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* Contact information: Crain: [email protected], Braun: [email protected], Gerl: [email protected]. We would like to thank Jean-Noël Barrot, Ayako Yasuda and seminar participants at the 2016 Private Equity Research Center Symposium, 2016 FMA Annual Conference, 2015 Coller Private Equity Findings Symposium, the University of California at Davis and the University of Texas for valuable comments.
The Levered Returns of Leveraged Buyouts: The Impact of
Competition*
Reiner Braun Technische Universität München (TUM)
Center for Entrepreneurial and Financial Studies
Nicholas Crain University of Melbourne
Faculty of Business and Economics
Anna Gerl Technische Universität München (TUM)
Current version, November 2018 First version, March 2015
ABSTRACT This paper investigates the relationship between leverage and returns in private equity buyout transactions. In contrast to the predictions of traditional capital structure theory, we find that transactions financed with large amounts of debt are associated higher transaction prices and lower returns to private equity sponsors. Consistent with the view that easy credit amplifies the intensity of bidding for deals, these relationships hold only when private equity buyers face competition from other funds, such as in deals sourced from investment bank auctions. Our results are distinct from changes in deal prices driven by private equity fundraising and the results are robust to alternative, plausibly exogenous, proxies for the competitiveness of deals. Finally, we show that the choice to pursue auction deals in particularly loose credit markets, when expected returns are low, is positively related to proxies for agency conflicts between fund managers and fund investors.
Keywords: Leverage, pricing, returns, competition, agency conflicts, leveraged buyouts
JEL classification: G32, G34
1
1 Introduction
In 1989, the private equity firm KKR raised nearly $21 billion of debt to finance the public-to-
private buyout of RJR Nabisco. The debt, which amounted to over six times the company’s
trailing EBITDA, provided several possible benefits. The tax savings would be large and
immediate. Servicing the debt would require steep cuts in the somewhat infamous perquisites
enjoyed by the firm’s incumbent management. Further, KKR believed that it would be difficult
for other bidders to find intermediaries with the experience and capacity to raise the capital
required to compete for the deal. However, with RJR Nabisco’s stable cash flows and the
strength of the late-80’s high yield bond market, several bidders competed for the deal, backed
by investment banks hoping to burnish their reputations and rise to the top of the leveraged
buyout (LBO) financing league table.1 To win the deal, KKR was forced to raise its bid by
nearly $4 billion. Eventually the fund would record a loss of $730 million on the investment
(Norris, 2004).
In this paper, we investigate how the performance of private equity buyout deals are
related to the amount of debt used to finance their purchase. The literature on private equity
has largely focused on how leverage may affect the value of the target firm.2 However, a private
equity buyout involves both a recapitalization of the target firm and a transfer of ownership.
As the anecdote about KKR’s experience with RJR Nabisco illustrates, private equity deal
returns are driven by both the value added by private equity ownership (including potential
benefits from additional leverage) and the price that can be negotiated with the sellers. Axelson
et al. (2013) find the price paid by private equity firms to acquire a portfolio company is
positively related to the amount of debt used to financing the purchase. This suggests sellers
of the target firm are better off when more debt is available to finance the deal, but tells us little
about the value captured by private equity.3 It could be that high levels of debt correspond to
the largest increases in the value of the target firm (through tax savings, for example). Sellers
1 Deal values were taken from ThomsonOne M&A database. Burrough and Helyar (2008) provide a journalist account of the bidding process for RJR Nabisco. 2 Jensen (1989) suggests that portfolio company leverage helps mitigate managerial agency problems. Kaplan and Strömberg (2008) suggest private equity funds may have an ability to time mispricing in debt markets. In addition, many papers have commented on the potential tax benefits of the additional debt used in buyout transactions. 3 In addition, Axelson et al. (2013) find that fund-level returns are negatively related to the average Debt / EBITDA among a fund’s deals. However, the identification of this effect relies on a small minority of deals from each fund that are captured in commercial deal leverage data. Our focus on the returns to individual deals offers two advantages. First, it gives us power to conduct tests in the cross-section, particularly with respect to the competitive environment in which the deal takes place. Second, conditional on a fund entering our sample, we have a nearly complete record of leverage and performance for each of its deals.
2
may capture a portion of this increase through higher deal prices, but the return to the private
equity sponsor may be higher as well. Alternatively, the increase in deal price may come at the
expense of private equity sponsors - in which case, we would expect the returns to private equity
sponsors to decline with leverage.
Our evidence comes from a large sample of deal-level data provided by several private
equity fund-of-funds (FoFs). The data was collected during the FoFs’ due diligence
investigations of buyout fund managers attempting to raise a new fund. It contains all the
previous investments of fund managers, whether or not the manager successfully raised
financing from the FoF. We are able to identify a large sample of 3,198 deals for which we
have information on both deal performance and leverage; the latter is taken directly from the
description of sources and uses of capital in the transaction.
We begin by considering the relationship between the realized returns to individual
buyout investments and the amount of debt used to finance the deal. We focus primarily on the
ratio of Debt to the target firm’s EBITDA, a measure of leverage-relative-to-firm fundamentals
that PE industry participants often use as a metric of the debt available to do a deal. We focus
on Debt / EBITDA rather than Debt / Equity, which is more common in the capital structure
literature, because the amount of equity contributed to the deal is itself an endogenous outcome
of the bargaining between buyer and seller.
We find a strong negative relationship between returns and Debt / EBITDA. One
additional turn of Debt / EBITDA corresponds to a nearly 2% decrease in the expected Internal
Rate of Return (IRR) to the deal’s private equity sponsor. This relationship holds after
controlling for deal characteristics (industry, region, etc.) that may be related to systematic risk.
The relationship also holds in the cross-section, which suggests that it is not likely to be driven
by time-varying changes in discount rates or other macro factors that may drive investment
returns.
Rather the causing lower returns, it seems more plausible that the negative relationship
between Debt / EBITDA and returns is an equilibrium outcome of the deal process, particularly
the competitive environment surrounding the deal. A portion of our sample, 947 deals, contains
information on whether the target firm was purchased through an investment bank auction or
sourced from “proprietary” deal flow. We find that the negative relationship between Debt /
EBITDA and returns only holds for auction deals. For deals sourced through auctions, one
3
additional turn of Debt / EBITDA corresponds to a nearly 5% lower IRR. We find no evidence
of this relationship in proprietary deals.
Two concerns limit our confidence in the evidence on deal source alone: First, the
portion of our sample that contains information on deal source is relatively small. A larger
sample would provide a more powerful test. Second, given that owners of the target may choose
the method by which they sell the firm, it is natural to worry that the process by which deals
select into an investment bank auction or proprietary deal is not independent of the expected
price sellers hope to receive. We address these concerns by splitting the sample based on
alternative proxies for the competitive environment surrounding a deal. The first is the
Enterprise Value (EV) of the deal.4 For both investment banks and PE funds, small deals
produce less benefit (fees and potential gains) while requiring similar costs (marketing and due
diligence) to larger deals. As a result, small deals are less likely to be sold through an auction
and less likely to receive interest from a large number of potential investors. Consistent with
our intuition about the effects of competition, we estimate that the relation between Debt /
EBITDA and returns in large deals is twice as strong as for small deals. Second, we split the
sample on the amount of capital recently raised by buyout funds specializing in the industry
and region of the target firm. Gompers and Lerner (2000) show that the ratio between the
amounts of capital committed to the PE industry and available investment opportunities varies
widely, leading to changes in the competition for deals.5 We find evidence of a much stronger
negative relation between Debt / EBITDA and deal returns following periods of high
fundraising.
We then examine how both debt and deal price change with credit market conditions.
As in Axelson et al. (2013) we interpret credit market conditions as an exogenous determinants
of the amount of leverage lenders are likely to provide. For the full sample and high competition
subsamples good credit conditions (low spreads in the high yield bond market) are related to
both larger amounts of debt and higher prices. However, in low competition subsamples we
find no evidence that credit market conditions are related to leverage or price. This suggests
that the leverage agreed to by lenders and private equity borrowers itself may be affected by
the competition surrounding the deal - for example, if private equity funds forced to pay higher
prices because of competition, do so by seeking additional debt. Returns would be low and
4 The potential endogeneity of EV is addressed in the methodology section. 5 In the appendix, we show that this money-chasing deals channel is separate from the effect of leverage on returns. Intuitively, the primary channel for the effect of recent fundraising on deal price (and thus lower returns) should be more equity in the deal, not more debt.
4
debt levels high, particularly for deals where the competitive advantage of the winning fund is
small.
Finally, we examine which funds pursue competitive deals. Malenko and Malenko
(2015) suggest that low reputation fund managers will have difficulty obtaining debt when
credit market conditions are poor because of concerns that they will expropriate lenders.
Axelson, Strömberg and Weisbach (2009) present an alternative model with similar
implications. They point out that the time limit on a PE fund investment period may cause fund
managers with unspent capital to pursue bad deals rather than let capital commitments expire.
Lenders’ willingness to provide debt capital to these agency-conflicted investors (some of
whom may have good opportunities) is determined by credit market conditions. The implication
of both models is that managers with reputation or agency concerns are likely to win more deals
when credit market conditions are good and their access to debt capital is less constrained.
Accordingly, we examine how a fund’s flow of new deals varies with credit market
conditions and proxies for firm reputation. We find that the deal flow of funds whose interim
performance is lower than that of their peers is particularly sensitive to credit spreads. This also
holds for the sub-sample of auction deals. Poorly performing funds win more auctions,
particularly when credit spreads (and expected returns) are low. In contrast, we find little
evidence that interim performance is related to the rate that funds complete proprietary deals.
This suggests that credit conditions predominantly effect poorly performing funds when they
face competition from other buyers who may have better access to capital.
Aside from the papers cited above, our results build on the literature related to the
financing and performance of buyout PE deals. Demiroglu and James (2010) and Ivashina and
Kovner (2011) find that fund manager characteristics (reputation and bank relationships,
respectively) are related to a fund manager’s ability to raise debt on favorable terms. Our results
suggest that any of the rents created by preferential access to debt capital are likely to decrease
as credit conditions improve and the competition for deals increases. Jenkinson and
Stucke (2011) find that the estimated tax savings from debt in public-to-private LBOs are
positively related to acquisition premiums. Our results show a corresponding effect on the
returns to private equity sponsors of large, competitive deals like the ones in their sample.
However, our results suggest that private equity sponsors may still earn rents when leverage is
harder to obtain and for deals that are less competitive, as the expected reduced taxing savings
from debt are compensated by the benefits of lower deal prices.
5
Our study also contributes to the literature on competition and its influence on the
private equity industry. Gompers and Lerner (2000) introduce the “money chasing deals”
phenomenon, arguing that increasing levels of fundraising lead to more intense competition
among VC firms for a finite amount of attractive investment opportunities. Our results contrast
with those of Guo, Hotchkiss and Song (2011) who propose that club deals, in which PE firms
form bidder syndicates, may help to reduce competition among PE funds.
Finally, our results relate to the empirical literature on macroeconomic conditions and
buyout fund returns. Robinson and Sensoy (2013) show that when funds invest their capital
during economic expansions (when high levels of leverage are also available), the performance
is poor. Similarly, Kaplan and Strömberg (2009) provide further evidence for the counter-
cyclicality in fundraising and performance for buyout funds. An alternative explanation is that
private equity investments may require a risk or liquidity premium that is particularly high when
economic conditions are poor (e.g., Franzoni, Nowak and Phalippou (2012) and Haddad,
Loualiche and Plosser (2015)). While we find that leverage (and thus returns) are driven by
credit conditions that are clearly related to broader economic conditions, we also find that the
negative relationship between leverage and returns is present in the cross-section.
