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Title: Bad Loan Externalities: Evidence from the Syndicated Loan Market
This version: October 25, 2015
Abstract
This study examines external impacts of distressed bank loans on the lending banks and other
borrowing firms. The banks, on average, lose almost 1% of their total market value, and the effect
spills over to other loan syndicate members. The distress news also impacts the banks’ other
borrowers, who experience seven-day mean cumulative abnormal returns of -0.31% for each
distress announcement. Distress externalities are worse when the bank is more exposed to the bad
loan, and for borrowers that are more relationship dependent. Future lending business is also
negatively affected, as loan rates rise by 67 BP following large distress damage, and lenders are
less likely to retain existing relationship borrowers.
JEL classification: G01; G21; G33.
Keywords: Financial intermediation; bank relationships; bankruptcy; lending constraints
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1. Introduction
In the Miller-Modigliani (1958) world of perfect capital markets, firms can source capital in
simple transactions, and they are unmoved by shocks at their lending banks. In imperfect
markets, with adverse selection and moral hazard, raising capital and changing its source can
result in complex and costly transactions (e.g., Holmström and Tirole, 1997). Many firms rely on
private bank loans as a source of low cost capital, and many banks supply firms with loans to
earn revenue. Most of the loans are relationship-based as borrowers often return to the same
lender when they need additional capital. Berger and Udell (1998) report the average relationship
lasts 7.8 years, and is longer for small and relatively information opaque firms.
In relationship lending, the bank is at the hub of a web of concurrent relationships with many
borrowers. It provides essential reputation support to the relationship borrowers. Bank reputation
is similar to a Club Good, as it is available only to borrowers in the bank’s relationship circuit.
Reputation is excludable because it is unavailable to borrowers outside the bank’s relationship
web. Within the web, reputation is non-rivalrous as each borrower enjoys the good without
exclusion. By establishing a lending relationship with the bank, each borrower can use the bank’s
reputation support continually, regardless of the number of borrowers in the web. The bank’s
marginal cost of supplying reputation to a new borrower is virtually zero. When the lender
becomes more reputable, each relationship borrower benefits from the improvement. On the
other hand, if the lender’s reputation deteriorates, it can simultaneously impact the relationship
borrowers as well. In addition to reputation support, the bank also provides capital support in
terms of current and future loans. When its resources are reduced due to sudden shocks, it is
unlikely to keep up the same level of capital support, which can in turn hurt the relationship
borrowers. The more relationship-dependent borrowers, such as those with stronger ties with the
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bank and those that rely heavily on the bank for monitoring, certification, and capital support, are
more likely to be affected by the nexus shocks than are the less dependent borrowers in the
relationship.
This broader recognition of the Club Good nature of the relationship web points to three
important areas that have been neglected in previous empirical studies of lending relationships.
First, earlier studies tend to focus on lending relationships outside the United States. For
example, Gibson (1995) uses Japan data to show lenders’ economic health affects borrowers’
investment behavior, a real external long-term effect beyond immediate losses in equity value.
Second, previous studies tend to focus on macro-level, crises shocks. In response to damages
from Korean bank crises, Bae et al. (2000) report sharp lending pull backs by banks on many
loans. Also, Chava and Purnanandam (2011) show following the 1998 Russian sovereign default,
most affected banks cut back future lending, especially those with more exposure to the
government’s bond default. Santos (2011) finds that after the 2007 to 2009 financial crisis shock,
loan spreads paid by U.S. firms are higher due to a reduction in the supply of total loanable
funds. The latter two studies report the negative impacts on the borrowers, in terms of access to
credit markets and their cost of capital. Evidence from these macro-crises shocks is limited and
should be interpreted with caution. During financial crises, the economic consequences fall
concurrently on all borrowers and lenders in the economy, thus preventing the observation of
spillover effects that could be due to individual loan defaults within the relationship web.1 In
addition, these shocks occur relatively infrequently. For example, Daniel et al. (2013) identifies
only one financial crisis as big as the 2009 crisis over the last century. Third, focusing on
aggregate loan shocks with such broad impacts confounds the analyses of individual bank loans
1 See also Kang and Stulz (2000), Ongena et al. (2000), Brewer et al. (2002), Gan (2003), and Carvalho et al. (2011).
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because the shocks can affect many aspects of the market at the same time. In that confounded
setting it is challenging to empirically identify and isolate the cause of a loss in firm value or a
preferential loan agreement.
This study, on the other hand, provides a new micro level understanding of the effects of
distressed loan shocks that fall on other borrowers in lenders’ relationship webs and on their
future lending businesses. The purpose is to examine how isolated bad loan shocks, resulting
from individual firm defaults or bankruptcies, simultaneously affect other borrowers in the
relationship bank’s web, and perhaps the structure of the web. The sample of default and
bankruptcy announcements spans multiple decades. The bad loan shocks are relatively isolated
across the economy, so they are unlikely to concurrently move the market. As a result, negative
impacts from individual bad loans shocks that fall on the web of relationship firms will be most
vivid, with few complicating macro level influences.
This paper contributes to the understanding of contagion effects of defaults and bankruptcies
(e.g., Chava and Purnanandam, 2011; Das, Duffie et al. 2007; Murfin, 2012). It extends the
evidence of bad loan externalities to include the consequences of bad loan shocks for all parties
in the syndicated loan market. It reports each party is affected differently by the individual loan
shocks. The degree of impact largely depends on the size of the bad loan, reputation of the
lender, and web borrower’s reliance on the lending relationship. Focusing on the stock
performance of the affected banks and their borrowers alleviates the endogeneity issue that is
common in corporate finance. The default and bankruptcy announcements are often isolated and
localized, so they are unlikely to trigger market-wide catastrophe. When web borrowers
underperform during the announcement periods, it is less likely to be caused by the bad market.
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To my knowledge, this is the first study that examines the differential impacts of bad loans
on lead arrangers, loan syndicate participants, and their web borrowers. Prior studies do not
examine how syndicate participants in their relationship web are affected by bad loans, and by
how much. In contrast, when a firm defaults on its loan, all lenders in the loan syndicate could be
affected to some degree. By comparing and contrasting the stock returns of lead arrangers and
syndicate participants, the incremental value loss associated with reputation damage can be
isolated from damages from capital depletion (the uncollectable interest and principal on the bad
loan, whose recovery rate is generally very low). The structure of a syndicated loan thus provides
a unique opportunity to disentangle the causes of bad loan externalities, financial constraints vs.
reputation damage, and the value of each. By focusing on isolated individual distress news, the
paper provides new understanding of the economics of lender-borrower connections.
The rest of the paper proceeds as follows. Section 2 outlines the general hypothesis and basic
predictions. Section 3 describes the data and methodology used to construct the sample. Section
4 presents the findings on the impact of loan distress announcement, and Section 5 concludes.
2. Literature review and empirical hypothesis
In the syndicated loan market, lead arrangers often have unique access to information about
their clients through repeat lending and close monitoring (Diamond 1984, Ramakrishnan and Thakor
1984, Fama 1985, Petersen and Rajan 1994). Market participants generally rely on such monitoring
and certification services. Lead banks experience significant value loss when the market realizes
they are unable to properly monitor their borrower (e.g., Dahiya et al., 2003). When an isolated
shock is large enough to impact its financial health and reputation, the web borrowers that are
connected to the lead via lending relationship suffer. For example, Houston et al. (2014) report
the abnormal bond return is negative and significant for the affected borrowers that share the
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same lead lender as the defaulted borrower. The damage is even stronger for the less-known and
more bank-dependent borrowers. There is no doubt that the lead lender plays an essential role in
the syndicated loan market, but the participants are also important part of it. They are responsible
for providing capital support. The bad loan can make it more difficult for them to lend in the near
future when it is relatively large or when there are multiple bad loans over a short period of time
due to capital depletion. A tightening of the credit market is observed following the financial
crisis (e.g., Bae et al., 2000; Chava and Purnanandam, 2011; Santos, 2011). As a result, their
relationship borrowers will be negatively affected although the effect will be much smaller than
those of the lead arrangers. The characteristics of both borrowers and lenders can have
tremendous influence on the level of impact. For example, when the affected bank is more
reputable, the impact is likely to be smaller because its reputation capital is too strong to be
affected by isolated bad loans. Also, more reputable banks tend to have more capital resources to
absorb the shocks. Thus, large or dominant banks are unlikely to be affected by isolated distress
news (Gopalan et al., 2011). Moreover, for borrowers that have multiple relationship lenders,
they are unlikely to be affected by the downfall of one-single bank because it is easy for them to
switch to different lenders. On the contrary, the bank-dependent borrowers should, in theory,
experience significant negative impact.
Furthermore, there are long-term economic consequences of the bad loans. The direction of
the impact should vary depending on the characteristics of both banks and borrowers. For the
more reputable banks that care more about maintaining existing clientele, they are likely to lower
the loan spread. The more reputable lenders often have higher-quality borrowers based on
positive assortative matching. Following reputation damage, the bank will need to make loan
terms more attractive to keep the high-quality borrowers. By considerably lowering the loan
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spread on future loans with the web borrowers, the bank is more likely to retain its good clients,
which will in turn help the recovery of its reputation. For the less reputable banks, however, it
may not be the case. Less reputable banks are often associated with the small and more bank-
dependent borrowers. Because the borrowers’ other connections are weak and to establish new
banking relationships is costly, they are locked in with the existing lender. As a result, the
affected bank can extract a higher loan fee from them to cover for the recent financial losses.
This suggests that loan spread will be higher following the distress damage especially for the less
reputable banks and bank-dependent borrowers.
A related question is the impact on the choice of lead arrangers for new syndicated loans
following extensive bad loan damages. Are borrowers more likely to switch to new lenders if
their existing lender experiences significant bad loan damages? Since the borrowers are distinctly
different the answer to the question should vary as well. Borrowers with multiple relationship
banks will incline to choose a different lead arranger for their new loans, as doubt about the
ability of their current lender to provide same quality of monitoring and funding service rises.
The deterioration of the lender’s welfare can no longer satisfy their needs, so the borrowers move
to a new relationship web that is more beneficial to them. However, this is unlikely to be the case
for the smaller and more bank-specific borrowers. Their close relationship with the bank makes
it hard for them to switch to a different lender and start a new lending relationship. Hence, not
only will the probability of switching to a new lender be affected by the degree of bad loan
damages, it also depends largely on borrower’s characteristics and the strength of the existing
lending relationship. Large distress damage is more likely to trigger a change of the lead
arranger, but primarily for the less reputable banks. Firms with strong ties with the current lender
are less likely to establish new lending relationships, ceteris paribus.
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3. Data and descriptive statistics
The primary sample includes all syndicated loans originated in the U.S. during 1988 through
2012, from Loan Pricing Corporation’s (LPC) DealScan, along with loan related information.
Standard & Poors (S&P) from Compustat rating database is used to identify firm default
announcements. New Generation Research's bankruptcy database is used for information on
Chapter 11 bankruptcy announcements. Center for Research in Security Prices (CRSP) provides
information related to stock price performance. Compustat gives other financial details of the
borrowing firms. The Federal Reserve’s Quarterly Call Report is used for bank related financial
information. Various data sources are then merged together using either established link files or
careful hand matching in the absence of unique and reliable indicator. Details are described
below.
