Non-bank Loan Investors and Borrowers’ Renegotiation

Prospects

Teodora Paligorova∗

Bank of Canada

E-mail: tpaligorova@bankofcanada.ca

Joao A. C. Santos∗

Federal Reserve Bank of New York

and

Nova School of Business and Economics

E-mail: joao.santos@ny.frb.org

September 2, 2015

JEL classification: G21, G23

Keywords: Corporate loans, renegotiation, loan syndicates, investor diversity, lead banks.

∗The authors thank Jason Allen, Berlin Mitchell, Enrique Schroth (discussant), Larry Wall and seminarparticipants at the EuroFIT Research Workshop on Corporate Loans, the University of Amsterdam, FederalReserve Bank of Atlanta for valuable comments. We thank Vitaly Bord for outstanding research assistance.The views stated herein are those of the authors and are not necessarily the views of the Bank of Canada, theFederal Reserve Bank of New York or the Federal Reserve System.

Non-bank Loan Investors and Borrowers’ Renegotiation Prospects

Abstract

We document that the growth of non-bank lenders in the syndicated loan market has a sig-nificant effect on loan renegotiations. Loans financed relatively more by non-bank lendersare associated with lower likelihood of renegotiation, while strong lead bank presence fa-cilitates renegotiations. Diversity among non-bank investors, either in terms of number ofinvestor types or investment shares, affects adversely the renegotiation prospects. We ad-dress the potential endogeneity between renegotiation prospects and syndicate structuresby using instrumental variable approach and alternatively by isolating a set of relativelyunexpected renegotiations which are unlikely to trigger endogenous adjustments to syn-dicate structures prior to renegotiations. Our findings highlight previously unrecognizedrole of the growing presence of non-bank lenders in the corporate lending business that isnegative treatment to renegotiations.

1 Introduction

Modern banking theories posit that bank loans are unique as banks have a comparative ad-

vantage in monitoring borrowers (e.g., Ramakrishnam and Thakor (1984)), which includes

screening loan applicants to identify their creditworthiness, as well as supervising and prevent-

ing borrowers from undertaking opportunistic behavior during the realization of the project.

Consistent with these theories, the corporate lending business was historically dominated by bi-

lateral agreements between banks and borrowers whereby banks kept the loans they originated

on their balance sheets.

With the development of the syndicated loan market, banks began to retain only a

portion of the loans they originated, placing the remainder with other institutional investors.1

Early on banks were the dominant investors in loan syndicates, but with the deepening of the

secondary loan market many other investors, including pension funds, hedge funds, mutual

funds, private equity firms and collateral managers, began to invest in corporate loans.2 In

1988, there were on average 1.3 non-bank investor categories in corporate loan syndicates in

the U.S. while that number had gone up to 3 up to 2010. Over the same period, not only the

diversity of investors in loan syndicates went up, but so did their market share (Figure 1).3

The growing presence of non-bank investors in loan syndicates has likely given banks

an opportunity to extend new loans and to likely do so under more favorable terms. The

increasing presence of non-bank investors in loan syndicates, however, may adversely affect a

distinct feature of traditional bank loans—the flexibility to renegotiate their loan terms. In

general, it will be easier for a borrower to renegotiate the loan terms with a single lender

than with a syndicate of diverse lenders. The presence of multiple lenders will give rise to

coordination problems, which will likely grow with the heterogeneity of investors. The reason

1 The U.S. syndicated loan market rose from a mere $339 billion in 1988 to $2.2 trillion in 2007, the yearthe market reached its peak.

2 The secondary loan market evolved from a market in which banks participated occasionally, most often byselling loans to other banks, to an active, dealer-driven market where loans are sold and traded much like otherdebt securities. The volume of loan trading increased from $8 billion in 1991 to $176 billion in 2005.

3 See Bord and Santos (2012) for analysis of the roles of different investors in the U.S. syndicated loan marketover the last two decades.

1

is that an increase in investor diversity will imply a rise in the number of different business

models and objective functions (Botlon and Sharfstein (1996), Gison et al. (1990)).4 Investors’

diversity will also make divergence of opinion about the benefit of renegotiation to rise by virtue

of differences in the information available to different investors. Lastly, if lead banks rely on

the growing presence of non-bank investors in the syndicate to lower their retained portion in

the loan, they may indirectly decrease borrowers’ prospects to renegotiate (e.g., Agarwal et al.

(2011), Adelino et al. (2013)). The share of the lead bank is important not only because it

affects the lead’s screening and monitoring incentives, but also because it affects its influence

over the syndicate participants in negotiation process.5

In this paper, we investigate the importance of investors’ diversity in syndicated loans

for borrowers’ prospects to renegotiate their credits. The U.S. syndicated loan market pro-

vides a unique opportunity to answer this question both because investors’ diversity has been

growing and because we now have detailed information on the composition of loan syndicates

throughout the life of the loan. We consider renegotiations that occur outside financial distress

or default and focus on renegotiations that increase the size of the credit. While focusing on

the role of investor diversity, we also account for other factors that may affect the outcome

of a renegotiation, including loan characteristics, the financial condition of the borrower and

the lead bank. Lastly, we capitalize on the panel structure of the data to isolate unexpected

renegotiations from expected ones to address concerns about the endogeneity of syndicate and

renegotiations. In addition, we also reply on an instrumental variable approach to estimate the

(unbiased) effect of lead bank and syndicate structure in renegotiations. Our instrumental vari-

able is the first-time arrival of bank participant lenders in the syndicate, which is arguably not

driven by the likelihood of renegotiation, and it affects the syndicate structure substantially.

We rely on the Shared National Credit database, which contains detailed information

on syndicated loans in the U.S. since the late 1980s. Critical for our purposes is the fact that

4For instance, investment funds or private equity firms may not have funding to accommodate a request by aborrower to increase the initial loan amount. Similarly, since CLOs have a limited life, they may not be willingto renegotiate the loan maturity that would go beyond their period of existence.

5 See Gorton and Pennacchi (1995) for models that capture the impact of lead bank share on monitoringincentives, and Sufi (2007), Ivashina (2009) for studies that argue that lead banks use their loan share to aligntheir incentives with those of syndicate participants and commit to future monitoring.

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the SNC database contains information about each investor in a given loan including their

exact loan share. In addition, our analysis benefits from information about credit line draw

downs, which allows us to define unexpected renegotiations to increase the credit size. In our

definition, unexpected renegotiations are preceded by a relatively large undrawn amount and

followed by a large withdrawal at the time of the renegotiation.

We find that the lead bank share plays an important role in the renegotiation outcome.

A change in the lead share from the 25th to the 75th percentile leads to an increase in rene-

gotiation probability from 13% to 24%. Compared to the unconditional renegotiation rate of

15.6%, this result suggests that lead banks’ retained shares have a relatively large impact on

the likelihood of renegotiations. In line with this finding, our results show that loans in which

the lead bank has divested its entire share are significantly less likely to be renegotiated.

We find that an increase in non-bank loan shares in a syndicate reduces the likelihood

of renegotiation. Consistent with this finding, loans with higher number of non-bank investors

are less likely to be renegotiated. Similar conclusion holds when we consider measures of

diversity that account for the ownership shares held by each type of non-bank investors as

well as measures that consider the concentration in the ownership structures among non-bank

investors.

Our findings are both novel and important because the growing presence of non-bank

investors in loan syndicates seems to weaken the option to renegotiate that is unique to bank

loans. It is well understood that borrowers value the possibility to renegotiate, even outside of

financial distress. Renegotiations provides borrowers with the opportunity to improve existing

contracts by adjusting them to changes in firms’ business plans, or yet to modify contracts

because of a covenant violation.6 Borrowers also value renegotiations because they give them

the opportunity to adjust the contract terms to reflect past or expected changes in credit

market conditions.

6 As Hart and Moore (1988) shows, in states of nature where the borrower’s cashflow is high, or more generallyits bargaining power high (by virtue of new favorable information, or new investment opportunities, or accessto additional funding sources) the borrower may be able to negotiate down any possibly onerous terms in theinitial contract or to adjust it to better fit its needs. For additional models showing that borrowers may be ableto renegotiate more advantageous terms when their relative bargaining power increases see Gorton and Kahn(2000) and Garleanu and Zwiebel (2009).

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Our paper is more closely related to the literature on loan renegotiations outside fi-

nancial distress, as in Roberts and Sufi (2009), Roberts (2014) and Mian and Santos (2011).

Roberts and Sufi (2009) rely on a sample of loans from DealScan and SEC fillings to investigate

loan renegotiations. Their study is largely a cross-sectional comparison of renegotiated and un-

renegotiated loans. They document that renegotiations are mostly driven by improvements in

credit quality and credit market conditions. They also show that renegotiations result in large

changes to the amount, maturity, and pricing of the contract. Roberts (2014) also considers a

sample of borrowers with syndicated loans. The author takes a dynamic view and investigate

how the determinants of renegotiation vary throughout the life of the loan. The results show

that loans for which the initial terms are restrictive possibly due to high information asym-

metries are more likely to be renegotiated more frequently. Mian and Santos (2011) rely on

the Shared National Credit data and focus on the role of loan maturity extensions in mitigat-

ing concerns about borrower liquidity. They document that firms refinance early when credit

conditions are good to prolong the effective maturity of their loans. Refinancing propensity

is more sensitive to credit market fluctuations for creditworthy firms consistent with the idea

that creditworthy firms choose to refinance at a lower rate when the cost of capital rises.

We are interested in understanding the determinants of loan renegotiations outside

financial distress and over the entire life of the loan. In this regard, our paper is closest to

Roberts (2014), who focuses on the set of borrowers’ controls that explain the renegotiation

patterns over the life of the loan, and Mian and Santos (2011) who document that borrowers’

conditions and overall credit conditions are key factors in explaining renegotiations that aim at

explaining maturity extensions. In contrast to these studies, our focus is on the renegotiation

impact of the syndicate structure and in particular the role loan investors’ diversity.

The rest of the paper is structured as follows. Section 2 discusses our methodology,

data, and sample. Section 3 reports results on the importance of lead bank’s loan share

and non-bank investor diversity on renegotiation prospects. Section 4 attempts to address

concerns with the endogeneity of loan syndicates. Section 5 presents the results of a set of

robustness tests we undertake on the role of the lead bank and non-bank investor diversity on

renegotiations prospects. Section 6 concludes the paper.

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2 Methodology, data and sample characteristics

2.1 Methodology

We rely on the following model to estimate the effects of loan investor diversity on likelihood

of renegotiation.

Pr(Renegotiatel,f,b,t) = Φ(αLEADl,b,t−1 + βDIV ERSITYl,t−1

+ γXl,t + λYf,t−1 + θZb,t−1 + TimeEffects). (1)

where Φ() is the standard normal cumulative distribution function; Renegotiatel,f,b,t takes

the value one if loan l is renegotiated to increase its size in year t and zero otherwise. We

use maximum likelihood estimation. We focus on renegotiations to increase the loan amount

because they require investors to increase their investments in the loan, unless they find another

investor who is willing to take (pay) their share. If the change in the loan amount is pre-

approved in the initial terms of the loan agreement, it is hard to infer the role of syndicate

structure over time. To address this issue we define a renegotiation as an amount increases that

is accompanied by maturity changes because often accordion clauses (pre-approved agreements)

affect only one feature of the loan and keep the rest unchanged. Most likely if more than one

loan term is changed, it is the result of renegotiation instead of a pre-approved automatic

change.

The two key variables of interest for our investigation are LEAD and DIV ERSITY.

The former variable measures the share of loan l that its lead bank b owns at the end of the

year prior to the renegotiation. We are interested in finding out whether larger ownership

stakes by lead banks facilitate renegotiations. As noted in the introduction, to the extent that

the lead bank’s incentives to monitor are correlated with its exposure to the loan, a larger

loan share will likely facilitate renegotiations because the lead bank is better informed about

the borrower’s funding needs and business prospects. A larger loan share by the lead bank

may also help facilitate renegotiations because it will add credibility to that bank’s role in

the renegotiation, which will likely influence the remaining syndicate members’ renegotiation

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decision. In the robustness section, we investigate the extreme case when the lead bank has

no exposure to the loan.

Our other key variable, DIV ERSITY, attempts to capture the importance of loan

investors’ diversity on borrowers’ success to renegotiate its loan. As we discussed in the intro-

duction, investor diversity is likely to hamper borrowers’ ability to renegotiate their credits.

Greater diversity indicates greater differences in the business models of investors and therefore

a higher likelihood of them not being able to coordinate and accommodate the demands that

come with a renegotiation request. It also means a higher likelihood of differences in informa-

tion acquisition about the borrower, which may fuel divergence of opinion about the merits of

the renegotiation.

We consider three variables to measure the importance of investor diversity in loan syn-

dicates.7 The first variable, Non-bank sharemeasures the share of the loan owned by non-bank

investors. Note that in addition to the lead bank and non-bank investors there are also bank in-

vestors in loan syndicates. The second variable, Lender type, measures the number of different

types of non-bank investors in the syndicate. We classify non-bank investors according to the

following nice possible categories: foreign bank, foreign firm, CLO, finance company, invest-

ment managers (mutual funds, hedge funds and pension funds), brokers, insurance companies,

private equity firm, and other lenders. We include foreign banks in this group because they

cannot rely on (insured) deposit funding. While the first variable treats all non-bank investors

as a group, the second variable considers the differences that emerge across the various types

of investors within that group. Our third variable, Non-bank share vol, which we measure

by the standard deviation of non-bank lenders’ loan shares, goes one step further in allowing

for differences among non-bank investors. In the robustness section, we propose two other

variables that also attempt to capture the diversity among the universe of non-bank investors

in the loan syndicate—the Hirfindahl index of their loan shares, HHI non-bank shares, and

the turnover among the set of nonbank investors in the syndicate, TURNOV ER.

