Post on 11-Oct-2020
Private Equity Fund of Funds vs. Funds – APerformance Comparison
Nathalie Gresch and Rico von Wyssa
October 13, 2010
Based on a comprehensive sample of 1641 funds this article investigates
the performance of private equity fund of funds versus direct fund in-
vestments. On a risk adjusted basis, fund of funds outperform the ag-
gregated direct funds. When separated into categories such as buyout,
venture and fund of funds, buyout funds exhibit the most attractive risk
return profile.
Analyzing how fund performance depends on macroeconomic variables,
direct funds generate procyclical returns: returns increase with high
public market performance and economic growth as well as declining
corporate bond yields. For fund of funds we cannot observe such a
pattern.
JEL classification: G11, G12
Keywords: Private Equity Fund, Fund of Funds, Performance
a Swiss Institute of Banking and Finance, University of St. Gallen, Switzerland.
We kindly thank SAM for providing the data.
Address for correspondence: Swiss Institute of Banking and Finance, University of St. Gallen, Rosen-
bergstrasse 52, CH-9000 St. Gallen, Switzerland. Email : heinrich.vonwyss@unisg.ch.
1 Introduction
In the last decade fund of funds have become one of the most important investor groups in
private equity funds. According to Preqin (2010), their share of total capital contributions
to direct private equity funds amounted to as much as 22% in 2009.1 Despite the increasing
importance of private equity fund of funds the risk and return characteristics of this fund
category are not yet fully understood. Due to the scarcity of data, the academic literature
on this topic, especially empirical research based on real fund of fund data, is rare. The
article closest to ours is Weidig and Mathonet (2004), who emphasize the positive return
characteristics of fund of funds. They use a sample of 1027 direct funds and a simulation
approach to obtain fund of fund returns and find that fund of funds, as a managed portfolio
of twenty funds, offer significant diversification effects relative to direct fund investments.
Furthermore, they report a similar risk profile of fund of funds with respect to a public
market index, with no total losses and a symmetric distribution of returns.
Most articles study risk and return characteristics as well as performance drivers of
direct fund investments. Ljungqvist and Richardson (2003) provide the first analysis of
private equity returns based on actual cash flows by looking at returns of large institutional
investors that invested into 73 funds between 1981 and 1993. They report that it takes
over eight years for the internal rates of return (IRRs) to turn positive and more than
ten years for private equity returns to exceed public equity returns. In addition, they
document an out performance of private equity relative to public market returns of 5%
or more per annum. Superior performance of private equity investments, expressed by
significant positive Jensen’s alphas, is also reported by Groh and Gottschalg (2006), who
use a sample of 199 cash flows from 133 transactions completed in the US between 1984
and 2004. Ick (2006) uses a data set containing information on 243 funds spanning from
1975 to 2003. He finds marginal out performance of private equity investments on a gross
of fee basis. Kaplan and Schoar (2005), who study returns of more than 746 funds of
vintage years 1980 to 2001, find that besides large heterogeneity across funds, private
equity investments earn returns (net of fees) approximately equal to the S&P 500. In
addition they document that the main drivers of performance are past fund performance
and fund size. Furthermore, they provide evidence for pro cyclical fund performance.
Phalippou and Gottschalg (2009) use a later version of the data set examined by Kaplan
and Schoar (2005), which includes 852 funds raised between 1980 and 2003. They report
1Compare The 2010 Preqin Private Equity Fund of Funds Review, p. 1.
1
average gross performance of 3% per annum above the S&P 500, but a net of fees under
performance of 3% per annum. Furthermore, they find that adjusting for risk decreases
performance by about 3% per year, resulting in a net of fees alpha of -6% per year. This
shows the importance of fees when measuring fund performance. Driessen et al. (2008),
by analyzing 958 mature funds of vintage 1980 to 1993, find a negative alpha and a high
CAPM beta for venture funds as well as a lower beta and a slightly positive but statistically
insignificant alpha for buyout funds.
While the authors above mainly compare private equity fund returns to public market
returns, the following articles concentrate on determinants of fund returns. Diller and
Kaserer (2007) use a dataset of 200 mature European private equity funds of vintage 1980
to 2003 and show that fund flows and general partners’ skills have a significant impact
on performance. In addition, they find a negative impact of the stock market return in
the vintage year of the fund on its final return. Moreover, fund returns are found to
be negatively correlated with the growth rates of the economy as a whole and unrelated
to public market returns. In contrast, Aigner et al. (2008), by using data on 358 funds
raised between 1974 and 2007, find that strong stock market and GDP growth during the
fund’s lifetime positively affect fund returns. Furthermore, they support the case for out
performance of experienced private equity funds and document that high interest rates
during the fund’s lifetime and high commitment volumes have an adverse impact on fund
returns. The approach in our article to explain fund returns is very similar to that of
Phallipou and Zollo (2005), who use a dataset of 705 funds raised between 1980 and 2003.
They provide evidence for positive correlation between fund performance with both the
business cycles and public stock markets. In addition they attribute low average fund
performance to weak returns of small and inexperienced funds. In summary, the findings
in previous literature, both concerning relative private equity performance and drivers of
returns, are rather divergent.
Our article extends the existing literature for the following reasons. First, the analysis
is based on real fund of fund data as opposed to simulated fund of fund returns. Second,
the data set includes detailed cash flow information which allows for the calculation of a
number of different performance measures and net of fees figures. This has the positive
effect that modeling of the missing values (e.g. fees) is redundant. Finally, as most of the
current research focuses on final IRR values, in this report the investigation is extended
to the full IRR structure, which allows for a more detailed performance comparison.
By using a data set provided by Preqin, we examine the following issues. First, we
2
investigate the performance of private equity fund of funds and relate it to the performance
of direct fund investments. Performance measures used for the evaluation besides total
value to paid-in (TVPI) and final IRRs are duration and some characteristics of the IRR
structure, such as the time it takes until the IRR turns positive or the maximum IRR
during the fund’s life. In addition, we compare the performance over different vintage
years. On an aggregate level, fund of funds’ risk and return characteristics are found
to be superior to direct funds, which is mainly due to less dispersion in fund of funds
returns. However, when split up by categories, buyout funds exhibit the most attractive
risk return features. Second, we analyze how macroeconomic conditions at the time of
investments as well as during the fund’s life influence fund performance. Regarding the
latter, we document for direct fund investments pro cyclical behavior with increasing fund
returns in line with rising stock market returns, growing real GDP and decreasing returns
on corporate BAA bonds. Moreover, when corporate bond yields and credit spreads are
high at the time investments are made, fund performance is higher too. Fund of funds
seem to be less prone to the general market development during the fund’s life. The most
significant explanatory variable for those is the real GDP growth rate prior to the start
date, with 52.6% of explained variation in returns.
The remainder of the article is structured as follows. In Section 2 we describe the
sample. Section 3 provides the results of the performance comparison of fund of funds
and direct fund investments. Section 4 investigates the exposure of fund performance to
macroeconomic factors and Section 5 briefly concludes.
