of an appraisal, leading to potentially stale pricing or a smoothing process. This causes issues with modern portfolio theory as the true volatility and correlation between asset classes can be understated.

• Determining the right performance metrics in order to compare the returns of private equity

with more liquid asset classes.

Two sets of data were used in order to conduct this analysis. The first one consists of periodical aggregate returns built on the sum of all the funds for a specific period of time (i.e. the sum of the cash flows and net asset values between the starting and the ending dates of the chosen period). The second set of data consists of cash flow patterns (amount and exact timing) based on data of individual funds.

In a first stage, Patrick Artus and Jérôme Teïletche concentrated on correcting aggregate quarterly returns generated by the smoothing process. Figure 1 presents the outcome of this calculation for venture capital. It appears that the portfolio with the highest Sharpe ratio (i.e. the portfolio that gives the highest return per unit of risk) consists of 3% venture capital, 2% quoted equities and 95% of bonds.

Moreover, the authors also looked at correcting returns generated by a smoothing process for the buyout segment of private equity. They were confronted with two key issues:

• The absence of a significant autocorrelation of aggregated returns on buyouts (i.e. a correlation

function between a single, not random variable at different times), which questions the principle of smoothing processes; and

• The difficulty of finding an aggregate return that gives a correct representation of the

dispersion of returns within the buyout segment.

Because the study of aggregate returns and their potential correction did not lead to a fully satisfactory solution, an analysis of the individual cash flows generated by European private equity funds was needed to create a more thorough understanding of the role of private equity in the portfolio of institutional investors.

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Executive Summary

Figure 1: Efficient frontier for portfolios of venture capital, public equities and bonds (European data; 1994 Q1 - 2003 Q2)

Source: EVCA/CDC Ixis Capital Markets, based on data provided by Thomson Venture Economics In the second stage, Christoph Kaserer and Christian Diller conducted an analysis of the cash flows of a total of 780 European private equity funds. As mentioned, the smoothing process does not determine the results of the cash flow analysis.

Initially, the authors described the time patterns of takedowns (also called draw downs) and disbursements. According to the study the average European private equity fund draws down 25% of the total committment when starting its business. Within the first three years 63% of the total committed capital will be invested in the fund. On the other hand, the calculations show that 53% of total disbursements are paid out to the limited partners within the first six years. By taking the ratio of these two measures, the authors argue that limited partners on average retracted the invested money after 7 and half years.

Next, Christoph Kaserer and Christian Diller calculated various performance measures. They found that the internal rate of return (IRR) based on cash flows for 201 funds, which were either liquidated or had a small residual net asset value, is around 12%. The results also indicate an average IRR of buyout funds of 13.4% and of venture capital funds of 10.6%. A calculated Excess-IRR of the MSCI Europe stands at 4.4% for an average European private equity fund. Because of the shortcomings of the IRR calculation method, the authors developed a bench-mark for returns based on the assumption that the limited partners, i.e. institutional investors, reinvested the distributions from the funds either in quoted equity or in bonds. This allows comparing returns of private equity with returns of public securities for the full life cycle of funds. Hereby the authors found an average public market equivalent of 0.93 and a value-weighted PME of 1.04. A PME larger than one indicates that the investments into the observed private equity funds generate a higher terminal value than an equivalent investment into the MSCI Europe. Additionally the authors estimated the return, risk and the correlation structure of private equity based on the PME measure. Again the authors claim that the share of private equity in portfolios of institutional investors should be around 5%.

10.0 9.5 9.0 8.5 8.0 7.5 7.0 0 5 10 15 20 25 30 35 40 45

Expected return (% per year)

Risk (% per year)

Frontier with raw statistics

Frontier with corrected statistics

Portfolio A' (minimum variance): 1% VC, 3% Equities, 96% Bonds

Portfolio B' (maximum of the Sharpe ratio): 3% VC, 2% Equities, 95% Bonds

Portfolio C' (Two assets portfolio): 16% VC, 0% Equities, 84% Bonds

Portfolio D' (maximum return portfolio): 100% VC, 0% Equities, 0% Bonds

A B C

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Executive Summary

One of the most striking findings of this research relates to the determination of the relative contribution of venture capital and buyout investments to institutional portfolios (see figure 2). In this case, the returns produced by European private equity funds and their hypothetical reinvestment in the JP Morgan European Government Bond Index by the limited partners suggests a maximum Sharpe ratio for institutional portfolios comprising 5% of venture capital and 3% of buyout investments.

Figure 2: Efficient frontier for portfolios of public equities, bonds, venture capital and buyout funds (Bond reinvestment strategy) (1972-2003)2

Source: EVCA/CEFS-TUM, based on data provided by Thomson Venture Economics

Before concluding, it should be pointed out that those results are based on historical returns. Nevertheless, the authors reason that investors should build their portfolios on the ability of the respective asset classes in order to outperform their track records.

Therefore, following basics should be taken into account in a strategic asset allocation. Bonds will probably not reproduce past returns due to the current low level of interest rates. Though it is true that the performance of the venture capital segment has been seriously reduced following the collapse of the Internet related investment trend in 1999/2000, this context has also made European private equity management teams experienced in managing investee companies through difficult times. Moreover, the increasing competition between buyout houses in Europe is emphasising the importance of differentiation via implementation of truly value adding strategies. The quantitative evidence based on historical returns and forecasts on the development of European private equity calls for a significant role of this asset class in the portfolio of institutional investors.

16% 14% 12% 10% 8% 6% 4% 0% 5% 10% 15% 20% 25%

Standard Deviation (σ)

Bonds = MVP (0% VC, 0% BO, 0% Eq., 100% Bds.) MSRP (5% VC, 3% BO,

7% Eq., 85% Bds.)

EP_9% (26% VC, 26% BO, 28% Eq., 20% Bds.)

BO VC

Equities

1 _{The following portfolios are marked in the diagram: MVP = Minimum variance portfolio,} MSRP = Maximum Sharpe ratio portfolio (for a risk free interest rate of 3%),

EP_9% = Efficient portfolio with an expected return of 9%.

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Introduction

What should be the share of private equity in the portfolio of an institutional investor? Modern portfolio theory offers an answer determined by the return, the volatility (in other terms the risk) and the correlation of private equity with other asset classes (typically, but not limited to, quoted stocks and bonds). Although offering a widely accepted framework, modern port-folio theory constitutes a challenge when applied to a set of asset classes comprising private equity. This challenge comes from the characteristics of the private equity asset class itself. According to modern portfolio theory, an asset class will see its share in an institutional investor’s portfolio increased when, for a determined level of correlation with other asset classes, its return increases and its risk (i.e. volatility) decreases. Conversely, for a determined level of return and risk, the share of an asset class will increase when its correlation with other asset classes diminishes. The impact of correlation relates to the intuitive notion of diversification. A small correlation means a higher degree of diversification.

