Master Thesis

Excess Returns for Private Equity Portfolio

Companies – A Comparison with public peers pre-

and post-financial crisis for the DACH region

Authors: Daniela Cueva & Sven Langelahn

Degree Program: MSc in Finance & Banking

Supervisor: Filippo Ippolito

Submission Date: June 25, 2018

2

Abstract

This research paper studies the relationship and the evolution of different financial

and economic returns for Private Equity portfolio companies in the DACH region

(Germany, Austria and Switzerland) compared to their direct listed peers pre- and

post-financial crisis. Our empirical evidence confirms that the excess returns

measured in Return on Equity of Private Equity-backed companies have decreased

overall by up to 2% in the years after the global financial crisis. Furthermore, we

discovered that the excess ROE by year declined from around 8% in 2006 to -0.8%

in 2009 and shows stable and similar results for both, the Private Equity portfolio

firms and the public peers in the following years until 2015. Our analysis comprises

randomly selected 70 Private Equity transactions (out of a total sample of 1,412 PE

transactions) between 2004 and 2012.

3

Acknowledgement

Firstly, we would like to thank our Master Thesis supervisor Filippo Ippolito for

supporting us with guidance and helpful advices for our very demanding topic in the

field of Private Equity.

Besides our advisor, our sincere thanks also goes to Manuel Barrón and Albert

Banal-Estañol, who provided us with supportive & motivating comments and insights

to further improve our thesis, and furthermore gave us the confidence to continue

with our current topic even there were several obstacles to overcome.

Finally, we would like to express our gratitude to our parents, to my girlfriend Sophie

and our friends for providing us with unfailing support and continuous encouragement

throughout our years of study and through the process of researching and writing this

thesis. This accomplishment would not have been possible without them.

Thank you all.

4

Table of Contents

List of Tables ............................................................................................................................ 5

List of Figures........................................................................................................................... 5

List of Abbreviations ................................................................................................................ 6

1. Introduction .................................................................................................................... 7

2. Literature Review........................................................................................................ 10

3. Private Equity in a nutshell........................................................................................ 12

5. Methodology ................................................................................................................ 19

a. Theoretical Framework .......................................................................................... 19

b. Selection of the Model: Propensity Score Matching ......................................... 20

c. The Regression ....................................................................................................... 22

6. Data .............................................................................................................................. 24

a. Data Description...................................................................................................... 24

b. Data Limitation ........................................................................................................ 27

7. Analysis & Results...................................................................................................... 28

a. Measures of Financial and Economic Performance ......................................... 28

b. Description of the Panel Data ............................................................................... 31

c. Measures of Liquidity ............................................................................................. 33

d. Findings of Regression .......................................................................................... 35

8. Summary & Conclusion ............................................................................................. 45

9. Appendix ...................................................................................................................... 48

10. List of References....................................................................................................... 57

5

List of Tables

Table 1: Number of PE companies from 2004 - 2012 ..................................................... 25

Table 2: Dependent variables used in the analysis ......................................................... 28

Table 3: Summary statistics of PE-backed companies and their public peers............ 31

Table 4: Control variables and dummies used in our analysis ....................................... 36

Table 5: Panel data fixed effects results for ROE ............................................................ 38

Table 6: Difference-in-Differences results for ROE.......................................................... 39

Table 7: Panel data fixed effects results for ROIC ........................................................... 40

Table 8: Difference-in-Differences results for ROIC ........................................................ 41

Table 9: Propensity Score Probit regression .................................................................... 42

Table 10: Propensity Score Matching from 2004-2016 ................................................... 43

Table 11: Panel data fixed effects results for ROA .......................................................... 48

Table 12: Panel data fixed effects results for Leverage Ratio........................................ 48

Table 13: Panel data fixed effects results for EBIT margin ............................................ 49

Table 14: Panel data fixed effects results for Net Income margin ................................. 49

Table 15: Panel data fixed effects results for Sales Growth........................................... 50

Table 16: Panel data fixed effects results for Cash Flow margin .................................. 50

Table 17: Yearly effect of PE by PSM ................................................................................ 51

Table 18: Cumulative effects of PE by PSM ..................................................................... 52

Table 19: Effects of PE entry per year by PSM ................................................................ 53

List of Figures

Figure 1: Structure of a typical Private Equity fund .......................................................... 14

Figure 2: Illustrative equity valuation bridge ...................................................................... 16

Figure 3: German Domestic Credit Supply by the Financial Sector .............................. 35

Figure 4: Yearly & accumulative effects of PE for ROE by PSM ................................... 44

Figure 5: PE entry effects for ROE by PSM ...................................................................... 45

Figure 6: Global median PE EBITDA multiples 2006 - 2017 .......................................... 54

Figure 7: PE firms by region 1980 - 2015 .......................................................................... 54

Figure 8: Global PE deal volume 2000 - 2017 .................................................................. 55

Figure 9: Global PE deal count 2000 - 2017 ..................................................................... 55

Figure 10: Global PE capital raised 2003 – 2017 (by fund type) ................................... 56

6

List of Abbreviations

Bn Billion

CDO Collateralized Debt Obligation

GDP Gross Domestic Product

GP General Partner

IPO Initial Public Offering

IRR Internal Rate of Return

LBO Leveraged Buyout

LP Limited Partner

M Million

MBI Management Buyin

MBO Management Buyout

PE Private Equity

ROA Return on Assets

ROE Return on Equity

ROIC Return on Invested Capital

SD Standard Deviation

SME Small and Medium Enterprises

TRBC Thomson Reuters Business Classification

VC Venture Capital

7

1. Introduction

Private Equity (PE) has become a widely used expression two decades ago and

nowadays it is considered as a serious funding alternative for many small- and

medium-sized as well as for a growing number of large-cap companies worldwide

and especially in Europe. Although Private Equity and its industry practices are not

always related to positive statements and opinions (e.g. in Germany, the

grasshopper is used as a synonym for a PE firm), the general perception is that

Private Equity is a useful and required funding alternative next to public and bank

funding.

Researchers have confirmed that a percentage of the publicly held corporations have

been replaced by emerging firms, which use public and private debt instead of public

equity as a major source of capital. These organizations, Private Equity firms, are

making remarkable gains and are improving their operating efficiency, employee

productivity and shareholder value (Jensen, 1989). The Jensen (1989) paper argued

that leverage buyouts (LBOs) created value to the company through high leverage.

Several authors have acknowledged Jensen’s opinion and have provided evidence

that LBOs create value by improving the operating performance of the acquired

companies by taking high levels of debt (Acharya, Hahn, & Kehoe, 2010). In the last

years, Private Equity funds have triggered concerns about their leverage levels. From

2006 until 2008, global PE groups raised almost $2 trillion in equity (Bernstein,

Lerner, & Mezzanotti, 2017). This situation could be alarming because for every

dollar rose in equity, they leverage more than two dollars of debt (Kaplan &

Strömberg, 2009).

The developments made in the credit structure in the years prior to the crisis brought

incentives to the credit market, especially for higher loans in LBO transactions

(Shivdasani & Wang, 2011). The expansion of the market of collateralized debt

obligations (CDOs) played a big role on the boom of LBOs. Shivdasani & Wang

(2011) explained that as investors demand for CDOs rose, CDO issuers had to

increase their collateral assets to issue this instrument, which resulted in an increase

in bank incentives to generate loans for LBOs funding.

The high valuations of the assets and the outperformance of the Private Equity

companies compared with the public firms have resulted in an increase in PE funds

8

and therefore a higher firepower for PE firms to buy private companies. In addition to

that, the monetary policy since the crisis has brought “easy money” to the sector;

nowadays investors have a much lower cost of capital than some years ago. Thus,

one of the results of the excess of liquidity in the PE sector is the increase in the

average purchase price multiples in Europe. The amounts Private Equity firms and its

funds have paid for new acquisitions were significantly high in the years before the

crisis, decreased until the crash in 2008 / 2009 and have been increasing

continuously from 2009 to an all-time high of 10.7x Enterprise Value / EBITDA in

2017 (Figure 6). The implied leverage of 5.5x Debt / EBITDA in those high acquisition

prices implies that the risk level is increasing and the Private Equity firms are willing

to pay higher prices with a higher leverage.

All these factors and especially the new high levels of debt have made us wonder

how the PE portfolio companies’ performance behaves under an external shock; for

this paper we analyse the behaviour before and after the financial crisis of 2008 and

take this event as the year of reference. Likewise, the information mention above

reaches our concern and interest in the PE portfolio company situation. The high

purchase prices, the high levels of leverage and the easy reachability of funds are

some of the reasons why the acquisitions get more expensive, the deals get riskier

and the performances of PE portfolio companies decrease. In line with the facts

before, the non-PE-backed peer companies, in our case public listed corporations in

the DACH region, have also easier access to financing and can foster and improve

their operating performance and profitability. All those factors lead us to the point to

examine the effects of this development on both parties over time. Therefore, the

focus of the present paper is to study the pre- and post-crisis performances of the PE

portfolio companies and how their excess returns compared to direct peers

developed over time with the year 2008 as a point of reference. The main result of

our research paper is that we found evidence for a significant decrease in excess

returns for Private Equity backed companies in terms of Return on Equity (ROE) after

the financial crisis and that we can observe similar net returns for both PE and public

shareholders from 2010 until 2015.

To give an overview of the Private Equity market in Europe and especially in the

DACH region and to underline the increasing importance and its prospects, we

compare the size and its current accelerated growth with the US Private Equity

9

market. The European PE market is large but not that well diffused as the US market

is. The PE investments in Europe correspond to 0.2% of the European GDP

compared to over 1% in the US (Preqin Global Private Equity Report 2018). It is

expected that after the official BREXIT in March 2019, the DACH region will further

grow in terms of importance and volume for the PE firms and funds. The PwC’s

Private Equity Trend Report confirms that premise: while the European market

experiences an overall deterioration, the number of acquisitions by PE companies in

the DACH region increased by 28% in 2016 (Roberts & Naydenova, 2017). As we

understand the importance of this region, our paper is focusing on Germany, Austria

and Switzerland.

At our best knowledge, one of the relevance of this research paper is that it will be

the first one that compares the performance of PE portfolio companies and their

direct peers without PE ownership before and after the crisis in the German-speaking

part of Europe, the DACH region. A second major driver for choosing this research

topic is the increasing importance of PE companies in the economy over the last two

or three decades, especially for North America and Europe the number of Private

Equity firms and funds have increased significantly (Figure 7). PE funds have

become a welcoming financing and funding alternative for private firms and in most

cases, the PE funds are able to provide more funding volume than equity markets or

bank loans. With a deal volume of around $1.27 trillion and approx. 8,100 deals in

2017, the Private Equity industry nearly reached its all-time high of $1.4 trillion from

the pre-crisis year 2007 (Figures 8 and 9).

Our data analysis considered randomly selected 70 PE portfolio companies out of a

total sample of 1,412 PE transactions between 2004 and 2012. The data analysed in

this paper was downloaded from Thomson Reuters EIKON, specifically from the

Private Equity and Venture Capital database and from the Bundesanzeiger, the

official publication of the Federal Republic of Germany published by the German

department of Justice. The data analysis was conducted with Stata.

The remainder of this paper is organized as follow: Chapter II gives a short overview

of recent research papers published in the field of performance evaluation for PE

funds & portfolio firms and the general importance of the Private Equity industry.

Chapter III introduces Private Equity as a standalone industry with its key players, the

10

structure of an LBO and further general definitions. After summarizing the theory,

chapter IV introduces our research hypotheses and the relevance of this paper. The

following chapter V covers the methodology and describes the empirical and

statistical models we are using. The set of data and its limitations in terms of quantity

and quality will be defined in chapter VI. The centrepiece of our paper – the analysis

and our empirical findings – will be presented in chapter VII. To conclude our Master

Thesis, chapter VIII gives a summary of the most interesting and significant findings,

presents an outlook for further research in the field of Private Equity and closes our

paper.

