Bank Effects and the Determinants of Loan Yield Spreads *

Li Hao**

February 24, 2004

JEL classification: G21.

Keywords: Loan yield spreads, bank characteristics, borrower characteristics, bank risk.

* The author is indebted to her supervisor, Gordon S. Roberts, for his continuous

guidance and support. The author would like to thank Yisong S. Tian and Melanie Cao for their valuable suggestions and comments. This paper has benefited from comments received from seminar participants at York University, Tenth Annual Conference of Multinational Finance Society and 2003 NFA Meeting. All errors are the responsibility of the author.

** Ph.D. Candidate, Schulich School of Business, Finance Area, York University, 4700 Keele Street, Toronto, Ontario, Canada, M3J 1P3 Phone: 416-736-2100 Ext: 77911. E-mail: lhao@schulich.yorku.ca.

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Abstract

This paper examines bank effects on loan yield spreads after controlling for borrower

and non-price loan characteristics. We assemble loan contract, borrower and bank

financial variables from three different databases and incorporate a broader range of bank

characteristics. Besides bank size, monitoring power, and bank risk, the number of lead

lenders specified at loan-level and defined differently from prior analogous measures, is

introduced and shown to be an important determinant of loan yield spreads. In this paper,

we define and include as lenders only those banks that have lending relationships with

borrowers and retain administrative, monitoring, or contract enforcement responsibilities.

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1. Introduction

The lender-borrower relationship has long been studied in the literature. There are two

sides to the lender-borrower relationship, the demand side and the supply side. One would

expect that factors from both sides have effects on loan yield spreads. An important strand

of research focuses on borrower effects on this relationship and on the setting of loan

contract terms. Taking the determination of collateral as an example, Chan and Kanatas

(1985) show that, in cases where the lender and the borrower have different opinions about

the borrower’s project, collateral will be offered by the borrower when the lender’s

valuation of the project is lower than the borrower’s. Higher quality borrowers signal their

creditworthiness by offering more collateral. Besanko and Thakor (1987) also find a

positive relationship between collateral and borrower creditworthiness. In contrast, Berger

and Udell (1990) and Harhoff and Korting (1998) find a positive relationship between

collateral and borrower risk in the context of small business loans. Aside from collateral, a

number of studies also examine the impact of borrower characteristics on the

determination of loan yield spreads (Angbazo, Mei and Saunders (1998), Gorton and Kahn

(2000), among others). In this strand of research, borrower effects on the determination of

loan contract terms have been widely explored while lender effects have not.

Another strand of research addresses the effects of lender characteristics on loan

contract terms. Studying different types of financial intermediaries, Carey, Post and

Sharpe (1998) find evidence that compared to banks, finance companies seem to be more

likely to make secured loans frequently and lend to riskier borrowers. Hubbard, Kuttner

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and Palia (2002) incorporate another lender attribute, bank financial health, and show that

low-capital banks tend to charge higher loan rates than well-capitalized banks. Coleman,

Esho and Sharpe (2002) examine further lender characteristics, and state that bank

monitoring ability, bargaining power, risk and syndicate structure have significant

influence in determining loan maturity and pricing. However, studies of lender effects on

the lender-borrower relationship and especially the determination of loan contract terms

remain scarce in this literature.

This paper is an empirical study of bank effects on the setting of loan pricing, taking

into account the influences of both bank and borrower attributes. Further lender

characteristics are included in this study. Notably, a new dimension of bank characteristics,

the number of lead lenders, which is same as the number of all lenders in a non-syndicated

loan and the number of lead lenders only in a syndicated loan., is introduced and motivated

by recognizing the important influence of single versus multiple banking relationships on

the lender-borrower relationship. It has been well documented that the number of banking

relationships a borrower maintains plays an important role in the lender-borrower

relationship. Petersen and Rajan (1994) find that small firms borrowing from multiple

banks are of lower creditworthiness than those borrowing from a single bank. For such

firms, borrowing from fewer banks generally increases credit availability and lowers the

cost of funds. Houston and James (2001) observe that firms relying on a single bank

exhibit greater sensitivity of investment to cash flow than firms maintaining multiple bank

relationships or borrowing from public debt markets. Harhoff and Korting (1998)

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empirically investigate the role of lending relationships in determining the costs of external

funding, and document that the number of relationships increases with the firm’s age, size,

and leverage. Carletti (2000) studies the link between the number of bank relationships

and banks’ incentives to monitor, along with the effect of such link on loan rates and firms’

choice between single and multiple relationships.

Recognizing the important influence of the number of banking relationships on the

lender-borrower relationship and insufficient attention paid to lenders’ effects on the

setting of loan contract terms in the literature, this paper further explores and examines

bank effects on loan pricing from a different perspective through incorporating a new

variable, the number of lead lenders at the loan level, along with other lender

characteristics. Our data set includes both syndicated loans and non-syndicated loans.

Based on lenders’ main responsibilities taken in the lending process, the number of lead

lenders is regarded same as the total number of lenders for non-syndicated loans while, in

the case of syndicated loans, it excludes participating lenders. We expect that the number

of lead lenders at the loan level affects the setting of loan contract terms.

In the case of a non-syndicated loan, the negotiation process between the borrower and

multiple lenders becomes more complex than is the case with a single lender. To some

extent, the presence of multiple lenders in a given loan contract could suggest that there is

unfavorable information about the borrower and thus that the original bank is unwilling to

lend to the borrower on its own. Including more lenders in a loan contract could diversify

risk and reduce each lender’s exposure to firm-specific risk, while also serving to

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discourage strategic default on the part of the borrower (Esty and Megginson (2003))1.

Therefore, the number of lead lenders, which is the number of all lenders in non-syndicated

loans, is expected to affect the determination of loan yield spreads.

In the case of a syndicated loan with multiple lead lenders, we expect that the costs of

such loans are higher. Lead lenders, which are typically more interested in generating fee

income, seek to structure and lead transactions. And, the lead lenders in syndicated loans

are not necessary the borrowers’ relationship banks rather than banks with expertise in loan

syndication. Moreover, the lead lenders in fully underwritten bids take the risk that the

market is not in favor of the deal and thus the lead lenders may have to provide the entire

loan amount. Therefore, the costs of those syndicated loans are generally higher to

compensate the lead lenders for greater credit and syndication risks.2 Given the higher

compensation for lead lenders, borrowers sometimes still call for multiple lead mandates in

order to enhance the chance of a successful syndication. The presence of multiple lead

lenders lowers each lead lender’s risk exposure and facilitates the process of loan

syndication as more lead lenders commit to invite participating banks who usually have

syndication relationship with the lead lenders. As a result, the number of lead lenders at the

loan level is assumed to be an important determinant on the setting of loan yield spreads.

1 Strategic default is particularly important in project finance. 2 Esty (2001) documents that there are two types of syndicated loans in terms of underwritten, one is fully underwritten bids and the other is best efforts bids. A fully underwritten bid indicates that the lead arranger(s) must take responsibility to provide the full amount of the loan on specified loan contract terms. A best effort bid indicates that the lead arranger (s) agrees to underwritten a portion of the loan – typically the amount that the arranger(s) intends to hold on its own balance sheet – and attempts to place the remainder of the loan in the bank market. In practice, approximately 70% of deals are done as fully underwritten bids. Data limitations do not allow us to distinguish between these two types of underwriting in our sample.

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Its effects on loan yield spreads are examined and explained in this study.

This study extends the existing literature by emphasizing the significant influence of

bank characteristics on loan yield spreads and provides evidence that borrower

characteristics are important determinants of loan yield spreads. Bank effects on loan yield

spreads are examined after controlling for the effects of borrower and non-price loan

characteristics, such as loan size, maturity, syndicate structure, etc. Our main findings are

that banks with greater monitoring power and riskier banks with lower capital-asset ratios

extract higher rents, which is consistent with the findings in prior studies. Importantly, the

new dimension of lender characteristics – the number of lead lenders for a given loan

contract – is shown to have a significant influence on loan yield spreads. The positive

relationship between the number of lead lenders and loan yield spreads reflects lead

lenders’ interest in generating fee income and request for compensating their greater credit

and risk exposure to potential loan syndication failure. It also suggests that the presence of

multiple lenders is associated with the duplication of monitoring, complex negotiation

processes, the possibility of unfavorable information about the borrower, and the intention

to discourage borrowers’ strategic default.