3.1 Loans
I define bad loans as outstanding loans of the firms that appear on either Bankruptcydata.com
or on S&P long term debt rating with a selective default (SD) ad default (D) rating. The
matching between Bankruptcydata.com and DealScan is done manually with firm names, while
the matching between Compustat and DealScan is accomplished with the help of Compustat-
Dealscan link file, which is created by Michael Roberts and WRDS (e.g., Chava and Roberts,
2008). For each bad loans identified following the match, I extract all loan-related contract
information from DealScan, such as the identity of lenders, both lead and participants, loan start
and end date, loan amount, loan type, etc. Each matched loan is categorized as either a default or
a bankrupt loan, which is important for comparison tests. When there are multiple bad loans for
one firm bankruptcy or default news, each loan is separately examined to ensure one loan
observation per bank. Unfortunately, both default and bankruptcy announcements can be
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partially anticipated. For one, there could be other rating agencies release default information
prior to S&P credit rating, so the market will be informed of the distress prior to my
measurement. For two, there could also be other news reports regarding the underperformances
of the distress firms, so the investors are aware of the high likelihood of distress of certain
borrowers. Under both scenarios, the market adjustment is likely to occur way before my
measurement period, which will make it harder for me to capture any impacts from my chosen
announcements because they are not “news”. As a result, the actual impact of the distress news
should be much larger than what I find due to the partial anticipation.
Following the identification of the lender, I obtain its relationship borrowers as well as their
outstanding loans at the time of the bad loan. Because banks often merge with other banks, its
name and companyid, the bank identifier on DealScan, changes. Manual adjustments account for
these changes. For example, prior to its Merill Lynch acquisition, Bank of America’s companyid
was 84685. After the acquisition, its name becomes Bank of America Merill Lynch, and its new
companyid is 127349. Since both companyids represent Bank of America, when it experiences a
bad loan shock, relationship borrowers are identified using both companyids. When lenders are
bank subsidiaries, the parent company is used for matching and testing. I define the affected
firms as the relationship borrowers of the lender that experiences bad loan shocks. When an
affected firm takes out multiple loans from the same bank, they are aggregated into one
observation.
3.2 Stocks
I use the Compustat-CRSP link to match the companies to CRSP firms in order to get their
stock performances. For those that can’t be identified using the link table, their names are hand-
matched to find more observations. There are quite a few companies and banks that are present
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on CRSP, so there could be a selection bias, but the bias should go against finding the predicted
results because the banks and firms that have sufficient stock data are usually larger and more
transparent to investors, making them less vulnerable to small shocks. Following Fernando, May,
and Megginson (2012), the main event-window is the seven-day window from 5 days prior to the
distress announcement (both default and bankruptcy) and 2 days after. CARs using other
windows are also reported. All abnormal returns are estimated using Fama-French-Carhart 4-
factor model with information from day-250 to day -50 from the announcement date.
3.3 Variable construction
A key independent variable is Exposure, a continuous measure of the importance of the
distressed firm to the affecting bank. Exposure is constructed by first identifying all outstanding
loans associated with the distressed firm at the time of distress announcement, then aggregating
them at the bank level. Since there are often multiple lenders on the deal, I multiply the DealScan
loan allocation variable by the total facility amount to obtain each lender’s loan proportion.
When the detail information about loan allocation is unavailable, the total facility amount is
divided by the number of lenders.
Lender sizes are quite different. Larger banks have more buffers to absorb the loan shocks, so
it takes a much bigger distress to make an impact. Therefore, the distress loan amount must be
scaled to control for the size effect. To successfully scale the number, the total distress amount is
divided by the average loan amount originated by the bank over the prior two years to get
Exposure, which is in percent. A higher Exposure means the affected bank has more exposure to
the distressed firm, capital-wise. Hence, banks with higher Exposure to the distressed firm are
expected to have more negative stock return reactions to the distress news.
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The variable that identifies recent distress loans (Surprise_distress) is also important to the
tests. It equals 1 when the distress happens within the first two years of loan origination and 0
otherwise. For the lead arrangers, as time passes it becomes harder to attribute bad loans to their
inadequate screening and monitoring since market conditions are likely to have changed since
the time the loan was originated. A distant default is less informative than a recent one. For the
syndicate participants, banks can sell their loan shares after the loan origination, which can shield
them from the bad loan impact. Even if the lenders decide to hold onto the loan until maturity, as
time elapses, they are able to recover more and more initial investment. Therefore, when the
distress loan is further down the road, its impact on other parties should be less.
A complete list of variable definitions is in Appendix.
3.5 Summary statistics
Table II Panel A provides an annual summary of the new, distressed, and affected loans,
banks, and firms, from January 1988 to January 2012. The sample period is determined by the
availability of bankruptcy data. 59,384 loans were originated over the sample period, with
inflation adjusted aggregate dollar amount of $15,065 billion. The number of loans increases
over years, and decreases following the financial crisis, reflecting the tightening of credit
markets. Banks are more cautious when issuing loans following the crisis (Murfin, 2012). The
first cluster of default and bankruptcy announcements is in the period 1999 to 2003, and the
second is during the recent financial crisis. The increased number of bad loans leads to more
affected entities, both banks and borrowers. A total of 1,108 firms either default or file for
Chapter 11 bankruptcy (Column 3). When the firm defaults first and then files for bankruptcy, it
enters into the summary statistics once at the original default date. The number of bad loans
exceeds the number of borrowers because many distressed borrowers have multiple loans
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outstanding (Column 4). The number of affected banks is much greater than what has been
documented in the literature with the inclusion of syndicate participants (Column 6). The number
of total lenders on each loan varies, but most are over three. The last column presents the total
number of borrowers that are affected by the distress news. Each affected bank or borrower only
enters into the summary once a year even if it is affected multiple times.
Insert Table II here.
Table II Panel B presents a brief summary of the accounting information of the entities
examined in the study. There is a large variation in the size of the distress borrowers. The
smallest 25% have $250 million or less in total assets, and the top 25% has over $1.7 billion in
total assets. Net income is negative across the distress sample, indicative of troubled financial
conditions. The distress firms are generally highly levered, and are often short in cash; showing
difficulty with paying back the outstanding loans. When something goes wrong in their daily
operation, these firms are less likely to recover and more likely to default or perhaps go
bankrupt.
Comparing to the distressed borrowers, the banks are generally much larger. Bank recovery
of bad loans is low, only about 20% of the loan charge off. This suggests a bad loan is likely to
cause a permanent damage to the bank. This agrees with the argument that bad loans can and
should have material damage to banks’ expected value, especially when they are relatively large
and come in clusters. To avoid potential chaos and better shield them from troubled loans, banks
are required maintain some loan loss allowance. The mean loan loss allowance is approximately
2% of banks’ total assets. The amount of loss allowance is constantly changing depending on
banks’ expectation about the performances of their loans. When the market becomes more
volatile, or when the banks are expected to have more bad loans in their portfolio, they are likely
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to adjust the loan loss allowance number upward, so the impact to its real operation can be
managed. In addition, when a bank is larger in size, its loan loss allowance is greater, so it is able
to absorb bigger loss on loans. As a result, the impact of bad loans should be smaller.
The affected borrowers share the same lender with the default or bankrupt firms, but unlike
the distressed firms, their financial positions are sound and promising. The primary focus of the
paper is on the publicly traded affected borrowers, which are relatively large and transparent.
Median market value of these firms is $1.6 billion. The bottom 25% have less than $500 million
in total assets, while the top 25% have over $4.6 billion. Unlike the distressed borrowers, the
affected borrowers’ net income is usually positive with a mean of $95 million, they tend to have
more cash holding than the financially troubled firms.
4. Empirical results
4.1 Univariate tests of CARs
Table III summarizes the average cumulative abnormal return using Fama-French-Carhart
four-factor model of the distressed borrowers, affected banks, and affected borrowers over
different windows surrounding the default or bankruptcy announcement date. Not surprisingly,
the equally-weighted four-factor adjusted abnormal return for the distress firm is -8.41% at the
announcement day, which is highly significant (Panel A). Over the longer seven-day event
window, the cumulative abnormal return is more negative, a stunning -20.58%. Distress firms
lose more than one fifth of their value due to the distress news. The loss is greater when it is a
bankruptcy filing instead of default announcement, which suggests market is still unclear about
the firm’s future outlook when it defaults on its debt obligation, but once it files for bankruptcy,
such expectation fades away. The difference between the two announcements’ impact is only
marginal significant in most event-windows.
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Insert Table III here
Panel B presents the four-factor adjusted abnormal return of the affected banks. Banks, in
general, experience negative stock return during the distress announcement. Although the event-
day abnormal return is only marginally significant, the cumulative abnormal returns using other
event-windows are all highly significant. The average seven-day bank CAR is -0.8%, and it is -
1.11% when using the eleven-day window, which spans from 5 days prior to the announcement
and 5 days afterwards. The impact of the distress news is long-standing. Opposite from the
distressed borrowers, which experience more negative returns for bankruptcy filing, the banks
experience more negative returns for defaults than bankruptcies. This is possibly due to the fact
that default often precedes bankruptcy, so it is a big surprise to the market. Similar results are
documented in the literature (Dahiya et al. 2003). However, more importantly, regardless of the
role played by the banks in the bad loans, lead arrangers or syndicate participants, the CARs are
all negative. Unexpectedly, the CARs of syndicate participants are actually more negative than
the lead, especially for the announcement day return. The difference is both statistically and
economically significant. Moreover, when dividing the banks into quintiles from highest to
lowest based on their exposure to the bad loans, the banks with the highest exposure to bad loans
perform significantly worse than the ones that has the lowest exposure to bad loans. The average
difference over the 11-day window is approximately -3.12%, which is statistically significant at
all confidence levels. It is evident that the degree of impact to each lender depends heavily on the
level of exposure it has toward the bad loan. When a bank fails to properly manage its exposure
to any individual loans, it is likely to experience a bigger loss when the loan goes bad.
When the impact is economically significant at the bank, its relationship borrowers are
adversely affected even if the shocks are not directly related to them. The negative shocks
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transmitted via the lending channel through the common lender when the lender is unable to
manage the damage at the bank level. Figure 1 illustrates the average return of the affected firms
appears to follow a random walk process prior to and after the distress announcement. There is,
however, a significant drop during the announcements of either default or bankruptcy.
[Insert Figure 1 here]
The four-factor adjusted abnormal returns for the web borrowers are significantly negative.
Similar to the impact on lenders, the default announcement affects their stock return more than
the bankruptcy news. The mean difference over the seven-day event window is -0.4%, which is
highly significant, both statistically and economically (Panel C). This suggests more information
is contained in default announcements because it is a bigger surprise to the investors. In addition,
opposite from the common belief that borrowers should experience greater value loss when their
lead arrangers suffer reputation damage, the test results reveal that borrowers’ losses are worse
when their lead arrangers are loan syndicate participants. Mean differences between the two
types of affected borrowers using multiple event-windows are marginally significant. However,
such outcomes could be attributed to the fundamental difference between the syndicate
participants and lead arrangers. In general, the lead arrangers are more reputable and larger in
size, which is less likely to be affected by some small and isolated bad loans. In addition, the
web borrowers perform significantly worse than their competitors that are in the same 4-digit
SIC industry. It suggests the negative impact from the lending channel can potentially jeopardize
firm’s position within the industry.