The limited liquidity of private equity makes the level of return, volatility and correlation not directly comparable with more liquid asset classes like quoted stocks and bonds. Intuitively, the lower level of liquidity should lead to a premium in the return produced by private equity. The question is then to know if the excess return in exchange of the limited liquidity justifies the inclusion of private equity in a portfolio. The answer is clearly depending on the investment horizon of the institutional investors.

Moreover, the limited liquidity brings with it that there is no market providing pricing guidance for assets in the portfolio on a continuous basis. As a result, the value of the assets in the portfolio is estimated via an appraisal, leading to a potential stale pricing or smoothing process. As a consequence, the true volatility of private equity and its correlation with other asset classes could be underestimated, leading to a potential over-commitment to the asset class according to modern portfolio theory.

Another impact of the limited liquidity, combined with the very nature of private equity, i.e. developing companies over a long period of time, leads to a J-curve effect. The J-curve effect is observed not only for cash flows but also internal rates of return (IRRs). Typically, due to the management fees, an investor will observe in the first years of a life of a fund negatives IRRs. It will take several years, usually more than six, before the investors can get a true picture of the performance of the funds in their portfolio. This can negatively impact the share of private equity in a portfolio depending on the age structure of the benchmark used in order to gauge the performance of the asset class.

Another issue stems from the performance metric used by the asset class. Because private equity fund managers have a direct influence on the timing of the cash flows (which is not the case for mutual funds managers for example), the performance of the industry is gauged by the IRR. But IRRs are not directly comparable with the returns extracted from indexes used to measure the performance of quoted stocks or bonds, because IRRs are dependent on the timing of the cash flows, while returns gained from indexes are not.

A very important issue is also related to the ability to invest in the best performing funds. The spread observed between good and bad performers is significantly higher in the private equity asset class than the one observed for quoted stocks or bonds. In other words, aggregate indexes might not give a true picture of the dispersion of performances.

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Introduction

All those issues have recently initiated new research (see bibliography for more information), but most of the work done so far was concentrating on US private equity funds. This paper is another step forward in understanding and gauging the role of private equity in the portfolio of institutional investors by focusing on European funds. Based on data collected by Thomson Venture Economics, this document follows two approaches in order to solve some of the issues presented above:

• The first part, “Asset allocation and European private equity: a first approach using aggregated

data” written by Patrick Artus and Jérôme Teïletche, CDC Ixis Capital Markets Research Department, deals with the stale pricing or smoothing process. In order to do this, this section is based on aggregate periodic IRRs of venture capital and buyout funds available through the VentureXpert database. A first efficient frontier is drawn from this analysis.

• The second part, “European Private Equity Funds, a Cash Flow Based Analysis”, conducted

by Christoph Kaserer and Christian Diller, Center for Entrepreneurial and Financial Studies

– University of Munich, is on a database comprising cash flows from 780 funds2_{. By producing}

Public Market Equivalent returns, this section also leads to the production of a second efficient frontier.

The conclusion confronts the results gained through the two approaches. Because all findings in the first and second part are based on historic returns, a discussion regarding future developments concludes the document.

2 _{It should be noted that Thomson Venture Economics provided the Center for Entrepreneurial} and Financial Studies – University of Munich with an anonym database, i.e. it was not possible to connect cash flows with a specific fund.

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Aggregated data

2. Asset allocation and European private equity:

A first approach using aggregated data

Patrick Artus Jérôme Teïletche

CDC IXIS Capital Markets Research Department

An analysis of the profitability of investments in private equity is no easy task. By definition, the value of investments is not known publicly at all times and does not result from the interaction of supply and demand on a centralised market. In practice, the value of investments in private equity is known in only a few specific occasions:

• While it remains within private equity: if the company receives new investments or if it

moves from a general partner’s portfolio to the portfolio of another (i.e. on a private equity secondary market);

• When it exits from the private equity sector: if another firm buys the company or if it is

introduced to the stock market.

Between these different periods, we can draw only on estimated values provided by the general partner.

Moreover, the conventional investment manner3 _{results in the internal rate of return (IRR)}

being the standard measure used throughout the industry. This contrasts with standard assets where profitability indexes are built as if the entire investment occurred in the initial period.

2.1 General remarks on private equity returns

All in all, the profitability measures of private equity show several unique features that must be taken into account, notably if one wishes to compare the profitability of private equity with that of other financial assets. More specifically, we will discuss in this section three characteristics of profitability in private equity:

(i) Biases of short- and long-term measures; (ii) IRR versus time-series returns;

(iii) Dispersion of performances of funds within a category or according to the age of investment and the type of companies in which the investment is carried out. Characteristic 1: Biases of short- and long-term measures.

As pointed out previously, the returns posted by private equity fund managers are disclosed only in a few specific circumstances: (i) if the investee company is introduced on the stock market; (ii) if the investee company is acquired; (iii) if it receives additional financing; (iv) if it files for bankruptcy (i.e. its value implicitly sinks to zero).

These characteristics entail several biases in measuring private equity returns. These biases are different in nature in the near and long term.

3 _{We understand conventional investment manner in this context to be the opposition between,} on the one hand, an initial investment (a draw-down or take down from the investor – the limited partner – to the fund managed by the venture capitalist – the General Partner) and consecutive disbursements carried out at irregular dates and, on the other hand, a final value at a period that is also random.

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Aggregated data

In the short term, posted returns are based on estimated values (appraisal returns), drawn up by the general partners. Because they are seeking to be cautious or responding to a simple human reflex, the general partners can be tempted to smooth these returns, i.e. wait for a positive or negative event to be confirmed before factoring it into the value of the investment. Lets assume a major shock, such as the stock market crash in October 1987, when the S&P 500 index plummeted 20% in just one day on 19 October. Back then it took one year before the market reached again its pre-crash level, but as early as the beginning of November 1987 the index was at just 10% off that level, its closing level

on 16 October4_{. Late October was consequently characterised by major volatility in}

stock market returns. Let us imagine a partner who must value its holdings in unlisted companies. The market of listed companies, or segments of this market, provides a reference for drawing up such valuations, because both markets, for listed and unlisted companies, are exposed to common macroeconomic or sector risks. Faced with large fluctuations in the stock market, the general partner could be tempted to wait for the market to cool down before drawing up a valuation. It is likely that this will result in returns on his investments that appear less volatile than those of the stock market. From a more general point of view, this process of smoothing returns, inherent to the fact that estimated values are used for private equity, induces at least two evident biases: (i) an under-estimation of volatility;

(ii) an under-estimation of the correlation with other assets, including those that constitute the reference for private equity (notably, stock market assets).

