Pricing and Performance of Loans Bundled with Underwriting
Yang Lu*
Department of Finance
Stern School of Business
New York University
Job Market Paper
January 12, 2007
ABSTRACT
Banks provide loans and underwriting services to the same corporate customer with
increasing frequency. Previous literature finds that loans that are bundled with
underwriting deals carry lower interest rates, consistent with either strategic behavior by
banks (“pay to play”) or informational economies of scope. However, I find that there is
no interest rate discount in bundled loans after adjusting for endogeneity arising from a
bank’s decision to manage its risk exposure to a client. My results support the story that
banks choose to provide bundled lending and underwriting services to higher-quality
customers. Tests of subsequent performance show that borrowers receiving bundled
services perform better than other borrowers in terms of future changes in credit risk
(proxied using KMV’s distance to default measure and Altman’s (1968) Z-score). Such
borrowers are also significantly less likely to default or receive credit rating downgrades.
The effects are stronger for smaller and unrated companies. Moreover, using a new
database of secondary market loan prices, I find that bundled loans have better return
performance in the secondary market.
* Department of Finance, Stern School of Business, New York University, 44 West 4th Street, Suite 9-193,
New York, NY 10012-1126. Phone: 212-998-0716. E-mail: ylu1@stern.nyu.edu.
I would like to thank my Ph.D. committee members Anthony Saunders, Alexander Ljungqvist, Yakov
Amihud, Kose John and Daniel Wolfenzon for constant encouragement and support. I also thank Edward
Altman, Steven Drucker, Amar Gande, Bill Greene, Victoria Ivashina, Michael Roberts, Rik Sen, Andre de
Souza, Ingo Walter, Johnathan Wang and seminar participants at NYU and FDIC for help and comments.
Finally, I thank Brooks Brady, Steven Miller, and Matthew Sanderson from Standard & Poor’s for help
with the data. All errors are mine.
The appropriate scope of banking activities has long been controversial in both academic
and regulatory circles. 1 The debate has been especially heated because of recent changes
in the industry. Consolidation among banks in the late 1990s alongside the repeal of the
Glass-Steagall Act in 1999 have increased banks’ ability to compete for corporate
customers by offering them both commercial loans and investment banking services. For
instance, between 1995 and 2004, the incidence of bundling loans with underwriting
services tripled, from 5% to 15% of corporate loans. The fraction was even larger in
terms of dollar value. This greater freedom raises the concern that commercial banks
might “discount” their loans to support their investment banking affiliates, which might
distort the competitive structure of underwriting markets. There are also concerns that in
bundled transactions, banks might lower their lending standards and extend loans to
otherwise unqualified clients in the pursuit of underwriting fees. If true, such behaviors
could increase the risk of large defaults and potentially hurt the stability of the banking
system.
Despite its apparent importance to the banks, their customers, and the financial system,
we know relatively little about the economic consequences of this aspect of banking
deregulation. This paper examines these consequences by studying the pricing and
performance of loans that are bundled with underwriting deals.
The following questions from Congressman John D. Dingell to the Federal Reserve
Board and the Office of the Comptroller of the Currency in 2002 illustrate the concerns:
“…since it appears that credit is being offered as a loss leader by commercial
banks to facilitate or leverage the extension of their investment banking
business, what are the implications of such mispricing on the supply of and
demand for credit? What are the implications of this underpricing for the
financial health of the smaller banks who participate in these syndicated
facilities? … To what degree is this tying activity a cause of the increased losses
being realized by large banks on loans to borrowers such as Enron who were
1
See Drucker and Puri (2005) for an excellent survey on this topic.
known to pay large investment banking fees? Is the “pay to play” practice
leading to a concentration of bad credit risks among an increasingly smaller
number of banks? What are the systematic implications of this distortion?”
These concerns are shared among corporate executives, 2 investment banks and the
financial press.3 To summarize, this “tying” or “pay to play” story suggests that banks
charge lower interest rates for loans that are bundled with underwriting deals and might
extend credit to otherwise unqualified borrowers, which could adversely affect the banks
themselves as well as the whole financial system.4
Another popular explanation for lower interest rates charged in bundled loans is the
existence of informational economies of scope. When a bank jointly provides lending and
underwriting services, it can use the same company-specific information on both fronts,
thus reducing its information acquisition costs. If it so chooses, the bank can then pass on
the cost saving to the customer in the form of a lower interest rate.
While both the pay to play story and the informational scope economies story can
potentially explain why banks offer discounted interest rates in bundled loans, previous
studies that test their validity have ignored the possibility that bundling may not be
exogenous. Specifically, what if some unobserved company characteristics that lead a
bank to provide the bundled transaction also lead the bank to charge a lower interest on
the loan in the bundled transaction? If we do not control for this selection, we might
overestimate the effects of bundling on loan pricing. Therefore, it is quite possible that
2
See the survey conducted by the Association for Financial Professionals in 2004.
For example, according to The Economist of January 9, 2003, many of the biggest banks have used cutprice loans to win lucrative business previously reserved for investment banks.
4
Although bundling does not necessarily imply “tying,” which is illegal for commercial banks, it is very
hard to tell one from another in practice. For example, in its interpretation of section 106 of the Bank
Holding Company Act Amendments of 1970 (see 68 Fed. Reg. 52024 (August 29, 2003)), Federal Reserve
gives the conditions under which bundling of lending and underwriting services constitutes illegal tying.
However, these conditions are very hard to verify due to the lack of explicit “tying” agreements between
bank and companies. This is probably the reason why the U.S. General Accounting Office (GAO) finds no
evidence of the tying practices (see the report at www.gao.gov/cgi-bin/getrpt?GAO-04-3).
3
2
the interest rate discount charged in bundled loans might simply reflect the banks’
selection based on their private information. 5
Having laid out the selection story, next I would like to get a better idea about which
unobserved company characteristics are most likely to affect both the bundling decision
and the loan pricing. Since possessing these characteristics is associated with receiving
lower interest rates, a natural candidate is probably borrower quality. It is reasonable to
assume that bank select better quality clients for bundled services. People have found that
when lending-relationship banks provide underwriting services to the borrowers, the
underwritten securities are generally better priced (Kroszner and Rajan (1997) and Gande
et. al. (1997), etc.). This is consistent with lending banks providing underwriting services
to higher-quality borrowers. One of the reasons why client quality affects bank’s
bundling decision is that when a bank provides bundled lending and underwriting
services to a customer, it leaves itself exposed to the same client on two fronts: it puts its
financial capital at risk on the lending front, and it puts its reputation capital at risk on the
underwriting front. If the borrower in question later performs badly, the bank will be
impacted adversely on both fronts. Thus, due to the increased risk, it is reasonable to
think a bank would be more careful in doing its due-diligence and would choose higherquality clients to whom to provide joints services.
A bank assesses a client’s quality based not only on public information but also on its
private information, which is not observable to the econometrician. Say a bank has two
clients with almost identical publicly observable characteristics, but the bank has more
favorable private information about one company. As a result, the bank might provide
bundled services to this client and charge a fair interest rate. However, this fair interest
rate charged in the bundled loan will appear lower to us as econometricians, since we do
not have access to bank’s private information. Therefore, it is quite possible that the yield
discount observed in prior work may then simply reflect the selection bias due to the
5
There is a close analogy between this story and Campa and Kedia’s (2002) argument for the
diversification discount. I argue that bundling is not exogenous and unobserved characteristics of borrowers
both cause a bank to provide bundled loans and to charge lower interest. Thus, the interest discount in
bundled loans might be due to selection bias. Campa and Kedia (2002) argue that firms self-select to
diversify. Certain unobserved firm characteristics, which cause firms to diversify, also cause them to be
discounted. Self selection may thus explain the diversification discount.
3
bank’s private information about the unobserved higher-quality of bundled-loan clients. I
call this the “private information” story or “unobserved higher-quality” story.
The “unobserved higher-quality” story provides new predictions about the pricing of
bundled loans and the quality of bundled-loan clients. Testing this story is not easy, since
it involves testing for the effects of unobservables. Here I employ two strategies. The first
strategy is to use an econometric method to explicitly correct for the bank’s selection in
its bundling decision. If the higher client quality hypothesis is correct, we would expect
to see no interest rate discount after correcting for the selection. The second strategy
examines the ex-post performance of bundled-loan clients or bundled loans; it is based on
the simple intuition that a bank’s private information regarding unobserved client quality
will be revealed eventually. If banks provide bundled loans predominantly to clients for
whom they have more favorable private information, then bundled-loan clients and
bundled loans should perform better ex-post. In addition, ex-post performance analysis
could also shed light on how bundling of lending and underwriting services affects the
health and stability of the financial system. This is the very reason why bundling has
attracted significant regulatory attention.
Following previous literature, I define a “bundled loan” as a loan to a borrower that
issues bonds or shares around the loan origination date and for which the lead lender acts
as lead underwriter. To make an apples-to-apples comparison, I define a “non-bundled
loan” as a loan with a security issue around the loan origination date that is underwritten
by a financial institution other than the lead lender. Therefore, all loans in my sample
have security issues around their origination dates, and the only difference between
bundled loans and non-bundled loans is whether or not the lead lender acts as lead
underwriter. My sample consists of 10,053 loan facilities from 1994 to 2004 covering
2,896 companies. Of these, 2,486 loans (25%) are classified as bundled.
Using OLS, I reproduce the results in previous studies and find that bundled loans have
lower yields than comparable non-bundled loans. However, the interest rate discount in
bundled loans disappears when I control for the selectivity in the bundling using a
treatment effects model. My instrument is based on time series and cross-sectional
4
variation in exogenous regulatory constraints on different commercial banks’ ability to
underwrite securities issues. Moreover, I find that the coefficient of the correction term
for selection, the inverse mills ratio, is negative and significant. This suggests that bank’s
private information about bundled-loan clients is negatively related to the interest rate.
The unobserved higher quality of bundled-loan clients allows a bank to offer them
bundled loans and to charge lower interest. Thus, the bundled-loan interest discount
found in previous studies appears to be due to unobserved higher client quality, not to
bundling per se. This finding is consistent with the “unobserved higher quality” story I
propose.
Examining the ex-post performance of borrowers that receive bundled services allows me
to further discriminate among the three candidate explanations. If, as the pay to play story
and anecdotal evidence suggest, banks take on bad credit or disregard high default
probability for the sake of their higher-margin underwriting business, bundled-loan
clients should perform worse ex-post compared to other borrowers. The informational
economies of scope story offers no clear prediction about ex-post performance, for it does
not take a stand on the quality of the customers involved. The “unobserved higher
quality” story, however, predicts that bundled-loan clients should perform better ex-post
since banks provide bundled service predominantly to their higher-quality clients; and
higher quality should eventually be revealed in the form of better ex-post performance.
To examine ex-post performance, I look at three different sets of performance proxies.
The first two are the distance to default (DD) measure (based on the KMV-Merton model)
and Altman’s (1968) Z-score measure, which are popular proxies for credit risk. I find
that after loan origination, the distance to default and Z-score measures improve for
bundled-loan clients and deteriorate for non-bundled-loan clients. My third performance
proxy is based on borrowers’ default rates and credit rating downgrade probabilities.
Using Standard & Poor’s default and rating migration data, I find that bundled-loan
clients default less frequently and are less likely to receive rating downgrades than are
non-bundled-loan clients in the sample. Superior performance among bundled-loan
clients is consistent with the “unobserved higher-quality” story. Interestingly, these
differences in ex-post performance are more pronounced among smaller and unrated
5
borrowers. This is consistent with the view that smaller companies and unrated
companies are generally more informationally opaque; therefore, a bank’s private
information should play a bigger role in differentiating a good client from a bad client.
Finally, the recent rapid development of a secondary market for syndicated loans
provides an opportunity to study the ex-post performance of the loans directly. Using
loan quote data from the LSTA/LPC secondary market price database, I compare the
return performance of bundled loans and non-bundled loans using the cumulative
abnormal return (CAR), buy-and-hold abnormal return (BHAR), and calendar-time
portfolio methods. Each shows that bundled loans perform better than non-bundled loans.
These results further support the hypothesis that banks provide bundled services
predominantly to higher quality clients.
This paper makes several contributions to the existing literature and to the public debate.
First, the majority of previous papers on universal banking focus the study on
underwritten securities. My paper is among the first few to study bundled loans, which
are an important component of bundled transactions. Second, this paper takes into
account a bank’s selection based on its private information. This had been ignored in
previous studies. That banks have private information about their clients is one of the
main reasons banks are viewed as “special” (Fama (1985)). My results show that ignoring
banks’ specialness may lead to incorrect inference. Third, this paper is the first to
investigate how bundling affects the ex-post performance of loans. Better ex-post
performance of bundled loans and bundled-loan clients is consistent with the conjecture
that banks provide bundled transactions predominantly to higher-quality clients. This
provides support for giving commercial banks more commercial freedom and suggests
that concerns about the possible negative effects of bundling on the health of financial
system seem unfounded. Fourth, my results add to the large credit risk literature by
highlighting that when studying default risk, it is important to take into account whether a
loan is bundled. This paper also adds to our understanding of the secondary loan market
by identifying an important performance driver in this market. Finally, this study adds to
the on-going discussion in regulatory circles and the academic literature concerning the
practice of product-tying by universal banks. Once I account for the decision to bundle, I
6
find that there is no interest rate discount in bundled loans. Thus, there is little evidence
of tying.