We investigate the impact of the lead bank’s loan share and the diversity in the loan

7When we refer to syndicate structure in the text we mean various measures of diversity as defined above.

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syndicate on borrowers’ ability to renegotiate their credits controlling for loan-, firm- and lead

bank-specific factors which is described next.

We begin by describing our set of loan-specific controls, Xl,t. We account for internal

loan rating assigned by the lead bank. Because it will be less risky for investors to increase

their investments in renegotiations in safer credits, we expect credits with better loan ratings

to be more likely to renegotiate. We include an indicator variable to distinguish credit lines

from term loans, and use CLdrawdown ratio to control for the fraction of the credit line the

borrower has already drawn down (CL drawdown ratio takes the value one for term loans).

The closer the credit line to being fully drawn down, the more likely the borrower will seek

a renegotiation to increase the size of its credit. We include a set of deal purpose dummy

variables to distinguish loans that are for debt repay, working capital, M&A, recapitalization,

capital expenditures or commercial paper back up. Some loan purposes may impose higher

funding needs and thus be more prone to renegotiations to increase the size of the credit.

Additionally, we include the set of dummy variables Loan Y ears to account for the age of

the loan since the borrower’s need to renegotiate its credit may vary as the loan approaches

maturity.

We next turn our attention to our set of firm-specific variables, Yf,t−1, all one year

lagged. We focus on the firm’s riskiness and its needs for funding since these are likely to be

two of the most important factors driving loan renegotiations, Leverage, is the ratio of total

debt over assets, to account for the firm’s financial condition since riskier firms are more likely

to encounter difficulties to renegotiate their credits. Since more profitable firms are less risky,

we control for the firm’s profitability (Profitability) as measured by the ratio of EBITDA to

Assets and by a market-based measure of the stock return. We complement these measures of

risk with Sales the log of sales, since larger firms tend to be more diversified across regions and

products. We use Sales Growth, the sales growth, to control for future growth opportunities

since fast-growing firms are more likely to request a renegotiation to increase the size of their

credits. CAPEX is capital expenditure and it is expected to be positively correlated with

renegotiations.

Our last set of controls, Zb,t−1, accounts for the financial condition of the lead bank.

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To the extent that the retained share by the lead bank in a given loan is correlated with the

bank financial conditions, it is important to control for bank characteristics. We control for

Capitalbk, the ratio of the bank’s equity to total assets, and Liquiditybk, the bank’s holdings of

cash and marketable securities as a fraction of total assets, because well capitalized banks and

banks with more liquidity will be better positioned to deal with the funding needs implied by

the loan renegotiations we consider. For this same reason we also control for Deposits bk, the

ratio of deposits to total assets, and Profitability bk, the bank’s return on assets. Deposits

continue to be the main source of funding of banks and more profitable banks will find it

easier to accommodate requests from borrowers for additional funding. Lastly, we control for

L assets bk, the log of bank’s total assets. Larger banks usually have access to more funding

sources. Also, importantly for our investigation, larger banks tend to have a larger share of

the syndicated loan market which will give them more influence over the syndicate members

in any bargaining that may emerge with the borrower’s renegotiation request.

We include year fixed effects to absorb time heterogeneity at the yearly level. The

year fixed effect captures all macro economic effect at the yearly level, which is the frequency

of our data. The standard errors are clustered and robust at the borrower level. As our

dependent variable is a dummy variable, we estimate all of our models with a probit model of

the probability of renegotiations. In the robustness section, we present results estimated with

logit fixed effects in order to take advantage of the panel structure of our data.

2.1.1 Endogeneity Issues

Our results may be affected by ‘time series’ endogeneity because the likelihood of loan renego-

tiations and syndicate structures may evolve simultaneously over time. It is less plausible that

the lead bank will influence the syndicate structure after the loan origination as this would

require the participation of many investors. However, the lead bank may be more willing to

change their own share in the loan. For example, following an improvement in financial condi-

tion, borrowers may seek to renegotiate loans both to improve the non-price terms and/or to

increase their size. Expecting these renegotiations, lead banks may adjust their retained shares

for the purpose of impeding or facilitating the renegotiation outcome. Looking at Figure 3,

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the lead share increases prior renegotiations, while it drops sharply in the year of renegotiation

and afterwards.8 On the other hand, non-bank lender’ shares decrease before renegotiations,

the diversity among non-bank lenders increases as measured by the non-bank share volatility

and syndicates have fewer lender types.

One way to alleviate concerns with endogeneity is to identify renegotiations that are

relatively unexpected and harder to predict by the lenders. These renegotiations will give

us more accurate inference about the causal effect of the syndicate structure on renegotiation

outcomes. To that end, we isolate a group of presumably unexpected renegotiations in the sense

that they are likely to be triggered by an unexpected large need for funds by the borrower.

To operationalize this idea, we first restrict the analysis to credit lines because the definition

depends on drawdown rates. Next, we identify renegotiations for which (a) the borrower has

a large portion of undrawn funds in the credit line over the entire life of the loan prior the

renegotiation and (b) immediately after the renegotiation the borrower draws down an amount

which is larger than the available credit prior to the renegotiation. The first condition assures

us that the borrower has plenty of available funding in its credit line as credit lines that are close

to being fully drawn down are more likely to be renegotiated to increase their size. The second

condition, in turn, guarantees that the borrower could not meet its funding needs without the

renegotiation. We classify these renegotiations as ‘unexpected,’ while the rest are considered

to be ‘expected.’

An alternative way to alleviate the endogeneity between renegotiations and syndicate

structures is to use an instrumental variable probit method which generates unbiased esti-

mates of LEAD and DIV ERSITY. At the first stage, the identification is achieved by the

inclusion of a variable(s) that is correlated with LEAD and DIV ERSITY but is uncorrelated

with the likelihood of renegotiations. A probit model on renegotiations that incorporates the

instrumented LEAD and DIV ERSITY is estimated at the second stage (i.e., equation 1).

We rely on presumably exogenous arrival of new bank participant lenders in the syndi-

cate. It appears that the arrival of new bank lenders for the very first time in the syndicate is

8While beyond of the scope of our analysis, an explanation for the development of the lead share after amountrenegotiation could be that lead banks transfer loan shares to other lenders to engage in new lending businesses.

9

not linked to renegotiation events but seems to be evenly widespread over the life of the loan

(See Figure 6). In addition, the data reveals that the arrival rate is relatively uniformly dis-

tributed over time and over loan years, which suggest that these bank lenders are not affected

by expected renegotiations.

At the first stage, we expect positive correlation between the lead share and the inflow

of new bank lenders. One potential explanation for the positive relationship between the lead

share and the rate of arrival of new bank lenders is that the latter may be attracted precisely to

syndicates with large lead bank shares because these loans are likely better monitored and less

affected by information asymmetry problems. It is not surprising, therefore, that new bank

participants who are likely less informed about borrowers’ quality take high lead shares as a

signal for weaker information asymmetry and credible commitment to monitoring. Similarly,

new bank participants may find it less appealing to invest in loans with large number of

investors and large non-bank shares, which are usually driven by coordination problems. We

do believe that because new bank lenders are less informed about loans’ and borrowers’ quality,

their decision to invest depends crucially on the syndicate structure and its capacity to insure

that the loan will be repaid.

2.2 Data

The main data source for this project is the Shared National Credit (SNC) program run by

the Federal Deposit Insurance Corporation, the Federal Reserve Board, and the Office of the

Comptroller of the Currency. The SNC program gathers, at the end of each year, confidential

information on all credits—new as well as credits originated in previous years—that exceed

$20 million and are held by three or more federally supervised institutions.9

For each credit, the program reports the identity of the borrower, the type of the credit

(e.g. term loan, credit line), its purpose (e.g. working capital, mergers and acquisitions),

origination amount and date, maturity date, and internal bank rating. In addition, the SNC

program reports information on whether the credit became nonaccrual and whether the bor-

9 The confidential data were processed solely within the Federal Reserve for the analysis presented in thispaper.

10

rower filed for bankruptcy over the last year. Finally, the program reports information on the

lead arranger and syndicate participants, including their identity and the share of the credit

that they hold.

The SNC data serve well our goal to investigate the impact of lenders’ diversity in

renegotiation outcomes. The SNC program contains detailed information on the composition

of the loan syndicate and the loan shares that each participant holds throughout the life of the

credit. Another reason is that the program tracks information on credit terms, giving us the

opportunity to identify instances in which it is renegotiated. We focus on renegotiations that

increase the size of the credit, but we also consider in the robustness section renegotiations

that extend the maturity of the credit. Finally, the program reports information over the last

two decades, which helps us to follow the loan, the lead share and syndicate diversity over a

long period in which syndicated lending experienced a rapid growth in the late 1990s with the

growth of the originate-to-distribute model in corporate lending.

It has been difficult so far to investigate lenders’ roles in renegotiations because of the

lack of the necessary data. DealScan includes information available only at the time of the

loan origination, and even then it has only very limited information on lenders’ loan shares.

For example, information on the lead bank loan share is missing for about 70 percent of

DealScan credits and information on syndicate participants’ shares is even more sparse. The

Loan Syndication Trading Association database contains information on loans traded in the

secondary market, but it has no information about the identity of the seller(s) or buyer(s),

ruling out its use to close the information gaps in DealScan. SEC fillings, which have also

been used in loan renegotiation studies, are valuable to complement the information reported

in DealScan with regards to the nature of the renegotiation, but they too do not contain

information on the ownership of the firm’s credits.

We complement the SNC data with information from the Moody’s Structured Finance

Default Risk Service Database, Intex CDO deal library, and from Standard and Poor’s Capital

IQ. Moody’s and Intex’s databases have information on structured finance products, including

the size, origination date, and names. We rely on these data sources to identify CLOs among

the syndicate participants reported in the SNC program. We use the Capital IQ database to

11

identify private equity firms, hedge funds, and mutual funds among the syndicate participants.

Additionally, we use Compustat to get information on the balance sheets of publicly listed

firms that appear in the SNC database. SNC contains loans from both privately held firms

and publicly listed firms. Lastly, we use CRSP to gather data on firms’ stock prices. We

describe next our sample.

2.3 Sample characteristics

We combine borrower, lead bank, and loan characteristics to form a loan-year panel dataset

from 1990 to 2010. We follow each loan from the year of origination or the year it first appears

in the SNC database (for loans originated before 1990) until the year of maturity or the end of

the sample period (for loans with maturity dates after 2010). Our sample contains information

for 30,566 loans taken by 3,834 publicly listed firms (we have available firm information for

20,016 loans). The full sample of public and non-public firms contains 90,654 loans taken by

9,486 firms.

Table 1 reports summary statistics for our sample. The top panel reports annual

balance sheet information for the publicly listed borrowers in the sample. We winsorize all

variables at the upper and lower one percentiles to mitigate the effect of outliers. The average

asset value is $4,102 million; the mean leverage is 35%, and the mean profitability defined

as EBITDA over assets is 3.7%; 26% of the borrowers have investment-grade (S&P long-term

issuer credit rating), 30% are below-investment grade, and the remaining are unrated. Overall,

the publicly listed borrowers in our sample are similar to the typical US sample of publicly-

listed firms.

The next panel in the table reports balance sheet summary statistics for lead banks in

our sample. To capture any potential effects that may arise from ownership transfers between

entities of the same holding company, we measure these controls at the holding company level.

For the ease of exposition, we continue to refer to these as bank controls. We also winsorize

the bank controls at the upper and lower one percentiles. The mean log of bank assets is 18.3.

The average bank has an equity-to-assets ratio of 5.9% and has 36.7% deposits.

The third panel in Table 1 characterizes the loans in our sample. The average loan

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amount is $191 million and the average maturity is 5 years. 61% of the loans are credit lines and

the remaining are term loans. Amount renegotiations occur in 15.6% loan-years (we exclude

the first year of the loan, which by definition cannot be classified as a renegotiation). The

SNC data reports information on the portion of the credit line that the borrower has utilized,

i.e., the ratio between the amount that has been drawn down and the total amount of the

credit line (CL drawdown ratio). The average draw-down rate in our sample is 56% (value of

this ratio for term loan equals one). The two most common loan purposes are working capital

(37.3%) and M&A (13.2%) financing. Looking at the ratings assigned by banks, 85.1% of the

loans receive investment grade rating by regulators (Loan rating).10

The bottom panel of Table 1 reports summary statistics about loan syndicates. Start-

ing with the lead bank, on average the lead bank owns 25.8% of the loan. The remaining

syndicate participants named as non-bank lenders, include foreign bank, foreign company,

CLO, finance company, investment managers (mutual funds, hedge funds and pension funds),

brokers, insurance companies, private equity firm, and other lenders. Foreign banks are classi-

fied as non-bank lenders because they do not have access to U.S. deposit insurance. Non-bank

investors own on average 38.9% of the loan.11 There are 1.58 different types of non-bank

investors with relatively high variation across loans.

We measure the concentration of non-bank lender shares in a syndicate with the

Herfindahl index (HHI non-bankshares) which is calculated as the sum of the squared non-

bank lender shares. The mean value of the HHI is 0.79 which shows the concentration of shares

among non-bank lenders. Another measure of diversity of non-bank lenders is the standard

deviation of the non-bank shares whose mean is 0.258. These two variables attempt to measure

the level of dispersion that exists among non-bank investors in the syndicate. The number of

types does so without considering the size of the investment, while HHI and the standard de-

viation of non-bank shares take into account the shares of each non-bank lender. High value of

10In the SNC program, loans are rated using the following ratings: pass, special mention, substandard,doubtful and loss. Each of these ratings can take values between 0 and 100, and they indicate the percentageof the loan receiving that rating. Consequently, loans may have different portions with different ratings.