The remainder of the paper is organized as follows: Section 2 describes the construction
of the sample, while Section 3 provides empirical evidence concerning the effect of the
competitive environment on leverage, pricing and returns. Section 4 examines the relation
between agency conflicts, deal leverage and returns, and Section 5 concludes.
2 Data
2.1 Data Sources
The primary basis for our analysis of buyout investments is a large, proprietary database
compiled by three European fund-of-fund managers. The database includes fund-level and
investment-level information on venture capital, private equity and other forms of alternative
assets. The subsample of buyout investments contains more than 13,500 portfolio companies
from around the globe, sponsored by 1,016 funds over a period from 1974 to 2012. One unique
feature of this database is that it contains detailed information at the deal-level, including
monthly gross cash flows between the fund and the portfolio company.
The data with rich information about fund manager, fund and investment come from the
FoFs’ due diligence process. PE FoFs are intermediaries that pool capital, typically from
6
institutions, and invest in PE funds. In exchange for fees, investors are able to allocate a portion
on their portfolio to PE, while delegating the process of evaluating and performing due
diligence on a large number of potential PE fund investments to the FoFs. PE fund managers
seeking an investment from the FoF are asked early in the process to provide the full track
record of historic and current funds, and respective deals, since inception. The data in our
sample includes all the fund managers and funds the FoFs performed due diligence on,
including those in which the FoF decided not to invest. This mitigates some of the concerns
regarding selection bias, which are discussed in detail below. Every time a fund manager
approaches the FoF to commit money to a new fund (and a due diligence process is started),
the record on past funds and investments is updated. The most recent updates on some fund
managers are from 2007, while some entries were brought up-to-date as late as mid-2014.
We merge the three individual due diligence databases and eliminate duplicate funds
and deals. Drawing on the full record of timed deal-level cash flows we are able to calculate
deal-level performance gross of fees for all investments. For the small number of past
investments that were not realized (9%) or were partially realized (22%) at the time due
diligence was performed, the database reports the Net Asset Value (NAV). For these
investments, we use the corresponding NAV as a proxy for cash flows to the fund and compute
deal-level performance using this information.
So far, few studies have had access to such a rich multi-level data set that includes
investment-level performance information. Braun, Jenkinson and Stoff (2015) also use some
information from the same database as a starting point to study performance persistence among
buyout fund managers; Lopez-de-Silanes, Phalippou and Gottschalg (2013) investigate the
performance dispersion and determinants of PE investments, drawing on a sample of PE firms
providing their respective private placement memorandum.
In this paper we take advantage of another unique feature of the FoF database. For a
subset of more than 3,000 buyout investments, the data contains financial details of the
transaction. We observe the enterprise value determined in the transaction, as well as the
amounts of debt and equity used. Further, our sample contains the earnings before interest,
taxes, depreciation and appreciation (EBITDA), as well as Industry Classification Benchmark
(ICB) code and country.
Finally, for another subset of these buyout investments the database indicates whether
the fund manager directly acquired the portfolio company from the seller, or whether the
purchase was made through an investment-bank run auction. To our knowledge, this large-scale
private equity study is the first one that links performance with such transaction details.
7
We follow Axelson et al. (2013) and match our sample of PE buyouts against public
market counterparts in the same year and, industry drawn from The Center for Research on
Security Prices (CRSP) and Standard & Poor's COMPUSTAT (North America) database.17
From Thomson Reuters Datastream/Worldscope, we obtain debt market and
macroeconomic information to gauge the effect of the market environment for PE. The major
variable to show the effects of debt markets on PE comes in form of the US high-yield spread
according to the Merrill Lynch High-yield Master Index, tracking the performance of below-
investment grade, US-dollar denominated corporate high-yield bonds publicly issued in the US
domestic market, minus US LIBOR. To account for the size of the economy, we provide
additional information on macroeconomic conditions, such as the Gross Domestic Product
(GDP) for each country in each year.
In the last part of our analysis, we investigate the factors influencing the number of
auctions in which a fund manager participates. In order to obtain more information on the
interim state of the fund, we benchmark against those with interim performance information in
the Preqin PE database. As Axelson et al. (2013) find this commercial database a reliable source
for PE sponsor characteristics, we are confident in using fund information, such as the
percentage of investment amount already called at the time of the investment or fund
performance variables.
2.2 Sample representativeness: Selection bias
For any database in the notoriously opaque PE asset class, there is an inherit challenge
to capture the investable universe to ensure representativeness. We are aware of potential
sample selection issues in our dataset that might originate from the following major channels:
First, our sample could be systematically flawed by omitting fund managers that did not seek
capital commitments from one of the three FoFs due to unobserved characteristics. Second,
some fund managers may avoid raising capital from FoFs, instead favoring direct relationships
with the institutional investors. However, having data from due diligence performed over
different years mitigates these selection issues. The private equity asset class is characterized
by strong boom and bust cycles in terms of fundraising (e.g., Gompers and Lerner (2000)), and
thus changing power dynamics in the GP-LP-relationship. As fund managers experienced
fundraising difficulties especially in the latest financial crises in the 2000s, they were forced to
17 Public companies are drawn from COMPUSTAT North America. In a subsample of only North American PE buyouts, we find qualitatively comparable results to the full international dataset. A correspondence table between SIC codes and one-digit ICB codes was created using current firms in Thomson Reuters Datastream, which contains both codes.
8
extend their investor base and thus are likely to be part of the due diligence process of our FoFs.
Third, as we take into account transactions with different realization status, we face a rather low
probability of underestimating the poor performance of funds not yet fully divested. Since the
FoFs force the fund managers to show all their past and current investments with complete
information, both performing and underperforming, our data set is unlikely to suffer from any
reporting bias unlike public commercial databases or data sourced by single LPs.
Related to the data compilation, we cannot rule out another source of survivorship bias:
When unsuccessful PE firms decided to quit the business and did not contact our FoFs for
capital commitments (again), they are unavoidably not part of our data compilation. This might
particularly apply to poorly performing first-time funds. However, Braun, Jenkinson and
Stoff (2015) argue that this is a fairly infrequent phenomenon. In addition, our data sample
contains funds of fund managers that failed to raise sufficient amounts of capital commitments
and thus were never closed. Overall, we are optimistic that our dataset, derived from large
institutional investors directly, is not biased towards a non-random sample in any significant
way. For a further discussion of the overall sampling process and potential sample selection
biases, please also refer to Braun, Jenkinson and Stoff (2015).
Another source of bias in this study might be due to the fact that we rely on self-reported
transaction details. While we do not know whether (and, if yes, why) the three FoFs selectively
asked some fund managers to provide additional details on their transactions, such as
information on EBITDA or debt, it is reasonable to assume that the likelihood to voluntarily
report additional details increases with success. Unless forced to report details on all historic
deals, fund managers may selectively report such details on their most successful deals to make
a good impression to the FoFs as potential LP investors. Therefore, restricting the sample on
buyouts for which these details are observable may introduce some additional positive selection
bias. However, in the next section we will introduce main sample characteristics in terms of
leverage, pricing and performance, and show that our final sample compares very well with
existing studies.
2.3 Sample characteristics
Table 1 provides a detailed overview over the composition and various characteristics
of our data set at portfolio company-level. The overall sample, after restricting to buyout deals
only being eligible for our research setting, includes 3,198 investments from 442 funds made
in the investment period between 1986 and 2006.
9
The EV/EBITDA multiple18 is an aptly used measure to analyze the transaction price of
a portfolio company and is calculated as the ratio of enterprise value (EV) to earnings before
interest, taxes, depreciation and amortization (EBITDA) at investment entry. The median
EV/EBITDA pricing multiple in our total sample is 6.7x. Axelson et al. (2013) report a higher
median value of 7.6x for a sample of 1,009 buyouts. The major reason for this discrepancy is
the difference in average transaction size between the two samples. Their sample, obtained by
combing several commercial databases, contains relatively large transactions with a median EV
of $677 million (mean: $1,514 million). They report that this is higher than the median EV of
$63 million (mean: $330 million) in the entire Capital IQ sample. Panel A in Table 1 displays
that EVs in our sample are much closer to these values. The median EV in our sample is $87
million (mean: $299 million). However, the subsample of the 25% largest transactions in our
sample is fairly comparable with Axelson et al. (2013). The median EV of these 798
transactions is $674 million (mean: $967 million). The median EV/EBITDA pricing multiple
in this subsample is 8.01x (mean: 8.51x) and even slightly higher than in Axelson et al. (2013).
As expected, the buyout transactions in our sample is highly levered. The median
Debt / Equity ratio in our sample is 1.49 (mean: 2.05) and indicates that on average about 60%
(mean: 66%) of the deal value is financed with debt. However, likely because buyout funds
focus on firms with strong cash flows, the amount of debt used in the transactions seems much
more modest when measured relative to the cash flow being produced by the firm. We find a
median Debt / EBITDA of 3.93 (mean: 4.05). Both these measures are smaller than the leverage
reported in Axelson et. al., but this appears to be predominantly related to the size of
transactions captured in the two samples. The mean Debt / Equity ratio of 2.4 for the largest
EV quartile in our sample indicates a debt share of approximately 71%, which is close to the
69% reported in Axelson et al. (2013). Similarly, the mean Debt / EBITDA value in the
subsample of largest transactions in our sample is 5.06 and very close to the value of 5.2 in their
study.
In addition, Table 1 displays descriptive statistics by time categories (Panel B) and by
regions (Panel C). Since this paper deals with the effect of competition, we also distinguish how
the fund manager has acquired the asset for a subsample of 947 portfolio companies. We
differentiate between portfolio companies sold via a competitive investment bank auction and
those that were acquired via a proprietary sales process.19 In such an auction, the owners of the
18 We winsorize the variables used in our sample at a 3% level to exclude extreme values and ensure comparability. 19 An investment bank’s auction process closely matches the individual value, English auctions used to model competition between funds in Malenko and Malenko (2015).
10
target firm employ an investment bank that solicits initial bids from a large number of potential
buyers.20 Out of the respondents to the initial round of solicitation, the investment bank helps
to select a portion of the respondents to participate in the future rounds. In each round, bidders
are granted more access to proprietary firm information with which to perform due diligence
and asked to submit an updated bid. Eventually the process settles on a winning bidder. For
buyout fund managers, seeking deal flow through auctions has relatively low expected search
costs as the amount of resources required to perform due diligence grows in each round with
the probability of winning the deal. The marketing efforts of the investment bank and the
relatively low entry costs ensure the participation of many bidders.21
For a buyout fund manager, the alternative to building a portfolio via winning
investment bank auctions is to generate “proprietary” deals. In this case, the portfolio company
is sold in a first chance acquisition and the private equity fund manager, as the buyer, is the first
one to purchase the portfolio company. In general, these deals involve high search costs to
identify potential targets that are not marketing themselves for sale. Practitioners we have
spoken with describe extensive networking and even cold-calling large numbers of firms that
meet a particular investment thesis. Potential targets may directly approach a private equity
fund that has developed a reputation for expertise in a particular industry or geographic area,
but evaluating these deals remains costly because of the due diligence involved. While
proprietary deal flow is costly to attract, private equity funds face less competition. When
raising a new fund, buyout fund managers often market the share of their portfolio that was
obtained from proprietary deals.
Panel D in Table 1 shows that in our sample performance is quite similar for both
groups. However, the average deal sold via an auction is significantly larger. The median EV
for auction deals is $136 million and more than twice the size than the median proprietary
transaction with $48 million. When rescaled by EBITDA, this difference in pricing is much less
pronounced. Nevertheless, the median EV/EBITDA pricing multiple for auction transactions is
with 6.85x still higher than the 6.43x median value for proprietary deals. Auctioned deals in
our sample are substantially more levered. The median Debt / EBITDA value for auctions of
20 Bankers typically approach both financial and strategic buyers. Our sample and corresponding analysis consists of only deals won by private equity (financial) buyers. Previous evidence suggests that the level of competition from both types of buyers are correlated, though good credit environments may favor financial buyers. See Gorbenko and Malenko (2014) and Martos-Vila, Rhodes-Kropf and Harford (2013). 21 Gorbenko and Malenko (2014) find an average of 16.5 participants in investment bank auctions of public targets that were eventually purchased by private equity buyout funds from 2000 to 2008. Our discussion with practitioners suggests that auction participation is somewhat lower for sales of private firms, but remains very competitive.