The remainder of the paper is structured as follows. The next section briefly discusses
prior related literature. Section II presents the data and sample construction. Section III
presents the results for the pricing of bundled loans. Section IV presents the results
concerning the ex-post performance of bundled-loan clients. Section V looks at the expost performance of bundled loans in the secondary market. Section VI concludes.
I. Literature Review
Theoretical papers about bundling lending and underwriting services (e.g. Kanatas and Qi
(1998, 2003), Puri (1999) and Rajan (2002)) model the tradeoff between the costs and
benefits of providing joint services. I briefly summarize their main points. Joint provision
of lending and underwriting services has three main potential benefits. The private
information banks collect through the lending relationship can be used to certify the
borrower’s value to the public market. This helps mitigate adverse selection problems,
possibly allowing the firm to sell its securities at higher prices. This is often referred to as
the certification hypothesis. Second, using the same information for different products
allows a bank to achieve informational economies of scope. Third, tying lending to
underwriting by discounting loans may benefit the bank through expansion of its
investment banking business. On the cost side, the literature has focused on potential
conflicts of interest. Chiefly, when a bank has negative private information about a firm,
it may help the firm issue public securities to repay its outstanding loans.
Most of the empirical work has focused on testing the certification hypothesis against the
conflicts of interest hypothesis. Researchers have used data from before the 1933 GlassSteagall Act (which separated lending and underwriting) and from the late 1980s (when
Glass-Steagall constraints began to be eased) to examine the ex-ante pricing and ex-post
performance of underwritten securities. Puri (1996), Kroszner and Rajan (1997), Gande
et. al. (1997), Roten and Mullineaux (2002), and Schenone (2004) investigate how prior
lending relationships affect the ex-ante pricing of underwritten public securities, such as
7
corporate bonds and IPOs of equity. Ang and Richardson (1994), Kroszner and Rajan
(1994), and Puri (1994) examine how lending relationships affect the default probability
of corporate bonds. Benzoni and Schenone (2004) examine the long-run performance of
equity offerings underwritten by lending-relationship banks. In general, these papers find
little evidence supporting the existence of conflicts of interest. Securities underwritten by
relationship banks are generally priced no worse and sometimes better than similar issues
by non-relationship banks. Overall, public securities underwritten by relationship banks
perform better than those underwritten by non-relationship banks. In addition, previous
studies also investigate whether bundling of lending and underwriting services affects
underwriting fees. Sufi (2004) and Drucker and Puri (2005) find that banks charge lower
underwriting fees when they jointly provide lending and underwriting services.
Nearly all these empirical papers analyze the underwriting part of the deal. Few have
examined key issues about the loan part. Noted exceptions include Brav et. al. (2006),
Calomiris and Pornrojnangkool (2006), and Drucker and Puri (2005). Brav et. al. (2006)
compare loans issued right after an IPO or an SEO with other loans and find no interest
rate differential between them. Note that given their focus on potential risk explanations
for long-run underperformance following equity issues, Brav et al. do not require that the
same bank provides the lending and underwriting services; thus the underwriter may not
be the lender. However, to test the stories outlined in the introduction, in this paper I
examine cases where the lending and underwriting services come from the same bank.
Calomiris and Pornrojnangkool (2006) investigate how the banking relationships that
combine lending and underwriting services affect the terms of lending and the
underwriting costs. They find that banks price loans and underwriting services in a
strategic way to extract value from their relationships. Drucker and Puri (2005)
investigate cases where banks jointly provide lending and SEO underwriting services to
the same customer around the same time. They use the propensity score matching method
to compare the spreads of bundled loans with those of other loans. They find bundled
loans have lower interest rates than other loans. One important limitation of these studies
is that they ignore a bank’s selection based on its private information. If the selection of
8
clients for bundled transactions is not random, then one cannot say for sure how bundling
affects loan pricing without adjusting for the selection carefully.
The secondary loan market has grown dramatically in recent years. However, relatively
few studies have used secondary market loan data. Altman, Gande and Saunders (2004)
compare the informational efficiency of the secondary loan market with the bond market
by checking the market reaction to news events like bankruptcy and default, and find that
the loan market is informatively more efficient. Allen and Gottesman (2005) investigate
the informational efficiency of the loan market compared to the equity market, and find
the equity market and syndicated loan market are highly integrated such that information
flows freely across markets. Moerman (2005) investigates how the information
asymmetry and financial reporting quality of a company affect the bid-ask spread of its
loans in the secondary market. She finds that bid-ask spread is positively related to
information asymmetry and timely incorporation of economic losses into financial
statements reduces the bid-ask spread.
II. Data and Sample
A. Sample Selection and Definition of Bundled Loans
My dataset combines data from different sources. Loan information (such as borrower
identity, lenders, origination date, yield spread, amount, maturity, loan purpose, loan type,
and borrower credit rating) comes from the Loan Pricing Corporation’s (LPC) DealScan
database. Secondary market data for syndicated loans are from the Loan Syndications and
Trading Association (LSTA) and LPC mark-to-market pricing service. Underwriting
information (such as issuer identity, underwriters, issue date, and security type) comes
from Thomson Financial’s Securities Data Corporation (SDC) Platinum database. Rating
migration and default data are from Standard & Poor’s Credit Pro database. I also use
CRSP and Compustat to retrieve relevant company information. Linking the different
databases together is not an easy task, especially since the loan databases only have
9
borrower names as the identifier.6 Therefore, I carefully hand-match the borrowers in
Dealscan to the issuers in SDC, and then I match to the companies in Compustat/CRSP.
My sample period runs from 1994 to 2004. The main reason for this is data availability.
The earliest loans that show up in the secondary market loan database were originated in
1994. Moreover, the loan data in Dealscan became comprehensive after 1994. These are
major reasons why my sample period begins in 1994. That there were few cases of
bundled lending before 1994 should allay any concern regarding my sample start time.
I use the following definition to capture instances in which a bank bundles lending and
underwriting services and jointly provides them to a customer. If a bank gives a solelender loan or leads a loan syndicate and also underwrites a security issue for the same
company in the time period from one year before to one year after the loan origination
date, I classify the loan as a “bundled loan.” Definition of the comparison group (i.e.
“non-bundled loans”) is very important to get meaningful inference. To compare
“bundled loans” with stand-alone loans is not fair in the sense that borrowers that also
issue securities around the loan may be fundamentally different from borrowers that do
not issue securities. To make an apples-to-apples comparison and provide a stronger test
of the “unobserved higher-quality” story against other stories, I define “non-bundled
loans” as follows. If a bank gives a sole-lender loan or leads a loan syndicate to a
company and the same company issues securities underwritten by a bank other than the
loan lead lender in the time period from one year before to one year after the loan
origination date, I classify the loan as a “non-bundled loan.” Therefore, all loans in my
sample have security issues around the loan origination dates, and the only difference
between bundled loans and non-bundled loans is whether underwriting is provided by the
lead lender or not.7
The choices of one year before and one year after are arbitrary. As a robustness check, I
also run the analyses using six-month intervals in the bundling definition. The results are
qualitatively similar. The definition of bundled loans is similar to that used in Drucker
6
7
Some of the loans have ticker information for the borrowers, but many of these prove unreliable.
Including stand-alone loans leads to stronger results.
10
and Puri (2005) with the exception that they only consider seasoned equity offerings
(SEO), whereas I consider all underwritten transactions, including all debt and equity
underwriting, to give a complete picture of bundling.8
I also apply several filters to the loan data. First, I only consider dollar-denominated,
completed loans to US companies. Second, I remove loans to borrowers with one digit
SIC code 6 (financial institutions) and 9 (government agencies, etc.) Third, since most
bundled loans involve public companies, I only consider loans involving them.
B. Loan Characteristics and Borrower Characteristics
The LPC DealScan database from which I obtain loan data has been extensively
documented in the literature.9 LPC reports loan data at the “facility” level as well as the
“deal” level. A deal can be structured into different facilities. Facilities differ in
origination date, type, amount, and maturity. The unit of observation used in this study is
a loan facility. All empirical results in this paper are qualitatively unchanged if I do the
analysis at the deal level, using the facility with the largest amount and earliest
origination date in the deal as a proxy.
The first part of the paper looks at loan pricing. To measure pricing, I use the variable
“All in Spread Drawn” (AISD), which is total annual spread paid over LIBOR for each
dollar drawn down. To control for other loan characteristics that have been shown to
affect loan pricing, I include loan amount, loan maturity, whether the loan is syndicated
or not, loan type, and loan purpose. To control for borrower credit risk and information
opacity, I include credit rating, size, leverage, equity return volatility, and profitability.
Dealscan provides the borrower’s long term debt credit rating at loan origination. I
supplement this with the rating information from the S&P Credit Pro database.
The second part of the paper examines the ex-post change in credit quality. Here I use
two proxies for credit quality. The first one is the distance to default (DD) measure based
on the KMV model and ultimately on the structural model of Merton (1974). Following
8
9
Removing private offerings and removing shelf-registered offerings don’t affect the results.
For detailed information about the Dealscan database, see Carey, Post, and Sharpe (1998).
11
KMV, I define distance to default based on how many standard deviations a company’s
asset value is currently above its debt value. See Appendix B for a detailed definition.
The second proxy is Altman’s (1968) Z-score, which is an index calculated from
accounting ratios. I compute both the distance to default measure and the Z-score
measure up to 2005.
My accounting data are from Compustat. To ensure I use accounting information that is
publicly available at loan origination, I use the following procedure similar to Bharath et.
al. (2005). For a loan made in calendar year t, I use fiscal year t data only if the loan
origination date is at least 6 months after the fiscal year end. Otherwise, I use fiscal year
t-1 data.
For more detailed variable definitions, see Appendix A.
C. Lender Characteristics and Previous Lending Relationships
In order to define bundled loans and control for lender characteristics and previous
relationships with borrowers, I must solve two issues. First, I need to identify the lead
banks in every loan. For sole-lender loans, this is trivial. For syndicated loans, since
many features of loan contracts are not standardized, grouping lenders into lead banks
and participants requires a few subjective criteria. Following Ivashina (2005), the
administrative agent is defined to be the lead bank whenever available. If the
administrative agent is not identified, I go down the list of book runner, lead arranger,
lead bank, lead manager, agent, and arranger. Second, in the late 1990s, many mergers
and acquisitions took place in the banking industry. I carefully track all mergers and
acquisitions among lenders, and following Ljungqvist, Marston, and Wilhelm (2006), I
assume that acquiring banks inherit the prior relationships and market shares of the target
banks.
Following previous literature, variables that capture lender reputation and relationship
strengths are constructed as follows. I use loan market share to proxy for bank
12
reputation.10 The loan market share of bank i in year t is defined as the dollar amount of
loans in LPC arranged by bank i in year t divided by the total dollar amount of loans
made that year. Following Ljungqvist, Marston, and Wilhelm (2006), the lending
relationship strength of bank i with company j is defined as bank i’s share of company j’s
previous loans.11 If a loan is lead-managed by more than one bank, each lead bank is
credited with an equal fractional share. Note my relationship strength variables vary from
zero (no relationship) to one (exclusive relationship). Thus, in addition to capturing the
existence of a relationship, these strength variables capture relationship intensity as
well.12
D. Summary Statistics
Table 1 shows the distribution and summary statistics of bundled loans. There are a total
of 10,053 loan facilities from 1994 to 2004 satisfying the condition to be included in the
study, i.e. there are security issues in the time period from one year before to one year
after the loan origination. These loans are extended to 2,896 companies. Of these, 2,486
(25%) are classified as bundled loans, i.e. the security issue around the loan is
underwritten by the lead lender. Panel A shows that overall, bundled lending trends
positively with time. In 2002, more than 42% of loans in my sample are classified as
bundled. Panel B breaks the sample based on loan type. I use 3 groups: Revolver
(including 364-day facility), Term loan (including term loan B-D (institutional term
loan)), and others.13 67% of the loans in my sample are revolvers. Revolving lines of
credit and term loans have similar fraction of bundled loans. Panel C breaks the sample
based on loan purpose, using seven groups: Acquisition lines, LBO/MBO, Takeover,
Debt Repay/Recapitalization, Corporate Purpose, Working Capital, and other purposes.14
10
This is similar to Megginson and Weiss (1991).
For company j at time t, I sum the loan amounts lead-managed by bank i and its predecessors in the
previous 5 years, then divide it by the total amount of loans borrowed by company j in the previous 5 years.
