Real Earnings Management in the
Financial Industry∗
Aytekin Ertan†
Yale University
November 13, 2013
Abstract
This study investigates earnings management in the context of syndicated loan originations, under which lead arrangers can recognize a disproportionate fraction of front-end
fees in the quarter of issuance. I find evidence that lenders initiate additional loans in the
last month of fiscal quarters when their reported EPS just meets or beats benchmarks. I
also observe that this boost is not costless. These loans are offered at a discount of 15–20
basis points and are associated with questionable quality as the borrowers experience
subsequent credit rating downgrades and CDS initiations. To further understand why
lenders underwrite and participate in costly suspect loans, I find evidence that these lead
banks concurrently engage in other methods of earnings management and that these loans
involve inexperienced and unsophisticated syndicates.
∗
I am grateful to Jake Thomas for his extensive guidance and support. I thank my dissertation committee members, Shyam Sunder, Frank Zhang, and Alina Lerman for continuous advice. I also thank Anwer
Ahmed, Gauri Bhat, Kalin Kolev, Stefan Lewellen, Jongha Lim, Gerald Lobo, Andrew Metrick, Marina
Niessner, Justin Murfin, Jayanthi Sunder, Florin Vasvari, Dushyantkumar Vyas, James Wahlen, Regina
Wittenberg-Moerman; Sriya Anbil, Stephen Karolyi, Peter Kelly, James Potepa, and seminar participants at
Yale Spring 2013 Accounting Research Conference for helpful comments and discussions.
†
Ph.D. Candidate, Accounting. Yale School of Management, 135 Prospect Street, New Haven, CT, USA,
06520. Phone: +1.203.584.5515. E-mail: aytekin.ertan@yale.edu, http://aytekinertan.commons.yale.edu
1
Introduction
While prior research has extensively documented earnings management via accruals, the
use of real activities to manage earnings has received relatively little attention (Graham,
Harvey, and Rajgopal, 2005; Roychowdhury, 2006). Real earnings management deserves
investigation because firms bear significant costs associated with the suboptimal operating
decisions they make to achieve financial reporting goals (e.g., Kedia and Philippon, 2009). In
a novel setting related to syndicated loan initiations, I investigate whether banks that serve
as lead arrangers initiate additional loans when their earnings fall short of benchmarks. Accounting rules (SFAS 91, 1986) generally spread loan origination fees over the life of the loan,
causing loan issuance to have a negligible impact on earnings in the issuance quarter. However, in the case of syndicated loans, lead banks can recognize a disproportionate fraction of
upfront fees received in the issuance quarter, which creates an opportunity to boost earnings
by originating more loans.1
Previous evidence suggests that the level of earnings management increases as the quarter
progresses, because the magnitude of anticipated earnings and the difference relative to
benchmarks becomes more apparent to managers.2 In order to incorporate this aspect, I
first identify “income-constrained” lender-quarters in which reported EPS equals or exceeds
the previous year’s same-quarter EPS by up to 3 cents.3 I then classify “suspect” loans
as loans issued in the third month of these income-constrained lender-quarters. I compare
suspect loans with loans initiated in the first two months of income-constrained quarters as
well as with loans initiated in non-income-constrained quarters. In this setting, one potential
cost of real earnings management is that the sum of fees and interest rates charged on suspect
loans issued to boost earnings is lower than that for comparable loans. This discount could
be offered to borrowers as an inducement to expedite already-awarded mandates or to take
on new deals through competitive bidding.
1
Lead arrangers are entitled to recognize syndication fees related to the portion they sell as long as they
charge the same effective interest rate as other participants. This matter is detailed in section 2.2.
2
For studies analyzing intra-quarter activities of financial (non-financial) firms, see Dechow and Shakespeare
(2009) (Cohen, Mashruwala, and Zach, 2010 and Chapman and Steenburgh, 2011).
3
I adopt a simple random-walk approach to employ a constant and clear target that is available to management
during the course of the quarter. However, I use analyst forecasts and a dynamic random-walk specification
as well. These issues are discussed in sections 3 and 4.3.
1
As the basis of my analysis, I construct a new data set that rigorously matches DealScan
lenders to Compustat. My findings are consistent with lead arrangers engaging in the hypothesized real earnings management in several respects. First, more loans are issued in the
third month of income-constrained quarters relative to the average of the first two months.
Although I find the same temporal pattern for non-income-constrained quarters, the incremental loan issuance in third months is significantly higher for income-constrained quarters.4 Furthermore, there is a decline in lenders’ loan syndication activity following incomeconstrained quarters, which suggests that income-constrained lenders shift loans from the
subsequent quarter to the current quarter. In line with the inference of expedited loan
origination, I observe that suspect loans are packaged about 10 days more quickly.5
Second, I find that suspect loans generate a lower income for lenders compared to nonsuspect loans. Compared to other loans, suspect loans are associated with lower spread
over LIBOR (21 basis points per year), but higher one-time fees (16 bps over 4–5 years,
on average), resulting in a net discount of 18 bps per year. These multivariate results are
obtained holding borrower, lender, loan, and time effects constant and are robust to various
alternative specifications. In particular, they are economically and statistically significant
after the adoption of marketflex (1998), a provision that gave lead arrangers discretion to
change the pricing of loans during the course of the syndication process (Miller, 2006). I
estimate that the fees that can be recognized at initiation correspond to a pre-tax EPS impact
of about 0.1 cent per suspect loan. Overall, this finding is in line with a lender preference
for short-term earnings over long-term value.
There is evidence of additional costs related to the timing and quality of suspect loans.
For example, borrowers of high-yield suspect loans suffer credit-rating downgrades by about
0.3 of a notch in the year following the issuance. This result is consistent with low-creditquality borrowers strategically timing their debt issuance to benefit from suspect loans. I also
provide evidence that credit default swap (CDS) initiations on borrowers of suspect loans
during the loan tenors are about 4% more likely than those on borrowers of non-suspect loans.
This finding might suggest that some lenders recognize problems with the loan (e.g., due to
4
I do not observe substantial differences on the demand side, i.e., between the borrowers of suspect and
non-suspect loans. It is important to note that explaining the temporal variation in loan issuance, i.e., peaks
in March, June, September, December, is not a main objective or claim of this study. While this variation
may be mainly driven by the borrower side, the cross-sectional variation in issuance with the supply side’s
earnings goals is unlikely to be an artifact of the borrower side.
5
From the initial agreement (the mandate award) to funding, this process takes six to eight weeks on average.
2
expedited screening) and seek insurance ex post. However, additional higher-order tests
imply that such costs are rather limited as there is not a systematic variation in subsequent
loan loss provisions by income-constrained lenders or in technical and payment defaults of
the borrowers in these cases.
Third, I investigate whether this costly real earnings management via syndicated loan
originations takes place concurrently with other means of earnings management. Consistent
with companies achieving financial reporting goals using a portfolio of earnings management
tools, I find evidence that income-constrained lender-quarters are associated with conditionally lower provisions for loan losses and higher realized security gains (Beatty, Ke, and
Petroni, 2002).
Finally, I observe that the composition of the syndicate differs between suspect and
non-suspect loans. While the evidence I provide is in line with lead arrangers of lending
syndicates trading off the costs and benefits of real earnings management, it is unclear
why the syndicate participants would choose to bear a share of the costs without directly
benefiting from the lead arranger’s earnings management. I find that the mix of lenders
is skewed toward non-banks for suspect loans even though these syndicates involve less
participation from a sophisticated subset of non-banks (e.g., hedge funds). Moreover, I
observe that lenders participating in suspect loans are less experienced in the syndicated
loan market. While these findings may suggest exploitation of the participants by the lead
arrangers, it is also possible that some of these participants bear the costs because they are
offset by reputational benefits or future business opportunities (Esty, 2001; Rhodes, 2009).
The main contribution of this study is filling an important gap in the financial accounting
literature by documenting new evidence of real earnings management by financial firms in
the context of syndicated loan originations. While manipulation of accruals has been the
dominant theme in the literature on earnings management (e.g., Healy and Wahlen, 1999;
Beatty, Ke, and Petroni, 2002), prior research has also provided evidence of real earnings
management by non-financial firms (e.g., Roychowdhury, 2006) and, to some extent, by
financial firms (e.g., Scholes, Wilson, and Wolfson, 1990). This work, however, is novel
in terms of the documented costs and within-quarter variation of manipulation activities
(Dechow and Shakespeare, 2009). Overall, this evidence should be of interest to regulators
and bank shareholders concerned with ongoing earnings management.
3
Furthermore, the evidence that suspect loans are associated with lower overall costs but
higher front-end fees is relevant to the banking and corporate finance literatures, in particular,
to the growing literature on the supply-side analysis of loan contracting (e.g., Berger and
Udell, 2004; Murfin, 2012).6 Given the importance of the syndicated loan market, the
variation I document in the cost of debt across lender-quarters improves our understanding
of the determinants of debt costs for borrowers as well as lender profitability (e.g., Gorton
and Winton, 2003; Ivashina and Scharfstein, 2010; Murfin and Petersen, 2012). To the extent
that higher front-end income reduces the incentives of lead lenders to monitor borrowers, the
reduced monitoring associated with suspect loans—like the lead bank’s share in the loan and
its reputation as in Dennis and Mullineaux (2000), Sufi (2007), and Gopalan, Nanda, and
Yerramilli (2011)—is relevant to the research on agency issues within loan syndications.
The layout of the paper is as follows. The next section provides background on real
earnings management and syndicated loans, includes a discussion of applicable accounting
standards, and presents my hypotheses. Section 3 discusses the data collection and samples;
Section 4 presents the empirical result, and Section 5 concludes the paper.
2
Background and Hypotheses
Firms’ propensities and abilities to manipulate real activities in order to achieve their
financial reporting goals have been a central topic in accounting research (e.g., Dechow
and Sloan, 1991; Bens, Nagar, and Wong, 2002). Prior literature has provided evidence
of real earnings management and highlighted its detrimental impact on firm value as a
consequence of agency problems (e.g., Kedia and Philippon, 2009; Cohen and Zarowin, 2010;
and Zang, 2012). However, documenting within-period variation in real corporate activities
has received little attention mostly due to the lack of machine-readable, intra-quarter data.
The exceptions, Cohen, Mashruwala, and Zach (2010), Chapman (2011), and Chapman and
Steenburgh (2011), provide granular evidence on the presence and costs of real earnings
management using firm-specific data on advertising and product promotions. These studies
build on Oyer’s (1998) evidence, which suggests that firms increase sales and cut prices in
the last quarter of the fiscal year compared to other fiscal quarters.
6
Throughout the paper, I refer to lenders as the supply side and to borrowers as the demand side.
4
To my knowledge, this is the first study investigating the link between the earnings
targets of financial institutions and syndicated lending practices.7 In the context of earnings
management related to the financial industry, the focus of accounting research has mainly
been on the demand side. Banks have mostly been considered counterparties that supply
funds, monitor borrowers, and negotiate contracts (e.g., Bharath, Sunder, and Sunder, 2008;
Ahn and Choi, 2009; and Bushman and Wittenberg-Moerman, 2012). The work examining
banks as active parties has highlighted the importance of tax planning, regulatory capital,
and financial reporting (e.g., Beatty, Chamberlain, and Magliolo, 1995; Collins, Shackelford,
and Wahlen, 1995; Beatty and Harris, 1999; and Owens and Wu, 2012). Importantly, Dechow
and Shakespeare (2009) document opportunistic timing and accounting choice as well as
within-quarter variation in banks’ securitization activities.
2.1
Syndicated Loans
A syndicated loan is a large credit agreement funded by more than one lender. This
process is arranged and managed by one or more financial institutions, the lead arranger(s).8
After a preliminary agreement (the mandate award) between the lead bank and the borrower, the lead bank chooses and invites other potential lenders by exchanging information
and holding proprietary meetings with them. Following the responses of the invitees, the
subscription level of the loan becomes clear and commitments are determined. This stage
is followed by administrative steps, finalization of the loan terms, and the signing of the
deal. Whereas the described process could be rather expedient for time-sensitive loans with
takeover purposes, large project financing deals can take several months before being funded
(Esty, 2001). Likewise, loans with longer tenor and larger tranches also require longer timetables.
Once the deal is signed, lenders receive front-end fees.9 These fees are often similar to
original issue discounts in public bond issuance and are mostly retained by lead arrangers.
Over the life of the loan, the other forms of payment made by the borrower are the interest
rate on the loan (spread and LIBOR) and annual fees on the drawn and undrawn portions of
7
See Altunbas, Gadanecz, and Kara (2006) for a discussion of the link between lending practices and various
time-varying lender characteristics.
8
Syndications are not necessarily about an arranger, a borrower and a number of participants. There may be
various intermediate roles. Throughout the paper, I rely on DealScan’s classification of lead arranger.
9
For details on fees, see Angbazo, Mei, and Anthony (1998); Gadanecz (2004); Rhodes (2009); Berg, Saunders,
and Steffen (2013).
