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: daniel.ilg@ku.de (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.

Acharya, V. V., Gottschalg, O. F., Hahn, M., Kehoe, C., 2013. Corporate

Governance and value creation: Evidence from private equity. Review of

Financial Studies 26 (2), 368�402.

Achleitner, A. K., Braun, R., Engel, N., 2011. Value creation and pricing in

buyouts: Empirical evidence from Europe and North America. Review of

Financial Economics 20 (4), 146�161.

Andrade, G., Kaplan, S. N., 1998. How Costly is Financial (Not Economic)

Distress? Evidence from Highly Leveraged Transactions that Became Dis-

tressed. The Journal of Finance 53 (5), 1443�1493.

Asquith, P., Wizman, T. A., 1990. Event risk, covenants, and bondholder

returns in leveraged buyouts. Journal of Financial Economics 27 (1), 195�

213.

Axelson, U., Jenkinson, T., Strömberg, P., Weisbach, M. S., 2013. Borrow

cheap, buy high? The determinants of leverage and pricing in buyouts.

The Journal of Finance 68 (6), 2223�2267.

Bae, K.-h., Goyal, V. K., 2009. Creditor rights, enforcement, and bank loans.

The Journal of Finance 64 (2), 823�860.

34

Berger, A. N., Bonaccorsi di Patti, E., 2006. Capital structure and �rm

performance: A new approach to testing agency theory and an application

to the banking industry. Journal of Banking & Finance 30 (4), 1065�1102.

Black, B. S., Gilson, R. J., 1998. Venture capital and the structure of capital

markets: banks versus stock markets1. Journal of Financial Economics

47 (3), 243�277.

Black, B. S., Gilson, R. J., 1999. Does Venture Capital Require an Active

Stock Market? Journal of Applied Corporate Finance 11 (4), 36�48.

Boubakri, N., Ghouma, H., 2010. Control/ownership structure, creditor

rights protection, and the cost of debt �nancing: International evidence.

Journal of Banking & Finance 34 (10), 2481�2499.

Cochrane, J. H., 2005. The risk and return of venture capital. Journal of

Financial Economics 75 (1), 3�52.

Cohn, J. B., Mills, L. F., Towery, E. M., 2014. The evolution of capital

structure and operating performance after leveraged buyouts: Evidence

from U.S. corporate tax returns. Journal of Financial Economics 111 (2),

469�494.

Cotter, J. F., Peck, S. W., 2001. The structure of debt and active equity in-

vestors: The case of the buyout specialist. Journal of Financial Economics

59 (1), 101�147.

Cressy, R., Munari, F., Malipiero, A., 2007. Playing to their strengths? Evi-

dence that specialization in the private equtiy industry confers competitive

advantage. Journal of Corporate Finance 13 (4), 647�669.

35

Cumming, D., Siegel, D. S., Wright, M., 2007. Private equity, leveraged

buyouts and governance. Journal of Corporate Finance 13 (4), 439�460.

Cumming, D., Walz, U., 2010. Private equity returns and disclosure around

the world. Journal of International Business Studies 41 (4), 727�754.

DeAngelo, H., DeAngelo, L., Rice, E. M., 1984. Going private: Minority

freezeouts and stockholder wealth. The journal of law & economics 27 (2),

367�401.

Djankov, S., La Porta, R., Lopez de Silanes, F., Shleifer, A., 2003. Courts.

Quarterly Journal of Economics 118 (2), 453�517.

Easterwood, J. C., 1998. Divestments and �nancial distress in leveraged buy-

outs. Journal of Banking & Finance 22 (2), 129�159.

Estrella, A., Hardouvelis, G. A., 1991. The Term Structure as a Predictor of

Real Economic Activity. The Journal of Finance 46 (2), 555�576.

Glaeser, E., Johnson, S., Shleifer, A., 2001. Coase Versus the Coasians. Quar-

terly Journal of Economics 116 (3), 853�899.

Groh, A. P., Liechtenstein, H. v., Lieser, K., 2010. The European Venture

Capital and Private Equity country attractiveness indices. Journal of Cor-

porate Finance 16 (2), 205�224.