2 Data
2.1 Full Sample
We use two main sources of data. Private equity fund cash flow data stems from a database
maintained by Preqin. Additionally, we obtain data on stock performance (MSCI World
Index), real GDP (US, chain weighted gross domestic product), corporate bond yields
(corporate BAA bonds as reported by Moody’s) and treasury bill rates (10 year treasury
bonds) from Bloomberg. The sample from Preqin provides the following data for each
fund: the amount and date of all cash flows (to/from investors), the quarterly net asset
values (NAVs) from 1979 to February 2010 and fund characteristics, such as fund type,
status, fund focus and size. Cash flows are net of fees and include all fee payments to
general partners as well as carried interest. The dataset consists of 1641 funds of which
3
204 are officially liquidated. Figure 1 shows the split-up of the different fund categories as
defined by Preqin.
Figure 1. Fund Categories
This figure illustrates the split-up of the different fund categories. Preqin distinguishes between
24 different fund types, of which Buyout, Venture, Early Stage and Fund of Funds are the largest
groups. Other fund types, representing more than 3% of total number of funds, include Real
Estate, Mezzanine and Distressed Debt. Finally, fund types with less than 3% of the funds are
Expansion (2.3%), Natural Resources (2.3%), Secondaries (2.2%), Balanced (2.1%), Late Stage
(2%), Early Stage: Start-up (1.2%), Early Stage: Seed (1.1%), Infrastructure (0.8%), Special Sit-
uation (0.7%), Co-investment (0.5%), Venture Debt (0.5%), Timber (0.3%), Co-Investment Multi-
Manager (0.2%), Real Estate Co-Investment (0.2%), Real Estate Secondaries (0.2%), Turnaround
(0.2%) and Direct Secondaries (0.1%).
2.3%2.3%
2.2%2.1%
2.0%1.2%
Mezzanine3.6%
Distressed Debt3.5%
Real Estate4.9%
Early Stage8.2%
Fund of Funds8.3% Venture (General)
22.0%
Buyout32.6%
1.1%
As for the geographic distribution, while the largest share of funds (>80%) invest in
the US, 11% of the funds focus on Europe, which leaves 9% concentrating on the rest of
the world. Table 1 summarizes the full sample. Due to the rapid industry growth in the
90s, the earlier years contain relatively fewer fund observations. We refer to “Combined”
as for all the fund types excluding fund of funds.
2.2 Samples for Performance Comparison
Since there is no market value for ongoing investments, accurate performance calculations
are only possible for sufficiently mature funds. Throughout Section 3, two samples of the
data are used. In sample A, we follow the approach of Phallippou and Gottschalg (2009)
4
Table 1. Sample Overview
The sample consists of combined funds (Combined), buyout funds (Buyouts), venture funds (Ven-
tures) and fund of funds (FoFs) raised between 1979 and 2001 (Vintage). All funds (All) are a
combination of 24 different fund types as classified by Preqin, of which Buyout (32.6%), Venture
(22%), Early Stage (10.5%) and Fund of Funds (8.3%) represent the largest share. Combined
stands for all the fund types excluding fund of funds. Out of the 1641 funds, 204 are officially
liquidated. Over 80% of the funds have a geographic focus on the US, 11% are investing in Europe
and 9% concentrate on the rest of the world.
Vintage Combined Buyouts Ventures FoFs All
1979 1 - 1 - 1
1980 3 2 1 - 3
1981 1 - 1 - 1
1982 3 - 2 - 3
1983 3 - 2 - 3
1984 6 3 3 - 6
1985 10 3 5 - 10
1986 11 3 4 1 12
1987 9 4 4 - 9
1988 10 5 3 1 11
1989 11 5 4 - 11
1990 19 6 5 - 19
1991 10 1 4 - 10
1992 24 8 7 - 24
1993 24 11 9 - 24
1994 38 18 9 1 39
1995 36 12 14 2 38
1996 50 21 8 - 50
1997 63 25 20 1 64
1998 89 36 21 4 93
1999 96 33 29 4 100
2000 138 34 53 8 146
2001 90 17 30 14 104
2002 75 27 15 13 88
2003 58 20 12 12 70
2004 98 30 14 11 109
2005 140 54 22 19 159
2006 155 54 27 16 171
2007 136 56 26 20 156
2008 88 32 11 6 94
2009 6 2 3 1 7
2010 6 1 1 - 6
Total 1507 523 370 134 1641
Liquidated 200 74 64 4 204
5
as well as Driessen and Phallipou (2008), which means that funds that have reached their
normal liquidation date, i.e., funds that are older than ten years, are included. As this
only leaves 15 FoFs, we use a second sample B that consists of funds that have either
been officially liquidated or were started before 2004 (i.e., funds that are older than six
years). We eliminate funds with no cash flow activities over the last six quarters of the
observation period as in Kaplan and Schoar (2005). This leaves 41 FoFs that we consider
to effectively be liquidated. In order to compare direct fund and FoFs performance, we
include in both samples only those direct funds, that are of the same vintage year as the
corresponding FoFs. We display the two subsamples A and B in Table 2.
Table 2. Sample for Performance Comparison
Sample A consists of funds that were either officially liquidated or are older than ten years (normal
fund life). Only funds of vintage years for which fund of funds data is available are included. There
are 343 combined funds (Combined), 132 buyout funds (Buyouts), 100 venture funds (Ventures)
and 15 fund of funds (FoFs) of vintage years 1986-1999 and 2005 which fulfill the criteria. Sample
B includes funds that were either officially liquidated or that are of vintage younger than 2004 and
that did not have any cash flow activities over the last six quarters. Again, funds are only included
when there are fund of funds in the same vintage year. 481 combined funds, 144 buyout funds,
153 venture funds and 41 fund of funds raised between 1986 and 2005 meet the requirements.
Sample A Sample B
Vintage Combined Buyouts Ventures FoFs All Combined Buyouts Ventures FoFs All
1986 11 3 4 1 12 11 3 4 1 12
1988 10 5 3 1 11 10 5 3 1 11
1994 38 18 9 1 39 33 15 7 1 34
1995 36 12 14 2 38 28 9 11 2 30
1997 63 25 20 1 64 54 21 18 1 55
1998 89 36 21 4 93 67 23 19 2 69
1999 96 33 29 4 100 72 22 24 2 74
2000 - - - - - 87 16 39 6 93
2001 - - - - - 50 7 16 10 60
2002 - - - - - 33 12 6 8 41
2003 - - - - - 36 11 6 6 42
2005 - - - 1 1 - - - 1 1
Total 343 132 100 15 358 481 144 153 41 522
6
2.3 Samples for Explaining Internal Rates of Return
The samples for the regression analysis and IRR patterns include the same direct funds
and FoFs as A and B. However, since that section aims at explaining the IRRs rather
than comparing performance measures across different fund types, we also include direct
funds of vintage years for which no data on FoFs in available. Table 3 gives an overview
of these two enlarged samples A+ and B+.
2.4 Possible Biases
Due to the nature of the sample, a number of biases could arise. First, FoFs returns
possibly include low performance funds in which they have invested, whereas some of
those funds might choose not to report to Preqin. Therefore the Preqin return data for
direct funds might be slightly upwards biased as compared to data on FoFs. Second,
the FoFs in sample B are relatively younger than Combined, Buyout or Venture funds.
While over 50% of Combined, Buyouts and Ventures are of vintage earlier than 2000, the
percentage of FoFs is only 24%. As net asset values usually grow over time, this could
lead to poorer results for FoFs. Third, as the data are self reported they are potentially
subject to selection biases. Finally, calculations are based on realized as well as unrealized
investments, which due to subjective accounting treatment of net asset values possibly
introduces further biases. Driessen et al. (2008) document that net asset values reported
by inactive funds are highly upward biased. This could affect performance measures for
both samples.