12
All empirical results continue to hold if I use the number of loans instead of dollar amounts in the
definition of market shares and relationship strength variables. In the cases where a loan facility has more
than one lead bank, I sum up the market shares and relationship strength variables across lead banks.
Results are robust to using the mean value or the maximum value across lead banks.
13
The empirical results are robust to other grouping schemes. For example, grouping 364-day facilities and
revolvers separately and grouping term loans and term loans B-D separately give similar results.
14
The empirical results are robust to grouping Acquisition lines, LBO/MBO and Takeover together; they
are also robust to grouping Corporate Purpose and Working Capital together.
11
13
The fraction of bundled loans differs across the loan purpose groups. I include year fixed
effect, loan type, and loan purpose control in all the analyses. Panel D breaks the sample
by credit rating. Investment-grade borrowers are more likely to receive bundled loans. In
addition, compared to non-bundled loans, the distribution of ratings for bundled loans is
tilted toward investment-grade borrowers: 39% of bundled loans are extended to
investment-grade borrowers, whereas only 28% of non-bundled loans are extended to
investment-grade borrowers. Superficially, this feature of the data is consistent with the
conjecture that banks select their clients more prudently when choosing bundling services
clients.
Panel E contrasts various loan, borrower, and lender characteristics between bundled
loans and non-bundled loans. Univariate comparison suggests that bundled loans yields
are lower than non-bundled loan yields. The median yield spread for bundled loans is 100
basis points, while the median yield spread for non-bundled loans is 150 basis points.
This difference is statistically significant, as is the difference in means. In addition,
bundled loans are generally larger in size, longer in maturity, and more likely to be
syndicated. Borrowers receiving bundled services are usually better rated, larger, more
highly leveraged, less volatile, and more profitable. The lead banks in bundled loans are
generally more reputable and have closer relationships to the borrowers. And the fraction
of loans lead managed by financial institutions other than commercial banks (say
investment banks) is higher in bundled loans. The key element in the story I propose is a
bank’s private information about unobserved client quality. To test this story, I will
carefully control for the observable differences between bundled loans and non-bundled
loans when examining their pricing and ex-post performance.
III. The Pricing of Bundled Loans
A. Empirical Model
Drucker and Puri (2005) document a yield discount between bundled loans and other
loans using the propensity score matching method. However, as the authors acknowledge,
matching models assume that unobservable private information does not affect loan
14
pricing.15 My “unobserved higher-quality” story says that the private information, which
banks use in deciding to jointly provide lending and underwriting services, is also used to
price bundled loans. Thus, one needs to adjust for the endogeneity present in the decision
to bundle before assessing interest rate differentials.16
I implement a “treatment effects” model 17 to explicitly adjust for the endogeneity of
bundling decision. I model loan yield spread as
YieldSpread = δ 0 + δ 1 ⋅ X + δ 2 ⋅ BundledLoan + ε
(1)
where X is a set of exogenous observable characteristics of loan, borrower, and lender,
and BundledLoan is a dummy variable taking the value one if the loan is bundled, and
zero otherwise. Coefficient δ 2 is the key parameter of interest. It estimates the interest
rate difference between bundled loans and non-bundled loans.
According to my “unobserved higher-quality” hypothesis, bundling is not exogenous. I
assume the bank’s decision model is
BundledLoa n = 1 if β ⋅ Z + υ > 0
(2)
BundledLoan = 0 if β ⋅ Z + υ <= 0
where Z is a set of observable variables that can potentially affect whether the loan is
bundled, and υ is an error term. Following the standard assumption in Heckman’s (1979)
two stage procedure, I assume the error terms ε and υ follow a bivariate normal
distribution with means zero and standard deviations σ e and 1 and correlation ρ. Under
this assumption,
15
See Li and Prabhala (2005) for an excellent survey of matching and self-selection models in finance.
I replicate the propensity score matching used in Drucker and Puri (2005) on my sample. Even in my
sample (which includes both equity and debt issues, and requires that all loans have underwriting around),
their results hold: Bundled loans have lower interest rates than matched non-bundled loans. These results
are available on request.
17
The same model has also been used in Campa and Kedia (2002) among others.
16
15
E (YieldSpread | BundledLoan = 1) = δ 0 + δ 1 ⋅ X + δ 2 + E (ε | BundledLoan = 1)
= δ 0 + δ 1 ⋅ X + δ 2 + ρ ⋅ σ e ⋅ λ1
where λ1 = E (υ | BundledLoan = 1) =
φ (β ⋅ Z )
Φ( β ⋅ Z )
and E (YieldSpread | BundledLoan = 0) = δ 0 + δ 1 ⋅ X + E (ε | BundledLoan = 0)
= δ 0 + δ 1 ⋅ X + ρ ⋅ σ e ⋅ λ2
where λ 2 = E (υ | BundledLoan = 0) =
− φ (β ⋅ Z )
1 − Φ(β ⋅ Z )
OLS estimate of δ 2 is given by
E (YieldSpread | BundledLoan = 1) − E (YieldSpread | BundledLoan = 0)
= δ 2 + ρ ⋅σ e ⋅
φ (β ⋅ Z )
(3)
Φ ( β ⋅ Z )(1 − Φ ( β ⋅ Z ))
Therefore, if the error terms ε and υ are correlated (i.e. ρ ≠ 0 ), then the OLS estimate
of δ 2 is biased, and the direction of bias depends on the sign of ρ. My “unobserved
higher-quality” hypothesis says that banks provide bundled services to higher-quality
clients. A bank’s private information about unobserved higher quality of clients, which
induces the bank to provide bundled services, also causes the bank to charge lower
interests. In equation (2), error termυ includes variables affecting the bank’s decision of
bundling not explained by observables. Thus, υ can be viewed as the bank’s private
information about client quality. If, as hypothesized by my story, a bank’s private
information and the interest rate charged are negatively correlated (i.e. correlation
ρ between error terms ε and υ is negative), then the estimated interest rate discount for
bundled loans using OLS is downward biased.
To account for the effects of selection bias, I follow a two-step estimation procedure
detailed in Maddala (1983). I first estimate equation (2) using a Probit model to get a
consistent estimator of β. I then use the estimated β to calculate λ 1 and λ 2 , the correction
terms for bank’s selection. In the second step, I estimate δ by estimating
16
YieldSpread = δ 0 + δ 1 ⋅ X + δ 2 ⋅ BundledLoan + δ λ ⋅ lambda + µ
(4)
where δ λ = ρ ⋅ σ e and lambda is the correction term for selection and defined as
lambda = λ1 ⋅ BundledLoan + λ 2 ⋅ (1 − BundledLoan)
In this equation, the coefficient δ 2 indicates whether there is an interest rate discount
after correcting for selection, and the coefficient δ λ captures the relation between bank’s
private information and loan interest rate.
B. Identification and Instrumental Variable
For identification, the bundled-lending decision equation (2) must include one or more
instrumental variables not included in the loan yield equation (1).18 An instrument must
satisfy two conditions: (a) it affects whether the loan is bundled or not; and (b) it is not
directly related to the interest rate. My choice of instrument is guided by economic
considerations and is based on suitably exogenous changes in regulation.
Recall that the difference between bundled and non-bundled loans is whether the security
issue around the loan origination date is underwritten by the lead lender. I use as
instrument time series and cross-sectional variation in regulatory constraints on a lender’s
ability to underwrite such securities. This variable is constructed from the graduated way
in which commercial banks were allowed to (re-) enter the underwriting market. On
January 18, 1989, the Federal Reserve began to allow so called Section 20 subsidiaries of
commercial banks to underwrite first corporate debt and later equity securities subject to
a 5 percent annual revenue cap. This cap was raised to 10 percent on September 14, 1989
and then to 25 percent on March 6, 1997 (announced on December 20, 1996). On
November 12, 1999, the cap was lifted following the passage of the Gramm-Leach-Bliley
Act. In addition to this time series variation, there is cross-sectional variation in the dates
18
Without instrumental variables, the inverse mills ratio terms are simply non-linear function of X, so the
model can still be identified by assuming normality. However, it is well known that identification by
functional form alone in this model often leads to very unstable and unreliable estimates of the parameters
(Little, 1985).
17
on which banks were granted underwriting authority for the first time, and these dates
sometimes varied for different types of securities for a given bank.
How binding the revenue cap is at a particular time clearly affects a lending bank’s
underwriting decision, which translates into whether a loan is bundled or not.
Unfortunately, commercial banks’ Section 20 underwriting revenues are not publicly
disclosed, so it is not possible to directly measure directly how constrained each bank is
at any point in time. Instead, to measure how constrained a bank might be at the time of a
loan client’s security issue, for each lead lender, I measure how long the bank has
operated under its then-current cap. Consider a hypothetical bank receiving Section-20
underwriting approval in 1990 and examine its underwriting situation over time. In 1991,
the bank was probably not bound by the 10% revenue cap since it just received
permission to underwrite securities. However, in 1996, having had five more years to
grow its underwriting business, the bank probably felt more constrained by the cap. The
increase of the revenue cap in 1997 loosened the constraint dramatically because the bank
suddenly received greater underwriting freedom.
Thus, the longer the current cap has been in place, the more likely it is that it will bind,
affecting a bank’s probability of bundling a loan in ways that are unrelated to any
characteristics of the borrower, and hence to the required interest rate. Formally, I
measure this as follows:19
Constra int = 1 −
(Tnext _ dereg − Tissue )
(Tnext _ dereg − max(T prev _ dereg , Tsec 20 _ app _ date ))
(5)
where Tnext _ dereg is the next regulatory change date after the security issue, Tissue is the
security issue date, T pre _ dereg is the previous regulatory change date before the security
issue, and Tsec 20 _ app _ date is the Section 20 subsidiary approval date of the lead lender.20 If
19
If there are more than two security issues around the loan, I will consider loan date. Using any of the
security issue dates (earlier date or later date) doesn’t change the results.
20
For investment banks, the constraint is set to be 0, since the regulation is only applied to commercial
banks. For loans before the approval of Section 20 subsidiary, the constraint is set to be 1 since the bank is
not eligible to underwrite the security at that time. By construction, the constraint variable is bounded
18
a security issue is closer to the next regulatory change date (so that the current cap has
been in effect for longer), the lender’s underwriting constraint is more likely to bind, so
the lender is less likely to underwrite the client’s security issue and the loan is less likely
to be bundled. At the same time, since this instrument is constructed from exogenous
regulatory changes, it is difficult to see how it would affect loan yields directly.
Univariate comparison in Table 1 shows that the constraint variable differs significantly
for bundled loans and non-bundled loans. Banks that offer bundled loans have a lower
underwriting constraint. The mean and median differences are both statistically
significant. Table 2 presents the first-stage probit results predicting whether a loan is
bundled or not as a function of the instrument and other controls. The coefficient on the
instrument is significant with the expected sign. Lead lenders with a higher underwriting
constraint at the time of the security issue are less likely to underwrite the deal.21
C. Regression Results
In Table 3, the OLS results suggest that bundled loans offer a yield discount of between
11 and 38 basis points depending on the specification. However, after I adjust for
endogeneity using a treatment effects model, the coefficient on the bundled lending
variable ( δ 2 ) becomes insignificant. The negative coefficient on δ 2 in the OLS
specification is soaked up by the coefficient on lambda, the inverse mills ratio. This sign
change of the coefficient on δ 2 indicates that there is a downward bias in the OLS
estimate. More importantly, the negative coefficient on lambda suggests that banks’
private information about unobserved characteristics of the borrower is negatively
correlated to the interest rate. Therefore, the private information most likely concerns the
borrower’s unobserved good quality, which induces the bank to provide both lending and
underwriting services to the same customer; it also causes the bank to charge a lower
interest rate. So the interest rate discount of bundled loans found in previous studies is
between 0 and 1 to make it comparable across different loans. Value 0 corresponds to no constraint or very
low constraint; on the other hand, value 1 corresponds to very high constraint or complete ineligibility.
21
Note that the instrument captures intrinsic variation in underwriting constraints; extrinsic variation
(between commercial banks and investment banks) is separately controlled for in Model 4 of Table 2.
19
due to the “unobserved” higher client quality, not the bundling. This result is consistent
with the “unobserved higher-quality” story.22
Other variables behave as expected and concur with findings in the existing literature.
Generally, I find larger loans, syndicated loans, and those of longer maturity have lower
yield spreads. Loans given to larger companies and companies with better credit ratings,
lower leverage, lower equity return volatility, or higher profitability offer lower yield
spreads. Bank characteristics and bank-company relationships also affect loan yields.
Loans from banks with better reputation, from banks with closer lending relationships
with the borrower, and from commercial banks offer lower yield spreads.
In conclusion, the results in Table 3 show that after adjusting for the endogeneity present
in the decision to bundle, there is no interest rate discount. The coefficient on the
selection correction term lambda is significant and negative. These results are consistent
with the “unobserved higher-quality” story.