The contributions of this paper to the existing literature are threefold. First,

considering influences from both the demand side and the supply side of credit borrowing,

we assemble bank and borrower financial variables in order to fully investigate bank

effects on the determination of loan yield spreads, controlling for the effects of non-price

loan features and borrower characteristics. Second, we incorporate a new dimension of

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bank characteristics in the study of bank effects on loan yield spreads. Specifically, we

introduce the number of lead lenders at the loan level as a variable affecting the setting of

loan yield spreads, along with bank size, bank risk, and bank monitoring power. Third,

continuing our focus on the role of multiple lenders, in the case of syndicated loans we

include all lead banks for a given loan contract in this study. For multiple-lead-bank

syndicated loans, we assign each lead bank a weight according to its contribution to the

loan facility based on its share of the syndicated loan, data which are available from

DealScan. By so doing, we avoid omitting data on multiple lead banks which provide

valuable information about bank effects on loan yield spreads. In contrast, Coleman, Esho

and Sharpe (2002) study only the lead bank which contributes the largest portion of the

syndicated loan. This could lead to an incomplete understanding of the effects of lender

characteristics due to the omission of substantial lender information in the case of

multiple-lead-bank syndicated loans.

The remainder of the paper is organized as follows. In the next section, we discuss

proxies for bank, borrower, and non-price loan characteristics. The central hypotheses

tested in this study are also discussed in section 2. Section 3 describes the data and the

empirical approach we use. Our empirical tests are reported in Section 4. Section 5

concludes.

2. Bank, borrower, non-price loan characteristics, and testing hypotheses

The objective of this paper is to examine bank effects on the determinants of loan yield

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spreads, while controlling for the effects of borrower characteristics and non-price loan

features on loan yield spreads. In the following, we discuss our hypotheses along with

proxies we use for bank, borrower, and non-price loan characteristics. We start with the

explanations of the proxies employed in this study.

2.1 Proxies for bank characteristics

Number of lead lenders

Prior studies have documented the influence that the number of lenders maintained by

a borrower exerts on loan contract terms. Most of the studies define the number of lenders

as the number of banking relationships the borrower keeps up through borrowing and cash

management activities. In contrast, this study defines the number of lenders as the number

of lead banks in a given loan contract by employing detailed loan-level data. Put another

way, we are concerned with the number of lead lenders at the level of individual loan

contract, whereas prior studies have defined the number of lenders as the number of banks

with which a borrower has borrowing relationships. For syndicated loans, we assume that

the lead bank’s characteristics have the greatest effects on the determination of loan

contract terms because the responsibilities for bargaining, monitoring and screening are

placed on the lead banks.

In prior studies, Petersen and Rajan (1994) use the number of banks from which the

firm borrows as a measure of borrower concentration. One of their main findings is that

borrowing from multiple lenders leads to increases in credit prices and decreases in the

availability of credit. The number of banks with which a borrower maintains relationships

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can therefore serve as a rough proxy for the borrower’s quality. Syndicated loans, however,

are not included in their study because they focus on small borrowers. Ongena and Smith

(2000) define the number of bank relationships in terms of cash management services. In

their data set, some of the recorded bank relationships are not traditional lending

relationships as cash management services include the collection of deposits, the

management of bank balances and overdrafts, foreign exchange management, and many

other services. Detragiache, Garella, and Guiso (2000), using Italian data, argue that a

borrower has a bank relationship if it borrows from a bank under one of the following six

loan categories: commercial paper discounted by the bank, lines of credits, export loans,

collateralized loans, medium-term loans, and long-term loans. One of the most important

sources of debt financing, the syndicated loan, is not included in that list. In this study, we

only define as lenders those banks that have lending relationships with borrowers and

retain administrative, monitoring, or contract enforcement responsibilities. The number of

lead lenders in this study is specified in each term loan facility (contract), and is different

from the current number of banking relationships the borrower maintains.

One advantage of this study is the focus on syndicated loans, one of the most important

sources of funding for medium and large borrowers. We employ the DealScan database

which provides detailed information on loan contract features.3 In a syndicated loan, a

group of financial intermediaries agrees to jointly issue a loan to a borrower. Dennis and

3 Other studies using DealScan for various research purposes include Carey, Post, and Sharpe (1998), Dennis and Mullineaux (1998), Dennis, Nandy and Sharpe (2000), Hubbard, Kuttner, and Palia (2002).

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Mullineaux (2000) document that in syndicated loans, “One lender will typically act as

managing agent for the group, negotiating the loan agreement, then coordinating the

documentation process, the loan closing, the funding of loan advances, and the

administration of repayments.” Lenders acting as managing agents retain administrative,

monitoring and contract enforcement responsibilities. These agents are assumed to have

relationships with borrowers, while the participating members are less likely to have such

relationships with the borrower since they are not generally involved in the negotiations

with or active monitoring of the borrower.

In the case of syndicated loans with a single lead bank responsible for negotiating the

loan contract with the borrower, we view these as loans with a single lender. Furthermore,

there are situations in which several banks assume the roles of originator, loan

administrator, and collateral administrator separately. Under these circumstances, the

syndicated loan is treated as a loan with multiple lead lenders. In our sample, 66.39% of

the syndicated loans have two or more lead banks. It is expected that the presence of

multiple lead lenders affects the lenders’ monitoring effectiveness and thus the setting of

loan contract rates. Considering the duplication of monitoring, sharing of benefits,

potential unfavorable information about the borrower, and complexity of the negotiation

process, including more lenders in a given loan contract could result in higher loan rates.

Esty (2001) states that, in syndicated loans, the lead lenders might not be borrowers’

relationship banks and rather they are experts on loan syndication. The lead lenders require

higher fees as being underwriters who might have to provide the entire loan amount if the

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deal is not accepted by the market. Therefore, we expect that there is a positive relation

between the number of lead lenders and loan yield spreads.

On the other hand, from the perspective of risk diversification, including more lenders

in a loan contract could reduce each lender’s exposure to firm-specific risk, while also

serving to discourage borrowers’ strategic default (see Esty and Megginson(2002)).

Following this line of reasoning, one would expect that multiple lenders might result in

lower loan rates. In this study, our reliance on the Compustat database for borrower

characteristics suggests that small firms might be underrepresented in our sample.

Considering that the typical borrowers in this study’s sample are more likely to be medium

or large-sized firms, which are supposed to be of higher quality and less prone to financial

distress, we predict that the effects of risk diversification might be limited and thus the

number of lead lenders have a positive relation with loan yield spreads.

Bank size

The benefits of bank size have been widely documented in the literature of bank

mergers and acquisitions (Kane (2000) and Milbourn, Boot, and Thakor (1999), among

others). Mergers and acquisitions activity in banking has been intense in the last decade in

many countries. A clear outcome of the bank merger trend is a tremendous increase in

bank size. Larger banks have greater market power and better access to government safety

net subsidies relative to smaller banks. Relatively smaller banks may be at a competitive

disadvantage in attracting the business of larger loan customers. Not surprisingly, bank

size influences a bank’s lending activities.

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In its explorations of the effects of bank size on loan yield spreads, this study employs

a measure of relative size which is defined as the ratio of bank size to borrower size, similar

to that used in Coleman, Esho and Sharpe (2002). Bank size and borrower size are

measured as the natural logarithms of bank and borrower’s total assets, respectively.

Notably, the calculation of bank size in this study differs from that of Coleman, Esho and

Sharpe (2002). In this study, to incorporate all lead banks’ features, we assign each lead

bank a weight based on its portion of the shares held by lead banks in each loan facility.

Each lead bank’s total assets is then multiplied by its weight, and the sum of all the lead

banks’ weighted assets is used to calculate bank size for each loan facility. In contrast, in

Coleman, Esho and Sharpe (2002), the total assets of the bank which contributes the largest

portion of the loan is used for calculating bank size. Their approach, by design, omits other

lead banks’ features from the calculation of bank size. Coleman, Esho and Sharpe (2002)

use relative size as a proxy for bank bargaining power vis-à-vis the borrower. The

negotiation process is a bilateral interaction involving the bank and the borrower.

Bargaining power is to a large extent dependent on asymmetric information between the

lender and the borrower, and also on the competence of outside lenders. As delegated

monitor, bank lenders have incentive and capability to collect information at a lower cost.

Given their monopoly of borrower information, there is the potential that bank lenders may

“hold up” the borrower by threatening to liquidate the borrower’s project. The size of the

bank relative to the borrower has explanatory power for bargaining power, but this power is

likely limited. Given that there is no perfect proxy for bargaining power, in this study, we

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use the relative size ratio in empirical testing to examine the effects of bank size on the

determinants of loan yield spreads. Following the argument in Coleman, Esho and Sharpe

(2002), it is expected that the relative size ratio is positively related to loan yield spreads.