11The syndicate consists of lead banks, participant banks and non-bank lenders. The sum of the shares of alllenders in each loan equals one.

13

the HHI index indicates that one or more types of non-bank investors hold significantly higher

shares. Similarly, high standard deviation indicates large differences among non-bank lender

shares.

In the remainder of the bottom panel, we report information for each of the non-bank

lenders that appear in loan syndicates. The average CLO loan share is 23% conditional on

the presence of at least one CLO in the syndicate. CLOs are present in 8.5% of the loans

observations. Investment managers are present in 7.8% of the loans and control on average

18.6% of the loan. Foreign lenders are present in 43% of the loans and they retain 42% of

the loan amount. Finance companies appear in 23.7% of the loans with an average share of

17.7%.12

2.3.1 The Diversity of investors and loan renegotiations

In our sample, 12.5% of the loan-years or 22% of the loans are renegotiated at least once; 15%

and 5% of the loans are renegotiated twice and three times twice, respectively, during their

lifetime. The mean value of amount increase is $111 million, which is 70% of the initial loan

value.

We compare loan and firm characteristics in the first loan-year for loans that are rene-

gotiated at least once and loans that have never been renegotiated in Table 2. Although a

renegotiation may occur at any time during the life of the loan life, we note that 55% of the

renegotiations occur in the second or third year. Based on columns (1)-(3) of Table 2, we note

that some results are consistent with the idea that lead banks design syndicates to facilitate

renegotiations from the very start of the loan. For example, non-bank investors own a smaller

portion of credits in renegotiated loans and non-bank lender types are fewer in renegotiated

loans. To the extent that non-bank investors are less friendly to renegotiations and non-bank

investors’ diversity makes it harder for the syndicate to agree upon a renegotiation request,

these differences are consistent with the idea that syndicates evolve in a way that facilitates or

impedes renegotiations. Some results, however, point in the opposite direction. For example,

12 See Bord and Santos (2012) for an analysis of the evolution of lender shares over time.

14

the lead bank, which is expected to retain a higher share in renegotiated credits actually holds

smaller shares. The lead bank share is believed to act as a commitment device to monitor

the borrower’s performance, which adds credibility to the negotiation process. In terms of

firm characteristics, renegotiated loans pertain to larger firms, with higher sales growth and

profitability. It is not surprising that such firms have stronger demand for credit including

through renegotiation, however, it is also possible that firms have become more profitable as

a result of the renegotiated credit.

In Figure 3 we plot the developments of the lead share and syndicate diversity before

and after the renegotiation event. These comparisons are important to understand the endo-

geneous evolution of the syndicate structure and in particular the idea that lead banks manage

syndicate structures ex-ante to either help valuable renegotiations or impede inefficient ones.13

Syndicate structures could be changing after the loan origination in order to facilitate or im-

pede future renegotiations. Looking at Figure 3 there are a host of significant developments

in loan syndicates pre-/post the renegotiation year. Some of the syndicate attributes, which

are believed to be friendly to renegotiations, change in the direction of making the syndicate

even friendlier up to the renegotiation event. For instance, both the non-bank share and the

number of types of non-bank investors in the syndicate decline over a three-year period be-

fore renegotiations compared to the post-renegotiation period. Similarly, lead shares exhibit

upward trend in the years preceding renegotiations and decrease after the renegotiation.

These patterns seem entirely consistent with the idea that lead banks manage loan

syndicates actively to facilitate or impede renegotiations. It is possible, however, that some

of the renegotiations are not easily foreseeable by the lenders and hence unlikely to elicit the

expected adjustment in syndicate structures. In the next section, we split the renegotiations

into expected and unexpected from lenders’ point of view, and compare the syndicate structures

of these two sets of credits.

13Berlin and Mester (1992) highlight the value of the option to renegotiate by showing that barriers to rene-gotiate lead to excessive defaults. Botlon and Sharfstein (1996) put forth a different view—when the lendercannot credibly threaten to liquidate the firm and it is easy to renegotiate, the borrower may take excessiverisks and miss to repay the loan in full. While in the former view, the lead bank will design a syndicate thatis friendly to renegotiations, in the latter view it will want to do the opposite in order to reduce the borrower’sadverse incentives.

15

2.3.2 Expected versus unexpected renegotiations

As we discussed in Section 2.1.1, to address the endogeneity between renegotiations and syn-

dicate structures, we isolate a group of presumably unexpected credit line renegotiations. We

classify renegotiations as unexpected if: a) the borrower has a relatively large undrawn amount

over the entire life of the loan until the renegotiation and b) the drawdown amount in the year

of renegotiation is larger than the amount it had left in the credit line. These conditions imply

that the borrower had to renegotiate its credit line in order to be able to draw down credit if

needed. We find that 12% out of all credit line renegotiations are unexpected.

We are interested to explore the lead share and syndicate structure in expected and

unexpected credits before and after renegotiation. We expect lead banks to be less proactive

in increasing their share prior renegotiations in unexpected credits. The definition is restricted

to credit lines because we cannot apply similar criteria to identify unexpected renegotiations

for term loans. Given that credit lines comprise 61% of the sample, focusing on credit lines

is not too restrictive. Further, to reduce concerns that the first renegotiation itself will pro-

duce information about the likelihood of subsequent renegotiations, we consider only the first

renegotiation in the life of each credit line.

Figure 4 plots the mean lead shares and non-bank shares for expected and unexpected

renegotiations. The lead share in expected renegotiations exhibits more pronounced upward

trend three years before renegotiations compared to the lead share in unexpected renegotia-

tions. This pattern confirms our conjecture that lead banks in ‘expected’ loan renegotiations

seem to evolve in a way that facilitates renegotiation outcomes. As for the ‘unexpected’ rene-

gotiation, the lead share also changes before and after the renegotiation event, however in much

less pronounced manner. The development of the non-bank shares on the right-hand side of

the figure provides further support that when renegotiations are expected, the non-bank share

is smaller while the lead share is larger, likely facilitating renegotiations.

A relevant question about our criteria for unexpected renegotiations is whether it over-

laps with developments in firm characteristics. For example, it is hard not to expect upcoming

renegotiations if borrowers’ credit rating improves and firms have high growth opportunities.

16

In other words, it is needed to reassure ourselves that our definition of unexpected credit lines is

not confounded by developments in firm characteristics that can make lenders foresee upcoming

renegotiations. To that end, in the bottom panel of Figure 4 we compare changes in capex and

sales growth before loan/after renegotiations for expected and unexpected renegotiations.14

Prior to a loan renegotiations, the growth opportunities for expected renegotiations are sig-

nificantly higher than for unexpected renegotiations, which reassures us that the demand for

extra credit in unexpected renegotiations is not directly linked to growth opportunities. The

development of the lead and non-bank shares in expected and unexpected renegotiations, and

the change in firm characteristics is consistent with our definition that firm characteristics

cannot easily reveal that certain renegotiations are either expected or unexpected. It is only

the draw-down behaviour of the borrower that determines whether renegotiation is expected

or unexpected.

3 Non-bank investors’ diversity and renegotiations

Bank loans were historically bilateral agreements between a borrower and a bank, which made

it easier to renegotiate since it is only required the agreement of a single bank.15 With the

syndicated loan model banks started to originate loans in which they would retain only a

portion of the loan and syndicate the remainder to other investors. Initially, most of these

investors were banks but their universe expended over time to non-bank investors (see Figure

1). Further, with the growth of the secondary loan market and corporate loan securitization,

it has become easier for all investors to alter their loan investment after the origination date.

It is possible that these changes made it more difficult for borrowers to renegotiate their

credits. For example, if lead arrangers decrease their loan share over the life of the loan, they

may be less willing to renegotiate because they may not be well informed about the borrowers’

financial conditions as they would have been if they had a larger exposure to the borrower.

14Each loan has only one renegotiation that can be either expected or unexpected since the sample coversonly the first renegotiation in a loan.

15 Typically loans are renegotiated to relax the restrictiveness of the initial terms of the contract, to capitalizeon an improvement in the borrower’s financial condition, to accommodate a change in the borrower’s fundingneeds, and/or due to changes in market conditions as shown in Mian and Santos (2011).

17

Additionally, lead arrangers with smaller shares will likely be less effective to cause a change in

the loan amendment in the voting process. Members of the loan syndicate have to agree upon

the renegotiation terms since loans are governed by private agreements but loan amendments

must be approved either by supermajority or unanimous vote.

The presence of multiple lenders may also deter renegotiations. This could be strategic

as in Botlon and Sharfstein (1996), who show that multiple lenders play the role of a commit-

ment device not to renegotiate the terms of the contract in the future.16 It may also derive

from the coordination problems (Gison et al. (1990)), or differences in bargaining power among

the lenders. Renegotiations with a single relatively large lender will be likely dominated by the

lenders’ willingness to renegotiate, while renegotiations in syndicates with evenly distributed

multiple lender shares may depend on the strategic interaction among lenders. These issues

arise in syndicates with multiple investors with similar business models, but they are likely to

be more acute when the universe of syndicate lenders is heterogeneous in terms of business

models. In this section, we first examine the role of lead arrangers in renegotiations related

to amount increase. Then we address the issue of pre-approved amount increases. Next, we

investigate whether the diversity of non-bank investors in the loan syndicate plays a role in

renegotiation outcomes. Last, we attempt to identify if there is a particular non-bank investor

type that plays a distinct role in renegotiation prospects.

Table 3 reports estimates from probit regressions in which the dependent variable is

a loan renegotiation in a give year. Columns (1)-(4) include only loan characteristics and

columns (5)-(8) add bank and firm controls. Starting with column (1), our main coefficient of

interest is the estimate on the lagged Lead bank share. The positive and significant estimate

on this variable confirms that the likelihood for a loan to undergo a renegotiation is higher

for high lead bank share. A change in the lead share from the 25th to the 75th percentile

is associated with a positive change in the predicted probability from 9% to 17%.17 Given

16According to Botlon and Sharfstein (1996), borrowers may take excessive risks when it is ex-ante easy torenegotiate. If lenders cannot credibly threaten to liquidate the firm, the firm may use the situation of lenders’weak bargaining power and rely on renegotiations that minimize their default losses. In such a way, certainsyndicate structures may be created for the purpose of hindering renegotiations, which points to the endogeneousnature of loan syndicates and renegotiations.

17The 25th and 75th percentile of the lead share distribution correspond to 10% and 40%, respectively. All

18

that the unconditional mean of renegotiation is 15.6%, this result implies that lead share has

relatively large effects on renegotiation outcomes (in section 5.3 we investigate the extreme

case when the lead bank has no investment in the loan).

Next, in column (2) we add the one-year-lagged non-bank loan share, that is the sum of

the shares of all lenders except for the lead and participant banks. We expect non-bank lenders

collectively to deter renegotiations. The business models of some of the lenders do not allow

them to increase their investments without costs (e.g., CLOs). Also, it is likely more difficult

for non-bank lenders to hedge the risk of default and having a larger investment increases that

risk. Using the same comparison as in column (1), a change in the non-bank shares from the

25th to the 75th percentile is associated with a decrease in predicted probability from 18% to

15%. The impact of the lead share in this specification is not changed, implying that both bank

and non-bank lenders matter for renegotiations, however in the opposite direction. In terms

of relative contribution, the positive impact of the lead share is stronger than the negative

impact of the non-bank lender share.

The diversity of non-bank lender shares is expected to play a role in renegotiations. For

example, if the pool of non-bank lenders has more dispersed structure in terms of percentage

shares, successful renegotiations may be less likely because fewer non-bank lenders with larger

shares dominate the voting outcome. Since non-bank lenders collectively oppose renegotiations,

the diversity of shares captures the lack of coordination that deters renegotiations and not

necessarily non-bank lenders’ choice to do so. In column (3) the negative estimate of the

standard deviation of non-bank shares confirms that diversity is a deterrent to renegotiations.18

Indeed, the renegotiation probability decreases from 13% to 11% when moving from the 25th

to the 75th percentile of the distribution of the standard deviation of non-bank lender shares.

The impact of the lead share estimate is preserved with greater magnitude than the impact of

the standard deviation of non-bank shares.

Finally in column (4) we offer an alternative diversity measure that is the number of

other covariates are held fixed at their mean values.

18Non-bank shares are calculated out to the loan amount financed by non-bank lenders. This measure allowsto directly estimate the impact of non-bank lenders isolating the role of banks.

19

non-bank lender types. This measure does not account for the shares of each investor but

rather accounts for different business model types that investor may use. The likelihood of

renegotiation is 14% if non-banks are absent from the loan and drops to 12% if the loan has

three different lender types.

With regard to the other controls we use in our models, they take the expected signs.

Credit lines are more likely to renegotiate when compared to term loans, and credit lines that

have been drawn down are more likely to be renegotiated to increase their size. Loans with

longer maturities left are more likely to be renegotiated. Lastly, investment grade borrowers

are less likely to renegotiate their loans that below-grade borrowers.

In columns (5) to (8) we report specifications that include bank and firm controls.

Adding these controls reduces the sample size because we have firm information only for

publicly listed borrowers, but it does not affect any of our previous findings. Figure 6 shows

margin plots for different values of lead bank share and syndicate diversity controls in the

specifications reported in columns (5) to (8). In column (5) lead share change from the 25th

to the 75th percentile leads to an increase in renegotiation probability from 13 to 24%. While

in column (6), a 25-75th percentile decrease in the non-bank share leads to a decrease in

renegotiations from 18% to 16%.