11
4.31 is much higher than the 3.65 for proprietary deals. With a 1.71 to 1.17 difference, the
pattern is the same for the Debt / Equity ratio.
Unlike commercial databases and most previous literature, we have all monthly deal-
level cash flow information gross of fees, i.e. before management and performance fees, to
ensure comparability among different limited partnerships, between the portfolio company and
the general partner, as well as valuations (NAV) for unrealized portfolio company investments.
Consequently, we are able to compute deal gross Internal Rate of Returns (IRR). The top line
in Table 1 shows that the median deal gross IRR in our sample of 3,198 buyouts is 27.7%. This
value is comparable with Lopez-de-Silanes, Phalippou and Gottschalg (2013) who report a
median IRR of 21.0% for their data set of 7,452 buyout deals.
3 Investment performance, pricing, leverage and competition
3.1 Leverage and returns
We begin by establishing some stylized facts about the correlations between the
leverage used to finance a deal and the returns generated on the equity contributed by the private
equity fund. We focus on the gross IRR of the deal, but obtain similar results using the return
multiple and the public market equivalent of Kaplan and Schoar (2005). Previous research has
shown that deals where the buyer is able to obtain high leverage are also deals where the buyer
pays a higher price (Axelson, et al. (2013)). By looking at the returns to the private equity
sponsor, rather than the purchase price, we provide more direct evidence of whether PE funds
are capturing a higher NPV.
Figure 1 presents graphical evidence that the expected returns of private equity
investments are increasing in Debt / Equity. We sort deals into quintiles according to Debt /
Equity, then compute the median gross IRR in each quintile. The figure’s bottom panel displays
the range of leverage used to fund deals in each quintile. Returns are monotonically increasing
in leverage measured as Debt / Equity. The lowest leverage quintile, with Debt / Equity ranging
from 0 to 0.63, has the lowest return with a median IRR of 21%. The highest leverage quintile,
with a Debt / Equity ratio ranging from 2.8 to 568.3, has a median return of 35%.
Figure 2 presents the equivalent pattern of returns with deals sorted on Debt / EBITDA.
Quintile 1 represents deals with the lowest leverage; Debt / EBITDA among these deals ranges
from 0 to 2.24. Quintile 5, the highest leverage quintile, has Debt / EBITDA ranging from 5.68
to 9.89. The top panel presents the mean gross IRR to deals in each quintile. Over very modest
amounts of leverage, the relationship matches the prediction from traditional corporate finance
12
theory. From Quintile 1 (the deals with the lowest leverage) to Quintile 2, we observe a
substantial increase in average returns of about 8%-points. After this initial increase, returns are
monotonically decreasing with leverage over the remaining quintiles. The drop between
Quintiles 2 and 5 is over 13%-points in IRR. Thus, unconditionally, over 80% of the sample
returns are strongly decreasing in Debt / EBITDA.
We extend the analysis of Figures 1 and 2 through ordinary least squares (OLS)
regressions where gross IRR for each deal is the dependent variable and leverage (as
Debt / Equity or Debt / EBITDA) is the main explanatory variable. The regressions control for
deal characteristics, such as portfolio company industry, that may drive both returns and
leverage. Dummy variables representing each quartile of deal enterprise value are included to
account for risks that may be correlated with target firm size. In all specifications, we control
for the realization status of the investment and ten ICB industries to account for industry-
specific risk. Furthermore, all specifications include fund fixed effects to account for different
approaches, e.g., investment styles, and different fund manager abilities. Standard errors in
these (and all following) regressions are clustered at the LBO deal-year level.
The corresponding results reported in Table 2 are consistent with the patterns evident
graphically in Figures 1 and 2. In Column 1, when leverage is measured as Debt / Equity at
entry, we find a strongly significant positive effect on deal gross IRR. One unit increase in the
Debt / Equity ratio corresponds to a 3.6% change in IRR. However, interpreting the coefficient
is difficult because of endogeneity concerns about the measurement of the firm’s equity. Our
measure of Equity comes from the sources of capital used to finance the purchase price. It
represents the amount of equity capital contributed to the deal, rather than the value of equity
immediately following the transaction. If the transfer to private equity ownership increases
firm value, then our measure of equity is likely to be biased low and the magnitude of this bias
is tied to the outcome of bargaining between the buyer and seller. Thus, for the remainder of
the paper we predominantly focus on Debt / EBITDA.
In Column 2 we find a negative and significant relation between buyout deal
Debt / EBITDA at entry and gross deal IRR. Increasing the amount of debt used to finance the
deal by one turn corresponds with a decrease in expected IRR of 1.7%. This suggests that
private equity firms perform poorly in deals that are highly levered relative to the firm’s
earnings. One concern about this interpretation is that the Debt / EBITDA available to buyout
funds may be correlated with macro factors that drive expected returns. For example, Haddad,
Loualiche and Plosser (2015) argue that aggregate changes in risk premia may drive buyout
13
returns and leverage. Controlling for investment year fixed effects in Column 3 shows that the
negative association between Debt / EBITDA and returns continues hold in the cross-section,
such that the relationship is unlikely to be driven by time series macro-factors. In Column 4
we include the Price / Dividend ratio of the S&P 500 as a proxy for time varying aggregate risk
premia. Consistent with the argument that time varying risk premia affects private equity
returns, the coefficient on the S&P 500 P/D ratio is negative and statistically significant.
However, the negative coefficient on Debt / EBITDA remains is slightly larger, and remains
statistically significant.
3.2 Leverage, returns and competition
We next examine how the relationship between leverage and returns varies with the
competitive environment in which a deal takes place. If the negative relation between
Debt / EBITDA and returns documented in Table 2 is an equilibrium outcome of the
competition between private equity funds, then we would expect the relationship to be strongest
for deals that are heavily marketed and receive interest from many funds or strategic acquirers.
We repeat the regressions of gross deal IRR on leverage over subsamples that differ in
characteristics that are likely to affect competition.
In Columns 1 and 2 of Table 3, we split the sample according to the source of the deal.
Panel A presents results from the pooled sample, while Panel B includes investment year fixed
effects. Column 1 exhibits results for a subsample of 387 LBOs that are classified as proprietary
deals. These deals were directly negotiated between the seller and private equity fund manager
without the seller widely soliciting other interest. In both the pooled sample, and the sample
with investment year fixed effects, we find that Debt / EBITDA is unrelated to deal returns in
proprietary deals. In Column 2 of each panel we report results from identical regressions on a
subsample of 560 deals that were sold by an investment bank auction with multiple bidders. In
each case the coefficient on Debt / EBITDA is statistically significant and negative. If the
portfolio company is acquired through a competitive auction, one additional turn of leverage is
associated with a 5.1%-points lower deal gross IRR. Including investment-year fixed effects
in the analysis results in a negligible decrease in the size of the coefficient.
In the remaining columns of Table 3, we present additional regressions over subsamples
that are split based on alternative proxies for the competitive environment surrounding each
deal. The goal of this analysis is to address two concerns regarding the subsamples based on
14
deal source. First, only about one third of the deal observations in our sample contains
information about the source of the deal. By relying on alternative proxies that are available for
all deals, we mitigate concerns that previous results are driven by factors affecting the
probability of observing deal source in the data. Second, it may be the case that the observed
source of a deal is an endogenous outcome of strategic choices by the seller, who even when
approached directly by a PE fund, may choose to initiate an investment bank auction when it
would be likely to produce a higher price. Given these concerns, the ideal alternative proxy
would be correlated with the competition surrounding a deal, available for all deals in the
sample, and, in the spirit of an instrumental variable, would be relatively unaffected by strategic
choices of the seller. We consider both the size of the deal and the magnitude of recent inflows
into the private equity industry.
Small deals (those where the enterprise value of the firm is low) are likely to receive
less interest from rival PE funds. Private equity deals require similar levels of diligence
regardless of size, and thus firms willing to invest in pursuing a small deal are only likely to do
so when there are few competitors and the expected probability of winning the deal is high.22
As a result the competition for smaller deals is less intense and that a small company is much
more likely to be acquired in a proprietary sourcing process than larger transactions (Preqin,
2014). While we view deal size as a proxy for the general level of competition surrounding a
deal rather than the specifics of the sale process, in unreported probit regressions we confirm
that consistent with our intuition deal size is positively related to the likelihood that a deal is
sourced from an investment bank auction.
We construct subsamples of small and large deals by splitting the deals at the median
EV of $87 million. Columns 3 and 4 of Table 3 present estimates of these regressions for a
subset of 1,599 small and 1,599 large deals, respectively. We find that the relationship between
Debt / EBITDA and deal gross IRR is stronger in larger deals, albeit with modest statistical
significance. The results in Column 3 of Panel A suggest that for larger deals in our sample, an
additional turn of Debt / EBITDA ratio is associated with a 2.4% lower return. This value
amounts to 1.3% and is statistically insignificant for smaller companies given in Column 4.
The point estimates for deal size subsamples in Panel B are nearly identical, but the coefficient
22 Although on the margin deal size is likely to be related to the ease of accessing credit, the assumption required is that very few deals have switched across the boundary set at median deal size from being a small to large deal as a result.
15
on Debt / EBITDA in large deals is not statistically significant at the 10% level (the p-value for
the coefficient is 0.13).
As an additional proxy for the level of competition surrounding a deal, we consider the
total buyout funds raised in the same industry and region three years prior to the respective
transaction, divided by current year’s regional gross domestic product (GDP) (see Braun,
Jenkinson and Stoff (2015) for more details on this variable). This proxy is motivated by the
“money chasing deals” phenomenon documented by Gompers and Lerner (2000), who find
evidence of increased competition following high levels of fundraising in the US venture capital
industry. One concern about this proxy is that excessive capital flowing into the private equity
industry may be responsible for higher deal prices. In Appendix A, we show evidence from
our sample that flows into the private equity industry has an effect on deal prices, but that it’s
primarily related to an increase in the equity capital contributed to deals.
Columns 5 and 6 of Table 3 show the corresponding regression results using a
subsample of 1,594 deals in a low competitive and 1,604 deals in a high competitive PE
fundraising environment, respectively. In line with the findings presented above, we observe
that the coefficient on Debt / EBITDA is much stronger at a significant level, in economic and
statistical terms, when a company acquired a portfolio company in a highly competitive
environment. While the effect of one additional turn of leverage is –1.1% and statistically
insignificant when there is few money chasing deals (Column 5), it is –3.1% and highly
statistically significant when competition in the buyout market is high (Column 6).
The negative correlation between Debt / EBITDA and deal returns has consistently
higher economic magnitude and statistical significant for deals which are likely subject to
competition between multiple private equity funds and strategic acquirers. One interpretation
is that observed Debt / EBITDA is a proxy for the ease of obtaining leverage for the deal.
Confidence in this interpretation suffers to the extent that Debt / EBITDA is
endogenous. For example, Debt / EBITDA is likely to be correlated with unobservable
differences between target firms, such as growth prospects. One remedy would be to find one
or more instruments for ease of obtaining leverage. Finding relatively strong instruments for
available leverage (such as changes in credit market conditions) is plausible. However, the
second stage is likely to lack power in the face of both noise created by the first-stage estimation
16
of Debt / EBITDA and the noise associated with realized returns.24 Instead, we argue a more
powerful test is to apply an instrumental variables approach to the deal price. Price does not
incorporate shocks to portfolio company value following the deal, and corresponds directly to
our conjecture that leverage in competitive deals is associated with a transfer of value from the
buyer to the seller.