Bank monitoring power

The roles of banks in information production and monitoring in the credit allocation

process have been widely explored. Since they are responsible for monitoring and

screening loan contracts, banks have the incentive and ability to mitigate adverse selection

and moral hazard problems, and provide flexibility by reconstructing loan contracts. Billet,

Flannery and Garfinkel (1995) use bank credit rating as a proxy for bank monitoring

effectiveness. They argue that high quality banks attempt to maintain their credit ratings

because higher credit ratings are associated with higher bank profits, which are the result of

their effectiveness in monitoring corporate borrowers. Therefore, a bank’s credit rating

could be used as a proxy for its monitoring power. Coleman, Esho and Sharpe (2002) use

the salary fixed effect, defined as the ratio of salary and benefits to total operating expense,

as a proxy for monitoring ability. They assume that staff abilities in monitoring activities

are reflected in their salaries, so that the salary expense ratio will mirror the resources

invested in monitoring activity and the competence of bank staff. In an alternative

approach, Johnson (1997) uses loan loss provisions to proxy reputation in bank monitoring

abilities, arguing that a change in loan loss provisions indicates a change in management’s

assessment of loan portfolio quality and/or a change in monitoring and screening abilities.

It is difficult to directly measure bank monitoring power because monitoring and

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screening activities are largely unobservable. Since banks attempt to maintain their

reputations through appropriate loan issuing and proper monitoring activities, we may

infer bank monitoring power from banks’ reported measures of loan quality, such as loan

loss provisions. It is the responsibility of the bank’s management to determine an adequate

loan and lease loss provision based on current knowledge of the bank's loan portfolios, and

to maintain a reviewable record of the basis for their determination of loan and lease loss

provisions. Bank management is thought to have superior information about default risks

in its loan portfolios compared to investors and other stakeholders. Therefore, we assume

that an assessment by bank management is a more accurate indication of bank monitoring

power, and that decisions on the level of loan loss provisions convey information about the

quality of bank monitoring activities. Accordingly, we employ the loan loss provision as

the proxy for bank monitoring power in this study.

Monitoring and information production, two main advantages of bank debt relative to

public debt, provide banks with an information monopoly which might be used to extract

higher rents. According to Rajan (1992) “hold-up” theory, one would expect that a bank

with superior monitoring power might extract higher rents. In other words, bank lenders’

monitoring power is positively related to loan yield spreads. As the proxy for bank lenders’

monitoring power, loan loss provisions are regarded as negatively associated with the

intensity of bank monitoring activities. The loan loss provision is thus expected to be

inversely related to loan yield spreads; the higher the loan loss provisions, the lower the

loan yield spreads.

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Bank risk

Bank lenders are exposed to many risks. These risks are operational and financial,

domestic and international, as well as on- and off-balance-sheet. In reality, these risks are

often interdependent. Liquidity risk, arising from the uncertainty of the timing of bank

cash flows, is one of the risks that bank lenders face in their business. Banks with seriously

impaired capital will find it extremely difficult to raise funds to replace maturing liabilities.

Liquidity risk is a crucial concern for bank lenders. In this study, we focus on the effects of

bank liquidity risk on the determinants of loan yield spreads.

As a proxy for bank liquidity risk, Hubbard, Kuttner and Palia (2002) choose the

capital-assets ratio, arguing that a riskier bank will have a lower capital-assets ratio and

charge a higher premium. They find that the cost of borrowing from low-capital banks is

higher than the cost of borrowing from well-capitalized banks, even after controlling for

borrower risk and information costs. Following the argument in Hubbard, Kuttner and

Palia (2002), in this study we also use the capital-assets ratio (equity capital/total assets) to

measure bank liquidity risk. We presume that banks with higher capital-assets ratios have

less liquidity risk, and charge lower premia. This suggests that a bank’s capital-assets ratio

is negatively related to loan yield spreads.

2.2 Proxies for borrower and loan characteristics

The effects of borrower characteristics on borrowers’ investment decisions have been

well explored. Those borrower characteristics which are more closely related to debt

agency problems are the main characteristics investigated in prior studies. There are two

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primary debt agency problems identified; one is the risk-shifting or asset substitution

problem (Jensen and Meckling (1976)), the other is the under-investment problem (Myers

(1977)) (Mao (2003)). Myers (1977) states that, with increases in the firm’s leverage,

equity holders have incentives to under-invest in positive NPV projects. Being aware of

these debt agency problems, debt holders price the debt appropriately and can be expected

to demand higher returns. Moreover, Chemmanur and Fulghieri (1994) find that firms

with a greater probability of encountering financial distress tend to choose bank loans

over public debt because banks may be amenable to renegotiating contract terms in the

event of financial distress. Houston and James (1996) document that firm size, the

importance of growth opportunities, overall leverage, the number of bank relationships,

and a firm’s access to public debt markets influence a firm’s decision to borrow from

banks. Particularly, they show that reliance on bank borrowing decreases with firm size

and reductions in overall leverage.

Given the important influences of borrower characteristics such as leverage, firm size

and firm solvency, on firms’ investment decisions, we include borrower leverage

(debt/assets), borrower size (natural logarithm of total assets), and borrower current ratio

(current assets/current liabilities) to measure borrower effects on loan yield spreads.4

These borrower variables serve as proxies for two groups of borrower characteristics:

borrower risk and information costs.5 One would expect that borrower size is negatively

4 The primary proxy for borrower leverage is the ratio of total debt (long-term debt plus debt in current liabilities) to borrower total assets (book value) (See Hubbard, Kuttner and Palia (2002) and Shane (2003)). 5 One could argue that these three borrower variables cannot completely measure all of the borrowers’ characteristics. In

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related to loan yield spreads since smaller firms are assumed to have higher risk due to

higher information costs, and larger firms are likely to be more diversified, which implies

lower expected bankruptcy costs and lower risk. This is consistent with the findings of

Peterson and Rajan (1994), who posit that adverse selection and moral hazard may have

more influence on small and young corporate borrowers. We include book-value

measures of leverage (debt/assets) as the proxy for borrower risk. As borrowers with

higher leverage ratios likely have higher risk, borrower leverage is expected to be

positively related to loan yield spreads.

To control for the effects of non-price loan characteristics on the determinants of loan

yield spreads, we include the facility amount size, term facility maturity, a dummy

variable indicating the loan’s secured status, and a dummy variable indicating whether the

loan is syndicated. If the loan is underwritten by a syndicate, it is likely to be successfully

distributed and associated with lower risk. This is equivalent to a reduction in syndication

risk for the originating bank(s) and a reduction in the firm-specific risk associated with

individual loans. This suggests that lower yield spreads are expected on syndicated loans.

Therefore, the syndicated loan indicator dummy variable is expected to be negatively

related to loan yield spreads. On the other hand, one might argue that a loan needed to be

syndicated may be associated with high risk, which leads to a positive relation between

the syndicate indicator dummy variable and loan yield spreads. In prior studies, it has

this study, considering that debt agency problems are more severe for small and risky firms (Myers (1977)), we are more concerned with borrower characteristics associated with borrower risk and information costs and thus control the effects of these borrower characteristics to investigate bank effects on loan pricing (Coleman, Esho and Sharpe (2002), Hubbard, Kuttner and Palia (2002)).

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been documented that loans with longer maturities are assumed to be associated with

firms with higher credit quality. Borrowers with lower credit quality are limited to

shorter-maturity loans and pay higher loan yield spreads due to higher potential default

risk. This suggests that maturity is negatively related to loan yield spreads. Borrowers

who have to pledge collateral are associated with higher firm-specific risk; collateral may

therefore be regarded as a signal of high risk. (Berger and Udell (1990), Harhoff and

Korting (1998)). Accordingly, the secured status is likely to be positively related to loan

yield spreads.6 Loan size is viewed as an important determinant of loan yield spreads.

Larger loans are more likely to be associated with large borrowers, for whom more

information is available. The presence of more information about these firms tends to

reduce lenders’ costs of monitoring, and for this reason, large borrowers might be charged

lower loan yield spreads. As a result, a negative relationship between the size of the loan

and loan yield spread is expected. A summary of the model and the expected signs of the

estimated coefficients are provided in Table 1.

[Table 1 here]

Although we estimate the model that regards the explanatory variables as exogenous,

some loan variables are likely to be endogenous. We address this issue in robustness

checks and estimate the model in a simultaneous equations framework. In spite of lacking

exogenous variables, the results suggest that the main findings of the estimated bank

6 However, in Chan and Kanatas (1985) and Besanko and Thakor (1987), higher quality borrowers pledge collateral, thereby signaling their creditworthness. Moreover, one could argue that loans secured by a pledge of specific assets or equity are associated with lower risk of principal and interest default, resulting in lower yield spreads.

