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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

References

Ajayi, T., Sosan, M., 2013. The roles of agent banks in syndicated loan transactions: a critical

analysis. Equity Research Consult.

Bae, K. H., Kang, J.K., Lim, C.W., 2002. The value of durable bank relationships: evidence from

Korean banking shocks. Journal of Financial Economics 64, 181-124.

Berger, A. N., Udell, G. F., 1995. Relationship lending and lines of credit in firm finance. Journal of

Business 68(3), 351-382.

Bernanke, B. S., Blinder, A., 1988, Credit, money, and aggregate demand. American Economic

Review 78(2), 435-39.

Besanko, D., Thakor A., 1987. Competitive equilibrium in the credit market under asymmetric

information. Journal of Economic Theory 42, 167-82.

Bester, H., 1985. Screening vs. rationing in credit market with imperfect information. American

Economic Review 75, 850-55.

Bharath, S. T., Dahiya, S., Saunders, A., Srinivasan, A. 2011. Lending relationships and loan contract

terms. Review of Financial Studies 24(4), 1141-1203.

Bharath, S. T., Dahiya S., Saunders, A., Srinivasan, A. 2007. So what do I get? The bank’s view of

lending relationship. Journal of Financial Economics 85, 368-419.

Bhattacharya, S., Chiesa, G., 1995. Proprietary information, financial intermediation, and research

incentives. Journal of Financial Intermediation 4(4), 328-357.

Boot, A.W. A., Thakor, A. V., Udell, G. F. 1991. Secured lending and default risk: equilibrium

analysis, policy implications and empirical results. The Economic Journal 101(406), 458-472.

Bord, V. M., Santos, J.A.C., 2012. The rise of the originate-to-distribute model and the role of banks

in financial intermediation. Federal Reserve Bank of New York Economic Policy Review

July, 21-34.

Brewer III, E., Genay, H., Hunter, W.C., Kaufman, G.G. 2002. The value of banking relationships

during financial crisis: evidence from failure of Japanese banks. FRB Chicago Working Paper

No. 2002-20.

Carvalho, D.,Ferreira, M., Matos, P., 2011. Lending relationships and the effect of bank distress:

evidence from the 2007-2008 Financial Crisis. Unpublished working paper. University of

Southern California.

Chan, Y., Kanatas, G., 1985. Asymmetric valuations and the role of collateral in loan agreements.

Journal of Money, Credit and Banking 17, 84-95.

Chang, Y, Fok, R.C.W, Lee,W., 2004. Bank relationships and their effects on firm performance

around the Asian Financial Crisis: evidence from Taiwan. Financial Management 33(2), 89-

112.

35

Chava,S., Purnanandam, A., 2011. The effect of banking crisis on bank-dependent borrowers. Journal

of Financial Economics, 99, 116-135.

Chava, S., Robert, M. R., 2008. How does financing impact investment? the role of debt covenants.

Journal of Finance 63, 2085-2121.

Dahiya, S., Saunders, A., Srinivasan, A., 2003. Financial distress and bank lending relationships.

Journal of Finance 58, 375-399.

Daniel, K., Moskowitz, T. J., 2013. Momentum crashes. Unpublished working paper. Columbia

Business School.

Das, S.R., Duffie,D., Kapadia, N., and Saita, L., 2007. Common failings: how corporate defaults

are correlated. Journal of Finance 62(1), 93-117.

Dennis, S., Mullineaux, D.J., 2000. Syndicated loans. Journal of Financial Intermediation 9, 404-426.

Diamond, D., 1984. Financial Intermediation and delegated monitoring. The Review of Economic

Studies 51(3), 393-414.

Fernando, C.S., May, A.D., and Megginson, W.L., 2012. The value of investment banking

relationships: evidence from the collapse of Lehman Brothers. Journal of Finance 62(1), 93-

117.

Francois, P., Missonier-Piera, F., 2005. The agency of loan syndicates. Unpublished manuscripts.

ESSEC Business School.

Gadanecz, B., 2004. The syndicated loan market: structure, development, and implications. BIS

Quarterly Review December, 75-89.

Gan, J. 2003. Collateral and credit cycle: evidence from the land-price collapse in Japan. Unpublished

working paper. Hong Kong University of Science and Technology

Gopalan, R., Nanda, V., Yerramilli, V., 2011. Does poor performance damage the reputation of

financial intermediaries? evidence from the loan syndication market. Journal of Finance

67(1), 235-370.

Holmström, B., Tirole, J., 1997. Financial intermediation, loanable funds, and the real sector.

Quarterly Journal of Economics, 112, 663-691.

Houston, J.F., Lin, C., Wang, J., 2014. Does bank monitoring matter to bondholders? Unpublished

working paper. University of Florida.

Kang, J.-K., Stulz, R.M., 2000. Do banking shocks affect borrowing firm performance? an analysis

of the Japanese experience. Journal of Business 73, 1-23.

Kovner, A., 2011. Do underwriters matter? the impact of the near failure of an equity underwriter.

Unpublished working paper. Federal Reserve Bank of New York.

Kovner, A., 2013. Federal Reserve Bank of New York, CRSP-FRB link.

36

Maskara, P. K., 2006. Participation of investment banks and non-bank financial institutions in

syndicated loans. Unpublished working paper. University of Kentucky.

Modigliani, F., Miller, M. H., 1958. The cost of capital, corporate finance and the theory of

investment. American Economic Review, 48, 261-97.

Mora, N., 2013. Lender exposure and effort in the syndicated loan market. Federal Reserve Bank of

Kansas Research Working Papers.

Murfin, J., 2012. The supply-side determinants of loan contract strictness. Journal of Finance 67(5),

1565-1601.

Ongena, S., Smith, D., Michalsen, D., 2003. Firms and their distressed banks: lessons from the

Norwegian banking crisis. Journal of Financial Economics 67, 81-111.

Rosenbaum, P.R., Rubin, D.B. 1983. The central role of the propensity score in observational studies

for causal effects. Biometrica 70, 41-55.

Santos, Jo˜ao A.C., 2011. Bank corporate loan pricing following the subprime crisis. Review of

Financial Studies 24 (6), 1916-1943.

Schiller, E. M., 2013. Co-lender issuers on defaulted loans. Dentons paper for the ACREL Annual

Meeting.

Stein, J., 1998. An adverse-selection model of bank asset and liability management with

implications for the transmission of monetary policy. RAND Journal of Economics 29, 466-

486.

Stiglitz, J. E., Weiss, A., 1981. Credit rationing in markets with incomplete information. American

Economic Review 71, 393-409.

Taylor, A. Sansone, A. 2007. The handbook of loan syndication & trading. New York: McGraw-Hill.

68.

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