3 Performance Comparison
Rather than providing an analysis of absolute returns or relating private equity to public
market returns, we want to compare the performance of different types of private equity
funds. The following section describes the three different return measurements, total value
to paid-in, internal rate of return and fund duration, used in the performance comparison
between direct funds and FoFs.
3.1 Methodology
The Total Value to Paid-In (TVPI), also called “multiple”, is calculated as the ratio
between the funds’ distributions and its contributions. Grabenwarter and Weidig (2005)
stress the fact that this definition can only be applied to funds that have been liquidated
7
Table 3. Sample for Regression Analysis and IRR Patterns
Sample A+ consists of funds that were either officially liquidated or are older than ten years (normal
fund life). There are 524 combined funds (Combined), 196 buyout funds (Buyouts), 157 venture
funds (Ventures) and 15 fund of funds (FoFs) of vintage years 1979-2005 which fulfill the criteria.
Sample B+ includes funds that were either officially liquidated or that are of vintage younger than
2004 and that did not have any cash flow activities over the last six quarters. 641 combined funds,
201 buyout funds, 209 venture funds and 41 fund of funds raised between 1979 and 2005 meet the
requirements.
Sample A+ Sample B+
Vintage Combined Buyouts Ventures FoFs All Combined Buyouts Ventures FoFs All
1979 1 - 1 - 1 1 - 1 - 1
1980 3 2 1 - 3 3 2 1 - 3
1981 1 - 1 - 1 1 - 1 - 1
1982 3 - 2 - 3 3 - 2 - 3
1983 3 - 2 - 3 3 - 2 - 3
1984 6 3 3 - 6 6 3 3 - 6
1985 10 3 5 - 10 10 3 5 - 10
1986 11 3 4 1 12 11 3 4 1 12
1987 9 4 4 - 9 9 4 4 - 9
1988 10 5 3 1 11 10 5 3 1 11
1989 11 5 4 - 11 11 5 4 - 11
1990 19 6 5 - 19 19 6 5 - 19
1991 10 1 4 - 10 10 1 4 - 10
1992 24 8 7 - 24 24 8 7 - 24
1993 24 11 9 - 24 22 9 9 - 22
1994 38 18 9 1 39 33 15 7 1 34
1995 36 12 14 2 38 28 9 11 2 30
1996 50 21 8 - 50 36 16 8 - 36
1997 63 25 20 1 64 54 21 18 1 55
1998 89 36 21 4 93 67 23 19 2 69
1999 96 33 29 4 100 72 22 24 2 74
2000 - - - - - 87 16 39 6 93
2001 4 - 1 - 4 50 7 16 10 60
2002 - - - - - 33 12 6 8 41
2003 1 - - - 1 36 11 6 6 42
2004 2 - - - 2 2 - - - 2
2005 - - - 1 1 - - - 1 1
Total 524 196 157 15 539 641 201 209 41 682
8
at the time of calculation. To include also non liquidated funds, the funds net asset value
serves as a proxy for future cash flows. This means that for non liquidated funds, the
TVPI is the ratio of the funds’ distributions plus the funds net asset value to its total
contributions. While the TVPI reflects the effectiveness of the investment with regard to
returning money to the investor, it completely disregards the time dimension as nominal
cash flow are used. This means that a TVPI of 2 does not declare whether the investor
doubles his money within two or within ten years. In this paper, we calculate TVPI for
all funds for which we could obtain an IRR. Since TVPI neglects the time dimension,
explanatory power increases when it is known over what time span the return is achieved.
Thus, average fund life for each of the categories as well as per vintage year is calculated
as well.
The Internal Rate of Return (IRR) is according to Grabenwarter and Weidig (2005)
the most widely used performance measure in private equity. It is the annualized effective
compounded rate of return that can be earned on the invested capital. As opposed to the
TVPI, the IRR describes how time efficient the fund has invested by considering discounted
instead of nominal cash flow. This means that the shorter the investment period for a
profitable investment, the higher the IRR. In addition to the dependence on time, the
IRR is money weighted, meaning that larger amounts account for a bigger part of the IRR
value.
Mathematically, the IRR is the discount rate that makes the net present value of all
cash flows equal to zero,
0 =∑t
CFt
(1 + IRR)t, (1)
where CF is the cash flow at time t and includes all capital contributions, capital distri-
butions as well as the last reported net present value of the respective fund.
Besides calculating IRRs by using all cash flows from the first contribution date until
the evaluation date, also IRRs where only part of the investment’s life is considered can
be computed. In this article, for each fund (if possible) the complete term structure of
IRRs is obtained by calculating an IRR for every cash flow date. Consider n cash flows
CFt at time t = 0, . . . , T where t = 0 is the base date. Calculating the IRR at time k
requires all cash flows with t ≤ k. I.e., for each cash flow, a new IRR is calculated by
using all precedent cash flows and including the new cash flow. Following this procedure
for all cash flows up to t = T results in the IRR term structure of a fund.
As the different fund’s cash flows do not occur at equal times from the base date,
9
and therefore the calculated IRRs correspond to different times, it is difficult to compare
results. To overcome this, the IRR term structures are normalized by interpolating values
at equal time steps from the base date. The time steps chosen in this report are quarterly
dates, meaning that for each fund, t = 1 corresponds to 90 days after the base date, t = 2
to 180 days after the base date and so forth. Given the different fund life, the IRR term
structure is interpolated up to 13,000 days (longest fund life). Outside the interval of the
first and last calculated IRR, the nearest calculated IRR is used as the interpolated value.
We apply the normalization to all funds for which more than one distinct IRR exists. In
cases of only one IRR, all points in time are assigned this value.
The Macaulay Duration measures the sensitivity of an asset’s price to interest rate
movements. Analogously to Phallipou and Gottschalg (2009) we calculate the duration
for a fund as the difference between the duration of distributions and contributions i.e.,
distributions and contributions are treated separately with opposite sign.
The distributions are the positive cash flows CF+ to the fund, whereas contributions
are the negative cash flows CF− from the fund. The calculation steps are as follows: First,
we calculate the present value PV ± of all the cash flows:
PV ± =∑t
CF±t
(1 + IRRt)t, (2)
where t is the time (in years) of the respective cash flow since the base date. We obtain
the time weighted present value by multiplying each cash flow with its t:
TWPV ± =∑t
t · CF±t
(1 + IRRt)t. (3)
Subsequently, the duration of the distributions Dur+ and the duration of the contributions
Dur− are computed as:
Dur± =TWPV ±
PV ± . (4)
Following Phallipou and Gottschalg (2009), the funds’ Duration is given by the difference
Duration = Dur+ −Dur−. (5)
The higher Dur+, the longer it takes until invested money is returned, and the higher
Dur−, the later capital has to be paid in. An investor favors a small Duration (or even
negative) which means a moderate Dur+ and a long Dur−.
