Bank Effects and the Determinants of Loan Yield Spreads *
Li Hao**
September, 2003
JEL classification: G21.
Keywords: Loan yield spreads, bank characteristics, borrower characteristics, bank risk.
* The author is indebted to her supervisor, Professor Gordon S. Roberts, for his
continuous guidance and support. The author would like to thank her committee members, Professor Yisong Tian and Professor Melanie Cao, for their valuable suggestions and comments. 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: 20635. Email: [email protected].
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Abstract
This paper examines the effects of bank characteristics on loan yield spreads after
controlling for borrower characteristics and non-yield-spread loan features. We assemble
loan contract variables, borrower and bank financial variables from the DealScan database,
the Compustat database and U.S. Federal Reserve Call Reports and incorporate a broader
range of bank characteristics to investigate bank effects on loan yield spreads. Bank
characteristics included in this study are bank size, monitoring power, and bank risk. In
addition, a new variable, the number of lenders in each loan contract, is introduced and
shown to be an important determinant of loan yield spreads. The measure of the number of
lenders in this study is different from those in prior studies as it focuses on the number of
lead lenders specified in each loan contract. Moreover, we define as lenders only those
banks that have lending relationships with borrowers and retain administrative, monitoring,
or contract enforcement responsibilities. We find evidence that bank characteristics
significantly influence loan yield spreads.
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1. Introduction
The lender-borrower relationship has long been studied in prior studies. There are two
sides of credit borrowing in the lender-borrower relationship, the demand side and the
supply side. One would expect that factors from both sides have effects on the
lender-borrower relationship. 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 price (Angbazo, Mei and Saunders
(1998), Gorton and Kahn (2000), among others). In this strand of the literature, 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
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likely to make secured loans frequently and lend to riskier borrowers. Hubbard, Kuttner
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 about 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 prices, taking
into account the influences of both bank and borrower attributes. Further lender
characteristics are included in this study of bank effects on loan prices. Notably, a new
dimension of bank characteristics, the number of lenders at the loan level, is introduced.
This is motivated by recognizing the important influence of single versus multiple banking
relationships documented in prior studies. It has been well documented that the number of
banking relationships a borrower maintains at a given moment 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
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and Korting (1998) 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 lenders on the lender-borrower
relationship, the current paper incorporates a new variable, the number of lenders at the
loan level, along with other lender characteristics, to examine bank effects on loan yield
spreads. The number of lenders at the loan level is expected to affect the setting of loan
contract terms. The effects of risk diversification, monitoring duplication, negotiation
complexity and bargaining power constitute the main issues. As for multiple-lead-lender
loans, the negotiation process between a borrower and multiple lenders becomes more
complex than is the case with a single lender. Considering the potential for monitoring
duplication and benefit sharing in cases of loans with multiple lenders, it is expected that
the lenders’ monitoring effectiveness is affected by the presence of multiple lenders, as is
the settings of loan contract terms. The bargaining power of each party will change
according to its level of commitment to the loan contract. Furthermore, 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 discourage
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strategic default on the part of the borrower (Esty and Megginson (2003)). In brief, the
number of lenders at the loan level is expected to affect the setting of loan contract rates.
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-yield-spread
loan characteristics. 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 lenders for a given loan contract – is shown to have a significant influence on
loan yield spreads. The positive relationship between the number of lenders at the loan
level and loan yield spreads 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-yield-spread loan features and borrower characteristics. Second, we incorporate a new
dimension of bank characteristics in the study of bank effects on loan yield spreads.
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Specifically, we introduce the number of 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 is 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 a biased 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-yield-spread loan characteristics. The central testing
hypotheses in this study are also discussed in section II. Section III describes the data and
the empirical approach we use. Our empirical tests are reported in Section IV. Section V
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
spreads, while controlling for the effects of borrower characteristics and non-yield-spread
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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.
2.1 Proxies for bank characteristics
Number of 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. By 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 lenders at the level of the individual loan
contract, whereas prior studies have defined the number of lenders as the number of banks
with which a borrower has borrowing relationships.
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
can therefore serve as a proxy for the borrower’s quality, but not in a strict way. 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
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traditional lending relationships. 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 for
empirical testing, 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
this 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.
As discussed above, the measure of the number of lenders differs from those used in
prior studies as it focuses on the number of lenders specified in each loan contract. The
number of 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. Finally,
for the purposes of this study, the relationship between the bank lender and the borrower is
limited to traditional lending business and does not include cash management services.
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.1 In a syndicated loan, a
1 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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group of financial intermediaries agree to jointly issue a loan to a borrower. Dennis and
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 some 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 lenders.
In our sample, 66.39% of the syndicated loans have two or more lead banks. We treat
the number of lead banks in multiple-lead-bank syndicated loans as the number of lenders.