5
the credit line (e.g., utilization and commitment fees). Lead banks also receive compensation
for advising and structuring deals. Overall, these payments are typically set within a band
once the mandate is awarded, and the final amounts may be determined toward the end
of negotiations. In particular, marketflex, a provision adopted following the Russian debt
crisis in 1998, gave lead arrangers greater flexibility to modify the pricing and non-pricing
terms even after the mandate award, thus adding a crucial dimension to the earlier setting,
in which loan terms were relatively fixed (Miller, 2006).
Currently, the primary market for syndicated loans is not as large as the peak of the last
decade but has been recovering from the recent financial crisis. Syndicated loans remain the
largest source of external financing in North America. 715 syndicated loans were originated in
the US in the first quarter of 2013 totalling $408 billion.10 These loans are preferred by some
borrowers because they are inexpensive and efficient relative to other types of debt financing.
Lenders favor syndications over sole-lender deals to diversify lending risks and comply with
lending limits. Earning extra fees and developing business relationships with borrowers and
other lenders are additional reasons for arranging syndication loans and participating in
lending consortia.
Variation in the cost of debt financing can be explained by a number of factors. Borrower’s credit quality, thus the repayment risk, constitutes the first-order factor in loan
pricing (e.g., Myers, 1977; Tirole, 2006). In the cross-section, accounting studies provide
evidence of several other determinants of the cost of debt, such as accounting quality, disclosure, and auditor choice (Bharath, Sunder, and Sunder, 2008; Sengupta, 1998; and Pittman
and Fortin, 2004). At the macro level, business and credit cycles are strongly linked with
debt yields (Rajan, 1994; Chava and Purnanandam, 2011). Also, Murfin and Petersen (2012)
document within-year seasonality in loan spreads: spring and fall borrowers issue cheaper
loans than winter and summer borrowers. On the supply side, capital constraints, liquidity,
loan portfolio performance, the appetite for borrowers of varying levels of credit quality, and
relationship concerns are some of the important cross-sectional determinants of loan terms
(Rhodes, 2009).
10
Source: http://www.bloomberg.com/professional/files/2013/04/2013-q1-global-syndicated-loans.
pdf
6
In general, the financial institutions that arrange and administer syndicated loans are
public companies. Considering the incentives of public companies to maintain certain levels
of performance under the presence of agency issues, I expect that lead banks strategically
manage their operations, including debt underwriting activities, to achieve financial reporting
objectives. Even though loan syndications are only one of the core businesses of financial
entities, they can non-trivially contribute to the bottom line if managed strategically.11 The
pertinent figure, syndication fees, which is difficult to pinpoint in financial statements, was
$125 million for Bank of America in the second quarter of 2005, corresponding to 2% of the
bank’s income before taxes and 2% of non-interest income.12
2.2
SFAS 91
The accounting treatment of fees associated with originating loans is governed by SFAS
91, issued in 1986. Par. 5, and Q&A par. 22, 35–44 state that since loan origination fees are
integral parts of the loan, despite being fully received at initiation, they should be deferred
and amortized over the life of the loan just like the annual fees and yield.13 However, in
the case of loan syndications, there are certain avenues through which closing a credit deal
impacts the bottom line.14
First and foremost, the revenue recognition criterion for syndicated loans, the effective
interest rate method, creates a channel that could impact the earnings of the lead arranger
in the quarter of issuance. Par. 11 governs this issue and, conceivably, is designed to make
sure that the amortized portion of arrangers’ proceeds from the loan matches the amount
deferred by participants.15 Lead banks syndicate out a substantial portion of the loan yet
11
Clearly, in the instance of “last-minute” loan initiations there is a payment due in association with the
spread given the limited amount of time left until the fiscal period-end. This amount can be non-trivial for
high-yield loans issued early in the quarter. However, because the cross-sectional variation in these cases
would be minimal, this amount is ignored in this paper.
12
Source (requires Factiva subscription):
http://global.factiva.com/aa/?ref=FNDW000020050801e17i000jh&pp=1&fcpil=en&napc=S&sa_from=
13
Note that fees associated with regular commercial & industrial and consumer loans are not recognizable at
initiation to the extent they are kept on balance sheet.
14
According to par. 6 and Q&A par. 18 on the treatment of loan origination costs, only the direct costs
and fees are to be offset whereas indirect costs must be recognized immediately. However, in the particular
case of syndicated loans, classification of costs is subject to discretion. This could enable the deduction of
costs (that are to be recognized immediately) from fees (that are to be deferred), potentially improving the
bottom line.
15
Paragraph 11 of SFAS 91 reads: “The enterprise managing a loan syndication (the syndicator) shall recognize
loan syndication fees when the syndication is complete unless a portion of the syndication loan is retained.
If the yield on the portion of the loan retained by the syndicator is less than the average yield to the other
syndication participants after considering the fees passed through by the syndicator, the syndicator shall
7
retain a fair amount of the fees (Rhodes, 2009). They recognize the excess amount of initial
fees as income, as long as the yield on the portion of the loan retained is not less than
the average yield on the loans held by the other syndicate participants. The breakdown of
these fees within the syndicate is almost always undisclosed and requires assumptions for
operationalization.16
Additionally, as stated in par. 12–14 regarding refinancing agreements, “any unamortized net fees or costs and any prepayment penalties from the original loan shall be recognized
in interest income when the new loan is granted.” In other words, the recognition in income
of previously received but not accrued cash payments from the borrower is permitted.17
Finally, there are other complications that SFAS 91 may not directly address, such as
loan-related advisory fees that are fully recognized as services are rendered, which would
improve banks’ earnings.18 In sum, banks’ management of earnings through manipulation
of syndicated loan originations can impact their contemporaneous earnings in a number of
ways.
2.3
Hypotheses & Research Design
I begin my analysis of real earnings management in the financial industry by focusing on
a subset of lender-quarters. My identification strategy relies on the just-meet-or-beat classification. I define income-constrained lender-quarters as these with reported EPS meetingor-beating (by up to 3 cents) the previous year’s same-quarter EPS. I then classify all loans
issued in the third month of these income constrained lender-quarters as suspect loans. I
use first two months of income-constrained lender-quarters and all months of non-income-
defer a portion of the syndication fee to produce a yield on the portion of the loan retained that is not less
than the average yield on the loans held by the other syndication participants”.
“Any excess should be recognized as revenue when the syndication is complete.” (Sangiuolo and Seidman,
2009)
16
For the distribution of fees in practice, see Esty (2001); for the accounting treatment, see Sangiuolo and
Seidman (2009).
17
Note that this is allowed “if the new loan’s effective yield is at least equal to the effective yield for such
loans.” Otherwise, the difference needs to be amortized. The statement does not elaborate further on the
determination of such loans, leaving it to managerial judgement.
18
On this subject, and on the effective interest rate method in general, accounting practitioners recognize these
grey areas. E.g., PricewaterhouseCoopers’ booklet of the “Complex Issues Banks Face” reads: “Identifying
the fees received and costs incurred associated with syndicated loans, and attributing these as part of
the effective yield can be an area of significant judgement.” (Although this booklet is designed for IFRS,
concerns with the effective interest method applies to GAAP as well.) Source: http://www.pwc.com/id/
en/publications/assets/pwc-ifrs_thecomplexissuesbankface0506.pdf
8
constrained lender-quarters as the control sample.
If income-constrained lead arrangers engage in the strategic management of loan syndications, they would increase loan issuance to book additional revenue. To test whether this
issuance frequency effect exists, I focus on the ratio of the number of loans issued in the
last month of the current quarter to the number of loans issued in all months of the current
quarter. (This allows within-quarter comparison.) A higher ratio signifies a larger concentration of issuance in the third month, holding quarterly issuance fixed. My preliminary
evidence shows that for all lender-quarters, on average, this ratio is higher than 1/3; that is,
loan issuance is higher during third months. Therefore, I mainly concentrate on the difference between the current issuance ratios of income-constrained and non-income-constrained
lender-quarters. This leads to my first hypothesis, stated in the alternative form.
H1. Third months constitute a larger share of loan issuance in the current quarter for
income-constrained lenders relative to non-income-constrained lenders.
To complement this hypothesis, I examine two dimensions of loan contracting related
to issuance frequency. First, I investigate whether suspect loans are packaged more quickly
by analyzing the number of days between the mandate award and deal activation dates.
Although loan timetables are complicated and mainly driven by loan characteristics, lead
arrangers can speed up loan arrangement towards the end of fiscal period by imposing stricter
deadlines or adjusting loan terms. Similarly, if suspect loans are expedited deals (which
would otherwise take place in the following quarter), then the issuance activity of incomeconstrained lenders should decline in the subsequent quarter. I examine this issue by defining
a second, forward-looking ratio as the number of loans issued in the last month of the current
quarter divided by the number of loans issued in all months of the subsequent quarter. (This
allows across-quarter comparison.) A higher ratio is consistent with lenders completing a
disproportionate amount of deals in the current quarter. This could be achieved by aggressive
bidding and/or shifting future deals to the current quarter.19
Second, I analyze the intensity of relationship lending associated with suspect loans.
It is possible that income-constrained lenders exploit or cooperate with a certain subset
of their borrowers. In particular, I focus on the incremental likelihood of suspect loans
19
To identify the former explanation that suggests “stealing borrowers”, I explore the shifts in loan issuance
across income-constrained and non-income-constrained lender-quarters.
9
being refinancing agreements and on the number of deals between the lead arranger and the
borrower prior to the corresponding contract.
In addition to issuance frequency, the pricing of suspect loans also deserves attention
because it would reveal the costs to lenders and the impact on arranger’s contemporaneous
earnings. From the supply-side’s perspective, each loan initiation constitutes short- and longterm cash inflows in the form of various fees and the yield. If lenders engage in the strategic
management of loan pricing, there could be a systematic variation in spreads and fees. Lead
arrangers might offer discounts to borrowers in order to win competitive bidding, induce
early refinancing, or accelerate an already-retained deal (especially post-marketflex). On the
other hand, lead arrangers may recognize the front-end fees associated with the syndicated
portion at loan initiation, which gives them an incentive to shift their focus to these fees.
Importantly, the costliness of this hypothesized earnings management can be investigated
by comparing the overall pricing of suspect loans with that of non-suspect loans. I test the
following hypotheses to measure the net costs associated with originating suspect loans and
to identify lender short-termism.
H2a. The total borrowing costs associated with suspect loans are lower than those associated
with non-suspect loans.
H2b. Suspect loans are associated with high front-end fees relative to non-suspect loans.
Given the findings from the hypothesis tests on pricing, I evaluate additional predictions
about cross-sectional variation on the demand side, performance of suspect loans, and other
activities of lead arrangers. I start by examining endogenous selection because it is possible
that the borrowers of suspect loans are systematically different than other borrowers in terms
of financial attributes or the preferred types of loans (e.g., revolvers vs. term loans). Then
I proceed with the analysis of subsequent credit events related to the loan (e.g., payment
and technical defaults) and to the borrower’s credit quality (e.g., change in credit ratings,
CDS initiations). Results from these tests help to clarify the amount of additional costs
associated with suspect loans. Next, I investigate lead arrangers’ view on suspect loans. If
they acknowledge and report that suspect loans are low quality, the issuance of these loans
could be followed by a systematic jump in the loan loss provisions of income-constrained
lenders. Finally, if real earnings management via syndicated loan originations is costly, it
10
is necessary to identify whether these lead arrangers concurrently engage in other means
of achieving earnings targets, treat real earnings management as a last resort, or disregard
these costs (since they originate to distribute).
Lastly, it is important to single out the behaviors of winners and losers, and to come
up with equilibrium explanations. Lead banks, syndicate participants, and borrowers have
their own incentives to take part in the syndicated loan market. If evidence supports the
aforementioned hypotheses, participant lenders would seem to bear the costs of suspect
loans. In this case, a rational explanation for suspect loans would be that not all potential participants solely value the direct cash flows associated with an individual lending
contract. Specifically, smaller and inexperienced creditors could seek to build relationships
with the rest of the contracting parties in order to generate future business opportunities,
while certain institutional lenders might assign particular importance to private information
about borrowers (Massa and Rehman, 2008; Bushman, Smith, and Wittenberg-Moerman,
2010; Massoud, Nandy, Saunders, and Song, 2011; and Ivashina and Sun, 2011b).20 Certainly, the exploitation of unsophisticated participants by lead arrangers is another, albeit
frictional/non-rational, explanation although prior research does not appear to support this
view by virtue of lead arrangers’ reputational concerns (e.g., Panyagometh and Roberts,
2010; Bushman and Wittenberg-Moerman, 2009).21 In all, examining the syndicate mix is
a direct way of understanding the motivation of all contracting parties.
H3. Non-bank and unsophisticated-lender participation in suspect-loan syndicates is greater
than that in other syndicates.
20
I discuss the profile of non-bank participants in greater detail in section 4.6.
21
The secondary market for loans, theoretically a key issue and plausible explanation, is not as central to the
argument as far as lead banks are concerned. Lead arrangers retain their share throughout the life of the loan
because of economic and bureaucratic (but not legal) barriers. On the population of the secondary market
for private loans, Benmelech, Dlugosz, and Ivashina (2012) report that “97% of CLOs were structured by
financial institutions that did not originate loans and instead acquired pieces of loans at syndication or in
the secondary market for the purpose of securitization.”