These biases are referred to alternatively under the generic terms of “stale pricing bias” or “smoothing bias”. The best indicator for such a process of smoothing returns is an

autocorrelative5 _{structure characterised by positive and very high values for the first}

lags. When asset allocation is analysed via standard tools, these biases have dramatic consequences. In particular, they lead to allocations that are excessively in favour of the asset of which the returns are smoothed since its risk is individually undervalued (via the standard deviation) or collectively with the other assets (via correlation).

The purpose of this study precisely consists in analysing the results in terms of allocation of private equity into investors’ portfolios made up of various standard assets. In particular, we have worked on relatively high frequency data, which is therefore likely to be significantly affected by the smoothing bias. When applying modelling as detailed in Appendix 1, we will see, however, that there are various solutions to offset this problem. These techniques are used in our empirical section.

Note that the impact on average returns is not treated within the theoretical framework discussed in this study. In particular, it supposes that the entire information of “real” returns is found in the track record of smoothed returns. In the longer term, the average of returns might also be moved upwards. One reason lies in the selection bias discussed hereafter. Another reason, closer to our concerns in this part, is that any additional performance of private equity is perhaps simply the compensation for the lower liquidity of this asset.

4 _{After just two days (21 October), the market had already pared back nearly 15%, before falling} anew subsequently.

5 _{The autocorrelation to the order k of a process denotes the expectation of the correlation between} realised value of the process at time t (e.g. a return) and its realised value k periods ago.

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Aggregated data

Access to this premium would be acquired only by investors accepting to “block” their funds for a very long time, while investors with a shorter horizon lose this premium by selling the illiquid asset on the secondary market at a discounted price, however such approach has been ignored in this study. Note that, ceteris paribus, one favours private equity over other more liquid assets.

Another solution consists in drawing exclusively on very long-term returns. With aggregated data, this gives rise to the problem that only extremely small samples are available for observations. Moreover, drawing on very long-term data does not protect the analysis totally against the existence of biases. In particular, the returns achieved in the long-term for private equity are likely to be affected by a selection bias. For, in practice, these returns are realised only when the company is acquired or is introduced on a stock market. In each case, the likelihood of observing a return is highly related to the likelihood that the value of the company is significant. This generates a selection bias, in the meaning that observed returns concern exclusively firms whose value has increased over time. Firms that have not seen their value rise, on the contrary, will be more likely to remain within the private equity segment.

Cochrane (2001) has sought to model this selection bias in the case of venture capital in

the United States6_{. All in all, according to his results, taking into account the selection}

bias would result in the (log-) average returns of venture capital dropping from 108% to 15% per year. Another solution consists in concentrating on fund level rather than individual direct investments. In particular, the returns thus achieved probably include both successful projects as well as failures (including bankruptcies). Baier et al. (2002) show that in this case the results obtained are consistent with those based on individual projects by taking into account the selection bias. Therefore, the data used in this study, at aggregate level as well as individual level, covers funds only, not individual direct investments. Characteristic 2: IRR versus time-series returns

As we pointed out previously, the IRR is the most appropriate measure of performance for investments in private equity. By contrast, for standard assets, i.e. equities and bonds, profitability is measured via performance indices. These indices do not take into account a specific structure of investments. Instead, they suppose that there are only two dates of interest: the starting point, which corresponds to the investment, and the end date, which corresponds to the date when the performance is recorded. Box 1 shows that it is extremely difficult to reconcile the two performance measures (see also examples in Part II). In fact, they can be equal in only the specific circumstance where the investment in private equity does not imply intermediate flow between the initial investment and the date of realisation.

To tackle this problem, two solutions for either individual funds or for aggregate level can be adopted. In the first case (treated in the second part of this study), one applies to standard assets the structure of private equity cash flows. The result is a measure called Public Market Equivalent (PME) where we suppose that every time the investor initiates a cash flow in private equity (either investment or distribution), he initiates an equivalent cash flow with respect to the standard asset. PME is the IRR of this second type of investment.

6 _{His model is based on a CAPM for the returns on venture capital (in log terms), a logistic} specification for the likelihood of being listed (or receiving additional financing) at date t and a linear specification for the likelihood of bankruptcy at date t, with the two probabilities being conditional on the value of the company at date t (the likelihood of remaining in the venture capital industry is deduced from the two other probabilities).

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Aggregated data

In the second case (which we treat in this first part), we define regular time intervals (quarterly or annual) to assess the profitability of private equity. More exactly, we calculate an IRR for one period. Net asset value (NAV) at the beginning of the period is booked as a negative cash flow. NAV at the end of the period is booked as a positive cash flow. The IRR that equalises both flows is similar to the Time Weighted Return (TWR) and represents a short-term measure of the profitability of private equity.

The empirical work carried out in this first part is based on the latter measure using data provided by Thomson Venture Economics (TVE). In this part we analysed private equity at an aggregate level (as the IRR remains the most appropriate measure for the analysis of individual funds) and compared it with other assets. However, several remarks must be made in advance with respect to this measure:

• In the definition given above, we have supposed that there was no intermediate cash

flow between the start date and the end date. In the case where an intermediate flow occurs, it is introduced into the calculation of the TWR. We then face again the problem of the comparison with standard assets. Nevertheless, if one concentrates on very short periods of time (such as quarterly periods), the likelihood of such a cash flow is relatively small.

• We include in the calculation only funds that: (1) have a “real” NAV at the end of

the period, in the sense that it is not estimated or automatically reported by Thomson Venture Economics; (2) has a real NAV reported at the beginning of the period when the fund is set up and has its first cash flow during the covered period. Note that these restrictions fail to curb the problem of stale pricing that we treat in our empirical part, in the sense that a NAV can be repeated and included in the calculation as long as the general partner carry it out.

• We work on aggregate TWRs. TVE gives importance to a measure called Pooled

TWR where the aggregation between the various funds is based on the sum of all cash flows for each fund by supposing one is dealing with just one fund. TVE also calculates simple averages of the IRR of each fund – and this can bias results in the case where small funds post returns that diverge markedly from the average – or averages of IRR weighted by the capital allocated to each fund – and this is meaningful only if all investments are carried out the beginning of the life of the fund only.

Excursion: IRR vs time-series returns

Returns of private equity investments are of a particular type. They are dependent on times when investments are made and generally take the form of several injections before value is realised in the end. This is why the concept of Internal Rate of Return

(IRR) is usually employed to measure performance for private equity. This measure is

different from the one usually employed for other assets, where it takes the form of a time-series return. How can we reconcile both measures of the performance of an investment?

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Aggregated data

Let be the sample period. For each period up to , the managers of the private equity project are

allowed to get a new financing. Let be the amount raised in period t. At time T, the value of the project is realised with terminal value . The IRR gives us an indication of the average per-period return associated with the different investment amounts .

Formally, the IRR is defined as the solution of:

. (1)

From (1), it is obvious that the IRR depends on the structure of payments. The more the investments are realised at the end of the period, the higher the IRR.