3.3 Leverage, price and debt market conditions
In this section we analyze how the leverage used in private equity buyouts and the price
paid for portfolio companies respond to credit market conditions. The goal is to examine how
deals with different levels of competition respond to plausibly exogenous changes in the
leverage available to private equity bidders. For competitive deals we expect improving credit
markets to be associated with more leverage and higher deal prices. For deals which are less
competitive we expect to find no change in deal price with credit market conditions. This would
suggest that differences across competition in returns documented in the previous section are
driven by the value captured by the seller through higher deal prices. It’s less clear how we
should expect leverage in less competitive deals to respond to credit market conditions. If
leverage increases as credit markets improve it would suggest that leverage is driven by similar
factors regardless of the competitive environment. In contrast, if leverage in non-competitive
deals is unrelated to credit market conditions then it suggests that competition itself helps
determine the leverage of the deal.
Our proxy for the credit market conditions in the highly levered debt market in which
private equity funds raise financing is the spread between the Merrill Lynch High-Yield Master
Bond and LIBOR (HY Spread). Axelson et. al (2013) find the same measure of HY spread to
be a significant determinant of leverage in their sample of buyout transactions
3.3.1 Deal Leverage
In Table 4 we regress log (Debt / EBITDA) from our sample of buyout deals against
HY spread. To control for differences in leverage which may be associated with size, we
include dummy variables corresponding to the quartile of EV in which the deal falls. We
control for other firm characteristics in two ways. In some specifications we include the median
log (Debt / EBITDA) observed in a COMPUSTAT firms in the same ICB industry and fiscal
24 In unreported results we do not find statistically significant evidence that credit market conditions are
related to returns.
17
year in which the deal takes place. In other specifications we include industry and region fixed
effects.
Panel A in Table 4 reports results from OLS regressions of LBO leverage on a set of
explaining factors for the final sample of 3,198 deals.25 For the total sample, we detect a
statistically significant and negative relation between the high-yield credit spread and
Debt / EBITDA leverage (Column 1). A one-unit higher high-yield credit spread is associated
with a 1.9% lower Debt / EBITDA ratio. In this regression, we find the coefficient on median
log of Debt / EBITDA of comparable public benchmark companies to be statistically
insignificant. In Column 2, we find the relationship between high-yield spread and LBO
Debt / EBITDA to be robust to excluding public matched leverage and including industry and
region fixed effects instead.
In line with our previous findings regarding the determinants of LBO pricing (and
consistent with Axelson et al. (2013) for larger buyouts), Panel B of Table 4 reveals that high-
yield credit spread significantly drives both LBO leverage measures and that there is no strong
link between industry and LBO leverage when competition is strong, but not if it is weak. In
none of the regressions, using our proprietary deal subsample (Columns 1 and 2) the high-yield
spread is economically or statistically relevant. However, in Column 1 the coefficient on
matched public Debt / EBITDA is significant and positive.
In turn, in Debt / EBITDA regressions on the auction subsample (Columns 3 and 4),
coefficients on high-yield credit spread are both statistically significant and negative. If
competition is high, time-series variation in debt market conditions determines LBO
Debt / EBITDA leverage. Further, matched industry leverage is insignificant for auctions.
These findings reinforce our interpretation of competition as a major channel in the usage of
leverage in private equity and its impact on pricing and returns.
Table 5 substantiates this picture using our alternative proxies for the competitive
environment surrounding the deal. Credit conditions play a more relevant role in explaining the
cross-sectional variation of LBO Debt / EBITDA leverage for large deals (Panel A) and those
investments done in a high PE fundraising environment (Panel B).
25 In several of these estimations, we lose observations, as the median EBITDA value of the matched public benchmark sample is negative. Hence, Debt / EBITDA leverage is not numerically interpretable for these observations.
18
3.3.2 Deal Pricing and Competition
We next examine the relation between the price at which deals take place and leverage
used to finance the deal for evidence that sellers are capturing more of the value from leverage
when competition is high. While similar in spirit to the analysis of the effects of leverage on
deal price in Axelson et al. (2013), the analysis in this section extends their results in by
examining subsamples with different levels of competition. Given the results in the previous
section, we expect to find no connection between leverage and returns in low competition deals
where changes in credit market conditions are not associated with additional leverage.
Analyzing the relation between leverage and deal price presents an omitted variables
problem which precludes simply running a regression with leverage as an explanatory variable.
Our measures of relative price (EV / EBITDA) and leverage (Debt / EBITDA) are nearly certain
to be correlated because of unobservable characteristics of the portfolio company (e.g., future
expectations of growth). Similar to Axelson et al. (2013) we use the spreads of high-yield
corporate bonds as a source of exogenous variation in credit market conditions at the time of
the transaction. A low high-yield credit spread indicates low costs for levering up a LBO
transaction and is therefore interpreted as loose credit market conditions. In this study, we
obtain high-yield spread for each buyout by deducting the US LIBOR rate from the Merrill
Lynch High-yield Master Index, both measured at investment entry. We include the high-yield
spread directly in regressions with EV / EBITDA as the dependent variable. In addition, we
estimate an instrumental variables model, where high-yield spread is used as an instrument in
the first-stage to capture exogenous variation in Debt / EBITDA. We note that in most
developed economies leverage increases with firm size. Potential reasons are that
diversification increases and the probability of financial distress decreases with firm size. As a
result, lower bankruptcy costs enable firms to take up more debt (Rajan and Zingales, 1995).
Hence, changes in the general credit market conditions may well have a different marginal
effect on the changes in LBO leverage, and ultimately prices, contingent on firm size. To control
for these effects, we include dummy variables for the EV at investment entry in all
specifications.
Panel A in Table 6 displays results for our full sample of 3,198 buyout deals. In order
to control for the economy-wide changes in discount rates or expectations of growth, we also
include the log EV multiple of industry matched public firms in Column 1. We find high-yield
spread to be significantly negatively related to LBO pricing. If debt is cheap, fund managers
pay higher EV / EBITDA prices for a given firm. In economic terms, a 1% increase in HY
19
spread corresponds a 1.0% decrease in EV / EBITDA pricing (exp(–.012) = 0.988). This value
is much smaller than the 4.7% reduction (exp(–.048) = 0.953) found in the sample of very large
transactions in Axelson et al. (2013). We confirm this our finding when replacing pricing of
matched public companies by industry and region fixed effects in Column 2.
By analyzing the relationship between credit market conditions and LBO pricing, we
implicitly assume that LBO leverage, as a result of credit market conditions, and pricing are
correlated. However, as Axelson et al. (2013) point out a correlation between these two does
not imply a causal effect of leverage and pricing since there are good reasons to believe that
they share unobserved common factors. Further, the correlation could partially stem from
measurement error of sharing the same EBITDA denominator. For these reasons, we use high-
yield credit spread as an instrument to predict LBO log Debt / EBITDA leverage in a first stage
regression (Column 3) and include this instrumented variable as one explanatory determinant
in the second stage (Column 4). The first stage regression, is very similar to the analysis in the
previous section. The highly statistically significant coefficient on high-yield spread in the first
stage indicates that in the overall sample there is a degree of association between credit market
conditions and debt used to finance buyout transactions. Because deal price and amount of debt
are transformed via logs, the coefficient on Debt / EBITDA should be interpreted as an
elasticity. The positively significant coefficient on instrumented leverage in the total sample
implies that a 10% change in Debt / EBITDA used to fund the deal is associated with a 1.12%
change in EV / EBITDA.
Altogether, Panel A in Table 5 displays similar patterns of credit market conditions,
LBO leverage and pricing as Axelson et al. (2013) report. However, we find the magnitude of
the association between high-yield credit spread and LBO pricing to be substantially weaker in
our sample that is much more heterogeneous in terms of firm size. We argue that in the specific
LBO context one other major reason for the discrepancies between their findings and ours may
well be different levels of competition. As we have argued above (refer to Section 3.1),
competition for small firms (as opposed to large companies in the sample in
Axelson et al. (2013)) should be lower which could explain weaker statistical and economic
effect strength in our sample.
To test the role of competition, in Panel B of Table 5 we report results obtained from
running identical regressions on the subsample of proprietary (Columns 1–2) and auction deals
(Columns 5–8), respectively. We find that credit market conditions are only a relevant
determinant of LBO pricing if competition for a buyout deal is high. If buyouts are proprietary
20
in nature, we find no signification relationship between high-yield credit spread and LBO EV
multiples. None of the coefficients on high-yield spread in Columns 1 or 2 is statistically
significant. We omit the instrumental variable analysis for the proprietary subsample because
consistent with the results from the previous section, high yield spread is unrelated to deal
leverage.
In contrast, regression results for the auction subsample confirm that the results in the
full sample are driven by competitive deals. The coefficient on high-yield credit spread is much
stronger and statistically significant in both LBO EV multiple regressions (Columns 3 and 4,
respectively). For example, in Model 3 a one-unit higher high-yield credit spread is associated
with a 2.3% lower EV / EBITDA pricing multiple (exp(–.023 = 0.977). The coefficient on high-
yield spread is economically and statistically significant (in Column 5), indicating that, in
contrast to low competition situations, here loose credit conditions are used to increase buyout
leverage. Accordingly, the coefficient on instrumented leverage in the second-stage regression
reported in Column 6 is statistically significant and positive in this high competition subsample.
Hence, we find that in the cross-section of competitive deals LBO leverage is indeed one reason
explaining why loose credit market conditions result in higher LBO pricing.
Table 7 reports the results from these robustness tests using buyout deal size (Panel A)
and PE fundraising activity (Panel B) as alternative competition proxies. These findings on
credit conditions and LBO pricing contingent on competition for deals are fully consistent with
our main results.
In Panel A, we find that high-yield spread is not correlated with LBO pricing in our
subsample of 1,599 deals with an EV smaller than the median value of $87 million. Assuming
that competition for small deals is much weaker than for larger buyouts, this is in line with the
findings for the proprietary subsample. In contrast, for our subsample of 1,599 large buyout
transactions we find that if credit conditions are loose LBO leverage is significantly higher
(Columns 3 and 4). High-yield spread also has significant explanatory power in the
corresponding first-stage regression (Column 5). The coefficient on instrumented leverage is
significant in the second stage (Column 6), and very large. The estimate suggests at 10%
change in the Debt / EBITDA used to finance large transactions is associated with a 7.63%
change in price.
When repeating the same empirical exercise for low and high PE fundraising
environment subsamples in Panel B of Table 7, we find the same patterns. High-yield spread is
21
not related to LBO pricing (Columns 1 and 2) in which little capital is chasing deals. In contrast,
the coefficient on high-yield spread is economically and statistically significant in the high PE
fundraising subsample (Columns 3 and 4. It is also significantly associated with LBO Debt /
EBITDA leverage in the first-stage regression (Column 5). Again, instrumented leverage is
significant in the second stage (Column 6), and the magnitude is large.
Altogether, the results on the relationship between leverage and pricing contingent on
competition presented in Tables 6 and 7 provide strong empirical evidence for a first-order role
of competition in determining both how leverage is used by private equity funds, and how
changes in available leverage effect gains to sellers. One interpretation is that for competitive
deals, improving availability of credit decreases the competitive advantage between potential
bidders. This would consistent with the models of Axelson, Stromberg and Weisbach (2009)
and Malenko and Malenko (2015). In both models the ability of private equity funds to raise
debt on favorable terms is related to agency problems which ease as credit becomes more
available.