The Zero Year represents the number of years it takes for the fund’s IRR to turn
positive. Given an interpolated IRR term structure defined for every time t, this is math-
ematically defined as the first root of the term structure. This date does not necessarily
10
correspond to a time of a cash flow. For some funds, i.e., when the first distribution is very
large, the interpolated IRR is positive from the beginning. In these cases the Zero Year
is the time difference between the first distribution date and the base date. Never Zero
are the number of funds for which the IRR term structure does never turn positive (and
therefore also has no first root). The Final IRR is the last IRR, i.e., the IRR obtained
when considering all capital flows and the last NPV. The Maximum IRR is the largest
IRR observed in the IRR term structure and often coincides with the Final IRR. The Fund
Life is the difference between the date of the last cash flow (usually a distribution) and
the base date. Finally, the Risk Return Ratio of the TVPI is given as either the average
TVPI minus one divided by the standard deviation of the TVPIs and the Risk Return
Ratio of the IRR is calculated as the average IRR divided by the standard deviation of
the IRRs.
3.2 Results
3.2.1 Fund of Fund vs. Combined Fund Performance
Table 4 resumes summary figures both for sample A and sample B. Average TVPI for
funds is with 1.61 and 1.48 for both samples larger than for FoFs with 1.32 and 1.25,
respectively. However, on a risk adjusted basis FoFs show an out performance of roughly
50% with risk return ratios of 0.53 vs. 0.34 for sample A and 0.60 vs. 0.30 for sample B.
Furthermore, while one out of three direct funds ends in a loss (i.e., TVPI smaller than
one), this is true only for one out of four FoFs. In addition, the size of the loss exceeds
the one for FoFs (+4% sample A and +18% sample B). The median TVPI is smaller than
the average TVPI for both samples and fund categories, indicating a negative skew in the
TVPI distribution. The difference between the maximum and minimum TVPI is much
larger for funds than for FoFs, which can partly be explained by the different sample size.
Findings for sample B can also be retraced in the probability distribution (Figure 2) and
the cumulative distribution (Figure 3) of the TVPI. Average fund lives for sample A and
sample B are 10.3 years and 8.48 years for Combined and 10.8 years and 7.23 years for
FoFs. This means that Combined has a longer fund life for sample A (+5%) and a shorter
life for sample B (-15%).
As for the IRR, results for sample A and B are mixed. For the former, the risk return
ratio for Combined is slightly better than for FoFs, which is mainly due to the much higher
average IRR of 7.28% vs. 4.23% for FoFs. However, with a minimum IRR of -73.72% and a
11
Tab
le4.
TV
PI
an
dIR
RS
tati
stic
sfo
rFu
nd
of
Fu
nd
san
dC
om
bin
ed
Fu
nd
s
Th
ista
ble
rep
orts
TV
PI
and
IRR
stat
isti
csfo
r343
dir
ect
fun
ds
(Com
bin
ed)
an
d15
fun
dof
fun
ds
(FoF
s)fo
rsa
mp
leA
as
wel
las
for
481
dir
ect
fun
ds
and
41fu
nd
offu
nd
sfo
rsa
mp
leB
,ra
ised
bet
wee
n1986
an
d2005.
TV
PI
isca
lcu
late
das
the
rati
oof
dis
trib
uti
ons
and
rem
ain
ing
net
asse
tva
lue
toin
vest
edca
pit
al.
Th
ep
rob
ab
ilit
yof
alo
ssis
the
per
centa
ge
of
fun
ds
exh
ibit
ing
TV
PI/
IRR
small
er
than
one/
zero
and
the
aver
age
loss
give
na
loss
isob
tain
edby
sub
tract
ing
the
mea
nof
all
TV
PIs
small
erth
an
on
e/IR
Rs
small
er
than
zero
from
one.
Th
eri
skre
turn
rati
ois
the
aver
age
TV
PI
min
us
on
e/av
erage
IRR
div
ided
by
the
stan
dard
dev
iati
on
(SD
).
Sam
ple
AS
am
ple
B
TV
PI
IRR
[%]
TV
PI
IRR
[%]
FoF
Com
bin
edF
oF
Com
bin
edF
oF
Com
bin
edF
oF
Com
bin
ed
Ave
rage
1.32
1.6
14.3
27.2
81.2
51.4
85.8
46.2
2
Med
ian
1.12
1.3
37.9
37.3
31.1
81.2
58.5
15.7
0
Max
2.67
20.3
823.8
496.8
32.6
720.3
824.0
096.8
3
Min
0.37
0.0
6-2
3.3
8-7
3.7
20.3
70.0
6-2
7.7
1-8
9.3
1
SD
0.60
1.8
112.9
720.7
90.4
21.5
811.3
620.5
5
Pro
b.
ofa
Los
s[%
]26
.67
32.9
426.6
733.2
426.8
036.3
826.8
336.5
9
Ave
rage
Los
s0.
340.3
8-1
2.1
4-1
2.1
40.1
80.3
6-8
.60
-12.4
5
Ris
kR
etu
rnR
atio
0.53
0.3
40.3
30.3
50.6
00.3
00.5
10.3
0
12
probability of loss of 33.24% as well as a negatively skewed distribution, it is not conclusive
whether Combined show a superior return profile. For sample B, the return distribution
for FoFs with less standard deviation, lower probability of loss, lower average loss and
higher risk return ratio looks more attractive, as documented in Table 4 and Figure 2 and
3. Due to the biases explained in Section 2.4, FoFs’ returns are downward biased, which
suggests that the out performance of FoFs in reality is even more pronounced.
Duration for FoFs is 0.62 for sample A and 0.99 for sample B. This is smaller than for
Combined with 1.50 and 1.49 respectively. The lower Duration for FoFs can be explained
by the higher Dur−, meaning that FoFs investors on average need to pay in capital at a
later stage than investors of direct funds. In summary, based on TVPI, IRR and Duration,
on an aggregate level, FoFs clearly outperform direct fund investments.
3.2.2 Fund of Fund Performance vs. Venture Fund and Buyout Fund Perfor-
mance
In this section we describe the findings for sample B, however, unless otherwise stated,
results for sample A are qualitatively the same. Table 5 sheds light on the return charac-
teristics for FoFs, Buyouts and Ventures.
Table 5. TVPI and IRR Statistics for Fund of Funds, Buyout Funds and Venture
Funds
This table reports TVPI and IRR statistics for 481 Combined, 41 FoFs, 153 Ventures and 144
Buyouts raised between 1986 and 2005. TVPI is calculated as the ratio of distributions and
remaining net asset value to invested capital. The probability of a loss is the percentage of funds
exhibiting TVPI/IRR smaller than one/zero and the average loss given a loss is obtained by
subtracting the mean of all TVPIs smaller than one/IRRs smaller than zero from one. The risk
return ratio is the average TVPI minus one/average IRR divided by the standard deviation (SD).
TVPI IRR [%]
FoF Buyout Venture FoF Buyout Venture
Average 1.25 1.62 1.43 5.84 11.80 1.11
Median 1.18 1.52 0.94 8.51 11.56 -2.13
Max 2.67 5.45 20.38 24.00 68.71 96.83
Min 0.37 0.18 0.13 -27.71 -28.77 -73.72
SD 0.42 0.74 2.43 11.36 15.05 22.58
Prob. of a Loss [%] 26.80 19.44 52.90 26.83 19.44 53.59
Average Loss 0.18 0.25 0.41 -8.60 -8.63 -13.03
Risk Return Ratio 0.60 0.84 0.18 0.51 0.78 0.05
13
Buyouts exhibit the most attractive risk return characteristics with a mean TVPI of
1.62, a standard deviation of 0.74 and only 19.4% probability of a loss. While the largest
TVPIs (up to 20.38) can be achieved with Ventures, the general risk profile of those
funds, due to the large dispersion appears unfavorable (risk return ratio of 0.18). These
findings suggest that investors seeking exposure to venture capital should consider a FoFs’
approach.