In terms of bank controls, banks’ deposit ratio and bank size are the only two variables

that play a role in renegotiation. Banks that rely more on deposit funding are more likely

to renegotiate possibly because they have more stable funding and thus can meet borrowers’

requests for credit. It is somewhat puzzling that larger banks are less likely to renegotiate. As

for the role of firm controls, faster growing, more profitable and higher capital expenditures

firms are more likely to renegotiate, which implies that renegotiations are likely initiated by

firms with greater investment opportunities.

In a nutshell, Table 3 shows that high lender shares impact significantly the likelihood

of renegotiations, which implies that any analysis of loan renegotiations requires information

on the structure of the lending syndicate. That table also shows that the lead bank plays a key

positive role in the renegotiation outcome while non-bank lenders adversely affect borrowers’

renegotiation prospects. The positive contribution of lead arrangers is of greater magnitude

20

than the negative effect of non-bank lenders. The negative effect of non-bank investors appears

to be driven by the diversity existing among these investors measured either by the dispersion

of their loan shares or the number of their types.

3.1 Accordion clauses in loan facilities

It is possible that some loan agreements allow the borrower to add a new term loan tranche or

increase the revolving credit loan commitments under an existing loan facility up to a specified

amount under certain terms and conditions. The advantage of this feature is that the increase

in the loan amount is pre-approved by the lenders so that the borrower does not have to get

the lenders’ consent if it increases the loan facility at a later date. If the change in the loan

amount is pre-approved, it is hard to draw conclusions about the role of syndicate structure

prior to the change of the loan contract. To address this issue we define a renegotiation as an

amount increases that is accompanied by maturity changes because often the accordion clauses

affect only one feature of the loan and keep the other unchanged. Most likely if more than one

loan term is changed, it is the result of renegotiation and not of a pre-approved contractual

agreement.

In Table 4 we estimate the same probit specifications as in Table 3 with a new dependent

variable that measures those renegotiations that are not pre-approved. Because most of the

amount increase renegotiations are also accompanied by either maturity increase or decrease

(10.8% of each loan-year), the dependent variable in this table does not undergo a substantial

change, which preserves the results across all regressions to those in Table 3. In column

(1) a change in the lead share from the 25th to the 75th percentile of the distribution is

associated with a change in the predicted probability from 6% to 11%. The magnitudes of

the other estimates are similar. Therefore, our results are not affected by the fact that some

renegotiations may be actually pre-approved at the loan origination phase.

3.2 Which non-bank lenders affect renegotiations?

Our results from Table 3 show that the likelihood of renegotiation decreases if non-bank lenders

collectively own a larger share of the loan. In this section we explore whether each non-bank

21

lender type impacts renegotiations in a similar way. There could be a number of channels

such as agency conflicts, legal constraints, coordination issues among multiple investors, and

different accounting treatment of losses that drive the effect on renegotiations. For that purpose

in Table 5 we estimate specifications that include the share of each lender type. In column (1)

we control for loan characteristics and in column (2) we add bank and firm controls.

Starting with column (1), lead banks and CLOs with larger shares increase the likeli-

hood of renegotiation, however, a host of lenders such as funds, finance companies, insurance

companies, and foreign banks are against renegotiations and consequently decrease renegotia-

tion likelihood. Given that the shares of broker and finance companies are small (Table 1), it

is not surprising that they do not affect renegotiation outcomes.

In terms of magnitudes, an increase from the 25th to the 75th percentile of the distri-

bution of the lead share is associated with a change in the predicted probability from 9% to

16%. It is worth noting that CLOs are the only non-bank lenders that act as “allies” of lead

banks in terms of facilitating renegotiations. One standard deviation increase in CLO share

leads to 1.2% increase in probability, while one standard deviation in the lead share is asso-

ciated with 20% increase in predicted renegotiation probability. Funds (mutual funds, hedge

funds and pension funds), finance companies and insurance companies, each has comparable

negative impact on renegotiation outcomes: one standard deviation increase in the respective

shares leads to 1.1%, 1.2% and 1.2% decrease in the likelihood of renegotiation. Finally, foreign

banks are a major player that acts against renegotiations: one standard deviation increase in

the share of foreign banks leads to 16% decrease in the likelihood of renegotiation.

In column (2) some of the lender share estimates lose significance when firm and bank

controls are added. One reason for this can be that lender shares and firm characteristics are

correlated and once the latter are included in the regression, the effects of some lender shares’

types becomes undistinguishable. Foreign banks and funds preserve their pronounced negative

effect on renegotiations and lead banks support strongly renegotiations similar to previous

specifications.

In columns (3) and (4) we consider regression models in which the dummy variable for

each lender type is interacted with the corresponding percentage share (the percentage is set to

22

zero if certain lender type is not present in the syndicate.) In such way, the interaction terms

between the percentage lender share and the dummy variables account for the zero values of

the lender share due to the presence in the syndicate. This approach is a suitable robustness

check since lender types exhibit heterogenous distribution across syndicates. The results of this

approach confirm two findings from columns (1) and (2): CLO lenders support renegotiations

while foreign banks are strongly against them.These results are somewhat surprising. It is

possible that the negative effect of foreign banks is related to the challenges that amount

renegotiations pose to them because they do not have access to insured deposit funding. With

regards to CLOs, it is possible that the positive effect of CLOs derives from these institutions

relationships with banks. While CLOs are seldom managed by banks, they usually use banks

as underwriters.

Our results indeed corroborate with a relationship story between CLOs and lead banks.

We find evidence of a substitution of loan investments between CLO and lead shares before

and after renegotiations. The lead share in the year prior renegotiations is 27% and it drops to

21% in the year after the renegotiation, while the opposite holds for CLOs: in the year prior

to renegotiation CLOs share is 0.23 and 0.25 in the year post renegotiation (conditional on

CLO presence). In addition, the CLO presence increases post-renegotiation from 7% to 9%. It

may be the case that CLOs increase their presence or increase their share if they are already

present in the syndicate when the loan is renegotiated with the support of lead banks who

decrease their loan investment post loan renegotiation, possibly to expand their new lending

business.

4 Endogeneity issues

All of the results we reported so far identify the effect of the syndicate structure on loans’

renegotiation prospects by controlling for the structure of the syndicate in the year before the

renegotiation takes place. To the extent that the syndicate structure changes to facilitate or

impede expected renegotiations, renegotiations and syndicate structures will evolve endoge-

nously over the life of the loan, making it difficult to draw causal implications for the effect of

23

lenders’ diversity on renegotiations. Certainly, syndicates may change over the life of the loan

for a number of other reasons. Further, while the lead bank has control over the share of the

loan it owns during the life of the loan, it will likely have only limited control over the structure

of the syndicate once it has been formed. Nonetheless, in this section we report the results

of three exercises in an attempt to alleviate concerns with the endogeneity of the syndicate

structure.

Our first exercise attempts to gauge the importance of the endogeneity problem by

investigating whether the syndicate structure at origination and the structure right before

renegotiation play different roles on the likelihood of renegotiations. If the syndicate structure

evolves for reasons completely unrelated to renegotiations then the structure at origination is

likely to play a relatively less important role in renegotiations. In contrast, if the syndicate

structure changes for reasons related to renegotiations, then the structure immediately before

the renegotiation is likely to play a relatively more important role.

This first exercise is useful for giving us an idea about the importance of the syndicate

endogeneity problem. In our second exercise we isolate a group of presumably unexpected

renegotiations that are likely to be triggered by an unexpected large need for funds by the

borrower. In the third exercise, using an alternative approach we alleviate the endogeneity by

using an instrumental variable estimation. At the first stage, we estimate OLS regressions of

the lead share or the measures of diversity as dependent variables using the arrival of new banks

as an instrumental variable. In the second stage, we estimate renegotiation probit models on

the predicted lead shares or syndicate diversity from the first stage.

4.1 Do initial syndicate conditions matter?

In Table 6 we report specifications that include the lead share and syndicate diversity measures

at origination. In column (1), we see that both the lead share at origination and the lead share

before renegotiations have impact on renegotiations. However, one standard deviation increase

in the lagged lead share leads to 20 percentage points higher probability of renegotiation, while

one standard deviation increase in the lead share at origination leads to 3 percentage points

higher renegotiation probability. In column (2), we add the non-bank shares at origination

24

and non-bank shares prior to renegotiations. One standard deviation increase in the non-bank

share at origination leads to 5% lower probability of renegotiations, while the effect is doubled

for the non-bank share before renegotiation. Similar conclusions hold for the other measures

of diversity in columns (3) and (4). When firm and bank controls are added to the regressions

in columns (5) to (8), the significance of non-bank shares, non-bank share volatility and types

at origination is lost, which may be due to the smaller sample. Overall, it seems that the

lead share and syndicate diversity predict future probabilities but their impact at origination

is much smaller or insignificant compared to their impact before the renegotiation.

These findings provide two important insights. First, an investigation of the role of

syndicate structures on loan renegotiation prospects has to account for the structure of the

syndicate before the renegotiation, not at the origination of the loan. Second, they are con-

sistent with the idea that the syndicate structure is endogeneous and evolves over time for

reasons related to loan renegotiations. In the next section, we attempt to control for the ef-

fects of endogeneity by investigating the impact of the syndicate structure on renegotiations

that are likely to be unexpected.

4.2 Expected versus unexpected renegotiations

To isolate a group of unexpected renegotiations we identify credit line renegotiations in which

the borrower has a large portion of undrawn funds in the credit line over the entire life of the

loan before renegotiations (we use 60% availability) and the borrower draws down immediately

after the renegotiation an amount that is larger than what it had available in its credit line

prior to the renegotiation. The first condition assures us that the borrower has plenty of

available funding before the renegotiation, thus it is unlikely the borrower to request amount

increase of the loan. The second condition, in turn, guarantees that the borrower cannot meet

its funding needs without having larger loan amount from the renegotiation.

In panel A of Table 7 we report estimates for expected renegotiations.19 It is likely

that the lead bank changes its share prior to renegotiation and as a result the estimate on the

19The base category of ‘expected’ does not contain ‘unexpected’ renegotiations.

25

lead share in the renegotiation regression is likely biased compared to the estimate of the lead

share in the unexpected renegotiations. The same can apply for all other measures of diversity.

In column (1) Panel A the estimate on the lead bank share is 1.778 while based on panel B

which reports estimates for unexpected renegotiations this estimate is 1.171. One standard

deviation increase in the lead share leads to 35 percentage points increase in the probability of

renegotiation in the first case and 23 percentage points in the second case. The estimates of

non-bank share, non-bank share vol and lender types in columns (2) to (4) in Panel A are of

larger magnitudes than these in Panel B. When firm and loan controls are included in columns

(5) to (8), the significance of the estimates is preserved and their magnitudes are slightly lower

across both Panel A and B. Importantly, the estimates in panel B across all specifications are

of smaller magnitude. Overall, this exercise shows the presence of a slight upward bias of the

estimate of the lead share and syndicate diversity when renegotiations are expected, however,

its impact does not seem to have strong implications for the interpretation of our results.

4.3 Instrumental variable results

In this section we take an alternative approach to address the endogeneity between renegoti-

ations and syndicate diversity. We rely on IV-probit estimation in which the identification is

achieved by the inclusion of an instrumental variable that is the fraction of new bank partic-

ipants scaled by the number of all lenders. To be a valid instrument, the new banks’ arrival

must fulfil two conditions. First, it must be correlated with the lead share and syndicate di-

versity variables, which means that the instrument must be correlated with the lead share and

syndicate diversity at the first stage. Second, the instrument must be uncorrelated with the

error term in the second stage probit regression, which means that new banks’ arrival affects

renegotiations only through lead share or syndicate diversity depending on the specification.

In Table 8 we report the estimates from optimized simultaneous maximum likelihood.

The first stage estimates of the IV variable new banks arrival are significant and take the

expected signs in all specifications. The significance confirms that the instrument is relevant.

There are theoretical arguments that can explain the positive sign between lead share and new

bank arrival. New banks may be attracted to invest in syndicates in which the lead bank has

26

large shares to ensure that they monitor borrowers. Given that these banks fund the loan

for the very first time, it is not surprising that they may rely on the lead bank’ monitoring

a lot more than otherwise. Similarly, it is not surprising that new banks may shy away from

syndicates with large non-bank shares and/or dispersed non-bank lender shares that are prone

to coordination problems. Therefore, new bank lenders is a relevant instrument of lead share

and syndicates’ diversity.

As far as the second condition is concerned, looking at Figure 6, new lenders preserve

stable shares both over time and the life of the loan, which suggests that the arrival of new

bank lenders is likely not linked to renegotiation events but are uniformly spread over years

(left-hand side graph of Figure 6) and over the life of the loan (right-hand side graph of Figure

6). In addition, when we inspect the pre-/post-renegotiation development of the new banks’

arrival, we find an equal drop from 34% before the renegotiation year to 27% in the year of

renegotiation year and 22% the year after renegotiation. The even drop in new bank lenders

pre/post renegotiation makes it hard to argue for renegotiation-driven adjustments in new

banks’ loan participation.

Looking at the estimates of interest from the second stage in Table 8 we observe that

the lead bank and syndicate diversity measures are significant and take the expected signs.

Figure 7 plots the marginal effects of lead bank share, non-bank share, non-bank share vol and

lender types based on specifications (5)-(8) of Table 8. These marginal effects are larger than

those from probit regression reported in Table 3. For example, if the lead share increases from

the 25th to the 75th percentiles (corresponding to 10% and 40% lead share on the horizontal

axes of in Figure 3), the probability of renegotiation is 13% and 24% respectively. However,

based on the IV probit estimates the 25th and the 75th percentiles of lead share correspond to

18% and 62% probability to renegotiate. Similar conclusion holds for the other specifications.

Overall, when addressing the issue of endogeneity the effects of the lead share and syndicate

diversity are even stronger.

