* Contact information: Crain: nicholas.crain@unimelb.edu.au, Braun: reiner.braun@tum.de, Gerl: anna.gerl@tum.de. 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

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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.

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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

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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.

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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.

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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

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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.

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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.

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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.

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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).

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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.

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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

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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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28

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.

39

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.

43

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