The results of the univariate test indicate both affected banks and borrowers experience
material loss due to distress announcement, but the test does not control for other factors that
could jointly influence stock returns, such as other borrower or bank specific characteristics. The
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need for better understanding what drives the stock return requires more comprehensive
multivariate cross-section tests.
4.2 Cross-section analysis of banks’ CARs
The first analysis examines determinants of bank stock performance over the distress
announcement. In particular, how default or bankruptcy news impacts banks’ stock return, what
factors exacerbate the influence, and what characteristics alleviate the impact. A bank that is
more exposed to the distressed borrower should be more affected by the news. Lead arrangers
should have more negative stock return because they experience both reputation and capital
damage.
To test the predictions, I estimate the following regression:
𝐵𝑎𝑛𝑘 𝐶𝐴𝑅𝑗,𝑡 = 𝛽0 + 𝛽1 ∗ 𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒𝑖,𝑗,𝑡 + 𝛽2 ∗ 𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒𝑖,𝑗,𝑡 ∗ 𝐿𝑒𝑎𝑑𝑖,𝑗,𝑡 + 𝛽3 ∗
𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒𝑖,𝑗,𝑡 ∗ 𝐿𝑒𝑎𝑑𝑖,𝑗,𝑡 ∗ 𝑇𝑜𝑝_𝑙𝑒𝑛𝑑𝑒𝑟𝑗,𝑡 + 𝛽4 ∗ 𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑒_𝑑𝑖𝑠𝑡𝑟𝑒𝑠𝑠𝑖,𝑡 + 𝛽5 ∗ 𝑋𝑖,𝑡 + 𝛽6 ∗ 𝑌𝑗,𝑡 +
𝜇𝑡, (1)
where 𝐵𝑎𝑛𝑘 𝐶𝐴𝑅𝑗,𝑡 is the seven-day four-factor adjusted cumulative abnormal return of bank j at
time t. 𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒𝑖,𝑗,𝑡 measures the exposure of bank j to distress firm i at time t.𝐿𝑒𝑎𝑑𝑖,𝑗,𝑡 and
𝑇𝑜𝑝_𝑙𝑒𝑛𝑑𝑒𝑟𝑗,𝑡 measure the role of the bank on the distressed loan and its reputation at time t,
respectively. 𝑋𝑖,𝑡 is a series of variables that capture the characteristic of distressed firm i at time
t, which includes aggregate outstanding loan characteristics, and 𝑌𝑗,𝑡 measures bank j’s
characteristics at time t. Year fixed effects (𝜇𝑡) are included to remove time trend since the
sample covers multiple decades. The regression sample includes both lead and syndicate
members. The results of the estimation are presented in Table IV.
Insert Table IV here.
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Various variables that capture characteristics of the distressed loans by each borrower and
bank fundamental information are included to control for important factors that might influence
the bank’s stock return. Exposure is significantly negative across all specifications (Panel A),
which agrees with the univariate results. When the affected bank is more exposed to the
distressed borrower, its stock return is more negative. A one standard deviation increase in
bank’s exposure to the distress firm leads to 0.37% more negative 4-factor adjusted cumulative
abnormal return (CAR) for the banks that are affected by the distress news. The impact is more
significant when the bank is a lead arranger, which experiences an additional 0.39% aggregate
loss in stock returns than other syndicate participants, ceteris paribus. This additional loss
reflects the value of monitoring and screening service assumed by the lead. The cost of
reputation damage is about the same as that of capital depletion. Banks suffer material loss even
if they are not the lead arrangers of the distressed loans. However, the loss in value primarily
applies to the less reputable banks. The most reputable lenders, on average, experience 0.92%
more positive cumulative return. The banks that are in the top of the pyramid are unlikely to be
affected by isolated distressed shocks. They are more equipped to shield themselves from any
negative shocks.
The coefficient of Surprise_distress is significantly negative both economically and
statistically. When a firm gets into financial trouble within the first two years of loan origination
its banks experience a 1% more negative CAR. The performance of recently issued loans is more
informative about bank’s current monitoring and screening ability. At the same time, banks are
less likely to package and sell the loans shortly after its origination, which makes them more
vulnerable to the capital loss that is associated with distress of newly issued loans. As a result of
both, banks are impacted significantly more when the distress news is relatively more recent,
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which is evident in all specifications. In addition, similar to what is revealed in the univariate
CAR test, a default announcement triggers more negative stock return than a bankruptcy
announcement, but the difference is no longer significant. Results hold after controlling for
various bank, distressed borrowers, and loans characteristics.
Column 7 suggests if the affected bank is a deposit bank, it is less likely to experience
negative stock return in response to the shocks. It is important to note that deposit banks are very
different from all other banks and bank subsidiaries. They are often much larger in size, and they
are highly regulated and backed by the government. One third of the banks in the sample are
deposit banks. In addition, it is worth noting that global banks perform significantly worse than
their counterparts, which suggests the distress is not regional. If the cause of the distress was
regional, the regional banks should have been affected more, so the opposite should have been
observed. However, it is not the case here. Banks that lend globally seem to be affected more.
Column 8 shows the result without year fixed effect, and instead includes two crisis indicator
variables. The two variables exactly correspond to the two cluster spikes in the sample. There are
clusters of distress announcements during these two periods. The variable Exposure is still
significantly negative after controlling for the effects of crisis. However, the results reveal that
bank CAR is more negative during the financial crisis period, about 1.2% more when the distress
occurs within that period. Since the focus of the study is on the economic impact of loan defaults
during stable economic time, it is important to see what happens when the same regression is run
using the sub-sample after removing the crisis period announcements. Because only the recent
financial crisis seems to have a significant impact on banks’ stock returns, only the observations
that are within the second crisis period are removed. The results are qualitatively the same (Panel
B). Hence, the impacts of distress news are not driven by crises. In addition, the impact of
19
distress news does not accumulate over time. Banks do not observe additional loss when they
have multiple distress announcements over the prior 6 months. In other words, the market treats
every distress announcement the same, and it does not allow the impact to be carried to the next
announcement.
Some may argue, at default stage, borrower can still negotiate with its lender, so the capital
depletion may not occur. If that is the case, there will be no impact on the participants as well as
their borrowers, so we will not be able to disentangle the two effects, reputation damage vs.
capital depletion. To alleviate that concern, I run the test on a sub-sample, which includes only
the bankruptcy announcement. Firms liquidate their assets at the bankruptcy stage, so capital
depletion to the lenders is inevitable. The average recovery rate of a bad loan from a bankrupted
company is about 15%. The results are similar for the bankruptcy-only sample (Panel B).
Collectively, the results of the first estimation strongly support the argument that both lead
arrangers and syndicate participants are negatively affected by distress news of their borrowers.
4.3 Cross-section analysis of borrowers’ CARs
The second test addresses the impacts on affected web borrowers. What happens to the
lenders’ other web borrowers when a loan in their portfolio goes bad, damaging their market
value? The affected borrowers generally have no direct relation with the distressed borrowers.
Their connection is through the lending channel. Unlike banks that are directly exposed to the
bad loans, the affected borrowers are exposed to banks’ financial well-being or the quality of the
common. When the distress shock is significant enough to negatively impact bank’s stock
performance which leads to negative change in the quality of the common, it also adversely
affects all other web borrowers. The goal here is to empirically identify factors or characteristics
that make the borrowers more or less susceptible to the change of the common. The degree of
20
impact is expected to be different, depending on borrower’s characteristics and the strength of
the firm-bank relationship. A borrower that is more information opaque, and more common-
dependent, should be affected more when the quality of the common deteriorates.
To test the predictions, I estimate the following regression:
𝐹𝑖𝑟𝑚 𝐶𝐴𝑅𝑘,𝑡 = 𝛽0 + 𝛽1 ∗ 𝐵𝑎𝑛𝑘 𝐶𝐴𝑅𝑗,𝑡 + 𝛽2 ∗ 𝑆𝑡𝑟𝑜𝑛𝑔_𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑗,𝑘,𝑡 + 𝛽3 ∗ 𝑋𝑘,𝑡 +
𝛽4 ∗ 𝑌𝑗,𝑡, (2)
where 𝐹𝑖𝑟𝑚 𝐶𝐴𝑅𝑘,𝑡 is the seven-day four-factor adjusted cumulative abnormal return of firm k at
time t, and 𝐵𝑎𝑛𝑘 𝐶𝐴𝑅𝑗,𝑡 is the CAR of bank j at time t. 𝑆𝑡𝑟𝑜𝑛𝑔_𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑗,𝑘,𝑡 captures the
relationship strength between firm k and bank j at time t. 𝑋𝑘,𝑡 is a series of variables that capture
firm specific characteristics at time t, including loan characteristics, and 𝑌𝑗,𝑡 measures bank j’s
characteristics at time t. The regression includes affected borrowers of both lead arrangers and
syndicate participants. The results of the estimation are presented in Table V.
Insert Table V here.
Unlike the previous regression of bank CAR, distressed borrower specific control variables
are not included because the borrowers are indirectly affected by the distressed loans via the
common lender, unless they are in the same industry as the distressed borrowers, or the distress
occurs during the crises. For each specification, various borrower and bank control variables are
included to make sure the abnormal return is not due to other factors.
In general, borrowers’ stock returns are positively correlated with both bank and industry
returns. When the banks are negatively affected by the distress news, as observed in previous
tests, the externality theory predicts the affected firms should also experience negative returns.
Every 1% increase (decrease) of banks’ CAR leads to 0.1% increase (decrease) of borrowers’
CARs. Therefore, when the banks are significantly affected by the bad loans, their existing
21
borrowers also experience damage. The bank influence is always positive and significant. In
addition, across all specifications, the variable Strong_relation appears to be always significantly
negative. Being more bank-dependent makes borrowers suffer more from the shocks to the bank.
Borrowers having strong relationship with the affected banks, on average, experience 1% more
negative cumulative abnormal return after controlling for other factors during the distress
announcement. When the borrower benefits more from and is more dependent on the bank’s
reputation and capital support, it is also more adversely impacted when the quality of the bank
goes down. This is also consistent with the view that when it is costly and difficult for the bank-
dependent borrowers to establish new lending relationship, they are more vulnerable to bank-
related shocks.
Surprisingly, affected borrowers in the same industry as the distressed borrowers don’t
experience more negative stock returns. The sign of Same_industry is always negative, but it is
statistically insignificant. On the contrary, when the distressed firm is in the upstream industry of
the affected borrowers, the affected borrowers experience more wealth loss. The result suggests
industry contagion effect is not as strong as what is commonly believed, and it is more likely in
the vertical direction from up to downstream than horizontally. However, the lender’s influence
still outweighs the two. The results from the fixed effect model appear to be similar. In addition,
the two crises indicators seem to have opposite impact on stock performance of the affected
firms. During the Bubble Crisis, affected borrowers respond positively to the change in banks’
quality. On the contrary, a distress announcement in the recent financial crisis adversely impacts
borrowers’ stock performances. The results of the sub-sample after excluding observations in
both crises periods are qualitatively the same. This reassures the results are not crisis-driven.
Even when the general economy is healthy and stable, and the credit market is relaxed and open,
22
borrowers with strong ties with their lenders are still more vulnerable to shocks affecting their
lenders. Availability of healthy relationship banks is always important to borrowers that rely on
such relationships.