4 Leverage, Performance and Managerial Agency Conflicts
4.1 Measuring agency incentives
In this section, we consider the role of agency problems in determining deal-level
leverage, pricing and returns. In particular, we consider the “use it, or lose it” problem where
at a given cutoff date, typically several years after the inception of the fund, any remaining
capital that has been not called from limited partners will expire.27 Our motivation is the model
of Axelson, Strömberg and Weisbach (2009) that explains the financing and compensation
structure of private equity funds as an optimal set of contracts in response to this agency
problem. Their intuition is that some fund managers facing such a cutoff will invest their
remaining capital regardless of the quality of investments available at the time. To mitigate this
incentive the performance-based portion of fund manager compensation, the carried interest, is
tied to the aggregate performance of the fund. This is effective for managers that have built a
valuable portfolio with their initial investments, and thus would not invest expiring capital in
negative NPV investments which may drag down aggregate fund returns. However, managers
whose initial investments have performed poorly are still likely to pursue bad investments rather
27 Unlike in many other asset classes, private equity funds are established with commitments of capital from their investors. Funds are not transferred from the investor to the fund manager until the managers call the capital to invest in a portfolio company or to pay themselves management fees.
22
than allow their remaining capital to expire. The investments of these managers are particularly
sensitive to the willingness of lenders to provide debt capital. The empirical implication is that
the relation between debt market conditions and returns is largely driven by the investments
made by these agency-conflicted managers.
To capture the major determinants of this agency incentive, we form two proxies: for
the likelihood that a fund’s committed capital will expire without being invested and for the
interim value of the fund’s existing portfolio. Our first proxy, investment speed, is formed each
fund-quarter by comparing the cumulative percentage of capital invested by a fund in our
sample relative to their vintage year peers in Preqin’s fund-level cash flow data. For funds in
our sample, the cumulative percentage of capital invested is calculated as the sum of all prior
cash flows from the fund to its portfolio companies, including initial purchases and follow-on
financings. In order to create a benchmark using Preqin data that is comparable to invested
capital calculated using our deal-level data, we are required to make an assumption about
management fees. Preqin data is created using the cash flows between the limited partners and
the fund. Some of the capital called by the fund is diverted directly to management fees rather
than being invested in portfolio companies. We adjust the quarterly cash flows from Preqin by
subtracting an additional 0.5% of committed capital from quarters [0–16], and 0.25% of
committed capital from quarters [17–24] under the assumption that these amounts are directly
used to pay management fees.28 We then calculate the cumulative percentage of capital invested
each quarter using these adjusted cash flows. Our measure of slow investment speed is an
indicator variable calculated each quarter for funds in our sample, which takes the value 1 when
the fund in our sample has a lower cumulative percentage of capital invested than the median
Preqin fund of the same vintage year.
Our second proxy for agency problems represents the value of the fund’s existing
portfolio. The ideal measure of interim performance would account for exited investments and
the NAV on the fund’s current holdings. However, our data set does not contain interim NAV
values at either the fund or the portfolio company level. Instead, each quarter we calculate the
Distributed to Paid-In (DPI) by taking the cumulative sum of all cash flows from portfolio
companies back to the fund and dividing it by the cumulative sum of invested cash flows in
these portfolio companies. We then eliminate the quarters at the beginning of the fund before
any cash flows have been distributed. This measure is often used to represent how much value
28 Metrick and Yasuda (2010) find that the majority of buyout private equity funds have management fees of 2% of committed capital per year, which typically step down over time, often at the end of the investment period. They find the median cumulative management fee over the lifetime of the fund is 12% of the fund’s committed capital.
23
has been returned to investors. Given the limitations of our data, we believe that the DPI
represents a useful measure for two reasons: First, high DPI indicates good performance by the
early investments in the fund. Managers of funds with high DPI have more to lose by making
poor investments with their remaining capital.29 Second, there is likely to be correlation among
portfolio companies in a fund, such that an early positive realization in one or more portfolio
companies raises the expected value of other investments in the portfolio. We calculate DPI for
each of the funds in the Preqin sample using the adjustment for management fees described
above. Poor interim performance is an indicator variable that takes the value 1 in each quarter
when the DPI for our sample fund falls below the median DPI of the Preqin funds in the same
vintage year.
4.2 Agency conflicts and their influence on private equity fund managers
This section provided evidence that agency problems are related to the likelihood PE
fund managers to participate in and win competitive deals. If, in the spirit of
Axelson, et al. (2013), managers need to put capital to work quickly before reaching the end of
the fund’s investment period, it seems likely that they would do so by bidding aggressively in
investment bank auctions, which have comparatively less search and due diligence costs. At the
same time, if lenders constrain the debt they will provide to agency-conflicted managers, we
would expect these managers to win fewer auctions when credit conditions are poor and lending
standards are particularly high.
In Table 8, we evaluate the tendency of managers to purchase firms through investment
bank auctions. Our main analysis focuses on Poisson regressions in which the dependent
variable is the number of portfolio companies purchased through auctions in a given year. A
potential problem with this approach is that only about one third of the deals in the sample
contain information about whether the deal was sourced from an investment bank auction or a
proprietary source. As a result, we limit the sample to funds that reported the source of at least
for 50% of their deals. In addition to the agency problem proxies, we include the high-yield
credit spread and controls for the percentage of a fund’s remaining capital and include fund
fixed effects. For comparison, we also evaluate the tendency of managers to purchase firms
through proprietary deal flow, and the managers overall deal activity.
29 This includes funds that have already started to distribute carried interest to their general partners, as many have clawback provisions based on the performance of subsequent investments.
24
In Column 1, prior to the introduction of agency conflicts, we find that the high yield
credit spread is negatively related to the number of auction deals for all fund managers.
However, the similar coefficients on high-yield spread in Columns 4 and 7 suggest that this
effect is common to all sources of deal flow, rather than in increased inclination towards
auctions when credit spreads are low.
In Columns 2, 5 and 8, we find that slow investment speed is negatively related to deal
activity across all types of deals. These regressions include fund fixed effects, such that this
results is not driven by persistent differences in the tendency of some fund managers to spend
their capital more quickly. The interaction term between slow investment speed and high-yield
spread is positive each deal type, but only statistically significant for the total number of deals.
In contrast, in Columns 3, 6 and 9 we find strong evidence that managers who are
performing poorly, increase the number of auctions in which they participate relative to other
types of deals. As in the previous analysis, we measure interim performance using the fund’s
interim DPI at the beginning of each year relative to vintage year peer funds in in Preqin. The
early years of a fund in which it has not yet realized any investments or distributed capital are
excluded, dropping the number of observations in each group by roughly one-third.30 In Column
3, we find that poor interim performance is negatively related to the participation in auctions
and that the interaction with high-yield spreads is positive.
5 Conclusions
Our study analyzes the impact of competition on the relationship between leverage,
pricing and returns based on a sample of 3,198 private equity buyout deals. We find that Debt
/ Equity is positively related to investment returns. However, we find a strongly negative
relationship between leverage measured as a ratio to firm fundamentals (Debt / EBITDA) used
to finance a deal and returns to the investment. The effect is primarily observed in sourced
through investment bank options, in which sellers solicit competitive bids from private equity
funds and strategic acquirers. Consistent with the view that the ease of accessing debt is
associated with more aggressive bidding, both prices and leverage increase following
improvements credit market conditions improve. None of these effects are evidence in
proprietary deals negotiated directly between the private equity fund and sellers of the target
30 In unreported analyses, we find similar results when the fund-years prior to any distributions are assigned a DPI of zero and are kept in the sample.
25
firm. Alternate proxies for competition such as deal sizes and recent PE fundraising conditions
provide strong evidence in support of our theory of competition.
Further, we show that the rate at which PE funds pursue and win auctions is related to
the fund’s interim performance. Fund’s with poor interim performance win relatively more
auction deals when credit spreads are low and debt is easy to obtain. We interpret this as
consistent with models that tie competitive advantage in debt markets to reputation and agency
costs.
Our results provide further evidence on the role of leverage in private equity
transactions. A common refrain in the private equity industry is that fund managers always
seek as much leverage is possible to financing each deal. Often this is framed as a way to
improve returns and increase the value of the portfolio company. Our results suggest that
seeking the maximum leverage possible may instead be necessary to bid aggressively to
compete for a deal. This does not suggest that leverage used by private equity in unrelated to
the value created in the target firm. In comparison to public firms, and non-PE owned private
firms, the large amounts of leverage in the capital structure of private equity portfolio
companies may be responsible for part of the value added by private equity ownership.
However, on the margin leverage is determined by competition, which seems unlikely to be
related to value of leverage the value of the target firm.
Given the competitive landscape in market conditions with low credit spreads and a
resulting overpricing of transactions, GPs carefully need to reconsider their strategies in how to
“win” deals and ultimately make them profitable. Besides auction and proprietary deals, other
forms of investments could include co-investments and club deals to share risks from especially
large deals. By doing so, private equity firms could protect themselves from new market
entrants, both from institutional investors such as hedge funds and private investors such as
high net worth individuals, in the market of private investments. Therefore, the effects of
competition on the private equity industry are further interesting questions, i.e. whether PE
firms are able to differentiate themselves from market players or gradually converge with other
financial investors. Besides getting access to deals, the usage of the appropriate level of debt
and the “right” composition of debt instruments to encounter increased costs from winning
deals in competitive markets will challenge managers of private equity companies.
26
6 References
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Axelson, U., Strömberg, P., & Weisbach, M. S. (2009). Why Are Buyouts Levered? The Financial Structure of Private Equity Funds. Journal of Finance, 64(4), 1549–1582.
Burrough, B., & Helyar, J. (2008). Barbarians at the Gate, New York, NY: Harper Collins. Braun, R., Jenkinson, T., & Stoff, I. (2015). How Persistent is Private Equity Performance?
Evidence from Deal-Level Data. Working paper. Demiroglu, C., & James, C. M. (2010). The role of private equity group reputation in LBO
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Table 1: Deal- and fund-level descriptive characteristics
This table presents deal-level descriptive characteristics for the final sample of 3,198 leveraged buyout deals from 442 funds made in the investment period between 1986 and 2006. We report the number of observations (Obs.), the enterprise value (EV) multiple (LBO EV / EBITDA) and two leverage multiples (LBO Debt / EBITDA ratio, LBO Debt / Equity ratio) and the LBO deal gross IRR. Panel A displays these descriptives by enterprise value while Panel B includes time categories according to Kaplan and Strömberg (2009). Panel C differentiates four main regions. Finally, in Panel D, we distinguish whether a portfolio company was sold via a competitive (English) auction or not.