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effects on loan yield spreads are robust to the simultaneous equations framework. Thus,

the results do not appear to be driven by specifications or errors in the single-equation

OLS framework.

3. Data Our interest is in the effects of bank characteristics on the determinants of loan yield

spreads. Therefore, we need to isolate the effects of bank characteristics, borrower

characteristics and non-price loan features on loan yield spreads. Information on banks,

borrowers and loans for each loan contract is required. Our main data sources are the

DealScan database, the Compustat database and U.S. Federal Reserve Call Reports.

Supplied by the Loan Pricing Corporation (LPC), the DealScan database includes

borrower identity and location; lender identity, lenders’ shares and lender roles7; loan

purpose, type, amount and contract date; and price, as well as a number of non-price

terms.8 The DealScan database provides relatively little detail relating to the borrower’s

and the lender’s financial positions. Financial variables reflecting borrower characteristics

can be obtained from Compustat, while lender characteristics are available in the Call

Reports provided by the U.S. Federal Reserve. The Call Reports are the regulatory filings

that all commercial banks having insured deposits submit each quarter. The Call Reports

include detailed information on the composition of bank balance sheets and some

7 From the DealScan database, the lender role is divided into the following types: Participant, Advisor Only, Co-agent, Co-arranger, Co-manager, Co-lead Manager, Co-Syndications Agent, Secondary Investor, Sub-participants, Technical Agent, Collateral Agent, Administration Agent, Agent, Arranger, Documentation Agent, Lead Bank, Lead Manager, Manager, Managing Agent, Sole Lender, Sr. Lender Manager, Sr. Managing Agent, Syndications Agent. 8 In DealScan, some of the “deals” involve more than one loan “facility” originated by the same borrower on that date. In this study, we conduct our analysis at the facility-level, treating each facility as a separate loan. This is because deals with multiple lenders do not always involve the same group of lenders in all facilities. Moreover, loan yield spreads are dependent on facility-level attributes.

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additional data on off-balance-sheet items. These data are reported at the level of the

individual bank.

In this study, the date of the facility is used as a key variable to match the annual report

data of banks and borrowers with the year-end data immediately preceding the facility date.

We obtain loan data for 1988-1999 from the DealScan, bank data for 1987-1998 from the

Call Reports, and borrower data for 1987-1998 from Compustat. As for lender type, we

include bank lenders only and exclude other types of lenders such as insurance companies,

mutual funds, etc. We begin with an extraction of the DealScan database which contains

data on 65,380 loan facilities originated by U.S. banks from 1988 to 1999. To ensure the

availability of borrower information, we require that the borrower’s country of origin is

USA and delete observations with missing borrower names and/or borrower tickers. Also,

we exclude loan facilities without lenders’ names or a loan facility active date. We are left

with 19,082 loan facilities after applying these filters. Using the names of the borrowers

and locations recorded in DealScan, we match the loan data with firm data from Compustat.

In total, 10,839 loan facilities are successfully matched. Next, we use the names of lead

banks in DealScan to link matched loan and borrower information with bank-level

information in Federal Reserve Call Reports. The Federal Reserve Call Reports supply

many financial, structural and geographical variables for bank lenders. For syndicated

loans, we assume that the lead bank’s characteristics have the greatest effects on the

determination of loan contract terms because the responsibilities for bargaining,

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monitoring and screening are placed on the lead banks.9 Considering the duties of the lead

bank (origination, loan administration, collateral administration, etc.) in syndicated loans,

we include lead banks in our sample.10 In the case of a loan with multiple lead banks, we

assign each lead bank a weight according to its portion of the total shares held by lead

banks. In doing so, we include information about all lead banks in each loan facility,

avoiding the exclusion of valuable bank information. After matching with bank data,

1,869 loan facilities with usable information remain. A large number of observations are

lost in the process of linking the loan information with bank-level data because many bank

names could not be found in Call Reports. Finally, we drop 17 observations identified as

outliers because the logarithm of borrower total assets is less than zero.

The remaining sample data consist of 1,852 loan facilities associated with 95 banks

and 740 firms11. Each firm, on average, has more than 2 loan facilities in this sample.

Since the DealScan database covers the loan syndication market, in our final sample

71.98% of loan facilities are syndicated loans and 28.12% are sole-lender loans. For

comparison’s sake, in the full DealScan sample 79.83% of loan facilities are syndicated

loans and 20.17% are sole-lender loans. Table 2 provides a description of all the

9 Dennis and Mullineaux (2000) document that the agent bank negotiates and drafts all the loan documents; participants can provide comments and suggestions but are not generally involved in the negotiations with the borrower. In some transactions, agent roles (origination, loan administration, collateral administration) are divided among several institutions. Fees are split in the case of multiple agents. Angbazo, Mei and Saunders (1998) state that lead banks retain primary administrative, monitoring, and contract enforcement responsibilities. Banks acting as managers perform administrative oversight duties although their share ownerships in the syndicated loan are on average smaller than lead banks. Participants do not perform special functions other than being signatories to the original loans. 10 We exclude banks whose lender role in a syndicated loan is that of participant, advisor only, secondary investor, sub-participants, technical agent, or collateral agent in syndicated loans. 11 Only 8 of the loans represent project finance.

22

explanatory and dependent variables used in the cross-sectional analysis of the effects of

bank, borrower, and non-price loan characteristics on loan yield spreads.

[Table 2 here]

Before investigating empirically the effects of bank characteristics on the determinants

of loan yield spreads, we begin by documenting the summary statistics of selected key

variables in Table 3.

[Table 3 here]

As shown in Table 3, the loan yield spreads (RATEAISD), measured by rates

all-in-spread drawn, is on average 178 basis points above the benchmark London interbank

offering rate (LIBOR).12 In DealScan, all-in-spread drawn is expressed as a spread over

LIBOR which takes into accounts both one-time and recurring fees associated with the

loan. The all-in-spread drawn is thus defined as the coupon spread, plus any annual fee,

plus any up-front fee divided by the maturity of the loan. For loans not based on LIBOR,

the LPC converts the coupon spread into LIBOR terms by adding or subtracting a constant

differential reflecting the historical averages of the relevant spreads. In this sample the

average maturity of the loan facilities (TFCMAT) is 3.62 years, and the mean loan facility

size is $0.27 billion.

This paper is similar in spirit to the study of Coleman, Esho and Sharpe (2002), in

which the influence of bank characteristics on loan pricing and maturity is examined. This

12 Other studies using the “all-in-spread drawn” to measure loan yield spreads include Angbazo, Mei and Saunders (1998), Hubbard, Kuttner and Palia (2002) among others. Loans are frequently priced off the prime rate, 6-month LIBOR, and 6-month Certificate of deposit (CD) rates.

23

study extends Coleman, Esho and Sharpe (2002) in two important regards. First, the

number of lead lenders in each loan contract is not included in their study. We incorporate

the number of lead lenders in each loan contract as one of the bank characteristics and

predict a significant effect on the determinants of loan yield spreads. Second, we include

multiple lead bank characteristics in the investigation of bank effects on the determination

of loan yield spreads. To do so, we assign each lead bank a weight according to its

contribution to the loan contract based on its share of the syndicated loan.

The summary statistics comparison of selected key variables from our sample and the

sample in Coleman, Esho, and Sharpe (2002) is presented in Table 4. Table 4 shows that

the bank lenders in this sample have mean total assets of $ 32 billion, mean total loans (net

of unearned income) of $ 21.6 billion, and mean total deposits of $22.6 billion, with a mean

cash/assets ratio 8%. Meanwhile, the borrowers have mean total assets of $2.9 billion, and

mean sales of $ 1.9 billion.

[Table 4 here]

As shown in Table 4, the mean relative size ratio in this sample is 1.88 which is much

smaller than the mean relative size ratio of 437.10 in the Coleman, Esho, and Sharpe (2002)

sample. One of the reasons for such a big difference is that, in Coleman, Esho and Sharpe

(2002) sample, only the lead bank contributing largest portion to each loan facility is

included while we include all lead banks in each loan facility in our sample. The typical

banks in the present sample are much smaller, and the typical borrowers are much larger in

terms of total assets, than those in the sample of Coleman, Esho and Sharpe (2002).