11
3
Data
I obtain the data on private loan contracts including information on borrower and lender
identities from Loan Pricing Corporation’s (LPC) DealScan database. Deal activation dates
are recorded at the daily level, allowing a granular within-quarter analysis of loan issuance.
DealScan lenders have their own identifiers, which need to be manually matched to Compustat.22 I construct a linking table by using lender identities as reported in DealScan.
Consistent with my research question, I limit my attention to lead arrangers, by relying on
lead arranger credit, the pertinent variable in DealScan Lender Shares data set. Focusing on
North American lenders, I combine subsidiaries, banking segments and regional offices under
a single bank name. (For example, Bank of America Illinois, Bank of America North America, Bank of America Securities and Bank of America are merged into Bank of America.)
To account for mergers and acquisitions, I use relevant information from from the Federal
Reserve System, Thomson Reuters’ SDC, and S&P’s Capital IQ. Loans originated by targets in the quarters corresponding to or immediately preceding the M&A deal are excluded
from the data. Appendix B describes the additional data sources as well as the matching
procedure in greater detail.
The data for my main analysis span the 19-year period between 1993 and 2011. I start
in 1993 because, (i) loan information in DealScan prior to 1993 is rather limited; (ii) I need
a hold-out period for calculating relationship lending and syndicate experience; and (iii)
there are concerns with the pre-1993 data adjustments in I/B/E/S, which I use in additional
tests. The main sample has a comprehensive coverage, but I separately address specific periods, such as the recent financial crisis (2008–2009), adoption of marketflex (1998), and the
Sarbanes–Oxley Act (2002). I obtain financial data on lenders and borrowers from Compustat North America Quarterly records, while, especially for the tests regarding other means
of earnings management pursued by lead arrangers, I use Compustat Bank Fundamentals
Quarterly and FR Y-9C filings.
Cheong and Thomas (2013) document the lack of variation in earnings surprises with
scale, for both cases where analyst forecasts and year-ago quarterly earnings are used as
benchmarks. Moreover, in my sample, the EPS discontinuity documented by earlier studies
22
Matching DealScan to FDIC call reports using entity names RSSD9010 entails similar complications. Note
also that the analogous issue for borrowers can be overcome by the link table provided by Chava and Roberts
(2008). I use their link table to build the borrower side of my Compustat-DealScan sample.
12
on earnings management (e.g., Burgstahler and Dichev, 1997; Degeorge, Patel, and Zeckhauser, 1999) is around 2 cents, rather than zero.23 Therefore, I define income-constrained
lenders as lender-quarters that meet or beat (by up to 3 cents) previous year’s same-quarter
EPS, and suspect loans as the loans arranged by income-constrained lenders in the last month
of the fiscal quarter.
I conduct my main analysis using GAAP earnings from the previous year because (i)
there are multiple alternative specifications for analyst forecasts (e.g., different forecast dates
or methods of consensus forecast measurement); (ii) expectations management and the incorporation of loan issuance news into forecasts could be an issue in the case of large financial
companies; and (iii) real earnings management takes place before the end of fiscal periods,
requiring a clear and fixed benchmark available to the management during the course of
the quarter. However, I also recognize that a simple random-walk method is not without
problems, especially when staleness, M&As, and the variety in macroeconomic trends are considered. To address these, I redefine income-constrained lenders using analyst forecasts and
a dynamic version of random walk; I present the pertinent findings in section 4.3, including
the main results regarding the cost of debt. I use the most recent analyst forecast, and as the
surprise distribution suggest, focus on the 0- to 2-cent bin. To take expectations management
into account, I classify the subset with a downward revision since the quarter-end forecast
as income-constrained lender-quarters. In the dynamic random-walk specification, which
takes macroeconomic trends into consideration, unlike the simple random-walk specification,
I identify the income-constrained group by evaluating the earnings surprise distribution in
different five-year periods (e.g., -1 to 1 cents for post-2007 and 1 to 3 cents for the period
preceding the crisis).
Due to data limitations and missing variables, I perform my analysis on three different
samples. The first is the unrestricted sample, which consists of the loans that are arranged
by North American public lenders included in my linking table. I use 85,363 borrower-lender
pairs in this sample to conduct univariate analyses such as comparing number of loans is-
23
One may speculate that quarterly earnings thresholds is not a major objective for banks due to their
complex structure and incentives. This distributional finding is consistent with banks managing earnings as
previously documented by Beatty, Ke, and Petroni (2002) and others. Specifically, positive 1¢–3¢ earnings
surprises in my sample are observed more than twice as much as negative 1¢–3¢ earnings surprises. (Similar
to Cheong and Thomas (2013), the mean and median surprises in my sample are around 2–3 cents as well.)
13
sued and frequency of refinancing agreements.24 Secondly, in the DealScan sample, data on
essential loan attributes such as amount, pricing, collateral status, as well as lender characteristics, such as size and leverage, are required to be non-missing. This sample includes 30,724
loan initiations from 2,724 distinct lender-quarters. Finally, in the Compustat-DealScan
sample, information on borrower characteristics needs to be non-missing in addition to loan
attributes.25 This restriction reduces the size of this sample to 11,584 loan originations and
1,893 distinct lender-quarters.26
The measurement of financial variables, as detailed in Appendix A, are generally consistent with prior research (e.g., Chava and Roberts, 2008). I use upfront fees from DealScan’s
Current Facility Pricing data set to proxy for syndication fees. I measure the amount that
the lead arranger is entitled to immediately recognize as fees multiplied by the portion of the
loan that the lead arranger syndicates out. I make this adjustment because lead arrangers
must defer one-time fees to the extent they retain the loan.27 This variable is further adjusted down for deals with multiple lead arrangers, as the amount that is received and can
be recognized at initiation also goes down.
Table 1 reports the summary statistics of the Compustat-DealScan sample by lenderquarter’s fiscal month (third month vs. first or second months) and earnings surprise status
(income constrained vs. non-income constrained). The statistical comparisons are made
across lender-quarters, holding the fiscal month of the quarter fixed. The spread and total
cost of borrowing are lower for suspect loans. While income-constrained lenders appear to
24
Hereafter, I will refer to observations as “loan initiations” instead of “borrower-lender pairs” since it is more
descriptive and intuitive. Essentially, though, the latter is more accurate since some initiations are included
in the sample more than once as they are arranged by multiple arrangers. In the sensitivity tests, I address
the implications of properly limiting the sample to unique loan initiations.
25
Two exceptions are upfront fees and lead bank’s share, which are missing to a non-trivial extent (60%-80%
of the time). Clearly, only non-missing observations are used in estimation models, if either or both of these
variables are used but there is not a designated fourth sample with additional restrictions.
26
In all samples, multiple facility tranches for a given loan package are collapsed into one, because the lack
of independence within packages with multiple tranches could cause erroneous and potentially inflated
estimates. Package amount is calculated as the sum of the facility amounts. The maturity, type and
purpose of the facility with the maximum maturity are retained as the maturity, type and purpose of the
package. By construction, I exclude sole lender deals as well; I focus on syndications (99.4%) and club deals
(0.6%).
27
This is not a perfect measurement due to the subtleties surrounding the process. First, lead arrangers
receive other benefits which could be recognized at initiation, as discussed. This measurement problem
leads to researcher’s underestimation of front-end revenues recognized by lead arrangers. However, they
also transfer a non-trivial amount of one-time fees to participant banks; such lack of disclosure of the
breakdown leads to researcher’s overestimation of fee revenues recognized by the lead arranger. Therefore,
the economic magnitude should be interpreted with caution. I am unable to answer whether these two
effects systematically vary between suspect and other loans, thus bias the statistical estimates.
14
charge lower fees than non-income-constrained lenders—which can be explained by lender
reputation (Fang, 2005)—the difference in fees is not significant in the last month of the quarter, suggesting a temporal increase in fees charged by income-constrained lenders. Borrower
characteristics do not vary substantially among groups, except for the lower profitability of
borrowers of suspect loans. This baseline finding casts some doubt on the view that observably stronger or weaker borrowers are associated with/take advantage of suspect loans.
Finally, CDS initiations during loan tenor are more likely for borrowers of suspect loans,
while a larger proportion of the borrowers of non-income-constrained lenders already have
CDS on their outstanding debt at the quarter of issuance.28
The correlations between borrower characteristics and loan attributes in the CompustatDealScan sample are presented in Table 2. Overall, the results support academic and anecdotal evidence, e.g., positive relations between collateral and spread, borrower size and loan
amount; negative relations between loan maturity and borrower credit quality, fees and
borrower Z-score. There is not a noticeable discrepancy between the two correlation matrices. Few differences involve either debt maturity or fees, e.g., ρ(f ees, borrowersize),
ρ(loan maturity, spread), which are elusive attributes (Johnson, 2003).
4
Results
4.1
Temporal distribution of loan issuance
Do lenders arrange more deals to generate additional revenues to achieve earnings tar-
gets? If so, do they shift loans across periods or outbid their competitors? In this subsection,
I answer these questions and test for the first hypothesis using the unrestricted sample. As
described above, this sample includes all loan initiations but does not contain information
about loan and borrower attributes, leaving only univariate comparisons feasible. In other
words, this is an analysis of the variation in loan issuance with lenders’ incentive to manage
earnings.
Results from pertinent univariate tests provide several insights into the questions above.
First of all, as illustrated in Figure 1, the within-year variation in loan issuance is noteworthy.
In DealScan universe, March, June, September, and December appear to be the dominant
months (consistent with the evidence provided on loan spreads by Murfin and Petersen,
28
Firm-level CDS initiation dates come from Thomson Datastream and span post-2001.
15
2012), particularly for public lenders.29 Except for September, the same conclusion holds for
my sample. This finding also suggests peaks towards fiscal quarter-ends, because some 90%
of the distinct lender-quarters in my sample have December fiscal year-ends, as required for
American bank holding companies. Indeed, Figure 2 supports this claim: third fiscal months
are associated with highest issuance.
Figure 2 also provides insights about Hypothesis 1. This figure shows the percentage
distribution of median loan issuance in dollars over fiscal months of the current and the
subsequent quarter combined. (For example, for income-constrained lenders, out of a total
$100 of issuance in the current and the subsequent fiscal quarter, $23 takes place in the third
month of the current quarter.) Relating to Hypothesis 1, the difference between the issuance
in third months and in first two months is larger for income-constrained lender-quarters. A
quarter-by-quarter analysis of the number of loans issued statistically supports this finding.
The median ratio of the number of loans issued in the last month of the current quarter to
that in all months of the same quarter is 47% for income-constrained lender-quarters. (For
non-income-constrained lender-quarters, this ratio is about 40%.) This finding is consistent
with Hypothesis 1: the capital market incentives of lead arrangers are linked with an increase
in their contemporaneous loan issuance.
Another inference that could be drawn from Figure 2 is about the subsequent issuance
activity of income-constrained lenders, which seem to experience a sharper decline in dollar
issuance during the following fiscal quarter. This difference, too, is statistically significant
for number of loans issued: the median forward-looking ratio, the number of loans issued
in the last month of the current quarter to that in all months of the subsequent quarter is
49% for income-constrained lender-quarters.30 (For non-income-constrained lender-quarters,
this ratio is about 40%.) This finding confirms the one above and, due to the subsequent
drop in issuance, suggests that income-constrained lenders create new deals or expedite the
already-retained mandates.31
29
International lenders are included in the DealScan statistics presented in Figure 1. To make the publicprivate classification, I use PublicPrivate, a variable in DealScan Company data set, but also follow up with
identifying parentid in the same data set. The former variable is mostly missing and would classify a private
subsidiary of a public lender as private, which would not be an accurate classification for this study.
30
The pattern in non-income-constrained lender behavior is not an artifact of bundling lenders that do well
(larger positive surprises) with lenders that do poorly (negative surprises). Untabulated tests indicate that
both groups have lower current and future issuance ratios than income-constrained lenders.
31
Another explanation for the results from the tests of Hypothesis 1 is that income-constrained lenders “steal”
deals from other lenders, which is possible especially when lending relationships are not a big determinant of
16
4.2
Syndication process and relationship intensity
Next, I examine whether these issuance activities are associated with accelerated negoti-
ation processes. It is possible that lead arrangers exercise their discretion on loan timetables
by imposing stricter deadlines or modifying loan terms. Table 3 presents the pertinent results. The main variable of interest is the interaction variable, IC lender × last month
signifying the loans arranged by income-constrained lenders in the final month of the fiscal
quarter.32 Controlling for loan, lender, and time effects, the coefficient estimates of suspect
loans in models 2 and 3 suggest that these loans are arranged significantly more quickly
(around 11 days) compared with non-suspect loans. This could be because an informational
advantage exists, i.e., suspect loans are inherently easier to arrange (e.g., Godlewski, 2009),
or the timetable of suspect loans is expedited in accordance with lead arranger’s objectives
(Ivashina and Sun, 2011a). As for other independent variables, these findings suggest that
whereas risky and larger loans seem to take longer to originate, those made for debt repayment purposes are associated with a swifter timetable.