Let be the one-period continuously compounded stock market return at t. Here, we assume that it is computed in log terms such that where denotes the level of the stock market at time t.

We denote by the average return observed over the sample period, .

In traditional asset allocation models these average returns are compared for different assets. The problem is that it is difficult to reconcile IRR and . Implicitly, is computed as if all the investment was made at the beginning of the period (ignoring the impact of compounding). So, except in the special case when for (i.e. all the investments are made in the first period of the private equity investment), it seems impossible to compare IRR and . To illustrate this point, let us imagine an investor who invests each period the same amount (say 1/T) at .

At time T, the total return on its portfolio would be , about half the return, which is expected when we say “the average return of the S&P 500 index was between 0 and T”. The only solution seems to reproduce the structure of investments in private equity funds using realised returns by other asset, that is:

. (2)

By comparing and , we get an idea about the difference in returns of private equity and of other assets. Assuming that IRR is small enough so that , we deduce that (saying private equity is more profitable than the stock market, while the investment dates are being the same in both cases) if and only if:

, (3)

that is, the IRR should be larger than a (structure of payments-) weighted stock market return. In the simplest case where stock market returns are constant, , the inequality (3) resumes to .

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Characteristic 3: Dispersion of returns versus aggregate measure.

The empirical analysis carried out in this study is based on the evolution over time of TWRs aggregated in a pool. In the case of Europe, we can draw on such private equity data only since 1980. Nevertheless, apparently the data of the beginning of the sample are not very representative. In particular, figure 3 clearly shows a structural change in the number of funds reporting since 1994. Consequently, our study will bear on the period 1994-2003.

Figure 3: Sample size of European returns

Source: EVCA/CDC Ixis Capital Markets, based on data provided by Thomson Venture Economics Beyond the choice of the sample period, our empirical analysis, based on aggregated measures, implies several approximations insofar as these aggregate measures portray only imperfectly the dispersion of returns.

A first source of dispersion of private equity returns is accounted for by the age of the investment. During the initial years, the investor in private equity has to expect negative cash flows and returns because of the initial investment and management fees paid to the general partner. This phenomenon is known as the J-curve phenomenon, illustrated in figure 4. At the beginning the return is negative, but subsequently the gradual increase in the valuation of the project little by little leads to positive returns. Generally speaking, the break-even point (i.e. when the IRR reaches zero) occurs around the fifth year of the investment. 300 250 200 150 100 50 0 ‘84 ‘85 ‘86 ‘87 ‘88 ‘89 ‘90 ‘91 ‘92 ‘93 ‘94 ‘95 ‘96 ‘97 ‘98 ‘99 ‘00 ‘01 ‘02

■ All private equity

■Venture capital

Number of funds

Year

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Figure 4: The J-curve phenomenon

Source: EVCA/CDC Ixis Capital Market

This phenomenon is found again indirectly when one analyses the profitability of the various segments of private equity. Figure 5 shows the return-to-risk ratio for these various segments. We can notably see that the returns for the general partner specialised in companies that have already developed (expansion and later-stage investments) present a higher average and a lower risk than that of funds specialised in start-up companies (seed and start-up investments). Implicitly, this result reflects the fact that the J-shaped curve phenomenon will be less pronounced for the former than for the latter because the underlying firms will be able to post results faster or will be faster to withdraw from the private equity field.

Figure 5: Risk/return profile for European private equity components (annual pooled weighted returns)

Source: EVCA/CDC Ixis Capital Markets, based on data provided by Thomson Venture Economics

20 15 10 5 0 -5 -10 0 1 2 3 4 5 6 7 8 9 10 IRR (%) Year 35 30 25 20 15 10 6 7 Standard deviation (%) Arithmetic mean (%) 8 9 10 11 12 Seed/Start-up

Buyout and Mezzanine

Venture capital

All private equity

Balanced venture capital

Expansion/Later stage

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Note: for exact definition of the different segments, please refer to chapter 3. Venture capital comprises seed/start-up and development/expansion/later-stage funds as well as balanced venture capital funds (i.e. investing in the two previous mentioned categories). The buyout segment consists of both buyout and mezzanine funds.

A second measure of dispersion concerns the diversity of performances by funds for a given period of time. To illustrate this phenomenon, the two charts below represent year after year the aggregate performance (pooled) as well as the first quartile (top of the vertical

bar)7_{and the last quartile (bottom of the vertical bar) of the distribution of performances}

of all the underlying funds. For the following exercise and the two graphs the terms venture capital has been defined broadly as all early and later-stage investments and while the buyout segment is solely buyout investments and excludes mezzanine. Two points can be noticed.

Figure 6: Dispersion of venture capital returns (1988 – 2002)

100 80 60 40 20 0 -20 -40 -60 ‘88 ‘89 ‘90 ‘91 ‘92 ‘93 ‘94 ‘95 ‘96 ‘97 ‘98 ‘99 ‘00 ‘01 ‘02 Year Pooled average

7 _{By construction, 25% of funds have a higher or equal performance than the first quartile and 25%} of funds have a less good or equal performance to the last quartile. Note then that the totality of the vertical bar covers the funds corresponding to 50% of the distribution closest to the median.

Aggregated data

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Figure 7: Dispersion of buyout returns (1988 – 2002)

Source: EVCA/CDC Ixis Capital Markets, based on data provided by Thomson Venture Economics On the one hand, the dispersion of performances of funds seems to have increased markedly over time, and this is partly a consequence of the fact that all the monitored funds have increasingly grown larger. On the other hand, in certain years, the pooled statistic favoured in our study provides no more than a rough – or even misleading – measure of the performance of most funds. In particular, in certain years, the pooled statistic is equivalent to the first quartile; this can pose problems when the first quartile is positive while at the same time most funds have reported a negative performance. Note, nonetheless, that this criticism applies above all to buyouts and to a lesser extent only to venture capital (see for example 2001 and 2002). This is hardly surprising insofar as the buyout category is far more heterogeneous. This is why in our study, we have given preference first to venture capital (2.2). The case of buyouts is treated later and we will see that the problem of heterogeneity is far more prevalent in this case (2.3).

2.2 Asset allocation among venture capital, equities and bonds in

the European case

In this part, we analyse the problem of allocation between three assets: venture capital, equities and sovereign bonds. For the reasons mentioned previously, the period of

analysis is 1994Q1-2003Q2. Equities are represented by the MSCI Europe index8_{; it}

covers total performances, i.e. they are made up of capital gains and dividends.

60 50 40 30 20 10 0 -10 -20 -30 -40 ‘88 ‘89 ‘90 ‘91 ‘92 ‘93 ‘94 ‘95 ‘96 ‘97 ‘98 ‘99 ‘00 ‘01 ‘02 Year Pooled average

8 _{This index was chosen because it is available for a long time while the Stoxx index is only available} since 1987. In the second part of this study, we need data back to the early eighties. Note that the correlation between the Stoxx and MSCI Europe quarterly returns is above 99% over the period for which both are available.