IV. Ex-Post Performance of Bundled-loan clients
The findings of the previous section are generated using an econometric model. The
reliability of the results depends on several assumptions (e.g. bi-variate normality of the
errors in selection and valuation model). To directly support the “unobserved higher
quality” story, I examine the ex-post performance of bundled-loan clients and bundled
loans. In the next two sections, I ask whether bundled-loan clients and bundled loans
perform better after loan origination. This will help to further distinguish the “unobserved
higher-quality” story from the other stories. According to the “unobserved higherquality” story, banks predominantly provide bundled services to clients for whom banks
have more favorable private information. If true, bundled-loan clients and bundled loans
will perform better ex-post. Moreover, examining ex-post performance can also shed
22
For robustness check, I also implement the traditional instrumental variable (IV) model. Again, I find no
interest rate discount using this model. To implement the IV model, I follow Procedure 18.1 in Wooldridge
(2001, page 623). This procedure is different from the traditional 2SLS, since the first stage is not a linear
model. Instead of using the fitted probability from the first stage to directly replace the dummy variable
BundledLoan in the second stage, this procedure uses the fitted probability as an instrument for bundling
status. Wooldridge argues that this procedure is better than directly replacing the dummy variable with the
fitted probability. See Wooldridge (2001) for more details.
20
light on the question how the practice of bundling affects the health and stability of the
financial system.
A. Ex-post Change in Distance to Default and Altman’s Z-score
To the extent that the relevant unobserved characteristic is a client’s credit quality, I
consider the distance to default (DD) measure (based on the KMV-Merton model) and
Altman’s (1968) Z-score; these are the popular proxies for credit quality. I measure expost performance starting at one year after the loan origination date, since I use a oneyear window after loan origination to define bundled loans. I track performance for the
next 2, 3, 4 and 5 years, respectively. For example, the change in distance to default for
the next 4 years is measured as DD 4 years from the loan origination minus DD 1 year
from the loan origination. Distance to default measures how many standard deviations a
company’s asset value is currently above its debt value. Altman’s Z-score is an index
calculated from accounting ratios. Higher values of DD and Z-score are generally
associated with higher credit quality. My story predicts one should observe a larger
increase (or a smaller decrease) in the DD and Z-score measures for bundled-loan clients
than for non-bundled-loan clients.
Univariate results in Table 1 show a clear difference in the ex-post performance of
bundled-loan clients vs. non-bundled-loan clients. Starting one year from loan origination,
the distance to default measure rises for bundled-loan clients and falls for non-bundledloan clients. The difference is both statistically and economically significant. For
example, looking at the change from t+1 to t+4, on average, distance to default for
bundled-loan clients rises by 0.528 and decreases by 0.116 for non-bundled-loan clients.
The difference is 0.644, which is large given the average distance to default level is 2.5.
Table 4 reports regression results with ex-post changes in DD and Z-score as dependent
variables. The unit of observation is still a loan facility. In addition to the controls used
before, I also control for the level of DD or Z-score measured at the month-end before the
loan origination date. The regression results mirror the univariate results and show that
distance to default rises significantly more for bundled-loan clients over the next 2 to 5
21
years. Regressions using the Z-score as the dependent variable give similar results. Other
variables behave as expected. For example, distance to default increases more for more
profitable companies and increases less for companies with higher equity return volatility.
Interestingly, the coefficients on the DD and Z-score level variable are significant
negative, suggesting both DD and Z-score exhibit mean reversion tendencies.
Since all the loans in my sample are accompanied by underwriting around their
origination dates, I also do the analysis controlling for underwriting types (debt or equity)
and event sequence (underwriting before the loan or after). The first two columns in
Table 5 report the results with these additional controls. Results indicate that
underwriting type and event sequence do not significantly affect the change in distance to
default. In an unreported regression, I find similar results using changes in Altman’s Zscore as the dependent variable.
The last two columns in Table 5 test if bundling affects smaller borrowers and larger
borrowers differently and if it affects unrated borrowers and rated borrowers differently.
Interestingly, the effects of bundling on ex-post performance are stronger for smaller
borrowers and unrated borrowers. This is consistent with the “unobserved higher-quality”
story. Smaller borrowers and unrated borrowers are usually more informationally opaque.
Therefore, for these borrowers, a bank’s private information will play a larger role in
differentiating client quality.
The results in this section show that distance to default and Altman’s Z-score increase
more after loan origination for bundled-loan clients. To the extent that these increases
indicate higher client quality, these findings provide additional support for the
“unobserved higher-quality” story.
B. Ex-post Default Rates and Rating Downgrade Probabilities of Bundled-loan clients
Actual default rates and rating downgrade probabilities are alternative measures of
borrowers’ ex-post performance. If bundled-loan clients are indeed higher quality clients,
one would expect to see lower default rates and lower rating downgrade probabilities for
them after loan origination.
22
In this section, I restrict my analysis to companies in my loan sample for which I have
default and rating migration information from Standard & Poor’s Credit Pro database.23
The loan sample is reduced to 4,374, involving 1,551 companies. Default and rating
change data are at the company level. The unit of observation in the analysis is still a loan
facility. I consider whether the borrower defaults or receives rating downgrades within
the next 2 to 5 years. Consistent with the previous section, I examine default and rating
changes starting from one year after loan origination. A default is defined as S&P setting
a company’s credit rating to “D”. A rating downgrade is defined as a borrower’s credit
rating dropping by at least one letter (e.g. from AA to A).24 Rating decreases within the
same letter level, such as from AA+ to AA, are not treated as downgrades.
The default and rating change data extend to March 2006. The latest loan origination date
in my sample is year end of 2004, so I can still track performance for the next two years.
This should mitigate some right-censoring concerns. As a robustness check, I also
conduct the same analyses on samples in which loans are not restricted by the artificial
end date of March 2006. For example, when I consider default rates within the next 3
years, I also analyze the sub-sample that includes only loans originating before March
2003. The results are qualitatively similar.
Figure 1 shows the distribution of default events at the borrower level. Out of the 1,551
companies in the sample, 337 (around 22%) defaulted between 1994 and March 2006 in a
total of 357 default events. Many defaults occurred in 2001 and 2002 after the dot-com
“bubble” burst. The default rates and default distribution across the years are comparable
to those reported in Duffie, Saita, and Wang (2005). The uneven distribution of default
rates across time calls for including year fixed effects in my analysis.
Table 6 reports the results of probit models in which the dependent variable equals one if
the borrower defaults within the next 2, 3, 4 or 5 years, respectively. Regardless of the
time horizon, bundled-loan clients are significantly less likely to default, and the
economic significance is large. For example, compared to non-bundled-loan clients,
23
Otherwise, I am not completely sure if the company does not default or if I fail to find it.
Here I ignore whether there is rating upgrade or not. If I instead define a rating downgrade to occur when
there are rating downgrades but no rating upgrades, the results are similar.
24
23
bundled-loan clients are 1.6% less likely to default within the next 3 years. Considering
the average three-year default rate is around 6%, this is statistically and economically
significant. Other control variables behave as expected. For example, consistent with
Shumway (2001), less profitable borrowers and borrowers with higher leverage and
larger equity return volatility default more.
In Table 7, I consider additional control variables. Consider a borrower’s default rate
within the next 3 years as an example. Although I have controlled for the borrower’s
credit rating in the analysis, I add the distance to default measure to my regressions to
further control for the borrower’s credit risk. As expected, borrowers with larger distance
to default are less likely to default. Furthermore, I also control for underwriting type and
event sequence as in the previous section. Interestingly, I find that borrowers with equity
issues around loan origination are significantly less likely to default. This is because
issuing more equity lowers the leverage and makes default less likely. Event sequence
does not predict default.
The last two columns examine how the effects of bundling on default rates depend on
borrower size and rating status. The effects are again stronger for smaller companies and
unrated companies. Note here the differences in the effects are statistically significant.
These findings are consistent with the “unobserved higher-quality” story.
Default is an extreme event. Therefore, as a robustness check, I also examine borrowers’
rating downgrade probabilities. Table 8 reports multivariate models of rating downgrade
probabilities. The dependent variable is a dummy with value 1 if the borrower receives a
rating downgrade within the next 3 years after loan origination. Results are similar to
those in Table 7. Borrowers of bundled loans are significantly less likely to receive rating
downgrades than borrowers of non-bundled loans. Although not statistically significant,
the results are more pronounced for smaller borrowers and unrated borrowers.
In conclusion, the results in this section show companies receiving bundled services
perform better ex-post. After loan origination, distance to default and Altman’s Z-score
increase more for them, and they are less likely to default or receive rating downgrades.
24
These results support the “unobserved higher-quality” story that bundled transactions are
predominantly provided to higher-quality firms.
V. Ex-Post Performance of Bundled Loans in the Secondary Loan Market
The previous section investigates ex-post performance of bundled loan borrowers in
terms of credit risk. This section looks at ex-post performance in terms of bundled loan
returns in the secondary loan market.
A. Brief Introduction of the Secondary Syndicated Loan Market
The markets for syndicated loans consist of a primary market and a secondary market.
Loans are originated and shared among the syndicate members in the primary market. In
the secondary market, a lender can sell portions of its loan after the close of the primary
syndication. The secondary loan market has grown rapidly in the past 15 years, with
trading volume increasing from $8 billion in 1991 to $176.34 billion in 2005 (see Figure
2, taken from LPC).25
In the beginning, mainly banks traded in the secondary loan market. Recently,
institutional investors, including prime funds, Collateralized Loan Obligations (CLO),
finance companies, hedge funds, and pension funds, have also joined the market.
Individual investors do not participate directly. This exclusion likely removes noise
trading impact on prices and makes syndicated loan prices more informationally efficient.
Altman, Gande, and Saunders (2004) demonstrate the informational efficiency of the
secondary loan market.
B. Sample Selection and Empirical Methods
25
There are many reasons behind the recent strong growth in the secondary loan market, including the
adoption of the Basel Capital Accords, the adoption of SEC Rule 144A, the foundation of the Loan
Syndication and Trading Association (LSTA), the development of the credit derivatives market, the
standardization of the settlement procedures, the decision of the rating agencies to rate corporate syndicated
loans, and so on. For more details about the secondary syndicated loan market, please see Allen and
Gottesman (2005), and Moerman (2005).
25
The LSTA/LPC mark-to-market pricing service contains bid and ask price quotes for
loans traded in the secondary market, averaged across dealers. Bid and ask prices are
quoted as a percentage of par (i.e. cents on one dollar of par value). LSTA obtains these
price quotes from trading desks at institutions that make markets for these loans. To
remove illiquid loans, each loan in the database must have at least two bid quotes and two
ask quotes. According to LPC estimates, the LSTA database covers over 80% of the
trading volume in the secondary loan market in the US. The database covers the period
from January 1999 to December 2004; data is reported on a weekly basis before
November 1999 and on a daily basis afterwards. To control for the overall performance
of the secondary loan market, I also obtain the weekly S&P/LSTA Leveraged Loan Index
(LLI) from Standard & Poor’s. 26 To mitigate concerns of stale quotes and infrequent
trading in the daily part of the LSTA database and to be aligned with the LLI index’s
frequency, my analysis is carried out on a weekly basis. All the empirical results in this
section are robust to using daily data after November 1999.
Since transaction prices are not reported in the database, I use the average of the mean
bid quotes and the mean ask quotes to proxy for the transaction price.27 To examine the
secondary market performance of loans, for each loan, I examine its weekly price change
and denote that as its “return”. Strictly speaking, price changes are not actual holding
period returns. Since loan quote prices are clean (they do not include accrued interest),
actual returns should include interest payments and any repayment during the holding
period. Unfortunately, interest payment dates or repayment information are not available.
I follow previous studies (e.g. Altman, Gande, and Saunders (2004) and Allen and
Gottesman (2005)) and only consider price changes.28 This is valid to the extent that I am
only interested in a loan’s ex-post performance, which should be fully captured by price
26
In order to provide investors with a performance benchmark, LSTA, in conjunction with Standard &
Poor’s/LCD, develop the S&P/LSTA Leveraged Loan Index (LLI). According to LSTA, LLI is the most
comprehensive loan index available to the secondary loan market. At December 31, 2001, the LLI
consisted of approximately 470 facilities and $104 billion in outstanding. This represents approximately
70% of the institutional universe, a coverage comparable to the S&P 500’s coverage of the equity universe.
27
Internal studies by LPC indicate that transaction prices are not considerably different from the average of
bid and ask quotes. The results are robust to using bid price or ask price instead.
28
As a robustness check, I also try to calculate the true holding return. I assume that interest payments
(LIBOR + spread) are equally distributed over the life of the loan and that there are no repayments before
maturity. To be consistent, here I use the S&P/LSTA Leveraged Loan Index with interests and repayments
adjustment. The results are qualitatively the same.