Analogue to TVPI statistics, with an average IRR of 11.8%, probability of loss of
19.44% and a risk return ratio of more than twice the risk return ratio of FoFs, Buyouts
outperform other fund categories with respect to IRR characteristics.
Results for TVPI are also demonstrated in Figure 2 and 3, while IRR returns are
depicted in Figure 4 and 5.
One might argue that since Buyouts are relatively older than FoFs, with 68% vs. 24%
of funds of vintage older than 2000, due to the J-curve effect of private equity returns, this
could explain part of the Buyouts out performance. However, as results are very similar
for sample A, this argument is questionable.
Figure 2. TVPI Distribution for Combined Funds, Fund of Funds, Buyout Funds and
Venture Funds
This figure illustrates the TVPI distribution for 481 Combined Funds, 41 Fund of Funds, 144
Buyout- and 153 Venture Funds raised between 1986 and 2005. Combined Funds compasses
Buyout- and Venture Funds and 184 funds of other types. TVPIs larger than 10 are added up and
displayed in the bin where TVPI is 10.
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10
0%5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
TVPI
Pro
babi
lity
Combined FundsFund of FundsBuyout FundsVenture Funds
empty line
14
Figure 3. Cumulative TVPI Distribution for Combined Funds, Fund of Funds, Buyout
Funds and Venture Funds
This figure illustrates the cumulative TVPI distribution for 481 Combined Funds, 41 Fund of Funds,
144 Buyout- and 153 Venture Funds raised between 1986 and 2005. Combined Funds compasses
Buyout- and Venture Funds and 184 funds of other types.
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
0%10
%20
%30
%40
%50
%60
%70
%80
%90
%
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8
TVPI
Cum
ulat
ive
Pro
babi
lity
● Combined FundsFund of FundsBuyout FundsVenture Funds
Figure 4. IRR Distribution for Combined Funds, Fund of Funds, Buyout Funds and
Venture Funds
This figure illustrates the IRR distribution for 481 Combined Funds, 41 Fund of Funds, 144 Buyout-
and 153 Venture Funds raised between 1986 and 2005. Combined Funds compasses Buyout- and
Venture Funds and 184 funds of other types.
−100 −90 −80 −70 −60 −50 −40 −30 −20 −10 0 10 20 30 40 50 60 70 80 90 100
0%5%
10%
15%
20%
25%
30%
35%
IRR [%]
Pro
babi
lity
Combined FundsFund of FundsBuyout FundsVenture Funds
15
Figure 5. Cumulative IRR Distribution for Combined Funds, Fund of Funds, Buyout
Funds and Venture Funds
This figure illustrates the cumulative IRR distribution for 481 Combined Funds, 41 Fund of Funds,
144 Buyout- and 153 Venture Funds raised between 1986 and 2005. Combined Funds compasses
Buyout- and Venture Funds and 184 funds of other types.
● ● ● ● ● ●●
●
●
●
●
●
●
●●
●● ● ● ●
0%10
%20
%30
%40
%50
%60
%70
%80
%90
%
−100 −90 −80 −70 −60 −50 −40 −30 −20 −10 0 10 20 30 40 50 60 70 80 90
IRR [%]
Cum
ulat
ive
Pro
babi
lity
● Combined FundsFund of FundsBuyout FundsVenture Funds
Regarding the cash flow structure, Dur+ is highest for FoFs, which in combination
with an average Dur− results in the lowest Duration of 1 as compared to 1.4 (+40%) for
Buyouts, 1.5 for Combined (+50%) and 1.7 (+70%) for Ventures. This indicates that
FoFs are leading in terms of cash flow timing.
FoFs and Ventures TVPI figures are in contrast to findings of Weidig and Math-
onet (2004). While they report similar risk return profiles for Buyouts, with numbers for
all TVPI measures lying between numbers of sample A and sample B in Table 5, figures
for Ventures and FoFs are more favorable in their paper. Their Ventures show lower stan-
dard deviations of TVPIs (2.97 in sample A and 2.43 in sample B compared to 1.9), less
probability of a loss (46% in sample A and 52.9% in sample B compared to 30%) and
smaller average loss (over 40% for both samples compared to 29%). As for FoFs, Weidig
and Mathonet (2004) calculate numbers for two different types of FoFs: average numbers
of 50,000 simulated FoFs once on the basis of 300 venture funds (FoF Venture) and once
on the basis of 200 European buyout funds (FoF Buyout). While mean TVPIs (1.7 and
1.8) are higher than for direct funds, standard deviations are much lower (0.2 and 0.5),
resulting in larger risk return ratios of 3.1 for FoF Buyouts and 1.7 for FoF Ventures.
In addition, they report a probability of any loss close to zero for both FoF types. The
16
discrepancy of results could be due to several reasons. First and most important, their
approach is based on simulated FoF returns whereas data in our article is real. Second, the
data on direct funds differ. The sample period of Weidig and Mathonet (2004) goes from
1980 to 1998, while direct funds for sample A are of vintage 1986-1999 and direct funds
for sample B of vintage 1986-2003. Thus, their sample includes the period of 1980-1985
which on average generated high returns for both fund categories and neglects more recent
vintages for which Ventures performed rather poorly. In addition, they only consider Eu-
ropean Buyouts whereas the Preqin sample contains more than 80% American Buyouts.
This should, however, lead to poorer results, as European Buyouts on average perform
worse than US Buyouts.2 Third, Weidig and Mathonet (2004) base their calculation on
gross of fees returns and then deduct management fees, set up costs and carry according
to a simplified calculation. The high FoF performance reported by them could therefore
also be due to an underestimation of fees.
In summary, TVPI and IRR characteristics of FoFs seem to be superior to Combined
and Ventures, but less attractive than Buyouts. In terms of cash flow timing, FoFs exhibit
the most favorable features.
3.2.3 Vintage Year Analysis
While the previous two sections concentrated on summary statistics over the whole sam-
ple period, the emphasis of this section lies on a performance comparison over different
vintage years. However, drawing conclusions for FoFs based on a single vintage year seems
somewhat arbitrary, as for vintages before the year 2000 the maximum amount of FoFs
per vintage year is two. Again, as outcomes for sample A and sample B are very similar,
we discuss only results for sample B.
Table 6 reveals results for different performance measurements for individual vintage
years as well as for the total range. In addition to Final IRR and TVPI, further measures
such as the mean ZeroYear, NeverZero, the maximum IRR and more detailed Duration
figures are shown.
Funds of vintage 1986 performed well by means of TVPI, with Combined, FoFs and
Buyouts generating very high returns and Ventures showing the most attractive risk return
ratios of TVPIs. With the exception of FoFs of vintage 1994, the trend continued for
vintage years 1988 and 1994-1995. Probably due to the effect of the dot com bubble,
performance declined in the subsequent years, with Combined and Ventures reaching the
2Compare Phalippou and Gottschalg (2009), p. 1752.
17
Tab
le6.
Perf
orm
an
ce
Com
pari
son
for
Diff
ere
nt
Vin
tage
Years
Th
ista
ble
rep
orts
TV
PI
and
IRR
stat
isti
csfo
r41
FoF
s,153
Ven
ture
san
d144
Bu
you
tsra
ised
bet
wee
n1986
an
d2005.