2. Literature Review

As mentioned in the first chapter, the studies and analyses of the Private Equity

business grew rapidly in the last decade although it exists since the 1980s, especially

in the USA and Europe. Jensen (1989) was one of the first researchers who

introduced the idea that PE firms reduce the disadvantages of public corporations

like dispersed ownership, weak corporate governance structure and an insufficient

level of leverage. The Private Equity model is concentrated on full ownership,

performance-based management incentives and significant leverage that reduces

costs, increases efficiency and therefore strengthens the company.

Kaplan and Schoar (2005) discovered that the returns of LBO funds, adjusted by

fees, are slightly below the S&P 500 and secondly that fund size and fund maturity

play an important role because funds with higher volume and longer maturity

generate higher returns. Furthermore, Kaplan and Strömberg (2009) gave evidence

that Private Equity investors used boom-and-bust-cycles to create economic value in

the long-term. On average, the two researchers recorded that PE firms advantage

from changing market conditions and their timing to create value in public-to-private

deals.

Several studies tried to confirm that PE firms take advantage of private information to

improve operative performance and enhance the value of their portfolio company. As

Kaplan and Strömberg (2009) mentioned in their study, incumbent managers do not

push for the highest price for existing shareholders and therefore give Private Equity

investors a favourable position to earn excess returns. Prior to this, Kaplan (1989)

11

examined the financial forecast quality of PE firms for their newly acquired portfolio

companies after the transactions. Under normal conditions, PE firms should benefit

from the asymmetric information relationship, but in fact, the current performance

post-transaction failed the forecasts.

In comparison, Leslie and Oyer (2009) discovered no significant benefits of PE

portfolio companies over their direct peers without PE-ownership in terms of

operational profitability (e.g. ROA, ROE or net income) and governance structure.

Regardless, management incentives for PE-backed companies were much higher

than for public companies. Leslie and Oyer also show that within the year of the IPO

of a PE portfolio company, the firm reports managerial incentives and leverage levels

very similar to their direct public-listed peers. The researchers suggest different

earning sources like tax advantages or value captures rather than performance

gains.

Recent research mainly focused on the effects of leverage on profitability within the

crisis and how stable those companies went through it. The focus of PE firms and

funds on companies with stable cash flow and room for economic and financial

improvement indicates that those companies are not too vulnerable in economic

downturns (Axelson et al., 2010). Another key finding by the authors was that low

borrowing costs strongly lead to higher leverage and this leverage resulted in

problems serving the interest payments. Similar to our research topic, Wilson et al.

(2011) assess the economic and financial performance of Private Equity backed

buyouts compared to peers for the United Kingdom, before and during the global

recession. Their paper concludes that PE portfolio companies achieved superior

performance in the period before and during the financial crisis of 2008. In terms of

profitability, the researchers find a positive effect of 3-5% for buyout firms, relative to

non-buyout firms. Next to profitability, the revenue and employment growth was

positive for the PE-backed firms for the sample period until 2010.

After giving an overview of the recent literature, the next chapter will introduce a

more general theory of the Private Equity industry and therefore focuses on the LBO

structure and the different approaches how PE firms generate superior returns with

their portfolio companies.

12

3. Private Equity in a nutshell

This section defines and determines the importance of Private Equity and gives the

readers without sufficient pre-knowledge a wider understanding and an overview of

the business model of Private Equity firms, its funds and the underlying market. The

following chapter is mainly based on the explanatory guide about Private Equity by

John Gilligan & Mike Wright, who published its third edition of the book “Private

Equity demystified” in 2014.

Private Equity (PE) became systemically important in the early 1980s and is mainly

described as risk capital investment in private companies with an established

business model mostly executed by buy-outs or buy-ins. The key stakeholders in a

Private Equity transaction are first of all the PE firm, which raises equity capital from

many different investors like institutional and wealthy private investors. Another

important party in the process are creditors such as banks or private debt funds

which provide the debt financing in a Leveraged-Buy-Out (LBO), and last but not

least the acquiring private company (the target company).

According to Gilligan & Wright, the main objective of PE firms is to generate capital

gains in forms of an increased shareholder value. “The idea is to buy equity stakes in

businesses, actively managing those businesses and then realising the value created

by selling or floating the business” (Gilligan & Wright, 2014: p. 14). This combined

approach of financial and entrepreneurial measurements is developed for later-stage

companies and compared to venture or growth capital, PE firms are willing to take

less risk and invest their money in a shorter horizon (mostly five to eight years).

In general, academic literature defines four different sub-classes of equity

investments: Venture capital, mezzanine / growth funds, distressed capital and

leveraged buyouts. Venture capital (VC) commonly comprises investments in young

companies like start-ups with usually negative or low profitability & cash flows.

Mezzanine capital is treated as a fund with both characteristics of debt and equity

and is included as a minority stake in a buyout transaction. Distressed capital is not

directly comparable to the other three forms due to the fact that it is rather focused on

mature companies with operating problems and a lack of growth and profitability.

There are several PE funds specifically focused on distressed and restructuring

companies which might have operating problems or issues of managerial quality. The

13

upcoming analysis in our paper is mainly focused on the last type of investment:

buyouts. This form refers to a buyout with an acquisition of the controlling stake using

a relatively high amount of debt and a minor stake in equity – this composition of

funds is stated as Leveraged Buyout (LBO). An LBO goes along with a strong

increase in leverage (a change of the financial structure), a reduction in free cash

flow due to higher interest payments and a change in ownership.

Since there are several sub-categories of buyouts, we focus on the two most

significant types measured by the status of the management in the transaction: a

management buyout (MBO) takes place when the existing management takes over

their own company in cooperation with a Private Equity firm. Gilligan & Wright call

MBO an insider buyout, in contrast to a management buy-in (MBI). In this case, the

Private Equity firm appoints a new management and replaces the existing one with

senior industry experts, mostly former well-known executives.

As illustrated by Gilligan & Wright (Figure 1), there are several stakeholders involved

in a Private Equity fund and in the underlying transactions. Central instrument is the

Private Equity fund which can be defined as an investment club in which several

investors like pension funds, insurance companies, family offices or wealthy

individuals can deposit their investment. Those funds are normally managed by a

fund manager and have a limited lifetime, always depending on the type of Private

Equity fund. The average lifetime of a fund is up to ten years (plus two years optional

extension period): six to seven years of investing in projects and three to four years

of winding-up.

Since the closed-end Private Equity fund normally has a legal structure of a

partnership, the limited partners (LPs) are the external equity investors which are

liable to the amount they have invested and the general partners (GPs) which are

normally the investment managers with unlimited liabilities. To reduce the level of

liability in practice, the GPs are in fact limited companies represented by the Private

Equity firms at the end. The LPs and GPs receive capital gains, dividends and

interest from the investment portfolio, whereas LPs traditionally get the pre-defined

(majority) returns and GPs the residual returns (including carried interests).

14

Figure 1: Structure of a typical Private Equity fund

Source: Gilligan & Wright (2014)

It is also important to mention the difference between the Private Equity fund and the

Private Equity firm. The PE firm normally advises the fund which finally executes the

acquisitions of the target companies. The collected equity capital will be leveraged

with private or bank debt to further increase the investment upsides of the fund. How

and where funds are incorporated depends on tax, regulatory, legal and financial

determinants. Most popular destinations in Europe are the United Kingdom (including

Jersey), Luxembourg and the Netherlands. The fundraising period for new Private

Equity funds is substantially important because the PE firm has to attract investors

and select the right investment partners. The fundraising volume increased

significantly over the last seven years to more than 700bn in 2017 (Figure 10).

Around 320bn refers to buyout funds which clearly indicates the highest share within

the investment funds. According to the Global Private Equity report 2018 by Bain &

Company, 2/3 of all PE funds that closed in 2017 met or exceeded their target

amounts.

According to Gilligan & Wright, the Private Equity transaction structure is one major

reason why the majority of the deals are very successful. The transaction is mainly

founded on an acquisition vehicle called “Newco”. This shell company acts as a

15

transitional vehicle which channels the equity and debt funding and is secured by the

assets of the target company. The debt financing depends on the success of the

transaction and is not issued in the case of failed deals. If everything is going well

and the transaction is approved from all parties, the Newco will be merged with the

target company and a new portfolio company is formed – including the high leverage

of the Newco which has to be managed and repaid by the free cash flow of the target

company. Since private and bank debt represents a significant portion of the

financing structure, it is important to mention that there are essential differences in

debt. The most senior and typical loan is the senior debt which normally covers the

highest amount in the transaction and is provided by international banks or other

institutional investors. Typical characteristics of those loans are the different tranches

of loans which are adapted to the individual needs and preferences of the PE

investors and the syndication because several banks together provide the loans to

spread the risk. The typical PE transaction consists of a term loan A (an amortising

loan) and term loans B and / or C which are bullet loans and normally sold in the

secondary market to debt investors like hedge funds for example. In terms of

seniority, the subsequent loan types are subordinated debt like 2nd lien debt and

mezzanine. Other financing options within the PE industry are bridge loans or high-

yield corporate bonds. Financial covenants are part of every financing structure

decision. Those covenants have the objection to limit the risk of investors like banks

or private debt funds and give them the right to renegotiate or terminate the lending

contract. Most popular covenants in Private Equity transactions are debt / EBITDA,

capital expenditures / sales or the debt-service coverage ratio. According to a paper

by Caselli, Garcia-Appendini and Ippolito (2013), better quality firms are more likely

to have covenant-rich contracts and therefore the number and quality of covenants

can give an indication of how stable and healthy the company might be and also give

implicit future growth prospects.

The Private Equity industry has shown high returns in the last decade. However, fees

for investors did not increase accordingly. There are three main types of fees:

Management fees are paid by fund investors based on the amount invested or

committed in the fund, historically this figure was about two percent of the total

capital. Second, performance fees are based on the operating result of a Private

Equity fund and are deducted from the total gain of the fund (around 15-20%). Last

16

but not least, transaction costs play a significant role in a PE deal (0.5-1.5% of deal

volume) and are paid to advisors and external consultants.

To value a final return on an investment, the exit and the sale process plays a key

role. There are several exit strategies but three options became most used in the last

decade in the USA and Europe: 1) Sale to a corporation, 2) Initial Public Offering

(IPO), and 3) Sale to another Private Equity fund (secondary buyout). According to

Gilligan & Wright and their illustrative equity valuation bridge (Figure 2), there are

four main valuation levers which impact the equity value at the end:

i. Change in the valuation method

ii. Change in company performance (mostly measured via EBITDA)

iii. Change in external market comparators

iv. Change in Net Debt

Figure 2: Illustrative equity valuation bridge

Source: Gilligan & Wright (2014)

The valuation method depends on the maturity of the company and normally

changes from cost valuation as the initial method to performance valuation for more

mature firms. The change in company performance is maybe the most logical

17

leverage for equity valuation since PE firms normally see large improvement potential

for the operating performance of their target companies. The possible changes in

external market comparators mean the market valuation for the asset. In other words,

the exit multiple depends on the industry, geography, market environment or many

other variables. Finally, the reduction in Net Debt has a strong positive impact on the

equity valuation at the sale date. PE firms normally apply the increase in operating

performance (EBITDA) and the reduction in Net Debt as greatest equity valuation

levers in their internal models.

A long-standing criticism of the Private Equity industry and its actors is the increasing

risk through leverage for the portfolio companies. Next to other researchers, Gilligan

& Wright have tried to summarize if PE has a positive impact on the overall economy

or not and concluded that it is critically important to be careful about the evidence

being used. The two authors also indicate that further quantitative research in the

field of Private Equity is needed and most difficulties are related to obtaining data

without bias and incompleteness.

To sum up the introductory chapter, we give some short references to the current

development in the Private Equity industry and then lead you to the hypotheses of

our research: Buyout returns for the general partners are decreasing slowly to a

lower but stable level (Bloomberg, 2018). Since the Private Equity industry has

become more competitive, the assets are often sold as secondary or tertiary buyout

and an enormous amount of cheap money competing for a limited set of deals, the

market is getting overpriced and the individual risk (especially for the target

companies) is increasing. Outstanding returns that GPs and LPs have earned with

undervalued assets some years ago are harder to find in the current market

environment. The volume of PE fundraising will continue to be very high until the

macroeconomic policies have not changed and investors all over the world are still

looking for public market exceeding returns.