The regression findings suggest there are benefits for borrowers to build multiple lending
relationships. Having a strong relation with a single bank may not be as beneficial as
documented in the existing literature. Although the CAR regression is strong and powerful, it
only applies to the affected borrowers with available stock return information, which in general
are more established and less opaque. The impact of bad loans on the smaller private relationship
borrowers cannot be observed due to lack of trading information. Unlike public firms, which
tend to have more sources of capital, the small private firms often find it more difficult to obtain
new capital for investment projects. They are likely to be affected more since they are more
bank-dependent.
4.4 Impact on future loan spread
One way to examine the distress impact on all relationship borrowers, one way or another, is
to look at spreads on loans made after the distress loan impact. Here the focus is on the
determinants of the loan spread, the all-in-drawn variable on DealScan. It is a spread over
London interbank offering rate (LIBOR) which takes into accounts both one-time and recurring
fee associated with the loan facility. It is determined by bank, borrower, and loan characteristics.
The purpose of the test is to provide additional evidence of bad loan externalities, and side-
effects of relationship lending, mainly the long-term impacts of bad loans, so there are specific
variables capturing the magnitude of bad loan shocks experienced by the lenders as well.
Because only the lead arrangers are responsible for setting the loan contract terms, the syndicate
participants are excluded from this test. The reputation theory suggests banks will charge lower
23
fees after they experience reputation damage. On the other hand, if the banks only intend to
recover from their capital loss, they should charge high fees on their future loans. By examining
the direction of the change, we can better understand which damage appears to be more
important from lender’s perspective.
The baseline estimation uses the following model:
𝐿𝑜𝑎𝑛 𝑠𝑝𝑟𝑒𝑎𝑑𝑗,𝑘,𝑡
= 𝛽0 + 𝛽1 ∗ 𝐿𝑎𝑟𝑔𝑒 𝑑𝑎𝑚𝑎𝑔𝑒𝑗,𝑡−1 + 𝛽2 ∗ 𝑆𝑡𝑟𝑜𝑛𝑔_𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑗,𝑘,𝑡 + 𝛽3
∗ 𝐿𝑎𝑟𝑔𝑒 𝑑𝑎𝑚𝑎𝑔𝑒𝑗,𝑡−1 ∗ 𝑆𝑡𝑟𝑜𝑛𝑔_𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑗,𝑘,𝑡 + 𝛽4 ∗ 𝐿𝑎𝑟𝑔𝑒 𝑑𝑎𝑚𝑎𝑔𝑒𝑗,𝑡−1
∗ 𝑆𝑡𝑟𝑜𝑛𝑔_𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑗,𝑘,𝑡 ∗ 𝑇𝑜𝑝_𝑙𝑒𝑛𝑑𝑒𝑟𝑗,𝑡 + 𝛽5 ∗ 𝑋𝑘,𝑡 + 𝛽6 ∗ 𝑌𝑗,𝑡 + 𝛽7 ∗ 𝑍𝑙,𝑡,
(3)
where 𝐿𝑎𝑟𝑔𝑒 𝑑𝑎𝑚𝑎𝑔𝑒𝑗,𝑡−1 captures the degree of distress damages experience by lender j at
time t-1. 𝑆𝑡𝑟𝑜𝑛𝑔_𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑗,𝑘,𝑡 captures the intensity of the relationship between lender j and
borrower k at time t. X, Y, and Z represent a series of control variables that measure borrower,
bank, and loan’s characteristics, respectively.
Insert Table VI here.
Controlling for bank, borrower, and loan characteristics, loan spread is significantly higher
when the lead arranger experiences large distress damage in the previous year (Column (1)
through (3)). Banks experiencing large bad loans damage in the prior 12 months, on average,
charge 70 more basis points (bps) on new loans they originate, ceteris paribus. The impact is
greater when the borrower is closely related to the lead arranger, which translates to an additional
increase in loan spread of approximately 132 basis points. However, if the affected bank is
24
highly reputable, the loan spread decreases by approximately 43 basis points. The estimates are
both statistically and economically significant across all specifications. When using a continuous
measure of distress damage, the results still hold. A one standard deviation increase in aggregate
distress damage leads to an increase of 15 bps in loan spread, and the impact is stronger when the
borrower is closer related to the bank (Column (4) through (6)). On the other hand, if the affected
bank is more reputable or the borrower is larger in size, the spread decreases accordingly.
In addition, loan spread increases more during both crises periods, especially the recent
financial crisis. When the credit market tightens, it becomes more expensive to borrow.
Borrowers in general pay 115 bps more when they take out loans during the recent financial
crisis. However, if the borrowers have strong relation with their lenders, their loan spread is
significantly reduced during both crises. The reduction in the bubble crisis is 68 bps, and it is
over 2% during the recent financial crisis. When the financial market is in turmoil, it is beneficial
to have close tie with a relationship bank because getting a new lender is much more costly.
When the credit market is tightened, strong lending relationship gives borrowers access to more
capital at a relatively low cost. Similar results hold in fixed effect models.
In sum, prior bad loan damages significantly increase loan spread on subsequent bank loans,
after controlling for other factors. Banks charge both new and existing clients for the capital loss
they suffer. This is more significant when the borrowers are smaller and more bank-dependent. It
also appears to be more prominent for the less reputable lenders. The highly reputable banks are
either less likely affected by trouble loans due to their large loan loss allowance, which
minimizes the needs for capital compensation, or they worry more about their reputation than
immediate capital recovery. The highly reputable banks are more willing to sacrifice loan fee in
25
exchange for business from their high-quality clients than the less reputable lenders who have
small and not so well-known borrowers that are locked in the relationship web.
4.5 Likelihood of switching lead arrangers
So far, the evidence indicates that both banks and their borrowers are adversely affected by
bad loans directly, reflected in negative announcement period’s stock returns. In addition,
borrowers seem to suffer in the long-run as well because they pay higher loan spreads that may
be charged to recoup capital losses the bank experiences from prior loan defaults. Borrowers rely
on their relationship banks for financial support and cross-monitoring benefits. The club good is
beneficial for the web borrowers. When the quality of the club good deteriorates, which
decreases the benefits web borrowers can enjoy, rational borrowers may seek future loans from
different lenders. However, not all borrowers can move freely between banks. Those that have
strong and exclusive relation with just one bank are less likely to switch lenders even if they
want to. On the other hand, borrowers that are less dependent on specific banks will find it easy
to switch to a new lender. Some may argue the choice between lender and borrower is mutual,
and it mainly depends on the change in firm and bank’s characteristics. When a borrower
improves in its quality, it may want to go with a higher ranked lender to satisfy its future needs
for capital and other services. On the other hand, lenders are also picking the borrowers that best
fit in their existing loan portfolio. If that is true, we should observe opposite results. Following
significant bad loan damages, lenders should be more cautious about their clientele. As a result,
they should be keeping the larger and safer borrowers and turn down the others. The results of
the test below help shed light on the main player in the lender-borrower matching game. In
addition to large bad loan impacts, I am also looking at other factors that make a firm more or
less likely to be associated with their lenders. Syndicate participants are not involved in the
26
initial lender-borrower selection process; instead, they are organized by the lead arrangers.
Therefore, the test sample excludes the syndicate participants.
A logistic regression is run to test the likelihood of having a different lender in the next loan
deal following strong distress impacts.
𝑃𝑟𝑜𝑏(𝑠𝑤𝑖𝑡𝑐ℎ𝑖𝑛𝑔)𝑘,𝑡
= 𝛽0 + 𝛽1 ∗ 𝐿𝑎𝑟𝑔𝑒 𝑑𝑎𝑚𝑎𝑔𝑒𝑗,𝑡−1 + 𝛽2 ∗ 𝐿𝑎𝑟𝑔𝑒 𝑑𝑎𝑚𝑎𝑔𝑒𝑗,𝑡−1 ∗ 𝑇𝑜𝑝 𝑙𝑒𝑛𝑑𝑒𝑟𝑗,𝑡
+ 𝛽3 ∗ 𝐿𝑎𝑟𝑔𝑒 𝑑𝑎𝑚𝑎𝑔𝑒𝑗,𝑡−1 ∗ 𝑆𝑡𝑟𝑜𝑛𝑔 𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑗,𝑘,𝑡 + 𝛽4 ∗ 𝑋𝑘,𝑡 + 𝛽5 ∗ 𝑍𝑙,𝑡
(4)
where the dependent variable Switching is a dummy variable that equals 1 when the borrower
uses a different lender for its new loan, and 0 otherwise. X and Z are vectors of control variables
that measure firm and loan characteristics, respectively. The model examines the impact of large
distress damage on choice of lenders. At the same time, it also illustrates the key factors than can
reduce/increase such impact.
Insert Table VII here.
In general, a firm is more likely to get a new lender if it is larger, less risky and has lower
leverage. On the other hand, if the bank is more reputable, the probability of being replaced is
lowered significantly. Having a top lender on the previous deal significantly reduces the
likelihood of getting a different lead arranger in the new deal (Column (1) through (3)). The
focus of the test is on the impact of large distress damage, so we examine if the variable,
Large_damage, is significant and how it affects the chances of getting a different lender.
Borrowers do consider what happens to their lenders’ loan portfolio when choosing lead
arrangers for their next loan deals. The impact is weaker when the borrower has a strong
27
relationship with the affected bank. Controlling for bank, borrower, and loan characteristics,
borrowers are less likely to retain the same lead arranger knowing it experienced large distress
damage in the prior 12 months (Column (1) and (2)). However, when the borrowers are closely
related to the banks, the likelihood of it getting a new lead arranger decreases significantly.
Interestingly, when borrowers improve in quality, they are more likely to switch to new lenders.
On the contrary, lenders are less likely being replaced when they improve in quality. This
suggests borrowers play significant roles when selecting lender in the loan market. Lenders, on
the other hand, don’t generally turn down clients. In addition, it appears during the bubble crisis,
borrowers are more likely to get new lenders following bad loan damages. To rule out the
possibility of observed outcome being crises driven, the same test is performed on a sub-sample
which excludes the crises periods. The results continue hold: borrowers with strong relationships
with the affected banks are more likely to stick with their old lenders following the damage, and
larger borrowers are more likely to switch to new lenders.
4.6 Robustness tests
Some may argue the distressed borrowers could share other common factors with the
affected borrowers in addition to the common lender. If that is the case, the negative impact on
the affected borrowers cannot be attributed to the lending channel. To address the concern of
endogeneity or the presence of an unknown factor that causes the bad performances of both
distressed and affected borrowers, a difference-in-difference test that compares the
announcement CAR of the control group to the treatment group. The treatment group in this case
is the affected relationship borrowers. The control group consists of firms that are similar to the
web borrowers but are in different relationship web at the time of distress. The details of the
matching process can be found in Appendix I. The matching helps to identify the firm(s) that are
28
similar to the affected borrowers but are in different relationship web. If the bad loan shocks
indeed travel through the lending channel due to the common lender, the web borrowers should
experience significantly more negative abnormal returns over the event window, and the impact
should dissipate shortly after.