26
changes in the secondary market. Finally, to be consistent, the S&P/LSTA Leveraged
Loan Index (LLI) I use is also based on market value only.
After matching the secondary syndicated loan market data with the primary market data
from DealScan,29 I identify the facilities in my loan sample that trade on the secondary
market. My final sample includes 462 syndicated facilities covering 199 companies.30 Of
these, 177 (38%) are classified as bundled loans.
To take a first glance at the performance of bundled and non-bundled loans, Figure 3
shows price indices for these types of loans together with the S&P/LSTA Leveraged
Loan Index (LLI). I construct the weekly price index for bundled loans as follows. First, I
calculate the average return of bundled loans for each week. Then the price index is
formed as the cumulative average return, with the index value for January 1, 1999 set to
1000. I form the price index for non-bundled loans likewise. Figure 3 shows that bundled
loans perform better than non-bundled loans.
However, the performance difference in Figure 3 may be due to inadequate controls for
known risk factors. To control for risks, I adopt two approaches to compare the
performance of bundled loans vs. non-bundled loans in the secondary market. First,
following Barber and Lyon (1997), I find matched non-bundled loans for each bundled
loan and examine the cross-section of cumulative abnormal return (CAR) and buy-andhold abnormal return (BHAR). Second, I use the calendar time portfolio method first
used by Jaffe (1974), which examine the returns using time series analysis. Both
approaches have certain limitations. For example, as pointed out by Fama (1998) and
Mitchell and Stafford (2000), CAR and BHAR methods may not adequately account for
29
I use the unique identification “Facilityid” in DealScan and the unique identification “Loan Identification
Number”(LIN) from secondary market database to match these two datasets. Facilityid is a unique number
assigned to each facility by LPC. LIN is a 13-letter string uniquely assigned to each syndicated loan traded
on the secondary market. I ignore those loans with LIN missing or less than 13-letters, because either they
are not covered by LPC, or they correspond to a deal (a combination of several facilities).
30
In this sample, there are 54 loans whose origination date is later than the first trade date (for 30 of them,
the gap is within one week). One possibility is the data recording error. Another possible reason is that
these loans are renegotiated loans. According to Kamstra, Roberts and Shao (2006), LPC assigns a new
Facililityid to a loan if it goes through a reduction in pricing, tenor extension, or a collateral release
stipulated as requiring 100% lender vote. However, LSTA will refer to the new loan, although the data
include quotes for original loans and new renegotiated loans. All the empirical results in this section are
robust to removing the 54 loans.
27
potential cross-sectional dependence in returns, while the calendar time portfolio method
is more vulnerable to stale quotes (which might be an important concern given that the
loan market is much less liquid than the equity market.)
B.1. CAR and BHAR
I define CAR and BHAR as follows. Define Ri ,t as the week t return of bundled loan i
and Rmatch _ nonbundled (i ),t as the week t return of the matched non-bundled loan or nonbundled loan portfolios. CAR for bundled loan i is defined as
T
CARi ≡ ∑ ( Ri ,t − Rmatch _ nonbundled ( i ),t )
(6)
t =1
BHAR for bundled loan i is defined as
T
T
t =1
t =1
BHARi ≡ ∏ (1 + Ri ,t ) −∏ (1 + Rmatch _ nonbundled ( i ),t )
(7)
Cross-sectional analysis of CAR and BHAR about the mean and median will show how
bundled loans perform relative to non-bundled loans. To identify the effect of bundling, I
need to find non-bundled loans which are similar to bundled loans. To find the match, I
require that matched non-bundled loans have the same credit rating, come to the
secondary market around the same time (within 30 days), and have the same event
sequence (underwriting before loan or after loan) as the bundled loan they match. I
consider a portfolio of 10 matched non-bundled loans whose sizes come closest to the
bundled loan.31 I calculate returns one year after the loan origination or at the time when
the loan goes to the secondary market, whichever comes later. I consider both one-year
and two-year returns.
B.2. Calendar Time Portfolio Method
31
In many cases, there are only one or two non-bundled loans that can be matched.
28
I form a bundled loan portfolio and hold each loan in the portfolio for a year. Specifically,
I add a bundled loan to my portfolio when it enters the secondary market or one year after
loan origination, whichever comes later; I drop it after the loan has stayed in the portfolio
for a year or if LSTA stops quoting it, whichever comes first. After calculating the equal
weighted return of the portfolio for each week, I regress the portfolio excess returns on
the four Fama-French-Carhart factors and the loan market excess index return:
( RbundledLoan ,t − R f ,t ) = α + β lm ( Rlm ,t − R f ,t ) + β m ( Rm,t − R f ,t ) + β s ⋅ SMBt + β h ⋅ HMLt + β u ⋅ UMDt + ε t
where RbundledLoan ,t is the return of the bundled loan portfolio for week t, R f ,t is the riskfree interest rate, ( Rlm ,t − R f ,t ) is the excess return of the loan market index (calculated
from the LLI index) and ( Rm ,t − R f ,t ) , SMB, HML and UMD are the traditional market,
size, book-to-market, and momentum factors. The non-bundled loan portfolios are
constructed and analyzed analogously. If the model adequately controls for the risk
factors affecting loan returns, then comparison of alpha for bundled loan portfolio and
non-bundled loan portfolio will show how bundled loans perform relative to non-bundled
loans after controlling for risks.
C. Results
Panel A of Table 9 shows the results for CAR and BHAR. On average, one year BHAR
and CAR of bundled loans are around 3.76% and 5.21%. Both are statistically significant.
Given that long run abnormal return might have positive skewness (Barber and Lyon
(1997)), I also compute the bootstrap adjusted t-test of the mean and Wilcoxon test of the
median. Results from bootstrap adjusted t-test are similar and both significant. Median
BHAR and CAR are 0.33% and 0.29%. Only the median BHAR is significant. The
results for two-year BHAR and CAR are qualitatively similar.
Panel B of Table 9 reports the time series regression results of calendar time portfolio
method. After adjusting for the risk factors, the bundled loan portfolios show no
abnormal returns; however, the non-bundled loan portfolios have significant negative
29
abnormal returns. These findings together with the positive BHAR and CAR results in
Panel A show that bundled loans perform better than non-bundled loans in the secondary
market; they are consistent with the findings in the previous sections and provide
additional support for the “unobserved higher-quality” story.
There are two important comments here. First, although I try to find good matches for
each bundled loan in constructing my CAR and BHAR statistics and try to control for
risk factors in the calendar time portfolio method, there is really no generally accepted
asset pricing model for loan returns to guide my choices of matching variables and risk
factors. Therefore, these results should be interpreted with caution. Second, although
better ex-post performance of bundled loans is consistent with the observation that these
loans are higher-quality ones, there is still an unresolved puzzle. I start to measure returns
one year after loan origination; thus, at that time, the market already knows whether a
loan is bundled or not. If the market is efficient, then one should not observe any
difference in return performance between bundled and non-bundled loans since the
market should have incorporated the information. So my findings suggest that either there
are certain levels of inefficiency in the secondary loan market, or I am not sufficiently
controlling for risk. I will leave differentiating between these explanations to future
research.
VI. Conclusion
Bank mergers and deregulation have increased the potential for banks to provide loans
and underwriting services to the same corporate customer. How does this greater freedom
affect banks, their customers, and the financial system? Previous literature has tried to
answer this question by examining the impact of bundling services on underwriting. This
paper looks at the other part of question: how bundling services affects lending. Financial
media and previous literature seem to suggest that loans bundled with underwriting deals
command lower interest rates due to banks’ strategic behavior (“pay to play”) or
informational economies of scope. In this paper, I find that there is no interest rate
discount in bundled loans after adjusting for the endogeneity arising from a bank’s
decision to manage its risk exposure to a client. My results could help alleviate concerns
30
about illegal product tying behavior of universal banks, which has attracted attention
from both the media and regulators, particularly in the wake of the Enron and WorldCom
bankruptcies.
My results fit nicely with an “unobserved higher-quality” story that banks choose to
provide joint lending and underwriting mainly to their higher-quality customers based on
the banks’ private information. This unobserved higher quality explains the interest rate
discount observed in previous studies. As further support for this story, I examine the expost performance of bundled-loan clients and bundled loans. I find that bundled-loan
clients have better ex-post performance, in terms of larger increases in the distance to
default and Altman’s Z-score measures, lower default rates, and lower rating downgrade
probabilities. These results are stronger for smaller companies and unrated companies. In
addition, using a new database for the secondary loan market, I find that bundled loans
perform better in the secondary loan market. All of these results support the “unobserved
higher-quality” story. The ex-post performance analysis also supports the rationale of the
Gramm-Leach-Bliley Act, which allows financial institutions to offer lending and
underwriting services under a single roof, despite concerns over the potential conflicts of
interest this generates. My results suggest that these concerns are unfounded; banks are
rationally selective when providing joint services.
There are still questions warranting further research. First, this paper shows that there is
no interest rate discount for loans bundled with underwriting. Then what benefits do the
customers of the bundled services gain? Maybe it is easier for them to obtain future
credits from the bank after receiving bundled services. Or maybe the customers will
receive discounts in underwriting fees. Drucker and Puri (2005) show that customers do
pay lower underwriting fees in secondary equity offerings when they also borrow from
the underwriter around the SEO. Is this true for other underwriting services? Second, in
this paper, I mainly investigate the decision process of the banks and focus on the private
information banks have about their customers’ quality. In reality, banks and customers
might associate by mutual choice. How to incorporate the two-sided matching problem in
the decision process warrants further work. Third, the secondary loan market has grown
31
rapidly in recent years. Despite its large size, we still have very limited understanding of
this market, and we still do not have a good asset pricing model for loan returns. These
are interesting topics for future research.
32
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35
Appendix A: Variable Definitions
Variable
Loan Characteristics
Definition
Source
BundledLoan
Dummy =1 if the lead bank of the loan also underwrites a security
issue for the borrower in the time period from one year before to
one year after the loan origination date, =0 otherwise
Dealscan/SDC
YieldSpread
All-in Spread Drawn, defined as total annual spread paid over
LIBOR for each dollar drawn down from the loan
Facility amount in millions
Maturity of the loan in months
Dummy=1 if the loan is syndicated, =0 otherwise
Dealscan
Facility Size
Maturity
Syndicate
Loan Type
2 dummy variables to capture 3 loan types:, Revolver (including
364-day Facility), Term Loan (including Term Loan B-D
(Institutional Term Loan)), Other loan type
Dealscan
Dealscan
Dealscan
Dealscan
6 dummy variables to capture 7 loan purposes: Acquisition lines,
LBO/MBO, Takeover, Debt Repay/Recapitalization, Corporate
Purpose, Working Capital, and other purposes.
Dealscan
S&P senior debt rating
(1 to 11)
Standard & Poor's Senior Debt Rating: AAA=1, AA=2, A=3,
BBB=4, BB=5, B=6, CCC=7, CC=8, C=9, D=10, NR or Rating
Missing=11 (In cases where rating is missing in Dealscan, I use
data from S&P Credit Pro database to fill in if available)
Dealscan/S&P
Credit Pro
Junk Rated
Dummy=1 if the S&P senior debt rating is junk rated (BB or
below), =0 otherwise
Dummy=1 if the S&P senior debt rating is missing, =0 otherwise
Dealscan/S&P
Credit Pro
Dealscan/S&P
Credit Pro
Compustat
Loan Purpose
Borrower Characteristics
Not Rated or Missing
Rating
Leverage
Equity Volatility
Book Leverage [=Book Debt/Asset, where Book Debt= Data181Data35+prefer stock, and prefer stock =Data10, Data56, or
Data130, in order of availability]
Stock return volatility calculated from daily return in previous year
(in percentage)
Market capitalization in billions, defined as stock price*share
outstanding measured at the previous month end of loan origination
CRSP
ROA
Distance to Default
(DD)
Return on Asset (Data172/Data6)
Proxy for default risk based on Moody's KMV. I use an
approximation based on Crosbie (1999): DD=(Market Value of
Assets-Debt)/(Market Value of Assets*Asset Volatility). See
appendix B for detailed construction method
Compustat
Compustat/CRSP
Altman's (1968) Z-score
Z-score based on Altman (1968). Z_score=3.3*EBIT/Total Asset
(data178/data6)+0.999*Sales/Total Asset
(data12/data6)+0.6*Market Equity/Total liability
(data199*data25/data181)+1.2*Working Capital/Total Asset
(data179/data6)+1.4*Retained Earning/Total Asset (data36/data6)
Compustat
Market Cap (ME)
CRSP
36
Lender Characteristics
Lead bank Loan Market
Share
Lead bank lending
relation strength
Dollar amount of loans lead by the lead bank as the fraction of the
total amount of loans issued in the market (measured at t-1)
Fraction of loans borrowed by the company that are arranged by the
lead bank in the previous 5 years based on dollar amount. 0 means
no lending relationship, 1 means exclusive lending relationship
Commercial bank lead
loan
Instrumental Variables
Dummy=1 if the lead bank is a commercial bank
Manual
work/Dealscan
Lender Underwriting
Constraint
1-(next regulation change date-security issue date)/(next regulation
change date-previous regulation date (or section 20 approval date,
which ever is later)). Regulation change dates are the dates when
Federal Reserve increased the revenue cap on section-20 subsidiary
(1989/9/14, 1997/3/6, 1999/11/12). Section 20 approval dates are
manually collected. This variable is bounded between 0 and 1 for
commercial banks, and 0 for investment banks.
