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Value Creation Drivers in Large Leveraged Buyouts
D. Ilg\ast
Catholic University Eichstätt-Ingolstadt, Chair of Public Finance, Auf der Schanz 49,
85049 Ingolstadt, Germany
Abstract
This work employs the most complete data sample of large private equity
backed transactions in Germany ranging from 1997 to 2008. This research
casts light on the impact of the holding period, the e�ects of divesting activ-
ities and expectations about the future shape of the economy on the perfor-
mance of deals. A longer holding period is associated with a lower internal
rate of return, and, contrary to expectations, this study �nds a return dimin-
ishing e�ect of selling subsidiaries of target companies. In addition, I apply
a new measure of business expectation: ifo slope. ifo slope incorporates the
theory of the interest term structure as a business economic development
indicator. Positive expectations at entry lead to higher returns in the future
and periods of economic downturn are stretching the length of the investment
dramatically. Finally, larger deals are �nanced more aggressively with debt
and generate more value by multiple expansion than smaller deals.
Keywords: leveraged buyout, private equity, management buyout,
performance, value creation
\ast Tel.: +49 841 937 1857, fax: +49 841 937 21857 0.Email address: [email protected] (D. Ilg)
August 19, 2015
JEL: G24, G34
�Some �nancial investors waste no thoughts on the people whose jobs they
destroy � they remain anonymous, faceless, fall upon �rms like swarms of
locusts, forage them and roam on. We �ght against this form of capitalism.�
Former Vice Chancellor Franz Müntefering (Social Democrat Party), 2005.
This statement provides a short outlook on why private equity and leveraged
buyouts in particular are seen quite di�erently in Germany opposed to the
mature Anglo-Saxon markets.
Private equity and leveraged buyouts play an important role in almost all
developed capital markets. Much research has been conducted on the e�ects
of private equity investments on the particular target company or the involved
economy. The impact of private equity �nancing on portfolio companies
can result in di�erences in growth/pro�tability (Guo et al., 2011; Kaplan,
1989; Wright et al., 2000; Smith, 1990; Cressy et al., 2007), employment
(Lichtenberg and Siegel, 1990; Jelic and Wright, 2011; Wilson et al., 2012;
Paglia and Harjoto, 2014), governance (Cumming et al., 2007; Thompson and
Wright, 1995; Jensen, 1986; Shleifer and Vishny, 1997; Jensen and Meckling,
1976), innovativeness (Long and Ravenscraft, 1993; Lerner et al., 2011) or
defaults (Palepu, 1990; Andrade and Kaplan, 1998; Rajan et al., 1995).
A considerable amount of literature has been published on value creation
in the U.K. and the U.S. These studies neglect the fact that the German econ-
omy shares distinct features, which make investments in general, and private
equity speci�cally, di�erent from other buyout markets especially the U.S.
2
and U.K. (La Porta et al., 2002; Cumming and Walz, 2010; La Porta et al.,
2006; Groh et al., 2010). The German market is di�erent in �ve ways: cor-
porate governance, investment protection, �nancing behavior, public opinion
and private equity market maturity.
In Germany, companies are forced to address a lot more stakeholders than
in the classical LBO markets. Focusing on the most important stakeholders
can uncover substantial improvement potentials. For example, Germany is
well-known for its representation rights of employees in supervising boards.
The nonexistence of aligning the compensation of mandatory board members
(i.e. labor representatives) to the performance of the supervised �rm could
result in greater principal-agent dilemmas (John and Senbet, 1998). Addi-
tionally, rigid labor protection laws before the labor reforms of the Schröder
government in 2004 can in�uence economic conditions for LBO investing ac-
tivities negatively by reducing e�ciency enhancement opportunities (Black
and Gilson, 1998). As a consequence of the reforms, the labor market became
highly robust, �exible and a role model for many economies.
Second, investments are not as well protected as in other countries; this
has several reasons. On the one hand, creditor protection is very high in Ger-
many (Bae and Goyal, 2009) and consequently equity investor rights must
su�er from asymmetrical protection, which can harm equity returns. More-
over, the privilege of debt might promote certain �nancing patterns. La Porta
et al. (1997, 1998) therefore report that the legal environment has a signif-
icant impact on the ability of local companies to attract and acquire debt
�nancing to reasonable costs (Boubakri and Ghouma, 2010). On the other
hand, however, equity investor rights itself are not protected well (McLean
3
et al., 2012). To overcome this issue, investors hold a large stake of owner-
ship, which can alter the value generation in buyouts and limit value creation
associated with cutbacks of agency costs. Additionally, German civil law is,
like in the rest of continental Europe but opposed to Anglo-Saxon countries,
the dominant form of jurisdiction. Glaeser et al. (2001) and Djankov et al.
(2003) found out that enforcing claims from commercial contracts is more
di�cult in such legal environments. All of those e�ects combined lead to an
relative increase in costs of capital (Lerner and Schoar, 2005) and thus limi-
tations in economic growth and investment activity (Mauro, 1995; Svensson,
1998).
In theory, value creation in a buyout can be generated by transferring
wealth from (former) stakeholders of the target to the private equity in-
vestor. One of these stakeholders can be, for instance, debtholders (Warga
and Welch, 1993; Muscarella and Vetsuypens, 1990; Asquith and Wizman,
1990; Palepu, 1990; Renneboog and Szilagyi, 2008). By increasing the risk of
the �nancing structure, the value of the call-like option, which is the equity
stake of the private equity investor, increases and transfers value when loan
terms are not adjusted adequately. Due to the strong creditor protection in
Germany, the wealth transfer is harder to achieve, so other value creation
levers might be tackled.
According to �nancing behavior, Germany is di�erent. Due to historical
reasons, German companies are mainly debt funded by bank loans. Banks
provide loans more cautiously, which could restrain su�cient �nancing for
LBO acquisitions and would result in a higher equity contribution. Moreover,
additional layers of subordinated debt could be scarce due to the lack of an
4
underdeveloped debt capital market (Jong et al., 2008). Finally, countries
with bank-centered capital markets lack the opportunity of exiting via an
IPO (Black and Gilson, 1998, 1999). All those e�ects weaken the equity
return potential of LBOs (Jeng and Wells, 2000; Kaplan and Schoar, 2005).