Table VIII shows that the difference in CAR between two groups is indeed highly significant
regardless the industry classification we use to identify the matches. The web borrower loses
approximately 0.5% more of its market value than its matched non-web borrowers over the
seven-day event window from 5 days prior and 2 days past the distress announcement. The
difference is statistically significant at all levels. In addition, when comparing the stock
performance of the two groups prior to the event, it appears that the web borrowers are either
indifferent from the control firms or perform significantly better. The same can be said about
their performances following the bad loan shocks. The result suggests the observed
underperformance of the web borrowers is temporarily and resulted from the common lender
they share with the distressed borrowers.
5. Conclusion
In sum, the study uses a new approach to examine economic consequences of bad loans. The
syndicated loan market consists of various relationship webs. All web borrowers share the same
club good provided by the bank. When the quality of the good improves, all borrowers enjoy the
benefits. On the other hand, when the good deteriorates in quality, all web borrowers suffer. The
increasingly connected financial market makes borrowers more vulnerable to external shocks
even when the shocks have nothing to do with their daily operations. The impact of a bad loan is
prominent during non-crisis time, which makes it important for understanding the entire spillover
effect. Affected banks and borrowers, on average, experience significantly negative stock return
29
at the time of the distress announcement. The impact is greater when the distress news comes in
more surprisingly. Prior studies have not examined the consequences of bad loans on syndicate
participants. This study finds they are affected but with a smaller magnitude than the direct
effects on the lead arrangers. Further findings show web borrowers are adversely affected when
the damage to the bank or the club good is economically significant. Although the reputation
damage of failed loans has been the main focus in studies of distress impacts, this study makes
clear that there are broader impacts of the bad loans.
From the firm’s point of view, it is good to have lending relationships, but strong and
exclusive relationship with just one lender can be more perilous. Being more committed to a
single bank raises their exposure to potential problems at the bank. Borrowers can be more
severely affected by news of distress in one of their bank’s loans, when they have strong
relationship with the bank. At the same time, reliance on the close relationship can reduce the
borrower’s power to bargain, exposing them to opportunistic bank behavior, which includes
higher loan spreads to compensate for prior capital loss.
This study provides additional evidence on downsides of lending relationship. The
interconnectedness is what spreads a small downfall of one firm to another, and eventually
becomes so big that everyone is affected. It is important for the banks and borrowers to better
shield themselves from these shocks to prevent the next financial crisis from happening.
Appendix I: Propensity Score Matching
In order to minimize the potential selection bias on the effect of bad loan impacts on web borrowers’
stock performances, I follow Rosenbaum and Rubin’s method (1983) to obtain close matches that are
similar to the web borrowers’ in all aspects. The idea is the web borrowers may connect to the distress
borrowers in some unobserved ways. It is the unobservable that causes the negative performances of both
firms instead of the lending channel. With a closely matched sample, such concern can be alleviated.
30
In addition to restricting the match samples to be in the same industry as the web borrowers, regardless
the industry classification, they need to have similar firm specific characteristics, such as size, leverage
ratio, cash, profitability, and etc. Four different industry classifications are used when identify matches in
the same industry as the affected borrowers to avoid industry bias. Moreover, in order to reduce the
regional concern, the selected matched samples need to be within similar distance to the distress firm. The
results of the first stage probability regression are shown below. Once the probability of each firm is
obtained, I impose a caliper of 0.05, which means there is only 5% difference in propensity score between
the affected firm (the web borrowers) and its matched firms. I then obtain the stock information of the
matched pairs to see if they are different.
Variables Propensity
matching
Intercept -0.9182***
(0.0487)
log (market value) 0.1939***
(0.0033)
Market to Book 0.0002
(0.0001)
Proximity -0.0538***
(0.0042)
Difference in lender
reputation
-0.0126***
(0.0013)
leverage 0.1814***
(0.0213)
liquidity -1.8998***
(0.0693)
ROA -0.2736***
(0.0315)
Return on operating
assets
0.4164***
(0.0578)
profit margin -0.00415**
(0.0020)
Cash -0.0054**
(0.0023)
Nobs 52986
R-sq 0.1
Appendix II: Variable definitions
This appendix provides the definition of all variables in the paper. All Compustat and Call Report are for
the firm’s most recent fiscal year and bank’s most recent quarter prior to the event, respectively. The
variables are in alphabetical order within each sub-category.
Variables Definition
31
Aggregate_damage Sum of outstanding distressed loans over the prior 12 months
Lead Dummy variable that equals to 1 when the bank is a lead arranger and 0 otherwise.
Coverage Dummy variable that equals to 1 when the firm is covered by at least one analyst
over the prior 12 months
Crisis 1 Dummy variable that equals to 1 when the year of observation is between 1999 and
2003, and 0 otherwise
Crisis 2 Dummy variable that equals to 1 when the year of observation is between 2008 and
2009, and 0 otherwise
Default Dummy variable that equals to 1 when the distress event is a default and 0 otherwise
Distance The number of years between the current loan deal and the most recent prior loan
deal taken by the same firm
Exposure Sum of all outstanding distressed loans over the prior 12 months divided by the
mean total loans offered by the bank over the past 24 months
Large_damage Dummy variable that equals to 1 when the aggregate_damage is ranked in the top
quintile from high to low, and 0 otherwise
Loan_size The natual logarithm of the loan principal amount
Multiple_lead Dummy variable that equals to 1 when there is more than 1 lead arrangers for the
loan and 0 otherwise
N_leads The number of lead arrangers for the loan
Secured Dummy variable that equals to 1 when the loan is secured and 0 otherwise
Strong_relation
Dummy variables that equals to 1 when the borrower's relationship with bank is
ranked in the highest quintile and 0 otherwise. Bank-borrower relationship is
measured by summing the amount of loans issued by bank j to firm i for the past 5
years and scaled by the total amount loans taken out by firm i over the same period.
Surprise_distress Dummy variable that equals to 1 when the distressed loan defaults within the first
2 years of loan origination and 0 otherwise
Top_lender
Dummy variable that equals to 1 when the bank's market share is ranked in the top
decile and 0 otherwise. Bank's market share is the ratio of total loans issued by the
bank over the prior 12 months to the total loans on Dealscan originated over the
same period. The ranking of bank's market share is done by year.
Yield Loan yield expressed as basis points over LIBOR
Borrower_rank_change
Borrowers are ranked into percentile each year based on the total amount of loans
they borrow during the year. The change is the difference between current percentile
rank and prior rank both obtained at the time of loan origination
Variables Definition
Lender_rank_change
Lenders are ranked ino percentile each year based on the total amount of loans
they originate during the year. The change is the difference between current
percentile
rank and prior one both obtained at the time of loan origination Return related:
32
Bank_car Fama-French-Carhart four-factor model estimated seven-day cumulative abnormal
return for the bank
Cash cash reported on Compustat not including short-term investment
Current_ratio Current assets divided by current liabilities, both obtained from
Compustat
Industry_car Four-factor model estimated seven-day CAR for Fama-French 48 industries.
Leverage The ratio of book value total debt to book value total assets, both from Compustat
Liquidity The ratio of cash to book value total assets, both from Compustat
Market value The sum of book value debt from Compustat and market value equity from CRSP
MV The natual logarithm of firm market value
ROA The ratio of earnings before interest, depreciation, and taxes (EBITDA) to total
assets
ROE The ratio of net income to book value of equity
Same_industry Dummy variable that equals to 1 when the affected firm is in the same industry as
the distressed firm, and 0 otherwise, based on Fama's 48 industry classification
Total assets Total book value assets reported on Compustat
Total liabilities Total book value liabilities on Compustat
Acknowledgement
33
I would like to express my special appreciation and thanks to my advisor Prof. Robert S. Hansen,
who has been a tremendous mentor for me. His advice on both research as well as on my career
have been priceless. I would also like to thank my committee members, Prof. Paul A. Spindt,
Prof. Sheri T. Tice, and Prof. C. Fee for serving as my committee members and offering me
tremendous help to push my work to the next level. Lastly, I would like to thank Mehmet Cihan,
Qiyuan Peng, and Venkat Subramaniam for insightful comments.
34
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37
Figure 1. Average daily abnormal returns of the affected borrowers.
The figure below illustrates the daily Fama-French-Carhart 4-factor abnormal returns of the affected
borrowers within the (-30,30) window of the default or bankruptcy announcement. All model parameters
are estimated over Day -250 to -50, where Day 0 is the distress announcement date.
-0.002
-0.0015
-0.001
-0.0005
0
0.0005
0.001
-30 -27 -24 -21 -18 -15 -12 -9 -6 -3 0 3 6 9 12 15 18 21 24 27 30
Abnormal Return (-30, +30)
38
Table I: Summary statistics
This table presents the basic summary statistics of the parties involved in the study. The sample period
goes from January 1988 to January 2012. Panel A reports the loan information obtained from DealScan
and distress data gathered from both bankruptcy filing reported on www.bankruptcydata.com and S&P
default rating from Compustat. The loan amount is adjusted for inflation. Panel B summarizes the key
accounting information of the entities involved. Firm accounting information is obtained from Compustat
using the most recent fiscal year prior to the event, and bank accounting information is retrieved from
Call Report using the most recent quarter prior to the event.
Panel A. Loan origination and distress by year
year number
of loans
Total loan
amount
($ Billion)
number of
distressed
firms
number of
distressed
loans
Total
distressed
loan
amount
($ Billion)
number
of
affected
banks
number of
affected
firms
1988 878 279.75 4 1 0.91 11 224
1989 876 388.04 16 16 0.39 9 222
1990 735 129.74 29 33 4.82 97 215
1991 595 92.98 48 52 9.58 120 147
1992 604 103.18 24 37 4.10 59 159
1993 961 110.19 22 28 7.14 77 159
1994 1475 218.41 20 14 2.48 46 239
1995 1716 277.57 31 23 3.47 81 215
1996 2523 318.72 25 23 3.24 90 286
1997 3029 504.33 20 19 1.95 49 356
1998 1828 414.70 42 47 3.13 98 280
1999 2149 363.78 96 98 14.97 277 353
2000 2517 439.02 113 164 18.12 304 378
2001 2586 462.05 136 165 24.39 416 417
2002 2786 498.87 115 149 28.89 417 427
2003 3202 605.15 80 95 10.04 296 449
2004 4271 975.99 41 54 7.75 228 499
2005 5027 1290.09 26 48 11.34 227 555
2006 5734 1828.63 18 32 4.10 131 503
2007 6047 2390.54 22 24 3.11 49 558
2008 3764 1390.53 44 78 36.94 221 363
2009 2095 650.85 78 183 116.80 487 206
2010 2905 802.71 28 69 12.63 146 289
2011 1078 513.83 19 24 9.56 91 142
2012 3 15.76 11 12 10.02 32 56
Total 59384 15065.40 1108 1488 349.85 4059 7697
39
Table I: Summary statistics (Continue.)