Com
bin
edco
mp
ass
esB
uyo
uts
,V
entu
res
and
184
fun
ds
ofot
her
typ
es.
TV
PI
isca
lcu
late
das
the
rati
oof
dis
trib
uti
on
san
dre
main
ing
net
ass
etva
lue
toin
vest
edca
pit
al.
RR
stan
ds
for
risk
retu
rn
rati
oan
dis
given
asth
eav
erag
eT
VP
Im
inu
son
e/av
erage
IRR
div
ided
by
the
stan
dard
dev
iati
on
.T
he
Fu
nd
Lif
eis
the
diff
eren
ceb
etw
een
the
date
of
the
last
dis
trib
uti
onan
dth
ed
ate
ofth
efirs
tco
ntr
ibu
tion
.T
he
mea
nze
roye
ar
isth
eti
me
as
mea
sure
din
years
itta
kes
unti
lth
eIR
Rtu
rns
posi
tive
.F
pV
is
the
nu
mb
erof
fun
ds
per
vin
tage
and
%N
ever
Zer
osh
ows
the
per
centa
ge
of
fun
ds
per
vin
tage
for
wh
ich
the
IRR
nev
ertu
rns
zero
.M
ax
IRR
isth
eh
igh
est
IRR
per
vin
tage
.W
hil
eD
ur+
isth
eD
ura
tion
ofdis
trib
uti
on
s,D
ur−
isth
eD
ura
tion
of
contr
ibu
tion
san
dD
ura
tion
isD
ur+
min
us
Du
r−.
Th
ela
stro
wsh
ows
aver
age
nu
mb
ers
for
all
fun
ds
(not
eth
atth
isis
not
equ
al
toth
eav
erage
of
vin
tage
years
figu
res,
as
that
wou
ldin
trod
uce
aw
eighti
ng).
Mea
nT
VP
IR
RT
VP
IF
un
dL
ife
Mea
nZ
ero
Yea
rN
ever
Zer
o[%
]#
Fp
V
Vin
tage
CF
oFB
VC
FoF
BV
CF
oF
BV
CF
oF
BV
CF
oF
BV
CF
oF
BV
1986
2.10
1.93
2.92
1.35
0.87
NA
1.26
3.2
615.9
121.1
215.6
815.8
28.7
99.8
28.3
69.9
29
00
011
13
419
882.
062.
671.
812.
541.
58N
A1.
841.4
714.7
217.5
114.5
813.7
67.3
15.8
27.4
37.1
60
00
010
15
319
942.
230.
481.
932.
100.
60N
A1.
080.3
811.2
113.1
410.1
911.8
95.0
1N
aN
5.1
55.2
033
100
13
71
33
115
719
952.
531.
501.
463.
570.
405.
930.
670.4
511.4
812.2
611.9
111.4
95.6
58.9
55.0
64.9
318
022
18
28
29
11
1997
1.65
0.37
1.50
1.90
0.55
NA
0.84
0.5
29.9
710.4
010.2
29.5
66.3
8N
aN
7.7
44.3
030
100
24
33
54
121
18
1998
1.56
1.04
1.39
2.06
0.23
0.60
0.80
0.2
49.3
39.0
09.3
89.3
66.9
29.5
67.6
57.1
037
50
26
63
67
223
19
1999
1.02
1.81
1.40
0.68
0.03
23.8
30.
60-0
.77
8.3
17.7
58.7
38.0
27.1
56.8
66.9
.7.9
353
032
79
72
222
24
2000
1.08
1.35
1.82
0.83
0.15
0.84
1.75
-0.7
77.4
37.8
27.8
57.3
46.9
17.2
86.3
78.0
053
33
669
87
616
39
2001
1.31
1.10
1.81
0.99
0.48
0.49
1.00
-0.4
46.5
56.8
46.8
56.5
45.5
46.9
24.9
46.7
536
40
14
56
50
10
716
2002
1.39
1.22
1.59
1.10
0.66
1.12
1.35
0.3
75.3
84.5
05.3
75.6
64.9
74.9
04.8
05.9
421
13
17
17
33
812
620
031.
351.
181.
691.
180.
460.
710.
520.8
84.2
53.7
64.2
23.8
14.1
74.4
14.2
43.8
425
17
18
17
36
611
620
051.
12N
A2.1
31.7
50
1A
ll1.
481.
251.
621.
430.
300.
600.
840.1
88.4
87.2
38.9
48.5
36.1
66.1
96.3
36.3
237
27
19
54
481
41
144
153
Max
IRR
[%]
Mea
nF
inal
IRR
[%]
RR
Fin
al
IRR
Du
rati
on
Du
r+D
ur−
Vin
tage
CF
oFB
VC
FoF
BV
CF
oF
BV
CF
oF
BV
CF
oF
BV
CF
oF
BV
1986
258
256
8.92
7.93
15.2
14.7
71.0
6N
A1.3
94.1
02.0
83.4
41.2
02.4
15.2
85.1
54.9
96.1
73.2
01.7
13.7
93.7
619
8824
2418
2413
.59
23.8
412
.51
17.4
82.3
8N
A3.5
51.9
01.6
11.0
82.1
90.1
85.2
04.9
25.5
54.5
73.5
93.8
43.3
64.3
919
9469
-20
6955
17.5
3-1
9.83
21.5
44.9
60.7
4N
A1.1
50.1
81.2
4-1
.23
1.0
90.4
74.6
27.0
53.9
66.4
03.3
88.2
82.8
75.9
.19
9597
1240
9718
.28
10.4
911
.03
22.9
50.6
94.9
10.6
00.6
71.6
91.0
72.9
90.9
54.5
85.2
25.6
43.8
42.8
94.1
52.6
42.9
019
9796
-23
2496
12.7
9-2
3.38
7.98
20.6
50.5
4N
A0.8
10.6
21.5
00.0
91.1
61.6
44.6
46.7
95.2
54.0
23.1
46.7
04.0
92.3
819
9877
217
442.
520.
665.
11-5
.04
0.1
20.5
50.5
4-0
.20
1.6
1-0
.34
1.6
62.2
94.9
86.4
75.1
95.6
53.3
76.8
13.5
43.3
619
9943
1923
13-2
.86
16.9
94.
63-1
0.5
6-0
.17
5.2
00.3
2-0
.88
1.5
61.3
61.1
12.2
55.5
25.5
85.2
26.3
93.9
64.2
24.1
14.1
420
0029
1929
18-0
.65
7.27
16.1
8-5
.87
-0.0
40.8
41.7
1-0
.52
1.6
60.7
31.1
51.9
35.9
75.3
84.9
36.6
14.3
24.6
53.7
84.6
820
0143
1343
266.
853.
1818
.84
-2.8
20.4
00.4
41.0
9-0
.23
1.1
10.9
71.0
61.4
74.8
85.8
94.2
25.8
73.7
74.9
23.1
54.3
920
0258
2431
1210
.95
9.95
17.5
93.2
70.6
11.2
51.4
20.3
31.4
01.4
21.2
60.9
94.3
54.3
44.1
04.8
52.9
52.9
22.8
43.8
620
0365
2065
1810
.20
5.01
16.3
97.0
90.6
10.3
00.6
60.9
81.1
51.1
41.3
21.0
23.5
03.6
63.7
63.6
42.3
52.5
32.4
42.6
120
059
9.31
NA
0.9
61.6
70.7
2A
ll97
2469
976.