Finally, I investigate the extent to which suspect loans are associated with relationship
lending and refinancing. Refinancing arrangements could be informationally convenient, and
SFAS 91 allows immediate recognition of previously deferred one-time fees once the new deal
is signed. Relying on the refinancing indicator in DealScan’s Package data set, I test but do
not find evidence of income-constrained lenders’ differential propensity to issue refinancing
loans (not tabulated).33 As for relationship lending, it is possible that income-constrained
lenders could take advantage of their relations with borrowers (e.g., Schenone, 2010). In
untabulated tests of means, I analyze the number of contracts between the arranger and the
borrower made within previous 5 years. Intriguingly, I find a stronger borrower-lender link for
non-income-constrained lenders: there are 1.2 (0.9) past loans in this group (in the incomeconstrained group). However, given the lack of controls, a within-lender analysis of this issue
the deal and/or when the borrower seeks competitive bidding. However, an untabulated time-series analysis
suggests that the loan count of non-income-constrained lenders does not seem to deteriorate in the periods
during which suspect loan presence is high. This finding is inconsistent with the main channel through
which income-constrained lenders generate deals being outbidding other lenders. Furthermore, I do not
find a significant relation between the total number of credit agreements issued during a given quarter and
the intensity of suspect loans. Even though such a test is fairly noisy, this finding is inconsistent with a
systematic effect of suspect loans on the total loan supply.
32
I thank Christophe Godlewski for sharing his loan timetable data.
33
It is important to note that this is a coarse approximation, and over 80% of deals being refinancing may
limit the variation in the tests (Roberts, 2012). The ideal variable is not a refinancing indicator but the
subset of these contracts which are premature, and whose negotiations are initiated by the lender side.
17
is more appropriate. Indeed, this conclusion is reversed within income-constrained lenders
(i.e., suspect loans are more relationship-intensive), though is not statistically significant.
4.3
Costs of borrowing
Are suspect loans priced differently than other loans after controlling for observable
determinants of loan pricing? Do income-constrained lenders prefer and pursue immediate
income over long-term value? I conduct multivariate analyses to answer these questions.
First, I consider loan spreads over LIBOR and syndication fees as the main dependent
variables. Then, I investigate their combined effect to deduce whether suspect loans are
associated with net discounts from borrowers’ point of view.
Table 4 explores the results from regressing all-in-drawn spread on lender, loan, borrower,
and time effects. The main variable of interest is the interaction variable, IC lender ×
last month signifying the loans arranged by income-constrained lenders in the final month
of the fiscal quarter, i.e., suspect loans. Clearly, fiscal month indicators ∈ {2, 3} are also
added to assign separate intercepts to each month within the quarter. Lender fixed effects
account for time-invariant lender effects, while additional lender characteristics (the lagged
values of lender leverage, size, book-to-market ratio, and past stock returns) control for
the effects of time-varying financial performance on lending behavior (Hubbard, Kuttner,
and Palia, 2002). Month-of-the-year dummies (year-quarter fixed effects) hold within-year
(across-year) variation constant. To the extent the number of observations permits, I also
use borrower fixed effects to mitigate the effects of demand-side selection on time-invariant
unobservables. Standard errors are clustered by borrower and by lender to account for
temporary, entity-level effects leading to correlated residuals.
Panel A presents results based on the DealScan sample. In these models I use loan
ratings, the codification in DealScan Market Segment data set, to account for an essential
loan attribute in the absence of borrower characteristics. In all specifications, the estimates
suggest that suspect loans are associated with around 13 bps lower spread, corresponding to
5.5% of the average spread.34 As expected, smaller loans and loans with longer tenor have
higher spread (Ivashina, 2009). The strong positive relation between collateral requirements
34
The average spread for suspect loans in the DealScan sample is 236 bps. (Detailed attributes of this sample
are not tabulated for brevity.) This is lower than the corresponding number in the Compustat-DealScan
sample, 172 bps. Yet this is not surprising since the latter group consists of publicly traded North American
companies, which are arguably less risky (at least from the perspective of North American lenders.)
18
and the spread indicates that lenders charge riskier loans with higher spread and require
collateral simultaneously (Berger and Udell, 1990). The negative link between the number
of performance pricing terms and the spread is consistent with Asquith, Beatty, and Weber
(2005); performance pricing clauses are more likely to be used for less risky borrowers.
Panel B presents results from similar loan-initiation level regressions estimated using
the Compustat-DealScan sample, which consists of loans originated to borrowers that are
publicly traded in the US. Additional variables proxying for borrower characteristics at loan
initiation are included in these tests to better control for demand-side selection. These regression results provide support for the main finding: suspect loans are conditionally cheaper.
The first model in this panel repeats the third model in Panel A to check robustness with
respect to different samples and additional regressors. The observed 22 bps discount (significant at 10% level) is consistent with the results in Panel A. The statistical significance
goes up after adding borrower characteristics, especially ROA, Altman’s Z-Score and the
book-to-market ratio. The main takeaway from Panel B is that the spread discount continues to persist after controlling for key attributes of the demand side (economically, 16-20
bps, about 9% of the average spread). The coefficient estimates of borrower characteristics
are consistent with prior studies (e.g., Strahan, 1999): larger, financially healthy and more
profitable borrowers get lower rates, whereas those with high leverage and poor cash flows
pay higher spreads. Moreover, the positive association between non-bank participation and
the spread is noticeable, suggesting that these entities tend to participate in riskier loans for
higher returns.
In these tests I also examine the sensitivity of my identification. First, I use a dynamic
specification of random walk to better incorporate macroeconomic trends. Second, I use
analyst forecasts, instead of time-series models, to reassess lenders’ earnings benchmarks.
As detailed in models 4 and 5 of Panel B, the results (16–20 bps) are statistically significant
and quantitatively similar to baseline findings.35
Table 5 reports results from fixed-effects regressions with fees as the dependent variable.
Because of DealScan’s limited coverage of upfront fees, these tests are conducted in smaller
samples. Adjusted fees is Fees multiplied by the portion of the loan sold by the bank (bank
allocation from DealScan Lender Shares data set). Since lead bank’s share is also sparsely
35
The statistical and economical significance of my findings with analyst forecasts is sensitive to using the
subset of banks that experience downward forecast revisions.
19
populated in DealScan, the sample size for regressions of Adjusted fees is even smaller. The
main findings from Table 5 suggest that suspect loans have larger fees holding loan, lender,
borrower, and time effects constant. Results in Panel A suggest an economic magnitude of
5.38–14.05 bps. Because of shared lending, Fees is larger than Adjusted fees; therefore, it is
conceivable to see the coefficient of interest being smaller when Adjusted fees is the dependent
variable than is the case when Fees is. However, this conclusion is reversed in relative terms:
5.4 bps in Adjusted fees (10 bps in Fees) corresponds to a marginal effect of 17% (14%).
In Panel B results obtained from the Compustat-DealScan sample support the main
findings. Model 3 suggests that when the sample for the regression of Fees is restricted
to non-missing Adjusted fees, the coefficient estimate of IC lender × last month becomes
insignificant. This variable is statistically and economically significant in models 4 and 5.36
This distinctive finding isolates and highlights the importance of the revenue recognition goal.
Importantly, this amount corresponds to a pre-tax EPS impact of about 0.09 cent per loan.37
As for the control variables, the association between fees and loan amount is consistent
with the link between compensation and complexity (Angbazo, Mei, and Anthony, 1998).
Similarly, the positive relation between spread and fees confirms prior literature that these
two pricing terms behave like complements, not substitutes (Berg, Saunders, and Steffen,
2013).
Having concluded that income-constrained lenders charge higher fees yet lower spread,
I examine the total cost of borrowing to identify whether these loans are “bargains” for borrowers. I construct Total cost of borrowing (T CB) by adding spread to annualized one-time
fees (that is, fees × 12 divided by maturity, measured in months). Even though the spread
already includes several types of annual fees, this measure does not include the charges on
the undrawn amount (e.g., commitment fees) or other potential front-end fees (e.g., letter of
credit fees). Table 6 presents the estimation results, which suggest for both main samples
that suspect loans are cheaper than other loans by the same lender at other times during the fiscal quarter and than contemporaneous loans initiated by other lenders.38 This
discount of 18.22 (19.96) bps in Compustat-DealScan sample (DealScan sample) suggests
36
Here the marginal effect is about 50%, though IC lender has a strong negative coefficient estimate, alleviating
the combined effect.
37
The average recognizable fees amount is around 20 bps for suspect loans. This times the average loan amount
($434mm), scaled by the average number of shares for income-constrained lenders (roughly 1 billion) equals
0.087 cent of an EPS impact per loan.
38
I exclude fees with lower than 5 bps to avoid degenerate results.
20
that the income-constrained lenders’ preference for short-term gain over long-term value is
costly—i.e., the direct cost of earnings management for the lending consortium. Similar
to the previous tests, these results are obtained after controlling for known and observable
determinants of loan pricing. Their magnitudes and significance are consistent with those
displayed in Table 4. The additive impact of one-time fees on TCB appears to be offset by
the subtractive impact of spread, which is charged annually.
Overall, these findings are in line with the second hypothesis: the capital market incentives of lead arrangers appear to impact loan pricing. Specifically, arrangers in these cases
charge lower spread and higher fees (H2b), in a way that the net conditional pricing is low
relative to comparable deals (H2a).
4.4
Other activities of income-constrained lenders
Why do lenders opt to engage in real earnings management if it is costly? What about
other means of achieving financial targets? Here, I provide explanations regarding nonlending activities in the supply-side. Specifically, I attempt to identify whether or not incomeconstrained lead arrangers concurrently engage in other activities in accordance with their
financial reporting goals. Consistent with Beatty and Harris (1999) and Beatty, Ke, and
Petroni (2002), I focus on provisions for loan losses and realized security gains. The relevant
data is obtained from FR Y-9C filings and reinforced with information from Compustat Bank
Fundamentals.
Table 7 presents the relevant results at the lender-quarter level. In addition to the original
specification in Beatty, Ke, and Petroni (2002), I also include on the RHS the number of
syndicated loans arranged in the current quarter to alleviate the effect of consolidating loans
into lender-quarter level. Results are economically consistent with prior work; e.g., provision
for loan losses are positively linked with reserves for loan losses and certain types of loans
(such as C&Is), while realized security gains are positively associated with unrealized security
gains. Importantly, as detailed in models 2 and 3, loan loss provisions are conditionally
smaller for income-constrained lenders. Although results from specification 4 do not suggest
a link between realized security gains and just meeting-or-beating earnings thresholds, this
conclusion is reversed after controlling for bank fixed effects as model 5 implies. In all, these
findings are in line with real earnings management via syndicated loan originations being
one of the means for achieving financial reporting goals.
21
4.5
Demand-side and aftermath
Are suspect loans made to certain types of borrowers? Are they associated with par-
ticular credit events following issuance? I focus on the demand side of the loan market to
answer these questions. That said, it is important to highlight that this study is rather like
a diff-in-diff-in-diff (time-borrower-lenders). While the demand-side could be the real force
behind the temporal issuance effect (e.g., borrowers may choose to adjust their short term
leverage or liquidity before the quarter end, resulting in the observed peak in third fiscal
months), this channel must also drive the cross-sectional variation with lenders’ earnings
goals to suggest spuriousness for my findings. Although I note some variation in borrower
characteristics at the issuance period such as borrower size and profitability, to the extent I
control for borrower and loan attributes, I posit that such a demand-side-only explanation
for suspect loans is rather less plausible.
Having noted these differences, I investigate the life of suspect loans to identify the traces
of selection effects as well as the additional costs associated with suspect loans. I start by
analyzing the changes in borrowers’ credit ratings after issuing suspect loans because credit
quality is a crucial metric for lenders (e.g., Tirole, 2006). If suspect loans are priced efficiently
or are made to borrowers with similar expected changes in future riskiness, borrowers of
suspect loans would not be associated with different future changes in credit ratings than
other borrowers. I present the results from these tests in Table 8. The dependent variable
is borrowers’ linearized credit rating one year after loan initiation.39 Indicator variables
denoting borrowers’ ratings at issuance are added on the RHS. The first specification includes
low-yield loans (i.e., loans that are not coded as leveraged or highly-leveraged in DealScan
Market Segments data set), while the second exclusively consists of high-yield loans (i.e.,
loans that are coded as leveraged or highly-leveraged in DealScan Market Segments data
set). The third model excludes observations with missing fees yet includes both types of
loans.
Several variables significantly predict changes in future credit ratings. Notably, borrowing costs, mainly driven by the spread component, are negatively associated with future credit
rating downgrades. This is consistent with (i) opportunistic debt issuance timing by borrowers, (ii) banks charging firms that have poor future performance with higher spreads by
39
S&P ratings are linearized from AAA = 21 to D = 0. Loans maturing within less than 12 months are
excluded from this analysis to ensure the lending contract is intact at the measurement date.
22
virtue of their superior screening ability and information set, and (iii) credit rating agencies
being sluggish in incorporating fundamentals. The third point is also supported by significant coefficient estimates of observable borrower characteristics.40 The main independent
variable, IC lender × last month, is insignificantly linked with future credit ratings in the
first specification. The second and third models report negative and significant coefficients
on the interaction terms. Economically, these coefficient estimates suggest a downgrade by
about 0.2–0.3 of a notch. In sum, this negative link between suspect loans and future credit
ratings sheds some light on borrower opportunism. I interpret this finding as additional costs
associated with suspect loans.