Panel B. Accounting information
Panel A
Distressed firm
Mean 25th Percentile Median 75th
Percentile
Total Assets $2,233.20 $250.49 $625.53 $1669.08
Total Liabilities $2,288.18 $216.01 $628.25 $1716.64
Cash $119.89 $2.79 $15.35 $63.42
Net Income -$308.45 -$211.9 -$65.43 -$12.19
Market Value $964.94 $140.23 $364.53 $834.57
Leverage 1.11 0.78 0.92 1.20
Liquidity 0.05 0.01 0.03 0.07
ROA -0.34 -0.31 -0.12 -0.03
ROE 0.06 -0.49 0.02 0.81
Panel B
Affected Bank
Mean 25th Percentile Median 75th
Percentile
Market Value $41,144.00 $4,916.40 $23,645.39 $59,261.70
Loan Charge Off $102,675.23 $33.36 $397.71 $38,564.18
Late Loans $46,381.94 $1.56 $357.42 $15,002.64
Total Recovery $17,213.75 $10.92 $114.54 $6,685.60
Loan Loss
Allowance $420,987.45 $834.60 $4,355.28 $171,966.08
Panel C
Affected Firm
Mean 25th Percentile Median 75th
Percentile
Total Assets $6,823.63 $500.60 $1,493.84 $4,453.04
Total Liabilities $4,784.53 $308.83 $984.04 $3,045.23
Cash $321.07 $10.36 $44.43 $174.62
Net Income $94.65 -$18.62 $18.42 $112.57
Market Value $5,469.83 $552.91 $1,575.45 $4,635.02
Leverage 0.69303 0.51289 0.65619 0.80887
Liquidity 0.06001 0.01167 0.0319 0.077761
ROA -0.025689 -0.024408 0.024709 0.059775
ROE -4.48706 -0.049422 0.080599 0.16936
40
Table II. Stock price reactions surrounding the distress announcement
This table reports the cumulative abnormal return (in percent) around the distress announcements. All model parameters are estimated over Day -
250 to -50, where Day 0 is the distress announcement date. Panel A presents the CARs of the distressed firms, and Panel B and C summarize the
CARs of both impacted banks and firms, respectively. The t-statistics for the difference in means is computed with the cross-sectional variances of
CARs and assumes unequal variances across the two samples. The comparison is done for the two types of distress news, default vs. bankruptcy,
and two distinctive bank roles, lead arranger vs. syndicate participants. The *, **, and *** indicate statistical significance at the 10%, 5%, and 1%
levels, respectively, in two-tailed tests.
Mean t-stat Mean t-stat Mean t-stat Mean t-stat
car(-5,1) -20.58%*** -5.26 -14.18%* -1.82 -22.92%*** -5.07 8.75% -0.99
car(-3,1) -18.97%*** -5.57 -15.08%** -2.36 -20.39%*** -5.06 5.31% -0.69
car(-1,1) -17.08%*** -5.21 -10.19%** -2.35 -19.61%*** -4.71 9.42% -1.28
car(-5,5) -11.18%* -1.82 3.01% 0.46 -20.14%** -2.26 23.15%* -1.87
car(-3,3) -12.34%** -2.41 -6.45% -1.09 -15.97%** -2.15 9.52% -0.90
car(0,0) -8.41%*** -3.57 -2.13% -0.68 -10.89%*** -3.63 8.76%* -1.69
Panel A: Distressed Firms
Full sample Default Bankruptcy Difference
Mean t-stat Mean t-stat Mean t-stat Mean t-stat Mean t-stat Mean t-stat Mean t-stat
car(-5,1) -0.80%*** -3.03 -1.16%*** -2.86 -0.67%** -2.04 -0.49% -0.83 -0.37% -0.58 -0.92%*** -3.28 0.55% 0.89
car(-3,1) -0.73%*** -3.22 -1.33%*** -3.79 -0.51%* -1.82 -0.83%* -1.62 -0.91%* -1.61 -0.67%*** -2.80 0.24% 0.45
car(-1,1) -0.53%*** -3.03 -0.98%*** -3.21 -0.37%* -1.75 -0.61%* -1.53 -0.61% -1.41 -0.51%** -2.70 0.11% 0.26
car(-5,5) -1.11%*** -3.21 -1.84%*** -2.90 -0.86%** -2.07 -0.99% -1.25 -0.47% -0.61 -1.31%*** -3.36 0.84% 1.02
car(-3,3) -0.90%*** -3.20 -1.69%*** -3.59 -0.62%* -1.80 -1.08%* -1.69 -0.79% -1.26 -0.94%*** -2.97 0.15% 0.23
car(0,0) -0.13% -1.31 -0.24% -1.48 -0.09% -0.73 -0.15% -0.68 -0.57%*** -2.83 -0.01% 0.08 0.58%** 2.55
Panel B: Affected Banks
Full sample Lead Participants DifferenceDifferenceDefault Bankruptcy
41
Panel B. Continue. High exposure vs. Low exposure
Variable Degree of exposure Mean t value Difference t value
car (-5,5) Low 0.16% 0.21
car (-5,5) High -2.96% -3.81 -3.12% -2.89
car (-5,1) Low 0.11% 0.00
car (-5,1) High -2.24% -0.05 -2.35% -0.03
car (-3,3) Low 0.11% 0.17
car (-3,3) High -1.18% -1.86 -1.29% -1.40
car (-1,1) Low -0.16% -0.42
car (-1,1) High -0.66% -1.75 -0.49% -0.91
car (0,0) Low 0.02% 0.08
car (0,0) High -0.19% -0.90 -0.20% -0.73
Mean t-stat Mean t-stat Mean t-stat Mean t-stat Mean t-stat Mean t-stat Mean t-stat
car(-5,1) -0.31%*** -3.50 -0.47%*** -5.15 -0.06% -1.50 -0.41%*** -3.87 -0.05% -1.00 -0.18%*** -3.49 0.13%* 1.73
car(-3,1) -0.28%*** -3.71 -0.65%*** -8.74 -0.05% -1.46 -0.60%*** -6.75 -0.09%** -2.14 -0.18%*** -3.95 0.08% 1.30
car(-1,1) -0.18%*** -5.02 -0.04% -0.67 -0.04% -1.51 0.00% -0.06 0.03% 0.91 -0.11%*** -3.08 0.14%** 2.83
car(-5,5) -0.57%*** -4.97 -0.78%*** -6.47 -0.10%* -1.85 -0.68%*** -4.96 -0.09% -1.31 -0.29*** -4.36 0.20%** 2.09
car(-3,3) -0.46%*** -4.10 -0.63%*** -6.46 -0.12%*** -2.83 -0.51%*** -4.60 -0.14%** -2.52 -0.24%*** -4.42 0.10% 1.33
car(0,0) -0.16%*** -3.00 0.00% -0.02 -0.03%** -2.16 0.03% 0.77 0.00% -0.04 -0.05%** -2.76 0.05%* 1.85
Bankruptcy Difference Lead Participants Difference
Panel C: Affected Firms
Full sample Default
42
Panel C Continue. Affected borrowers vs. Industry competitors
Variable Group Mean t value Difference t value
car (-5,5) competitor 33.45% 13.27 car (-5,5) affected borrower -0.57% -4.97 -34.02% -16.36
car (-5,1) competitor 21.19% 10.54 car (-5,1) affected borrower -0.31% -3.50 -21.50% -12.95
car (-3,3) competitor 20.09% 10.63 car (-3,3) affected borrower -0.46% -5.02 -20.56% -13.26
car (-3,1) competitor 14.76% 9.34 car (-3,1) affected borrower -0.28% -3.71 -15.04% -11.57
car (-1,1) competitor 9.28% 9.01 car (-1,1) affected borrower -0.18% -3.00 -9.47% -11.14
car (0,0) competitor 5.15% 8.15 car (0,0) affected borrower -0.16% -4.10 -5.30% -10.17
43
Table III. Cross-section analysis of affected banks’ CARs.
This table presents the regression results of the following equation,
𝐵𝑎𝑛𝑘 𝐶𝐴𝑅𝑗,𝑡 = 𝛽0 + 𝛽1 ∗ 𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒𝑖,𝑗,𝑡 + 𝛽2 ∗ 𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒𝑖,𝑗,𝑡 ∗ 𝐿𝑒𝑎𝑑𝑖,𝑗,𝑡 + 𝛽3 ∗ 𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒𝑖,𝑗,𝑡 ∗ 𝐿𝑒𝑎𝑑𝑖,𝑗,𝑡 ∗ 𝑇𝑜𝑝_𝑙𝑒𝑛𝑑𝑒𝑟𝑗,𝑡 + 𝛽4 ∗
𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑒_𝑑𝑖𝑠𝑡𝑟𝑒𝑠𝑠𝑖,𝑡 + 𝛽5 ∗ 𝑋𝑖,𝑡 + 𝛽6 ∗ 𝑌𝑗,𝑡 + 𝜇𝑡 , (1)
where Bank CAR is the cumulative abnormal return over the seven-day window surrounding the distress announcement, and is computed using
Fama-French-Carhart 4-factor model. 𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒𝑖,𝑗,𝑡 measures the exposure of bank j to distress firm i at time t. 𝑋𝑖,𝑡 is a series of variables that
capture the characteristics of distress firm i at time t, which includes aggregate outstanding loan characteristics, and 𝑌𝑗,𝑡 measures bank j’s
characteristics at time t. Year fixed effects (𝜇𝑡) are included to remove time trend since the sample covers multiple decades. Both lead arrangers
and syndicate participants observations are included in the analysis. Detailed definitions of the control variables are given in Appendix. Panel
presents the results using complete sample, and Panel B shows the results without crisis-period observations. Heteroskedasticity-consistent p-
values clustered at the firm level are reported in parentheses. The superscripts ***, **, and * indicate significance at the 1%, 5%, and 10% levels,
respectively.