225.
8411
.80
1.1
10.3
00.5
10.7
80.0
51.4
90.9
91.3
91.7
15.0
05.0
74.8
45.6
33.5
14.0
83.4
53.9
2
18
lowest mean TVPIs of 1.02 and 0.68 in 1999, while the through for FoFs and Buyouts was
in 1997 and 1998 respectively. Not surprisingly Buyouts of vintage 2000 performed rather
well, presumably taking advantage of low valuations in the aftermath of the crisis. As can
be observed, eight out of ten times when TVPI for Ventures and FoFs increased/decreased
from the previous vintage year, TVPI for Buyouts moved towards the opposite direction.
This tendency also applies to Final IRR. It appears that changes in the returns for Ventures
and FoFs are inversely correlated to the changes in Buyout returns. From an investor’s
or asset management’s perspective it could thus be prudent to include both fund types
(Buyouts as well as either Ventures or FoFs) to better diversify the investment portfolio.
Column three shows that fund life strongly decreased for all fund categories. While in
1986 average fund life was longer than 15 years, in 1999 it was almost halved to around
eight years. As fund life from vintage year 1995 onwards on average decreases by one with
each vintage year increase, this effect is obviously due to the fact that cash flow data is
only available up to the first quarter of 2010. Surprisingly, FoFs with 7.23 years exhibit
the shortest average fund life.
The mean ZeroYear is on average over all vintages with 6.16 years lowest for Combined,
followed by 6.19 years for FoFs. Mean ZeroYear is generally large for vintages 1986, 1988
and 1998-2000 and decreases substantially over the vintage years 2001-2004.
The average share of NeverZero for all vintages coincides with the probability of a
negative IRR (see Table 5, prob. of a loss), which means that no fund that once crossed
the zero IRR line exhibits a negative final IRR. Obviously, when there are many funds
that never achieve positive IRRs in a vintage year, the mean Final IRR for that year is
low. As one could expect, maximum IRRs were achieved with Ventures of vintages 1995
and 1997.
Most of the time, high TVPIs coincide with high Final IRRs. This is not entirely true
for vintage year 1986, which shows some of the largest TVPIs for Combined, FoFs and
Buyouts but not topmost Final IRRs, which is probably a result of the long fund life for
funds of vintage 1986.
Regarding Duration, we note that since vintage 2000, both Dur+ and Dur− constantly
decreased, indicating that cash flows are being exchanged at a faster rate. Generally,
vintage years 1994 as well as 2001-2003 show the most favorable Durations. FoFs, in most
years have among the largest Dur+. However, as they exhibit very large Dur− as well,
average Duration over the whole sample period is lowest.
All in all, funds of vintage years 2002-2003 seem to perform well, with short mean
19
ZeroYear, low percentage of funds that exhibited a negative Final IRR, decent level of Final
IRRs, low Durations and effectual TVPIs. Regarding mean TVPI, mean Final IRR and
the percentage of NeverZero, except for FoFs in 1988, overall performance for vintage years
1986, 1988 and 1994-1995 is also considerable. As opposed to that, lowest performance is
for all fund categories shown for funds of one of the vintage years between 1997 and 1999,
which is probably due to the effects of the dot com bubble. Since return changes of FoFs
and Ventures seem to be negatively correlated to return changes of Buyouts, it could be
prudent to include both Buyouts and either FoFs or Ventures to the portfolio in order to
better diversify investments.
4 Regression Analysis
4.1 Methodology
In this section, we aim to explain private equity fund returns. Similar to Phalippou and
Zollo (2005), we include the stock market return, real GDP growth rate, corporate BAA
bond yields (CBAA yields) as reported by Moody’s and credit spreads as explanatory
variables. The CBAA yields and the credit spreads, which capture the probability of
default and the expected recovery in case of default in the economy, reflect the cost of
financing buyout investments. Both variables also correspond to different stages in the
business cycle and are thus relevant for all fund categories. High credit spreads and high
CBAA yields are usually found in difficult market environments (i.e., economic recession).
Finally the GDP growth rate and stock market return assess the impact of the market
sentiment in the year the fund first invested (start year) on final fund returns (Phalippou
and Zollo, 2005). In this study, the dependent variables are the average Final IRRs per
Start Year.3 To exploit the macroeconomic impact on different fund categories, the average
IRRs of All, Combined, FoFs, Buyouts and Ventures are explained separately.
We run two different types of regressions, corresponding to the different time periods,
which we refer to as the Fund Life Regression and the Start Year Regression. For the Fund
Life Regression, we calculate returns of the different explanatory variables over the fund’s
life to measure how macroeconomic factors during the funds life influence its returns. For
the Start Year Regression, we calculate factor returns for the year prior to the investment
start. This allows for an evaluation of the impact of the economic environment at start of
3Note that the Start Year is not always the same as the vintage year, i.e. for the total sample, the Start
Year is on average eight months later than the Vintage Year.
20
the fund life on fund returns. First, the average Final IRR as well as the average fund life
for each Start Year are computed. The fund life is thereby set as the difference between
the date of the last transaction (usually a distribution) and the first contribution. This
is calculated for each fund and then averages for all funds of the same Start Year are
computed. Then, for the Fund Life Regression, the real GDP growth rate, MSCI return
and CBAA spread over the average fund life per Start Year are calculated,4 i.e. when
a fund has Start Year 1980 and average fund life for this Start Year is 8.9, returns for
all variables are calculated for the period 1980-1989. For the Start Year Regression, we
take the MSCI return and real GDP growth rate for the year before the Start Year into
account. In addition, we consider the absolute value of the CBAA yield and the credit
spread (calculated as the difference between the CBAA yield and treasury bond yield) at
the end of the year before the Start Year. We repeat this for each Start Year, i.e., when a
fund has Start Year 1980, we consider the MSCI return and real GDP growth rate between
1979 and 1980 and the CBAA yield as well as the credit spread as of December 31, 1979.
In order to evaluate the economic importance of the variables, we standardize them by
subtracting their sample mean and dividing by their sample standard deviation. Finally,
we regress the three Fund Life regression variables and the four Start Year regression
variables against the mean Final IRRs per Start Year of the six different fund categories.
Since the variables exhibit high correlations among each other, we regress them against
the IRRs one at a time. Carrying out the analysis for sample A+ as well as for sample B+
results in 84 single regressions.
4.2 Results
We present the results of the analysis in Table 7. While Panel A reports figures for sample
A+, Panel B shows regression outputs for sample B+.
For Combined, Buyouts and Ventures signs of the coefficients for the Fund Life Re-
gression are with one minor exception all pointing towards the same direction. Funds that
invest during times with high public market performance and economic growth as well
as declining corporate bond yields show higher IRRs. This suggests that private equity
funds generate pro cyclical returns, which is in line with the findings of Phallipou and
Zollo (2005). Coefficients are statistically significant for all variables for Combined and
All in sample B+, as well as for CBAA Spread (Buyouts sample A+) and GDP growth
(Ventures sample A+ and B+). For Ventures, the real GDP growth rate during the time
4The CBAA spread is the CBAA yield at End Date minus the CBAA yield at Start Date.