Obs. Mean Median SD Mean Median SD Mean Median SD Mean Median SD Mean Median SD
All LBO deals 3,198 299.37 87.03 526.80 7.45 6.68 3.69 4.05 3.93 2.17 2.05 1.49 1.99 0.37 0.28 0.64
Panel A: Enterprise ValueQuartile 1 (smallest 25%) 692 14.89 15.05 7.03 6.68 5.52 4.54 3.15 2.79 2.25 1.78 1.08 2.06 0.37 0.27 0.67Quartile 2 816 49.32 48.00 14.61 6.83 6.19 3.45 3.57 3.50 1.96 1.96 1.34 2.06 0.39 0.29 0.64Quartile 3 821 142.60 134.70 46.19 7.62 6.89 3.18 4.20 4.23 1.94 1.99 1.52 1.86 0.32 0.26 0.60Quartile 4 (largest 25%) 869 908.81 610.00 708.17 8.46 7.95 3.34 5.06 5.06 2.05 2.40 1.94 1.96 0.41 0.28 0.64
Panel B: Time Categories1974–1989 62 97.35 36.70 158.56 7.79 6.17 4.44 5.59 4.82 2.70 5.48 5.95 3.10 0.44 0.34 0.501990–1994 279 93.52 40.50 174.01 6.97 6.12 4.13 3.97 3.86 2.39 2.49 1.78 2.34 0.47 0.38 0.561995–1999 915 212.20 69.30 392.72 7.29 6.62 3.92 4.01 3.96 2.23 2.20 1.52 2.17 0.33 0.24 0.692000–2004 1,245 325.32 96.15 535.23 7.14 6.50 3.28 3.76 3.68 1.94 1.77 1.38 1.63 0.40 0.32 0.572005–2007 697 467.81 152.23 696.89 8.34 7.67 3.67 4.49 4.44 2.20 1.87 1.48 1.73 0.34 0.19 0.70
Panel C: RegionsNorth America 1,012 356.21 114.68 567.68 7.54 6.74 3.45 4.51 4.43 1.93 2.71 2.00 2.36 0.36 0.26 0.65Europe 2,034 264.59 73.72 488.48 7.36 6.66 3.74 3.88 3.68 2.23 1.77 1.35 1.70 0.37 0.28 0.62Asia 124 439.03 126.96 712.34 8.09 6.67 4.49 3.48 3.31 2.33 1.64 1.15 1.91 0.45 0.33 0.70RoW 28 153.16 39.02 436.48 7.25 5.79 4.35 2.11 1.08 2.08 0.84 0.32 1.74 0.49 0.31 0.78
Panel D: Proprietary vs. auctionProprietary 387 180.83 47.56 389.18 7.49 6.43 4.33 3.65 3.28 2.44 1.95 1.17 2.17 0.36 0.26 0.64Auction 560 391.37 136.16 590.85 7.63 6.85 3.56 4.31 4.40 2.22 2.40 1.71 2.30 0.36 0.27 0.62
Deal Gross IRREV/EBITDA D/EBITDAEV D/E
29
Table 2: Performance and leverage – full sample This table presents the results from Ordinary Least Squares (OLS) regressions of deal-level gross Internal Rate of Return (IRR) performance on two channels of leverage and a set of control variables. The IRR is defined as the interest rate that makes the net present value of a series of monthly cash flows, positive and negative, from an investment in a portfolio company zero. The first leverage variable LBO Debt / EBITDA describes a portfolio company’s net debt (D) to earnings before interest, taxes, depreciation and amortization (EBITDA) ratio at investment entry and serves as a proxy for the debt-to-fundamentals value of a portfolio company. Column 1 shows the results for the final sample of 3,198 transactions, while Column 2 uses an alternative measure of leverage, namely a portfolio company’s net debt (D) to equity (E) ratio at investment entry (LBO Debt / Equity). Column 3 reports the results for a model taking into account both measures of leverage, while we add investment year fixed effects (FE) in Column 4. In each regression model, we include a set of controls. By including the log of the enterprise value (EV), we control for the investment size of each deal. The binary variable “partially realized” (resp. “fully realized”) equals one if the transaction was partially (resp. fully realized) and zero otherwise. Further, all regressions include fixed effects (FE) for industry, region and fund. Industry fixed effects consist of ten basic one-digit ICB codes. Region fixed effects indicate where the investment took place and include the four categories North America, Europe, Asia and Rest of the world (RoW). Investment year fixed effects (FE) ranging from 1976 to 2006 denote the year when the portfolio company receives its first infusion of capital from the PE sponsor. The standard errors reported beneath each coefficient are clustered at the LBO deal-year level at entry. *, ** and *** denote statistical significance at the 10%, 5% and 1%, respectively.
VARIABLES
(1) (2) (3) (4)
LBO Debt / EBITDA -0.017** -0.016* -0.019**(0.008) (0.008) (0.008)
LBO Debt / Equity 0.036***(0.008)
S&P 500 Price / Div -0.564***
(0.093)
EV quartile 2 0.008 0.015 0.019 0.020
(0.039) (0.040) (0.038) (0.038)
EV quartile 3 -0.012 0.012 0.011 0.017
(0.048) (0.049) (0.045) (0.046)
EV quartile 4 0.047 0.105* 0.106* 0.116**(0.052) (0.050) (0.052) (0.051)
Realization Status (ref: Unrealized)Partially realized 0.288*** 0.295*** 0.311*** 0.310***
(0.041) (0.037) (0.041) (0.036)
Fully realized 0.469*** 0.478*** 0.492*** 0.492***(0.057) (0.055) (0.069) (0.053)
Industry FE Yes Yes Yes YesRegion FE Yes Yes Yes YesFund FE Yes Yes Yes YesInvestment year FE No No Yes No
Constant 0.232 0.360** 1.104*** 2.595***(0.145) (0.137) (0.268) (0.407)
Observations 3,198 3,198 3,198 3,198R-squared 0.257 0.252 0.285 0.266
LBO Deal Gross IRR
30
Table 3: Performance and leverage – Competitive Environment
This table presents the results from Ordinary Least Squares (OLS) regressions of deal-level gross Internal Rate of Return (IRR) performance on two channels of leverage and a set of control variables. Columns 1 and 2 exhibit results for a subsample of 387 LBOs that were directly sold to acquirers. Columns 3 and 4 show identical regressions on a subsample of 560 auctions. The IRR is defined as the interest rate that makes the net present value of a series of cash flows, positive and negative, from an investment in a portfolio company zero. The first leverage variable LBO Debt / EBITDA describes a portfolio company’s net debt (D) to earnings before interest, taxes, depreciation and amortization (EBITDA) ratio at investment entry and serves as a proxy for the debt-to-fundamentals value of a portfolio company. The second measure of leverage refers to a portfolio company’s net debt (D) to equity (E) ratio at investment entry (LBO Debt / Equity). In each regression model, we include a set of controls. By including the log of the enterprise value (EV), we control for the investment size of each deal. The binary variable “partially realized” (resp. “fully realized”) equals one if the transaction was partially (resp. fully realized) and zero otherwise. Further, all regressions include fixed effects (FE) for industry, region and fund. Industry fixed effects consist of ten basic one-digit ICB codes. Region fixed effects indicate where the investment took place and include the four categories North America, Europe, Asia and Rest of the world (RoW). The standard errors reported beneath each coefficient are clustered at the LBO deal-year level at entry. *, ** and *** denote statistical significance at the 10%, 5% and 1%, respectively.
31
Proprietary Auction Small Cap Large CapLow PE
FundraisingHigh PE
Fundraising
(1) (2) (3) (4) (5) (6)
LBO D/EBITDA 0.002 -0.051** -0.013 -0.024* -0.011 -0.031**(0.015) (0.024) (0.009) (0.013) (0.009) (0.011)
EV quartile 2 0.041 0.083 0.019 -0.017 0.015(0.084) (0.117) (0.045) (0.063) (0.046)
EV quartile 3 0.071 0.212 -0.031 0.035(0.164) (0.145) (0.067) (0.059)
EV quartile 4 0.274 0.229 0.085 0.117 0.104(0.169) (0.177) (0.062) (0.079) (0.061)
Realization Status (ref: Unrealized)Partially realized 0.324** 0.305*** 0.278*** 0.318*** 0.338*** 0.297***
(0.126) (0.094) (0.043) (0.061) (0.068) (0.032)
Fully realized 0.477** 0.657*** 0.504*** 0.470*** 0.609*** 0.413***(0.216) (0.136) (0.049) (0.088) (0.080) (0.068)
Industry FE Yes Yes Yes Yes Yes YesRegion FE Yes Yes Yes Yes Yes YesFund FE Yes Yes Yes Yes Yes Yes
Constant -1.129*** 0.911*** 0.531 0.499* 0.402 0.376**(0.316) (0.301) (0.334) (0.270) (0.262) (0.149)
Observations 387 560 1,599 1,599 1,594 1,604R-squared 0.447 0.419 0.328 0.324 0.333 0.295
Panel A: LBO Deal Gross IRR
32
Proprietary Auction Small Cap Large CapLow PE
FundraisingHigh PE
Fundraising
(1) (2) (3) (4) (5) (6)
LBO D/EBITDA -0.009 -0.047* -0.009 -0.024 -0.009 -0.031**(0.013) (0.026) (0.009) (0.015) (0.009) (0.012)
EV quartile 2 0.044 0.105 0.018 0.010 -0.001(0.101) (0.143) (0.042) (0.065) (0.046)
EV quartile 3 0.122 0.243 -0.017 0.016(0.181) (0.153) (0.063) (0.058)
EV quartile 4 0.258 0.233 0.086 0.149* 0.083(0.186) (0.195) (0.062) (0.086) (0.069)
Realization Status (ref: Unrealized)Partially realized 0.224* 0.351*** 0.267*** 0.343*** 0.352*** 0.312***
(0.122) (0.113) (0.041) (0.072) (0.080) (0.033)
Fully realized 0.316 0.736*** 0.485*** 0.495*** 0.630*** 0.426***(0.212) (0.147) (0.042) (0.120) (0.089) (0.072)
Industry FE Yes Yes Yes Yes Yes YesRegion FE Yes Yes Yes Yes Yes YesFund FE Yes Yes Yes Yes Yes YesInvestment year FE Yes Yes Yes Yes Yes Yes
Constant 0.021 0.500 1.363** 0.911* 1.310*** 0.665***(0.403) (0.602) (0.544) (0.495) (0.323) (0.153)
Observations 387 560 1,599 1,599 1,594 1,604R-squared 0.513 0.468 0.354 0.367 0.378 0.315
Panel B: LBO Deal Gross IRR with Investment Year Fixed Effects
33
Table 4: Leverage – full sample, proprietary and auction This table presents the results from OLS regressions of LBO leverage on the high-yield (HY) spread, a set of various financial information of matched public market companies in the same month, region and industry classification as the comparable LBO transaction and a set of control variables. We report different regression models for the full sample of 3,198 deals in Panel A and for the subsamples by deal type of 387 proprietary deals and the subset of 560 auction deals in Panel B. The high-yield (HY) spread denotes the US high-yield rate at investment entry according to the Merrill Lynch High-yield Master minus US LIBOR. The public log Debt / EBITDA and Debt / Equity are the median values of debt (D) to EBITDA ratio resp. debt (D) to equity (E) ratio of matched public market companies in the same year (month), region and industry classification as the comparable LBO transaction. The regressions of Columns 2 and 4 in Panel A and Columns 2, 4, 6 and 8 in Panel B include fixed effects (FE) for industry and region. Industry fixed effects consist of ten basic one-digit ICB codes ranging from one (Oil & Gas) to 9000 (Technology). Region fixed effects indicate where the investment took place and include the four categories North America, Europe, Asia and Rest of the world (RoW).The standard errors reported beneath each coefficient are clustered at the LBO deal-year level at entry. *, ** and *** denote statistical significance at the 10%, 5% and 1%, respectively.