As a complementary test, I look into subsequent lender and borrower behaviors with
respect to CDS initiations and spreads. In order to avoid the incidental parameters problem
and to abstract average partial effects, I choose an OLS specification. As model 4 of Table
8 indicates, the probability of a CDS contract being initiated on the borrower’s outstanding
debt during the loan’s stated tenor is associated with an incremental probability of 4.1% for
suspect loans. I view this finding as a sign of income-constrained lenders’ skepticism about
the borrowers’ abilities to repay and seeking insurance ex post.
Do borrowers of suspect loans vary in their propensity to default? I test whether borrowers of suspect loans are more likely to be in violation of their loan covenants within
(that is, before and after) one year of the loan initiation.41 Untabulated tests indicate that
the violation rates between suspect and other loans are not significantly different in any
specifications that include a vector of control variables and fixed effects. Similarly, future
payment defaults do not seem to vary systematically between suspect and non-suspect loans.
This lack of findings is not surprising to the extent the former are endogenous (i.e., jointly
determined with the spread) and the latter are rare.
Finally, however a coarse way to identify the effects of suspect loan issuance, I examine
the recognition and consequences of suspect lending at the lender-level. Specifically, I test
whether income-constrained lenders subsequently experience different changes in loan loss
provisions relative to other lenders. Lead arrangers could report a loan loss if they recognize
that suspect loans are less likely to be repaid. I do not find any systematic or significant
40
Very large R-squareds are noticeable. Though this is partly due to controlling for current credit ratings
(ratings are quite sticky), excluding this variable still results in models with R-squareds around 80%.
41
I obtain the data on covenant violations spanning 1993–2008 from Amir Sufi’s website.
23
differences in subsequent loan loss provisions with the incidence of just meeting-or-beating
earnings thresholds.
4.6
Composition of suspect syndicates
Are suspect loans an outcome of efficient contracting? Why do the parties bearing extra
costs participate in these deals? The results above suggest an overall discount in loan pricing
and possible future issues with suspect loans. While borrowers appear to obtain “bargains”,
the cumulative effect for the lead arranger is not obvious. Despite lower spreads, benefits
from higher syndication fees and hitting earnings benchmarks could plausibly offset the costs
(e.g., Bartov, Givoly, and Hayn, 2002). However, syndicate participants seem to bear only
the costs to the extent they are not entitled to the higher fees.
In this regard, it is important to note that participating in “suspect syndicates” could be
a rational choice under certain conditions. For instance, institutional investors participating
in the syndicated loan market act as lenders of last resort aiming for conditionally higher
spreads (Lim, Minton, and Weisbach, 2013; Dugan, 2013) or, as Ivashina and Sun (2011b)
document, some institutional participants engage in equity trades using private information
about borrowers. It is also possible that objective functions of certain syndicate participants
value building future business relationships with the borrower, lead arranger, or other participants. Thus, they may tolerate forgoing the direct forms of compensation (Rhodes, 2009).
Clearly, the alternative explanation to the rational choice argument is that participants are
exploited by income-constrained lead arrangers.42
To this end, I analyze the syndicate structure of suspect loans. Table 9 presents the
estimation results from OLS regressions. The first model shows that non-bank participation
in suspect loans is higher than that in other loans. Economically, non-bank participation is
3.15% higher in suspect loans, corresponding to a marginal increase of 18%.43 I recognize
42
This would contradict the lead banks’ reputational concerns, as suggested by Gopalan, Nanda, and Yerramilli (2011) and Panyagometh and Roberts (2010), although Rhodes (2009) provides evidence that small
bank participation in syndicated deals has decreased over time. Suspect loans, together with subsequent
learning by small banks, could explain why syndicated lending has fallen out of favor with small bank
participants.
43
Empirically, I calculate non-bank participation as the number of non-banks scaled by total number of lenders
in the syndicate. (The data come from DealScan’s Lender Share and Company data sets.) Evidently, this is
equal-weighting. Substantial data issues in the distribution of loan among the syndicate members precludes
a value-weighted analysis. Another issue might be the right skewed distribution of non-bank participation.
My results remain unaltered if the dependent variable is used in logarithmic or binary form.
24
that this finding could be a result of various factors because not all non-bank lenders are
the same. Indeed, as model 2 displays, the probability of an institutional and sophisticated
lender participating in the contract is 2.87% lower for suspect loans.44 Finally, suspect-loan
syndicates are less experienced (measured as the natural logarithm of the syndicate-median
value of the number of loans arranged or joined by each participants within 5 years prior to
the corresponding contract). Overall, these findings suggest that suspect loans are skewed
toward inexperienced and unsophisticated lenders. These entities may be targeting future
business with the contracting parties, attempting to enter the loan market, or exploited by
the lead arrangers.45
4.7
Sensitivity checks
In this subsection, I analyze the sensitivity of my results to a number of alternative
specifications. Table 10 explores the robustness of my main findings (i.e., the coefficient
estimates of IC lender × last month from fixed-effects OLS regressions, with the spread and
fees as dependent variables) by presenting the results from a battery of tests.
Bundling all other loans into the same category may be too coarse. Superior lead arrangers’ rent extraction may be related to their time-varying earnings performance, which
may not be accounted for by lender fixed effects or other lender characteristics. Likewise, it is
possible that lenders are income constrained because they offer discounts (reverse causality).
To address this issue, I separate the single group of non-suspect loans into loans originated by
lenders that are with earnings surprises greater than 5 cents and loans originated by lenders
that are with negative earnings surprises. Reverse causality predicts that lenders with worse
contemporaneous earnings surprises should also be associated with conditional discounts,
partially or fully mitigating the statistical power of income-constrained lenders. As detailed
in Table 10, the main conclusions are insensitive to these alternatives when I run my main
44
I use an indicator variable because sophisticated non-bank lenders participate in a small subset of loans and
the variation in the degree of participation is not meaningful.
45
Selling suspect loans in the secondary market could be another rationale for the discounts offered in suspect
loans (e.g., Gorton and Pennacchi, 1995). However, securitization and the subsequent sale of syndicated
loans are not popular among lead arrangers due to contractual frictions and reputational concerns. For
example, Ivashina (2005) points out that even though lead banks are not explicitly prohibited from selling
their share of the loan on the secondary market, they are committed to holding the share that they retained
at loan origination. It is because loan sales in the secondary market entail the borrower’s and syndicate’s
consent and the syndicated loan market is illiquid and private. In this sense, from the banks’ perspective,
syndication and securitization are substitutes rather than complements. Still, it is possible, along with
other actions, that participants unload suspect loans from their books. A full investigation of this issue,
however, is beyond the scope of this paper.
25
tests using separate intercepts; nor do I find substantially different coefficient estimates for
high-earnings-surprise lender-quarters and low-earnings-surprise lender-quarters.
I address potential omitted variable concerns by repeating my main estimation tests with
a number of additional regressors that are excluded from the main specification because they
reduce the sample size.46 Including concurrent changes in loan loss provisions by banks or
measures of relationship lending on the RHS does not have a substantial impact on the main
results.
Moreover, I do not find a substantial variation in the coefficient estimates of IC lender
× last month over years. One exception is that both coefficient estimates and t-statistics
of IC lender × last month are stronger after 2002 in the total-cost-of-borrowing tests. This
is consistent with the work by Cohen, Dey, and Lys (2008), which reports that real earnings management have become more prevalent after the Sarbanes-Oxley Act. Importantly,
untabulated pricing results are not significant during the 1993–1998 period, suggesting that
the pricing flexibility provided to arrangers by marketflex may have facilitated this means of
real earnings management. Additionally, I present a summary of the results from regressions
that exclude the recent financial crisis and its aftermath. These findings are economically
significant. Also, as detailed in the last row of Table 10, using natural logarithms of the
dependent variables to mitigate the impact of outliers does not invalidate my conclusions.
Lastly, in untabulated tests, I reduce dimensionality by factor analysis and run my regressions using common factors to explain loan spreads. Collinearity among my control
variables would not bias the coefficient of the main variable of interest, IC lender × last
month, or change the fact that OLS is still efficient and yields the best linear unbiased estimator. However, this experiment is useful for comprehending the actual forces driving the
cost of debt in my estimation sample and making sense of the coefficients of explanatory
variables. These results confirm the validity of the main coefficient of interest, IC lender
× last month, (-20.66**) and suggest 6 distinct factors as determinants of the cost of borrowing, all of which are significantly associated with the spread: loan and borrower size;
borrower profitability; borrower leverage, loan maturity and collateral; lender size, leverage and composition of the syndicate; covenants and performance pricing; borrower’s stock
46
Of note, I do not exclude borrowers from financial or utility industries, since there is no ex ante reason for
doing so. Though, I confirm that my main results are not sensitive to having these types of companies in
my estimation samples.
26
market performance and book-to-market ratio.47 When included, lender’s earnings-target
incentives are classified under the fourth factor.
5
Conclusions
Taking advantage of granular records of syndicated loan agreements, I find intra-quarter
evidence of costly real earnings management in the financial industry. Specifically, I investigate lead banks’ opportunistic timing and pricing of syndicated loan issuance in third months
of fiscal quarters. I observe that publicly-traded lenders that just meet-or-beat their previous
year’s quarterly earnings initiate more loans and charge higher upfront fees but offer overall
discounts via lower spreads over LIBOR. These comparative statements are valid both within
(comparing suspect loans with other loans by the same lender in the same fiscal quarter)
and across lenders (comparing suspect loans with loans by other lenders in the same fiscal
month).
Taken together, this evidence is in line with income-constrained lenders’ preference for
short-term income over long-term value. Borrowers of leveraged loans experience credit rating downgrades and CDS initiations on their debt following the suspect loan initiations,
suggesting that borrowers with conditionally poor prospects select suspect loans. Suspect
loans are associated with greater non-bank and unsophisticated-lender participation, consistent with a lack of expertise as well as with the conjecture that syndicate members agree to
the discounts associated with suspect loans, because they may receive some benefits other
than direct compensation in the forms of spread and fees.
Overall, this study contributes to the accounting literature, by providing granular and
new evidence of costly real earnings management in the financial industry, and to the corporate finance literature, by introducing a new determinant of the cost of debt financing.
However the operation-specificity of the evidence remains a caveat, limiting the generalizability of the findings to the greater financial industry. This paper calls for more research
on the composition and term structure of debt pricing. Future research can also explore the
variation in other aspects of financial contracting with lender or managerial incentives.
47
Listed in descending eigenvalues, based on whose criteria the 6 factors are retained. The verbal classification
is subjective and made based on the magnitudes of factor loadings.
27
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Appendix A: Variable definitions
Variable
Spread
Fees
Loan amount
Loan maturity
Covenants
Performance
pricing clauses
Collateral
Retained share
Loan purpose
Loan type
Refinancing
Lending
relationship
Number of participants
Ratio of nonbanks
Total cost of
borrowing
Adjusted fees
Leverage i
Profitability
(Broa)
Size (Bsize)
Stock
performance (Bret)
Book-to-market
(Bb2m)
EBITDA
Altman’s
Z-Score (BAltman’s Z)
Credit ratings
i
Definition or Calculation
All-in-drawn spread over floating rate
Upfront fees
Total facility amount in loan package
Maximum tenor of facilities in loan package
(months)
Number or financial covenants in loan package,
0 if missing
Number or performance pricing clauses in loan
package, 0 if missing
Value of secured dummy in loan package
Value of bankallocation in loan package
Corporate purposes, debt repayment, working
capital, takeover, CP backup, or other.
Type of loan: Revolver, term loan, or other.
Refinancing indicator as is
Number of previous loan arrangements between
lender and borrower
The number of lenders participating in the deal
DealScan
DealScan
DealScan
DealScan
Source
Current Facility Pricing
Current Facility Pricing
Facility
Facility
DealScan Financial Covenant, Net
Worth Covenant and Package
DealScan Performance Pricing and
Package
DealScan Facility
DealScan Facility
DealScan Facility
DealScan Facility
DealScan Package
DealScan Facility
DealScan Lender Shares
The number of non-commercial bank lenders in
the deal scaled by number of participants
Spread + F ees × 12/M aturity
DealScan Lender Shares and Company
DealScan
F ees × (1 − Retained share/100)
DealScan
(dlcq + dlttq)/atq or (atq − ceqq)/atq if debt values are missing
Return on assets: ibq/atq
Compustatii
Lagged value of total assets, logged in regressions
l.atq iii
Raw
returns,
calculated
over
last
full
quarter
before
loan
origination
(prccq/ajexq − l.prccq/l.ajexq +
dvpspq/ajexq)/(l.prccq/l.ajexq)
ceqq/(prccq × cshoq)
Compustat
oibdpq/l.atq, if missing ibq + txtq + xintq + dpq
1.2 × (actq − lctq)/atq + 1.4 × (req/atq) + 3.3 ×
oiadpq/atq + 0.6 × prccq × cshoq/ltq + 0.999 ×
saleq/atq iv
splticrm is used in linearized form (0 for D and
21 for AAA) or as indicator variables for each
rating and one for no rating
Compustat
Compustat
Compustat
Compustat
Compustat
Compustat S&P Ratings
Lender and borrower characteristics are calculated similarly.
ii
North American Fundamentals Quarterly dataset is used and variables are as of the quarter-end preceding
the loan initiation unless stated otherwise.
iii
l. is quarterly lag operator, implying an additional lag on top of quarter-end values.
iv
Missing oiadpq is replaced with ibq + txtq + xintq.