Aggregated data

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The bonds used here are those of all the countries of the European Community (weighted in market value terms; JP Morgan index). Once more, we are dealing with total performances, including capital gains and the payment of coupons. Note that for standard assets (equities & bonds), we have adjusted performances for management fees in order to be in line with venture capital returns that are adjusted for fixed and variable fees. We have assumed that management fees are 50 bps for equities and 20 bps for bonds, typical of the fees paid by institutional investors.

In a first section, we deal with the problem of asset allocation in a standard manner. In a second section, we illustrate the presence of smoothing of the venture capital returns. The third section proposes a correction of the impact of smoothing on the variance of venture capital and its correlation with equities. The last section proposes reformulating the problem of allocation from corrected statistics. The details of the methodology are provided in the Appendix I.

2.2.1 A naïve approach of asset allocation

The standard problem of asset allocation needs to estimate the average, the standard deviation and the correlation matrix of returns on various assets. These various statistics are detailed in table 1 below and table 2 on the next page.

It can be seen that over the period 1994-2003, the various assets have presented an average return that ranges from 7.4% for bonds to 9.8% for venture capital via 8% for equities. The risks associated to these various assets are also very different, with a naturally lower risk for government bonds and a similar risk for venture capital and equities although it is slightly lower in the former case. All in all, the Sharpe ratio is far higher for bonds and equivalent in the case of venture capital and equities. Table 2 shows a 33% correlation between venture capital and equities and for both, venture capital and equities, a correlation with bonds close to zero, which is more favourable for venture capital. Table 1: Descriptive statistics for quarterly returns (as %; after management fees)

Venture Capital Equities Bonds

Risk/return profile

Geometric average/quarterly figures 2.0 1.5 1.9

Geometric average/annualized figures 8.2 6.3 7.7

Arithmetic mean/quarterly figures 2.4 2.0 1.8

Arithmetic mean/annualised figures 9.8 8.0 7.4

Standard Deviation / quarterly figures 9.9 10.5 2.4 Standard Deviation / annualised figures 19.8 21.1 4.7

Sharpe ratio (risk-free rate = 3.6%) 31% 24% 84%

Dispersion measures Minimum -13.7 -23.8 -3.6 Lower quartile -5.6 -2.4 0.4 Upper quartile 7.2 6.9 3.3 Maximum 22.9 23.3 6.2

Aggregated data

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Table 2: Correlation matrix

Source: EVCA/CDC Ixis Capital Markets, based on data provided by Thomson Venture Economics On the basis of such data, we obtain the efficient frontier as shown in figure 8 (after management fees).

The chart details a few points drawn from this efficient frontier9_{. The portfolio with a}

minimal variance (A) is made up of 5% of venture capital, 2% of equities and 93% of bonds. The portfolio that maximises the Sharpe ratio when the risk-free asset is introduced (B) is made up of 8% of venture capital, 2% of equities and 90% of bonds. Other efficient frontier portfolios allow a higher ratio with a higher risk. They allocate assets increasingly to venture capital at the expense of equities and bonds. The weight of equities rapidly decreases from a maximum of 2% attained at the minimum variance portfolio and is equal to zero on point C. Then, venture capital substitutes to bonds from this portfolio onwards until one reaches the portfolio (RH scale) made up only of venture capital.

Venture Capital Equities Bonds

VC 1.000 0.334 -0.014 Equities 0.334 1.000 0.064 Bonds -0.014 0.064 1.000 10.0 9.5 9.0 8.5 8.0 7.5 7.0 0 5 10 15 20 25

Return (% per year)

Risk (% per year)

Portfolio A (minimum variance): 5% VC, 2% Equities, 93% Bonds

Portfolio B (maximum of the Sharpe ratio): 8% VC, 2% Equities, 90% Bonds

Portfolio C (Two assets portfolio): 22% VC, 0% Equities, 78% Bonds

Portfolio D (maximum return portfolio): 100% VC, 0% Equities, 0% Bonds

9 _{Strictly speaking, the efficient frontier, which maximises return for a given level of risk, starts from} the portfolio with minimal variance. All the dots located below the portfolio with minimal variance (denoted by A in the chart above) are dominated (other portfolios allow a higher expected return to be achieved for a same level of risk) and do not belong to the efficient frontier stricto sensu.

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All in all, the portfolios thus constituted give venture capital a substantial weight, notably at the expense of equities. As we will now see, this result is partly linked to a probable process of smoothing of venture capital returns.

2.2.2 The smoothing of venture capital returns

The smoothing process of returns (or, in other words, the stale pricing bias) has a major implication on the dynamics of observed returns: they tend to be very auto-correlated. Table 3 shows the autocorrelation coefficient for lags ranging from 1 to 4 for the various assets. While equities seem to be non-autocorrelated, bonds and especially venture capital are marked by a major autocorrelation of their returns. While the autocorrelation of

bonds is difficult to interpret10_{, a smoothing process can probably account for that of}

venture capital.

Table 3: Autocorrelation structure

Source: EVCA/CDC Ixis Capital Markets, based on data provided by Thomson Venture Economics One can seek to detect the smoothing process empirically, as is detailed in the Appendix I. It is supposed the “real” venture capital returns are determined by an underlying factor (general state of the economy), which the stock market reflects satisfactorily. It is further supposed that observed venture capital returns are a moving average function of the “real” venture capital returns, i.e. they are smoothed. Starting from this hypothesis and drawing on the fact that stock market returns are not autocorrelated, the coefficients associated with the smoothing process can be calculated via the estimate of an equation that regresses venture capital returns on constant and variable lags of Equity market returns (including contemporary returns), while the number of lags is assessed by a 90% significance test. Over the period 1994-2003 and in the case of Europe, this leads to the following estimate:

4)

By comparison, a regression of venture capital returns on just contemporary returns on equities leads to the following estimate:

5)

Lag Venture Capital Equities Bonds

1 0.528* -0.038 0.413*

2 0.439* 0.078 0.215

3 0.263 0.116 0.174

4 0.039 0.203 0.180

Note: an asterisk denotes an autocorrelation significantly different from zero at the 95% confidence level.

10_{A potential explanation is the downward trend in interest rates in the 1990s, with the continued} disinflation process until the introduction of the euro and the steady improvement in public finances from 1993 to 2000.

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From (4), we can deduce the implicit smoothing process of venture capital (see Appendix I; equations A9 and A10). More specifically, if we denote by the “real” (i.e. not smoothed) venture capital returns in period t, we can deduce the following relationship with observed returns :

6) ,

so that , , with the notations of the Appendix I. Simplified, observed venture capital returns of a given quarter are approximately an equal-weighted average of “real” venture capital returns over three quarters (including

the current quarter). In an aggregated approach, the Herfindahl11_{index associated}

with this structure is equal to .