Adverse media coverage about private equity investors in 2005, caused by
the metaphor of locusts shaped by Franz Müntefering, later vice chancellor
of Germany, led to mistrust and defense reactions between investors and
targets. Those e�ects could reduce the e�ectiveness of monitoring e�orts
and therefore, performance.
All this aforementioned evidence resulted directly or indirectly into the
lagged evolution of the German private equity market. In 2005, in the middle
of the research period, Germany just ranked fourth place in Euro amount in-
vested and third in number of investments despite being the biggest economy
in Europe.1
The present study concentrates solely on the German market since its
distinctive aspects described above. However, this results in a smaller sam-
ple size, which is encountered by employing statistical methods with small
sample corrections enabling the author to draw valid and robust conclusions,
although the sample is small.
Therefore, this study makes three major contributions to the research on
value creation in large buyouts in Germany. First, this work employs the
largest and most complete data sample in Germany. Second, the author
applies small sample corrections and methodologies to account for extreme
1According to the European Private Equity Venture Capital Association (EVCA)
5
value-incurred biases. Third, this study introduces a new measure of business
expectations: ifo slope. It incorporates the theory of the interest term struc-
ture as a business development indicator by subtracting the current economic
situation from the future level of the ifo expectations
This paper is structured as follows: Section 1 provides a discussion about
the value creation process in LBOs and introduces research hypotheses. Sec-
tion 2 describes the dataset and section 3 presents the empirical �ndings and
some tests of robustness. Section 4 concludes. As such, all data herein are
aggregated and not attributable to any single deal or PE house.
1. Theory and hypotheses
1.1. Theory of value creation
One of the major questions, especially in Germany and its public opinion,
is how private equity �rms are generating their returns. The theory of the
wealth transfer hypothesis is widely spread: wealth is just transferred from
one stakeholder of the acquisition target to the new LBO investor. This
hypothesis neglects the e�ects of value generation at all. Therefore, it is
of high interest to evaluate the sources of value creation in buyouts. For
the whole sample, we assume that no dividends are paid to equity holders
during the holding time. This is a viable assumption due to widely-used
covenant restrictions about the payment of dividends when debt is not paid
back completely and consistent with earlier evidence (Cohn et al., 2014).
The decomposition of value enhancement was calculated by the following
6
formula:2
100% =mt\Delta CT - t
TP+
\Delta mT - tCt
TP+
\Delta mT - t\Delta CT - t
TP+
\sum Tn=1 FCFn
TP+
\sum Tn=1 TCn
TP(1)
TP are the cumulative proceeds �owing to the equity holders, mt is the
EBITDAmultiple at entry, ∆CT - t is the di�erence in EBITDA between entry
and exit, ∆mT - t is the di�erence in EBITDA multiple valuation between
entry and exit, FCF represents the free cash �ows accumulated during the
investment period and TC stands for transaction costs. It is assumed that all
free cash �ow is used to repay debt. This seems a fair assumption according
to the lack of free cash �ow induced by high levels of debt (Jensen, 1986).
By multiplying with TP, we get absolute TP. In the next step, we calculate
times money (TM) by
TM = TP/Et - 1 (2)
Et is here denoted as equity at entry. By adding back 1, we get the money
multiple (MM), which we use to calculate the internal rate of return (IRR)
by using the following formula:
IRR =T - t\surd MM - 1 (3)
IRR and TM are employed as the dependent variables in the later anal-
ysis. It can be seen from Figure 1 that, opposed to the public opinion, the
free cash �ow e�ect (33.1%), which covers IRR e�ects from deleveraging and
2See seminal work from Daniel Pindur: Value Creation in Successful LBOs
7
tax shields do not exclusively create value. Moreover, the equity return is
generated mainly by increasing operational and strategic e�ciency resulting
in improved earnings (44.1%). Changes in entry and exit prices only account
for 23.1% of the IRR.
Figure 1: Value Contribution of Single Value Drivers to Total Value Creation
This table reports the aggregation of the value creation in the underlying sample calcu-
lated by cross-sectional means. Growing EBITDA and free cash �ows e�ects are dominant.
To assess the value creation even further, the author disaggregated the
sources of earnings variations in the sample more deeply (see Figure 2).
Changes in EBITDA, which serves as the main metric for corporate valu-
ation in an LBO context, are mainly due to EBITDA margin improvements
(65.2%) and not so much on sales variations (26.5%), which is in contrast
to Achleitner et al. (2011) and their full range sample. This can be seen as
8
evidence that larger buyouts increase value more by reducing slag than by
utilizing growth opportunities.
Figure 2: Factors of EBITDA contribution
This table reports the aggregation of EBITDA growth in the underlying sample calculated
by cross-sectional means. EBITDA growth is mainly obtained from margin improvements.
1.2. Hypotheses
A considerable amount of literature has been published on drivers of �rm-
level returns. These studies concentrate on di�erent characteristics of the
value creation process and are limited to the Anglo-Saxon markets. Drivers
of fund-level returns are neglected in this review.
9
The holding period of portfolio companies can in�uence the return pat-
terns of transactions. Longer holding periods deteriorate returns measured
by IRR. Acharya et al. (2013) as well as Cumming and Walz (2010) report
a signi�cant negative in�uence of the holding period on the IRR. Further
statistical tests revealed that returns are functions of the holding period
(Cochrane, 2005). Given that IRR is a time-sensitive measure of returns,
shorter investments should yield a higher performance. Nikoskelainen and
Wright (2007) claim that their signi�cant �ndings can be attributed to the
fact the investors prefer faster exits when a window of opportunity opens
even when the full enhancement potential is not yet exhausted. Therefore,
the hypothesis must be:
H 1. The shorter the holding period of an LBO investment, the higher the
realized performance.