It is expected that the presence of multiple lead banks affects the lenders’ monitoring
effectiveness and thus the setting of loan contract rates due to the potential for monitoring
duplication and benefits sharing. Also, unfavorable information about the borrower could
be inferred from the presence of multiple lead banks, and thus the original bank is
unwilling to lend to the borrower on its own. Given these concerns (duplication of
monitoring, sharing of benefits, potential unfavorable information about the borrower, and
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complexity of the negotiation process), including more lenders in a given loan contract
could result in higher loan rates. On the other hand, including more lenders in a loan
contract could diversify risk and 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. However, 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 would expect the number of lenders to 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.
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 the
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natural logarithm of bank and borrower’s total assets. 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 in the calculation of bank size, 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. This 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 bank and the borrower, and
also on the competence of outside banks. 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 probably limited.
In this study, we use 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.
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Bank monitoring power
The role of banks in information production and monitoring of borrowers in the credit
allocation process has been widely explored. Since they are responsible for monitoring
and screening loan contracts, banks have the 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 rating because a higher
credit rating is 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 is 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
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
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loss provisions. Loan loss provisions are recorded as losses of loan principal that are
probably and reasonably estimable. 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 has superior
information about default risks in its loan portfolios compared to investors and other
stakeholders. Therefore, we assume that an assessment of bank management may be a
more accurate indication of bank monitoring power, and that decisions on the level of loan
loss provisions may convey information about the quality of bank monitoring activities.
As such, 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 “hold-up” theory in Rajan (1992), 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 in doing business. 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 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 banks with less liquidity risk 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
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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
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)). Mayers (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 examine borrower effects on loan yield spreads.2
2 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)).
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These borrower variables serve as proxies for two groups of borrower characteristics:
borrower risk and information costs.3 One would expect that borrower size is negatively
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 more 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. Loans with longer maturities are assumed to
3 One could argue that these three borrower variables cannot completely capture all of the borrowers’ characteristics. Considering that debt agency problems are more severe for small and risky firms (Myers (1977)), we control for the effects of borrower characteristics associated with borrower risk and information costs to investigate bank effects on loan pricing (Coleman, Esho and Sharpe (2002), Hubbard, Kuttner and Palia (2002)).
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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)). As such, the secured status is likely to be positively
related to loan yield spreads.4 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. The endogenous
characteristics of these explanatory variables might be a concern. We will address this
issue later in the robustness checks section.
[Table 1 here]
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
4 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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characteristics and non-price loan characteristics 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 roles5; loan
purpose, type, amount and contract date; and price, as well as a number of non-price
terms.6 The DealScan database provides relatively little detail relating to the borrower’s
financial position and the lender’s financial position. Financial variables reflecting
borrower characteristics can be obtained from the Compustat database, 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 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 database, bank data for 1987-1998
from the Call Reports, and borrower data for 1987-1998 from the Compustat database. As
for lender type, we include bank lenders only and exclude other types of lenders such as 5 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. 6 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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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 the USA and delete observations with missing borrower names and/or
borrower tickers. Also, we exclude loan facilities without lenders’ names or 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 the Compustat database. 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 U.S. Federal Reserves Call Reports.
The U.S. Federal Reserve’s 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, monitoring and screening are placed on the lead
banks.7 Considering the duties of the lead bank (origination, loan administration, collateral
administration, etc.) in syndicated loans, we include lead banks in our sample.8 In the case
of a loan with multiple lead banks, we assign each lead bank a weight according to its
7 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. 8 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.
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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 firms. 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 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
variables in Table 3.
[Table 3 here]
As shown in Table 3, the loan yield spread (RATEAISD), measured by rates
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all-in-spread drawn, is on average 178 basis points above the benchmark London interbank
offering rate (LIBOR).9 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 study is similar in spirit to the research of Coleman, Esho and Sharpe (2002), in
which the influence of bank characteristics on loan pricing and maturity is examined. Even
after controlling for borrower and non-price loan characteristics, they find that bank
characteristics (bank monitoring ability, bargaining power, risk, and syndicate structure)
significantly affect the setting of loan maturity and pricing. Our study extends Coleman,
Esho and Sharpe (2002) in two important regards. First, the number of lenders in each loan
contract is not included in their study. We incorporate the number of 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
9 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.
22
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 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)’s 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).
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,
23
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 lenders, bank monitoring power, bank size, and bank risk, while controlling for the
effects of borrower characteristics and non-yield-spread loan features. Defining loan yield
spreads as a function of bank, borrower and non-yield-spread 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.10 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.
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]
10 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.
24
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-yield-spread 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
the determinants of loan yield spreads. We next discuss in detail the effects of bank,
borrower and non-yield-spread loan characteristics on the determinants of loan yield
spreads, focusing on the regression in Table 6.
25
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.
The difference in the results might arise from the different relative size ratios in these
two samples. In the current sample, the mean relative size ratio is 1.88, which is much
smaller than the 437.10 calculated for the Coleman, Esho and Sharpe (2002) sample. The
typical bank in our sample has mean total assets of $ 31.98 billion while the typical bank in
the Coleman, Esho and Sharpe (2002) sample has mean total assets of $ 90.60 billion.