2.2.3 The correction of venture capital variance and of the correlation between venture capital and equities

By drawing on the estimated structure of the smoothing process, we can correct the following statistics (the most affected by smoothing):

• The standard deviation of venture capital returns;

• The contemporary correlation of venture capital returns with those on equities.

In the first case, the corrected standard deviation is given by (see Appendix):

, i.e. 34% in annualised terms.

In the second case, the corrected correlation between venture capital and equities is given by (see Appendix):

.

In the Appendix, we suggest that another way to correct the biases linked to smoothing is to draw on data with a lower frequency. From the annual data over the period 1994-2002, we obtain a standard deviation of 27.5% per year and a correlation between venture capital and equities of 0.714.

11 _{Applied to the present context, the index varies between 0 if the smoothing takes an infinite form} (i.e. the returns are extremely smooth) and 1 if they are not smooth at all. The lower the index, the more returns seem to be smoothed. For the reader’s information, a similar calculation in the case of the US venture capital – with the Nasdaq as the reference market – leads to a value of 0.238. Therefore, European returns seem to be less smooth.

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Consequently, the two comparison methods do not give absolutely similar results12_.

Nevertheless, it should be noted that the differences tend to cancel one another out (with the annual estimates, the smaller individual risk of venture capital – given by the standard deviation – is offset in the portfolio by its higher correlation with equities), and as a result, in practice, the allocations obtained with either correction are broadly equivalent. Furthermore, the two corrections point in the same direction: (i) venture capital risk is higher than suggested by the original returns data; (ii) it is more highly correlated to the stock market.

2.2.4 A “corrected“ asset allocation

Drawing on corrected statistics, we obtain the following new efficient frontier represented by the blue curve. For comparison purposes, we simultaneously show the efficient frontier obtained previously with the original data.

Source: EVCA/CDC Ixis Capital Markets, based on data provided by Thomson Venture Economics Two points can be noticed:

• On one hand, the correction of the standard deviation and of the correlation naturally

leads to an efficient frontier that is located lower on the right of the risk/return plan. This means that for an equivalent average level of return, the risk associated with new portfolios is high (e.g. portfolio D’ versus portfolio D that posts the same average return but a higher risk; 34% vs. 20%).

12_{One needs to be aware of the fact that the sampling period is not absolutely equivalent between} the quarterly and annual data, because of the loss of the first half of 2003 with respect to annual data. 10.0 9.5 9.0 8.5 8.0 7.5 7.0 0 5 10 15 20 25 30 35 40

Risk (% per year)

Frontier with raw statistics

Frontier with corrected statistics

Portfolio A' (minimum variance): 1% VC, 3% Equities, 96% Bonds

Portfolio B' (maximum of the Sharpe ratio): 3% VC, 2% Equities, 95% Bonds

Portfolio C' (Two assets portfolio): 16% VC, 0% Equities, 84% Bonds

Portfolio D' (maximum return portfolio): 100% VC, 0% Equities, 0% Bonds A B C D

Aggregated data

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• On the other hand, the weight of venture capital tends to decrease to the benefit of bonds and to a lesser extent to equities. For the lower part, we mention as an example the portfolio A’ with minimal variance (1% of venture capital against 5% for A) or the portfolio B’ with a minimum Sharpe ratio (3% of venture capital against 8% for port-folio B). For the upper part, we mention the portport-folio C’ where there are no more equities in the portfolio (16% of venture capital against 22% for portfolio C). To summarise the impact of the correction on all portfolios, we see that the share of venture capital is reduced in portfolios. However, the weight given to venture capital remains substantial, notably in the portfolios that would be chosen by investors who are not very risk-averse (upper part of the frontier).

2.3 Introducing buyout funds into the portfolio

In the previous section, we have considered the case of venture capital funds only. The other important category of private equity is buyout funds, which represent around 40% of the total number of funds. Due to a bigger size than venture capital funds, buyout funds represent more than 60% of the total private equity funds raised between 1998 and 2002. The following figures do represent the number of funds, which serve as the universe upon which Thomson Venture Economics calculates aggregated returns. The first figure shows raw numbers while the second one expresses for each year and for each category the number of funds as a proportion of the maximum number of funds for a given category in the whole sample (this peak was achieved in 2000 or 2001 depending on categories). In the previous section, we have chosen to begin our analysis in 1994 as it did appear a structural break in the number of venture capital funds covered by TVE at this time. The figures illustrate that in the case of buyouts, this break took place probably later, in 1996. So one should be cautious with returns of this segment before that date.

Figure 10: Sample size for European returns

300 250 200 150 100 50 0 ‘84 ‘85 ‘86 ‘87 ‘88 ‘89 ‘90 ‘91 ‘92 ‘93 ‘94 ‘95 ‘96 ‘97 ‘98 ‘99 ‘00 ‘01 ‘02 Year

■Venture Capital

■Buyout

Aggregated data

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Figure 11: Sample size for European returns as proportion of maximum number of funds

Source: EVCA/CDC Ixis Capital Markets, based on data provided by Thomson Venture Economics The following figures investigate whether the pooled average TWR are sensible measures of the whole distribution of buyout funds returns, as they jointly represent the pooled average (the blue point) and the upper quartile (top of the vertical bar) and the lower quartile (bottom of the vertical bar). This is not the case, at least very less than in the case of venture capital (see above for the same representation for the venture capital case). For instance, one can see that in 1999, the pooled average was larger than the upper quartile (note that this is something which is possible in statistical terms – the maximum return is equal to 1144% in a year). For 2001 and 2002, we see that the pooled return is near the upper quartile. This does induce a significant positive return for the pooled sample (+6.4% in 2001 and +1.3% in 2002) while the returns were neg-ative for the large majority of funds. For comparison, we have the following statistics for the same two years: average return -5.5% and -6.2%; median return -1.4% and –9.5%; capital-weighted average return -6.4% and -6.9%. Due to this fact, we have decided to consider simple average as representative measures of buyout returns.