According to the e�ect of divestments, selling assets, which do not add
value to the core-business, should yield a positive return. Easterwood (1998)
analyzed the outcome of divesting activities on the wealth e�ect and reports
a positive signi�cant outcome on the value of buyout companies, which run
through divesting activities. Smith (1990) was not able to show the con-
tribution of divestments to the value generation. In theory, the rationale
for divesting non-core subsidiaries and divisions is twofold (DeAngelo et al.,
1984; Kaplan and Stein, 1993): �rst, underperforming business activities are
disposed in order to strengthen margins. Second, non-core divisions should
be transferred to owners, which receive higher utility by controlling these
items. The additional cash �ow from a sale could be then used to pay down
debt more quickly. A divestment in the holding period should therefore in-
10
crease the performance of an investment.
H 2. LBO investments with divestments of subsidiaries show a higher per-
formance.
Future economic expectations are linked to the performance and valuation
of a company. Favorable prospects at entry should support the target com-
pany's ability to enhance top-line and bottom-line performance and therefore
deleveraging and valuation at exit (Valkama et al., 2013).
As a measure of business expectations, the author constructed a new
variable: ifo slope. Like applying the slope of the term structure for utiliz-
ing expectations about the future state of the interest rate levels and thus
an estimate about future business conditions (Harvey, 1988; Estrella and
Hardouvelis, 1991; Jorion and Mishkin, 1991), the author transferred the
concept of the slope parameter (measured by the di�erence) between the
long term interest rate level (normally 10 years) and short term levels (one
to three years) to the di�erence between the ifo business expectations and
the current state of the German economy. At positive parameters, the shape
of the economy is expected to improve and vice versa.
H 3. LBO investments with positive business expectations at entry show
higher performance.
During poor economic conditions, fund managers have the incentive to
delay writing-o� loss-making investments or underperforming exits. Write-
o�s or reporting negative returns would yield a hostile perception of the fund
management and hereby hurt their ability to raise new capital from poten-
tial investors in the future. Cumming and Walz (2010) report signi�cantly
11
di�erent holding periods between times of good economic conditions and re-
cessionary times. Once a recession occurs during the investment period, a
deal is considered a recessionary transaction. The periods of economic down-
turns are de�ned by the OECD.
H 4. The holding period of LBO investments is longer in recessionary times.
The larger a �rm, the higher the agency costs. At some degree of �rm size,
monitoring gets too costly and the waste of free cash �ow increases (Jensen,
1986). A mean of reducing agency costs is increasing leverage (Wruck, 1990).
Singh and Davidson III (2003) and Berger and Bonaccorsi di Patti (2006)
report that the agency costs to the PE investor increase signi�cantly with the
size of the �rm and can be reduced by a higher debt load. Humphery-Jenner
and Powell (2014) as well as Moeller et al. (2004) found out that larger
companies perform worse than smaller ones when conducting acquisitions.
This could be regarded as evidence for higher agency costs in large companies.
Assuming that LBO equity investors are aware about that fact, they tend
to �nance larger deals more aggressively. Additionally, Nikoskelainen and
Wright (2007) claim that larger companies have a history of performing on
debt and more collateral is available for higher debt funding at lower �nancing
costs.
H 5. The larger the transaction size at entry, the more leverage is used to
�nance the transaction.
As shown in hypothesis 5, large companies have higher agency costs and
are therefore equipped with more debt. More debt limits the growth op-
portunities, so adding value must stem from other sources like margin im-
12
provements, leverage or multiple expansion. Moreover, larger deals have
the advantage of higher visibility in the exit market and have the required
volume for going public, especially in Germany (Nikoskelainen and Wright,
2007), where there is de facto a minimum volume for public listings. Given
the higher visibility of larger companies, information asymmetries may not
arise extensively, which lowers entry prices and therefore acquisition premia
in general (Lehn and Poulsen, 1989; DeAngelo et al., 1984; Ofek, 1994). Fi-
nally, as larger corporations exhibit more possibilities of divesting non-core
divisions higher potential for eliminating the conglomerate discount exists
(Singh, 1993). The multiple variation e�ect is measured by the absolute
contribution of the multiple variation between entry and exit.
H 6. The larger the transaction size at entry, the more value is generated by
the multiple variation e�ect.
2. Sample data
2.1. Data description
The sample data is collected with the support of the Bundesverband
Deutscher Kapitalbeteiligungsgesellschaften e.V. (BVK). The BVK collected
data from the largest leveraged buyouts in Germany with the consent of
well-known LBO players.
The included deals need to ful�ll the following criteria:
\bullet an enterprise value of more than EUR 750 million
\bullet a controlling stake of a minimum of 25 percent held by at least one
LBO company
13
\bullet the controlled �rm underlies German jurisdiction, the target must em-
ploy more than one thousand people in Germany and the revenue coun-
try share of Germany needs to exceed at least 30 percent
40 transactions from 1997 to 2008, which are completely exited, are in-
cluded. The following characteristics were collected: the annual report prior
one year before the transaction was closed including cash �ow data, con-
trolling stake, deal prices and �nancing structure. PricewaterhouseCoopers
is managing and maintaining the database and was providing a data-room
infrastructure to the author. See AppendixA for an overview about the deal
universe and the corresponding �nancial investors.
From the 40 transactions, 12 were complete and suitable for analysis. Ad-
ditional research using commercial databases exhaustively and hand-collecting
yielded a controlled sample of 22 deals. Macroeconomic data was comple-
mented. The ifo slope is the di�erence between ifo business expectations and
recent ifo business climate at entry. See Table 1 for descriptive statistics.