18

4. Hypotheses

Our central hypothesis and therefore our principal objective focus on the

development of excess returns for Private Equity portfolio companies compared to its

peers. Due to the current market and the macroeconomic environment, the excess

returns should decrease compared to peers without PE-ownership. Excess returns

will be measured in terms of sales growth, EBIT margins, net income margin, ROA,

ROE & ROIC. After comparing PE-companies with non-PE-backed companies, we

will study the pre-and post-market conditions and additionally analyse the impact of

the financial crisis on the Private Equity industry in general.

The second objective of this paper is to find the main reasons for the change in

profitability. After the crisis, macroeconomic policies have increased the liquidity in

the markets, the interest rates have decreased substantially and as a result, some

investments have become profitable when in the past they were not. We believe that

this situation had a negative overall effect on the excess returns of the Private Equity

portfolio companies, and it resulted in a decrease of their excess profitability after the

crisis. At the same time, we have observed the increase of prices in the M&A market

over time and especially after the crisis, which has negatively impacted the

profitability of PE companies. If PE companies have to use higher amounts of money

for the acquisition / payment of the purchase price of new companies, then it might

be reasonable to think that this could result in a decrease of the extra volume of

money they can offer to improve the efficiency and profitability of the acquired

companies, investing less in the portfolio company. In our paper, the last hypothesis

is not possible to prove due to a lack of data availability and quality.

Therefore, the hypotheses of this paper are the following:

Primary Hypothesis:

𝐻1,0 = The excess returns of Private Equity – backed companies have

decreased after the crisis compared to the peers without PE ownership

Secondary Hypotheses:

𝐻2,0 = Macroeconomic policies have decreased the excess profitability of PE-

backed companies after the crisis

19

𝐻3,0 = The increase in M&A prices has decreased the profitability of PE-

backed companies after the crisis

𝐻4,0 = The level of leverage of PE-backed companies brings more sensibility

(e.g. return volatility) within these companies compared to their public peers

5. Methodology

a. Theoretical Framework

The different incentives that drive the portfolio firms and the Private Equity

companies create a tangible conflict of interest, which could bring problems to the

interests of both individuals. The main goal for a healthy company is to assure future

gains and a sustainable growth. On the other hand, the PE companies’ main

objective is to get high returns as fast as possible and to be able to accomplish that,

they expect that the PE-backed companies grow fast in order to sell the company in

the medium term and maximize their returns. The combination of the two incentives

causes moral hazard problems.

The research by Michael Jensen and William Meckling (1970) headed the

development of the Principal-Agent Problem theory. The problem arises when the

Agent acts on its own interest when making the decision instead of the Principal. In

the special situation of our research, the Agent is the PE-backed company and the

Principal is the PE Company.

Before and after the acquisition of a company by a PE fund, there are several costs

and investments for them. PE companies pre-invest significant amounts in the due

diligence process, similarly they post-invest in monitoring a long period of time until

their returns can be plausible, these costs are caused by the asymmetric information

problem (Mehta, 2004). In general, the Private Equity sector contains two primary

information problems: asymmetric information or hidden information and moral

hazard or hidden action (Pratt and Zeckhauser, 1985). The agent employs its better-

quality information to maximize its benefits without considering the principal in the

business decisions and hidden actions can result when the principal is not able to

observe the effort of the agent (Mehta, 2004).

20

In this particular case, the Private Equity firm is depending on the success of the

portfolio company. In this interaction, some agency costs are created such as the

cost of monitoring, this situation results in higher costs and fewer returns for PE firms

(Mehta, 2004). For the current research paper, we want to estimate the effect that

Private Equity has on the profitability of the companies. Consequently, it is not within

our scope to estimate the profitability of the Private Equity firm or fund itself. As

mentioned in chapter 3, one of the characteristics of PE firms after executing the

buyout, one of the first steps of the new majority owner is to change the

management, thus we believe that this action protects PE firms from the agency

problems. Nevertheless, the PE-backed companies are still exposed to these agency

problems where PE firms reach for their own benefit, which are not always good for

the portfolio companies. An example we discovered in our research are the profit

transfer agreements which results in a substantially decrease of the net income of

the PE-backed companies.

b. Selection of the Model: Propensity Score Matching

In randomized experiments, the results between the treatments could be compared

because units are slightly similar while in non-randomize experiments the direct

comparisons could be misleading and with errors because the units exposed to one

treatment are systematically different from the other study group (Rosenbaum &

Rubin, 1983). In the Rosenbaum & Rubin (1983) paper, the suggestion to solve that

problem is to balance scores. The balancing score function (𝑏(𝑥)) is composed by

observed covariance 𝑥 such that the conditional distribution of 𝑥 given 𝑏(𝑥) is the

same for treated (𝑧 = 1) and control (𝑧 = 0) group. At any value of the balancing

score, the difference estimation of the average between the treatment and control

effect would be unbiased and pair matching in balancing scores could bring unbiased

outcomes. The authors stated that the first best would be if treated and control units

would exactly match each other, in this case the sample distribution of two groups

would be identical plus exact matching on balancing score will result on an unbiased

outcome. However, these conditions are impossible to obtain, so methods that

pursue similar matches are used regularly.

21

Matching involves a set of statistical techniques to build the best-compared group

based on observed characteristics. When there are different variables to do the

match with, the problem of dimensionality could appear and the best solution to the

problem is the Propensity Score Matching Model. In this approach, the enrolled unit

is no longer matched with the non-enrolled unit, instead the model computes the

probability that the unit’s treatment group will enroll in the program based on a set of

observable characteristics. This probability is called propensity score, which takes

values between 0 and 1 (Gertler, Martinez, & Prema, 2016).

The propensity score matching method tries to imitate the randomize assignment of

the control and treatment group, thus this method belongs to the group of quasi-

experimental methods. The impact is estimated by the comparison of the average

outcomes of the treatment group and the average outcomes of the statistically

matched subgroup. In practice, propensity scores close to 1 cannot be matched

because it does not exist peers with that level of similarities, thus units with a high

score in the program are so different from control units that there is no good match

for them (Gertler, Martinez, & Prema, 2016).

However, as we mention above the propensity score matching methodology helps us

to treat the data as a quasi-experiment and the combination of this methodology with

others could eliminate some biases created on the peer selection. If we combine the

PSM with the difference in differences we reduce the risk of bias in the estimation

(Gertler, Martinez, & Prema, 2016).

The combined methodology is implemented with the following steps:

1. Perform the matching based on observable baselines

2. Estimate the change in outcomes between before and after periods for each

unit in the treatment group (first difference)

3. Estimate the change in outcomes between before and after periods for the

peers for each unit in the treatment group (second difference)

4. Subtract the second difference from the first one (D-D method)

5. Compute the average of the double differences

This methodology was the first one to be used to compute the estimations of the

effects of PE. Nonetheless, the present paper uses diverse methodologies for the

estimation of the effects. We make a 1:1 matching in order to later use it with a D-D

22

model and a Panel Data Fixed Effects. For a two-period analysis, the Fixed Effects

and D-D coefficients are similar and give us the same information. For the three

periods of analysis, the Fixed Effects methodology was the only one we used.

The propensity score matching methodology in Stata was used by the commands

“pscore”, “teffects psmatch”, “attnd” and “attk”. This methodology estimates the

matches with a probit regression and then gives the coefficient of interest (the effect

of the treatment). Within the PSM methodology there are different types of models to

get the objective coefficient. We use the nearest neighbour and the Kernel

approaches. The main idea of the approach is the multivariate distance matching

(Jann, 2017):

𝑀𝐷(𝑋𝑖 ,𝑋𝑗) = √(𝑋𝑖 − 𝑋𝑗)′ ∑ −1 (𝑋𝑖 − 𝑋𝑗) ; 𝑀𝐷 as a distance metric

where ∑ −1

is the covariance matrix of X (control variables) according to

Mahalanobis matching. The nearest neighbour matching model is based on the

following: for each observation 𝑖 in the treatment group, the 𝑘 closest observation in

the control group is found. A particular control could be used in multiple occasions as

a match and in case of ties (several controls with equal MD), use all ties as a match.

Alternatively, the Kernel model use all control as matches and gives larger weights to

controls when MD is small (Jann, 2017). The changes in the results from one

approach to the other should not be significant however both models were used in

order to give robustness to our results.

c. The Regression

For the first methodology, we use the Fixed Effect model regression as we see in the

equation 1, where 𝑦𝑖𝑡 is the dependent variable and would take the values of the

financial ratios mentioned in the chapter 7 a. The right part of the regression shows

the constant, 𝑋𝑖𝑡 (control variables), the treatment dummy (𝑃𝐸𝑑𝑢𝑚𝑚𝑦𝑖𝑡 ) and time

dummy (𝑇𝑡).

𝑦𝑖𝑡 = 𝛽0 + 𝛽1𝑋𝑖𝑡 + 𝛽2𝑃𝐸𝑑𝑢𝑚𝑚𝑦𝑖𝑡 + 𝑇𝑡 + 𝜂𝑖 + 𝛾𝑡 + 𝑢𝑖𝑡 Eq. (1)

Our principal hypothesis is: The excess of returns of PE-backed companies has

decreased after the crisis compared to the peers without PE ownership. Therefore,

23

we want to estimate the effect of PE on all the periods and additionally the effect of

PE on the time before and after the crisis. For this, we worked with the interactions

between the PE dummy and a time dummy that takes the value of 1 if the year

observation is in the crisis period (2008-2011). Thus, the main regression to analyse

will be the following:

𝑦𝑖𝑡 = 𝛽0 + 𝛽1𝑋𝑖𝑡 + 𝛽2𝑃𝐸𝑑𝑢𝑚𝑚𝑦𝑖𝑡 + 𝛽3𝐶𝑟𝑖𝑠𝑖𝑠 𝑃𝑒𝑟𝑖𝑜𝑑𝑖 + 𝛽4𝐼𝑛𝑡𝑒𝑟𝑎𝑐𝑡𝑖𝑜𝑛𝑖𝑡 + 𝑢𝑖𝑡 Eq. (2)

As shown in equation 2, the interaction dummy refers to a PE-backed company

situated in the period of the crisis. Consequently, 𝛽4 will provide us the result for the

acceptance or denial of our principal hypothesis and will deliver the estimation for the

effect of PE after the crisis.

To prove 𝐻2,0 , it was necessary to complement the analysis with one more period

after the crisis, which we call the liquidity period in the economy (2012-2015). The

second hypothesis is: Macroeconomic policies have decreased the excess

profitability of PE-backed companies after the crisis. In order to estimate this, we

employ the following regression:

𝑦𝑖𝑡 = 𝛽0 + 𝛽1𝑋𝑖𝑡 + 𝛽2𝑃𝐸𝑑𝑢𝑚𝑚𝑦𝑖𝑡 + 𝛽3𝐶𝑟𝑖𝑠𝑖𝑠 𝑃𝑒𝑟𝑖𝑜𝑑𝑖 + 𝛽4𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦 𝑃𝑒𝑟𝑖𝑜𝑑𝑖 +

𝛽5𝐼𝑛𝑡𝑒𝑟𝑎𝑐𝑡𝑖𝑜𝑛1𝑖𝑡 + 𝛽6𝐼𝑛𝑡𝑒𝑟𝑎𝑐𝑡𝑖𝑜𝑛2𝑖𝑡 + 𝑢𝑖𝑡 Eq. (3)

As shown in equation 3, the Interaction1 dummy refers to a PE-backed company

situated in the period of the crisis and the Interaction2 refers to PE-backed

companies situated in the liquidity period (2012-2015). Consequently, 𝛽6 will provide

us the result for the acceptance or denial of our second hypothesis and will deliver

the estimation for the effect of PE in the liquidity time.