Guo, S., Hotchkiss, E. S., Song, W., 2011. Do buyouts (still) create value?

The Journal of Finance 66 (2), 479�517.

Harvey, C. R., 1988. The Real Term Structure and Consumption Growth.

Journal of Financial Economics 22 (2), 305�333.

36

Humphery-Jenner, M., Powell, R., 2014. Firm size, sovereign governance, and

value creation: Evidence from the acquirer size e�ect. Journal of Corporate

Finance 26 (0), 57�77.

Jelic, R., Wright, M., 2011. Exits, Performance, and Late Stage Private

Equity: the Case of UK Management Buy-outs. European Financial Man-

agement 17 (3), 560�593.

Jeng, L. A., Wells, P. C., 2000. The determinants of venture capital funding:

Evidence across countries. Journal of Corporate Finance 6 (3), 241�289.

Jensen, M. C., 1986. Agency Costs of Free Cash Flow, Corporate Finance,

and Takeovers. American Economic Review 76 (2), 323.

Jensen, M. C., Meckling, W. H., 1976. Theory of the �rm: Managerial behav-

ior, agency costs and ownership structure. Journal of Financial Economics

3 (4), 305�360.

John, K., Senbet, L. W., 1998. Corporate governance and board e�ectiveness.

Journal of Banking & Finance 22 (4), 371�403.

Jong, A. d., Kabir, R., Nguyen, T. T., 2008. Capital structure around the

world: The roles of �rm- and country-speci�c determinants. Journal of

Banking & Finance 32 (9), 1954�1969.

Jorion, P., Mishkin, F., 1991. A multicountry comparison of term-structure

forecasts at long horizons. Journal of Financial Economics 29 (1), 59�80.

Kaplan, S. N., 1989. The E�ects of Management Buyouts on Operating Per-

formance and Value. Journal of Financial Economics 24 (2), 217�254.

37

Kaplan, S. N., Schoar, A., 2005. Private equity performance: Returns, per-

sistence, and capital �ows. The Journal of Finance 60 (4), 1791�1824.

Kaplan, S. N., Stein, J. C., 1993. The evolution of buyout pricing and �nan-

cial structure in the 1980s. Quarterly Journal of Economics (433), 313�357.

La Porta, R., Lopez de Silanes, F., Shleifer, A., 2006. What Works in Secu-

rities Laws? The Journal of Finance 61 (1), 1�32.

La Porta, R., Lopez de Silanes, F., Shleifer, A., Vishny, R. W., 1997. Legal

Determinants of External Finance. The Journal of Finance 52 (3), 1131�

1150.

La Porta, R., Lopez de Silanes, F., Shleifer, A., Vishny, R. W., 1998. Law

and �nance. Journal of Political Economy 106 (6), 1113�1155.

La Porta, R., Lopez de Silanes, F., Shleifer, A., Vishny, R. W., 2002. Investor

protection and corporate valuation. The Journal of Finance 57 (3), 1147�

1170.

Lehn, K., Poulsen, A., 1989. Free Cash Flow and Stockholder Grains in Going

Private Transactions. The Journal of Finance 44 (3), 771�787.

Lerner, J., Schoar, A., 2005. Does Legal Enforcement A�ect Financial Trans-

actions? The Contractual Channel in Private Equity. Quarterly Journal

of Economics 120 (1), 223�246.

Lerner, J., Sørensen, M., Strömberg, P., 2011. Private equity and long-run

investment: The case of innovation. The Journal of Finance 66 (2), 445�

477.

38

Lichtenberg, F. R., Siegel, D. S., 1990. The e�ects of leveraged buyouts on

productivity and related aspects of �rm behavior. Journal of Financial

Economics 27 (1), 165�194.

Long, W. F., Ravenscraft, D. J., 1993. LBOs, Debt and R&D Intensity.

Strategic Management Journal 14, 119�135.

Mauro, P., 1995. Corruption and growth. Quarterly Journal of Economics

110 (3), 681�712.

McLean, R. D., Tianyu, T., Zhao, M., 2012. Why does the law matter?