Table 1 illustrates that times money of all transactions is 2.4x on average
and has a median of 2.2x. Consequently, the IRR has a mean and a median
of 50% and 22% respectively. These results are lower than the reported
outcomes by Valkama et al. (2013), Nikoskelainen and Wright (2007) as well
as Cumming and Walz (2010) but higher than Achleitner et al. (2011) and
in accordance with Acharya et al. (2013). The size of the transactions ranges
from 45me to 3be with a mean of 784me and a median of 557me. The
deals exhibit EBITDA at entry reaching from 6me to 353me as well as
means and medians of 112me and 87me respectively. The average holding
period is 46 months, and the median equals to 39, which corresponds to 3
14
Table 1: Descriptive Statistics of the Controlled Sample
Variable Median Mean Std. Dev. Min Max
Times Money 2.154 2.379 2.794 -2.223 11.539
IRR 0.221 0.503 0.702 -0.403 2.701
Holding Period (in Months) 38.717 45.602 22.288 15.967 95.467
Size (Enterprise Value at Entry in me) 556.650 783.559 779.043 45.000 3000.000
Log Size (Enterprise Value at Entry) 13.230 13.098 1.077 10.714 14.914
ifo Slope 1.200 0.889 4.620 -8.800 8.400
Leverage 0.667 0.641 0.195 0.000 0.889
EV/EBITDA at Entry 6.450 6.157 1.259 3.200 8.000
EV/EBITDA at Exit 8.090 8.305 2.131 4.700 13.160
EBITDA at Entry (in me) 86.491 112.926 87.267 6.081 352.999
EBITDA at Exit (in me) 126.164 168.581 168.636 12.744 747.340
Revenue at Entry (in me) 795.094 1104.604 1134.333 67.752 4557.099
Revenue at Exit (in me) 859.826 1245.924 1451.847 74.308 6256.800
Investor Participation 0.745 0.691 0.236 0.273 1.000
Management Participation 0.082 0.092 0.086 0.000 0.327
EBITDA Margin at Entry 0.090 0.146 0.122 0.052 0.572
EBITDA Margin at Exit 0.156 0.177 0.131 0.035 0.609
Multiple Variation (in me) 162.479 170.086 161.153 -52.539 578.642
Observations 22
15
years and 10 months and 3 years and 3 months respectively. This is in line
with previous research (Axelson et al., 2013; Nikoskelainen and Wright, 2007;
Valkama et al., 2013; Acharya et al., 2013). Interestingly, the majority of
transactions were performed during times of favorable business expectations:
the ifo slope exhibits positive values in the average and median transaction
(0.9 and 1.2). Values greater than zero mean that the future is regarded
more positively than the prevailing conditions. Leverage reaches from 100%
equity �nancing to 89% debt �nancing and a mean and median of 64% and
67% respectively. The �ndings observed mirror those of previous studies
about average debt levels in LBO deals (Axelson et al., 2013). Management
participation is comparable to earlier research: mean participation of 9% and
a median share of 8%. These �ndings are in agreement with Cotter and Peck
(2001)'s �ndings, which show similar patterns. In contrast, the sponsor's
participation di�ers and is slightly higher with a mean of 69% and a median
of 75%. This could be due to the lack of small investor protection in Germany
and the resulting need of a higher share. The transactions by entry reach
from 1997 to 2006. Chart 3 describes the frequency of the deals per year.
One can clearly observe a dip after the burst of the Dot-com bubble in
2002. This is in coherence with the development of the total private equity
investment volume in Germany (from 4.435be in 2001 to 2.506be in 2002).3
The evolution of the average multiples over the years is shown in Figure 4.
Especially, in later years, when exit multiples are available, one can see a sig-
ni�cant gap between buying and selling valuation in this sample. This could
3According to BVK
16
Figure 3: The Frequency of Buying-in Transactions Every Year
This table reports the yearly number of deals of the dataset reaching from 1999 to 2006
including 22 observations.
of course be biased by buying targets from low valued sectors (e.g. textile &
apparels, food or construction)4 and just selling targets stemming from high
valued industries (e.g. software, retail trade or machine manufacturing).
According to hypothesis 2 and 4, the sample was split into investments
with divesting activities and non-divesting activities as well as deals that
underwent recessionary periods and deals that were invested during growth
phases (see Table 2). Transactions, where investors refrained to divest parts
4Market based multiple valuation for large and public companies in Germany deter-
mined by the Finance-Magazin as of November 2014
17
of businesses have a higher times money and IRR than deals that faced refo-
cusing e�orts. This is somehow surprising because divestments are considered
as an approach of increasing margins and therefore the value of the target.
Recessionary investments show mixed results. Times money indicates
that the mean times money is higher at recession deals and the median times
money implies that growth transactions yield higher returns. The metric IRR
shows a clearer picture. Growth deals are by far more lucrative to investors.
The mixed results can be explained by the extended holding period. Given
that times money is insensitive to the holding period, the metric times money
is not in�uenced by longer investment whereas IRR is.
Overall, both statistics (Table 1 and 2) show clear signs of the existence
of extreme values indicated by larger deviations between mean and median
in the sample. This needs to be taken care of in the following sections.
18
Figure 4: Comparison of Entry & Exit Multiples
This table compares the entry and the exit multiples calculated by cross-section means.
The exit multiples are generally higher than the entry multiple, which can be an indica-
tion of superior negotiation skills.
19
Table2:Summary
StatisticswithDetailed
AnalysisofDivestm
ents
andRecessions
FullSample
ControlledSample
DivestingActivities
Non-divestingActivities
RecessionwhileInvested
Growth
whileInvested
Observations
40
22
517
13
9
Mean
Median
Mean
Median
Mean
Median
Mean
Median
Mean
Median
TimesMoney
2.379
2.154
0.544
0.112
2.919
2.670
2.620
2.006
2.031
2.670
IRR
0.503
0.221
0.039
0.040
0.639
0.412
0.279
0.207
0.827
0.694
HoldingPeriod
45.602
38.717
38.953
37.300
47.557
40.033
59.031
60.133
26.204
25.200
Size
783.559
556.650
911.349
376.994
745.974
565.300
767.040
649.340
807.421
527.794
ifoSlope
0.889
1.200
-1.420
-0.600
1.559
1.300
0.023
1.200
2.122
2.300
Leverage
0.641
0.667
0.537
0.643
0.672
0.682
0.684
0.653
0.579
0.688
EV/EBITDAatEntry
6.157
6.45
5.732
6.600
6.282
6.300
6.442
6.750
5.744
6.100
EV/EBITDAatExit
8.305
8.09
8.808
8.400
8.157
8.000
8.175
8.180
8.491
8.000
EBITDAatEntry(inme)
112.926
86.491
138.373
87.673
105.445
86.310
100.668
73752
130.638
87.673
EBITDAatExit(inme)
168.581
126.164
145.733
61.999
175.301
126.528
147.768
126.528
198.644
125.800
RevenueatEntry(inme)
1104.604
795.094
962.705
994.600
1146.339
667.753
692.505
667.753
1699.857
1003.504
RevenueatExit(inme)
1245.924
859.826
897.219
862.542
1348.485
802.825
746.150
802.825
1967.820
862.542
InvestorParticipation
0.691
0.745
0.719
0.770
0.683
0.720
0.685
0.710
0.699
0.843
ManagementParticipation
0.092
0.082
0.142
0.120
0.077
0.050
0.078
0.050
0.112
0.086
EBITDAMargin
atEntry
0.146
0.09
0.137
0.137
0.149
0.090
0.184
0.137
0.091
0.087
EBITDAMargin
atExit
0.177
0.156
0.144
0.152
0.187
0.164
0.217
0.164
0.119
0.125
20
2.2. Sample selection
Research on the subject has been mostly restricted to going-private deals.