21
Table 7. Explaining IRRs
This table reports the results of OLS regressions to explain the drivers of the IRR for different fund
categories. The dependent variables are the mean Final IRRs per Start Year of combined funds
(Combined), fund of funds (FoFs), buyout funds (Buyouts), venture funds (Ventures) and all funds
(All). Explanatory variables are per Start Year (year in which first contribution took place): 1)
MSCI return and real GDP growth rate during the average fund life as well as the difference of the
average corporate bond yield (CBAA yield as reported by Moody’s) at the end of the investment
and the Start Year, 2) MSCI return and real GDP growth rate for the year before the Start Year,
as well as average corporate bond yield (CBAA yield as reported by Moody’s) and credit spread
at the end of the year before the Start Year. ∗ denotes significance of the coefficient at a level of
10%, ∗∗ at a level of 5%. Standard deviation of the coefficients are given in parentheses.
Panel A: Explaining IRRs for Sample A+
Fund Life Regression Start Year Regression
IRR MSCI GDP CBAA MSCI GDP CBAA Credit Spread
FoFs -0.017 0.006 0.025 -0.051 -0.115∗∗ 0.035 0.063
(0.060) (0.060) (0.059) (0.057) (0.041) (0.058) (0.055)
Combined 0.010 -0.009 -0.011 -0.016 -0.009 -0.002 0.033∗
(0.018) (0.018) (0.018) (0.018) (0.018) (0.018) (0.017)
Buyouts 0.017 0.020 -0.037∗∗ -0.003 -0.040∗∗ 0.025 0.032∗
(0.018) (0.018) (0.017) (0.019) (0.016) (0.018) (0.017)
Ventures 0.027 0.060∗∗ -0.034 0.023 0.000 0.025 -0.034
(0.026) (0.023) (0.026) (0.026) (0.027) (0.026) (0.026)
All 0.012 -0.006 -0.013 -0.017 -0.010 0.000 0.034
(0.017) (0.017) (0.017) (0.017) (0.017) (0.017) (0.016)
Panel B: Explaining IRRs for Sample B+
Fund Life Regression Start Year Regression
IRR MSCI GDP CBAA MSCI GDP CBAA Credit Spread
FoFs -0.017 -0.003 0.022 -0.011 -0.061 0.013 0.022
(0.039) (0.039) (0.038) (0.039) (0.034) (0.039) (0.038)
Combined 0.025∗ 0.030∗∗ -0.028∗∗ 0.001 -0.004 0.024∗ 0.005
(0.013) (0.012) (0.012) (0.014) (0.013) (0.013) (0.013)
Buyouts 0.005 0.003 -0.012 -0.005 -0.031∗∗ 0.010 0.027∗
(0.016) (0.016) (0.016) (0.016) (0.015) (0.016) (0.015)
Ventures 0.024 0.048∗∗ -0.031 0.006 -0.010 0.026 -0.026
(0.020) (0.018) (0.019) (0.020) (0.020) (0.020) (0.020)
All 0.026∗∗ 0.031∗∗ -0.029∗∗ 0.000 -0.005 0.025∗∗ 0.005
(0.012) (0.011) (0.011) (0.013) (0.013) (0.012) (0.013)
22
of investment is the only significant factor. Even though none of the Fund Life Regres-
sion variables are statistically significant for FoFs, the signs of coefficients indicate that as
opposed to the other fund categories, FoF returns do not follow public stock markets or
economic growth and can be said to be more robust. This robustness towards the economic
environment could be due to the following reasons. First, a FoF has more diversification
of the end investment, i.e., of portfolio companies which are heavily dependent on the
macroeconomic environment. Second, a FoF has more vintage year diversification than a
direct fund. Both these effects could lead to more stable returns.
In case of the Start Year Regression, the MSCI return one year prior to the first
investment is not statistically significant for any dependent variable and generally only
explains very little of the variation in fund returns. At times with high real GDP growth
previous to the Start Year, IRRs were lower for all categories. For both samples, with
the exception of Ventures, funds that started investing in periods of high corporate bond
yields and high credit spreads, i.e., in a difficult market environment, outperformed. As
opposed to the Fund Life Regression loadings, this contradicts the results of Phallipou and
Zollo (2005). For FoFs, the most significant explanatory variable is the real GDP growth
rate prior to the Start Date, with significant coefficient and 52.6% of explained variation
(R square) in sample A+. As opposed to that for Combined, for sample A+ the credit
spread and for sample B+ the CBAA yield at the Start Date are significant. As expected
for Buyout returns, the growth rate of the economy one year prior to the Start Year is a
very important signal, as can be observed from the significance level and with the negative
coefficient being rather large as compared to other explanatory variables. When there’s
an economic downturn, Buyouts can take advantage of low valuations. On the other hand,
a positive coefficient for credit spreads is surprising. It would be anticipated that when
financing is expensive, Buyouts under perform.
To summarize, while signs of coefficients for All, Combined, Buyouts and Ventures
suggest a pro cyclical behavior of fund returns, fund of funds seem to be less prone to the
general market development during the fund’s life. Generally, R squares are rather low for
most of the regressions, indicating that there are more relevant factors than the chosen
macroeconomic conditions explaining the IRR structure.
23
5 Conclusion
Based on a comprehensive dataset of 1641 private equity funds raised between 1979 and
2005, we study risk and return characteristics of different fund categories. Unlike previ-
ous studies, real FoF data is used, allowing for an authentic performance comparison of
FoFs and direct fund investments. We show that based on TVPI and IRR, FoFs exhibit
a more attractive risk return profile than aggregate direct fund investments. For both
performance measures, FoFs exhibit a higher risk return ratio, less probability of loss and
lower average loss. When split up by fund category, Buyouts feature the most favorable
risk return characteristics, followed by FoFs, Combined and Ventures. Examining the cash
flow structure, we document that the duration of the contributions is highest for FoFs,
which in combination with an average duration of distributions, results in the lowest total
duration of 1 as compared to 1.4 (+40%) for Buyouts, 1.5 for Combined (+50%) and 1.7
(+70%) for Ventures. This indicates that FoFs are leading in terms of cash flow timing
as investors on average need to pay in capital later and receive distributions earlier than
investors of other fund categories.
We also assess the performance by vintage years and find that overall, funds of vintage
years 2002-2003 seem to perform well, with low percentages of funds that exhibit a negative
Final IRR, decent levels of Final IRRs, low durations and effectual TVPIs. Regarding
mean TVPI and mean Final IRR overall performance for vintage years 1986, 1988 and
1994-1995 is also considerable. As opposed to that, lowest returns are for all fund categories
shown for funds of one of the vintage years between 1997 and 1999, which is probably due
to the effects of the dot com bubble. It appears that changes in the returns for Ventures
and FoFs are inversely correlated to the changes in Buyout returns. From an investor’s or
asset management’s perspective it could thus be prudent to include both Buyouts as well
as either Ventures or FoFs to better diversify the investment portfolio.
Finally, analyzing how macroeconomic conditions at the time of investments as well
as during the fund’s life influence fund performance, we show that direct fund returns
increase with MSCI and real GDP growth. This indicates pro cyclical return behavior.
As opposed to that, fund of funds seem to be less prone to the general market develop-
ment during the funds life. Generally, there is for all fund categories only little variation
explained by the used factors, suggesting that there are more relevant factors than the
chosen macroeconomic conditions explaining the IRR structure.
24
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