Investor protection and its e�ects on investment, �nance, and growth. The

Journal of Finance 67 (1), 313�350.

Meuleman, M., Amess, K., Wright, M., Scholes, L., 2009. Agency, strategic

entrepreneurship, and the performance of private equity-backed buyouts.

Entrepreneurship, theory and practice 33 (1), 213�239.

Moeller, S. B., Schlingemann, F. P., Stulz, R. M., 2004. Firm size and the

gains from acquisitions. Journal of Financial Economics 73 (2), 201�228.

Muscarella, C. J., Vetsuypens, M. R., 1990. E�ciency and Organizational

Structure: A Study of Reverse LBOs. The Journal of Finance 45 (5),

1389�1413.

Nikoskelainen, E., Wright, M., 2007. The impact of corporate governance

mechanisms on value increase in leveraged buyouts. Journal of Corporate

Finance 13 (4), 511�537.

39

Ofek, E., 1994. E�ciency Gains in Unsuccessful Management Buyouts. The

Journal of Finance 49 (2), 637�654.

Paglia, J. K., Harjoto, M. A., 2014. The e�ects of private equity and ven-

ture capital on sales and employment growth in small and medium-sized

businesses. Journal of Banking & Finance 47 (0), 177�197.

Palepu, K. G., 1990. Consequences of leveraged buyouts. Journal of Financial

Economics 27 (1), 247�262.

Phan, P. H., Hill, C. W. L., 1995. Organizational Restructuring and Eco-

nomic Performance in Leveraged Buyouts: An Ex Post Study. The

Academy of Management Journal 38 (3), p 704�739.

Pregibon, D., 1980. Goodness of Link Tests for Generalized Linear Mod-

els. Journal of the Royal Statistical Society: Series C (Applied Statistics)

29 (1), 15.

Rajan, R. G., Zingales, L., Seward, J., 1995. What Do We Know about

Capital Structure? Some Evidence from International Data. The Journal

of Finance 50, 1421�1460.

Renneboog, L., Szilagyi, P. G., 2008. Corporate Restructuring and Bond-

holder Wealth. European Financial Management 14 (4), 792�819.

Shleifer, A., Vishny, R. W., 1997. A Survey of Corporate Governance. The

Journal of Finance 52 (2), 737�783.

Singh, H., 1993. Challenges in Researching Corporate Restructuring. Journal

of Management Studies 30 (1), 147�172.

40

Singh, M., Davidson III, W. N., 2003. Agency costs, ownership structure and

corporate governance mechanisms. Journal of Banking & Finance 27 (5),

793�816.

Smith, A. J., 1990. Corporate ownership structure and performance: The

case of management buyouts. Journal of Financial Economics 27 (1), 143�

164.

Svensson, J., 1998. Investment, property rights and political instability: The-

ory and evidence. European Economic Review 42 (7), 1317�1341.

Thompson, R. S., Wright, M., 1995. Corporate governance: The role of re-

structuring transactions. The Economic Journal (430), 690�703.

Valkama, P., Maula, M., Nikoskelainen, E., Wright, M., 2013. Drivers of

holding period �rm-level returns in private equity-backed buyouts. Journal

of Banking & Finance 37 (7), 2378�2391.

Warga, A., Welch, I., 1993. Bondholder losses in leveraged buyouts. Review

of Financial Studies 6 (4), 959�982.

Wilson, N., Wright, M., Siegel, D. S., Scholes, L., 2012. Private equity portfo-

lio company performance during the global recession. Journal of Corporate

Finance 18 (1), 193�205.

Wright, M., Hoskisson, R. E., Busenitz, L. W., Dial, J., 2000. Entrepreneurial

Growth Through Privatization: The Upside of Management Buyouts.

Academy of Management Review 25 (3), 591�601.

41

Wruck, K. H., 1990. Financial distress, reorganization, and organizational

e�ciency. Journal of Financial Economics 27 (2), 419�444.

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

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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

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continued from previous page

Company (Year) Sponsor

Vinnolit (2000) Advent International

WincorNixdorf (1999) KKR & GS Capital Partners

44