32
Appendix B: Matching DealScan lenders to Compustat
The linking table constructed by Michael Roberts (Chava and Roberts, 2008), which
substantially facilitates the studies involving DealScan borrowers, provides S&P’s entity
identifiers (gvkey) of DealScan borrowers. A similar task for lenders is less straightforward
due to the complex corporate nature of financial institutions and the way DealScan is structured. Specifically, mergers and acquisitions are overwhelmingly common and complicated
in the financial industry, and a fair amount of operating subsidiaries are active in the syndicated loan market. DealScan’s Company data set includes parent and ultimate-parent
identifiers, which are useful for tracking down the hierarchy among institutions. Similarly,
the linking table provided by the New York Fed is an important bridge between DealScan
and CRSP-Compustat universe.v Although these variables are not historical, they provide a
sufficiently reliable initial basis for constructing the desired DealScan-Compustat link. When
SEC Filings (via EDGAR), Compustat Names, DealScan’s Company and Facility datasets
are jointly utilized and used for cross-check, the merging accuracy goes up. Identifying M&As
in the financial industry entails a careful analysis using official M&A records, which are available on the Chicago Fed’s website.vi SDC Platinum and Compustat Corporate Tracker are
other viable alternatives.
Here, I briefly describe one of the entities in my linking table, JP Morgan Chase &
Co. (henceforth, JPM), to illustrate the issues with the merging procedure and the ways I
solve them.vii In DealScan, by tracking JPM’s ultimateparentid, one can observe that it has
arranged over 20,000 deals. (However, note that this quick analysis classifies as JPM loans
the loans originated by entities that would be a part of JPM, even before the mergers in
question took place.) DealScan’s unique lender identifier, companyid, indicates that there
are 107 distinct lenders under the JPM umbrella, each of which arranged at least two loans
in North America since 1993. This would not be a major a challenge if these lenders were
only limited to regional offices (e.g., JP Morgan Delaware) and business segments (e.g., JP
Morgan Securities), or the goal was not linking entities at the gvkey level.
v
Source: http://www.newyorkfed.org/research/banking_research/datasets.html
vi
Source: http://www.chicagofed.org/webpages/publications/financial_institution_reports/
merger_data.cfm
vii
Figure A1 presents a simplified time line for JPM’s major corporate activities.
33
Chemical
Chase
Manhattan
JP Morgan
YEAR
2968
2968
2943
2968
2968
2968
7562
1996
2000
Bank One
Bear Stearns
2004
2008
Figure A1: Summary of JP Morgan Chase & Co.’s corporate time line and gvkeys
DealScan bundles Chase, JP Morgan, Chemical Bank, Bear Stearns etc. with JPM’s
ultimateparentid. On the Compustat-side, the post-1993 story of JPM starts with Chemical
Bank [gvkey=2968]. In 1996, Chemical acquired Chase Manhattan and although Chemical
was the acquirer, the entity adopted the Chase name. In Compustat, the name corresponding
to gvkey, 2968, is replaced with Chase and the name corresponding to Chase’s former gvkey,
2943, is updated as Chase-old. Similarly, Chase’s acquisition of J P Morgan [gvkey=7562] in
2000 results in the joint name of J P Morgan Chase & Co and the retention of 2968 as the
gvkey of the final entity. Later ,during the last decade, JPM acquired Bank One (2004) and
Bear Sterns (2008); these deals are more straightforward to process. Yet it is still important
to link, for instance, Bank One’s loans with Bank One [gvkey=1998] prior to the acquisition,
and with JPM [gvkey=2968], thereafter.
Finally, there are two more issues because of the way DealScan is structured. First,
JPM’s components’ (such as Chase’s or Bank One’s) subsidiaries with less obvious names
(e.g., First Chicago, National Bank of Detroit) need to be treated in the same way as the
parent. Here parentid helps to some degree but S&P’s Capital IQ provides a clearer coverage
for accurate tracking. Second, in DealScan, it is possible to see some defunct lenders initiating
loans years after they got acquired or deactivated for other reasons. To control for this, one
can (i) carefully track down the deactivation date of the brand name (rather than that of the
corporation), (ii) look up borrower 10-K/Qs for information about the lending contracts, or
(iii) rely on DealScan’s evaluation. For this linking table, I have adopted the third option but
followed up with manual random checks in accordance with the first two options. Ultimately,
the linking table is at the entity-quarter level, matching multiple companyids (because there
are multiple DealScan lenders under a single big name) to multiple gvkeys (because of M&A
activities).
34
Fmonth
1
2
3
1
2
3
Number of deals
Month
Private lenders (AllPublic
DealScan)
lenders (All DealScan)
This study
Jan
2257
7326
4131
18,000
Feb
2593
8488
4778
Mar
3954
15421
7035
16,000
Apr
3121
9322
5757
14,000
May
3457
10418
6291
Jun
4467
13517
8059
12,000
Jul
3863
11162
6071
Aug
3227
10274
5915
10,000
Sep
3394
13403
5723
Oct
3092
9310
5562
8,000
Nov
3349
9730
5462
Dec
4060
12624
7387
IC
104
114
156
113
95
106
688
Non-IC
11300
11000
13100
11300
11000
12100
69800
Fmonth
1
2
3
4
5
6
6,000
4,000
2,000
0
Jan
Feb
Mar
Apr
May
Private lenders (All DealScan)
Jun
Jul
Aug
Sep
Oct
Public lenders (All DealScan)
Nov
Dec
This study
Figure 1: Within-year variation in loan issuance
24%
22%
Amount issued
20%
18%
16%
14%
12%
10%
1
2
3
4
5
6
Month
Current fiscal quarter
Subsequent fiscal quarter
Income Constrained
Non-Income Constrained
Figure 2: Distribution of loan issuance over months in currrent and subsequent fiscal quarter
35
Table 1: Summary Statistics
This table presents pertinent summary statistics of the Compustat-DealScan sample. All variables are
as defined in Appendix A. There are 265, 464, 4,033, 6,791 loan observations in the four mini panels,
respectively. (The statistics of Upfront fees and Retained share are based on fewer observations. Similarly,
variables involve CDS are from after 2001.) A lender is income constrained if meets its year-ago quarterly
earnings or beats it by up to 3¢. Fmonth ∈ {1, 2, 3}, denoting the month of the fiscal quarter (of lender).
Continuous control variables are Winsorized at 1% and 99% levels. * (+ ) denotes statistically significantly
different mean statistics at the 1% (10%) level between top and bottom mini panels holding fiscal month
fixed.
Panel A: Loans by income-constrained lenders
Loan amount ($mil)
Maturity (months)
Spread (bps)
Upfront fees (bps)
Total cost of borrowing (bps)
Collateral dummy
Number of covenants
Number of perf. terms
Brating (linearized)
Broa (×100)
Bsize (log)
Bret (%)
Blev
Bb2m
BAltman’s-Z
Bebitda
Ratio of institutions
Number of participants
CDS firm at initiation
CDS initiation during loan tenor
Retained share (%)
mean
434*
49.8
172+
39
214+
0.64
2.16+
0.698
11.2
0.467*
6.71*
5.55
0.275
0.604
2.05
0.032+
0.061
8.23
0.164+
0.097+
23.75
F month = 3
median
p25
190
73
60
36
150
97.5
25
12.5
195
129
1
0
2
1
1
0
11
9
0.999
0.119
6.52
5.31
2.87
-8.61
0.267
0.137
0.493
0.282
1.56
0.946
0.032
0.017
0
0
6
2
0
0
0
0
17.14
10
p75
500
60
225
50
286
1
3
1
13
1.87
7.98
17.8
0.402
0.746
2.54
0.047
0.083
12
0
0
33.33
mean
440*
49
186
34.4*
238
0.67+
2.04+
0.647
11
0.938
6.7*
2.44
0.282
0.562
2.15
0.037
0.047
8.61
0.168*
0.066
26.55*
F month = 1, 2
median
p25
200
75
60
36
150
100
25
11.8
206
130
1
0
2
1
1
0
11
9
1.15
0.181
6.63
5.42
2.34
-11.2
0.278
0.152
0.453
0.297
1.65
1.02
0.036
0.021
0
0
6
3
0
0
0
0
20.90
13.16
p75
500
60
250
50
293
1
3
1
13
1.98
7.83
14.3
0.396
0.697
2.59
0.051
0.058
11
0
0
35.71
mean
588
49
187
58.6
245
0.61
1.98
0.673
11.3
0.834
7.13
4.02
0.282
0.603
2.03
0.036
0.06
9.62
0.207
0.076
21.85
F month = 1, 2
median
p25
265
100
60
36
175
87.5
25
15
220
127
1
0
2
1
1
0
11
9
1.07
0.252
7.08
5.9
2.97
-9.03
0.274
0.146
0.478
0.303
1.53
0.85
0.034
0.021
0
0
7
3
0
0
0
0
16
10
p75
700
60
250
67.6
325
1
3
1
13
1.99
8.32
15.5
0.398
0.75
2.52
0.049
0.083
13
0
0
28
Panel B: Loans by all other lenders
Loan amount ($mil)
Maturity (months)
Spread (bps)
Upfront fees (bps)
Total cost of borrowing (bps)
Collateral dummy
Number of covenants
Number of perf. terms
Brating (linearized)
Broa (%)
Bsize (log)
Bret (%)
Blev
Bb2m
BAltman’s-Z
Bebitda
Non-bank participation
Number of participants
CDS firm at initiation
CDS initiation during loan tenor
Retained share (%)
mean
540
49.4
187
49
250
0.61
2.06
0.678
11.3
0.86
7.04
4.87
0.277
0.588
2.16
0.035
0.059
9.45
0.214
0.070
22.86
F month = 3
median
p25
250
100
60
36
175
87.5
30
12.5
226
130
1
0
2
1
1
0
11
9
1.11
0.238
7.01
5.79
3.79
-8.92
0.264
0.143
0.484
0.299
1.64
0.944
0.034
0.02
0
0
7
3
0
0
0
0
16.67
10.29
36
p75
600
60
250
50
328
1
3
1
13
2.05
8.23
16.7
0.392
0.72
2.66
0.048
0.071
13
0
0
30
37
Amount
1.00
0.17
-0.19
-0.01
-0.19
-0.28
-0.15
0.08
0.04
0.64
-0.09
0.07
-0.06
-0.13
0
Amount
1.00
0.12
-0.15
0.03
-0.12
-0.23
-0.19
0.01
0.07
0.64
-0.01
0.08
-0.09
-0.12
0.04
Loan amount ($mil)
Maturity (months)
Spread (bps)
Upfront fees (bps)
Total cost of borrowing
Collateral dummy
Number of covenants
Number of perf. terms
Broa
Bsize
Bret
Blev
Bb2m
BAltman’s-Z
Bebitda
Loan amount ($mil)
Maturity (months)
Spread (bps)
Upfront fees (bps)
Total cost of borrowing
Collateral dummy
Number of covenants
Number of perf. terms
Broa
Bsize
Bret
Blev
Bb2m
BAltman’s-Z
Bebitda
1.00
0.03
0.07
-0.02
0.13
0.1
0.04
0.12
0.03
0.02
0.13
-0.13
-0.05
0.12
Maturity
1.00
-0.02
0.23
0
0.04
0.09
0.12
0.16
0.12
-0.06
0.14
-0.22
-0.09
0.17
Maturity
1.00
0.56
0.23
-0.07
-0.16
0.01
-0.11
0
0.1
0.01
-0.13
0.05
Fees
1.00
0.41
-0.08
-0.38
-0.14
-0.32
0.07
-0.02
0.11
-0.25
-0.09
TCB
1.00
0.16
-0.22
-0.2
-0.44
0
0.16
0.14
-0.21
-0.15
Collat
1.00
0.19
0.07
-0.22
0.16
-0.03
0.01
0.12
0.07
Coven
1.00
0.15
0.17
0.06
-0.06
-0.12
0.11
0.14
Ppric
1.00
0.07
0.1
-0.07
-0.3
0.27
0.5
Broa
1.00
-0.1
0.14
-0.07
-0.14
0.01
Bsize
1.00
0.46
0.94
0.46
0.11
-0.29
-0.28
-0.27
0.01
0.17
0.29
-0.25
-0.23
Spread
1.00
0.54
0.16
-0.05
-0.19
-0.08
0.03
-0.04
0.11
0.09
-0.16
-0.04
Fees
1.00
0.38
0.01
-0.34
-0.27
-0.15
-0.01
0.14
0.25
-0.25
-0.21
TCB
1.00
0.19
-0.22
-0.19
-0.44
0.03
0.13
0.19
-0.15
-0.14
Collat
1.00
0.21
0.02
-0.29
0
0.03
0
0.04
0.07
Coven
1.00
0.14
0.1
0.01
-0.05
-0.09
0.08
0.13
Ppric
1.00
0.12
0.07
-0.13
-0.25
0.33
0.53
Broa
1.00
-0.01
0.13
-0.08
-0.16
-0.01
Bsize
Panel B: Compustat-DealScan Sample, Loans by all other lenders
1.00
0.54
0.93
0.4
0.05
-0.33
-0.18
-0.38
-0.04
0.12
0.23
-0.21
-0.17
Spread
1.00
0.01
0.11
0.08
0.12
Bret
1.00
-0.11
0.01
0.21
0.12
Bret
Panel A: Compustat-DealScan Sample, Loans by income-constrained lenders
1.00
0.02
-0.43
-0.06
Blev
1.00
-0.04
-0.48
-0.02
Blev
1.00
-0.28
-0.33
Bb2m
1.00
-0.27
-0.35
Bb2m
1.00
0.36
BAlt.Z
1.00
0.34
BAlt.Z
1.00
Bebitda
1.00
Bebitda
This table presents correlation matrices of borrower characteristics and attributes of loans initiated by income-constrained and non-incomeconstrained lender lenders.A lender is income constrained if meets its year-ago quarterly earnings or beats it by up to 3¢. All variables are as
defined in Appendix A.
