Master Thesis Excess Returns for Private Equity Portfolio ......Master Thesis Excess Returns for...
Transcript of Master Thesis Excess Returns for Private Equity Portfolio ......Master Thesis Excess Returns for...
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
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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.
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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.
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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
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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
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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
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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
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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
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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
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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)
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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.
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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
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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).
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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
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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
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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
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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.
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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
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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).
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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