Nonetheless, there is more than one type of entry: conglomerate carve-outs,
sponsor-to-sponsor, or public-to-private privatization. Almost all studies ne-
glected this fact, which could impose a selection bias because value generation
is signi�cantly di�erent between the entry types (Nikoskelainen and Wright,
2007; Meuleman et al., 2009). Additionally, much research is solely based
on positive observations of the performance measure. No-receiverships, deals
that are omitted due to the total loss of the sponsor's equity, are often not
included. This could imply another possible selection bias by including only
successful LBOs. The present research incorporates all entry modes and
captures no-receiverships, and is therefore free of sample selection biases.
3. Results
3.1. Ordinary least square analysis
Although this paper's data sample is limited to 22 observations, a uni-
variate regression was performed. Small samples do not restrict regressions
in general. The estimation of the coe�cient is still valid although inference
statistics like hypothesis tests can be biased. Regression results need to be
examined carefully to deal with possible ine�ciencies in the estimations pro-
cess of regression parameters.
To con�rm the hypotheses, and the descriptive statistics, classical ordi-
nary least squares (OLS) was applied to estimate the impact of the indepen-
dent variables on the endogenous metrics. As one can see, in Table 3, H 1
21
cannot be rejected completely. The di�erent performance measures show an
elusive result. A negative e�ect of the holding period on times money was
not observable. This is consistent with prior research (Phan and Hill, 1995).
However, the holding period showed a signi�cant negative e�ect (-1.5%p) on
IRR at the 10% level (R2 of 21.6%). This is remarkable in two ways: �rst, it
supports H 1. Second, although the internal rate of return is time sensitive,
it is even more interesting that there is a negative impact linked to changes in
the holding period. Due to heteroscedasticity, a robust regression was used
to estimate the IRR in the regression setting.
H 2 was rejected by both metrics at the 5% signi�cance. Times money (-
2.375, R2 of 13.3%) and IRR (-60.0%p, R2 of 13.5%) both indicate that a sale
of business units leads to a lower performance in portfolio companies. In the-
ory, an LBO investor screens targets for operational and strategic e�ciency
improvement potential. Afterwards, underperforming branches, subsidiaries
and divisions are sold to improve overall EBITDA margin. One reason why
this could fail is that divestments are sold to an unfavorable price compared
to the loss of EBITDA of the division due to information asymmetries, time
pressure due to �nancial distress, size discounts, or limited liquidity in the
divestment market. Testing for speci�cation problems is not meaningful with
dummy variables in this context and therefore omitted.
Good economic expectations are essential at the beginning of the invest-
ment. The importance of good business expectations can be con�rmed at
both performance measures (H 3 ). ifo slope (0.122) is signi�cant according
to times money at the 10% level with an R2 of 4.1% whereas the e�ect on
IRR (4.1%p) is signi�cant at the 1% level with a R2 of 7.3%. Therefore, a
22
favorable ifo slope can support the top-line in�uenced growth of EBITDA
and thus valuation at exit.
The results obtained from the preliminary analyses of H 1 and H 3 are
directly related to H 4 and the impact of recessions on the holding period.
The holding period is almost 33 months longer (signi�cant at a 1% level) at
deals, where an economic contraction is occurring during the lifetime of the
investment. The goodness of �t is strong, given an R2 of 54.9%. The reason
for that could be threefold: �rst, investors hang on underperforming targets
too long instead of writing-o� or exiting with a negative return. Second,
due to the recession, it takes longer for an investor to mine the full growth
potential of a target, hereby, a viable exit is deferred to the future. Third,
company valuations collapse and exit routes close very fast. Investors are
locked-in in an investment. A heteroscedastic robust estimation method was
applied to account for indications of heteroscedasticity.
H 5 was con�rmed at the signi�cance of 1%. An 1% change in �rm size
increases debt �nancing, expressed by leverage, by 6.4%p (R2 of 12.4%). This
can stem from several reasons. Larger �rms exhibit in general higher valua-
tions and higher acquisition premia. This deviation from fundamentals (like
the Enterprise-to-EBITDA ratio) can be bridged by a higher debt portion.
Second, value generation crafted by sales growth is limited due to preexisting
high levels of sales, and thus, the value lever �nancial engineering becomes
more critical. Finally, the larger the portfolio company, the more governance
issues can arise due to limited monitoring capabilities of the investor. Higher
debt �nancing is an adequate way of aligning the interests of the agent (tar-
get management) with those of the principal (LBO investor). This is in line
23
with research conducted by Achleitner et al. (2011).
According to the results of H 5, larger deals are limited in their growth
options. That is why target companies can easier generate value by extensive
�nancial engineering or by multiple expansion. H 6 claims a more important
contribution of the multiple expansion at larger deals and can be con�rmed
at the 10% level. Large deals tend to generate their value by a 47me higher
multiple expansion compared to small companies (R2 of 9.9%).
To control for incorporating all relevant variables, tests of variable omit-
tance are employed. No regression can reject the null of no omittance of a
relevant variable, which is a clear indication that the regressions are correctly
speci�ed and that there is no exogenous variable missing. To test for robust-
ness, the Link-test (Pregibon, 1980) was also applied and able to support the
outcomes.
24
Table3:OLSRegressionResults
DependentVariable
IndependentVariable
Statistics
DiagnosticTests
H1)
TimesMoney
HoldingPeriod
Coe�cient
-0.007
Jarque-Bera-Test
11.92***
Std.Dev.