Table 2: Correlations
Table 3: Lenders’ earnings targets and the length of syndication process
This table presents results from loan-initiation level regressions of length of syndication
process on lender and borrower characteristics. All specifications include fiscal month,
month of the year, year-quarter, loan ratings, lender, and borrower industry fixed effects.
IC lender stands for income-constrained lenders. It is an indicator variable equals 1 for loans
that are arranged by lenders which meet their year-ago quarterly earnings or beat them by
up to 3¢; and 0 otherwise. last month is an indicator variable equals 1 for loans initiated
in the last month of lender’s fiscal quarter; and 0 otherwise. length of negotiations is the
number of days between the award of the mandate and deal activation date. All variables
are as defined in Appendix A. Heteroskedasticity-robust standard errors are clustered by
lenders and are reported in parentheses. ***, **, and * denote results significant at the 1%,
5%, and 10% levels.
IC lender
IC lender × last month
length of
negotiations
(I)
length of
negotiations
(II)
length of
negotiations
(III)
-1.05
(3.96)
-9.64*
(5.65)
-1.76
(4.18)
-11.25*
(6.40)
-2.66
(4.50)
-11.28**
(5.55)
7.03***
(1.45)
4.37***
(1.21)
4.37**
(2.15)
4.90*
(2.92)
-1.10
(1.51)
0.50
(1.80)
1,067
0.38
NO
NO
1,067
0.44
YES
YES
1,067
0.44
YES
YES
ln(Spread)
ln(Loan Amount)
ln(Maturity)
Secured dummy
Number of financial covenants
Number of perf. pricing terms
Observations
R-squared
Loan purpose dummies
Loan type dummies
38
Table 4: Lenders’ earnings targets and loan spreads
This table presents results from loan-initiation level regressions of all-in-drawn-spread on lender
and borrower characteristics. All specifications include fiscal month, month of the year, yearquarter, lender, loan type and purpose dummies as well as lender characteristics. Earnings
surprise calculations in Panel A are as described in Section 4.3. IC lender stands for incomeconstrained lenders. It is an indicator variable equals 1 for loans that are arranged by lenders
which meet their year-ago quarterly earnings or beat them by up to 3¢; and 0 otherwise. last
month is an indicator variable equals 1 for loans initiated in the last month of lender’s fiscal
quarter; and 0 otherwise. All variables are as defined in Appendix A. Panel A includes all borrowers and Panel B explores the subsample that consists only of publicly traded North American
firms with sufficient non-missing data to calculate borrower characteristics and industry membership. Heteroskedasticity-robust standard errors are clustered by borrower and by lender and are
reported in parentheses. ***, **, and * denote results significant at the 1%, 5%, and 10% levels.
Panel A: DealScan Sample
IC lender
IC lender × last month
Spread
(I)
Spread
(II)
Spread
(III)
Spread
(IV)
0.25
(9.64)
-14.18*
(8.36)
0.90
(6.44)
-12.78**
(5.81)
-19.86***
(2.39)
27.98***
(6.16)
95.63***
(3.72)
2.40
(5.10)
-12.73***
(4.89)
-8.77***
(1.57)
19.84***
(3.86)
41.26***
(5.19)
-2.19*
(1.16)
-27.10***
(1.86)
7.98
(5.12)
-11.44*
(6.02)
-5.38***
(1.62)
15.63***
(4.48)
23.67***
(3.84)
-1.85
(1.24)
-28.93***
(2.11)
30,724
0.17
NO
NO
NO
30,724
0.48
NO
NO
NO
30,724
0.52
YES
YES
NO
30,724
0.83
YES
NO
YES
ln(Loan Amount)
ln(Maturity)
Secured dummy
Number of financial covenants
Number of perf. pricing terms
Observations
R-squared
Loan ratings
Borrower country FE
Borrower FE
39
Panel B: Compustat-DealScan Sample
IC lender
IC lender × last month
ln(Loan Amount)
ln(Maturity)
Secured dummy
Number of financial covenants
Number of perf. pricing terms
Spread
Spread
Spread
RW
RW
RW
(I)
(II)
8.77
(6.51)
-22.03**
(9.66)
-15.52***
(2.20)
-0.30
(3.79)
78.50***
(3.48)
3.20*
(1.81)
-27.11***
(2.67)
11,584
0.48
NO
NO
NO
Borrower profitability
Borrower size
Borrower stock performance
Borrower leverage
Borrower book-to-market
Borrower Altman’s Z-Score
Borrower EBITDA
Observations
R-squared
Borrower credit ratings
Borrower industry FE
Borrower FE
(III)
Spread
Dynamic
RW
(IV)
Spread
Analyst
Fcast
(V)
4.12
(6.28)
-19.84**
(9.43)
-6.47**
(2.74)
-5.07
(3.76)
33.72***
(3.46)
1.61
(1.48)
-18.05***
(2.55)
-279.90***
(61.55)
-11.36***
(2.27)
-7.31
(7.29)
46.61***
(10.03)
25.09***
(4.76)
-5.00***
(0.66)
-74.35
(53.87)
1.36
(5.77)
-17.53**
(8.67)
-5.52**
(2.32)
-4.67*
(2.70)
23.46***
(5.06)
3.48**
(1.35)
-15.01***
(2.91)
-173.36**
(74.38)
-10.63**
(4.08)
-15.02**
(6.13)
45.01***
(10.59)
32.34***
(5.07)
-3.75***
(0.87)
95.33*
(56.18)
0.81
(6.47)
-20.02**
(10.08)
-6.49**
(2.74)
-5.06
(3.76)
33.72***
(3.45)
1.62
(1.48)
-18.07***
(2.56)
-279.61***
(61.63)
-11.33***
(2.27)
-7.35
(7.29)
46.65***
(10.04)
25.05***
(4.76)
-5.00***
(0.66)
-74.05
(53.74)
-0.97
(5.77)
-16.69*
(9.69)
-6.47***
(2.44)
-4.89
(3.95)
43.70***
(3.09)
2.74*
(1.40)
-21.27***
(2.48)
-294.38***
(60.18)
-12.85***
(2.33)
-5.61
(7.31)
55.95***
(9.99)
26.40***
(4.58)
-4.90***
(0.64)
-83.84
(57.47)
11,584
0.49
YES
YES
NO
11,584
0.57
YES
NO
YES
11,584
0.57
YES
YES
NO
11,481
0.56
YES
YES
NO
40
Table 5: Lenders’ earnings targets and syndication fees
This table presents results from loan-initiation level regressions of upfront fees on lender and borrower
characteristics. All specifications include fiscal month, month of the year, year-quarter, lender, loan type
and purpose dummies as well as lender characteristics. IC lender stands for income-constrained lenders. It
is an indicator variable equals 1 for loans that are arranged by lenders which meet their year-ago quarterly
earnings or beat them by up to 3¢; and 0 otherwise. last month is an indicator variable equals 1 for loans
initiated in the last month of lender’s fiscal quarter; and 0 otherwise. Adjusted fees is upfront fees as
reported in DealScan multiplied by the share lender distributes. All variables are as defined in Appendix
A. Panel A includes all borrowers and Panel B explores the subsample that consists only of publicly
traded North American firms with sufficient non-missing data to calculate borrower characteristics and
industry membership. In the last model of Panel B, the dependent variable is limited to be in the (0, 100)
interval, which slightly reduces the sample size. Heteroskedasticity-robust standard errors are clustered
by borrower and by lender and are reported in parentheses. ***, **, and * denote results significant at
the 1%, 5%, and 10% levels.
Panel A: DealScan Sample
Spread
IC lender
IC lender × last month
ln(Loan Amount)
ln(Maturity)
Adjusted
fees
(I)
Adjusted
fees
(II)
Fees
Fees
Fees
(III)
(IV)
(V)
0.05***
(0.01)
-2.27
(1.64)
5.38**
(2.50)
1.07
(0.67)
0.11
(0.96)
0.05***
(0.01)
-2.18
(1.51)
5.40**
(2.55)
1.37*
(0.72)
-0.26
(0.97)
3.91***
(1.31)
0.10
(0.46)
0.25
(1.08)
0.16***
(0.02)
-7.86*
(4.65)
10.05*
(5.62)
-0.45
(1.10)
1.42
(1.93)
5.87**
(2.89)
-2.71**
(1.23)
-4.73*
(2.57)
0.25***
(0.02)
-5.79
(4.36)
12.29**
(5.22)
0.23***
(0.02)
-5.57
(4.25)
14.05**
(5.59)
3.55**
(1.38)
-2.74
(1.89)
1.07
(2.75)
-2.24**
(0.91)
-10.40***
(1.85)
2,420
0.34
NO
2,420
0.35
NO
2,420
0.42
YES
5,464
0.34
NO
5,464
0.36
YES
Secured dummy
Number of financial covenants
Number of perf. pricing terms
Observations
R-squared
Borrower country FE
41
Panel B: Compustat-DealScan Sample
Spread
IC lender
IC lender × last month
ln(Loan Amount)
ln(Maturity)
Secured dummy
Number of financial covenants
Number of perf. pricing terms
Adjusted
fees
(V)
0.24***
(0.02)
-5.19
(4.68)
17.86**
(8.27)
1.87
(4.24)
3.20
(4.67)
1.06
(3.36)
-3.58*
(1.84)
-9.79**
(3.84)
-29.75
(46.20)
1.96
(3.83)
-18.67**
(8.39)
13.08
(19.59)
-1.27
(3.92)
-0.35
(0.83)
103.84
(86.35)
-0.46
(8.25)
0.16***
(0.03)
-12.01*
(6.66)
7.05
(9.02)
-1.19
(2.81)
6.44
(4.80)
4.02
(4.25)
-2.27
(2.88)
-1.70
(5.88)
-40.04
(50.09)
2.16
(2.59)
19.33**
(8.32)
-18.54
(23.77)
1.57
(5.71)
1.98*
(1.19)
-93.46
(101.85)
17.03
(15.46)
0.05***
(0.01)
-8.36***
(2.93)
13.75***
(4.77)
0.62
(1.63)
-0.15
(1.40)
2.80*
(1.68)
1.65**
(0.65)
2.83
(2.03)
-15.76
(20.83)
1.50
(1.21)
3.23
(3.85)
9.81**
(4.94)
-1.01
(1.63)
0.95**
(0.42)
-18.08
(27.48)
-3.45
(4.09)
0.06***
(0.01)
-6.93***
(2.69)
11.15**
(5.02)
-0.44
(1.61)
0.69
(1.52)
2.70
(1.82)
0.70
(0.69)
1.78
(1.98)
-29.12
(22.72)
1.11
(1.31)
3.07
(4.26)
9.34*
(5.16)
-0.51
(1.70)
0.75*
(0.40)
-24.13
(30.65)
-5.17
(4.58)
2,287
0.42
YES
YES
898
0.53
YES
YES
898
0.52
YES
YES
849
0.54
YES
YES
Fees
Fees
(I)
(II)
0.25***
(0.02)
-1.90
(4.56)
12.88*
(6.87)
3.38**
(1.47)
2.52
(2.84)
-0.88
(3.02)
-3.96**
(1.70)
-9.86***
(3.32)
2,287
0.39
NO
NO
Borrower profitability
Borrower size
Borrower stock performance
Borrower leverage
Borrower book-to-market
Borrower Altman’s Z-Score
Borrower EBITDA
Ratio of non-banks
Observations
R-squared
Borrower credit ratings
Borrower-industry FE
(III)
Adjusted
fees
(IV)
Fees
42
Table 6: Lenders’ earnings targets and the total cost of borrowing
This table presents results from loan-initiation level regressions of total cost of borrowing on lender
and borrower characteristics. All specifications include fiscal month, month of the year, year-quarter,
and lender dummies as well as lender characteristics. IC lender stands for income-constrained lenders.