24

Similarly, the facility amount size in this sample is greater than that in the Coleman, Esho

and Sharpe (2002) sample. Moreover, 71.98 % of loan facilities in this sample are

syndicated loans while 92% of the sample loans are syndicated loans in the Coleman, Esho,

and Sharpe (2002) sample. These sample differences might lead to the differing signs of

the coefficients of the relative size ratios in the empirical results of this study and Coleman,

Esho, and Sharpe (2002). Discussion of the sign of the coefficient of relative size ratio will

be presented later in the interpretation of empirical results.

Empirical methodology

In this study, we focus on the effects of bank characteristics, which include the number

of lead lenders, bank monitoring power, bank size, and bank risk, while controlling for the

effects of borrower characteristics and non-price loan features. Defining loan yield spreads

as a function of bank, borrower and non-price loan features, we examine the effects of bank

lenders, borrowers, and non-price loan characteristics on the determinants of loan yield

spreads, using ordinary least squares.

Table 5 contains a matrix of Pearson correlation coefficients among the dependent and

explanatory variables. These correlations reveal some simple relationships among the

variables.13 The relative size ratio is positively correlated with loan yield spreads. Both

loan loss provision and capital assets ratio are negatively correlated with loan yield spreads.

13 To detect multicollinearity, we apply variance inflation factor (VIF) which can be expressed as VIF=1/ (1-R-square). A general rule is that the VIF should not exceed 10 (Belsley, Kuh, & Welsch, 1980). In this study, we use each explanatory variable as the dependent variable to run a regression and obtain the R-square and VIF. None of the VIFs we obtained exceed 10.

25

This is consistent with the idea that banks with intensive monitoring power and less bank

risk extract a higher spread for their monitoring and lending activities.

[Table 5 here]

4. Empirical results

Table 6 presents the results of the examination of the relationships between loan yield

spreads and bank characteristics, borrower characteristics, and non-price loan features. We

compute White’s (1980) heteroskedasticity-consistent standard errors to account for

heteroskedaticity.

[Table 6 here]

In this study, we are interested in the effects of bank characteristics on the determinants

of loan yield spreads. As shown in Table 6, regression 1 reports the results without

considering the effects of bank characteristics, while the variables reflecting bank

characteristics are included in regression 2. The pure effects of non-price loan

characteristics on loan pricing are examined in regression 3. Bank effects and non-price

loan characteristics are incorporated in regression 4. It is clear that the regression

equations as a whole are significant based on the F-values. Comparing the adjusted R-

squares in these four regressions, it is clear that the inclusion of bank characteristics among

the explanatory variables in regressions 2 and 4 improves the entire model’s explanatory

power. Moreover, most of the coefficients of variables reflecting bank characteristics are

statistically significant. This suggests that bank characteristics have significant effects on

26

the determinants of loan yield spreads. We next discuss in detail the effects of bank,

borrower and non-price loan characteristics on the determinants of loan yield spreads,

focusing on the regression in Table 6.

4.1 Bank Effects

The coefficient of the relative size ratio, which is the ratio of bank over borrower size,

is significantly negative. This result is inconsistent with the finding in Coleman, Esho and

Sharpe (2002). In their study, they find relative size is positively related to loan yield

spreads and negatively related to maturity, while in this study the coefficient of relative size

ratio is negative.

As discussed earlier, the difference in the results might arise from the different relative

size ratios in these two samples. The mean relative size ratio in this sample is much smaller

than that calculated for the Coleman, Esho and Sharpe (2002) sample. Only the lead bank

contributing the largest portion of the loan for each loan facility is included in Coleman,

Esho and Sharpe (2002), there is 52 lead banks represented in their sample with the top 5

banks appearing in 76 % of the sample.14 By contrast, we include all lead banks in each

loan facility and have 95 banks presented in this sample.

Our reliance on the Compustat database for borrower characteristics suggests that

small firms might be underrepresented in our sample. The typical borrower in this sample

has mean total assets of $ 2.91 billion compared to borrower mean total assets of $ 0.83 14 These top five banks are Bank of America, Chase Manhattan Corporation, Citibank, Bank One Corporation and Fleet Boston Corporation (Coleman, Esho and Sharpe (2002).

27

billion in Coleman, et al (2002). Borrowers in this sample are more likely to be medium or

large-sized firms in terms of total assets. Thus, it is expected that these borrowers are more

likely to have less firm-specific risk and thus enjoy lower loan yield spreads. Moreover,

the typical lenders corresponding to these borrowers are not large banks in terms of total

assets. Following the bargaining power argument in Coleman, Esho and Sharpe (2002),

the borrower’s bargaining power is expected to increase with firm size. Taken together, the

typical borrower in this sample is more likely a medium or large-sized firm with less

firm-specific risk. Thus, it is not surprising to observe the negative sign of the coefficient

of the relative size ratio.15

As shown in Table 6, the coefficient of bank loan loss provisions is significantly

negative as expected. Loan loss provision is used as the proxy for bank monitoring power

and is inversely related to bank monitoring power. A decrease in the level of loan loss

provisions indicates an increase in managerial assessment of loan portfolio quality and

enhanced bank monitoring and screening abilities. For a bank, a lower level of loan loss

provisions conveys favorable information about the bank’s quality of monitoring activities.

Therefore, banks with low levels of loan loss provisions have superior monitoring power

and thus charge higher premia.

15 The coefficient of the relative size ratio is not significant, as shown in regression 4 in Table 6. Our argument focusing on the issue of size may arguably be too narrow. An alternative interpretation for the negative relation between the relative size ratio and loan yield spreads can be inferred from the following. Empirical studies of the U.S. banking industry document the significant effects of a bank’s size on its lending business. Larger banks are more likely to lend to medium and large companies, assuming these borrowers having less firm-specific risk. Given that banks prefer lending to big borrowers, if a big bank lends to a small borrower then one would expect that the small borrower must have high credit quality and strong bargaining power in order to borrow from the bank. As such, we presume that those small borrowers who borrow from big banks are associated with a lower risk premium, and thus the relationship between the relative size ratio (lender size over borrower size) and loan yield spreads is negative.

28

The significantly negative coefficient of the bank lenders’ capital-assets ratio indicates

that a bank with a lower capital-assets ratio charges higher loan yield spreads. This result is

supporting evidence for the contention that a more risky bank with a lower capital-assets

ratio will charge a higher premium.16 This result is consistent with our expectation and

also with the findings in Hubbard, Kuttner and Palia (2002) and Coleman, Esho and Sharpe

(2002).

The coefficient of the number of lead lenders is significantly positive and relatively

large in magnitude. As discussed earlier, the positive sign of the coefficient reflects lead

lenders’ interest in generating fee income and their requests for compensating greater credit

and risk exposure to loan syndication failure. The positive sign can also indicate that the

presence of more lenders in the loan facility reflects the originating bank’s unwillingness to

lend to the borrower on its own and could suggest the existence of unfavorable information

about the borrower’s credit quality. Dennis and Mullineaux (2000) argue that, in the case

of syndicated loan, the originating bank is willing to syndicate those loans on which it has

less favorable “inside information”. Simons (1993) examines empirically the motives for

syndications and reports that diversification is the primary motive for syndication. From

the perspective of bank governance functions, Esty and Megginson (2002) state that a loan

syndicate with a large number of lenders can deter strategic default on the part of the

borrower by making it more costly to default. This deterrence effect indicates that, with

16 As a robustness check, we have also rerun the regression with alternative proxies for bank risk, loan deposit ratio and cash assets ratio (not reported), and we obtain very similar results. Coleman, Esho and Sharpe (2002) use loan deposit ratio as the proxy for bank liquidity risk. The cash assets ratio is used in Kashyap, Rajan and Stein (2002) to measure bank liquid-assets.

29

more banks in the syndicate, borrowers face a higher risk of being shut out of future

borrowing if they default. From another perspective, one might expect that loans with

diffused lender structure are associated with risky borrowers. Taken together, one would

expect that the borrower might be at a disadvantage facing multiple banks and be charged

with higher spreads. The results of the present study confirm that the number of lead

lenders is in fact positively related to loan yield spreads.

4.2 Borrower and Loan Effects

As shown in Table 6, the coefficient of the current ratio is significantly negative,

consistent with our expectation and findings in prior studies. The negative sign suggests

that borrowers with lower current ratios are associated with higher loan yield spreads.

Since a borrower’s current ratio is regarded as a proxy for borrower risk, the higher the

current ratio, the lower the probability the borrower has short-term solvency or liquidity

problems.

The significant negative coefficient of borrower size is consistent with our hypothesis.