0.019
WhiteTest
0.90
R2
0.003
RESET
0.29
IRR
1Coe�cient
-0.015*
Jarque-Bera-Test
6.39**
Std.Dev.
0.008
WhiteTest
9.55***
R2
0.216
RESET
1.23
H2)
TimesMoney
Divesture
Dummy
Coe�cient
-2.375**
Jarque-Bera-Test
12.47***
Std.Dev.
0.927
WhiteTest
0.62
R2
0.133
RESET
2�
IRR
Coe�cient
-0.600**
Jarque-Bera-Test
13.18***
Std.Dev.
0.225
WhiteTest
0.83
R2
0.135
RESET
2�
H3)
TimesMoney
ifoSlope
Coe�cient
0.122*
Jarque-Bera-Test
13.31***
Std.Dev.
0.064
WhiteTest
1.36
R2
0.041
RESET
0.52
IRR
Coe�cient
0.041***
Jarque-Bera-Test
14.80***
Std.Dev.
0.013
WhiteTest
1.76
R2
0.073
RESET
0.25
H4)
HoldingPeriod1
RecessionDummy
Coe�cient
32.827***
Jarque-Bera-Test
1.02
Std.Dev.
6.090
WhiteTest
4.11**
R2
0.549
RESET
2�
H5)
Leverage
Size
Coe�cient
0.064***
Jarque-Bera-Test
15.74***
Std.Dev.
0.020
WhiteTest
1.75
R2
0.124
RESET
0.08
H6)
MultipleVariation
Size
Coe�cient
47141.750*
Jarque-Bera-Test
8.00**
Std.Dev.
27048.190
WhiteTest
0.58
R2
0.099
RESET
0.51
1Heteroscedasticrobust
regressionwasperform
edto
accountforheterscedasticityin
residuals.
2Notmeaningfulwhile
regressingdummyvariables.
Jarque-Bera-Test
wasperform
edto
test
forthenon-norm
ality,theWhiteTest
tocontrol
forheteroscedasticityandtheregressionspeci�cation-errortest(RESET)to
testforH0:noomittanceofavariable.***,
**and*denote
signi�canceatthe1%,5%
and10%
respectively.Allvariablesare
atleastsigni�cantata10%
levelexcept
H1andhere
timesmoney;Thisisclearevidencethattheholdingperiodispositively
relatedto
theinternalrate
ofreturn
althoughthemetricaccounts
forthetimehorizon.
25
3.2. Bootstrap regression analysis
As there is a small sample employed, the t-tests and the corresponding
p-values could be biased. To deal with this issue, the author incorporated the
methodology of bootstrapping the OLS regression. Bootstrapping employs
resampling from the sample to empirically calculate inference statistics with
a bootstrap estimate of the standard deviation. The approach assumes that
the underlying and controlled sample is the population and the bootstrapped
samples are then draws from the sample itself. These methods do not require
any assumptions about distributions of errors like normality, and therefore
provide more accurate inferences when extreme values are present and the
sample size is small.
Performing an OLS bootstrapped regression analysis with 1000 replica-
tions, H 3 and ifo slope remain signi�cant at the IRR whereas times money
becomes insigni�cant. This could be explained in combination with H 4 :
deteriorating business conditions will end in a recession, which extends the
holding period and therefore a�ects time-sensitive IRR negatively (see Ta-
ble 4 for details). H 6 is rejected.
Almost all regressions show signs of non-normality in their residuals (ex-
cept H 4 ), which is a signal for the possible existence of extreme values. The
�ndings suggest that extreme values might have an e�ect on the proper esti-
mation of the OLS parameters and methods that are more robust are more
suitable.
26
Table4:OLSBootstrapped
Regressionresults
DependentVariable
IndependentVariable
Statistics
DiagnosticTests
H1)
TimesMoney
HoldingPeriod
Coe�cient
-0.007
Jarque-Bera-Test
11.92***
Std.Dev.
0.020
Link-test
p-val
0.76
Adj.-R
2-0.047
IRR
Coe�cient
-0.015**
Jarque-Bera-Test
6.39**
Std.Dev.
0.007
Link-test
p-val
0.10
Adj.-R
20.177
H2)
TimesMoney
Divesture
Dummy
Coe�cient
-2.375**
Jarque-Bera-Test
12.47***
Std.Dev.
0.953
Link-test
p-val
�
Adj.-R
20.090
IRR
Coe�cient
-0.600***
Jarque-Bera-Test
13.18***
Std.Dev.
0.223
Link-test
p-val
�
Adj.-R
20.091
H3)
TimesMoney
ifoSlope
Coe�cient
0.122
Jarque-Bera-Test
13.31***
Std.Dev.
0.078
Link-test
p-val
0.27
Adj.-R
2-0.007
IRR
Coe�cient
0.041***
Jarque-Bera-Test
14.80***
Std.Dev.
0.016
Link-test
p-val
0.42
Adj.-R
20.026
H4)
HoldingPeriod
RecessionDummy
Coe�cient
32.827***
Jarque-Bera-Test
1.02
Std.Dev.
5.818
Link-test
p-val
�
Adj.-R
20.527
H5)
Leverage
Size
Coe�cient
0.064***
Jarque-Bera-Test
15.74***
Std.Dev.
0.024
Link-test
p-val
0.83
Adj.-R
20.080
H6)
MultipleVariation
Size
Coe�cient
47141.750
Jarque-Bera-Test
8.00**
Std.Dev.
29522.730
Link-test
p-val
0.28
Adj.-R
20.054
1Notmeaningfulwhileregressingdummyvariables.
Jarque-Bera-Testwasperform
edto
testforthenon-norm
ality,the
linktest
totest
forH0:noomittanceofavariable.***,**and*denote
signi�canceatthe1%,5%
and10%
respectively.
27
3.3. Median quantile regression analysis
As chapters 3.1 and 3.2 show signs of extreme values (indicated by larger
di�erences between mean and median values) and existing non-normality
in the residuals, classical OLS regression, even bootstrapped, might not be
suitable of capturing the extreme values because it �ts the regression on the
expense of extreme values, and thus coe�cients are not estimated e�ciently
anymore.
A more robust estimation method is the median quantile regression (MQR).