44
Variables
Column (1) Column (2) Column (3) Column (4) Column (5) Column (6) Column (7) Column (8)
Intercept 2.24*** 2.29***
(0.0008) (0.0006)
Exposure -0.31** -0.35*** -0.35*** -0.33*** -0.37*** -0.34*** -0.47*** -0.47***
(0.0116) (0.0019) (0.0019) (0.0035) (0.0011) (0.0024) (0.0001) (0.0001)
Exposure* -0.28** -0.39*** -0.38** -0.40*** -0.39*** -0.4*** -1.03** -1.1**
lead (0.0444) (0.0092) (0.0102) (0.0079) (0.0084) (0.0066) (0.0299) (0.0231)
Exposure*lead 0.92** 0.93** 0.95** 0.97** 0.97** -0.39*** -0.40***
*top_lender (0.0289) (0.0269) (0.0244) (0.0222) (0.0203) (0.0035) (0.0027)
Surprise_ -0.95* -0.83* -0.82* -0.84* -0.84* -0.82* -1.08*** -1.07***
distress (0.0612) (0.0949) (0.0954) (0.0921) (0.0921) (0.0954) (0.0046) (0.0049)
Default -0.85 -0.86 -0.76 -0.86 -0.87 -0.75 -0.71
(0.1366) (0.204) (0.2879) (0.2044) (0.1968) (0.2638) (0.2800)
Coverage -0.11 -0.04 -0.14 -0.12 -0.15 -0.7 -0.15
(0.8554) (0.9438) (0.8164) (0.8313) (0.8028) (0.2854) (0.7926)
Multiple_lead -0.26 -0.28 -0.33
(0.6516) (0.5622) (0.5021)
Global -1.12** -1.18**
(0.0257) (0.0208)
n_prior_distress_news 0.01
(0.4631)
Secured -0.60
(0.3198)
MV 0.07
(0.254)
Deposit_Bank 1.39**
(0.0263)
Bubble crisis
Financial crisis
Nobs 649 649 649 649 649 649 649 649
Year FE Yes Yes Yes Yes Yes Yes No No
Adj-R2 0.09 0.09 0.09 0.09 0.1 0.1 0.08 0.08
Panel A
45
Table III. Cross-section analysis of affected banks’ CARs (Continue)
Variables
Full Sample
Intercept 1.38* 2.38*** 2.44*** 2.54*** 2.62***
(0.0671) (0.0002) (0.0001) (0.0009) (0.0007)
Exposure -0.40*** -0.42*** -0.43*** -0.54*** -0.54***
(0.0001) (0.0001) (0.0001) (0.0001) (0.0001)
Exposure* -0.4*** -1.49*** -1.56*** -1.2** -1.31**
lead (0.0032) (0.0007) (0.0004) (0.0370) (0.0271)
Exposure*lead 1.06*** -0.53*** -0.55*** -0.36** -0.38**
*top_lender (0.0063) (0.0001) (0.0001) (0.0235) (0.0182)
Surprise_ -1.13** -1.12*** -1.12*** -1.03** -1.02**
distress (0.0208) (0.0011) (0.0011) (0.0262) (0.0274)
Default -0.75 -0.57 -0.56
(0.256) (0.3692) (0.3806)
Coverage -0.21 -0.11 -0.16 -0.15 -0.20
(0.7039) (0.8322) (0.7625) (0.804) (0.7315)
Multiple_lead -0.07 0.19 0.12 -0.05 -0.12
(0.9) (0.6976) (0.8080) (0.9373) (0.8435)
Global -1.25*** -1.33*** -1.29** -1.37**
(0.0082) (0.0059) (0.0422) (0.0332)
n_prior_distress_news 0.0064 0.01
(0.3715) (0.4297)
Secured -0.08
(0.8874)
MV 0.06
(0.3215)
Deposit_Bank
Bubble crisis 0.03
(0.9575)
Financial crisis -1.21*
(0.0961)
Nobs 649 462 462 531 531
Year FE No No No No No
Adj-R2 0.08 0.12 0.12 0.09 0.09
Panel B
Excluding Crises Excluding Defaults
46
Table IV. Cross-section analysis of affected firms’ CARs.
This table present the regression results of the following equation,
𝐹𝑖𝑟𝑚 𝐶𝐴𝑅𝑘,𝑡 = 𝛽0 + 𝛽1 ∗ 𝐵𝑎𝑛𝑘 𝐶𝐴𝑅𝑗,𝑡 + 𝛽2 ∗ 𝑆𝑡𝑟𝑜𝑛𝑔_𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑗,𝑘,𝑡 + 𝛽3 ∗ 𝑋𝑘,𝑡 + 𝛽4 ∗ 𝑌𝑗,𝑡 , (2)
where 𝐹𝑖𝑟𝑚 𝐶𝐴𝑅𝑘,𝑡 is the cumulative abnormal return over the seven-day window surrounding the distress
announcement, and is computed using Fama-French-Carhart 4-factor model. 𝑆𝑡𝑟𝑜𝑛𝑔_𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑗,𝑘,𝑡 captures the
relationship strength between firm k and bank j at time t. 𝑋𝑘,𝑡 is a series of variables that capture firm specific
characteristics at time t, including loan characteristics, and 𝑌𝑗,𝑡 measures bank j’s characteristics at time t. The
regression includes affected borrowers of both lead arrangers and syndicate participants. Detailed definitions of the
control variables are given in Appendix. The sub-sample analysis excludes distress news announced during financial
crisis. Heteroskedasticity-consistent p-values clustered at the firm level are reported in parentheses. The superscripts
***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
Sub_sample
Variables Column (1) Column (2) Column (3) Column (4) Column (5) Column (6) Column (7)
Intercept -0.08 0.12 0.10 0.02*** 0.02** 0.52
(0.6913) (0.7244) (0.9105) (0.0084) (0.0200) (0.5467)
bank_car 0.08*** 0.08*** 0.06** 0.12* 0.11* 0.03 0.07**
(0.0062) (0.0071) (0.0494) (0.0665) (0.0721) (0.4631) (0.0244)
bank_car*Global -0.09 -0.11
(0.2522) (0.1017)
bank_car*upstrea
m_downstream-0.05
(0.8831)
Strong_relation -1.1* -1.11* -1.34** -1.42** -1.46** -1.25* -1.6**
(0.0889) (0.0889) (0.0433) (0.0370) (0.0106) (0.0606) (0.0203)
industry_car 0.01*** 0.01*** 0.01*** 0.01*** 0.01*** 0.01*** 0.01***
(0.0005) (0.0005) (0.0005) (0.0003) (0.0001) (0.0005) (0.0005)
NIMV 2.93** 2.7** 2.92** 2.79* 2.78** 2.61* 1.74
(0.0271) (0.042) (0.0411) (0.0613) (0.0279) (0.0717) (0.2455)
same_industry -1.28 -1.34 -1.04 -1.40 1.29 -1.06 -1.21
(0.3325) (0.3112) (0.4369) (0.3119) (0.2859) (0.4271) (0.3704)
top_lender -0.35 -0.45 0.01* -0.32 -0.29
(0.4127) (0.3441) (0.0749) (0.6203) (0.5454)
coverage 0.80 1.23** 1.24*** 0.70 0.58
(0.1051) (0.0164) (0.0051) (0.1625) (0.2479)
leverage -0.35 -0.01 0.01 -0.47 -0.29
(0.6646) (0.9478) (0.6294) (0.5653) (0.734)
bank_mv 0.13* 0.03 0.10
(0.0977) (0.7156) (0.2261)
Full Sample
47
Table IV. Cross-section analysis of affected firms’ CARs. (Continue)
Sub_sample
Variables Column (1) Column (2) Column (3) Column (4) Column (5) Column (6) Column (7)
global -0.02*** -0.03***
(0.0001) (0.0001)
n_prior_distress 0.01 0.01
(0.1443) (0.1164)
upstream_downst
ream-0.09***
(0.0045)
Bubble crisis 1.05** 1.11** 1.25***
(0.0248) (0.0182) (0.0021)
Financial crisis -0.74 -0.01 -0.15
(0.3806) (0.5771) (0.8506)
Nobs 2902 2902 2902 2902 2902 2902 2613
Year FE No No No No No Yes No
Adj-R2 0.02 0.02 0.02 0.03 0.03 0.03 0.02
Full Sample
48
Table V. Determinants of loan spread following distressed loans.
This table presents the regression results of the following equation,
𝐿𝑜𝑎𝑛 𝑠𝑝𝑟𝑒𝑎𝑑𝑗,𝑘,𝑡 = 𝛽0 + 𝛽1 ∗ 𝐿𝑎𝑟𝑔𝑒 𝑑𝑎𝑚𝑎𝑔𝑒𝑗,𝑡−1 + 𝛽2 ∗ 𝑆𝑡𝑟𝑜𝑛𝑔 𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑗,𝑘,𝑡 + 𝛽3 ∗ 𝐿𝑎𝑟𝑔𝑒 𝑑𝑎𝑚𝑎𝑔𝑒𝑗,𝑡−1 ∗ 𝑆𝑡𝑟𝑜𝑛𝑔 𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑗,𝑘,𝑡 + 𝛽4 ∗
𝐿𝑎𝑟𝑔𝑒 𝑑𝑎𝑚𝑎𝑔𝑒𝑗,𝑡−1 ∗ 𝑆𝑡𝑟𝑜𝑛𝑔 𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑗,𝑘,𝑡 ∗ 𝑇𝑜𝑝𝑙𝑒𝑛𝑑𝑒𝑟𝑗,𝑡+ 𝛽5 ∗ 𝑋𝑘,𝑡 + 𝛽6 ∗ 𝑌𝑗,𝑡 + 𝛽7 ∗ 𝑍𝑙,𝑡 , (3)
where 𝐿𝑎𝑟𝑔𝑒 𝑑𝑎𝑚𝑎𝑔𝑒𝑗,𝑡−1is a dummy variable that equals to 1 when the aggregate default damage experience by bank j at year t-1 is in the top quintile of distress
damage after ranking it from high to low, and 0 otherwise. X, Y, and Z represent a series of control variables that measure borrower, bank, and loan’s characteristics,
respectively. Detailed definitions of the control variables are given in Appendix. The alternative measure of distressed loan damage is a continuous variable that
aggregates the amount of all outstanding distressed loans. The sub-sample analysis excludes loans originated during the crisis periods. Heteroskedasticity-consistent p-
values clustered at the firm level are reported in parentheses. The superscripts ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
Variables Full Sample Sub-sample
Column (1) Column (2) Column (3) Column (4) Column (5) Column (6) Column (7) Column (8) Column (9) Column (10) Column (11)
Intercept 193.30*** 192.84*** 184.28*** -109.46*** -107.35** -129.94*** 181.66*** -134.99***
(0.0001) (0.0001) (0.0001) (0.0076) (0.0132) (0.0072) (0.0001) (0.0046)
Large_damage 66.29*** 62.04*** 73.60*** 5.66 75.09*** 7.76
(0.0001) (0.0001) (0.0001) (0.4573) (0.0001) (0.4758)
Strong_relation 43.08*** 45.20***
(0.0001) (0.0001)
Large_damage* 36.5** 131.86* 29.42* 288.54** 265.04**
strong_relation (0.0375) (0.0984) (0.0829) -0.0205 (0.0279)
Large_damage*strong -43.60 -203.19* -183.2
relation*top_lender (0.5966) (0.0928) (0.1347)
Aggregate_damage 16.60*** 15.33*** 15.64*** -3.89* 15.91***
(0.0001) (0.0001) (0.0001) (0.0586) (0.0001)
Aggregate_damage 2.02*** 3.62*** 3.12*** 1.45
*strong_relation (0.0001) (0.0007) (0.0009) (0.2293)
Aggregate_damage*st -1.68* -1.74* 1.16
rong_relation*top_lender (0.0989) (0.0901) (0.3786)
49
Table V. Determinants of loan spread following distressed loans (Continue)
Variables Full Sample Sub-sample
Column (1) Column (2) Column (3) Column (4) Column (5) Column (6) Column (7) Column (8) Column (9) Column (10) Column (11)
Top_lender -18.19*** -19.26*** -16.67*** -21.65*** -22.20*** -18.58*** -0.71 -7.02 -16.40** -13.5*