VARIABLESLBO log
D/EBITDALBO log
D/EBITDA(1) (2)
HY spread -0.019** -0.014*(0.009) (0.008)
Industry median log D/EBITDA 0.066(0.045)
EV quartile 2 0.207*** 0.201***(0.048) (0.044)
EV quartile 3 0.447*** 0.419***(0.038) (0.031)
EV quartile 4 0.678*** 0.609***(0.043) (0.040)
Industry FE No YesRegion FE No Yes
Constant 0.982*** 0.893***(0.085) (0.297)
Observations 2,744 3,198R-squared 0.105 0.135
LBO log D/EBITDA
LBO log D/EBITDA
LBO log D/EBITDA
LBO log D/EBITDA
(1) (2) (3) (4)
HY spread -0.037* -0.009 -0.060*** -0.054***(0.021) (0.018) (0.015) (0.013)
Industry median log D/EBITDA 0.266*** 0.050(0.089) (0.081)
EV quartile 2 0.170 0.182 0.128 0.239**(0.113) (0.106) (0.129) (0.109)
EV quartile 3 0.336*** 0.357*** 0.361*** 0.416***(0.098) (0.101) (0.097) (0.109)
EV quartile 4 0.767*** 0.701*** 0.634*** 0.713***(0.122) (0.130) (0.109) (0.082)
Industry FE No Yes No YesRegion FE No Yes No Yes
Constant 1.054*** 1.306*** 1.298*** 2.564***(0.154) (0.151) (0.133) (0.221)
Observations 331 387 490 560R-squared 0.096 0.163 0.130 0.203
Panel A: Full Sample
Panel B: Subsample by Deal Type
Full Sample
Proprietary Auction
34
Table 5: Leverage – enterprise value and fundraising competition proxy This table presents the results from Ordinary Least Squares (OLS) regressions of LBO leverage on the high-yield (HY) spread, a set of various financial information of matched public market companies in the same year (month), region and industry classification as the comparable LBO transaction and a set of control variables. We report different regression models for the subsamples by enterprise value (EV) of 1,599 small cap deals and 1,599 large cap deals in Panel A and for the subsamples by PE fundraising of 1,594 deals made in a low and 1,604 deals in a high PE fundraising environment in Panel B. As dependent variables, we use the LBO Debt / EBITDA (logarithmized) and the LBO Debt / Equity (logarithmized). The first leverage variable LBO Debt / EBITDA describes a portfolio company’s net debt (D) to earnings before interest, taxes, depreciation and amortization (EBITDA) ratio at investment entry and serves as a proxy for the debt-to-fundamentals value of a portfolio company. The second measure of leverage refers to a portfolio company’s net debt (D) to equity (E) ratio at investment entry (LBO Debt / E). The high-yield (HY) spread denotes the US high-yield rate at investment entry according to the Merrill Lynch High-yield Master minus US LIBOR. The public log Debt / EBITDA and Debt / Equity are the median values of debt (D) to EBITDA ratio resp. debt (D) to equity (E) ratio of matched public market companies in the same year (month), region and industry classification as the comparable LBO transaction. By including the log of the enterprise value (EV), we control for the investment size of each deal. The regressions of Columns 2, 4, 6 and 8 in Panels A and B include fixed effects (FE) for industry and region. Industry fixed effects consist of ten basic one-digit ICB codes ranging from one (Oil & Gas) to 9000 (Technology). Region fixed effects indicate where the investment took place and include the four categories North America, urope, Asia and Rest of the world (RoW).The standard errors reported beneath each coefficient are clustered at the LBO deal-year level at entry. *, ** and *** denote statistical significance at the 10%, 5% and 1%, respectively.
VARIABLESLBO log
D/EBITDALBO log
D/EBITDALBO log
D/EBITDALBO log
D/EBITDA(1) (2) (3) (4)
HY spread -0.019 -0.004 -0.018 -0.024**(0.012) (0.009) (0.011) (0.010)
Industry median log D/EBITDA 0.132 0.006(0.083) (0.044)
EV quartile 2 0.208*** 0.189***(0.047) (0.046)
EV quartile 4 0.233*** 0.197***(0.026) (0.032)
Industry FE No Yes No YesRegion FE No Yes No Yes
Constant 1.000*** 1.003*** 1.408*** 1.315**(0.118) (0.301) (0.082) (0.469)
Observations 1,380 1,599 1,364 1,599R-squared 0.018 0.075 0.037 0.079
LBO log D/EBITDA
LBO log D/EBITDA
LBO log D/EBITDA
LBO log D/EBITDA
(1) (2) (3) (4)
HY spread -0.013 -0.004 -0.029** -0.026***(0.021) (0.012) (0.010) (0.007)
Industry median log D/EBITDA 0.048 0.117*(0.090) (0.056)
EV quartile 2 0.306*** 0.275*** 0.060 0.066(0.050) (0.048) (0.055) (0.066)
EV quartile 3 0.470*** 0.448*** 0.360*** 0.338***(0.057) (0.044) (0.049) (0.051)
EV quartile 4 0.744*** 0.694*** 0.551*** 0.514***(0.063) (0.045) (0.044) (0.048)
Industry FE No Yes No YesRegion FE No Yes No Yes
Constant 0.853*** 1.375*** 1.210*** 0.393(0.164) (0.356) (0.098) (0.575)
Observations 1,312 1,594 1,432 1,604R-squared 0.093 0.150 0.118 0.129
Large Cap
Panel A: Subsample by Enterprise Value (EV)
Small Cap
Panel B: Subsample by PE Fundraising
Low PE Fundraising High PE Fundraising
35
Table 6: EV / EBITDA pricing – full sample, proprietary and auction
This table presents the results from Ordinary Least Squares (OLS) and Instrumental-Variables (IV) regressions of LBO enterprise value (EV) multiple on the high-yield (HY) spread, the LBO log Debt / EBITDA, the Public log EV multiple and a set of control variables. We report different regression models for the full sample of 3,198 deals in Panel A and for the subsamples by deal type of 387 proprietary deals and the subset of 560 auction deals in Panel B. As dependent variable, we use in all specifications the EV multiple as the enterprise value (EV) to earnings before interest, taxes, depreciation and amortization (EBITDA) ratio at investment entry for each LBO deal. The high-yield (HY) spread denotes the US high-yield rate at investment entry according to the Merrill Lynch High-yield Master minus US LIBOR. The LBO log Debt / EBITDA describes a portfolio company’s net debt (D) to earnings before interest, taxes, depreciation and amortization (EBITDA) ratio at investment entry (logarithmized) and serves as a proxy for the debt-to-fundamentals value of a portfolio company. The Public Log EV multiple is the median value of EV to EBITDA ratio of matched public market companies in the same year (month), region and industry classification as the comparable LBO transaction (logarithmized). By including the log of the enterprise value (EV), we control for the investment size of each deal. The regressions of Columns 2 in Panel A and Columns 2 and 6 in Panel B include fixed effects (FE) for industry and region. Industry fixed effects consist of ten basic one-digit ICB codes ranging from one (Oil & Gas) to 9000 (Technology). Region fixed effects indicate where the investment took place and include the four categories North America, Europe, Asia and Rest of the world (RoW). The standard errors reported beneath each coefficient are clustered at the LBO deal-year level at entry. *, ** and *** denote statistical significance at the 10%, 5% and 1%, respectively.
VARIABLES
LBO log EV multiple
LBO log EV multiple
LBO log EV multiple (first
stage)
LBO log EV multiple (second
stage)(1) (2) (3) (4)
HY spread -0.012* -0.017** -0.107***(0.006) (0.006) (0.020)
LBO D/EBITDA 0.112**(0.055)
Public log EV multiple 0.125** -0.346* 0.164***(0.048) (0.192) (0.032)
EV quartile 2 0.090** 0.086*** 0.469*** 0.037(0.033) (0.030) (0.115) (0.035)
EV quartile 3 0.215*** 0.203*** 1.078*** 0.094(0.034) (0.031) (0.122) (0.058)
EV quartile 4 0.311*** 0.288*** 1.936*** 0.095(0.037) (0.035) (0.123) (0.109)
Industry FE No Yes No NoRegion FE No Yes No No
Constant 1.569*** 1.941*** 4.524*** 1.063***(0.138) (0.135) (0.474) (0.197)
Observations 3,198 3,198 3,198 3,198R-squared 0.086 0.121 0.124 0.439
LBO log EV multiple
LBO log EV multiple
LBO log EV multiple
LBO log EV multiple
LBO log EV multiple (first
stage)
LBO log EV multiple (second
stage)(1) (2) (3) (4) (5) (6)
HY spread -0.012 -0.013 -0.023** -0.028*** -0.063***(0.011) (0.009) (0.010) (0.009) (0.020)
LBO D/EBITDA 0.367**(0.158)
Public log EV multiple 0.076 0.126 -0.059 0.147*(0.120) (0.104) (0.192) (0.086)
EV quartile 2 0.086 0.089 0.129 0.128 0.267* 0.031(0.077) (0.074) (0.081) (0.078) (0.140) (0.069)
EV quartile 3 0.223*** 0.226*** 0.218* 0.208* 0.444*** 0.055(0.068) (0.067) (0.105) (0.102) (0.131) (0.089)
EV quartile 4 0.330*** 0.291*** 0.365*** 0.357*** 0.714*** 0.103(0.103) (0.096) (0.088) (0.085) (0.126) (0.119)
Industry FE No Yes No Yes No NoRegion FE No Yes No Yes No No
Constant 1.683*** 1.358*** 1.611*** 2.557*** 1.322*** 1.126***(0.267) (0.107) (0.239) (0.092) (0.449) (0.204)
Observations 387 387 560 560 560 560R-squared 0.062 0.136 0.123 0.149 0.138 0.295
Panel B: Subsample by Deal Type
Auction
Full Sample
Panel A: Full Sample
Full Sample
Proprietary
36
Table 7: EV / EBITDA pricing – enterprise value and fundraising competition proxy
This table presents the results from Ordinary Least Squares (OLS) and Instrumental-Variables (IV) regressions of LBO enterprise value (EV) multiple on the high-yield (HY) spread, the LBO log Debt / EBITDA, the Public log EV multiple and a set of control variables. We report different regression models for the subsamples by enterprise value (EV) of 1,599 small cap deals and 1,599 large cap deals in Panel A and for the subsamples by PE fundraising of 1,594 deals made in a low and 1,604 deals in a high PE fundraising environment in Panel B. As dependent variable, we use in all specifications the EV multiple as the enterprise value (EV) to earnings before interest, taxes, depreciation and amortization (EBITDA) ratio at investment entry for each LBO deal. The high-yield (HY) spread denotes the US high-yield rate at investment entry according to the Merrill Lynch High-yield Master minus US LIBOR. The LBO log Debt / EBITDA describes a portfolio company’s net debt (D) to earnings before interest, taxes, depreciation and amortization (EBITDA) ratio at investment entry (logarithmized) and serves as a proxy for the debt-to-fundamentals value of a portfolio company. The Public Log EV multiple is the median value of EV to EBITDA ratio of matched public market companies in the same year (month), region and industry classification as the comparable LBO transaction (logarithmized). By including the log of the enterprise value (EV), we control for the investment size of each deal. The regressions of Columns 2 in Panel A and Columns 2 and 6 in Panel B include fixed effects (FE) for industry and region. Industry fixed effects consist of ten basic one-digit ICB codes ranging from one (Oil & Gas) to 9000 (Technology). Region fixed effects indicate where the investment took place and include the four categories North America, Europe, Asia and Rest of the world (RoW). The standard errors reported beneath each coefficient are clustered at the LBO deal-year level at entry. *, ** and *** denote statistical significance at the 10%, 5% and 1%, respectively.