It is costly for banks to monitor and screen these small firms because smaller firms

presumably have more information asymmetries and more risk. Therefore, smaller

corporate borrowers are usually charged higher loan yield spreads.17

The results from Table 6 that deserve mention are the coefficients of the loan 17 The variance inflation factor (VIF) for this case indicates that, multicollinearity is not a severe problem here. Furthermore, in view of the correlation between these two explanatory variables, borrower size (logarithm of borrower total assets) and relative size ratio (logarithm of bank total assets/logarithm of borrower total assets), we replace borrower size (logarithm of borrower total assets) with firm size (logarithm of borrower total sales) and obtain similar empirical results (not reported).

30

characteristics variables. The statistically significant relationship between loan yield

spreads and term facility maturity is consistent with findings in Strahan (1999), Dennis

Nandy and Sharpe (2000). The negative sign of the coefficient of maturity suggests that

maturity is inversely related to loan yield spreads, as loans with longer maturity are more

likely associated with borrowers with higher credit quality (Gottesman and Roberts

(2003)).

The negative relationship between loan yield spreads and facility size is in line with

findings in Angbazo, Mei and Saunders (1998). This can be interpreted as indicating that

the originating bank is exposed to a lower level of firm-specific risk since borrowers

corresponding to large loans are more likely to be large firms which are assumed to have

lower levels of risk.

The coefficient of loan distribution, a dummy variable equal to 1 if the loan is

underwritten by a syndicate of banks, is negative. The negative sign of this coefficient can

be explained by the argument that syndicated loans lower the risk of an unsuccessful

distribution of loans due to risk diversification for bank lenders. Accordingly, loan yield

spreads are lower in syndicated loans.

As expected based on Table 1, a positive relationship between secured status and loan

yield spreads is obtained. This positive sign of the coefficient of secured status suggests

that since riskier borrowers are required to pledge collateral and firm specific risk cannot

be sufficiently reduced by the security guarantees, secured borrowings are positively

associated with loan yield spreads. This positive relationship between secured status and

31

loan yield spreads is consistent with findings in Angbazo, Mei, and Saunders (1998),

Dennis, Nandy, and Sharpe (2000).

4.3 Robustness checks

This section discusses results from several robustness checks of our original results.

The first robustness check focuses on results in three different sub-samples: term loans,

revolvers, and others. The second explores the sensitivity of the results to the

single-equation OLS framework. The third robustness check discusses the results from

the models employing alternative lender number variables, such as total number of lenders

and the number of participants, and thus compare with the original results.

In this sample, as shown in Table 6, we pool term loans, revolvers and other types of

loan agreements together. This approach could be subjected to criticism. Coleman, Esho

and Sharpe (2002) suggest that the estimates obtained by Hubbard, Kuttner and Palia (2002)

could be biased as a result of their practice of imposing identical relationships across loan

types. Different types of loans have different properties. For example, unlike the term loan,

the revolver offers the borrower the right (but not the obligation) to draw down, repay and

redraw all or part of the loan at their discretion (Rhodes (2000)). Revolvers are very

important in fostering the bank-customer relationship and in bank commercial and

industrial lending. A revolver facility provides an ongoing line of credit that may be drawn

down, repaid and re-borrowed many times over the life of the facility. The expected size of

the loan to be drawn down is often not certain as the loan amount will be associated with

32

the borrower’s future circumstances and the loan constraints assigned on the loan contract.

Compared to fixed term loan facilities, revolvers are more likely to be associated with

quantity risk (take-down risk) (Ho and Saunders (1983). This could lead to higher required

yield spreads. As for the focus on revolvers, Dennis, Nandy and Sharpe (2000) point out

that the contract terms on revolvers of risky firms differ from those of less risky firms. In

this study, to avoid questionable pooling of different types of loan agreements together, we

separately re-estimate the model for three sub-samples containing term loans, revolvers

and other types of loan agreements, as shown in Table 7.18

[Table 7 here]

From Table 7, the results in the revolvers sub-sample are similar to the original

findings as shown in Table 6, suggesting that bank characteristics have significant effects

on loan yield spreads. Attention needs to be paid to the differences among the regression

results for those three sub-samples. Bank characteristics have less effect on the

determinants of loan yield spreads for the term loan and other types of loan agreement

sub-samples than for revolvers. One would expect that the effects of bank and borrower

characteristics on the setting of loan contract terms could be more accurately reflected in

revolvers. Besides the basic differences between revolvers and other types of credit loans,

the difference in sample size could also be a reason for the weak regression results for the

term loan and other types of loan sub-samples: the sample sizes for the latter two are much

18 A term loan is for a specific amount of money which is to be repaid in full by an agreed date. A revolver is also available for a specific amount of money for an agreed period of time, but unlike the term loan, it offers the borrower the right (but not the obligation) to draw down, repay and redraw all or part of the loan at their discretion. (Syndicated Lending, Tony Rhodes, 3rd edition)

33

smaller than that of the revolver sub-sample.

Turning to the second area of robustness analysis, applying the single-equation OLS

technique can be subjected to criticism. Dennis, Nandy and Sharpe (2000) point out the

important interrelationships among contract terms. They model maturity, secured status

and pricing within a simultaneous decision framework, documenting significant

bi-directional relationships between maturity and secured status and a uni-directional

relationship from both maturity and secured status to loan pricing (all-in-spread). Coleman,

Esho and Sharpe (2002) employ OLS to investigate the effects of bank characteristics on

loan pricing and maturity. In their study they estimate the models using OLS, assuming

maturity and yield spread involve a recursive model with a uni-directional relationship

from maturity to the yield spread.

To explore the sensitivity of the results to the single-equation OLS framework and its

specifications, we estimate the model in a reduced form of a simultaneous equations

framework. Similar in spirit to the empirical approach in Coleman, Esho and Sharpe

(2002), we re-estimate our model, as the loan yield spread, maturity and secured status

involve a recursive model with uni-directional relationships from both maturity and

secured status to loan yield spreads.19 In both the maturity equation and secured status

equation, we include bank, borrower and non-price loan characteristics. The regression

results are provided in Table 8.

19 In the recursive model of this study, both maturity and secured status are included in the loan yield spreads equation as explanatory variables, while loan yield spread is not included in the maturity and secured status equations.

34

[Table 8]

As shown in Table 8, the results suggest that the main findings of the estimated bank

effects on loan yield spreads are robust to the reduced form framework. Accordingly, the

results do not appear to be driven by specifications or errors in the single-equation OLS

framework.

To explore the sensitivity of our original results to alternative lender number variables,

we rerun the models employing alternative lender number variables such as total number of

lenders and the number of participants. It is clear that total number of lenders captures the

number of all types of lenders including both lead lenders and participants. And, the

number of participants excludes lead lenders for a given loan contract. As shown in Table

9, regression 1 provides the results from the original model in Table 6. Regression 2 shows

the effects of total number of lenders on the setting of loan yield spreads. This significantly

positive relation between total number of lenders and loan yield spreads is similar in spirit

to the association between the number of lead lenders and loan yield spreads. This is

because lead lender’ characteristics have the most influences on the setting of loan contract

terms in that the responsibilities for bargaining, monitoring and screening are placed on

lead lenders. Accordingly, the reasoning for the positive relation between the number of

lead lenders and loan yield spreads are also applicable here for the positive relation

between total number of lenders and loan yield spreads. Therefore, the presence of more

lenders results in higher loan yield spreads. The effects of risk diversification seem

dominated by the effects of monitoring duplication and negotiation complexity in our

35

given sample.

[Table 9]

Moreover, as shown in regression 3 of Table 9, the coefficient of the number of

participants is also significantly positive. Notably, regression 4 presents the results which

incorporate the effects of both the number of lead lenders and the number of participants on

loan yield spreads. As shown in regression 4, the coefficient of the number of participants

becomes much less significant relative to that in regression 3 when both the number of lead

lenders and the number of participants are included in the model as explanatory variables.

It is clear that the effects of lead lenders on loan yield spreads are significant and dominate

the effects of participant on loan yield spreads. This is supportive for the important roles

played by lead lenders who maintain the responsibilities of monitoring, administrative

activities and loan enforcement, while participating lenders just simply provide credits for

borrowers; this is consistent with our expectation that lead lenders account for most of the

effects that total number of lenders has on loan yield spreads.

The positive relation between the number of participants and loan yield spreads, as

shown in Table 9, is possibly associated with the differences between syndicated loans and

non-syndicated loans. A syndicated loan with more participating lenders involved could

indicate that this loan is too risky and thus needs more lenders to participate to lower the

firm-level risk that each lender exposes. Considering high potential risk in the loan with

more participating lenders, we expect that there is a positive relation between the number

of participants and loan yield spreads. On the other hand, the presence of more

36

participating lenders could lead to risk diversification and thus lower loan prices. The

magnitude of risk diversification executed by participating lenders might be limited, which

is in line with their roles in loan contracts. Accordingly, a positive relation between the

number of participating lenders and loan yield spreads is not surprising.