It is an indicator variable equals 1 for loans that are arranged by lenders which meet their year-ago
quarterly earnings or beat them by up to 3¢; and 0 otherwise. last month is an indicator variable
equals 1 for loans initiated in the last month of lender’s fiscal quarter; and 0 otherwise. Total cost
of borrowing is loan spread (which already includes annual fees) plus annualized upfront fees. All
other variables are as defined in Appendix A. Panel A includes all borrowers and Panel B explores the
subsample that consists only of publicly traded North American firms with sufficient non-missing data
to calculate borrower characteristics and industry membership. Heteroskedasticity-robust standard
errors are clustered by borrower and by lender and are reported in parentheses. ***, **, and * denote
results significant at the 1%, 5%, and 10% levels.
Panel A: DealScan Sample
IC lender
IC lender × last month
ln(Loan Amount)
ln(Maturity)
Secured dummy
Number of financial covenants
Number of perf. pricing terms
Observations
R-squared
Loan purpose, type, and ratings
Borrower country FE
Total cost
of borrowing
(I)
Upfront
fees
(II)
All-in-drawn
spread
(III)
-0.21
(10.67)
-19.96*
(10.45)
-14.04***
(3.16)
-31.61***
(10.44)
56.20***
(7.71)
-5.73***
(1.72)
-34.23***
(5.34)
-8.93**
(3.97)
11.73***
(4.30)
2.98**
(1.32)
-1.30
(2.57)
1.68
(4.07)
-2.04**
(1.04)
-10.18***
(2.25)
11.79
(9.69)
-24.47***
(8.37)
-14.99***
(2.83)
10.22*
(6.05)
45.92***
(6.11)
-3.77**
(1.52)
-25.49***
(4.47)
6,385
0.56
YES
YES
6,385
0.36
YES
YES
6,385
0.60
YES
YES
43
Panel B: Compustat-DealScan Sample
IC lender
IC lender × last month
ln(Loan Amount)
ln(Maturity)
Secured dummy
Number of financial covenants
Number of perf. pricing terms
Borrower profitability
Borrower size
Borrower stock performance
Borrower leverage
Borrower book-to-market
Borrower Altman’s Z-Score
Borrower EBITDA
Observations
R-squared
Loan purpose & type dummies
Borrower credit ratings and industry FE
Total cost
of borrowing
(I)
Upfront
fees
(II)
All-in-drawn
spread
(III)
-4.58
(10.23)
-18.22*
(10.92)
-0.79
(5.24)
-54.63***
(5.97)
31.19***
(6.45)
-4.42**
(2.23)
-27.68***
(5.49)
-460.50***
(114.33)
-15.31***
(4.25)
107.30***
(19.63)
24.75***
(7.18)
-5.15***
(1.47)
43.34
(103.68)
87.81***
(11.52)
-6.83
(4.59)
16.37*
(9.03)
3.21
(3.27)
3.60
(3.97)
0.55
(3.82)
-3.83**
(1.59)
-10.81***
(3.33)
-35.77
(39.34)
1.85
(2.75)
10.85
(15.47)
-0.44
(2.85)
-0.20
(0.72)
77.67
(75.30)
0.95
(7.47)
1.31
(9.07)
-20.97**
(10.53)
-2.75
(4.80)
-26.63***
(4.74)
26.93***
(5.58)
-0.94
(1.88)
-18.62***
(4.42)
-379.36***
(107.28)
-14.82***
(3.52)
73.05***
(17.49)
17.40***
(5.88)
-4.40***
(1.27)
-17.15
(86.00)
75.31***
(10.64)
2,200
0.67
YES
YES
2,200
0.45
YES
YES
2,200
0.69
YES
YES
44
Table 7: Lenders’ operations and earnings targets
This table presents results from lender-quarter regressions of loan loss provisions
and realized security gains on relevant lender characteristics. Dependent variables
are in percentage points. Loan variables are scaled by total loans (bhck2122);
while security gains are scaled by total assets (bhck3210). The FR Y-9C coding is
as follows: non-performing loans=(bhck5525-bhck3506+bhck5526-bhck3507+bhck1616),
loan loss reserves=bhck2123, commercial and industrial loans=bhck1766, agricultural
loan=bhck1590, real estate loans=bhck1410, loans to individuals=bhck1975, unrealized gains=bhck8434, realized gains=(bhck3521+bhck3196). IC lender stands for
income-constrained lenders. Extreme values of dependent variables are removed.
Heteroskedasticity-robust standard errors are clustered by lender and are reported in
parentheses. ***, **, and * denote results significant at the 1%, 5%, and 10% levels.
Log(total assets)
# syndicated loans arranged
∆non-performing loans
Loan loss reserves
Agricultural Loans
Real Estate Loans
Comm. & Ind. Loans
Loans to individuals
LLP
LLP
LLP
(I)
(II)
(III)
0.0124
(0.009)
0.0236***
(0.006)
4.8108
(5.512)
2.5746**
(1.283)
-1.8241
(1.272)
0.1963***
(0.049)
0.4224***
(0.085)
0.8145***
(0.094)
0.0102
(0.009)
0.0240***
(0.006)
4.4218
(5.581)
3.0522**
(1.280)
-1.7290
(1.271)
0.1968***
(0.050)
0.4102***
(0.084)
0.8180***
(0.094)
0.0652**
(0.027)
0.0218***
(0.006)
4.3076
(6.166)
2.5481*
(1.409)
-4.5449***
(1.374)
0.3221
(0.213)
0.3952**
(0.163)
0.3631
(0.262)
Unrealized gains
IC Lender
Observations
R-squared
Year fixed effects
Firm fixed effects
1,652
0.25
YES
NO
Realized
Gains
(IV)
Realized
Gains
(V)
0.0049
(0.003)
0.0028
(0.003)
0.0137
(0.010)
-0.0003
(0.003)
5.0422**
(2.030)
0.0103**
(0.005)
1,796
0.25
YES
YES
-0.0367***
(0.013)
-0.0218*
(0.011)
4.8449*
(2.493)
-0.0004
(0.006)
1,652
0.26
YES
NO
1,652
0.38
YES
YES
1,796
0.10
YES
NO
45
Table 8: Suspect loans and the borrowers’ future credit events
This table presents results from loan-initiation level regressions of future changes in S&P credit ratings on lender and borrower characteristics. All specifications include current credit ratings, fiscal
month, month of the year, year-quarter, lender, loan type and purpose dummies as well as lender
characteristics. Rating year + 1 is borrower’s credit rating one year from loan initiation. The sample
is restricted to loans with a tenor of at least 12 months. The first (second) specification includes Lowyield (High-yield) loans as described in the paper. IC lender stands for income-constrained lenders.
It is an indicator variable equals 1 for loans that are arranged by lenders which meet their year-ago
quarterly earnings or beat them by up to 3¢; and 0 otherwise. last month is an indicator variable
equals 1 for loans initiated in the last month of lender’s fiscal quarter; and 0 otherwise. All other
variables are as defined in Appendix A. Heteroskedasticity-robust standard errors are clustered by
lender and are reported in parentheses. ***, **, and * denote results significant at the 1%, 5%, and
10% levels.
IC lender
IC lender × last month
ln(Loan Amount)
ln(Maturity)
Secured dummy
Number of financial covenants
Number of perf. pricing terms
Borrower profitability
Borrower size
Borrower stock performance
Borrower leverage
Borrower book-to-market
Borrower Altman’s Z-Score
Borrower EBITDA
ln(Spread)
Rating
year + 1
Rating
year + 1
Rating
year + 1
fees
subsample
(III)
CDS
initiation
Low-yield
High-Yield
(I)
(II)
-0.109
(0.12)
0.270
(0.16)
-0.116***
(0.02)
0.053**
(0.02)
-0.094***
(0.03)
0.012
(0.02)
-0.019
(0.03)
3.483**
(1.37)
0.139***
(0.02)
0.597***
(0.11)
0.023
(0.17)
-0.334***
(0.04)
0.044**
(0.02)
3.055***
(0.76)
-0.130***
(0.04)
0.106
(0.08)
-0.173*
(0.10)
-0.082**
(0.03)
0.167***
(0.04)
-0.061
(0.08)
-0.015
(0.01)
0.012
(0.04)
2.232**
(1.01)
0.197***
(0.02)
0.995***
(0.11)
-0.564***
(0.18)
-0.352***
(0.06)
0.027
(0.02)
7.234***
(0.98)
-0.220***
(0.05)
0.152
(0.11)
-0.327**
(0.14)
-0.076
(0.06)
0.123**
(0.06)
-0.007
(0.09)
-0.008
(0.03)
-0.002
(0.06)
1.409
(0.88)
0.120**
(0.05)
0.648***
(0.16)
-0.569***
(0.16)
-0.349***
(0.07)
0.102***
(0.04)
2.083
(1.38)
-0.254***
(0.09)
-0.027
(0.03)
-0.007
(0.01)
0.041**
(0.02)
0.014***
(0.00)
0.042***
(0.01)
0.004
(0.01)
0.007**
(0.00)
-0.003
(0.01)
-0.022
(0.05)
0.003
(0.01)
0.017
(0.01)
0.016
(0.02)
-0.005
(0.00)
0.003**
(0.00)
0.104
(0.08)
-0.010**
(0.00)
2,984
0.92
2,567
0.78
994
0.96
8,272
0.15
ln(Fees)
Observations
R-squared
46
year + 1
(IV)
Table 9: Lenders’ earnings targets and syndicate structure
This table presents results from loan-initiation level regressions of measures regarding syndicate structure on lender and borrower characteristics. non-bank participation is the ratio of number of non-bank
lenders to total number of participants in the contract agreement. sophisticated is an indicator variable that equals 1 if the lender is identified as an institutional investor by DealScan Company data
set; and 0 otherwise. syndicate experience is the median value of the number of past deals participated
in or arranged by each participant in the syndicate. All specifications include fiscal month, month
of the year, year-quarter, loan ratings, lender, and borrower industry fixed effects as well as lender
characteristics. IC lender stands for income-constrained lenders. It is an indicator variable equals
1 for loans that are arranged by lenders which meet their year-ago quarterly earnings or beat them
by up to 3¢; and 0 otherwise. last month is an indicator variable equals 1 for loans initiated in the
last month of lender’s fiscal quarter; and 0 otherwise. All variables are as defined in Appendix A.
Heteroskedasticity-robust standard errors are clustered by borrower and by lender and are reported
in parentheses. ***, **, and * denote results significant at the 1%, 5%, and 10% levels.
IC lender
IC lender × last month
ln(Loan Amount)
ln(Maturity)
Secured dummy
Number of financial covenants
Number of perf. pricing terms
Borrower profitability
Borrower size
Borrower stock performance
Borrower leverage
Borrower book-to-market
Borrower Altman’s Z-Score
Borrower EBITDA
Spread
Number of participants
Observations
R-squared
non-bank
participation (%)
(I)
sophisticated
∈ {0, 1} (%)
(II)
ln(syndicate
experience)
(III)
-0.52
(0.78)
3.15*
(1.85)
-0.40
(0.29)
0.98**
(0.40)
-0.18
(0.68)
0.01
(0.13)
-0.40
(0.40)
3.60
(8.74)
1.04***
(0.28)
0.20
(0.70)
2.51*
(1.32)
-1.49***
(0.40)
-11.95
(9.94)
0.43***
(0.14)
0.03***
(0.00)
0.15***
(0.03)
0.93
(1.15)
-2.87**
(1.16)
-1.08***
(0.34)
0.67**
(0.31)
-0.24
(0.44)
-0.09
(0.15)
0.15
(0.36)
-4.64
(9.10)
0.19
(0.31)
0.89
(0.74)
1.20
(1.36)
-1.23***
(0.28)
4.41
(6.99)
0.11
(0.18)
0.02***
(0.00)
0.19***
(0.05)
-0.149
(0.116)
-0.333*
(0.195)
0.101***
(0.038)
-0.024
(0.053)
-0.088
(0.054)
-0.010
(0.017)
0.034
(0.042)
0.954
(0.775)
0.114***
(0.025)
0.111
(0.079)
0.057
(0.138)
-0.063
(0.042)
-1.142
(0.961)
0.002
(0.012)
0.000
(0.000)
-0.012***
(0.002)
7,504
0.17
7,504
0.08
4,254
0.20
47
Table 10: Sensitivity tests
This table summarizes the results from sensitivity analyses. The baseline regression
with borrower and lender characteristics as well as fixed effects are run with Spread
or Fees as dependent variables in Compustat-DealScan sample. Only the coefficient
estimates of IC Lender × Last M onth and sample sizes are reported for brevity. Loan
loss provisions are forward looking quarterly changes scaled by total assets. Relationship
lending is calculated as the number of deals between lender and borrower within 5 years
prior to corresponding loan initiation. Duplicates are removed in order of lead share
and bank size. Heteroskedasticity-robust standard errors are clustered by lender or by
borrower and by lender. ***, **, and * denote results significant at the 1%, 5%, and
10% levels.
DV = Spread
β̂
#Obs.
Specification
DV = F ees
β̂
#Obs.
Separate intercepts used for + and − surp.
-16.10**
11,584
18.24**
2,200
Loan loss provisions included on the RHS
-17.47**
9,978
15.18**
1,787
Relationship lending included on the RHS
-20.61**
11,584
18.30*
2,200
Pre-2008
-14.69*
8,735
12.46*
1,703
Duplicate loan initiations removed
-20.69**
9,977
19.75**
2,025
-0.05*
11,584
0.22**
2,200
Dependent variables used in log specification
48