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% ‘84 ‘85 ‘86 ‘87 ‘88 ‘89 ‘90 ‘91 ‘92 ‘93 ‘94 ‘95 ‘96 ‘97 ‘98 ‘99 ‘00 ‘01 ‘02 Year

■Venture Capital

■Buyout

Aggregated data

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Figure 12: Dispersion of buyout returns (1988 – 2002)

Source: EVCA/CDC Ixis Capital Markets, based on data provided by Thomson Venture Economics An important point is whether the returns of buyout funds are affected by a stale pricing bias. We could not find any clear evidence on that point, at least much less than in the case of venture capital. First, there is no sign of positive autocorrelation in the returns of buyout funds. Second, the returns are only weakly related to lagged equities returns. One might note furthermore that:

• These results on the stale pricing problem are not dependent on the aggregation

method for buyout funds. There is no clear evidence of autocorrelation whenever funds returns are aggregated as a pool, as a simple average or using committed capital weights;

• An analysis of US data shows that the limited evidence – if any – of stale pricing in the

case of buyouts compared to the case of venture capital is again observed. Note, however, that the difference is marked stronger in the European case;

• Whereas, in the case of venture capital funds, the smoothing process is a satisfactory

explanation of the difference between quarterly and annual returns characteristics, it is difficult to reconcile the apparent lack of smoothing process for buyout and the fact that the standard deviation of buyout returns and their correlation with equities’

returns both increase with annual data13_{. This last point again underlines than one}

should be cautious with aggregated returns of buyouts, notably when one tries to compare them with other assets (including other private equity categories).

100 80 60 40 20 0 -20 -40 -60 ‘88 ‘89 ‘90 ‘91 ‘92 ‘93 ‘94 ‘95 ‘96 ‘97 ‘98 ‘99 ‘00 ‘01 ‘02 Year Pooled Average

13_{With the simple aggregation method, the standard deviation for annual returns is 18.8% versus} 10.0% (annualized) for quarterly returns. The correlation between buyouts and equities annual returns is 76% versus 11% for quarterly returns.

Aggregated data

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Our analysis is based on the period 1994-2002 (data were not made available to us for buyouts in 2003). The following table gives the whole set of statistics (including corrected figures for

venture capital returns) used for the asset allocation problem14_.

Table 4: Statistics used for the asset allocation problem

Source: EVCA/CDC Ixis Capital Markets, based on data provided by Thomson Venture Economics These statistics lead to the efficient frontier of portfolios represented in the following figure. By and large, the results are very favourable to buyouts. Its share goes from 17% in the minimum variance portfolio up to more than 95% in the portfolio where there are no more bonds (entitled ‘the two assets portfolio’ in figure 13). From this point, its share decreases regularly being replaced by venture capital up to the maximum return portfolio (not represented here for readability of figure 13) where there are only venture capital funds. Note that the maximum proportion of equities is only 2.5% and that this is attained in the minimum variance portfolio. Note also that venture capital only appears in the final part of the curve (the riskier one).

Figure 13: Efficient frontier with quarterly returns (portfolio composed of buyout, venture capital, public equities and bonds)

Returns (%) Venture Capital Buyouts Bonds Equities

Average 11.3 9.9 7.4 7.7

Std deviation 34.0 10.0 4.8 20.6

Correlation matrix Venture Capital Buyouts Bonds Equities

VC 1.000 0.329 -0.018 0.502

BO 0.329 1.000 0.039 0.114

Bonds -0.018 0.039 1.000 0.053

Equities 0.502 0.114 0.053 1.000

14_{Note that the average arithmetic return is largely higher for venture capital once we exclude} the first two quarters of 2003.

10.5 10.0 9.5 9.0 8.5 8.0 7.5 7.0 0 2 4 6 8 10 12 14

Risk (% per year)

Max Sharpe ratio portfolio

0% VC; 26.5% BO; 2.0% Equities; 71.5% Bonds Two assets portfolio

4.2% VC; 95.8% BO; 0% Equities; 0% Bonds

Minimum variance portfolio 0% VC; 17% BO; 2.5% Equities; 80.5% Bonds

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2.4 Concluding remarks

In this chapter, we analysed the asset allocation problem of private equity compared with other assets from the perspective of aggregated data, which was reconstituted from individual funds. Among the various potential biases, which are discussed in the introduction of this study, we have insisted on a bias known as “the stale pricing problem” or “the smoothing problem”. It implies that for illiquid assets, measures of individual and joint (i.e. with other assets) risks are likely to be downward biased. This has potentially large consequences in an asset allocation framework. We have illustrated the extent to which venture capital returns are exposed to this bias and how to correct for this (either using lower frequency data or inferring the smoothing process from the whole structure of returns). For buyouts, results are less clear-cut. We suspect that there might some difficulties with the aggregation of individual buyout funds. We have notably illustrated that popular aggregated measures (such as pooled aggregates) are not totally convincing representative measures of the whole distribution of returns. Summarising, the results presented above show that even when accounting for the stale pricing problem, there is a large interest for the insertion of private equity funds in the portfolio of European institutional investors. In our opinion, two interpretations of this result can be advanced:

• Either it highlights the fact that interest in private equity is very high and investors

should raise its weight relative to the present situation up to a bracket ranging from 5% to 10% of all assets. For, even after taking into account the corrections that are detrimental for private equity and keeping in mind that we are dealing with net returns whereas returns on other assets are gross, the quantitative results are very positive for private equity;

• Or it can be considered that the corrections made in this study, although they lead

to notable differences from the original results, remain insufficient since they do not factor in a selection bias by not correcting the average of returns. Notably, this problem is posted to be important in the case of buyout funds as shown by the very high average return.

The analysis on individual data proposed in the second part can help dealing with this indecision.

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Cash flow based

3. European private equity funds –

A cash flow based performance analysis

Christoph Kaserer

Christian Diller

Center for Entrepreneurial and Financial Studies (CEFS-TUM) Technische Universität München

This part of the study is based on a data set on European private equity funds that has been

provided by Thomson Venture Economics (TVE)15_{. Before we look at the cash flow based}

performance analysis, we are going to describe shortly the data set used for this work. It should be noted that TVE uses the term private equity to describe the universe of all venture

investing, buyout investing and mezzanine investing16_{. Actually, we have been provided with}

various information related to the timing and size of cash flows, residual net asset values (NAV), fund size, vintage year, fund type, fund stage and liquidation status for a total of 794 funds. Some 14 of these funds have been funds of funds. We excluded these funds from our data set as they combine a number of single private equity funds and, hence, provide redundant information for the purpose of this study. Moreover, given the small sample size it will not be possible to draw general conclusions with respect to the performance of this particular fund type. As far as the different fund types and stages are concerned it should be noted that we use, in accordance with TVE, the following definitions:

Type definitions:

• Venture capital funds (VC): TVE uses the term to describe the universe of venture investing.

It does not include buyout investing, mezzanine investing, fund of fund investing or secondaries. Angel investors or business angels are also not be included in the definition.

• Buyout funds (BO): TVE uses the term to describe the universe of buyout investing and

mezzanine investing. It does not include venture investing, fund of fund investing or secondaries. Angel investors or business angels are also not be included in the definition.

Stage definitions:

• Early-stage (ES): A fund investment strategy involving investment in companies for product

development and initial marketing, manufacturing and sales activities17_.

• Balanced/Diversified (B): A venture fund investment strategy that includes investment in

portfolio companies at a variety of stages of development (seed, early-stage, later-stage).