27

5 Robustness

In this section we report several robustness checks including alternative measures of non-bank

diversity, amount renegotiations separately for credit lines and term loans, zero lead share as

an extreme case of lead bank share, and logit fixed effects estimation.

5.1 Alternative definitions of syndicate diversity

We focus on the results from two alternative measures of non-bank lenders’ diversity. Our first

measure is HHI no-bank shares which is an Hefindahl index that measures the concentration

of non-bank lender shares. As for the non-bank volatility measure, we calculate the non-bank

shares out of the loan amount financed by non-bank lenders. High values of the measure imply

the presence of a single (or a small number) non-bank lender with relatively large loan share,

which will likely dominate the renegotiation process. To the extent that the dominant non-bank

lender opposes renegotiations, we should observe a negative relationship between HHI non-

bank shares and renegotiations. However, we should observe the opposite relationship if the

dominant investor is renegotiation friendly. In Table 5 we show that foreign banks are strictly

against while CLOs are in favor of renegotiations which suggests that if the former is the

dominant lenders the relation between HHI and renegotiation will be negative, however if the

latter is the dominant lender the relationship will be positive. There could be other less obvious

combinations between lenders types and their shares, which makes this exercise interesting.

The results in Table 9 columns (1) suggest that the coefficient of HHI non-bank shares and

renegotiations is negative likely because the non-bank lenders that make the syndicates more

concentrated oppose renegotiations. In column (2) the significance of the coefficient is not

preserved likely due to the non-random distribution of concentrated loans across firms and

banks.

Our second alternative measure of diversity reports the percentage turnover of new

lenders in the syndicate on a yearly basis. We can compute this measure because we have

the identity of each individual lender at the end of the year during the life of the loan. New

lenders in a given year are defined as those who do not appear in the previous year of the

28

syndicate. On average, the syndicate has 25% new lenders each year. Based on Table 9,

columns (3) and (4) the arrival of new non-bank lenders in the year before the loan renegotiation

is negatively associated with the likelihood of renegotiation. This may be due to the fact that

the new non-bank lenders are not as well informed as departing investors about the borrowers’

creditworthiness and therefore may not be willing to increase their exposure to the borrower.

5.2 Renegotiations of credit lines and term loans

Our sample is dominated by credit lines, but about one third of the observations are term loans.

The results of our investigation of expected versus unexpected renegotiations suggest that our

findings on the impact of the syndicate structure on renegotiation prospects apply to credit

lines since this part of our investigation focuses on credit lines. It is unclear though whether

our findings also apply to term loans. This difference is important because as we documented

in Table 4, term loans are more likely to be renegotiated than credit lines. The difference

between credit lines and term loans may matter not only because the contracts are difference

and the universe of borrowers may be different, but perhaps more importantly because the

universe of investors is different. Banks, for example, play more important role in credit lines

than in term loans, consistent with Holmstrom and Tirole (1997) and Kashyap et al. (2002)

that banks have a comparative advantage in the provision of liquidity insurance to corporations

via credit lines.20

According to our data source, on average, non-bank investors own 50% of term loans,

but only 35% of credit lines. The diversity of non-bank investors is also smaller in credit lines

than in term loans. For example, on average there are 2.9 types of non-bank investors in term

loans, but only 1.3 types in credit lines.21

In order to ascertain whether our findings apply to credit lines as well as term loans,

20 Holmstrom and Tirole (1997) show that, in contrast to financial markets, banks can insure firms againstliquidity shocks that disrupt their investments by offering them credit lines. Kashyap et al. (2002), in turn,show that as long as the liquidity needs of depositors and corporations are not correlated it is advantageous tocombined deposit taking with credit line provision because it saves on the costly liquidity buffer banks need tokeep to meet unexpected deposit withdrawals and draw downs on credit lines.

21 See Bord and Santos (2012) for an analysis of the relative importance of different investors in credit linesand term loans in the U.S. syndicated loan market over the last two decades.

29

we reestimated the models the models reported in Table 4 separately for credit lines and term

loans. The results of this investigation are reported in Table 10. The top panel shows the

results for credit lines while the bottom panel shows the results for term loans. A quick look

at that table confirms our findings on the role of the lead bank as well as on the diversity of

non-bank investors hold both for credit lines and term loans. The results on the lead bank

share appear to be slightly weaker for term loans, but that is likely the result of the decline in

the sample size when we expand our set of controls to account for borrower- and bank-specific

factors (columns 5-8 of the bottom panel). When we do not account for these factors, we

also find that term loans in which the lead bank has a large loan share are more likely to be

renegotiated in order to increase their size.

5.3 Renegotiations with zero lead banks’ shares

The results we presented thus far show that the higher the retained lead share, the higher the

chances the loan will be renegotiated or alternatively, when the lead bank owns a smaller share

of the loan these loans are less likely to be renegotiated. This finding poses an interesting

question: what happens in the extreme case when the lead bank divests itself entirely from the

loans it originates? The lead bank will likely be less informed about the borrower’s financial

condition than it would be had it retained an investment in the loan. Additionally, the lead

bank will find it more difficult to convince the participants in the syndicate to increase their

exposure to the borrower in renegotiations to increase the size of the loan. In general, lead

banks keep a portion of the loan at the time of the loan origination, but in some cases lead

banks sell the entirety of their loan shares in the years after loan origination. For example, the

lead bank has zero investment in 4.3% loans in the year of origination and 8.24% afterwards.22

To ascertain the potential impact of the absence of an investment by the lead bank on

the renegotiation prospects, we rerun the models reported in Table 3, but this time we include

a dummy variable Zero Lead, which takes the value one if the lead bank has no investment in

the loan instead of the continuous variable LEAD bank share. The results of this investigation

22The zero lead share in the entire sample is 8.3%

30

are reported in Table 11. As we can see from column (1), loans in which the lead bank has no

exposure are indeed less likely to be renegotiated.

According to model 1, when the lead bank has no exposure to the loan, the loan is

12% less likely to be renegotiated than the remaining loans. The coefficient on our dummy

variable Zero Lead continues to be negative and highly statically significant when we control

for our proxies of investor diversity (columns 2-4). The magnitude of that coefficient decreases,

but it continues to be negative and statistically significant when we expand our set of controls

to account for bank- and borrower-specific factors (except in model 8 which controls for the

number of lender types). This result shows that the absence of a loan investment by the

lead bank is detrimental to borrowers’ prospects to renew their loans. It adds support to our

previous finding that the ownership of a large share of the loan by the lead bank increases

borrowers’ chances to renegotiate their loans successfully.

5.4 Logit Fixed Effects

In this section we report results from logit fixed effect estimation. It is possible that the lead

share and syndicate structure are correlated with an unobservable variable at the loan level

whose inclusion could distort our results. To alleviate this concern, in Table 12 we reestimate

table 3 using a conditional fixed effect model. This estimation is possible because we follow the

loan over time. The estimate clearly confirm that lead share and syndicate structure variables

take the expected signs for specifications without firm and bank controls (columns 1-4) and

for specifications with these controls (5-8). This suggests that an omitted loan factor is not a

driver of our results.

6 Conclusion

The bilateral agreements that use to govern corporate bank-borrower relationships were in-

creasingly replaced with multilateral agreements between the borrower and a syndicate of

lenders. With the growing interest of non-bank investors in the corporate lending business, the

initially bank-dominated syndicates became increasingly heterogenous in terms of having vari-

31

ous non-bank lenders. A consequence of this transformation is that while bilateral agreements

are believed to provide incentives to monitor the borrowers and facilitate laon renegotiations,

multilateral agreements are believed to have the opposite effects, particularly when they include

diverse investors.

The results of our investigation show that a reduction in the lead share of the loan is

detrimental to borrowers’ renegotiation prospects. Our findings show that the diversity of in-

vestors is a key factor behind the adverse effect of non-bank investors’ on renegotiations. Given

that these results continue to hold when we restrict our sample to the subset of renegotiations

that are likely to be unexpected, this suggests that the syndicate structure, in particular the

relative importance of the lead bank and the diversity among non-bank investors, do affect

negatively borrowers’ renegotiation prospects.

Although the ability to renegotiate is a distinctive feature of traditional bank loans

that borrowers likely value, our findings do not necessarily imply that the arrival of non-bank

investors in the corporate lending business is detrimental to borrowers. For example, to the

extent that renegotiations are not efficient, the growing appetite of non-bank investors to

participate in loan syndicates makes it easier for lead banks to credibly design syndicates that

are renegotiation proof.

It is possible that part of the non-bank lending growth is driven by banks’ appetite

to increase their own lending capacity and benefit from the corresponding origination fees.

Under this scenario, the expansion of bank lending was made possible with the arrival of

non-bank investors in the corporate lending business which comes at the cost of weakening of

renegotiation prospects. It would be interesting to investigate whether the growing presence

of non-bank lenders in loan syndicates has led to an increase in bank lending and whether the

decline in loan renegotiation prospects has had any real effect on borrowers.

Our findings suggest other potentially interesting areas for future research. The sample

is dominated by renegotiations that occur outside financial distress. Another fruitful area is to

investigate the role of the syndicate structure in renegotiations that occur in financial distress

and which may require concessions by investors.

While investigating the role of individual non-bank investors, we uncovered two results

32

that are somewhat unexpected—foreign banks are not renegotiation friendly while CLOs seem

to favor renegotiations. We suggest that the former result may be related to foreign banks’

inability to access insured deposit funding because the renegotiations we consider require ad-

ditional investments in borrowers while the latter result may be related to the relationships

that exist between banks and CLOs. CLOs are seldom managed by banks, but nearly all of

them use banks as underwriters. In addition CLOs increase their share and presence in the

loan at the time of renegotiation, while the opposite happens for the lead bank. It would seem

worthwhile to further investigate these findings both because foreign banks have an important

presence in loan syndicates, and because the securitization of corporate loans, after the decline

it experienced during the Great Recession, is again on the rise.

33

References

Adelino, M., Gerardi, K., and Willen, P. (2013). Why don’t lenders renegotiate more home

mortgages? Redefaults, self-cures and securitization. Journal of Monetary Economics,

60:835–853.

Agarwal, S., Amromin, G., Ben-David, I., Chomsisengphet, S., and Evanoff, D. (2011). The

role of securitization in mortgage renegotiation. Chicago Fed Working Paper, 2.

Berlin, M. and Mester, L. (1992). Debt covenants and renegotiation. Journal of Financial

Intermediation, pages 95–133.

Bord, V. and Santos, J. (2012). The rise of the originate-to-distribure model and the role of

banks in financial intermdiation. FRBNY Economic Policy Review, July:21–34.

Botlon, P. and Sharfstein, D. (1996). Optimal debt structure and the number of creditors.

Journal of Political Economy, 104:1–25.

Garleanu, N. and Zwiebel, J. (2009). Design and renegotiation of debt contracts. Review of

Financial Studies, 22:749–781.

Gison, S., Kose, J., and Lang, L. (1990). Troubled debt restructurings: An empirical analysis

of private reorganization of firms in default. Journal of Financial Economics, 6:315–353.

Gorton, G. and Kahn, J. (2000). The design of bank loan contracts. Review of Financial

Studies, 13:331–364.

Gorton, G. and Pennacchi, G. (1995). Banks and loan sales marketing nonmarketable assets.

Journal of Monetary Economics, 35:389–411.

Holmstrom, B. and Tirole, J. (1997). Financial intermediation, loanable funds, and the real

sector. Quarterly Journal of Economics, 112(3):663–691.

Ivashina, V. (2009). Asymmetric information effects on loan spreads. Journal of Financial

Economics, 92:300–319.

34

Kashyap, A., Rajan, R., and J.Stein (2002). Banks as liquidity providers: An explanation for

the coexistence of lending and deposit-taking. Journal of Finance, 57 (1):33–73.

Mian, A. and Santos, J. (2011). Liquidity risk and maturity management over the business

cycle. Federal Reserve Bank of New York, mimeo.

Ramakrishnam, R. and Thakor, A. (1984). Information reliability and a theory of financial

intermediation. Review of Economic Studies, 51:415–432.

Roberts, M. (2014). The role of dynamic renegotiation and asymmetric information in financial

contracting. Journal of Financial Economics, 116:61–81.

Roberts, M. and Sufi, A. (2009). Renegotiation of financial contracts: Evidence from private

credit agreements. Journal of Financial Economics, 93:159–184.

Sufi, A. (2007). Information asymmetry and financing arrangements: Evidence from syndicated

loans. Journal of Finance, 62:629–668.

Appendix

Accord takes one if loan amount increases and its maturity changes as well; zero otherwise

Assets is the annual firm assets in $million.

Brokers share is the sum of percentage shares of all broker lenders in a loan; Brokers dummytakes one if at least one lender is classified as broker company.

Capital Expenditures is one if the loan is used for capital expenditures and zero otherwise.

CP backup is an indicator variable that takes one if the loan is used to back up commercialpaper and zero otherwise.

Credit line equals one if the loan is a credit line (revolving credit, revolving credit convertingto term loan, line of credit, commercial letter of credit, revolving credit, standby letterof credit and demand loan.

CL drawdown ratio is the ratio of credit line drawdown to total available credit. The ratiotakes the value one if the loan is classified as a term loan.

CLO share is the sum of shares for all CLO (Collateralized Loan Obligations) lenders in agiven loan; CLO dummy takes one if at least one lender is classified as a CLO.

Capital bk is the annualized ratio of quarterly equity over quarterly risk-weighted assets.

35

Deposits bk is the annualized ratio of quarterly deposits over quarterly assets.

Debt Repay is one if the loan is for repayment of previous debt.

Foreign lender share is the sum of percentage shares of foreign lender in a loan; Foreign lender dummytakes one if at least one lender is either a foreign bank or a foreign company and zerootherwise.