MQR minimizes absolute-deviations and is thus more robust to extreme val-
ues than OLS. OLS minimizesn\sum
i=1
e2i whereas MQR minimizesn\sum
i=1
| ei| . MQR
features another attribute, which enables the usage. MQR does not demand
any assumptions about the distribution of the residuals.
Applying bootstrapping on the more robust median quantile regression
analysis provides a similar impression as discussed before. Table 5 presents
the results of the MQR. All coe�cients keep their original sign and thus
are stable. The holding period remains signi�cant at the IRR, whereas di-
vestment activities become insigni�cant in IRR performance. In contrary
to bootstrapped OLS, ifo slope becomes signi�cant in terms of times money
again. The in�uence of a recession on the holding period (H 4 ), the depen-
dence of size and leverage (H 5 ) and multiple variation (H 6 ) remain highly
signi�cant, and are consistent with prior results.
Speci�cation problems are not observable but the non-normality assump-
tion is violated except for H 4. As discussed above, the severance of this
violation can be relaxed given that MQR does not require a certain distribu-
tion of the residuals.
28
Table 6 provides a summary of estimated signs of the coe�cients and lev-
els of signi�cance. The vast majority of the results is robust and is completely
stable in estimated coe�cient sign.
29
Table5:MedianQuantileBootstrapped
RegressionResults
Dependentvariable
Independentvariable
Statistics
Diagnostictests
H1)
TimesMoney
HoldingPeriod
Coe�cient
-0.023
Jarque-Bera-Test
10.78***
Std.Dev.
0.029
Link-test
p-val
0.04**
PseudoR
20.027
IRR
Coe�cient
-0.013*
Jarque-Bera-Test
7.70**
Std.Dev.
0.007
Link-test
p-val
0.30
PseudoR
20.124
H2)
TimesMoney
Divesture
Dummy
Coe�cient
-2.557**
Jarque-Bera-Test
12.14***
Std.Dev.
1.053
Link-test
p-val
�
PseudoR
20.093
IRR
Coe�cient
-0.372
Jarque-Bera-Test
14.33***
Std.Dev.
0.265
Link-test
p-val
�
PseudoR
20.048
H3)
TimesMoney
ifoslope
Coe�cient
0.161*
Jarque-Bera-Test
13.10***
Std.Dev.
0.086
Link-test
p-val
0.28
PseudoR
20.072
IRR
Coe�cient
0.032**
Jarque-Bera-Test
14.89***
Std.Dev.
0.014
Link-test
p-val
0.99
PseudoR
20.111
H4)
HoldingPeriod
RecessionDummy
Coe�cient
34.933***
Jarque-Bera-Test
0.47
Std.Dev.
8.227
Link-test
p-val
�
PseudoR
20.355
H5)
Leverage
Size
Coe�cient
0.068**
Jarque-Bera-Test
15.55***
Std.Dev.
0.027
Link-test
p-val
0.32
PseudoR
20.112
H6)
MultipleVariation
Size
Coe�cient
49939.930*
Jarque-Bera-Test
7.91**
Std.Dev.
26078.820
Link-test
p-val
0.86
PseudoR
20.141
1Notmeaningfulwhileregressingdummyvariables.
Jarque-Bera-Testwasperform
edto
testforthenon-norm
ality,the
linktest
totest
forH0:noomittanceofavariable.***,**and*denote
signi�canceatthe1%,5%
and10%
respectively.
30
Table6:Summary
ofRegressionAnalysisResults
DependentVariable
IndependentVariable
OLS
OLSBootstrapped
MedianBootstrapped
H1)
Tim
esMoney
HoldingPeriod
Coe�
cientSign
\circleddash \circleddash
\circleddash
Signi�cance
--
-
IRR
Coe�
cientSign
\circleddash \circleddash
\circleddash
Signi�cance
***
*
H2)
Tim
esMoney
Divesture
Dummy
Coe�
cientSign
\circleddash \circleddash
\circleddash
Signi�cance
**
**
**
IRR
Coe�
cientSign
\circleddash \circleddash
\circleddash
Signi�cance
**
***
-
H3)
Tim
esMoney
ifoSlope
Coe�
cientSign
\oplus \oplus
\oplus
Signi�cance
*-
*
IRR
Coe�
cientSign
\oplus \oplus
\oplus
Signi�cance
***
***
**
H4)
HoldingPeriod
RecessionDummy
Coe�
cientSign
\oplus \oplus
\oplus
Signi�cance
***
***
***
H5)
Leverage
Size
Coe�
cientSign
\oplus \oplus
\oplus
Signi�cance
***
***
**
H6)
MultipleVariation
Size
Coe�
cientSign
\oplus \oplus
\oplus
Signi�cance
*-
*
\oplus denotesapositivecoe�cientsign,whereas\circleddash anegativecoe�cientsign.***,**and*denote
signi�canceatthe1%,5%
and10%
respectively.�
connotesnosigni�cantresults.
31
4. Conclusion
The present study was designed to determine the e�ects of value levers on
the value creation process of LBO investor while holding an investment. This
study has shown that holding period, divestments and the shape of the future
economy have a signi�cant impact on the value creation in terms of times
money and internal rate of return. First, holding period is negatively linked
to performance. Longer investments tend to decrease the IRR. Second, in
contrast to earlier �ndings, however, a negative impact of divestures on the
returns was detected. This can be attributed to unfavorable selling prices
due to size or liquidity discounts or can be seen as evidence for �nancial
distress and resulting need to generate cash �ows to meet debt and interest
payments. Third, another important �nding was that the di�erence between
business expectations and the current state of the economy, described by the
ifo slope, could be seen as good indicator for the outcome of an investment.
Additionally, the other results of this study did show some remarkable
insights. During recessions, the holding period increases more than double
to about �ve years. This can be due to the refusal of investors to materialize
their loses by exiting for an unfavorable transaction price or writing-o� their
investment completely. When controlling for size, the results show that larger
deals tend to be higher leveraged and multiple expansion is more important
for the value generation. Larger �rms face higher agency costs, and given that
investors are aware of that fact, they are eager to increase leverage in order
to use the disciplining e�ect of debt. A second reason could be that larger
targets are considered less risky and have therefore a higher debt capacity.