(0.0012) (0.0011) (0.0047) (0.0001) (0.0002) (0.0023) (0.9107) (0.2834) (0.017) (0.0638)
Switched 14.14*** 14.68*** 14.02*** 16.26*** 21.66*** 20.07*** 11.31** 15.60*** 17.86*** 24.11*** 13.94**
(0.0048) (0.0038) (0.0052) (0.0012) (0.0001) (0.0001) (0.0244) (0.0029) (0.0016) (0.0001) (0.0134)
Secured 55.47*** 44.53*** 45.96*** 54.51*** 45.16*** 44.94*** 40.39*** 41.44*** 38.27*** 37.76*** 28.44***
(0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001)
Borrower_size -5.41*** -3.96* -5.72*** -5.94*** -4.97** -6.64*** -10.98*** -11.14*** -9.80*** -10.47*** -16.73***
(0.0067) (0.0756) (0.0096) (0.003) (0.0267) (0.0028) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001)
Loan_size 0.78 -1.76 -2.08 -0.05 -1.51 -1.46 -4.27* -2.81 -0.39 0.21 -6.88***
(0.7087) (0.425) (0.3429) (0.9825) (0.5006) (0.5107) (0.0533) (0.2035) (0.8763) (0.935_ (0.0059)
Maturity -3.61*** -5.67*** -3.84*** -3.91*** -5.92*** -3.83*** -2.69** -2.52** -3.07** -3.07** -1.97
(0.0013) (0.0001) (0.0012) (0.0005) (0.0001) (0.0013) (0.0226) (0.0326) (0.025) (0.0261) (0.1427)
Current_ratio -1.01 -0.99 -1.06 -1.05 -1.04 -1.07 -0.62 -0.70 -0.73
(0.1336) (0.1357) (0.1162) (0.1143) (0.1093) (0.1024) (0.3207) (0.267) (0.2279)
Leverage 95.33*** 95.59*** 93.80*** 94.2*** 94.81*** 95.73*** 82.04*** 81.97*** 91.18***
(0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001)
Multiple_lead -0.74 -0.56 -0.57 -0.36 -2.64*** -2.67*** -0.66 -0.35 -2.18**
(0.4083) (0.5018) (0.5235) (0.6812) (0.0032) (0.0029) (0.4463) (0.6871) (0.012)
Bubble crisis 26.91*** 149.89**
(0.0001) (0.0418)
Financial crisis 114.60*** -118.23
-0.0001 -0.2861
Large_damage* -43.30***
Bubble crisis (0.0009)
Large_damage* 40.24*
Financial crisis (0.0841)
50
Table V. Determinants of loan spread following distressed loans (Continue)
Variables Full Sample Sub-sample
Column (1) Column (2) Column (3) Column (4) Column (5) Column (6) Column (7) Column (8) Column (9) Column (10) Column (11)
Large_damage*Strong -67.49*
_relation*Bubble crisis (0.0635)
Large_damage*Strong -213.15***
_relation*Financial crisis (0.0002)
Aggregate_damage* -6.38*
Bubble crisis (0.0748)
Aggregate_damage* 12.47**
Financial crisis (0.0203)
Aggregate_damage* 0.55
strong_relation*Bubble crisis (0.5685)
Aggregate_damage* -5.90***
strong_relation*Financial crisis (0.0001)
Nobs 13426 13426 13426 13426 13426 13426 13426 13426 8845 8845 8845
Year FE No No No No No No Yes Yes No No Yes
Borrower FE No No No No No No Yes Yes No No Yes
Adj-R2 0.05 0.06 0.07 0.06 0.07 0.08 0.09 0.11 0.13 0.09 0.08
51
Table VI. Probability of switching to a different lender following the distressed loans
This table reports the regression results of the following equation,
𝑃𝑟𝑜𝑏(𝑠𝑤𝑖𝑡𝑐ℎ𝑖𝑛𝑔)𝑘,𝑡
= 𝛽0 + 𝛽1 ∗ 𝐿𝑎𝑟𝑔𝑒 𝑑𝑎𝑚𝑎𝑔𝑒𝑗,𝑡−1 + 𝛽2 ∗ 𝐿𝑎𝑟𝑔𝑒 𝑑𝑎𝑚𝑎𝑔𝑒𝑗,𝑡−1 ∗ 𝑇𝑜𝑝 𝑙𝑒𝑛𝑑𝑒𝑟𝑗,𝑡 + 𝛽3
∗ 𝐿𝑎𝑟𝑔𝑒 𝑑𝑎𝑚𝑎𝑔𝑒𝑗,𝑡−1 ∗ 𝑆𝑡𝑟𝑜𝑛𝑔 𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑗,𝑘,𝑡 + 𝛽4 ∗ 𝑋𝑘,𝑡 + 𝛽5 ∗ 𝑍𝑙,𝑡
(4)
where the dependent variable Prob(switching) is a dummy variable that equals to 1 when the borrower
uses a different lender for its new loan, and 0 otherwise. Just like before, X and Z are series of control
variables that measure firm and loans characteristics, respectively. Detailed definitions of the variables
are given in Appendix. The sub-sample analysis excludes loans originated during the crisis periods.
Heteroskedasticity-consistent p-values clustered at the firm level are reported in parentheses. The
superscripts ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
52
Variables Full Sample Sub-sample
Column
(1)
Column
(2)
Column
(3)
Column
(4)
Column
(5)
Intercept -1.13*** -1.12***
(0.0001) (0.0001)
large_damage 0.46** 0.42* 0.05 0.01 0.35
(0.0385) (0.0581) (0.6328) (0.9665) (0.1431)
top_lender -0.32*** -0.32*** -0.15*** -0.13*** -0.25***
(0.0001) (0.0001) (0.0001) (0.0002) (0.0042)
large_damage*top_lender -0.09 -0.01 0.05 0.03 -0.16
(0.7199) (0.9577) (0.6448) (0.8137) (0.5547)
large_damage*strong_relation -0.83*** -0.62*** -0.64*** -0.67**
(0.0006) (0.0001) (0.0001) (0.01)
borrower_size 0.19** 0.18* 0.11*** 0.12*** -0.20*
(0.0468) (0.059) (0.0001) (0.0001) (0.0687)
secured 0.07 0.06 -0.00 0.03 0.04
(0.4159) (0.4858) (0.9948) (0.4252) (0.6511)
yield 0.03 0.03 0.04*** 0.04*** 0.03
(0.1898) (0.179) (0.0001) (0.0001) (0.1189)
loan_size 0.05 0.04 0.03** 0.03** 0.02
(0.2177) (0.2673) (0.0295) (0.0295) (0.6178)
n_leads 0.03 0.02 0.01 0.01 0.04*
(0.2286) (0.2924) (0.186) (0.1402) (0.0904)
distance 0.32*** 0.32*** 0.17*** 0.18*** 0.39***
(0.0001) (0.0001) (0.0001) (0.0001) (0.0001)
leverage -0.27 -0.23 -0.04 -0.08 -0.19
(0.1373) (0.217) (0.406) (0.1224) (0.3165)
current_ratio 0.08 0.09 0.00 0.01 0.16*
(0.2461) (0.2148) (0.7899) (0.662) (0.0575)
borrower_rank_change 0.01 0.01 0.01*** 0.01*** 0.01**
(0.2287) (0.236) (0.0012) 0.0008) (0.046)
lender_rank_change -0.35*** -0.35*** -0.12*** -0.10*** -0.29***
(0.0001) (0.0001) (0.0004) (0.0064) (0.0008)
Bubble crisis 0.13***
(0.0005)
Financial crisis -0.01
-0.8573
Nobs 25011 25011 25011 15002 15002
Year FE Yes Yes No No Yes
Borrower FE Yes Yes No No Yes
53
Table VII. CAR of affected borrowers vs. matched pairs.
This table presents the differences in CAR (%) between the affected borrowers and their matched pairs
obtained using Propensity Matching Score Method. Details of the process are described in Appendix I.
All model parameters are estimated over Day -250 to -50, where Day 0 is the distress announcement date.
Panel A reports the results using Fama 48 industry classification. Panel B reports the results using Fama
12 industry classification. Panel C reports the results using 3-digit SIC code and Panel D reports the
results using 2-digit SIC code. The t-statistics for the difference in means is computed with the cross-
sectional variances of CARs and assumes unequal variances across the two samples.
Panel A: Non-sample vs. Sample Firms (FAMA 48 industry)
Sample firms Non-sample firms
Difference in
means
Mean t-stat Mean t-stat Mean t-stat
car(-5,1) -0.31% -3.50 0.18% 2.13 -0.49% -4.01
car(-3,1) -0.28% -3.71 -0.21% -3.14 -0.07% -0.67
car(-3,3) -0.46% -5.02 -0.61% -7.72 0.15% 1.24
car(-5,5) -0.57% -4.97 -0.17% -1.58 -0.41% -2.60
car(0,0) -0.16% -4.10 -0.13% -3.71 -0.03% -0.51
car(-1,1) -0.18% -3.00 0.11% 2.05 -0.30% -3.61
car(-30,-6) -0.15% -0.76 -1.08% -6.39 0.94% 3.62
car(6,30) -0.53% -2.84 -1.27% -7.30 0.75% 2.92
car(-120,-15) -1.55% -5.62 -2.39% -9.84 0.84% 2.28
Panel B: Non-sample vs. Sample Firms (FAMA 12 industry)
Sample firms Non-sample firms
Difference in
means
Mean t-stat Mean t-stat Mean t-stat
car(-5,1) -0.31% -3.50 0.13% 2.89 -0.45% -4.66
car(-3,1) -0.28% -3.71 -0.21% -5.51 -0.07% -0.88
car(-3,3) -0.46% -5.02 -0.38% -8.61 -0.08% -0.85
car(-5,5) -0.57% -4.97 0.16% 2.70 -0.73% -6.03
car(0,0) -0.16% -4.10 0.05% 2.64 -0.21% -5.04
car(-1,1) -0.18% -3.00 0.14% 4.45 -0.33% -4.93
car(-30,-6) -0.15% -0.76 -0.42% -4.32 0.27% 1.32
car(6,30) -0.53% -2.84 -0.15% -1.52 -0.38% -1.94
car(-120,-15) -1.55% -5.62 -1.94% -13.86 0.39% 1.34
54
Table VII. CAR of affected borrowers vs. matched pairs (Continue)
Panel C: Non-sample vs. Sample Firms (3 digit SIC)
Sample firms Non-sample firms
Difference in
means
Mean t-stat Mean t-stat Mean t-stat
car(-5,1) -0.31% -3.50 0.52% 4.44 -0.83% -5.42
car(-3,1) -0.28% -3.71 -0.06% -0.61 -0.22% -1.72
car(-3,3) -0.46% -5.02 -0.46% -4.05 -0.01% -0.03
car(-5,5) -0.57% -4.97 0.11% 0.73 -0.68% -3.49
car(0,0) -0.16% -4.10 0.00% -0.01 -0.16% -2.36
car(-1,1) -0.18% -3.00 0.21% 2.65 -0.40% -3.78
car(-30,-6) -0.15% -0.76 -0.56% -2.33 0.42% 1.26
car(6,30) -0.53% -2.84 -1.85% -7.20 1.32% 4.09
car(-120,-15) -1.55% -5.62 -2.59% -7.46 1.03% 2.21
Panel D: Non-sample vs. Sample Firms (2 digit SIC)
Sample firms Non-sample firms
Difference in
means
Mean t-stat Mean t-stat Mean t-stat
car(-5,1) -0.31% -3.50 0.24% 3.22 -0.56% -4.78
car(-3,1) -0.28% -3.71 -0.19% -3.07 -0.09% -0.97
car(-3,3) -0.46% -5.02 -0.52% -7.24 0.06% 0.53
car(-5,5) -0.57% -4.97 0.04% 0.43 -0.61% -4.14
car(0,0) -0.16% -4.10 -0.06% -1.93 -0.10% -1.96
car(-1,1) -0.18% -3.00 0.10% 2.07 -0.29% -3.68
car(-30,-6) -0.15% -0.76 -0.95% -6.23 0.80% 3.27
car(6,30) -0.53% -2.84 -1.06% -6.71 0.53% 2.20
car(-120,-15) -1.55% -5.62 -2.56% -12.02 1.01% 2.94