Finance company share is the percentage share of a finance company; Finance company dummytakes one if at least one lenders is a finance company.

IGrade (BGrade) equals one if a borrower has an investment grade (non-investment grade)the month before the loan origination. We use credit ratings from S&P long-term debtrating at a monthly basis.

Investment manager share is the sum of the percentage shares of hedge funds, mutual fundsor pension funds; Investment manager share dummy takes one if at least one lender isclassified as either a hedge fund, a mutual fund or a pension fund.

Insurance company share is the sum of percentage shares of all insurance companies in agiven loan; Insurance company dummy takes one if at least one lender is an insurancecompany.

HHI non-bank shares is the Herfindahl-Hirschman index of the non-bank lender shares. Itis the sum of the squares of all non-bank share types. For the purpose of that measure,non-bank shares are defined as the fraction of loan amount held by each non-bank lenderdivided by the total non-bank amount in a loan. Higher values are associated with greaterconcentration.

Levearge is the ratio of firm debt to total assets.

L assets bk is the natural log of yearly bank assets in hundreds of millions.

Liquidity bk is the annualized ratio of quarterly cash and short-term investments to quarterlyrisk-weighted assets.

Lead bank share is the percentage share of the lead bank in a loan.

Lender types is the sum of different lender types in a loan.

Loan amount(mil) is the amount of the loan.

Loan rating is rating of the loan provided by the regulator. It takes one if the loan isinvestment grade (PASS) and zero otherwise.

L sales is the log of annual firm net sales.

Maturity left is the difference between loan maturity year and current loan year.

M&A is an indicator variable that takes one if the loan is used for financing merger andacquisition deals and zero otherwise.

Non-bank shares is the sum of shares for all lenders except for lead and participant banks.

36

Non-bank shares vol is the standard deviation of all lender shares except for the lead andparticipant banks.

New banks arrival is the ratio of the number of new bank lenders that appear for thevery first time in a loan over the total number of syndicate lenders. It is used as anInstrumental Variable.

Other lender share is the sum of percentage shares of unclassified lenders; Other lender dummytakes one if at least one lender is unclassified.

Profitability bk is the annualized ration of quarterly bank net income before taxes overquarterly risk weighted assets.

Private equity share is the sum of the percentage shares of private equity; Private equity dummytakes one if at least one lender is private equity.

Profitability is the ratio of firms’ annual operating income to assets.

Reneg( amt) takes one if the loan amount increases compared to the previous year and zerootherwise.

Reneg mat takes one if the loan maturity increases compared to the previous year and zerootherwise. We exclude amount renegotiations from the definition.

Recapitalization is one if the loan is used for recapitalization and zero otherwise.

Sales growth is the firm sales’ quarterly growth on a yearly basis.

Stock return is the yearly stock return based on daily data.

Stock returnvol is the standard deviation of stock returns based on daily data.

Turnover is the ratio of the number of new non-bank lenders relative to the previous yearover total number of lenders.

Unrated equals one if a borrower does not have credit rating in a given year.

Zero Lead is a dummy variable that takes one if the lead share has zero share in the loan.

37

Table 1: Sample Summary StatisticsThis table presents firm, (lead) bank, and loan summary statistics. All variables are defined in the Appendix.

Mean SD

Firm Controls

Assets ($ Mil) 4,102 7,742Sales growth 0.134 0.322Leverage 0.353 0.217Profitability 0.037 0.024Stock return 0.144 0.527Stock return vol 0.001 0.002Igrade 0.262 0.44Unrated 0.454 0.498

Lead Bank Controls

L assets bk 18.359 1.918Profitability bk 0.003 0.002Capital bk 0.074 0.017Deposits bk 0.367 0.251Liquidity bk 0.051 0.041

Loan Controls

Credit line 0.614 0.487CL drawdown ratio 0.565 0.416Loan amount (mil) 191 439Reneg amt 0.156 0.362Accord 0.108 0.311Maturity left (years) 3.809 2.366M&A 0.132 0.338Debt repay 0.032 0.17Recapitalization 0.031 0.173Capital expenditures 0.027 0.162CP backup 0.027 0.163Loan rating 0.851 0.351Zero lead 0.083 0.277

Lenders’ Retained Shares

Lead bank share 0.258 0.189Participant bank share 0.463 0.223Non-bank shares 0.389 0.330Non-banks share vol 0.258 0.179Lender Types 1.535 1.801HHI non-bank shares 0.79 0.26CLO share 0.23 0.178CLO dummy 0.085 0.279Foreign lender share 0.422 0.276Foreign lender dummy 0.437 0.496Finance company share 0.177 0.165Finance company dummy 0.237 0.425Investment manager share 0.186 0.154Investment manager share dummy 0.078 0.268Brokers share 0.077 0.089Brokers dummy 0.071 0.257Insurance company share 0.079 0.109Insurance company dummy 0.058 0.233Private equity dummy 0.049 0.216Private equity share 0.051 0.073Other lender dummy 0.251 0.434Other lender share 0.177 0.171

38

Table 2: Differences Across Renegotiated and Never Renegotiated LoansColumn (1) reports the mean loan and firm characteristics in the first loan-year for loans that have beenrenegotiated at least ones. Column (2) reports the mean loan and firm characteristics in the first loan-year forloans that have never been renegotiated.

RENEGOTIATED NEVER RENEGOTIATED t-test

Lead bank share 0.285 0.312 -0.027***Non-bank share 0.385 0.399 -0.014***Non-bank share vol 0.281 0.269 -0.011***Non-Bank types 1.581 1.469 -0.112***L Sales 5.274 5.212 0.061**Sales growth 0.201 0.175 0.026***Leverage 0.336 0.338 -0.002Profitability 0.04 0.038 0.002**Stock return 0.202 0.145 0.057***Stock return vol 0.001 0.0011 0.000**Capex 0.045 0.044 0.001

39

Table 3: Renegotiations and Syndicate DiversityThe table reports probit regression estimates. The dependent variable takes one if a loan is renegotiated (amountincrease) in a given year and zero otherwise. Each specification includes year and loan-year dummies. Standarderrors are robust and clustered at the loan level. All controls are lagged the year before loan renegotiation. Alldefinitions are listed in the Appendix. *** denotes 1% significant level, ** denotes 5% significant level, and *denotes 10% significant level.

(1) (2) (3) (4) (5) (6) (7) (8)

Lead bank share 1.185*** 0.994*** 1.086*** 1.128*** 1.403*** 1.320*** 1.387*** 1.324***(0.064) (0.064) (0.064) (0.065) (0.152) (0.153) (0.153) (0.153)

Non-bank share -0.412*** -0.205***(0.021) (0.056)

Non-bank share vol -0.652*** -0.123*(0.051) (0.063)

Lender types -0.025*** -0.032***(0.004) (0.009)

Loan rating 0.416*** 0.391*** 0.417*** 0.406*** 0.274*** 0.275*** 0.275*** 0.270***(0.019) (0.020) (0.019) (0.020) (0.052) (0.052) (0.052) (0.052)

CL drawdown ratio 0.240*** 0.246*** 0.228*** 0.248*** 0.157*** 0.169*** 0.156*** 0.172***(0.017) (0.017) (0.017) (0.017) (0.035) (0.035) (0.035) (0.035)

Maturity left 0.049*** 0.057*** 0.051*** 0.051*** 0.139*** 0.140*** 0.138*** 0.142***(0.003) (0.003) (0.003) (0.003) (0.009) (0.009) (0.009) (0.009)

Credit line 0.662*** 0.616*** 0.658*** 0.650*** 0.726*** 0.709*** 0.727*** 0.708***(0.018) (0.018) (0.018) (0.018) (0.047) (0.047) (0.047) (0.047)

Igrade -0.068*** -0.076*** -0.050* -0.090*** -0.014 -0.022 -0.012 -0.040(0.026) (0.026) (0.026) (0.026) (0.034) (0.034) (0.034) (0.034)

Unrated -0.050** -0.074*** -0.061*** -0.070*** 0.041 0.029 0.039 0.021(0.019) (0.019) (0.019) (0.020) (0.032) (0.032) (0.032) (0.032)

Bank ControlsProfitability bk 1.638 1.092 1.826 0.331

(7.896) (7.886) (7.898) (7.894)Capital bk 0.667 0.189 0.625 0.416

(0.948) (0.959) (0.949) (0.955)L assets bk -0.018* -0.018* -0.018* -0.016

(0.010) (0.010) (0.010) (0.010)Deposits bk 0.271*** 0.250** 0.266*** 0.267***

(0.098) (0.097) (0.098) (0.098)Liquidity bk -0.610 -0.618 -0.616 -0.497

(0.447) (0.448) (0.446) (0.450)Firm controlsL sales -0.008 0.001 -0.007 -0.003

(0.011) (0.011) (0.011) (0.011)Sales Growth 0.397*** 0.403*** 0.397*** 0.405***

(0.041) (0.042) (0.041) (0.041)Leverage -0.095 -0.062 -0.093 -0.064

(0.067) (0.068) (0.067) (0.068)Profitability 1.285** 1.077* 1.273** 1.180**

(0.566) (0.570) (0.566) (0.567)Stock returns 0.084*** 0.086*** 0.084*** 0.086***

(0.023) (0.023) (0.023) (0.023)Stock return vol -15.880 -12.844 -16.101 -12.536

(11.207) (11.251) (11.216) (11.260)Capex 0.884*** 0.924*** 0.890*** 0.886***

(0.283) (0.284) (0.283) (0.282)Obs 90,654 90,654 90,654 90,654 20,016 20,016 20,016 20,016Pseudo-R2 0.072 0.087 0.065 0.087 0.103 0.114 0.121 0.145

40

Table 4: Non-accordion Clauses and Syndicate DiversityThe table reports probit regression estimates. The dependent variable Accord takes one if a loan amountincreases and loan maturity changes compared to the previous year. Each specification includes year and loan-year dummies. Standard errors are robust and clustered at the loan level. All controls are lagged the year beforeloan renegotiation. All definitions are listed in the Appendix. *** denotes 1% significant level, ** denotes 5%significant level, and * denotes 10% significant level.

(1) (2) (3) (4) (5) (6) (7) (8)

Lead bank share 1.241*** 0.994*** 1.161*** 1.175*** 1.489*** 1.378*** 1.458*** 1.452***(0.072) (0.072) (0.072) (0.072) (0.164) (0.165) (0.164) (0.164)

Non-bank share -0.510*** -0.265***(0.024) (0.062)

Non-bank share vol -0.402*** -0.158**(0.041) (0.071)

Lender types -0.029*** -0.016*(0.005) (0.007)

Loan rating 0.390*** 0.354*** 0.380*** 0.377*** 0.256*** 0.256*** 0.254*** 0.252***(0.022) (0.023) (0.022) (0.023) (0.059) (0.059) (0.059) (0.059)

CL drawdown ratio 0.195*** 0.201*** 0.194*** 0.204*** 0.034 0.051 0.033 0.044(0.019) (0.019) (0.019) (0.019) (0.041) (0.041) (0.041) (0.041)

Maturity Left 0.060*** 0.071*** 0.063*** 0.062*** 0.157*** 0.160*** 0.158*** 0.159***(0.004) (0.004) (0.004) (0.004) (0.010) (0.010) (0.010) (0.010)

Credit line 0.648*** 0.585*** 0.645*** 0.633*** 0.639*** 0.617*** 0.640*** 0.629***(0.021) (0.021) (0.021) (0.021) (0.055) (0.055) (0.055) (0.055)

Igrade -0.091*** -0.104*** -0.092*** -0.115*** -0.048 -0.059 -0.049 -0.060(0.028) (0.028) (0.028) (0.028) (0.037) (0.037) (0.037) (0.037)

Unrated -0.077*** -0.107*** -0.095*** -0.100*** 0.032 0.016 0.030 0.023(0.021) (0.021) (0.021) (0.021) (0.034) (0.035) (0.034) (0.035)

Bank Controls No No No No Yes Yes Yes YesLoan Controls No No No No Yes Yes Yes YesObservations 90,654 90,654 90,654 90,654 20,016 20,016 20,016 20,016Pseudo-R2 0.076 0.089 0.102 0.111 0.123 0.122 0.141 0.131

41

Table 5: Loan Renegotiations and Non-Bank LendersThe table reports probit regression estimates. The dependent variable takes one if a loan is renegotiated ina given year and zero otherwise. Each specification includes year and loan-year dummies. Columns (3) and(4) report estimates that consider the interaction of lender shares and dummy variable for the presence ofa particular lender type. The reported estimates are interaction terms. Loan, Firm, Bank controls are notreported. All definitions are listed in the Appendix. *** denotes 1% significant level, ** denotes 5% significantlevel, and * denotes 10% significant level.

(1) (2) (3) (4)

Dummy Variable

Lead bank share 1.050*** 1.322*** 1.037*** 1.295***(0.064) (0.153) (0.064) (0.154)

CLO share 0.170* 0.044 0.561*** 0.465*(0.102) (0.254) (0.151) (0.258)

Funds share -0.164 -0.633** 0.042 -0.153(0.103) (0.301) (0.131) (0.355)

Finance company -0.117** -0.128 0.178** 0.107(0.058) (0.131) (0.079) (0.163)

Broker company -0.179 0.108 -0.226 0.193(0.211) (0.428) (0.279) (0.568)

Insurance Company -0.385* -0.830 -0.125 -0.558(0.226) (0.760) (0.280) (0.863)

Foreign bank -0.549*** -0.248*** -0.572*** -0.218***(0.025) (0.068) (0.034) (0.080)

Foreign company -0.249 -0.626 0.781 0.107(0.809) (2.198) (0.886) (0.163)

Other -0.067 -0.035 0.074 0.051(0.063) (0.140) (0.083) (0.173)

Loan Rating 0.418*** 0.269*** 0.413*** 0.266***(0.020) (0.052) (0.020) (0.052)

CL Drawdown ratio 0.190*** 0.131*** 0.193*** 0.137***(0.018) (0.038) (0.018) (0.038)

Maturity left 0.058*** 0.142*** 0.059*** 0.142***(0.003) (0.009) (0.003) (0.009)

Credit line 0.611*** 0.688*** 0.610*** 0.684***(0.018) (0.048) (0.019) (0.048)

Igrade -0.007 -0.025 -0.019 -0.043(0.027) (0.034) (0.027) (0.034)

Unrated -0.049** 0.032 -0.059*** 0.021(0.019) (0.032) (0.020) (0.032)

Firm Controls No Yes No YesBank Controls No Yes No YesObs. 90,654 20,016 90,654 20,016Pseudo-R2 0.083 0.103 0.084 0.103

42

Tab

le6:

Renegotiationsand

Syndicate

Diversity:Syndicate

DiversityAtOrigination

Thetable

reportsprobitregressionestimates.