For the second methodology, we use the PSM model, which first provides the

information about how the control variables affect the probability to enter PE and then

with the matching scores, estimates the effect of the treatment. Furthermore, we

want to evaluate the effect for every year hence we create dummies of time for each

year. For this, we apply the following regressions for each year from 2004 until 2015:

Pr (𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡)𝑖𝑡 = 𝛽0 + 𝛽𝑖𝑋𝑖𝑡 + 𝑢𝑖𝑡 Eq. (4)

𝑦𝑖𝑡 = 𝛽0 + 𝛽1𝑋𝑖𝑡 + 𝛽2𝑃𝐸𝑑𝑢𝑚𝑚𝑦𝑖𝑡 + 𝛽3𝑌𝑒𝑎𝑟𝑖 + 𝛽4𝐼𝑛𝑡𝑒𝑟𝑎𝑐𝑡𝑖𝑜𝑛𝑖 + 𝑢𝑖𝑡 Eq. (5)

24

Equation 4 corresponds to the first step of the model. This regression is run by using

a PROBIT model, which computes the probability of PE entry and estimates the

effect that each of the observable variables have on the probability of treatment. The

main outcome of this regression is the score matching for each observation. Equation

5 shows the second step of the model: the dependent variable for this model is the

ROE, which is regress by the control variables, the PE dummy, the time dummy (that

changes for every year) and the interaction between the time dummy and the PE

dummy.

The use of two different methodologies assures the robustness of our results and

gives us more confidence in the estimations of the effects. Also, each methodology

gives us different information, which helped to enrich the analysis of the current

research paper.

6. Data

a. Data Description

This paper examines the Private Equity excess returns for portfolio companies

compared to their peers before and after the crisis for the DACH region. Whether

those additional returns for PE-backed portfolio companies are going down after the

crisis or not depend on several determinants which we are going to study. Next to the

individual company data of our sample, we include credit supply in Germany as an

economic determinant / proxy for liquidity which will be explained in more detail at the

end of this chapter and additionally in chapter 7c.

Since the Private Equity industry does not show the highest degree of disclosure

characteristics, our research approach of comparing returns, profitability and growth

of Private Equity portfolio companies with direct listed peers was heavily dependant

on the data availability and its quality. Due to the fact that we additionally focused on

a specific geographic region with Germany, Austria and Switzerland and as well a

very limited time frame (four years before and after the financial crisis in 2008), we

knew that the scope of PE companies was limited.

25

We have collected our data from Thomson Reuters EIKON, specifically from the

Private Equity and Venture Capital database. In the first step, we have chosen

several criteria for the PE companies in terms of:

- Deal structure: Levered Buyout (LBO), Management Buyouts (MBOs),

Management Buyins (MBIs), Acquisitions, Secondary buyouts &

Secondary purchase

- Country of incorporation of target company: Germany, Austria &

Switzerland (DACH region)

- Date of transaction: 2004 – 2012

- Ownership: Majority stake (>50%)

For the above-mentioned criteria, we were able to extract in total 1,412 companies.

Since Thomson Reuters could not provide the financial data directly in the so called

Private Equity Screener, we had to screen and verify the data manually and

individually. According to the time scheduled for this research paper, we decided to

take a random selection of these 1,412 companies with the intention to represent all

years based on their share of the total companies (Table 1).

Table 1: Number of PE companies from 2004 - 2012

Year # of total companies # of sample companies

2004 121 4

2005 164 13

2006 211 15

2007 233 10

2008 169 8

2009 108 5

2010 134 5

2011 132 6

2012 138 4

TOTAL 1,412 70

Due to data unavailability, this approach was not fully successful but we were able to

gather data for all years on a minimum level (minimum four companies). Finally, we

26

randomly selected 70 companies with data availability and completeness for all the

years and the table shows the selection which represents a similar percentage of

sample companies and total companies for each year.

In the second step, we benchmarked the 70 PE-backed companies to public peers

with the same approach as Alemany and Marti (2005) did. This process covers the

industry (industry classification from Thomson Reuters: TRBC – Thomson Reuters

Business Classification), the size (revenue before PE-entry therefore no PE-effect in

the selection process), the company age (in our definition, year of incorporation has

to be before 2000) and the geography (DACH region with focus on the corresponding

country of the PE-backed company) in a manual procedure. This very time-

consuming process was mainly done with Thomson Reuters EIKON because the

given peer classification was helpful. If there was no peer classification given, we did

a manual web research for a listed peer with the above-mentioned criteria. The

financial data for the peers was finally extracted from Thomson Reuters EIKON.

An example of this peering process is the following: in 2005, the Hamburg-based

fashion company Tom Tailor Group was acquired by the PE firm Alpha Group. In the

year before, the clothing company made revenues of around €300m and was

operating with own stores and wholesalers in more than ten countries. The peer

analysis with TRBC gave us the listed clothing company Gerry Weber International

AG as the firm with the highest degree of conformance. In the case of very unique or

seldom business models, we tried to find the most similar listed peer.

One of our secondary hypotheses was the role of macroeconomic policies and what

influence they have had on the returns of PE-back companies after the crisis. This

topic mostly refers to the very broad field of liquidity. Since liquidity is not easy to

measure and has many different definitions, we tried to identify the most meaningful

indicator to test the impact of the increased volume of liquidity after the financial crisis

of 2008. As the focus of our study is on the DACH region, especially on Germany, we

selected the German domestic credit supply provided by the financial sector as a

good proxy for liquidity in the market. This ratio of credit supply over the Gross

Domestic Product (GDP) shows the availability of credit for German consumers and

corporations. The data were extracted from the online database of World Bank.

27

In the following, our present paper works with two different sets of data: first, we use

a mean approach for three different time windows. The different periods represent

different states of the economy, the boom, followed by the worldwide crisis and finally

the period of a slow recovery due to macroeconomic policies. The means are

calculated for four years for each period (2004-2007, 2008-2011 & 2012-2015). This

approach comprises 356 observations and we use the fixed effect and Diff-in-Diff

methodologies. The second data set is an unbalanced panel from 2004 to 2015 with

1,418 observations where one period characterises one year.

b. Data Limitation

Our data selection and the procedure of analysing might represent some limitations

and biases which are mostly owed to time and data quality. Thomson Reuters and

the German Bundesanzeiger are the sources we use in our analysis, both have very

high-quality standards. Any potential data errors are based on those two databases.

The manual selection of only one benchmark / peer is based on our best knowledge

and effort. Since the analysis focuses mostly on mature Private Equity portfolio

companies, we tried to exclude venture capital firms which normally show abnormal

growth and returns in the first years of business (e.g. we do not include seed, early

stage or expansion investments). Furthermore, we do not include financial service

companies like bank, brokers or insurance companies in our sample because their

profit & loss statement and balance sheet show a different structure and are hardly

comparable with those of industrial companies.

A potential data limitation might arise because of the different years companies enter

into Private Equity / were acquired by Private Equity firms. Since we are analysing

three different time frames (pre-crisis which is from 2004 to 2007, post-crisis from

2008 to 2011 and liquidity-period from 2012 to 2015) for the first methodology

approach, the means are calculated over the time period of four years (e.g. 2004-

2007). Companies entering in 2006 were considered for two years and have missing

values for two years. These data gaps might affect the final results of our analyses.

Another important remark has to be made in terms of potential exits of Private Equity

firms from their portfolio companies. In average, PE firms invest for a horizon of five

to eight years in the companies, sometimes this time can go up to ten years under

28

certain circumstances (e.g. an economic crisis resulted in lower results and therefore

a lower valuation at the targeted exit date). In our research approach, we assume

that the Private Equity companies entered at the beginning of the time frame (e.g.

2004 or 2005), will stay maximal ten years and therefore are not taken into account

after this period.

7. Analysis & Results

This section will first introduce the financial data we use for our analysis, describes

the rationale of the data and after that, we describe the summary results of the

variables. Afterwards, we analyse liquidity and its importance in the PE industry and

finally, we present and discuss the results of our regressions and give possible

approaches to interpret those findings.

a. Measures of Financial and Economic Performance

Since this paper is mainly focusing on the comparison of financial and economic

performance between private and public corporations, the analysis consists of eight

main indicators which are widely used in the financial economy to evaluate

profitability and financial stability. Those eight ratios will be the dependent variables

in our coming analysis and are displayed in table 2.

Table 2: Dependent variables used in the analysis

Variables Explanation of the variables

Sales growth Geometric growth of sales

EBIT margin Operating profitability

Net Income margin Net after-tax income

Return on Equity (ROE) Profitability of the book value of shareholders equity,

calculated as Net Income / Shareholders Equity

Return on Assets (ROA) Profitability of book value of total assets, calculated as

EBIT / Total Assets

29

Return on Invested

Capital (ROIC)

Profitability on invested capital, calculated as (Net Income

– Dividends) / (Shareholders Equity + Long-Term Debt)

Leverage ratio Financial ratio between interest-bearing debt over total

assets

Cash flow / Sales Free cash flow as percentage of total sales

The eight indicators capture different measures of economic profitability and stability.

The sales growth ratio indicates if the business shows an increasing trend in terms of

revenues and they are easily comparable between PE-backed and non-PE-backed

companies. In theory, small-sized companies like SMEs are able to reach higher

sales growth figures because they could fill a specific niche with high growth

potential, they could have a more dynamic approach to tackle new challenges or

have possible advantages from less bureaucracy. In downturns, both PE-backed and

public companies will be affected in a similar way although few studies e.g. by

Bernstein, Lerner and Mezzanotti (2017) show that PE-backed firms are more

resistant against economic crashes because they have more resources and greater

debt & equity inflows than their peers.

Secondly, we analyse five different ratios of operating performance. Private Equity

firms are very keen on improving the operating performance of their portfolio

companies through different approaches to maximise their return after the investment

period. Normally, EBITDA (Earnings before interest, tax, depreciation and

amortization) is used as a measure for operating profitability in Corporate Finance

related papers. Due to the lack of data quality and availability in the annual

publications of the private firms, we decided to use EBIT (Earnings before interest

and tax) as an alternative, like other papers did as well, e.g. Nikoskelainen & Wright

(2007). The advantage of taking EBIT or EBITDA is that the capital structure is not

taken into account. Especially for companies’ post-LBO, the funding structure

changes significantly and reduces the net earnings. EBIT and EBITDA are also

regularly used for quoting multiples and prices. One bias which might arise are the

different account standards which influence the depreciation and amortisation and

therefore the EBIT. Since we only choose firms from one region, the differences in

accounting and reporting standards can be disregarded. The net income margin

could be interesting in our analysis because it is the final outcome for the equity

30

shareholders, in our case the PE firms and the fund investors. The owner has to

decide if they keep the net profit in the company or distribute it to the shareholders

after a successful year. This cashing-out is a normal procedure in the PE industry in

order to generate the first, fixed returns of the investment.

The other three main operating performance variables are depending on the

deployed capital – shareholders equity, total assets and invested capital

(shareholders equity plus long-term debt). Return on Equity (ROE) represents the net

earnings for the shareholders and is seen as one of the major basis of decision-

making of an investment. In average, PE investors are seeking returns of more than

25 percent per investment. Return on Assets (ROA) and Return on Invested Capital

(ROIC) evaluate the operating profitability depending on the deployed capital in the

everyday activities. Those rations heavily vary with the underlying industry and

business model. It is hardly possible to compare the returns of a service provider with

an oil or mining company. ROIC is not widely used in Corporate Finance so far but

indicates a more neutral assessment of operating profitability because it takes into

account the net earnings in relation to the funding structure.

The funding and capital structures are explicitly analysed with the next financial

variable: the leverage ratio. This figure shows the relationship of interest-bearing debt

over total assets which becomes increasingly important after an LBO. Since PE-

backed companies have higher leverage than their peers, the sensitivity of profits is

greater and the dependence on stable cash flows to pay interests and amortization is

essential.

This point leads us to the last ratio in our analysis: the free cash flow to sales ratio is

maybe the most important coefficient to determine the ability to serve all obligations

while continuing or increasing their “normal” operating business. This ratio is normally

part of all covenant contracts with banks or other investors in an LBO, next to the

debt-service coverage ratio or fixed-charge coverage (Gilligan & Wright, 2014).