Collectively, the results suggest that lead lenders have significant influences on the

determination of loan yield spreads and account for most of the effects of total number of

lenders on loan yield spreads.

5. Conclusions

The lender-borrower relationship has been widely explored, with a focus on how it is

affected by borrower, loan and, to a lesser extent, lender characteristics. Relatively little

study has been devoted to the effects of the number of lenders in individual term loan

facilities, likely because prior work on the number of lenders has concentrated on

firm-level rather than loan contract-level data. In order to study the effects of the number

of lead lenders as well as other bank characteristics on loan yield spreads, we assemble

loan contract variables, borrower financial variables, and bank financial variables from the

DealScan database, the Compustat database and Federal Reserve Call Reports, respectively.

Incorporating a broader range of bank characteristics, we find that they have significant

effects on loan yield spreads after controlling for the effects of borrower and non-price loan

characteristics. Banks with greater monitoring power and riskier banks with lower

capital-asset ratios are found to extract higher rents, which is consistent with findings in

37

prior studies. Importantly, a new dimension of lender characteristics – the number of lead

lenders – is shown to have a significant influence on loan yield spreads.

Our study also provides evidence that borrower characteristics are significant

determinants of loan yield spreads, and extends the existing literature by emphasizing the

significant influence of bank characteristics on loan yield spreads. Moreover, we find that

the loan type is related to the effects of borrower and bank characteristics on loan contract

terms.

38

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Shockley, R. and A. Thakor, 1997, Bank loan commitment contracts: data, theory and

tests, Journal of Money, Credit, and Banking, 29, 517-534.

Strahan, P. E., 1999, Borrower risk and the price and nonprice terms of bank loans,

Working paper, Department of Finance, Boston College.

Table 1

Summary of Hypotheses

Hypotheses Characteristics Proxies Expected Sign Realized Sign

Borrower Characteristics

Borrower Solvency current ratio - ve - veAsymmetric Infomration borrower size - ve - veCredit Quality leverage +ve +ve

Bank Characteristics

Bank Risk relative size + ve - veBank Monitoring loan loss provision - ve - veBank Liquidity Risk capital asset ratio - ve - veMonitoring Duplicate number of lenders + ve + ve

Non-Price Loan Characteristics

Controls maturity - ve - vefacility size - ve - veloan distribution - ve - vesecured + ve + ve

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Table 2

Description of Dependent and Explanatory Variables

Variable Description

RATEAISD Rates all-in-spread drawn, defined as the basis point coupon spread overLIBOR plus the annual fee and plus the upfront fee spread over the duration of the revolver

Current Ratio Borrowers' current assets over current liabilitiesBorrower Size Natual logarithm of borrower total assetsLeverage Borrower's total debts over total assetsTax Assets Ratio Borrower's total income taxes over total assetsRelative Size Ratio Natual logarithm of bank total assets over natual logarithm of borrower total assetsLoan Loss Provision Provision for loan and lease lossCapital Asset Ratio Bank's equity capital over total assetsNumber of Lenders The number of lead banks in each term facilityTFCMAT The term facility maturityFacility Size Natual logarithm of the amount term facility sizeLoan Distribution Dummy variable equal to 1 (0 otherwiae) if the loan is underwritten by a syndicate of banksSecured Dummy variable equal to 1 (0 otherwise) if the loan is secured

Note. This table provides a description of all the explanatory variables and dependent variable used in the cross-sectional analysis of the effects of bank, borrower, and other loan characteristics on loan yield spreads.

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Table 3

Descriptive Statistics for Dependent and Explanatory Variables

Variable Number Mean Standard Deviation Minimum Maximum

RATEAISDa 1690 178.1 114.9 15.00 755.0Current Ratio 1523 2.169 1.796 0.100 40.67Borrower Size ($billion) 1852 2.900 12.91 0.001 257.4Leverage 1819 0.320 0.262 0.000 2.137Relative Size Ratio 1848 1.884 1.428 0.411 47.72Loan Loss Provisionb 1852 156.8 379.1 -104.0 2507.0Capital Asset Ratio 1848 0.073 0.019 0.034 0.266Number of Lenders 1852 3.103 3.454 1.000 28.00Maturityc (year) 1753 3.620 2.340 0.080 20.00Facility Size ($billion) 1852 0.270 0.630 0.00001 7.000Loan Distribution 1843 0.723 0.448 0.000 1.000Secured 1235 0.741 0.438 0.000 1.000

Note. This table presents summary statistics of the explanatory and dependent variables. The loan agreements were originated during the period January1988 - December 1999.aRATEAISD is rates all-in-spread drawn. In DealScan, all-in-spread is expressed as a spread over LIBOR which takes into accounts both one-time and recurring fees associated with the loan.bLoan Loss Provision is a measure of bank loan quality. It is the responsibility of the bank's management to determine an adequate loan and lease loss provision based on current knowledge of the bank's loan portfolios.cTFCMAT is the term facility maturity.

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Table 4

Comparison of Selected Key Variables in Our Sample and the Sample in Coleman, Esho and Sharpe (2002)

Variable Mean S.D. Minimum MaximumA B A B A B A B

RATEAISD (b.p) 178.11 125.50 114.93 93.91 15.00 2.68 755.00 505.40Maturity(year) 3.62 4.12 2.34 2.10 0.08 0.11 20.00 30.00Facility Size ($million) 271.06 361.30 634.18 700.20 0.01 0.60 7000 10500Borrower Size 5.74 20.54 2.07 1.89 0.18 14.91 12.46 26.15Tax Assets Ratio 0.02 0.03 0.04 0.03 -0.17 -0.20 0.35 0.21Cash Assets Ratio 0.08 0.09 0.04 0.04 0.01 0.02 0.22 0.20Loan Deposit Ratio 0.89 0.89 0.21 0.14 0.32 0.42 2.80 1.38Bank Size 9.14 25.23 1.67 1.02 3.06 18.04 13.00 26.21Number of Lenders 3.10 1.77 3.45 1.11 1.00 1.00 28.00 19.00Relative Size Ratio 1.88 437.10 1.43 931.00 0.41 0.01 47.72 9681

Bank Total Assets ($billion) 31.98 90.60Borrower Total Assets ($billion) 2.90 0.83Percentage of Syndicated Loan 0.72 0.92Borrower Sales ($billion) 1.90Bank Total Loans ($billion) 21.60Bank Total Deposit ($billion) 22.60Note. This table presents the comparison of selected variables from out sample and the sample in Coleman, Esho and Sharpe (2002). Column A contains the summary statistics of the variables from our sample. Column B contains the summary statistics of the variables in the sample of Coleman, Esho and Sharpe (2002).Bank size, borrower size, and facility size are the Natual logarithm of bank total assets, borrower total assets,and term loan facility amount size.

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Table 5

Correlation Matrix : Full Sample

Relative Loan Capial NumberCurrent Borrower Size Loss Asset of Facility Loan

RATEAISD Ratio Size Leverage Ratio Provision Ratio Lenders Maturity Size Distribution SecuredRATEAISD 1Current Ratio 0.06604 1Borrower Size -0.63589 -0.21197 1Leverage -0.02288 -0.31794 0.13839 1Relative Size Ratio 0.31599 0.06152 -0.56556 -0.04899 1Loan Loss Provision -0.05896 0.10264 -0.05549 -0.05719 0.11194 1Capital Assets Ratio -0.11564 -0.10594 0.10052 0.05794 -0.03296 -0.05541 1Number of Lenders -0.38491 -0.20579 0.63195 0.20558 -0.29643 -0.13544 0.06118 1Maturity -0.19516 -0.04493 0.2011 0.12213 -0.15281 0.02164 0.05093 0.1714 1Facility Size -0.64443 -0.19868 0.82855 0.21899 -0.44416 -0.03704 0.116 0.65332 0.29947 1Loan Distribution -0.51486 -0.18317 0.60376 0.20865 -0.36894 0.04824 0.08299 0.37784 0.3197 0.72075 1Secured 0.48162 0.06893 -0.38457 0.03247 0.17024 -0.01946 -0.0822 -0.2243 0.06355 -0.31158 -0.19542 1