Thedep

enden

tvariable

takes

oneifaloanisrenegotiatedin

agiven

yearandzero

otherwise.

Each

specification

includes

yearandloan-yeardummies.

Standard

errors

are

robust

andclustered

attheloanlevel.Allcontrols

(notatorigination)are

lagged

theyearbefore

loan

renegotiation.

Loan,Firm,Bankcontrols

are

notreported

;regressionssp

ecificationsare

thesameasin

Table

3.

All

defi

nitionsare

listed

inthe

Appen

dix.***den

otes1%

significantlevel,**den

otes5%

significantlevel,and*den

otes10%

significantlevel.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Leadbankshare:originationyear

0.282***

0.192***

0.244***

0.254***

0.150*

0.167*

0.146

0.130

(0.049)

(0.051)

(0.064)

(0.050)

(0.081)

(0.091)

(0.118)

(0.103)

Leadbankshare

0.985***

0.844***

1.204***

0.831***

1.286***

1.131***

1.438***

1.141***

(0.072)

(0.074)

(0.099)

(0.073)

(0.169)

(0.177)

(0.206)

(0.171)

Non-bankshare:originationyear

-0.134***

0.001

(0.045)

(0.080)

Non-bankshare

-0.471***

-0.473***

(0.067)

(0.145)

Non-bankshare

vol:

originationyear

-0.343***

-0.239

(0.051)

(0.158)

Non-B

ankshare

vol

-0.373***

-1.896***

(0.058)

(0.244)

Len

der

types:originationyear

-0.014*

-0.008

(0.007)

(0.012)

Len

der

types

-0.106***

-0.071***

(0.010)

(0.017)

LoanControls

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

FirmsControls

No

No

No

No

Yes

Yes

Yes

Yes

BankControls

No

No

No

No

Yes

Yes

Yes

Yes

Obs

90,654

90,654

90,654

90,654

20,016

20,016

20,016

20,016

Pseudo-R

20.102

0.113

0.096

0.078

0.102

0.111

0.143

0.121

43

Table 7: Expected and Unexpected RenegotiationsThe table reports probit regression estimates. The dependent variable in Panel A/B takes one if a renegotiationis defined as expected/unexpected and zero otherwise. Each specification includes year and loan age dummies.The sample covers loan-years up to the first renegotiation for a given loan. Standard errors are robust andclustered at the loan level. All controls are lagged the year before loan renegotiation. All definitions are listedin the Appendix. *** denotes 1% significant level, ** denotes 5% significant level, and * denotes 10% significantlevel.

(1) (2) (3) (4) (5) (6) (7) (8)

PANEL A: EXPECTED

Lead Bank Share 1.778*** 1.642*** 1.664*** 1.629*** 2.023*** 1.993*** 1.983*** 1.838***(0.148) (0.148) (0.149) (0.149) (0.287) (0.288) (0.288) (0.290)

Non-Bank Share -0.380*** -0.200*(0.056) (0.105)

Non-Bank Share vol -0.718*** -0.255*(0.130) (0.139)

Lender Types -0.087*** -0.120***(0.017) (0.035)

Loan Rating 0.485*** 0.468*** 0.485*** 0.461*** 0.295*** 0.295*** 0.298*** 0.287***(0.024) (0.024) (0.024) (0.024) (0.062) (0.062) (0.063) (0.062)

CL drawdown ratio 0.335*** 0.336*** 0.324*** 0.348*** 0.284*** 0.298*** 0.281*** 0.311***(0.017) (0.017) (0.017) (0.017) (0.040) (0.040) (0.041) (0.041)

Maturity left 0.035*** 0.042*** 0.036*** 0.039*** 0.117*** 0.119*** 0.117*** 0.121***(0.004) (0.004) (0.004) (0.004) (0.010) (0.010) (0.009) (0.010)

Igrade 0.051* 0.045 0.067** 0.014 0.059 0.052 0.066 0.027(0.028) (0.028) (0.028) (0.028) (0.039) (0.039) (0.042) (0.039)

Unrated -0.088*** -0.113*** -0.101*** -0.126*** 0.042 0.031 0.038 0.016(0.022) (0.022) (0.022) (0.022) (0.037) (0.037) (0.041) (0.038)

Firm Controls No No No No Yes Yes Yes YesBank Controls No No No No Yes Yes Yes YesObs 57,865 57,865 57,865 57,865 13,092 13,092 13,092 13,092Pseudo-R2 0.078 0.098 0.091 0.089 0.091 0.088 0.079 0.099

PANEL B: UNEXPECTED

Lead Bank Share 1.171*** 1.021*** 1.081*** 1.064*** 1.554*** 1.468*** 1.514*** 1.453***(0.079) (0.079) (0.079) (0.079) (0.177) (0.178) (0.174) (0.177)

Non-Bank Share -0.427*** -0.268***(0.023) (0.065)

Non-Bank Share vol -0.645*** -0.333**(0.054) (0.138)

Lender Types -0.054*** -0.050***(0.006) (0.011)

Loan Rating 0.036 0.013 0.031 -0.002 -0.052 -0.053 -0.051 -0.077(0.051) (0.052) (0.051) (0.052) (0.132) (0.132) (0.131) (0.132)

CL drawdown ratio -1.037*** -1.069*** -1.061*** -1.041*** -1.315*** -1.314*** -1.317*** -1.301***(0.038) (0.039) (0.039) (0.039) (0.109) (0.109) (0.109) (0.112)

Maturity left 0.069*** 0.077*** 0.071*** 0.074*** 0.155*** 0.156*** 0.155*** 0.159***(0.006) (0.007) (0.006) (0.007) (0.020) (0.020) (0.018) (0.020)

Igrade -0.500*** -0.503*** -0.484*** -0.534*** -0.399*** -0.401*** -0.394*** -0.443***(0.073) (0.073) (0.073) (0.074) (0.103) (0.103) (0.103) (0.103)

Unrated -0.179*** -0.192*** -0.196*** -0.213*** -0.073 -0.076 -0.078 -0.105(0.047) (0.047) (0.047) (0.048) (0.082) (0.083) (0.083) (0.084)

Firm Controls No No No No Yes Yes Yes YesBank Controls No No No No Yes Yes Yes YesObs 50,765 50,765 50,765 50,765 10,879 10,879 10,879 10,879Pseudo-R2 0.101 0.143 0.122 0.165 0.101 0.111 0.123 0.099

44

Table 8: IV Probit EstimatesThe table reports IV probit regression estimates using maximum likelihood estimation. At the first stage, thedependent variables are lead share, non-bank share, non-bank share vol and lenders types in columns (1-4) and(5-8). The instrument is New bank lenders defined as the ration of new banks over total number of lendersin each loan. At the second stage, the dependent variable takes one if a loan is renegotiated in a given yearand zero otherwise. Each specification includes year and loan-year dummies. Standard errors are robust andclustered at the loan level. All controls are lagged the year before loan renegotiation. Loan, firm, bank controlsare not reported; regressions specifications are the same as in Table 3. All definitions are listed in the Appendix.*** denotes 1% significant level, ** denotes 5% significant level, and * denotes 10% significant level.

(1) (2) (3) (4) (5) (6) (7) (8)

First StageNew banks arrival 0.172*** -0.984*** -0.145*** -2.425*** 0.090*** -1.807*** -0.102*** -1.705***

(0.003) (0.049) (0.026) (0.024) (0.005) (0.145) (0.005) (0.047)Second StageLead bank share 2.427*** 5.452***

(0.126) (0.383)Non-bank share -0.468*** -0.354***

(0.004) (0.034)Non-bank share vol -2.882*** -4.176***

(0.125) (0.192)Lender types -0.184*** -0.335***

(0.009) (0.024)Loan Controls Yes Yes Yes Yes Yes Yes Yes YesBank Controls No No No No Yes Yes Yes YesFirm controls No No No No Yes Yes Yes YesObservations 90,654 90,654 90,654 90,654 20,016 20,016 20,016 20,016Exogeneity Test -0.394*** 0.231*** 0.489*** 0.311*** -0.708*** 0.354*** 0.962*** 0.486***/athrho/ (0.024) (0.015) (0.027) (0.017) (0.072) (0.034) (0.077) (0.045)

45

Table 9: Robustness: Alternative Measures of DiversityThe table reports probit regression estimates. The dependent variable takes one if a loan is renegotiated in agiven year and zero otherwise. Each specification includes year and loan-year dummies. Standard errors arerobust and clustered at the loan level. All definitions are listed in the Appendix. *** denotes 1% significantlevel, ** denotes 5% significant level, and * denotes 10% significant level.

(1) (2) (3) (4)

Lead bank share 1.110*** 1.395*** 1.159*** 1.384***(0.064) (0.152) (0.064) (0.152)

HHI non-bank shares -0.180*** -0.021(0.015) (0.031)

Turnover -0.122*** -0.121***(0.012) (0.021)

Loan rating 0.421*** 0.275*** 0.417*** 0.276***(0.019) (0.052) (0.019) (0.052)

CL drawdown ratio 0.228*** 0.156*** 0.229*** 0.121***(0.017) (0.035) (0.017) (0.037)

Maturity left 0.050*** 0.138*** 0.050*** 0.140***(0.003) (0.009) (0.003) (0.009)

Credit line 0.661*** 0.727*** 0.661*** 0.706***(0.018) (0.047) (0.018) (0.048)

Igrade -0.046* -0.012 -0.077*** -0.025(0.026) (0.034) (0.026) (0.034)

Unrated -0.056*** 0.040 -0.055*** 0.041(0.019) (0.032) (0.019) (0.032)

Bank ControlsProfitability bk 1.757 1.753

(7.898) (7.909)Capital bk 0.649 0.578

(0.948) (0.955)L assets bk -0.018* -0.018*

(0.010) (0.010)Deposits bk 0.268*** 0.281***

(0.098) (0.098)Liquidity bk -0.615 -0.606

(0.446) (0.447)Firm controlsL Sales -0.008 -0.007

(0.011) (0.011)Sales growth 0.397*** 0.404***

(0.041) (0.041)Leverage -0.095 -0.064

(0.067) (0.067)Profitability 1.282** 1.290**

(0.566) (0.565)Stock returns 0.084*** 0.085***

(0.023) (0.023)Stock Return Volatility -16.134 -13.668

(11.210) (11.164)Capex 0.888*** 0.879***

(0.283) (0.283)Obs 90,654 20,016 90,654 20,016Pseudo-R2 0.096 0.165 0.101 0.101

46

Table 10: Robustness: Credit Lines and Term LoansThe table reports probit regression estimates. The dependent variable takes one if a loan is renegotiated in agiven year and zero otherwise. Each specification includes year and loan-year dummies. Standard errors arerobust and clustered at the loan level. All definitions are listed in the Appendix. *** denotes 1% significantlevel, ** denotes 5% significant level, and * denotes 10% significant level.