To better organize our panel data, we use several dummies for being PE-backed or

not and for the three different periods (Prior-crisis, post-crisis and liquidity period) we

analyse. These periods represent different economic situations and also different

environments for Private Equity firms and the overall credit market. The boom period

from 2004 to 2007 is characterized by economic growth, high earnings and an

31

exorbitant increase in credit supply. From 2008 to 2011, the growth rates and

profitability ratios decreased significantly and the global economy went into a

recession. The last period from 2012 to 2015 is affected by all-time low interest rates,

relatively low growth ratios for corporates and increased levels of economic and

political uncertainty.

b. Description of the Panel Data

This subchapter will summarize and describe the data we collected on the dependent

variables. The data is divided into PE-backed and peer statistics and presents the

number of observations, the means and the standard deviations for each of the three

different time windows. The number of observations varies due to the distribution of

new PE-backed company entries as displayed in Table 1 and due to missing values

and data quality issues.

Table 3: Summary statistics of PE-backed companies and their public peers

Different to recent research and also different to our expectations, the PE-backed

companies show lower revenue growths as well as EBIT margins for the period

before the financial crisis (2004-2007). The average excess returns for the public-

Obs Mean SD Obs Mean SD Obs Mean SD

Revenue growth 97 0.1310 0.1392 239 0.0704 0.1540 276 0.0636 0.1452

EBIT margin 97 0.1105 0.1247 239 0.0987 0.1143 276 0.1080 0.1129

Net income margin 97 0.0578 0.0900 239 0.0480 0.0926 276 0.0497 0.0927

ROE 97 0.2085 0.1951 239 0.1471 0.2239 276 0.1344 0.1934

ROA 97 0.1089 0.1080 239 0.0981 0.1036 276 0.1028 0.0901

ROIC 97 0.0808 0.0848 239 0.0723 0.1054 276 0.0805 0.1217

Leverage ratio 97 0.5072 0.1859 239 0.4698 0.1912 276 0.4976 0.2045

CF/Revenue 97 0.0505 0.0650 239 0.0534 0.1230 276 0.0616 0.1228

P

E

-

b

a

c

k

e

d

Ratios2012-20152004-2007 2008-2011

Obs Mean SD Obs Mean SD Obs Mean SD

Revenue growth 99 0.1387 0.1431 239 0.0759 0.1748 280 0.0531 0.1086

EBIT margin 99 0.1541 0.1030 239 0.1177 0.0892 280 0.1101 0.0993

Net income margin 99 0.0941 0.0840 239 0.0678 0.0754 280 0.0670 0.0712

ROE 99 0.1731 0.1344 239 0.1169 0.1279 280 0.1087 0.1238

ROA 99 0.1375 0.0941 239 0.1028 0.0826 280 0.0916 0.0704

ROIC 99 0.1272 0.0966 239 0.0905 0.0911 280 0.0824 0.0823

Leverage ratio 99 0.2523 0.2147 239 0.2814 0.2383 280 0.2607 0.2178

CF/Revenue 99 0.0691 0.0866 239 0.0587 0.0844 280 0.0476 0.0811

P

e

e

r

s

Ratios2012-20152004-2007 2008-2011

32

listed peers is about four percent on EBIT margin level while the revenue growth over

the four-year period is only slightly higher for the peers. The Return on Equity shows

interesting and logical results as the ROE is higher for the PE-backed companies

with around 20.9% compared to 17.3% for the peers in the years until 2007. The LBO

structure consists heavily of leverage and therefore the equity ratio (Shareholders

equity as a percentage of total assets) will decrease consequently. This change in

the capital structure is also visible in the leverage ratio (50.7% for PE-backed

companies vs. 25.2% for non-PE-backed firms). The cash flow generation as a

percentage of sales is given by around five percent for PE-portfolio companies and

6.9% for their peers.

The post-crisis period from 2008 to 2011 represents a time when the economies all

around the world hit a bottom and recovered at a relatively slow pace. Our data can

prove that most of the margins and returns dropped in this time period, but overall the

decrease for PE-backed companies was smaller than for public companies. This

result confirms prior research by Bernstein, Lerner and Mezzanotti (2017) who also

show that PE-backed companies are more resilient in economic downturns than their

non-PE-backed peers. The average EBIT margins for Private Equity portfolio

companies between 2008 and 2011 are around 9.9% while the direct peers generate

11.8% as operating profit. Return on Assets shows very similar results for both

groups of companies, 9.8% for PE-backed firms and 10.3% for their public

counterparts. The drop in ROA for PE-backed companies from the pre- to the post-

crisis period is relatively low which can be possibly explained by capital expenditures

and investments in profitability improvements of the assets by the PE firms in the

years after the crisis. While the leverage ratio for PE-backed companies decreased

slightly by 3.7% to around 47%, the public peers increased their share of interest-

bearing debt to total assets in the crisis by around three percent to 28%.

The third period from 2012 to 2015 - the liquidity period - indicates overall better

statistics in terms of profitability and growth than the post-crisis period from 2008 to

2011 for PE-backed companies, however the public peers show lower results

compared to the previous period. Since the macroeconomic policies at that time gave

an advantage for lending cheap money for investments, the overall economic state in

Europe was characterised by uncertainty and worries about the extensive

indebtedness of states of Southern Europe. Nevertheless, the DACH region was in a

33

solid and growing condition and the average sales growth per annum was about

6.4% for PE-backed companies and 5.3% for the peers. EBIT margins and ROA

increased to 10.8% and 10.3%, respectively for the PE portfolio companies and

decreased to 11.0% and 9.2%, respectively for the public peers. The Return on

Equity slightly dropped for both groups of companies, this might be caused by an

increase in equity capital with a nearly stable net income or a decline in asset

turnover. Regarding the leverage ratio, the PE-backed companies increased their

debt portion on average to around 49% while the listed peers decreased their

leverage to 27%.

In our analyses, the standard deviation measures the historic volatility of our financial

and economic parameters for the specified time frame. The most important

conclusion from the panel summary is that the historic standard deviations for both

the PE-backed companies and the listed peers are very close to each other with only

a few exceptions: the volatility of the ROE is significantly higher for the Private Equity

portfolio companies, depending on the chosen period between 6.1% (pre-crisis) and

9.6% (post-crisis) greater. An average volatility of around 22.4% for the post-crisis

period implies that some returns for shareholders significantly differ from the mean

after the crisis, potentially due to outliers with negative net income and therefore

negative ROE. Overall, the leverage ratios show the highest average volatility in our

sample with around 20%, and the maximal volatility of 23.8% for the peers in the

post-crisis period. The liquidity period from 2012 to 2015 shows high standard

deviations for both the PE-backed companies and the listed counterparts of 20.5 %

and 21.8% respectively. The above-mentioned liquidity period, its characteristics and

its potential impact on PE-backed and public company returns will be introduced

more precisely in the next subchapter.

c. Measures of Liquidity

This brief chapter will introduce one of the most important drivers of the price

increase of Private Equity transactions in the last years, the excess amount of

liquidity in the markets. The macroeconomic policies after the financial crisis of 2008

and particularly after the European sovereign debt crisis of 2011 / 2012 were mostly

focused on an extension of market liquidity to stabilize the economies and financial

34

markets all around the world (e.g. the European Central Bank with its unconventional

program to buy €60bn covered bonds each month from 2010 onwards). The access

to liquidity was seen as a major determinant to reduce fear and uncertainty in the

markets, for corporations and also states. Since our research focus is on Germany,

Austria and Switzerland, those countries and its economies all show relatively stable

performance within and after the recent crises.

To understand and underline the importance of liquidity for our analysis for the

Private Equity industry, we have to consider how liquidity impacts the business

models of Private Equity firms and its funds. Banks and other financial intermediaries

have the function of liquidity transformation and in this case, liquidity provision to

consumers and corporations after the crisis was the main intention and mandate of

the European Central Bank and their macroeconomic policies. Since banks and other

financial intermediaries channel the money supply of central banks, they play an

essential role in our research focus. In Germany, corporations traditionally use bank

loans for financing and funding investments and operations, compared to companies

in the USA which are more dependent on public markets with equity and bond

instruments. The leverage in LBOs has increased in the last years and especially the

financial sectors is providing loans and financing for Private Equity transactions. In

our analysis, we have selected the German domestic credit supply by the financial

sector (Figure 3) as a proxy for the liquidity in the corporate market and the key

objective is to examine the relationship between liquidity and the returns of PE-

backed companies and its public peers. This approach might be a potential

explanation of higher prices and lower excess returns of the PE industry and we are

particularly interested if the PE industry benefits from the increased availability of

money in the markets but on the other hand, the higher liquidity also results in lower

returns in comparison to the public listed comparable firms.

The secondary market for Private Equity companies has grown significantly in the

last years and it is one of the most important drivers for an increased price level for

Private Equity transactions (Bernstein, Lerner, & Mezzanotti, 2017). Figure 4

indicates the all-time high deal multiple with around 10.7x EBITDA in average for

2017 globally, compared to 5.6x in 2009 and 7.9x in 2013. The higher market volume

as well as the increased competition lead to more risky deals and higher leverages. A

leverage of 6x to 6.5x EBITDA became common in 2017 (Bloomberg, 2018).

35

Figure 3: German Domestic Credit Supply by the Financial Sector

Source: World Bank

This chapter briefly underlines the importance of liquidity for the Private Equity

industry and its business model and introduces our proxy for liquidity and the liquidity

period (2012-2015). We are going to use the proxy in the next chapter to better

understand the development of the returns and profitability of the Private Equity

industry in comparison to their listed peers.

d. Findings of Regression

The central idea of our paper is to analyse the behaviour of the returns between

Private Equity-backed companies and its peers and test if the excess returns,

especially for the shareholders, are decreasing after the financial crisis of 2008. This

chapter will provide several statistical analyses to check if we can approve our

hypotheses or not. As we have introduced our dependent variables in chapter 7 a.,

we need control variables and dummies to perform the Difference-in-Differences

approach, the Propensity Score Matching and the panel data fixed affects analysis.

100

110

120

130

140

150

160

170

180

190

200

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

German Domestic Credit Supply by the Financial Sector (as % of German GDP)

36

Table 4: Control variables and dummies used in our analysis

Variables Explanation of the variables

Control Variables

Number of employees Number of full-time employees, mentioned in the Annual

Report of the companies

Company age Company age measures the age of the company on the

year of the buyout (Buyout year - incorporation year)

Initial sales growth Initial sales growth indicates the sales growth of the year

before the Private Equity transaction / buyout

Initial EBIT margin Initial EBIT margin indicates the operating profitability of

the year before the Private Equity transaction / buyout

Dummies

PEdummy 1 if observation is PE-backed, 0 if it is a public peer

timecrisis 1 if the observation belongs to time period from 2008-

2011, 0 if not

timeliquidity 1 if the observation belongs to time period from 2012-

2015, 0 if not

Interaction1 Product of PEdummy and timecrisis; 1 if PEdummy=1 and

timecrisis=1, 0 if not

Interaction2 Product of PEdummy and timeliquidity; 1 if PEdummy=1

and timeliquidity=1, 0 if not

The control variables measure the size of the company in terms of the number of

employees, the company age indicates how many years the firm has operated before

the observation date (the PE-transaction) and the initial sales growth and EBIT

margin before the transaction. The two economic variables are an indication of how

well the company performed before the PE-interaction. The dummies used in the

analysis show different characteristics of the companies, e.g. if they belong to Private

Equity or listed peers or what time the companies have entered Private Equity (pre-

crisis, post-crisis or liquidity period). For the first methodology analyses, we work with

the arithmetic means of the three different periods.

37

In a first step, we investigate the behaviour of the PE-backed companies in the three

periods of time, especially how the profitability and financial stability has changed

after the crisis of 2008 compared to their matched public peers. We are using a panel

data fixed effects approach. With this method, we study the implications for each of

our eight dependent variables with respect to different parameters. As we can see in

Table 5, we regress three different models; Model 1 includes all four control variables

and is only comparing two periods of time (crisis vs. pre-crisis), this is model is equal

to the D-D estimation and as we can see in Table 5 and 6, the D-D coefficient is -

0.011 while the FE coefficient is -0.016. Both results are similar and significant, which

confirms the robustness of our model since we can find significant and similar

parameters with diverse models. Model 2 and 3 include the three periods of time, in

the case of Model 2 the four control variables are incorporated, as we can observe

not all of them are significant. Model 3 only contains the significant control variables,

but the results on the coefficients of the dummies and the interactions do not differ

significantly the models, this is another robustness sign for the founded outcomes.