VARIABLES
LBO log EV multiple
LBO log EV multiple
LBO log EV multiple
LBO log EV multiple
LBO log EV multiple (first
stage)
LBO log EV multiple (second
stage)(1) (2) (3) (4) (5) (6)
HY spread -0.003 -0.009 -0.021*** -0.024*** -0.028***(0.009) (0.007) (0.006) (0.007) (0.009)
LBO D/EBITDA 0.763**(0.368)
Public log EV multiple 0.173*** 0.078 -0.088 0.145***(0.058) (0.069) (0.090) (0.054)
EV quartile 2 0.090** 0.085***(0.034) (0.029)
EV quartile 3 -0.211*** 0.062(0.032) (0.090)
EV quartile 4 0.099*** 0.094***(0.019) (0.020)
Industry FE No Yes No Yes No NoRegion FE No Yes No Yes No No
Constant 1.415*** 1.803*** 1.936*** 2.337*** 1.854*** 0.621(0.167) (0.171) (0.152) (0.130) (0.218) (0.589)
ObservationsR-squared 1,599 1,599 1,599 1,599 1,599 1,599
0.017 0.068 0.045 0.078 0.034
LBO log EV multiple
LBO log EV multiple
LBO log EV multiple
LBO log EV multiple
LBO log EV multiple (first
stage)
LBO log EV multiple (second
stage)(1) (2) (3) (4) (5) (6)
HY spread -0.007 -0.013 -0.020*** -0.023*** -0.036***(0.010) (0.008) (0.006) (0.003) (0.010)
LBO D/EBITDA 0.544**(0.233)
Public log EV multiple 0.196*** 0.020 -0.189* 0.123**(0.065) (0.067) (0.107) (0.056)
EV quartile 2 0.108** 0.080** 0.061 0.069 0.081 0.017(0.039) (0.036) (0.049) (0.049) (0.066) (0.026)
EV quartile 3 0.223*** 0.187*** 0.194*** 0.194*** 0.359*** -0.001(0.038) (0.037) (0.048) (0.047) (0.065) (0.079)
EV quartile 4 0.324*** 0.274*** 0.287*** 0.276*** 0.544*** -0.009(0.034) (0.028) (0.058) (0.058) (0.065) (0.118)
Industry FE No Yes No Yes No NoRegion FE No Yes No Yes No No
Constant 1.375*** 2.077*** 1.865*** 1.824*** 1.594*** 0.998***(0.178) (0.247) (0.153) (0.237) (0.262) (0.289)
Observations 1,594 1,594 1,604 1,604 1,604 1,604R-squared 0.076 0.117 0.098 0.138 0.112 0.056
Large Cap
Panel A: Subsample by Enterprise Value (EV)
Small Cap
Panel B: Subsample by PE Fundraising
Low PE Fundraising High PE Fundraising
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Table 8: Agency problem – Poisson regressions
This table presents the results from Poisson regressions of the number of auctions, the number of proprietary deals and the number of all deals on the average (Avg.) high-yield (HY) spread, the fund’s remaining capital and size, different agency proxies and interactions of the corresponding agency proxy with the HY spread and a set of control variables. As dependent variables, we use the number of auctions (No. of Auctions), the number of proprietary deals (No. of Proprietary) and the number of deals (No. of Deals). The avg. high-yield (HY) spread denotes the average US high-yield rate at investment entry according to the Merrill Lynch High-yield Master minus US LIBOR. A fund’s remaining capital is measured in $ million of the investing PE fund after the first investment. The fund size (logarithmized) in $ million indicates the size of the respective fund that makes the investment. Further, we use as agency proxies the investment speed as the fund’s remaining capital relative to other funds (Columns 2, 5 and 6) and the investment interim performance as the fund’s interim performance measured as the money multiple of all deals with more than three years of experience (Columns 3, 6 and 9). The respective agency proxy is interacted with the HY spread. The regressions include fixed effects (FE) for fund in Columns 2–3, 5–6 and 8–9. The standard errors reported beneath each coefficient are clustered at the LBO deal-year level at entry. *, ** and *** denote statistical significance at the 10%, 5% and 1%, respectively.
VARIABLES
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Avg. HY spread -0.084*** -0.055** -0.127*** -0.033* -0.012 -0.012 -0.055*** -0.023 -0.051***(0.016) (0.023) (0.029) (0.020) (0.028) (0.025) (0.012) (0.021) (0.016)
Remaining capital 0.216 0.495*** 1.052*** 0.369* 0.658*** 0.968*** 0.294*** 0.565*** 0.963***(0.143) (0.109) (0.187) (0.203) (0.135) (0.129) (0.083) (0.083) (0.079)
Log fund size 0.207*** -0.102** 0.072***(0.022) (0.052) (0.020)
Slow Investment Speed -0.344* -0.464* -0.426***(0.191) (0.257) (0.142)
Avg. HY spread*Slow Investment Speed 0.036 0.037 0.041**(0.027) (0.025) (0.016)
Poor Interim Performance 1.404*** -0.216 0.497***(0.318) (0.272) (0.129)
Avg. HY spread*Poor Interim perf. -0.216*** 0.047 -0.059***(0.058) (0.042) (0.021)
Fund FE No Yes Yes No Yes Yes No Yes Yes
Constant -0.645*** 0.539 -16.073*** 0.975*** -1.102* -0.013 0.840*** 0.560* 0.249**(0.183) (0.369) (4.149) (0.257) (0.622) (0.160) (0.123) (0.288) (0.100)
Observations 624 624 399 605 603 389 639 637 408
No. of Proprietary No. of DealsNo. of Auctions
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Figure 1: Deal-level Gross IRR by Debt / Equity Quintile
The figure shows the median Gross Internal Rate of Return (IRR) of private equity fund buyout investments across different levels of debt used to finance the deal. Deals are sorted into quintiles based on Debt / Equity, with the range of each quintile displayed in the lower panel. The Gross IRR represents the performance of each investment before any fees assessed by the fund, and is calculated using the cash flows between the fund and portfolio company. The upper panel displays the Median Gross IRR calculated by equal weighting deals in each quintile.
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Figure 2: Deal-level Gross IRR by Debt / EBITDA Quintile
The figure shows the median Gross Internal Rate of Return (IRR) of private equity fund buyout investments across different levels of debt used to finance the deal. Deals are sorted into quintiles based on Debt / EBITDA, with the range of each quintile displayed in the lower panel. The Gross IRR represents the performance of each investment before any fees assessed by the fund, and is calculated using the cash flows between the fund and portfolio company. The upper panel displays the Median Gross IRR calculated by equal weighting deals in each quintile.
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7 Appendix: Available leverage versus money chasing deals
In this appendix, we provide a brief analysis to separate our results on leverage from the
“money-chasing deals” phenomenon documented in Gompers and Lerner (2000). In Section 3,
we use the level of recent PE industry fundraising as a proxy for competition, which we
hypothesize effects the rents which PE funds can earn by levering portfolio companies. Our
concern is that because PE fundraising and available deal leverage may depend on similar
macroeconomic factors, their effect on the returns to buyout private equity deals may be
difficult to distinguish. We wish to show that the decrease in returns associated with deal
leverage is indeed separate from the money-chasing deals effect.
First, we note out that our measure of available PE capital is the sum of the past three
years of fundraising calculated by industry-region. This is motivated by the long time required
to raise and deploy PE capital. Correspondingly, the capital committed, but not yet called by
PE funds, is likely to change with a substantial lag with respect to the business cycle (Robinson
and Sensoy (2013)). In contrast, debt is raised in the spot market for loans, which is likely to
respond quickly to changes in macro factors. Consistent with this intuition the correlation
between our fundraising measure and our measure of credit market conditions, the high-yield
spread, is a modest 0.09. Of course PE fundraising is measured at the industry-region level,
while the high-yield spread is an aggregate measure which only varies over time. It may be that
there is a substantially higher correlations between our fundraising measure and unobserved
factors determine available leverage at the industry-region level. Rather than rely exclusively
on the correlation between these measures, our second approach shows that credit markets and
excess private equity fundraising differ in their relation to the financing of deals in a way that
suggests that they are distinct channels.
Intuitively, improving credit markets should increase the available leverage, increasing
the debt used to finance deals. In contrast, following periods of high private equity fundraising,
the “dry-powder” available in the industry-region should be related to the amount of equity
used to finance a deal. If instead, the leverage effect we document in the body of the paper is
simply an artifact created by correlation between debt market conditions and the variables
which drive private equity fundraising, we would expect both variables to have similar effects
on deal financing.
Table A1 presents results which relates credit market conditions (HY spread) and PE
fundraising to deal prices (EV / EBITDA) and financing (Equity / EBITDA, Debt / EBITDA).
All specifications include control variables for the valuation and financing of public companies
41
matched by industry-region. The results for changes in credit market conditions mirror those in
Tables 5 and 7 in the body of the paper. Increasing high-yield spreads (worsening credit
markets) is negatively related to the price paid for acquisitions (EV / EBITDA) in the full
sample (Column 3) and the Auction Subsample (Column 9). In addition, high-yield spread is
negatively related to the amount of debt in the auction subsample (Column 7). PE fundraising
has a similar effect on the price paid for acquisitions in the Full (Column 2) and Auction
(Column 9) sample. However, PE Fundraising consistently has no effect on the amount of debt
used to finance the deal. In contrast, PE fundraising is positively related to the amount of equity
used in the deal in the full sample (Column 2) and both subsamples (Columns 5 and 8). These
results support the notion that credit market conditions and PE fundraising effect the price of
deals through separate channels.
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Table A1: Leverage and money chasing deals – full sample, proprietary and auction
This table presents the results from Ordinary Least Squares (OLS) regressions of LBO leverage, LBO equity and LBO enterprise multiples on the high-yield (HY) spread, the private equity (PE) fundraising environment, a set of various financial information of matched public market companies in the same year (month), region and industry classification as the comparable LBO transaction and a set of control variables. We report different regression models for the full sample of 3,198 deals (Columns 1–3), for the subset of 387 proprietary deals (Columns 4–6) and the subset of 560 auction deals (Columns 7–9). As dependent variables, we use the LBO Debt / EBITDA (logarithmized), the LBO E / EBITDA (logarithmized) and the LBO EV multiple (logarithmized). The LBO log Debt / EBITDA describes a portfolio company’s net debt (D) to earnings before interest, taxes, depreciation and amortization (EBITDA) ratio at investment entry (logarithmized) and serves as a proxy for the debt-to-fundamentals value of a portfolio company. The LBO log Equity / EBITDA describes a portfolio company’s equity (E) to earnings before interest, taxes, depreciation and amortization (EBITDA) ratio at investment entry (logarithmized). The LBO log EV multiple is a portfolio company’s enterprise value (EV) to EBITDA ratio (logarithmized). The high-yield (HY) spread denotes the US high-yield rate at investment entry according to the Merrill Lynch High-yield Master minus US LIBOR. The financial information of matched public companies include the log Debt / EBITDA, log Equity / EBITDA and the Public EV multiple. The private equity fundraising environment is an aggregation of buyout funds raised over the prior three years (in a given industry and region) divided by current year’s regional gross domestic product (GDP). All regressions include fixed effects (FE) for industry and region. Industry fixed effects consist of ten basic one-digit ICB codes. Region fixed effects include different geographical regions such as North America, Europe, Asia and Rest of World (Row). The standard errors reported beneath each coefficient are clustered at the LBO deal-year level at entry. *, ** and *** denote statistical significance at the 10%, 5% and 1%, respectively.
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VARIABLES
LBO log D/EBITDA
LBO log E/EBITDA
LBO log EV multiple
LBO log D/EBITDA
LBO log E/EBITDA
LBO log EV multiple
LBO log D/EBITDA
LBO log E/EBITDA
LBO log EV multiple
(1) (2) (3) (4) (5) (6) (7) (8) (9)
HY spread -0.012 -0.011 -0.017*** -0.023 -0.014 -0.014 -0.045** 0.002 -0.021**(0.011) (0.010) (0.005) (0.029) (0.021) (0.011) (0.016) (0.016) (0.009)
PE fundraising 17.172 215.051*** 68.242*** -65.947 248.106*** 33.802 37.790 296.087*** 106.844***(23.827) (24.116) (15.061) (82.786) (66.485) (32.024) (50.342) (51.610) (26.008)
Industry median log D/EBITDA -0.028 0.134 -0.095(0.078) (0.261) (0.100)
Industry median log E/EBITDA 0.109 0.123 0.403**(0.100) (0.158) (0.155)
Public EV multiple 0.074 -0.026 0.245(0.064) (0.105) (0.158)
Industry and Region FE Yes Yes Yes Yes Yes Yes Yes Yes Yes
Constant 1.243*** 0.739** 1.879*** 1.765*** -0.528 1.549*** 2.834*** -0.196 2.096***(0.298) (0.265) (0.185) (0.165) (0.396) (0.288) (0.212) (0.571) (0.360)
Observations 2,744 3,198 3,198 331 387 387 490 560 560R-squared 0.067 0.090 0.072 0.118 0.169 0.095 0.111 0.146 0.092
Full Sample Proprietary Auction