• Development, later-stage and expansion: (DEV, LS & EX): Development funds provide for

the major growth expansion of a company whose sales volume is increasing. Although the company has clearly made progress, it may not yet be showing a profit. The money invested is used to finance the initial development of the young company. Later stage fund investment involves financing the expansion of a company which is producing, shipping and increasing its sales volume

• Buyout (BO): TVE uses the term to describe the universe of buyout investing and mezzanine

investing. It does not include venture investing, fund of fund investing or secondaries. Angel investors or business angels are also not be included in the definition. The definition involves e.g. leverage buyouts (LBOs), management buyouts (MBOs) and bridge financing.

15_{TVE is recording private equity data for five different world regions. One of them is Europe.} 16_{Fund of fund investing and secondaries are also included in this broadest term. TVE is not using}

the term to include angel investors or business angels, real estate investments or other investing scenarios outside of the public market.

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Cash flow based

As one can see from table 5, around 59% of the sample funds are venture capital funds, while the remaining 41% are categorised as buyout funds. The average fund size according to the

TVE-data is €182.18m17_{. Variation in fund size is considerably high, as the largest fund is}

132 times as large as the median fund. Moreover, as one might expect, buyout funds are on average about 3.7 times as large as Venture capital funds. As far as the liquidation-status is concerned, it should be noted from table 6 that only 95 out of the total of 780 funds have been liquidated before the end of the sample period, ending 30 June 2003. The average size of the liquidated funds is considerably smaller than that of the non-liquidated funds. Evidently, the average fund size has become larger for more recent vintage years. This effect may be driven by the growth of the private equity industry over the 1990s.

Table 5: Sample funds by size and type

Source: EVCA/CEFS-TUM, based on data provided by Thomson Venture Economics Table 6: Sample funds by liquidation status

As far as the stage of the sample funds is concerned, it can be seen from Table 7 that one quarter are early stage funds. As one may expect, the size of the funds differs perceivably depending on their stage.

18_{It should be noted that TVE is calculating the fund size on the basis of committed capital.}

Type All funds VC funds BO funds

No. of Funds 780 458 322 in % 58.7% 41.3% Size in mio. € Average 182.18 86.69 318.92 Median 47.70 31.60 85.10 Stdev 512.14 243.85 720.27 Funds * Size 142,100.40 39,705.78 102,693.20 in % 27.9% 72.3%

Type All funds liquidated funds non-liquidated funds

No. of Funds 780 95 685 in % 12.2% 87.8% Size in mio. € Average 182.18 52.14 201.54 Median 47.70 26.20 53.00 Stdev 512.14 103.62 544.51 Funds * Size 142,100.40 4,953.10 138,055.10 in % 3.5% 97.2%

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Finally, the vintage year distribution of the sample funds can be found in figure 14, the vintage year being the year of fund formation and its first draw down of capital. In accordance with the growth of the private equity industry during the 1990s an unprecedented vintage activity took place in the period 1997 to 2000. However, also during the period 1987 to 1996 a continuous fundraising activity at a fairly impressive level took place. With the exception of the year 1992 about 30 to 40 new funds entered the market every year during this period. Table 7: Sample funds by size and stage

Source: EVCA/CEFS-TUM, based on data provided by Thomson Venture Economics Figure 14: Number of funds by vintage year (number of funds: 780)

Cash flow based

Venture Capital Funds Buyout Funds LS & DEV

ES B & EX BO Late Stage/

Developed/ Buyout Stage All funds Early Stage Balanced Expansion Funds

No. Of Observ. 780 198 116 144 322 in % 25.4% 14.9% 18.5% 41.3% Size in mio. € Average 182.18 70.57 144.13 60.79 318.92 Median 47.70 27.95 40.35 30.61 85.10 Stdev 512.14 122.31 435.79 110.79 720.27 Funds * Size 142,100.40 13,972.86 16,718.80 8,753.76 102,693.20 in % 9.8% 11.8% 6.2% 72.3% 100 90 80 70 60 50 40 30 20 10 0 ‘72 ‘73 ‘74 ‘75 ‘76 ‘77 ‘78 ‘79 ‘80 ‘81 ‘82 ‘83 ‘84 ‘85 ‘86 ‘87 ‘88 ‘89 ‘90 ‘91 ‘92 ‘93 ‘94 ‘95 ‘96 ‘97 ‘98 ‘99 ‘00 ‘01 ‘02 ■ VC ■BO Year Number of funds

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Cash flow based

3.1 Cash flow characteristics of European private equity funds

In this section we are describing the cash flow patterns of European private equity funds. The peak of aggregate takedowns (also called draw downs, i.e. the money committed by the investors or limited partners actually invested in the fund) as well as distributions or disbursements, i.e. the money returned by the fund managers (or General Partner) to the investors (or limited partners) is in the year 2000, as shown in figure 15. Takedowns of committed capital by all the sample funds aggregated to €18.4bn in this year; simultaneously, distributions aggregated to €13.5bn. It should be noted that according to EVCA reports the aggregated volume of funds raised in the European private equity industry in the year 2000 was almost €44bn. Hence, we can infer that for this particular year the sample of funds provided by TVE for the purpose of this study covers about 43% of the fund volume tracked by the EVCA.

The growth in the private equity industry is strongly correlated with the performance of the public equity market. In fact, figure 15 impressively proofs that the growth of private equity investments during the 1990s was strongly correlated to the lasting positive stock market performance during this period.

Figure 15: Time pattern of aggregated sample funds’ cash flows (number of funds: 780)

20000 18000 16000 14000 12000 10000 8000 6000 4000 2000 0 V alue in mio. € ‘72 ‘73 ‘74 ‘75 ‘76 ‘77 ‘78 ‘79 ‘80 ‘81 ‘82 ‘83 ‘84 ‘85 ‘86 ‘87 ‘88 ‘89 ‘90 ‘91 ‘92 ‘93 ‘94 ‘95 ‘96 ‘97 ‘98 ‘99 ‘00 ‘01 ‘02 Year

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Cash flow based

Figure 16: Funds’ takedowns and public equity market performance (number of funds: 780)

As far as structural issues of cash flow patterns are concerned four questions appear in this context. First, how long does it typically take until the general partner has taken down the committed capital? Second, what is the typical time pattern of disbursement? Third, how long does it typically take for a limited partner to get back his invested capital? Fourth, are these patterns different depending on fund size?

An answer to the first question is given by figure 17. The average fund draws down 25% of the total investment volume when starting its business. Within the first three years 63% of total committed capital is invested in the fund. It should be noted that according to Ljungqvist/Richardson (2002) the average US fund draws down 57% of the committed capital within the first three years. Moreover, it seems that capital drawdown is faster for venture capital funds than for buyout funds. However, the difference is not that large, as general partner of venture