Dealscan/Manual
Work
Default and Rating Migration Variables
Default within next X
Dummy=1 if the borrower defaults within next X years after loan
years (X=2, 3, 4, 5)
origination, =0 otherwise. A borrower is defined to default if S&P
Downgrade with next X
years (X=2, 3, 4, 5)
set the rating to "D".
Dummy=1 if the borrower receives rating downgrade within next X
years after loan origination, =0 otherwise. Borrower receives rating
downgrade if the credit rating of the company decreases at least one
letter (e.g. from AA to A). The rating decrease within the same
letter level (e.g. from AA+ to AA) is not treated as downgrade.
Dealscan
Dealscan
S&P Credit Pro
S&P Credit Pro
Secondary Market Analysis Variables
Loan Return
Weekly change in loan prices
LSTA/LPC
mark-to-market
pricing services
S&P/LSTA Loan Index
Excess Return
Equity Market Excess
Return
SMB
Weekly return calculated from weekly S&P/LSTA Leveraged Loan
Index minus the risk free rate
Weekly excess return of equity market portfolio (aggregated from
daily excess return of equity market)
Weekly Fama-French size factor (aggregated from daily FamaFrench size factor)
Weekly Fama-French value factor (aggregated from daily FamaFrench value factor)
Weekly Momentum factor (aggregated from daily momentum
factor)
Standard &
Poor's
Kenneth French's
website
Kenneth French's
website
Kenneth French's
website
Kenneth French's
website
HML
UMD
37
Appendix B: Calculation of Distance to Default (DD)
Distance to default is a market based measure of default risk. It is ultimately based on the
structural model of Merton (1974). Here I use an approximation based on Crosbie (1999):
V A − Debt
/σ A
VA
where VA is the market value of asset, σ A is the asset volatility, and Debt is face of the
debt, taken to be a firm’s short term debt plus one-half its long-term debt (Compustat data
items 9 and 44 respectively), following KMV. This measure has an easy interpretation: it
measures how many standard deviations a company’s asset value is currently above its
debt value.
Distance to Default (DD) ≡
VA and σ A are not directly observable and estimated from the following procedure
similar to Vassalou and Xing (2004). Using daily data from the twelve months prior to
the date at which I wish to calculate the distance to default, I calculate the volatility of the
equity return, σ E , providing that I have at least thirty observations. I propose initial
VE
value of σ A : σ A =
σ E , and plug this value of σ A into the following equation
V E + Debt
to infer V A for each trading day of the past twelve months.
V E = V A N (d1 ) − X ⋅ e − rT N (d 2 )
where d1 =
ln(V A / Debt ) + (r + σ A2 / 2)T
σA T
and d 2 = d1 − σ A T
Here I set T=1 and r is set to be t-bill rate.
This yields a time series of V A . Then I calculate the implied log return on assets each day
and use that return series to generate the new estimates of σ A . I proceed in this manner
until it converges (the absolute difference in adjacent σ A is less than 10 −4 ).
There is another way to define distance to default:
Distance to default ≡
ln(V A / Debt ) + ( µ − σ A2 / 2)T
σA T
where µ is the daily change in ln( V A ) over the past 12 months. If I use this definition,
the results are similar.
38
Table 1: Summary Statistics of Bundled Loans vs. Non-bundled Loans.
The sample consists of loans satisfying the following condition: borrower also issues securities (debt or
equity) in the time period from one year before to one year after the loan origination date. Bundled loans
are defined as loans that the lead bank of the loan acted as underwriter for the security issue. Accordingly,
non-bundled loans are defined as loans that the lead bank of the loan and underwriter of security issue are
different financial institutions. Loan data are from LPC’s DealScan database and security issuance data are
from SDC Platinum. The sample period is from 1994 to 2004. For the loan data, I also restrict the sample to
dollar denominated completed loans to US public companies, excluding loans to financial institutions and
government agencies etc. (first SIC digit 6 or 9). Final sample consists of 10,053 loan facilities. For
securities issuance data, I consider both public and private offerings of both equity and debt. All variables
are defined as in Appendix A.
Panel A: Distribution of bundled loans over time (# of facilities)
Year
Non-bundled loans
Bundled Loans
1994
850
139
1995
767
107
1996
998
168
1997
1192
221
1998
860
193
1999
593
236
2000
574
219
2001
514
344
2002
420
315
2003
406
290
2004
393
254
Total
7567
2486
Panel B: Distribution of bundled loans by loan type (# of facilities)
Loan Type
Non-bundled loans
Bundled Loans
364-day facility
1069
636
Revolver
4097
933
Term loan
1004
231
Term loan B-D
327
159
Other loan type
1070
527
Panel C: Distribution of bundled loans by loan purpose (# of facilities)
Loan Purpose
Non-bundled loans
Bundled Loans
Acquisition lines
387
117
LBO/MBO
52
21
Takeover
870
355
Debt Repay /Recapitalization
1892
350
Corporate purpose
2419
774
Working Capital
850
230
Other loan purpose
1093
639
Panel D: Distribution of bundled loans by borrower rating (# of facilities)
Rating
Non-bundled loans
Bundled Loans
Investment Grade
2108
968
27.86%
38.94%
Junk Grade
1820
777
24.05%
31.26%
Not Rated or Missing
3639
741
48.09%
29.81%
Total
989
874
1166
1413
1053
829
793
858
735
696
647
10053
Fraction
14.1%
12.2%
14.4%
15.6%
18.3%
28.5%
27.6%
40.1%
42.9%
41.7%
39.3%
24.7%
Total
1705
5030
1235
486
1597
Fraction
37.3%
18.5%
18.7%
32.7%
33.0%
Total
504
73
1225
2242
3193
1080
1732
Fraction
23.2%
28.8%
29.0%
15.6%
24.2%
21.3%
36.9%
Total
3076
Fraction
31.5%
2597
29.9%
4380
16.9%
39
Table 1: Summary Statistics of Bundled Loans vs. Non-bundled Loans (Continued).
Panel E: Difference between bundled loans and non-bundled loans
Non-bundled
loans
Loan Characteristics
(N=7567)
Yield Spread
Mean
167.366
Median
150.000
Loan Size (facility amount in millions)
Mean
278.688
Median
100.000
Maturity (months)
Mean
45.191
Median
37.000
Syndicate
Mean
0.693
Median
.
Borrower Characteristics
S&P senior debt rating (from 1 to 11)
Mean
7.575
Median
6.000
Leverage
Mean
0.654
Median
0.614
Equity Return Volatility (in percentage)
Mean
2.936
Median
2.536
Market Cap (billions)
Mean
4.544
Median
0.682
ROA
Mean
-0.030
Median
0.034
Lender Characteristics
Lead bank Reputation (t-1)
Mean
0.069
Median
0.029
Lead bank lending relation strength
Mean
0.435
(previous 5 years based on $)
Median
0.273
Commercial bank lead loan
Mean
0.931
Median
.
Instrumental Variable
Lender Underwriting Constraints
Mean
0.436
Median
0.351
Ex-post Performance
change in Distance to Default from t+1 to t+2
Mean
-0.079
Median
-0.076
change in Distance to Default from t+1 to t+3
Mean
-0.078
Median
-0.060
change in Distance to Default from t+1 to t+4
Mean
-0.116
Median
-0.150
change in Distance to Default from t+1 to t+5
Mean
-0.162
Median
-0.248
change in Altman's Z-score from t+1 to t+2
Mean
-0.324
Median
-0.032
change in Altman's Z-score from t+1 to t+4
Mean
-0.770
Median
-0.191
Bundled
Loans
(N=2486)
140.252
100.000
566.791
285.000
48.886
39.000
0.878
.
P-value
<.0001
<.0001
<.0001
<.0001
<.0001
0.1702
<.0001
.
6.341
5.000
0.660
0.636
2.655
2.378
9.965
2.362
0.024
0.034
<.0001
<.0001
0.3327
<.0001
<.0001
<.0001
<.0001
<.0001
0.0511
0.1016
0.136
0.116
0.542
0.701
0.858
.
<.0001
<.0001
<.0001
<.0001
<.0001
.
0.192
0.000
<.0001
<.0001
0.260
0.177
0.612
0.491
0.528
0.442
0.281
0.201
0.004
0.032
-0.016
0.019
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
40
Table 2: Probit Models of Bundled Loan on Instrumental Variable and Other
Controls (First Stage of Treatment Effects Model).
This table presents the first-stage probit estimates of a dummy for whether or not a loan is bundled on the
instrumental variable and other controls. The dependent variable, BundledLoan, in an indicator equal 1 if
the loan is “Bundled” as defined in Table 1. All right-hand side variables are defined as in Appendix A. All
models are estimated using probit maximum likelihood estimation. Heteroskedasticity-consistent t-statistics
are shown in italics. Intercepts, year fixed effects and industry fixed effects are included in all models, and
not reported. Loan type and loan purpose are controlled in Probit2 – Probit4, and not reported. I use ***, **,
and * to denote significance at the 1%, 5%, and 10% level (two-sided), respectively.
Dependent Variable= BundledLoan
Instrumental Variable
Lender Underwriting Constraint
Probit1
Probit2
Probit3
Probit4
-0.954***
-15.68
-0.755***
-11.43
-0.668***
-9.11
-0.394***
-5.12
0.0003***
9.14
0.005***
8.16
0.401***
9.05
0.0002***
6.67
0.004***
6.67
0.21***
4.06
0.119
1.12
0.086
0.81
0.136
1.21
0.097
0.79
0.154
0.85
-0.007
-0.07
0.32***
4.2
-0.082***
-4.92
0.002*
1.93
0.623***
3.61
8954
0.146
1232.3***
7519
0.133
1006.22***
0.0001***
3.58
0.004***
5.95
0.151***
2.84
0.044
0.41
-0.006
-0.06
0.016
0.14
-0.021
-0.16
0.088
0.47
-0.037
-0.34
0.278***
3.5
-0.081***
-4.71
0.001
1.07
0.545***
3.11
2.864***
15.93
0.151***
3.74
-0.607***
-9.35
7519
0.168
1246.44***
Loan Characteristics
Facility Size
Maturity
Syndicate
Borrower Characteristics
rated "A"
rated "BBB"
rated "BB"
rated "B"
rated "CCC" or below
not rated or rating missing
Leverage
Equity Return Volatility
Market Cap
ROA
Lender Characteristics
Lead Bank Reputation
Lead Bank Lending Relationship Strength
Commercial Bank Lead Loan
N
Pseudo R2
Wald-test: all Coeff. = 0 (χ2)
9876
0.093
923.53***
41
Table 3: The Pricing of Bundled Loans.
This table presents the results of OLS regressions and the treatment effects model as specified in equation
(4). The dependent variable is “All In Spread Drawn” (AISD). All variables are defined as in Appendix A.
In the treatment effects model, I use Probit4 in Table 2 as the first-stage. Industry and year fixed effects are
included in all models and not reported. Loan type and loan purpose are controlled in all models and not
reported. t-statistics based on robust standard errors are shown in italics. For OLS regressions, the standard
errors are Heteroskedasticity-consistent. For the treatment effects model, since the second stage involves a
generated regressor, lambda, which is estimated with sampling error, the second-stage covariance matrix is
not consistent. Consistent standard errors are calculated by bootstrapping with 500 replications. I use ***, **,
and * to denote significance at the 1%, 5%, and 10% level (two-sided), respectively. This table shows that
after adjusting for endogeneity, there is no interest rate discount for bundled loans.
42
Table 3: The Pricing of Bundled Loans (Continued).