The first as well as the most interesting variable of investigation in our research is the

Return on Equity. The ROE determines the net earnings for the shareholders which

are in this case either the Private Equity firms or the investors in public corporations.

The following equation and Table 5 demonstrates the robust findings of the

regression:

𝑅𝑂𝐸𝑖𝑡 = 0.1729 − 0.000823 ∗ 𝐶𝑜𝑚𝑝𝑎𝑛𝑦𝐴𝑔𝑒 + 0.3514 ∗ 𝐼𝑛𝑖𝑡𝑖𝑎𝑙𝐸𝑏𝑖𝑡𝑀𝑎𝑟𝑔𝑖𝑛 − 0.0434

∗ 𝑡𝑖𝑚𝑒𝑐𝑟𝑖𝑠𝑖𝑠 − 0.0496 ∗ 𝑡𝑖𝑚𝑒𝑙𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦 + 0.0481 ∗ 𝑃𝐸𝑑𝑢𝑚𝑚𝑦 − 0.0138

∗ 𝐼𝑛𝑡𝑒𝑟𝑎𝑐𝑡𝑖𝑜𝑛1 − 0.0194 ∗ 𝐼𝑛𝑡𝑒𝑟𝑎𝑐𝑡𝑖𝑜𝑛2

We can see that in general, the effect of the crisis (2008-2011) had a negative impact

on the mean ROE of the companies in our sample, PE-backed and listed companies

have decreased in total 4.3% in the period after the crisis compared to the pre-crisis

period (2004-2007). This result is no surprise due to the weaker economic

environment and the overall lower earnings. The reduction in the mean ROE in the

third period, the period of excess liquidity, compared to both periods before (2004-

2011) is given by 4.9%. This reduction can be partly explained by the de-leveraging

of the PE portfolio companies since we have only transactions until 2012 in our panel

38

data and by an increase in equity capital ratios to make the companies more robust

against shocks, as a consequence of the latest crisis.

Table 5: Panel data fixed effects results for ROE

Overall, PE portfolio companies show in average 4.8% higher ROE than their peers,

visible in the PEdummy variable in the equation above. The interaction effects

between PE-backed companies and listed peers confirms that the excess returns are

decreasing by 1.4% in the period from 2008-2011 and therefore verifies our first

hypothesis with the ROE as a key determinant. For the liquidity period from 2012 to

2015, we can see the same effect with a reduction of the excess returns of 1.9%

compared to the periods before. Since there is no single reason for this reduction in

the excess returns, the combination of a greater competition in the PE industry, much

higher acquisition prices and the availability of cheaper funding for public and non-

PE-backed peers might lead to our finding. Another important fact we have

discovered in our data research: We identified several PE-backed companies with an

increasing level of profit transfer agreements. Those agreements are normally used

for holding structures with several subsidiaries to channel the profits in one

corporation to reduce the tax payments or simplify the accounting requirements. In

our case, we discovered several PE portfolio firms who created a synthetic holding

structure and received profit transfers from its portfolio companies in the holding

Variables Model 1 Model 2 Model 3

Dependent Variables:

ROE

Control Variables:

Number of Employees 9.18E-07 8.50E-08

Company Age -0.000644* -0.000815** -0.000823**

Initial Sales Growth 0.120373 0.120043

Initial EBIT Margin 0.584241** 0.335989* 0.351339*

Dummies:

Of Time

timecrisis -0.039592*** -0.043490*** -0.043391**

timeliquidity -0.049748*** -0.049579*

Of Treatment

PEdummy 0.063979** 0.0512469** 0.048147*

interaction1 = (Pedummy*timecrisis) -0.0165565* -0.013720* -0.013776*

interaction2 = (Pedummy*timeliquidity) -0.019292* -0.019369*

R-sqr 0.1488 0.1187 0.116

Observations: 356

**Inference: *** p<0.01; ** p<0.05; * p<0.1

39

period. This approach is a possibility of early cash-out profits from its investments,

especially if the market is going to decrease in the future in terms of exit multiples.

Those profit transfer agreements decrease the ROE and ROIC in our analysis since

we did not adjust those payments.

Table 6: Difference-in-Differences results for ROE

The Diff-in-Diff analysis (Table 6) confirms the results from the first regression. The

buyout companies represent the treatment group in this analysis, the public peers the

control group. With this statistical method, the excess Return on Equity results for

PE-backed companies are decreasing by 1.1% to overall 4% if we compare pre-crisis

(2004-2007) and post-crisis (2008-2011). The comparison of the post-crisis time and

the period of excess liquidity (2012-2015), we see a further decrease of superior

ROE by 0.6%.

The next variable of interest is the Return on Assets. The outcome for this variable

does not show very robust figures since we get different coefficients for the dummies.

Although we are using the same type of model for the analysis, we receive

coefficients with a wide spread. The results of this regression are displayed in the

Table 10.

The analysis for the Return on Invested Capital (ROIC) shows robust and significant

results for the third period (the liquidity period) while the crisis period cannot give any

vigorous evidence with the FE model. The following equation and Table 7 summarise

the results of our panel data fixed effect analysis:

Crisis - Boom

Control Treated DID (T-C)

Before 0.124 0.175 0.051*

After 0.079 0.119 0.040*

-0.011

**Inference: *** p<0.01; ** p<0.05; * p<0.1

Liquidity - Crisis

Control Treated DID (T-C)

Before 0.093 0.131 0.038*

After 0.085 0.117 0.033*

-0.006

**Inference: *** p<0.01; ** p<0.05; * p<0.1

40

𝑅𝑂𝐼𝐶𝑖𝑡 = 0.0578 − 0.00044 ∗ 𝐶𝑜𝑚𝑝𝑎𝑛𝑦𝐴𝑔𝑒 + 0.3456 ∗ 𝐼𝑛𝑖𝑡𝑖𝑎𝑙𝐸𝑏𝑖𝑡𝑀𝑎𝑟𝑔𝑖𝑛 − 0.0234

∗ 𝑡𝑖𝑚𝑒𝑐𝑟𝑖𝑠𝑖𝑠 − 0.0329 ∗ 𝑡𝑖𝑚𝑒𝑙𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦 − 0.0261 ∗ 𝑃𝐸𝑑𝑢𝑚𝑚𝑦 + 0.0163

∗ 𝐼𝑛𝑡𝑒𝑟𝑎𝑐𝑡𝑖𝑜𝑛1 + 0.0318 ∗ 𝐼𝑛𝑡𝑒𝑟𝑎𝑐𝑡𝑖𝑜𝑛2

Table 7: Panel data fixed effects results for ROIC

The ROIC specifies the net return in proportion of the invested capital. Higher

leverage reduces the ratio due to a higher denominator (assume a stable numerator)

and this could be a reason why the ROIC is 2.6% lower for PE-backed companies

than for their comparable peers. The interaction coefficients show that the Private

Equity portfolio companies increase their ROIC compared to the public firms after the

crisis and also in the liquidity period. The ROIC ratio coefficient of 3.2% in the

liquidity period is statistically significant and might refer to the better capital

management of the PE companies in the time between 2012 and 2015. The diff-in-

diff coefficients confirm the results of the panel data fixed effects approach. Before

the crisis, the control group shows 2.7% higher ROIC results which decreased in the

crisis by 2% to only 0.7%. In the period from 2012 to 2015, the Returns on Invested

Capital shows higher coefficients for the PE-backed companies with an excess ROIC

of 0.5%. However, the increase of 1.5% between the crisis and liquidity period is

statistically not significant in the D-D model shown in Table 8.

Variables Model 1 Model 2 Model 3

Dependent Variables:

ROIC

Control Variables:

Number of Employees 1.45E-07 -1.46E-07

Company Age -0.000329* -0.000441** -0.000438**

Initial Sales Growth 0.091946 0.053432

Initial EBIT Margin 0.522289*** 0.326665* 0.345596**

Dummies:

Of Time

timecrisis -0.024412*** -0.023554** -0.023437**

timeliquidity -0.032944** -0.032903***

Of Treatment

PEdummy -0.016167* -0.025896* -0.026140*

interaction1 = (Pedummy*timecrisis) 0.015118* 0.016749 0.016343

interaction2 = (Pedummy*timeliquidity) 0.032159* 0.031789*

R-sqr 0.2223 0.1136 0.1123

Observations: 356

**Inference: *** p<0.01; ** p<0.05; * p<0.1

41

Table 8: Difference-in-Differences results for ROIC

The leverage ratio demonstrates robust and significant results for the dummies, but

not for the control variables (Table 12). The coefficients for the four dummy variables

barely change within the different models and therefore we are able to say that the

leverage ratio is a suitable indicator for measuring the differences between the

companies and the time windows. In the crisis period, the overall leverage ratio rises

around 1.7% for the total sample of companies, PE-backed and public firms). In the

time window from 2012 to 2015, the ratio of interest-bearing debt over total assets

decreases by 2.5%, most probably because of the de-leveraging of the PE

companies and due to the fact that no new PE-companies are entering in this time

and therefore we find a lower overall leverage. The effect of being in PE ownership

for leverage is clearly visible and we find a 26.9% higher leverage ratio for the PE

firms compared to the peers. In terms of interaction between the different time

periods, we see a stronger reduction in leverage for the PE portfolio firms with -5.6%

for the crisis period and -10.2% for the liquidity period. The Private Equity backed

companies were able to reduce the leverage faster than their peers over time, mostly

due to the fact that the exit of some of the investments could happen before our

estimated time of eight to ten years.

The operating profit, measured in earnings before interest and tax (EBIT) does not

show many significant results and give us limited space for interpretations (Table 13).

The coefficients are only significant for the time dummies which indicate lower results

for the post-crisis and liquidity period. The coefficients do not change much between

the different models and show for the timecrisis dummy a value of -2.3% and for the

Crisis - Boom

Control Treated DID (T-C)

Before 0.08 0.053 -0.027*

After 0.056 0.048 -0.007*

0.02

**Inference: *** p<0.01; ** p<0.05; * p<0.1

Liquidity - Crisis

Control Treated DID (T-C)

Before 0.07 0.06 -0.01

After 0.059 0.064 0.005

0.015

**Inference: *** p<0.01; ** p<0.05; * p<0.1

42

timeliquidity dummy a reduction in the EBIT of 2.6%. We cannot give more evidence

in this context due to lack of significance of the coefficients. Similar results we can

find for the net income margin and the sales growth (Tables 14 & 15). For the net

income margin, the time dummies show significant and robust results with a negative

trend of -1.7% for the crisis period of 2008 to 2011 and -1.6% for the liquidity time up

to 2015. For the sales growth variable, we observe time dummies which are highly

significant and robust. The sales growth in the crisis period indicates negative growth

of 4.7%, in the liquidity period -7.3%. If the company is backed by a Private Equity

firm, it demonstrates overall higher sales growth by 1.7% which illustrates the

expansion strategy of most Private Equity firms in Europe. For the last dependent

variable, the cash flow over sales ratio, we only find two control variables with

meaningful coefficients (Table 16). None of the dummies are significant therefore it is

hardly possible to interpret those results for the cash flows. Although cash flow

possibly is the most used financial variable in the Private Equity industry next to

EBITDA, the cash flow over sales ratios are very volatile and do not show a logical

behaviour neither over time nor in comparison of PE-backed companies to public

listed peers.

Table 9: Propensity Score Probit regression

After analysing the panel data fixed effects, we are going to start with the second

methodology approach, the Propensity Score Matching method. The first step is to

run a Probit regression between the probability of being backed by Private Equity and

the independent variables used to do the matching process (Table 9). As we can

Variables Coefficient P-value

Dependent: Pedummy

Independent:

Number of Employees -5.87E-06 0.006

Company Age -0.0015662 0.027

Initial Sales Growth -3.208771 0.000

Initial EBIT Margin -1.589986 0.000

Constant 0.4617212 0.000

Pseudo R-sqr 0.0432

Obs: 1419

Treatment:667

Control: 752

**The region of common support is [.13615071, .7567352]

43

see, the number of employees and the company age do not influence the probability

of treatment, but the initial sales growth and the initial EBIT margin show negative

coefficients, that indicate that for higher values in these variables, the probability of

treatment is lower. In easy words, the higher the initial sales growth and / or the initial

EBIT margin, the less probable the company will be acquired by a Private Equity firm.