Table 6OLS Estimations of the Determinants of Loan Yield Spreads : Full Sample

Variable Regression 1 Regression 2 Regression 3 regression 4

Intercept 287.48 348.87 247.88 274.18(20.85)*** (18.30)*** (32.81)*** (21.94)***

Borrower CharacteristicsCurrent Ratio -4.78 -4.02

(-2.57)*** (-2.19)**Borrower Size -12.33 -20.61

(-4.11)*** (-6.19)***Leverage 13.69 4.30

(1.26) (0.40)Bank CharacteristicsRelative Size Ratio -4.36 0.8

(-2.49)** (0.51)Loan Loss Provision -0.03 -0.02

(-3.89)*** (-4.10)***Capital Asset Ratio -230.21 -317.49

(-1.74)* (-2.46)**Number of Lenders 8.00 4.24

(5.25)*** (3.34)***Non-Price Loan Characteristics Maturity -0.21 -0.25 -0.19 -0.19

(-1.83)* (-2.19)** (-1.89)* (-1.90)*Facility Size -14.66 -18.43 -22.26 -27.09

(-5.21)*** (-6.38)*** (-12.85)*** (-12.30)***Loan Distribution -33.01 -20.56 -38.14 -29.25

(-3.85)*** (-2.39)** (-4.89)*** (-3.71)***Secured 89.3 86.42 87.55 86.44

(12.87)*** (12.74)*** (15.09)*** (15.04)***Adjusted R-square 0.5099 0.5373 0.5051 0.5205Number of Observations 952 948 1144 1140F value 142.47 101.07 292.93 155.69Pr > F <.0001 <.0001 <.0001 <.0001Note. This table shows the estimates of the effects of bank, borrower and other loan characteristics on the determinants of loan yield spreads. ***, **, * indicate significance at 1%, 5%, and 10% levels, respectively.t-statistics are calculated using White's heteroskedasticity-consistent standard errors.Sample sizes for these regressions vary on the basis of the availability of all explanatory variables for each regression.

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Table 7OLS Estimations of the Determinants of Loan Yield Spreads : Subsamples

Term Loans Revolvers Others

Variable Regression 1 Regression 2 Regression 3

Intercept 555.42 353.27 251.03

(7.78)*** (16.38)*** (3.63)***Borrower CharacteristicsCurrent Ratio -5.65 -4.94 -5.83

(-1.27) (-2.26)** (-1.10)

Borrower Size -45.36 -21.79 3.35

(-4.74)*** (-5.59)*** (0.24)

Leverage -12.30 13.95 -39.75

(-0.50) (1.12) (-1.04)

Bank CharacteristicsRelative Size Ratio -36.97 -3.74 -1.31

(-3.12)*** (-2.14)** (-0.17)

Loan Loss Provision -0.02 -0.02 -0.0006

(-0.99) (-3.06)*** (0.02)

Capital Asset Ratio -76.56 -210.05 -214.78

(-0.21) (-1.32) (-0.44)

Number of Lenders 4.25 8.82 6.57

(1.19) (4.89)*** (1.28)Non-Price Loan CharacteristicsMaturity -0.81 -0.29 0.11

(-2.84)*** (-1.86)* (0.20)Facility Size -1.39 -19.15 -27.54

(-0.21) (-4.95)*** (-2.77)***Loan Distribution -27.58 -17.01 -65.24

(-1.33) (-1.68)* (-2.26)**Secured 78.44 75.15 147.89

(3.76)*** (9.79)*** (6.65)***Adjusted R-square 0.4276 0.5372 0.6883

Number of Obs 221 623 104

F value 16.01 66.52 21.87

Pr > F <.0001 <.0001 <.0001

Note. This table presents the estimates of the effects of bank, borrower and other loan characteristicson the determinants of loan yield spreads in three sub-samples, term loans, revolvers and others.***, **, * indicate significance at 1%, 5%, and 10% levels, respectively.t-statistics are calculated using White's heteroskedasticity-consistent standard errors.

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Table 8The Determinants of Loan Yield Spreads : Single-Equation OLS vs Reduced Form

Single-Equation OLS Reduced FormVariable Yield Spread Maturity Secured StatusIntercept 348.87 339.22 21.21 1.2

(18.30)*** (17.36)*** (4.03)*** (13.29)***Borrower CharacteristicsCurrent Ratio -4.02 -4.80 0.85 0.009

(-2.19)** (-2.40)** (1.44) (0.86)Borrower Size -20.61 -20.91 -3.28 -0.13

(-6.19)*** (-5.99)*** (-3.28)*** (-7.70)***Leverage 4.30 3.41 7.33 0.2

(0.40) (0.31) (2.26)** (3.60)***Tax Asset Ratio 5.82

(0.30)Intangible Ratio 0.06

(4.52)***Bank CharacteristicsRelative Size Ratio -4.36 -3.34 -0.72 -0.01

(-2.49)** (-2.00)** (-1.45) (-1.35)Loan Loss Provision -0.03 -0.02 0.004 -0.000006

(-3.89)*** (-2.98)*** (1.7)* (-0.17)Capital Asset Ratio -230.21 -180.85 103.74 0.80

(-1.74)* (-1.22) (2.37)** (1.06)Number of Lenders 8.00 9.12 0.95 -0.01

(5.25)*** (5.64)*** (1.98)** (-1.21)Non-Price Loan CharacteristicsMaturity -0.25 -0.1

(-2.19)** (-0.81)Facility Size -18.43 -20.45 3.95 0.03

(-6.38)*** (-6.41)*** (4.20)*** (1.61)Loan Distribution -20.56 -13.43 15.46 0.05

(-2.39)** (-1.50) (5.96)*** (1.12)Secured 86.42 85.25

(12.74)*** (11.93)***Adjusted R-square 0.5373 0.5636 0.2363 0.2048Number of Obs 948 754 754 754F value 101.07 89.54 24.33 20.42Pr > F <.0001 <.0001 <.0001 <.0001Note.This table shows the estimates of the effects of bank, borrower and other loan characteristics on thedeterminants of loan yield spreads in both single-equation OLS framework and reduced form framework. ***, **, * indicate significance at 1%, 5%, and 10% levels, respectively.t-statistics are calculated using White's heteroskedasticity-consistent standard errors.

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Table 9Robustness Check: The Number of Lead Lenders vs Other Lender Number Variables

Variable Regression 1 Regression 2 Regression 3 regression 4

Intercept 348.87 359.06 354.70 357.00(18.30)*** (18.45)*** (18.11)*** (18.31)***

Borrower CharacteristicsCurrent Ratio -4.02 -4.27 -4.52 -4.10

(-2.19)** (-2.33)** (-2.46)** (-2.24)**Borrower Size -20.61 -21.81 -20.16 -21.94

(-6.19)*** (-6.42)*** (-5.97)*** (-6.46)***Leverage 4.30 9.37 12.78 6.51

(0.40) (0.88) (1.19) (0.60)Bank CharacteristicsRelative Size Ratio -4.36 -4.72 -4.48 -4.67

(-2.49)** (-2.69)** (-2.54)** (-2.67)**Loan Loss Provision -0.03 -0.03 -0.03 -0.03

(-3.89)*** (-3.88)*** (-4.12)*** (-3.81)***Capital Asset Ratio -230.21 -233.69 -247.03 -228.12

(-1.74)* (-1.67)* (-1.75)* (-1.63)Number of Lead Lenders 8.00 6.13

(5.25)*** (3.40)***Number of Total Lenders 3.44

(5.37)***Number of Participants 4.02 2.08

(4.43)*** (1.94)*Non-Price Loan Characteristics Maturity -0.25 -0.24 -0.19 -0.25

(-2.19)** (-2.13)** (-1.89)* (-2.22)**Facility Size -18.43 -18.00 -22.26 -18.63

(-6.38)*** (-6.29)*** (-12.85)*** (-6.46)***Loan Distribution -20.56 -17.46 -38.14 -17.75

(-2.39)** (-2.00)** (-4.89)*** (-2.03)**Secured 86.42 84.54 87.55 85.22

(12.74)*** (12.45)*** (15.09)*** (12.53)***Adjusted R-square 0.5373 0.5379 0.5334 0.5387Number of Observations 948 948 948 948F value 101.07 101.31 99.54 93.24Pr > F <.0001 <.0001 <.0001 <.0001Note. This table shows the estimates of the effects of bank, borrower and other loan characteristics on the determinants of loan yield spreads. ***, **, * indicate significance at 1%, 5%, and 10% levels, respectively.t-statistics are calculated using White's heteroskedasticity-consistent standard errors.Sample sizes for these regressions vary on the basis of the availability of all explanatory variables for each regression.