LHS Var=YieldSpread
BundledLoan
OLS1
-37.541***
-10.76
OLS2
-19.679***
-6.59
OLS3
-11.313***
-4.61
OLS4
-11.203***
-4.49
Treatment
Effects
Model
28.493
1.25
-23.35*
-1.75
-0.026***
-6.18
0.281***
4.61
-71.963***
-18.27
-0.012***
-8.23
-0.064
-1.15
-23.743***
-5.83
-0.01***
-6.96
-0.093*
-1.68
-18.503***
-4.57
-0.011***
-5.57
-0.082
-1.62
-20.002***
-5.74
-4.989
-1.57
9.062***
2.69
68.145***
15.23
117.98***
19.92
145.969***
6.73
57.415***
14.86
17.129***
2.98
27.532***
19.69
-0.223***
-3.96
-37.54***
-2.58
-4.748
-1.42
10.022***
2.86
70.229***
15.46
116.734***
19.45
140.178***
6.57
57.036***
14.51
17.554***
3.09
26.395***
19.05
-0.26***
-5.21
-34.819**
-2.4
-5.452
-0.77
9.711
1.37
70.234***
9.44
117.605***
14.61
138.665***
11.29
57.014***
8.08
14.343***
2.82
27.05***
25.95
-0.272***
-3.66
-38.497***
-4.96
-85.491***
-3.34
-15.377***
-5.73
-35.533***
-4.58
65.288***
2.73
6440
0.567
Lambda
Loan Characteristics
Facility Size
Maturity
Syndicate
Borrower Characteristics
rated "A"
rated "BBB"
rated "BB"
rated "B"
rated "CCC" or below
Not Rated or Missing Rating
Leverage
Equity Return Volatility
Market Cap
ROA
Lender Characteristics
Lead Bank Reputation
Intercept
192.496***
12.23
186.514***
11.92
34.14**
2.26
-46.195***
-4.48
-14.11***
-5.64
-45.265***
-5.03
80.004***
4.64
N
R-square
8159
0.068
7752
0.336
6504
0.554
6440
0.566
Lead Bank Lending Relationship Strength
Commercial Bank Lead Loan
43
Table 4: Ex-Post Performance of Bundled-Loan Clients (Change in Distance to Default and Altman’s Z-score).
This table presents the results of ex-post performance of borrowers measured by changes in distance to default (DD) and Altman’s Z-score. The changes are all
measured relative to the level one year after the loan origination month end (t+1). The dependent variables for the first four columns are change in borrower’s
distance to default from one year after loan origination to X years after loan origination (X=2, 3, 4, 5). The dependent variables for the last 2 columns are change
in borrower’s Altman’s (1968) Z-score from one year after loan origination to X years after loan origination (X=2, 4). Results for X=3 and 5 are similar and not
reported. All variables are defined as in Appendix A. Loan type, loan purpose, industry and year fixed effects are included in all models and not reported.
Heteroskedasticity-consistent t-statistics are shown in italics. I use ***, **, and * to denote significance at the 1%, 5%, and 10% level (two-sided), respectively.
This table shows that bundled-loan clients perform better ex-post.
Dependent Variable=
Bundled Loan
Distance to Default before Loan
changes in distance to default (DD) from t+1 to
t+2
t+3
t+4
t+5
0.147***
0.276***
0.136***
0.117**
4.19
6.63
2.74
2.09
-0.105***
-0.202***
-0.216***
-0.32***
-5.73
-8.63
-8.27
-10.22
Z-score before loan
Loan Characteristics
Facility Size
Maturity
Syndicate
Borrower Characteristics
rated "A"
rated "BBB"
rated "BB"
rated "B"
changes in Z-score from t+1 to
t+2
t+4
0.084*
0.145*
1.78
1.75
-0.211***
-6.52
-0.413***
-8.27
0.0000
0.85
0.0000
0.01
0.018
0.5
0.0000
1.15
-0.001**
-2.16
0.097**
2.12
0.0001*
1.95
-0.001
-0.97
0.036
0.67
0.0001*
1.91
-0.001**
-2.08
0.145**
2.37
-0.0001
-0.34
0
-0.2
-0.031
-0.29
-0.0001
-1.32
0.002*
1.74
-0.084
-0.5
-0.012
-0.14
0.09
1.06
0.053
0.59
0.001
0.01
-0.073
-0.72
0.068
0.64
0.055
0.48
0.023
0.18
-0.06
-0.56
0.099
0.88
0.062
0.52
-0.032
-0.25
-0.366***
-2.98
-0.298**
-2.27
-0.391***
-2.76
-0.589***
-3.61
-0.273***
-2.75
-0.398***
-3.9
-0.348***
-2.82
-0.437***
-2.93
-0.423**
-2.51
-0.616***
-3.89
-0.652***
-3.39
-0.792***
-3.46
rated "CCC" or below
not rated or rating missing
Leverage
Equity Return Volatility
Market Cap
ROA
Lender Characteristics
Lead Bank Reputation
Lead Bank Lending Relationship
Strength
Commercial Bank Lead Loan
Intercept
N
R-square
0.225
1.35
0.084
0.97
-0.182***
-3.07
-0.011
-0.77
-0.001
-1.07
0.394***
4.08
-0.023
-0.12
0.238**
2.25
-0.037
-0.45
-0.082***
-4.55
0
0.38
0.69***
4.69
0.213
0.95
0.235**
2.08
-0.107
-1.16
-0.085***
-3.73
-0.001
-0.54
0.624***
2.68
-0.314
-1.44
-0.132
-0.98
-0.145
-1.24
-0.103***
-3.66
0
0.18
0.445
1.56
-0.302
-1.55
-0.371***
-3.47
-0.244
-1.1
-0.123***
-2.96
0.001
0.49
-0.125
-0.2
-0.329
-0.89
-0.708***
-4.45
0.426
1.31
-0.225***
-2.82
-0.004**
-1.98
2.673***
2.68
0.024
0.14
0.017
0.51
0.063
1.25
0.301**
2.03
-0.131
-0.6
-0.002
-0.05
0.104
1.55
0.604***
3.35
0.316
1.08
0.037
0.78
0.109
1.51
0.284
1.39
0.223
0.61
0.037
0.69
-0.064
-0.74
0.639**
2.54
0.306
1.36
-0.059
-0.82
-0.106
-0.75
1.238***
4.82
-0.147
-0.27
-0.101
-0.83
-0.028
-0.13
1.599***
3.87
5568
0.192
4657
0.382
3819
0.458
3024
0.508
5289
0.132
3687
0.246
45
Table 5: Ex-post Change in Distance to Default Conditional on Information
Opaqueness
This table presents the results of borrower’s ex-post performance by considering additional controls and
effects of information opaqueness. Dependent variable is change in distance to default (DD) from one year
after loan (t+1) to 4 years after loan (t+4). Considering other time horizon generates similar or even
stronger results. Recall all the loans in my sample have underwriting around. So in first column, I control
for the event sequence, i.e. whether the underwriting is before loan or after loan. In second column, I
additionally control for the underwriting type, debt or equity. In third column, I examine the different
effects of bundling on ex-post performance for rated and unrated companies. In last column, I examine the
different effects of bundling on ex-post performance for smaller and larger companies. Smaller companies
are those with market capitalization below sample median. Larger companies are those with market
capitalization above sample median. All other variables are defined as in Appendix A. Industry and year
fixed effects are included in all models and not reported. Loan type and loan purpose are controlled in all
models and not reported. Heteroskedasticity-consistent t-statistics are shown in italics. I use ***, **, and * to
denote significance at the 1%, 5%, and 10% level (two-sided), respectively. This table shows that better
performance of bundled-loan clients are more pronounced for not rated companies and smaller companies.
Dependent Variable= change of distance to default from t+1 to t+4
BundledLoan
BundledLoan*Not rated ( β 1 )
Control for
Event
Sequence
0.137***
2.75
Control for
Underwriting
Type
0.132***
2.63
Effects of
Rating
0.173**
2.24
BundledLoan*Rated ( β 2 )
0.115*
1.87
BundledLoan*Small ( β 3 )
0.174**
2.24
BundledLoan*Large ( β 4 )
= 1 if underwriting before loan
-0.217***
-8.32
-0.065
-1.58
-0.041
-0.9
-0.219***
-8.39
-0.065
-1.59
-0.037
-0.8
-0.219***
-8.39
0.114**
1.98
-0.064
-1.56
-0.037
-0.82
-0.218***
-8.36
0.0001**
1.98
-0.001
-0.95
0.038
0.71
0.0001**
1.97
-0.001
-0.96
0.037
0.7
0.0001*
1.96
-0.001
-0.98
0.036
0.68
0.0001**
2
-0.001
-1
0.037
0.69
-0.058
-0.54
0.103
-0.056
-0.53
0.108
-0.056
-0.53
0.105
-0.055
-0.51
0.109
-0.067
-1.63
= 1 if equity underwriting
Distance to Default before Loan
Loan Characteristics
Facility Size
Maturity
Syndicate
Borrower Characteristics
rated "A"
rated "BBB"
Effects of
Borrower
Size
46
rated "BB"
rated "B"
rated "CCC" or below
not rated or rating missing
Book Leverage
Equity Return Volatility
Market Cap
ROA
Lender Characteristics
Lead Bank Reputation
Lead Bank Lending Relationship
Strength
Commercial Bank Lead Loan
Intercept
N
R-square
P-value for test:
P-value for test:
β1 = β 2
β3 = β4
0.92
0.068
0.57
-0.025
-0.19
0.21
0.93
0.236**
2.08
-0.108
-1.17
-0.087***
-3.83
-0.0005
-0.44
0.609***
2.61
0.96
0.079
0.66
-0.013
-0.1
0.219
0.96
0.247**
2.17
-0.112
-1.22
-0.086***
-3.72
-0.0004
-0.39
0.603***
2.59
0.94
0.074
0.62
-0.02
-0.15
0.212
0.93
0.228*
1.9
-0.111
-1.21
-0.085***
-3.67
-0.0004
-0.41
0.6**
2.57
0.97
0.077
0.64
-0.02
-0.15
0.21
0.92
0.244**
2.14
-0.112
-1.22
-0.086***
-3.75
-0.0004
-0.36
0.598**
2.57
0.322
1.1
0.035
0.73
0.11
1.53
0.34
1.62
0.3
1.02
0.036
0.77
0.109
1.51
0.357*
1.7
0.292
1
0.037
0.79
0.104
1.42
0.365*
1.73
0.312
1.06
0.035
0.75
0.11
1.53
0.355
1.69
3818
0.464
3818
0.464
3818
0.464
3818
0.464
0.5071
0.5465
47
Table 6: Bundled-loan Clients Are Less Likely to Default.
This table presents results of probit model about whether borrower defaults. The unit of observation is a
loan facility, and the sample is restricted to borrowers for which I can obtain default and rating migration
data from Standard & Poor’s. The dependent variable equals 1 if the borrower defaults within the next X
years as of March 2006 (X=2, 3, 4, 5), and 0 otherwise. All variables are defined as in Appendix A.
Intercept, loan type control, loan purpose control, industry and year fixed effects are included and not
reported. Heteroskedasticity-consistent t-statistics are shown in italics. I use ***, **, and * to denote
significance at the 1%, 5%, and 10% level (two-sided), respectively.
Probit Model about whether borrower defaults within the next X years after loan origination
2 year
3 year
4 year
5 year
BundledLoan
-0.792***
-0.428***
-0.249***
-0.184**
-5.4
-3.87
-2.64
-2.07
Loan Characteristics
Facility Size
0.0003***
0.0002**
0.0002***
0.0002***
3.77
2.06
3.7
3.52
Maturity
-0.005***
0.001
0
-0.001
-2.62
0.91
-0.11
-0.97
Syndicate
-0.177
-0.207
-0.337***
-0.364***
Borrower Characteristics
-1.26
-1.61
-2.94
-3.58
rated "A"
-0.881***
-1.096***
-1.166***
-1.144***
-2.65
-3.68
-5.18
-4.68
rated "BBB"
0.006
-0.095
0.066
-0.142
0.02
-0.57
0.49
-1.17
rated "BB"
0.118
0.202
0.237**
0.143
rated "B"
rated "CCC" or below
Not Rated or Missing Rating
Leverage
Equity Return Volatility
Market Cap
ROA
Lender Characteristics
Lead Bank Reputation
Lead Bank Lending Relationship
Strength
Commercial Bank Lead Loan
N
Pseudo R-square
0.58
0.68***
3.61
0.993***
3.35
0.008
0.03
0.419***
2.69
0.165***
3.71
-0.097*
-1.93
-0.774***
-2.85
1.46**
2.53
-0.178
-1.51
-0.255
-1.57
1.47
0.456***
3.25
0.203
0.55
-0.17
-0.65
0.233*
1.74
0.246***
6.2
0.039
0.94
-0.409
-1.4
0.229
0.43
0.038
0.38
-0.212
-1.46
1.98
0.518***
4.11
0.324
1.07
-0.082
-0.4
0.243*
1.92
0.199***
5.63
0.086**
2.41
-0.665**
-2.13
0.11
0.24
0.081
0.91
-0.016
-0.12
1.36
0.338***
2.94
-0.005
-0.01
-0.376*
-1.91
0.307**
2.48
0.186***
5.76
0.057*
1.69
-1.121***
-3.5
-0.059
-0.13
0.013
0.15
-0.016
-0.13
3004
0.299
3064
0.229
3110
0.210
3150
0.211
48
Table 7: Default Rates of Bundled-loan Clients Conditional on Information
Opaqueness.