For an investor, larger targets are harder to grow and given that they are
32
burdened with higher debt, are therefore limited in their ability to increase
sales. Consequently, value creation must stem from alternative sources like
the multiple expansion. Alternatively, larger companies are more visible in
the market prior to the buyout and information asymmetries, which may arise
at entry, are thus less likely to substantiate. This could shrink overpayment
and therefore makes multiple expansion more important.
However, with a small sample size, caution must be applied. A larger data
sample could provide evidence that is more distinctive. Although univariate
analyses were highly signi�cant and small sample corrections were applied,
these results should be tested in a multivariate regression setting as soon as
the appropriate sample size is available.
Given that the existing data sample does not cover the performance dur-
ing the �nancial crisis, more information on transactions a�ected by the
economic crisis would help to establish a greater con�dence in the accuracy
on this matter. For instance, the robustness of ifo slope could be tested
further during the recent crisis.
There is abundant room for further progress to employ MQR to analyze
the value creation in the top and �op quartiles of the LBO deals. For in-
stance, top-notch investments may bene�t from deleveraging while extensive
leverage could be a liability for underperforming deals. Finally, in future in-
vestigations, it might be possible to compare country speci�c results within
the same attractiveness of LBO environments and in contrast, those �nd-
ings with economies with a di�erent LBO investment climate. This would
provide insights whether value creation di�ers depending on the market and
regulatory environment.
33
Acknowledgments
I would like to thank Prof. Schneider for detailed discussions and his valu-
able feedback, Attila Dahmann and the Bundesverband Deutscher Kapital-
beteiligungsgesellschaften e.V. for providing data, PricewaterhouseCoopers
for granting me access and hosting me in their o�ce and Alexander Scheld
for support from a practical and pragmatic point of view.
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AppendixA. Deal Universe in the Dataset
Company (Year) Sponsor
A.T.U. Autoteile Unger (2004) KKR
Accordis/Niederlande (1999) CVC Capital Partner
Amadeus/Spanien (2005) BC Partners & Cinven
Armacell (2001) CVC Capital Partner & Gilde Investments
Autobahn Tank & Rast (1998) Lufthansa, Apax Partners & Allianz Capital Partners
Bartec (2002) Allianz Capital Partners
Bavaria Yacht (2007) Bain Capital
Beru (2000) The Carlyle Group
Brenntag (2004) Bain Capital
Brenntag (2006) BC Partners
Bundesdruckerei/Authentos (2000) Apax Partners
Casa Reha (2005) Advent International
CBR (2004) Cinven
Celanese (2004) Blackstone Group
Charles Vögele/Schweiz (1997) Permira
Cognis (2001) Permira, GS Capital Partners & SV Life Sciences
Corposan (1997) CVC Capital Partners
Debitel (2004) Permira
Demag Holding (2002) KKR
Dialog Semiconductor (1998) Apax Partners
Dometic/Schweden (2005) BC Partners
Duales System Deutschland (2005) KKR
Dynamit Nobel (2004) KKR
Dywidag Systems International (2007) CVC Capital Partners
Edscha (2003) The Carlyle Group
Elster Group (2005) CVC Capital Partners
Euro Dental Holding (1997) Permira
Flint Group/BASF Printing Systems/USA (2004) CVC Capital Partners
Gerresheimer Glas (2004) Blackstone Group
Grammer (2001) Permira
Grohe (1999) BC Partners
Grohe (2004) Texas Paci�c Group & CSFB Private Equity
H. C. Starck (2006) Advent International & The Carlyle Group
Herlitz (2005) Advent International
Honsel (2000) The Carlyle Group
HT Troplast (2004) Advent International & The Carlyle Group
continued on next page
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continued from previous page
Company (Year) Sponsor
Hugo Boss (2007) Permira
IFCO Systems (2003) Apax Partners
ISTA (2003) CVC Capital Partners
Jack Wolfskin (2002) Bain Capital
Kabel Baden-Württemberg (2001) Blackstone Group
Kabel Deutschland (2003) Apax Partners, Providence Equity Partners & GS Capital Partners
Kalle Nalo (1997) CVC Capital Partners
Kiekert (2000) Permira
Kion Group (2006) KKR & GS Capital Partners
Klöckner Pentaplast (2001) Cinven
Klöckner Pentaplast (2007) Blackstone Group
LR-International (2004) Apax Partners
MAN Roland Drucksysteme (2006) Allianz Capital Partners
Memorex Telex (1997) Permira
Merlin Entertainment/UK (2005) Blackstone Group
Messer Griesheim (2001) Allianz Capital Partners & GS Capital Partners
Metzeler (2000) CVC Capital Partners
Mobilcom (2005) Texas Paci�c Group
Moeller (2003) Advent International
MTU Aero Engines (2003) KKR
Nordsee (1997) Apax Partners
OXEA Gruppe (2007) Advent International
Premiere Fernsehen (2003) Permira, Bayern LB, HVB & BAWAG
ProSiebenSat1 Media (2006) Permira & KKR
Rodenstock (2003) Permira
Rungis (1997) CVC Capital Partners
RWE Solutions-Nukem (2006) Advent International
RWE Solutions-SAG (2006) Advent International
Sanitec/Finnland (2001) BC Partner
Scandlines (2007) 3i, Allianz Capital Partners & Deutsche Seereederei
Schmalbach-Lubeca (2000) Allianz Capital Partners
Single Temperiertechnik (1997) Permira
Sirona dental systems (1997) Permira
Smur�t Kappa Packaging/Irland (1998) CVC Capital Partners
Sport�ve (2004) Advent International
Springer Science+Business (2003) Cinven & Candover
Sueddekor/2D Holding (2004) Bain Capital
Sulo (2004) Blackstone Group & Apax Partners
Takko (2002) Permira
Takko (2007) Apax Partners
Tenovis (2000) KKR
UFA-Film Theater (1998) Apax Partners
Unity Media (2003) BC Partners & Apollo Management
Versatel (2005) Apax Partners
Viatris (2002) Advent International
continued on next page
43
continued from previous page
Company (Year) Sponsor
Vinnolit (2000) Advent International
WincorNixdorf (1999) KKR & GS Capital Partners
44