This supports the main idea of the Private Equity industry and its business model,

which is based on the selection of undervalued and underestimated private

companies. The output of the matching process gives us the possibility to evaluate

the PE effects with different methods.

Table 10: Propensity Score Matching from 2004-2016

After the Probit regression, we calculate the estimated impact by two methodologies;

matching nearest neighbour and matching Kernel. The results of being backed by

Private Equity are given by 2.8% and 3.2% respectively. This finding indicates that

the ROE is about 2.8% - 3.2% higher than the ROE of the public peers (Table 10).

As we can see in figure 4, the effects of Private Equity are positive and increased in

the boom period (Matching Kernel method). The years from 2004 to 2007 represent a

strong superior ROE for Private Equity portfolio companies, especially the years

2006 and 2007 with about 8.4% and 7.8% respectively. The green line in figure 4

specifies the accumulative effect of being PE-backed. During the crisis of 2008, the

excess ROE for PE-backed companies was still 3.9%. In the following years, the

excess ROE ratios are stable around zero or slightly negative for some years (Tables

ROE

Methods Matches Estimate Min Max

Matching nearest neighbors 267 0.028**

Normal 0.00598 0.05072

Percentile 0.00713 0.05335

Bias corrected 0.00366 0.04998

Matching Kernel 667 0.032**

Normal 0.01584 0.04806

Percentile 0.01639 0.04854

Bias corrected 0.01639 0.04854

**Inference: *** p<0.01; ** p<0.05; * p<0.1

**100 repetitions for bootstrapping of standard errors

44

17-18). The accumulated effect for ROE shows a stable development after 2012 with

approx. 4% higher than the public peers.

Figure 4: Yearly & accumulative effects of PE for ROE by PSM

After analysing the yearly & the accumulative effect of the whole sample of PE-

backed companies, we find evidence that the PE entry effects for Return on Equity

for Private Equity backed companies which have entered PE in the specific year

show different but also very interesting movements in the selected period of time. As

visible in figure 5 and more detailed in table 19, we discovered a positive effect and a

superior Return on Equity over time for companies entering between 2004 and 2007.

The excess ROE value for buyouts happened in 2007 amounts to 7%. PE-backed

companies entering in the years 2008 to 2010 show a lower ROE than its control

group. Private companies which have been acquired by Private Equity funds in 2010

show a 4.9% lower ROE over time then the listed peers in the DACH region. In

contrast to 2010, we can discover a strong superior ROE of 8.9% for companies

entering Private Equity in 2011. Overall, we cannot find a clear trend for the different

years although we find evidence that the PE portfolio companies which have been

acquired by PE firms during the crisis until 2010 show lower ROE than their peers.

-2%

0%

2%

4%

6%

8%

10%

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Yearly Effect Accumulated Effect

45

Figure 5: PE entry effects for ROE by PSM

Finally, we analyse the fourth hypothesis of our research paper, which focuses on the

relationship of the sensibility of returns and the level of leverage. This assumption

proves if the PE-backed companies with significantly higher leverage ratios show

higher volatility in returns, in this case in Returns on Equity. We regressed the

leverage level with the volatility of the returns, observed a positive relationship but we

couldn’t find significant coefficients for this analysis. Therefore, we can only show

that leverage has a positive impact on ROE which is widely known but we are not

able to verify the higher return sensibility of PE portfolio companies.

8. Summary & Conclusion

A great number of researchers have studied the returns of Private Equity firms and

its investors compared to equity stock markets. Since investors are continuously

looking for new investment opportunities to fulfil their return targets, especially in our

current low interest rate environment, Private Equity becomes one of the most

popular alternative asset classes. In contrast to focus on the PE firms and funds with

its decent returns, our paper is focusing on the single Private Equity portfolio

companies and therefore we have analysed the core level and one of the

fundamental reasons why PE firms can earn around 25% IRR per annum. The

central value levers for Private Equity firms are the improvement of the profitability,

-6%

-4%

-2%

0%

2%

4%

6%

8%

10%

2004 2005 2006 2007 2008 2009 2010 2011 2012

PE Entry Effect

46

the business expansion and in general the increase of all financial and capital ratios

of the portfolio companies to sell the company for the highest possible price after the

holding period of normally five to eight years. Exactly those financial ratios, we have

examined for PE-backed companies compared to public firms and always in relation

to the current development of greater competition, cheaper funding and more risky

investments.

As introduced in chapter 4, the key hypothesis of this paper is that PE-backed

companies in the DACH region show a decreasing level of excess returns after the

global financial crisis and that the superior returns for the Private Equity portfolio

companies compared to their non-PE-backed peers are converging. With a panel

data fixed effects as well as a Difference-in-Differences approach, we can affirm that

the average excess ROE decreased by 1.1% - 1.4% from the pre-crisis to the post-

crisis period and by 0.6% - 1.9% from the post-crisis to the liquidity period. These

results implicate that the peers are able to increase the profits for their shareholders,

partly due to better funding opportunities, an easier access to liquidity and therefore a

higher leverage ratio (average leverage ratios for public peers increased by around

4% - Table 3), on the other hand due to greater emphasis on stable earnings after

the drop in the crisis. Beyond that the Private Equity backed firms are de-leveraging

after the crisis therefore the equity ratio is increasing. The ROIC is decreasing for the

peers over time and in the latest period from 2012 to 2015, PE-backed firms show

greater returns on invested capital. For the variables net income, EBIT margin and

ROA, we only find a few reliable results in this analysis and cannot give compelling

interpretations.

The Propensity Score Matching approach gives evidence that the Returns on Equity

for PE-backed firms before the crisis have shown higher excess results than after the

crisis. However, PE portfolio firms can generate a 4% higher ROE ratio on an

accumulated basis for the selected time frame from 2004 to 2015. After 2009, the

average ROE for our treatment companies is very similar or slightly below the results

for the comparable firms.

To conclude our research paper, we are able to declare that in terms of Return on

Equity, the excess returns of Private Equity portfolio companies compared to non-

PE-backed peers are decreasing after the boom period in the two following periods

until 2015 and therefore we can confirm our key hypothesis. It is important to

47

mention, that the net income values were influenced by the profit transfer

agreements and therefore some ratios (especially the ROE as our key variable) are

undervalued. Our secondary hypotheses capture different topics related to the

excess returns of Private Equity portfolio companies: the hypothesis that

macroeconomic policies have played a major role in the decline can be confirmed

partly because we identified a negative relationship between excess returns and the

liquidity period (2012-2015) for ROE. Although we identified a stable ratio of nearly

zero excess returns after 2010 for PE-backed companies. The third hypothesis

assesses the negative impact of higher M&A prices on PE-backed company

profitability. Due to insufficient data for the PE-transaction prices, we were not able to

answer this question to a final and reasonable level. Next to this, is not possible to

accept or reject the fourth hypothesis of a positive relationship between return

sensibility and the level of leverage.

However, since our explicit sample size comprises 70 randomly selected companies

out of a total sample of 1,412 transactions between 2004 and 2012, further research

is needed to better understand the relationship and its development of Private Equity

backed companies compared to their public peers during and after the financial crisis

of 2008 for the DACH region. Another interesting research focus might be the impact

of different types of transactions in the field of Private Equity (MBO, MBI, expansion

capital etc.) or the study of excess returns per industry / sector of PE-backed

companies (e.g. differences between healthcare and industrial companies).

48

9. Appendix

Table 11: Panel data fixed effects results for ROA

Table 12: Panel data fixed effects results for Leverage Ratio

Variables Model 1 Model 2 Model 3

Dependent Variables:

ROA

Control Variables:

Number of Employees 1.56E-07 -2.57E-07

Company Age -0.000405* -0.000461** -0.000823**

Initial Sales Growth -0.019927 -0.043903

Initial EBIT Margin 0.560424*** 0.4108978** 0.351339*

Dummies:

Of Time

timecrisis -0.020340*** -0.022711*** -0.043391***

timeliquidity -0.030748*** -0.049579***

Of Treatment

PEdummy 0.002403* 0.005511* 0.048147*

interaction1 = (Pedummy*timecrisis) -0.0024109 -0.004056* -0.013776

interaction2 = (Pedummy*timeliquidity) 0.024724* -0.019369

R-sqr 0.2323 0.1656 0.116

Observations: 356

**Inference: *** p<0.01; ** p<0.05; * p<0.1

Variables Model 1 Model 2 Model 3

Dependent Variables:

Leverage

Control Variables:

Number of Employees -5.26E-08 -8.66E-07

Company Age -0.000229 -0.000185

Initial Sales Growth -0.488927 -0.592755

Initial EBIT Margin -0.175901 -0.249815

Dummies:

Of Time

timecrisis -0.018798* -0.017766* -0.017745*

timeliquidity -0.024468* -0.025526*

Of Treatment

PEdummy 0.248237*** 0.239702*** 0.269185***

interaction1 = (Pedummy*timecrisis) -0.055735* -0.054080* -0.056232*

interaction2 = (Pedummy*timeliquidity) -0.100555*** -0.102491***

R-sqr 0.3879 0.3384 0.3065

Observations: 356

**Inference: *** p<0.01; ** p<0.05; * p<0.1

49

Table 13: Panel data fixed effects results for EBIT margin

Table 14: Panel data fixed effects results for Net Income margin

Variables Model 1 Model 2 Model 3

Dependent Variables:

EBIT Margin

Control Variables:

Number of Employees 0.000001* 0.000001* 0.000001*

Company Age 0.0000358 -0.000109

Initial Sales Growth -0.152659** -0.159539** -0.159846**

Initial EBIT Margin 0.899342*** 0.781360*** 0.788182***

Dummies:

Of Time

timecrisis -0.024483*** -0.022446*** -0.022972***

timeliquidity -0.025539*** -0.026493***

Of Treatment

PEdummy 0.001489 -0.006865 -0.006227

interaction1 = (Pedummy*timecrisis) 0.008739 0.009327 0.009618

interaction2 = (Pedummy*timeliquidity) 0.021729** 0.022035**

R-sqr 0.5892 0.476 0.4731

Observations: 356

**Inference: *** p<0.01; ** p<0.05; * p<0.1

Variables Model 1 Model 2 Model 3

Dependent Variables:

Net Income Margin

Control Variables:

Number of Employees 0.000001* 0.000001** 0.000001*

Company Age 0.000049 -0.000072

Initial Sales Growth -0.082883 -0.078971

Initial EBIT Margin 0.666018*** 0.540403*** 0.523656***

Dummies:

Of Time

timecrisis -0.019331*** -0.017462*** -0.017946***

timeliquidity -0.015885** -0.016601**

Of Treatment

PEdummy -0.006366 -0.012093 -0.010671

interaction1 = (Pedummy*timecrisis) 0.008346 0.009278 0.009861

interaction2 = (Pedummy*timeliquidity) 0.010159 0.010725

R-sqr 0.4797 0.3529 0.3439

Observations: 356

**Inference: *** p<0.01; ** p<0.05; * p<0.1

50

Table 15: Panel data fixed effects results for Sales Growth

Table 16: Panel data fixed effects results for Cash Flow margin

Variables Model 1 Model 2 Model 3

Dependent Variables:

Sales Growth

Control Variables:

Number of Employees -3.82E-07 -1.30E-07

Company Age -0.000029 0.00004

Initial Sales Growth 0.409508*** 0.152759** 0.160039**

Initial EBIT Margin 0.003623 0.073458

Dummies:

Of Time

timecrisis -0.058129*** -0.047637*** -0.047630***

timeliquidity -0.073057*** -0.072959***

Of Treatment

PEdummy 0.023008* 0.019523* 0.017349*

interaction1 = (Pedummy*timecrisis) -0.003948 -0.004758 -0.004645

interaction2 = (Pedummy*timeliquidity) -0.001373 -0.001229

R-sqr 0.2003 0.1346 0.1318

Observations: 356

**Inference: *** p<0.01; ** p<0.05; * p<0.1

Variables Model 1 Model 2 Model 3

Dependent Variables:

Cash Flow Margin

Control Variables:

Number of Employees -6.94E-08 -4.69E-08 -0.000251*

Company Age -0.0002358 -0.000254*

Initial Sales Growth 0.058285 -0.037763

Initial EBIT Margin 0.456622*** 0.400381*** 0.396879***

Dummies:

Of Time

timecrisis -0.0025345 0.001925 0.001902

timeliquidity -0.001981 -0.002037

Of Treatment

PEdummy 0.0084503 0.003694 0.004794

interaction1 = (Pedummy*timecrisis) 0.0052557 0.005166 0.005142

interaction2 = (Pedummy*timeliquidity) 0.018059 0.018047

R-sqr 0.1689 0.125 0.1239

Observations: 356

**Inference: *** p<0.01; ** p<0.05; * p<0.1

51

Table 17: Yearly effect of PE by PSM

ROE - Matching Nearest Neighbors

Effects Obs Treatment Matches Estimate Min Max

Effect of PE in 2004 1419 3 3 -0.31** -0.3941373 -0.182162

Effect of PE in 2005 1419 18 18 0.098* -0.0154133 0.2920388

Effect of PE in 2006 1419 33 33 0.078** -0.0045497 0.1518777

Effect of PE in 2007 1419 43 42 0.073** 0.0063693 0.1337314

Effect of PE in 2008 1419 52 51 0.052* -0.0037888 0.1545445

Effect of PE in 2009 1419 57 57 0.009 -0.0567489 0.0915098

Effect of PE in 2010 1419 62 61 0.019 -0.034059 0.1077471

Effect of PE in 2011 1419 68 64 0.024 -0.0440113 0.1245286

Effect of PE in 2012 1419 70 66 0.000 -0.0654769 0.0724432

Effect of PE in 2013 1419 70 64 -0.046* -0.1127306 0.0040056

Effect of PE in 2014 1419 70 67 0.016 -0.0400035 0.0667279

Effect of PE in 2015 1419 66 64 -0.034 -0.0968571 0.0135359

Effect of PE in 2016 1419 34 34 -0.040 -0.1612038 0.0471597

ROE - Matching Kernel

Effects Obs Treatment Matches Estimate Min Max

Effect of PE in 2004 1419 3 3 0.006 -0.1058787 0.1427966

Effect of PE in 2005 1419 18 18 0.038 -0.0259509 0.1207089

Effect of PE in 2006 1419 33 33 0.084*** 0.0006693 0.1579868

Effect of PE in 2007 1419 43 43 0.078*** 0.0325334 0.1369456

Effect of PE in 2008 1419 52 52 0.039* -0.0126237 0.1041439

Effect of PE in 2009 1419 57 57 -0.008* -0.0640402 0.0439488

Effect of PE in 2010 1419 62 62 -0.004* -0.0558561 0.038121

Effect of PE in 2011 1419 68 68 0.011 -0.049927 0.0694766

Effect of PE in 2012 1419 70 70 -0.005* -0.0687047 0.0373061

Effect of PE in 2013 1419 70 70 0.003* -0.0578716 0.0438461

Effect of PE in 2014 1419 70 70 -0.005* -0.0499242 0.0251218

Effect of PE in 2015 1419 66 66 -0.001* -0.0536021 0.046929

Effect of PE in 2016 1419 34 34 -0.038 -0.1605741 0.0309361

**Inference: *** p<0.01; ** p<0.05; * p<0.1

** Confidence Interval (95%): bias corrected

52

Table 18: Cumulative effects of PE by PSM

ROE - Matching Nearest Neighbors

Effects Obs Treatment Matches Estimate Min Max

Effect of PE in 2004 1419 3 3 0.006 -0.1104198 0.1612114

Effect of PE until 2005 1419 21 19 0.023 -0.0794774 0.1430606

Effect of PE until 2006 1419 54 46 0.049* -0.0343281 0.105476

Effect of PE until 2007 1419 97 81 0.054** 0.0065576 0.1056362

Effect of PE until 2008 1419 149 116 0.058*** 0.0177801 0.0955259

Effect of PE until 2009 1419 206 160 0.051*** 0.0108466 0.1034845

Effect of PE until 2010 1419 268 198 0.041*** 0.0001884 0.0706492

Effect of PE until 2011 1419 336 234 0.020* -0.0088961 0.0536141

Effect of PE until 2012 1419 406 263 0.035** -0.0085193 0.0653948

Effect of PE until 2013 1419 476 283 0.037*** 0.0103926 0.0595769

Effect of PE until 2014 1419 546 291 0.032*** -0.0097291 0.0550161

Effect of PE until 2015 1419 612 285 0.042*** 0.0207864 0.0583146

Effect of PE until 2016 1419 646 264 0.024*** 0.0071813 0.047478

ROE - Matching Kernel

Effects Obs Treatment Matches Estimate Min Max

Effect of PE in 2004 1419 3 3 0.006 -0.1131088 0.1513551

Effect of PE until 2005 1419 21 21 0.031 -0.0465387 0.1311249

Effect of PE until 2006 1419 54 54 0.062** 0.011324 0.1014382

Effect of PE until 2007 1419 97 97 0.073*** 0.0435386 0.1142571

Effect of PE until 2008 1419 149 149 0.067*** 0.0323472 0.1085096

Effect of PE until 2009 1419 206 206 0.050*** 0.0069058 0.0699719

Effect of PE until 2010 1419 268 268 0.042*** 0.0089593 0.06553

Effect of PE until 2011 1419 336 336 0.040*** 0.0181853 0.0643611

Effect of PE until 2012 1419 406 406 0.036*** 0.0166731 0.0575279

Effect of PE until 2013 1419 476 476 0.036*** 0.0188695 0.0583865

Effect of PE until 2014 1419 546 546 0.036*** 0.0134509 0.0556798

Effect of PE until 2015 1419 612 612 0.036*** 0.0186228 0.0566221

Effect of PE until 2016 1419 646 646 0.032*** 0.0166856 0.0549012

**Inference: *** p<0.01; ** p<0.05; * p<0.1

** Confidence Interval (95%): bias corrected

53

Table 19: Effects of PE entry per year by PSM

ROE - Matching Nearest Neighbors

Effects Obs Treatment Matches Estimate Min Max

Effect of PE entry in 2004 1419 40 39 0.018*** -0.0331905 0.0692339

Effect of PE entry in 2005 1419 164 147 0.030* -0.0122585 0.0666081

Effect of PE entry in 2006 1419 175 114 0.013 -0.02706 0.0517548

Effect of PE entry in 2007 1419 98 86 0.07*** 0.0177635 0.1182327

Effect of PE entry in 2008 1419 80 69 -0.063*** -0.133105 -0.0084326

Effect of PE entry in 2009 1419 37 36 0.012 -0.0829346 0.0779902

Effect of PE entry in 2010 1419 30 15 -0.024 -0.115733 0.078004

Effect of PE entry in 2011 1419 32 22 0.079* -0.0130129 0.1738476

Effect of PE entry in 2012 1419 11 10 -0.015 -0.1775383 0.0923007

ROE - Matching Kernel

Effects Obs Treatment Matches Estimate Min Max

Effect of PE entry in 2004 1419 40 40 0.023** -0.0100752 0.0658633

Effect of PE entry in 2005 1419 164 164 0.017* -0.0175617 0.0394422

Effect of PE entry in 2006 1419 175 175 0.009 -0.0278377 0.0376037

Effect of PE entry in 2007 1419 98 98 0.043** 0.0142311 0.091863

Effect of PE entry in 2008 1419 80 80 -0.015* -0.0636437 0.035879

Effect of PE entry in 2009 1419 37 37 0.000 -0.0473558 0.0714092

Effect of PE entry in 2010 1419 30 30 -0.049** -0.0907006 0.0218925

Effect of PE entry in 2011 1419 32 32 0.089** 0.0142402 0.2060327

Effect of PE entry in 2012 1419 11 11 0.03 -0.044768 0.0458805

**Inference: *** p<0.01; ** p<0.05; * p<0.1

** Confidence Interval (95%): bias corrected

54

Figure 6: Global median PE EBITDA multiples 2006 - 2017

Source: McKinsey Global Private Markets Review 2018

Figure 7: PE firms by region 1980 - 2015

Source: Preqin / The Economist

55

Figure 8: Global PE deal volume 2000 - 2017

Source: McKinsey Global Private Markets Review 2018

Figure 9: Global PE deal count 2000 - 2017

Source: McKinsey Global Private Markets Review 2018

56

Figure 10: Global PE capital raised 2003 – 2017 (by fund type)

Source: Bain Global Private Equity Report 2018

57

10. List of References

Acharya, V., Hahn, M., & Kehoe, C. (2010). Corporate Governance and Value

Creation: Evidence from Private Equity. NYU working paper No. 2451/27878.

Alemany, L. and J. Marti (2005). Unbiased Estimation of Economic Impact of Venture

Capital Backed Firms, EFA, Moscow Meetings Paper

Axelson, U., Jenkinson, T., Strömberg, P., Weisbach, M. (2013). Borrow cheap, buy

high? The determinants of leverage and pricing in buyouts. Journal of Finance, Vol.

68(6), 2223-2267

Bernstein, S., Lerner, J., & Mezzanotti, F. (2017). Private Equity and Financial

Fragility during the Crisis. Harvard Business School.

Browning, M. (2012). Two examples of structural modelling. Notes for "Structural

modelling". Department of Economics, University of Oxford.

Caselli, S., E. Garcia-Appendini & F. Ippolito (2013). Contracts and Return in Private

Equity Investments. Journal of Financial Intermediation. Vol. 22 (2), 201-217

Cressy, R., A. Malipiero & F. Munari (2007). Playing to their strengths? Evidence that

specialization in the Private Equity industry confers competitive advantage. Journal of

Corporate Finance, Vol. 13, 647–669.

Gertler, P., Martinez, S., & Prema, P. (2016). Impact Evaluation in Practice (Vol.

Second Edition). Washington: World Bank.

Gilligan, J. and J. Wright (2014). Private equity demystified: an explanatory guide (3rd

ed.). London, ICAEW Corporate Finance Faculty.

Groh, A., H. von Liechtenstein & K. Lieser (2010). The European venture capital and

private equity country attractiveness indices. Journal of Corporate Finance, Vol. 16

(2), 205–224

Jann, B. (2017). Why Propensity Scores Should Be Used for Matching. Berlin: Stata.

Jensen, M. C. (1989). Eclipse of the Public Corporation. Harvard Business Review,

Vol. 67, 61–74.

58

Kaplan, S., & Strömberg, P. (2009). Leveraged Buyouts and Private Equity. Journal

of Economic Perspectives, Vol. 23, 121–146.

Kaplan, S.N. (1989). The Effects of Management Buyouts on Operating Performance

and Value. Journal of Financial Economics, Vol. 24(2), 217-254

Kaplan, S.N. and A. Schoar (2005). Private Equity Performance: Returns,

Persistence and Capital Flows. The Journal of Finance, Vol. 60 (4), 1791-1823

Leslie, P. and P. Oyer (2009). Managerial Incentives and Value Creation: Evidence

from Private Equity. Unpublished Working Paper, Stanford University & National

Bureau of Economic Research

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

mechanisms on value increase in leveraged buyouts. Journal of Corporate Finance,

Vol. 13, 511-537

Roberts, S., & Naydenova, E. (2017). Private Equity Trend Report 2017.

PricewaterhouseCoopers (PwC).

Rosenbaum, P., & Rubin, D. (1983). The Central Role of the Propensity Score in

Observational Studies for Causal Effects. Biometrika, Vol. 70(1), 41-55.

Shivdasani, A., & Wang, Y. (2011). Did Structured Credit Fuel the LBO Boom? Vol.

66 (4), 1291–1328.

Wilson, N., M. Wrigt, D.S. Siegel & L. Scholes (2011). Private Equity portfolio

company performance during the global recession. Journal of Corporate Finance,

Vol. 13(1), 193-205