(1) (2) (3) (4) (5) (6) (7) (8)

PANEL A: CREDIT LINES

Lead bank share 1.374*** 1.174*** 1.308*** 1.230*** 1.665*** 1.613*** 1.653*** 1.617***(0.079) (0.078) (0.079) (0.079) (0.176) (0.178) (0.177) (0.178)

Non-bank share -0.465*** -0.135**(0.027) (0.066)

Non-bank share vol -0.309*** -0.058*(0.043) (0.031)

Lender types -0.072*** -0.024*(0.007) (0.013)

Loan rating 0.552*** 0.521*** 0.544*** 0.520*** 0.355*** 0.356*** 0.355*** 0.351***(0.026) (0.027) (0.026) (0.027) (0.064) (0.064) (0.064) (0.064)

CL drawdown ratio 0.444*** 0.438*** 0.443*** 0.447*** 0.356*** 0.360*** 0.356*** 0.362***(0.020) (0.020) (0.020) (0.020) (0.044) (0.044) (0.044) (0.044)

Maturity left 0.048*** 0.056*** 0.050*** 0.053*** 0.154*** 0.154*** 0.154*** 0.155***(0.004) (0.004) (0.004) (0.004) (0.011) (0.011) (0.011) (0.011)

Igrade -0.060** -0.064** -0.059** -0.091*** -0.009 -0.012 -0.009 -0.020(0.028) (0.028) (0.029) (0.029) (0.036) (0.036) (0.036) (0.037)

Unrated -0.052** -0.075*** -0.066*** -0.090*** 0.060* 0.054 0.059* 0.050(0.022) (0.022) (0.022) (0.023) (0.036) (0.036) (0.036) (0.036)

Firms Controls No No No No Yes Yes Yes YesBank Controls No No No No Yes Yes Yes YesObs 59,021 59,021 59,021 59,021 15,896 15,896 15896 15896Pseudo-R2 0.065 0.051 0.078 0.045 0.143 0.091 0.102 0.112

PANEL B: TERM LOANS

Lead bank share 0.799*** 0.665*** 0.764*** 0.875*** 0.596* 0.415 0.576* 0.408(0.116) (0.117) (0.117) (0.116) (0.310) (0.314) (0.310) (0.315)

Non-bank share -0.255*** -0.380***(0.034) (0.110)

Non-bank share vol -0.196*** -0.126*(0.067) (0.071)

Lender types -0.015*** -0.047***(0.005) (0.013)

Loan rating 0.200*** 0.188*** 0.197*** 0.220*** 0.086 0.084 0.084 0.079(0.029) (0.029) (0.029) (0.029) (0.092) (0.092) (0.092) (0.093)

Maturity left 0.058*** 0.063*** 0.059*** 0.056*** 0.123*** 0.127*** 0.123*** 0.132***(0.004) (0.004) (0.004) (0.004) (0.018) (0.019) (0.018) (0.020)

Igrade -0.140* -0.133* -0.146* -0.114 0.065 0.042 0.058 -0.029(0.076) (0.077) (0.076) (0.076) (0.106) (0.107) (0.106) (0.108)

Unrated -0.097*** -0.115*** -0.105*** -0.075* -0.068 -0.094 -0.073 -0.119(0.037) (0.038) (0.037) (0.038) (0.073) (0.074) (0.073) (0.075)

Firms Controls No No No No Yes Yes Yes YesBank Controls No No No No Yes Yes Yes YesObs 31,633 31,633 31,633 31,633 4,120 4,120 4,120 4,120Pseudo-R2 0.051 0.098 0.045 0.067 0.065 0.098 0.078 0.064

47

Table 11: Robustness: Zero LeadThe table reports probit regression estimates. The dependent variable takes one if a loan is renegotiated in agiven year and zero otherwise. Each specification includes year and loan-year dummies. Standard errors arerobust and clustered at the loan level. All definitions are listed in the Appendix. *** denotes 1% significantlevel, ** denotes 5% significant level, and * denotes 10% significant level.

(1) (2) (3) (4) (5) (6) (7) (8)

Zero lead -0.116*** -0.154*** -0.124*** -0.140*** -0.033* -0.060* -0.034* -0.070(0.020) (0.020) (0.020) (0.021) (0.018) (0.033) (0.02) (0.044)

Non-bank share -0.370*** -0.232***(0.020) (0.054)

Non-bank share vol -0.251*** -0.090(0.035) (0.061)

Lender types -0.022*** -0.035***(0.004) (0.008)

Loan rating 0.424*** 0.401*** 0.419*** 0.415*** 0.283*** 0.284*** 0.282*** 0.282***(0.019) (0.020) (0.020) (0.020) (0.052) (0.052) (0.051) (0.052)

CL drawdown ratio 0.217*** 0.215*** 0.214*** 0.222*** 0.104*** 0.120*** 0.104*** 0.124***(0.017) (0.017) (0.017) (0.017) (0.037) (0.037) (0.035) (0.037)

Maturity left 0.053*** 0.062*** 0.055*** 0.055*** 0.143*** 0.145*** 0.144*** 0.147***(0.003) (0.003) (0.003) (0.003) (0.009) (0.009) (0.007) (0.009)

Credit line 0.652*** 0.615*** 0.657*** 0.650*** 0.702*** 0.686*** 0.703*** 0.687***(0.018) (0.019) (0.018) (0.018) (0.048) (0.048) (0.047) (0.048)

Igrade -0.074*** -0.077*** -0.071*** -0.089*** -0.028 -0.039 -0.030 -0.057*(0.026) (0.026) (0.026) (0.026) (0.034) (0.034) (0.033) (0.034)

Unrated -0.060*** -0.110*** -0.085*** -0.091*** 0.035 0.018 0.033 0.009(0.019) (0.019) (0.019) (0.020) (0.031) (0.032) (0.031) (0.032)

Bank ControlsProfitability bk 0.940 0.287 0.923 -0.572

(7.873) (7.864) (7.756) (7.875)Capital bk 0.638 0.113 0.594 0.391

(0.938) (0.950) (0.908) (0.946)L assets bk -0.022** -0.021** -0.021** -0.018*

(0.010) (0.010) (0.010) (0.010)Deposits bk 0.261*** 0.223** 0.254*** 0.243**

(0.097) (0.097) (0.092) (0.097)Liquidity bk -0.539 -0.525 -0.519 -0.391

(0.444) (0.446) (0.439) (0.448)Firm controlsL Sales -0.006 0.008 -0.003 0.003

(0.011) (0.011) (0.010) (0.011)Sales growth 0.435*** 0.446*** 0.439*** 0.447***

(0.041) (0.041) (0.041) (0.041)Leverage -0.072 -0.026 -0.059 -0.031

(0.067) (0.068) (0.064) (0.068)Profitability 1.291** 1.060* 1.249** 1.189**

(0.558) (0.564) (0.547) (0.561)Stock returns 0.081*** 0.083*** 0.082*** 0.083***

(0.022) (0.022) (0.022) (0.022)Stock return vol -20.095* -17.467 -19.849* -17.466

(11.265) (11.324) (11.066) (11.331)Capex 0.890*** 0.934*** 0.905*** 0.893***

(0.283) (0.285) (0.272) (0.284)Obs. 90,654 90,654 90,654 90,654 20,016 20,016 20,016 20,016Pseudo-R2 0.091 0.101 0.111 0.097 0.091 0.11 0.098 0.123

48

Table 12: Robustness: Logit Fixed Effect EstimationThe table reports logit fixed effect regression estimates. The dependent variable takes one if a loan is renegotiatedin a given year and zero otherwise. Each specification includes year and loan-year dummies. Standard errors arerobust and clustered at the loan level. Loan, Firm, Bank controls are not reported; regressions specifications arethe same as in Table 3. All definitions are listed in the Appendix. *** denotes 1% significant level, ** denotes5% significant level, and * denotes 10% significant level.

(1) (2) (3) (4) (5) (6) (7) (8)

Lead bank share 2.285*** 2.113*** 2.222*** 2.046*** 2.999*** 2.747*** 3.405*** 2.701***(0.143) (0.146) (0.143) (0.145) (0.345) (0.356) (0.441) (0.351)

Non-bank share -0.598*** -0.624***(0.106) (0.229)

Non-bank share vol -0.688*** -0.515***(0.107) (0.189)

Lender types -0.147*** -0.125***(0.017) (0.031)

Loan rating 0.581*** 0.578*** 0.582*** 0.573*** 0.382** 0.384** 0.350* 0.381**(0.064) (0.064) (0.064) (0.064) (0.155) (0.155) (0.188) (0.155)

CL Drawdown ratio 0.240*** 0.260*** 0.243*** 0.305*** 0.182* 0.212** 0.063 0.260**(0.053) (0.053) (0.053) (0.053) (0.107) (0.107) (0.124) (0.108)

Maturity left 0.511*** 0.512*** 0.512*** 0.513*** 0.609*** 0.609*** 0.647*** 0.613***(0.012) (0.012) (0.012) (0.012) (0.023) (0.023) (0.028) (0.024)

Igrade 0.159 0.142 0.150 0.101 0.145 0.126 0.286* 0.095(0.111) (0.111) (0.111) (0.111) (0.152) (0.153) (0.165) (0.153)

Unrated 0.139* 0.120 0.124 0.099 -0.078 -0.082 -0.022 -0.086(0.081) (0.081) (0.081) (0.082) (0.132) (0.132) (0.150) (0.132)

Bank ControlsProfitability bk -13.461 -15.278 -13.906 -16.978

(21.331) (21.360) (25.098) (21.379)Capital bk 2.995 3.286 0.065 3.135

(3.548) (3.551) (4.408) (3.555)L assets bk -0.101 -0.096 -0.077 -0.099

(0.077) (0.077) (0.092) (0.077)Deposits bk 0.132 0.168 0.385 0.124

(0.508) (0.508) (0.597) (0.508)Liquidity bk -3.091* -3.012* -1.785 -3.018*

(1.643) (1.644) (1.924) (1.646)Firm controlsL Sales -0.113 -0.091 -0.087 -0.085

(0.105) (0.106) (0.120) (0.106)Sales growth 0.404*** 0.411*** 0.340** 0.409***

(0.118) (0.119) (0.135) (0.119)Leverage -0.825** -0.769** -0.677* -0.746**

(0.321) (0.322) (0.371) (0.322)Profitability 10.977*** 10.800*** 11.130*** 10.791***

(2.268) (2.273) (2.637) (2.274)Stock returns -0.002 0.002 0.016 0.003

(0.055) (0.055) (0.066) (0.055)Stock return vol -83.774** -84.349** -56.396 -79.176*

(41.266) (41.379) (49.508) (41.556)Capex -0.627 -0.609 -0.883 -0.619

(1.140) (1.140) (1.363) (1.141)Obs 7,540 7,540 7,540 7,540 3,005 3,005 3,005 3,005Pseudo-R2 0.098 0.102 0.089 0.091 0.089 0.074 0.079 0.102

49

Figure 1: The Share of Lender Types

020

4060

8010

0

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

CLO

Lead Bank

Pension Fund

Finance Company

Other

Foreign Bank

Insurance

Foreign Company

Broker Company

Hedge Fund

Mutual Fund

Private Equity

Bank

Note: This graph shows the percentage contribution of each lendertype in the total loan amount by year.

Figure 2: The Relationship Between Renegotiations and Lender Shares at YearlyLevel

.05

.1.1

5.2

.25

.45 .5 .55 .6Non−Bank Share

Yearly Share of Renegotiations Fitted values

Renegotiations and Non−Bank Share

.05

.1.1

5.2

.25

.4 .45 .5 .55 .6Bank Share

Yearly Share of Renegotiations Fitted values

Renegotiations and Bank Share

Note: The left-hand side graph shows fitted linear regressions of the percent of renegotiations in a year and

lagged (mean) lead bank shares. The right-hand side graph shows fitted linear regressions of the percent of

renegotiations in a year and lagged (mean) bank lender shares.

50

Figure 3: Syndicate Structure Development over the Life of the Loan

.22

.23

.24

.25

.26

Lead

Ban

k S

hare

−3 −2 −1 0 1 2

.29

.3.3

1.3

2.3

3N

on−

Ban

k S

hare

−3 −2 −1 0 1 2

.262

.264

.266

.268

.27

Non

−B

ank

Sha

re V

ol

−3 −2 −1 0 1 2

1.1

1.2

1.3

1.4

1.5

Lend

er T

ypes

−3 −2 −1 0 1 2

Note: These plots show mean values of lead share, non-bank share, non-bank share vol and lender types before

and after the renegotiation year (denoted with 0 on the horizontal axis). On the horizonal axes, -3, -2, -1 denote

the year prior renegotiation and 1, 2 the year after.

51

Figure 4: Syndicate Structure Development over the Life of the Loan:Expected vs Unexpected Renegotiations

.2.2

2.2

4.2

6.2

8Le

ad B

ank

Sha

re

−3 −2 −1 0 1 2

Expected Unexpected

.28

.3.3

2.3

4.3

6N

on−

Ban

k S

hare

−3 −2 −1 0 1 2

Expected Unexpected

.03

.035

.04

.045

.05

CA

PE

X

−3 −2 −1 0 1 2

Expected Unexpected

.05

.1.1

5.2

Sal

es G

row

th

−3 −2 −1 0 1 2

Expected Unexpected

Note: These plots show mean values of lead share, non-bank share, non-bank share vol and lender types before

and after unexpected and unexpected renegotiations. The renegotiation year is denoted with 0 on the horizontal

axis.

52

Figure 5: Distribution of First-Time Banks’ Arrival in the Syndicate

0.0

5.1

.15

.2N

ew B

anks

/All

Lend

ers

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

Instrumental Variable: New Banks Arrival

0.0

5.1

.15

.2N

ew B

anks

/All

Lend

ers

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Instrumental Variable: New Banks Arrival

Note: The left-hand side graph shows the mean percentage of new banks’ arrival (the ratio of new banks over

total syndicate members). On the right-hand side graph, loan years are shown on the horizontal axis.

53

Figure 6: Probit Margin Plots

.1.2

.3.4

.5P

roba

bilit

y of

Pos

itive

Out

com

e

.1 .22 .4 .6 .7Lead Share

.15

.16

.17

.18

.19

Pro

babi

lity

of P

ositi

ve O

utco

me

0 .1 .25 .35 .5Non−Bank Share

.09

.1.1

1.1

2.1

3.1

4P

roba

bilit

y of

Pos

tive

Out

com

e

0 .15 .27 .44 .62Non−Bank Share Vol

.1.1

1.1

2.1

3.1

4P

roba

bilit

y of

Pos

itive

Out

com

e

0 1 2 3 4 5Lender Types

Note: These plots show predicted probabilities from probit models in Table 3, columns (4-8) as a function

of lead share, non-bank share, non-bank share vol and lender types.

54

Figure 7: IV Probit Margin Plots

0.2

.4.6

.81

Pro

babi

lity

Of P

ositi

ve O

utco

me

.1 .22 .4 .6 .7Lead Bank Share

.1.2

.3.4

.5P

roba

bilit

y O

f Pos

itive

Out

com

e

0 .1 .25 .35 .5Non−Bank Share

0.1

.2.3

.4.5

Pro

babi

lity

Of P

ositi

ve O

utco

me

0 .15 .27 .44 .62Non−Bank Share Vol

0.1

.2.3

.4P

roba

bilit

y O

f Pos

itive

Out

com

e

0 1 2 3 4 5Lender Types

Note: These plots show predicted probabilities from IV probit models in Table 8, columns (4-8) as a function

of lead share, non-bank share, non-bank share vol and lender types.

55