This table presents results of probit model about whether borrower defaults by considering additional
controls and effects of information opaqueness. . The dependent variable equals 1 if the borrower defaults
within the next 3 years as of March 2006, and 0 otherwise. Considering other time horizon generates
similar results. The unit of observation is a loan facility, and the sample is restricted to borrowers for which
I can obtain complete default and rating migration data from Standard & Poor’s. Recall all the loans in my
sample have underwriting around. So in first column, I control for the event sequence, i.e. whether the
underwriting is before loan or after loan. In second column, I additionally control for the underwriting type,
debt or equity. In third column, I examine the different effects of bundling on default rate for rated and
unrated companies. In last column, I examine the different effects of bundling on default rate for smaller
and larger companies. Smaller companies are those with market capitalization below sample median.
Larger companies are those with market capitalization above sample median. All other variables are
defined as in Appendix A. Industry and year fixed effects are included in all models and not reported. Loan
type and loan purpose are controlled in all models and not reported. Heteroskedasticity-consistent tstatistics are shown in italics. I use ***, **, and * to denote significance at the 1%, 5%, and 10% level (twosided), respectively. This table shows that lower default rates of bundled-loan clients are more pronounced
for not rated companies and smaller companies.
Dependent Variable=whether borrower defaults within the next 3 years after loan origination
BundledLoan
BundledLoan*Not rated ( β 1 )
Control
for Event
Sequence
Control for
Underwriting Effects of
Type
Rating
-0.492***
-4.07
-0.472***
-3.97
Effects of
Borrower
Size
-1.649***
-4.44
BundledLoan*Rated ( β 2 )
-0.409***
-3.32
BundledLoan*Small ( β 3 )
-0.657***
-3.75
BundledLoan*Large ( β 4 )
-0.286**
-1.97
= 1 if underwriting before loan
0.12
0.182
0.17
0.178
0.78
1.27
1.18
1.21
-0.477***
-0.503***
-0.496***
-4.54
-4.69
-4.72
-0.25***
-0.25***
-0.26***
-0.254***
-4.49
-4.7
-4.92
-4.8
= 1 if equity underwriting
Distance to Default before Loan
Loan Characteristics
Facility Size
Maturity
Syndicate
0.0000
0.0000
0.0000
0.0000
0.49
0.22
0.23
0.13
0.001
0.003
0.003
0.003
0.87
1.46
1.45
1.54
-0.245*
-0.2
-0.198
-0.184
-1.86
-1.51
-1.5
-1.37
49
Borrower Characteristics
rated "BBB"
-0.184
-0.298*
-0.374**
-0.277
-1.05
-1.66
-2.02
-1.57
0.018
-0.094
-0.179
-0.09
0.13
-0.68
-1.25
-0.64
rated "B"
0.212
0.173
0.099
0.197
1.47
1.21
0.67
1.37
rated "CCC" or below
-0.025
-0.045
-0.126
0.005
-0.06
-0.11
-0.32
0.01
not rated or rating missing
-0.214
-0.293
-0.3
-0.275
-0.84
-1.2
-1.2
-1.12
Leverage
0.306**
0.257*
0.255*
0.25*
2.15
1.76
1.74
1.72
0.142***
0.153***
0.149***
0.151***
3.03
3.16
3.07
3.13
0.091
0.075
0.076
0.053
1.24
1.01
1.03
0.88
-1.848***
-1.881***
-1.897***
-1.898***
-4.43
-4.63
-4.65
-4.59
rated "BB"
Equity Return Volatility
Market Cap
ROA
Lender Characteristics
Lead Bank Reputation
Lead Bank Lending Relationship
Strength
Commercial Bank Lead Loan
Intercept
N
Pseudo R-square
P-value for test: β 1 = β 2
P-value for test: β 3 = β 4
0.286
0.103
0.084
0.014
0.53
0.18
0.15
0.02
0.043
0.088
0.092
0.103
0.41
0.81
0.84
0.95
-0.282*
-0.241
-0.238
-0.246
-1.88
-1.59
-1.54
-1.62
-0.96
-0.94
-0.897
-0.748
-1.48
-1.38
-1.31
-1.1
2503
0.237
2503
0.254
2503
0.258
2503
0.257
0.0011
0.0799
50
Table 8: Bundled-loan Clients Are Less Likely to Receive Rating Downgrade.
This table presents probit estimation results on whether a borrower’s credit rating is downgraded. The unit
of observation is a loan facility of a borrower for which I have complete default and rating migration data
from Standard & Poor’s. The dependent variable equals 1 if the borrower receives a credit rating
downgrade within the next 3 years as of March 2006, and 0 otherwise. Considering other time horizon
generates similar results. All variables are defined as in Appendix A. Controls for loan type and loan
purpose, and industry and year fixed effects are included but not reported Heteroskedasticity-consistent tstatistics are shown in italics. I use ***, **, and * to denote significance at the 1%, 5%, and 10% level (twosided), respectively. This table shows that bundled-loan clients are less likely to receive rating downgrade,
and this effect is more pronounced for not rated companies and smaller companies.
Dependent Variable
=whether borrower receives rating downgrade within the next 3 years after loan origination
BundledLoan
BundledLoan*Not rated ( β 1 )
Benchmark
-0.163***
-2.59
Control for
Event
Sequence
and
Underwriting
Type
-0.162**
-2.29
Effects
of Rating
Effects of
Borrower
Size
-0.411**
-2.13
BundledLoan*Rated ( β 2 )
-0.134*
-1.8
BundledLoan*Small ( β 3 )
-0.228**
-2.27
BundledLoan*Large ( β 4 )
-0.12
-1.44
-0.028
-0.043
-0.26
-0.4
-0.23
-0.105***
-0.16***
-2.85
-0.113***
-0.165***
-2.92
-0.114***
-0.163***
-2.9
-0.113***
-3.46
-3.71
-3.76
-3.72
0.0000
0.0000
0.0000
0.02
-0.04
-0.04
= 1 if underwriting before loan
= 1 if equity underwriting
Distance to Default before Loan
Loan Characteristics
Facility Size
Maturity
Syndicate
Borrower Characteristics
rated "A"
rated "BBB"
-0.024
-0.001
-0.0004
-0.0004
0.0000
-0.04
-0.0003
-0.63
-0.38
-0.38
-0.34
-0.211***
-0.201**
-0.2**
-0.197**
-2.68
-2.56
-2.55
-2.51
0.98***
0.945***
0.908***
0.944***
7.84
7.54
7.03
7.53
0.352***
0.324***
0.288***
0.324***
3.3
3.02
2.58
3.02
51
rated "BB"
rated "B"
rated "CCC" or below
not rated or rating missing
Leverage
Equity Return Volatility
Market Cap
ROA
Lender Characteristics
Lead Bank Reputation
Lead Bank Lending Relationship
Strength
Commercial Bank Lead Loan
0.38***
0.371***
0.336***
0.373***
4.27
4.19
3.64
4.2
0.4***
0.392***
0.393***
0.358***
3.95
3.98
3.53
4.02
-0.016
-0.021
-0.054
-0.021
-0.07
-0.09
-0.23
-0.09
-0.232
-0.229
-0.235*
-0.229
-1.62
-1.6
-1.66
-1.6
-0.165
-0.181
-0.185*
-0.181
-1.49
-1.62
-1.65
-1.63
0.136***
0.136***
0.137***
0.137***
4.36
4.35
4.35
4.37
0.01
0.007
0.007
0.001
0.39
0.29
0.29
0.03
-0.122
-0.124
-0.117
-0.12
-0.5
-0.52
-0.49
-0.5
-0.065
-0.106
-0.112
-0.126
-0.22
-0.35
-0.37
-0.42
0.047
0.057
0.056
0.057
0.75
0.91
0.9
0.92
0.093
0.094
0.096
0.091
0.96
0.97
0.98
0.93
0.095
0.187
0.216
0.241
0.2
0.39
0.46
0.5
N
Pseudo R-square
P-value for test: β 1 = β 2
2883
0.072
2883
0.074
2883
0.075
2883
0.075
P-value for test:
0.3421
Intercept
β3 = β4
0.1693
52
Table 9: Bundled Loans Perform Better in the Secondary Loan Market.
This table presents the results of ex-post performance of bundled loans vs. non-bundled loans in the
secondary loan market. Panel A reports cumulative abnormal returns (CAR) and buy-and-hold abnormal
returns (BHAR) of bundled loans for 1 year and 2 years. For each bundled loan, I find the matched nonbundled loans and calculate CAR and BHAR using equation (6) and (7) in the paper. There are 103
bundled loans with valid matched non-bundled loans. I match non-bundled loans based on rating, time to
secondary market, event sequence and size. Panel B reports the results using calendar time portfolio
method. I construct the bundled loan portfolio and non-bundled portfolio as follows: I add a bundled loan
to my portfolio when it enters the secondary market or one year after loan origination, whichever comes
later; I drop it after the loan stays in the portfolio for a year or if LSTA stop quoting it, whichever comes
first. After calculating the equal weighted return of the portfolio for each week, I regress the portfolio
excess returns on the four Fama-French-Carhart factors and loan market excess index return. t-statistics
based on Newey-West robust standard errors (using 2 lags) are shown in italics. I use ***, **, and * to denote
significance at the 1%, 5%, and 10% level (two-sided), respectively.
Panel A: CAR and BHAR
1 year
CAR
N
103
Mean
5.21% **
t
2.436
p-value
0.017
bootstrap t
2.485
p-value
0.013
BHAR
103
3.76%
2.314
0.023
2.352
0.019
Median
p-value
0.33%
0.041
0.29%
0.137
2 years
**
**
N
Mean
t
p-value
bootstrap t
p-value
CAR
103
13.09%
2.372
0.020
2.400
0.017
Median
p-value
0.46%
0.083
**
*
BHAR
103
5.34%
1.955
0.053
1.939
0.053
0.59%
0.045
*
**
Panel B: The Calendar Time Portfolio Method: Regression of Weekly Excess Return of
Bundled and Non-bundled Loan Portfolios (Holding loans for 1 year)
Excess Return
Excess Return of
of Bundled Loan
Non-bundled Loan
Dependent Variable:
Portfolio (BL)
Portfolio (NBL)
Alpha
-0.001
0.0001
-0.002***
-0.001*
-1.35
0.25
-3.4
-1.77
S&P/LSTA Loan Index Excess Return
0.897**
0.972***
2.55
4.27
Equity Market Excess Return
-0.014
-0.028
0.057**
0.042
-0.48
-0.91
2.03
1.55
SMB
0.089** 0.047
0.074**
0.029
2.27
1.09
2.45
1.02
HML
0.008
-0.032
0.087**
0.044
0.17
-0.68
2.53
1.43
UMD
-0.079
-0.073
-0.039*
-0.033
-1.41
-1.29
-1.84
-1.65
N
R-square
313
0.054
313
0.102
313
0.054
313
0.129
53
Figure 1: Distribution of Defaults.
The figure shows the distribution of borrower default events. In this figure, I only consider public
borrowers with loan data in my sample and with complete rating history data from Standard & Poor’s.
There are 1,551 such companies. Default data reported in this figure is from 1994 to March 2006. I define
default to occur when S&P sets the company’s credit rating to “D”. 337 sample companies default and
there are 357 default events.
number of default events
90
80
70
60
50
40
30
20
10
0
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Q1
year
Figure 2: Trading Volume of Secondary Syndicated Loan Market.
The figure shows the trading volume of secondary syndicated loan market. The “par” loans are loans
selling at 90% of its face value or above and “distressed” loans are loans selling at below 90% of its face
value.
Source: Reuters LPC Traders Survey
54
Figure 3: Bundled Loans Perform Better than Non-bundled Loans.
The figure shows the secondary market price index of bundled loans and non-bundled loans, together with
the S&P/LSTA Leveraged Loan Index for the sample period of January 1, 1999 to December 31, 2004. The
price index for bundled loans is constructed as follows. First, I calculate the average return of bundled loans
for each week. Then a price index is formed as the cumulative average return, with the index value for
January 1, 1999 set to be 1000. [Price Index (t) =Price Index (t-1) * (1+Average Return(t-1,t))]. The price
index for non-bundled loans is formed similarly. The S&P/LSTA Leveraged Loan Index (loan market
index) is rescaled so that the level for January 1, 1999 equals 1000. The LLI used here is based on market
value only (excluding interests).
Figure: Price index of bundled loans and non-bundled loans in secondary market
1100
1050
1000
price index
950
900
850
800
750
700
Bundled_Loan_index
NonBundled_Loan_index
650
Loan_Market_index
00
4
7/
1/
2
00
4
1/
1/
2
00
3
7/
1/
2
00
3
1/
1/
2
00
2
7/
1/
2
00
2
1/
1/
2
00
1
7/
1/
2
00
1
1/
1/
2
00
0
7/
1/
2
00
0
1/
1/
2
99
9
7/
1/
1
1/
1/
1
99
9
600
date
55