Since only the lead bank contributing the largest portion of the loan for each loan facility is
included in Coleman, Esho and Sharpe (2002), there are only 52 lead banks represented in
their sample with the top 5 banks appearing in 76 % of the sample.11 In our sample, we
include all lead banks in each loan facility. In total, 95 banks are presented in our 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
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 11 These top five banks are Bank of America, Chase Manhattan Corporation, Citibank, Bank One Corporation and Fleet Boston Corporation (Coleman, Esho and Sharpe (2002).
26
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 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.12
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.
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
12 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.
27
supporting evidence for the contention that a more risky bank with a lower capital-assets
ratio will charge a higher premium.13 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 lenders is significantly positive and relatively large in
magnitude. The positive sign of the coefficient indicates 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 loans, the
agent bank may have information unavailable to the syndicated participants. 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. Thus, one would expect
that the fact that syndicates are more diffuse reflects to some extent that there is less
favorable information about the borrower. Moreover, in syndicated loans, lead banks hold
large portions of the loan and therefore have an incentive to monitor, while lead banks
holding smaller stakes (such as managers) may be likely to engage in a free ride since
monitoring is costly. From the perspective of bank governance functions, Esty and
Megginson (2002) state that a loan syndicate with a large number of lenders can deter
13 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.
28
strategic default on the part of the borrower by making it more costly to default. This
deterrence effect indicates that, with more banks in the syndicate, borrowers face a higher
risk of being shut out of future borrowing if they default. Taken together, one would expect
that the borrower might be at a disadvantage facing multiple banks and be charged with
higher spreads due to potential bargaining complexity, monitoring duplication or the
free-ride problem. The results of the present study confirm that the number of 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 more difficult 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.14
14 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
29
The results from Table 6 that deserve mention are the coefficients of the loan
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.
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 fact that syndicated loans lower the risk of an unsuccessful distribution
of loans due to risk diversification for bank lenders. As a result, 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
results (not reported).
30
associated with loan yield spreads. This positive relationship between secured status and
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 the results. The first
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.
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. 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 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.
Revolvers are very important in fostering the bank-customer relationship and in bank
31
commercial and industrial lending since the revolver, unlike the term loan, 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)). In this study, to avoid the criticism of pooling
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 7.15
[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
smaller than that of the revolver sub-sample.
Applying the single-equation OLS technique can be subjected to criticism. Dennis,
15 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)
32
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 secure status
involve a recursive model with uni-directional relationships from both maturity and
secured status to loan yield spreads.16 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.
[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. Thus, the results
16 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.
33
do not appear to be driven by specifications or errors in the single-equation OLS
framework.
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 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 U.S. Federal Reserve Call Reports,
respectively. Incorporating a broader range of bank characteristics, we find that bank
characteristics have significant effects on loan yield spreads after controlling for the effects
of borrower and non-yield-spread 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 prior studies. Importantly, a new dimension of lender
characteristics – the number of lenders – is shown to have a significant influence on loan
yield spreads.
Our study also provides evidence that borrower characteristics are important
determinants of loan yield spreads, and extends the existing literature by emphasizing the
34
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.
35
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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
Other Loan Characteristics
Controls TFCMAT - ve - vefacility size - ve - veloan distribution - ve - vesecured + ve + ve
40
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.
41
Table 3
Descriptive Statistics for Dependent and Explanatory Variables
Variable Number Mean Standard Deviation Minimum Maximum
RATEAISDa 1690 178.11 114.93 15.0 755.0Current Ratio 1523 2.17 1.80 0.10 40.67Borrower Size ($billion) 1852 2.90 12.91 0.00 257.39Leverage 1819 0.32 0.26 0.00 2.14Relative Size Ratio 1848 1.88 1.43 0.41 47.72Loan Loss Provisionb 1852 156.78 379.07 -104.0 2507Capital Asset Ratio 1848 0.07 0.02 0.03 0.27Number of Lenders 1852 3.10 3.45 1.00 28.00TFCMATc (year) 1753 3.62 2.34 0.08 20.0Facility Size ($billion) 1852 0.27 0.63 0.00001 7.00Loan Distribution 1843 0.72 0.45 0.00 1.00Secured 1235 0.74 0.44 0.00 1.00
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 accountsboth 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 adequateloan and lease loss provision based on current knowledge of the bank's loan portfolios.cTFCMAT is the term facility maturity.
42
Table 4
Comparison of Selected Variables in Our Sample and Other Sample
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 2.68 755.00 505.40TFCMAT(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 TFCMAT 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 1TFCMAT -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 Spread : 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)***
Other Loan CharacteristicsTFCMAT -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 0.5205
Number of Observations 952 948 1144 1140
F value 142.47 101.07 292.93 155.69
Pr > F <.0001 <.0001 <.0001 <.0001 This table shows the estimates of the effects of bank, borrower and non-price 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 Spread : 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)Other Loan CharacteristicsTFCMAT -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 characteriston 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 8OLS Estimations of the Determinants of Loan Yield Spreads : Full Sample
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)Other Loan CharacteristicsTFCMAT -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 the determinants of loan yield spreads in both single-equation OLS framework and reducedform 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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