Reputation and monitoring ability in loan syndications *
Hua Jessie Zhang**
September 24th, 2003
* JEL classification: G21. Key words: Loan syndication, asymmetric information. The author would like to thank her supervisor, Professor Gordon S. Roberts, for his support and advice. The author also would like to thank Kin Chung Lo, Kamphol Panyagometh, Jonathan Yan, Matthew Bowler as well as Stephen Sapp for their helpful comments and suggestions. 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].
Reputation and monitoring ability in loan syndications
Abstract
Syndicated loans are an increasingly important financial instrument, occupying
40% of the US corporate finance market today. Employing a combination of modeling
and empirical testing, we provide and confirm the mechanics of how information about
the true motive and monitoring ability of lead banks is updated from a market perspective
through a sequence of loan syndication decisions. A repeated Bayesian game model with
incomplete information for syndicate members is constructed followed by empirical tests
to support the model. Further, lead banks are divided into two groups: “active” and
“drop-out” groups. Advancing upon previous study, we propose that the actions of the
“active” group should be consistent with the reputation hypothesis, while those of the
“drop-out” group could be motivated by either the exploitation effect or the
overconfidence effect. Two testable hypotheses to discriminate between the two effects
are proposed. The empirical results of this study confirm that the actions of the “active”
group are consistent with the reputation hypothesis and those of the “drop-out” group are
consistent with the overconfidence hypothesis.
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Reputation and monitoring ability in loan syndications
I. Introduction
A syndicated loan is one in which a syndicate of lenders consisting of two or
more banks contracts with a borrower to provide loans on common terms and conditions
governed by a set of common documents (Syndicated Lending (2000)). By the late
1990s syndicated loans had become a rapidly growing emerging market, and an
increasingly important financing tool.
Syndicated loans possess qualities of both public and private debt. Public debt
tends to be long-term, with relatively loose covenants, and in most cases cannot be
renegotiated. By contrast, bank loans tend to be shorter term, with extensive covenants,
and can be restructured. Berlin and Loeys (1988), Berlin et al. (1992), and Rajan et al.
(1995) examined and confirmed these contractual characteristics extensively. A
syndicated loan has characteristics of both public and private debt, as it is the sale of a
“bundle” of loans to a group of institutional participants. In contrast, a non-syndicated
bank loan sells a “whole” contract to a borrower.
The syndicate of participating lenders delegates some monitoring responsibility to
the lead bank(s) prior to finalization of the loan syndication. The loan agreement
specifies which decisions require the consent of certain proportions of the member banks.
A standard syndicated loan agreement usually requires unanimous agreement for any
reduction in fees, principal, interest, or for changes in the terms of credits. However,
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there are provisions that allow the lead bank to declare a default event, typically after
consulting with the other syndicate members.
Loan syndication invites agency problems such as adverse selection and moral
hazard. As mentioned by Esty (2001), the banks invited to participate in the loan
syndication are usually not banks with a previous relationship with the borrower, but
rather banks with a prior syndication relationship with the lead bank. An example of
asymmetric information is also illustrated by Simon (1993): since the syndicate
participants rely mainly on the borrower credit information contained in documents
provided by the lead bank, the lead bank may have additional inside information about
the borrower unavailable to the syndicate members. For example, in cases where the
borrower has been a long-time customer of the lead bank, the lead bank might have some
information not reflected in the borrower’s public financial statements. Dennis and
Mullineaux (2000) give a few examples of such information, citing “judgments
concerning management expertise, the nature of customer-supplier relationships, or the
borrower’s capacity to adapt successfully to changing market conditions.” Because loan
syndication involves relationship banking and significant amounts of underwriting
revenue, the originating banks may have incentives to syndicate loans even when the
inside information is not favorable, thereby creating an adverse selection problem.
Loan syndication also involves moral hazard, as Gorton and Pennachi (1995) and
Dennis and Mullineaux (2000) have pointed out. Once it is sold to syndicate participants,
the participants’ portion of the loan is removed from the balance sheets of the lead banks
and appears on the syndication participants’ statements. As a result, the burden of the
incentive to monitor the loan is shifted from the lead bank to the syndication participants.
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Dennis and Mullineaux (2000) were the first to study the factors affecting the
decision to syndicate a loan and, in cases of loan syndication, the percentage of the loan
syndicated to participating members. Using the DealScan database from the Loan Pricing
Corporation (LPC), they found that the decision to syndicate a loan, together with the
percentage syndicated out to participating members, is affected from the degree of
transparency of the borrower, the loan and lead bank characteristics and the reputation of
the lead banks as syndication originators. In particular, the more transparent the borrower
is, as evidenced by the existence of a credit rating or by being listed on a stock exchange,
the more likely the loan is to be syndicated and sold in greater proportions to syndication
participants.
In the same vein, Panyagometh and Roberts (2003) proposed two alternative
hypotheses to explain loan syndication behavior in general: a reputation hypothesis
versus an exploitation hypothesis. Under the reputation hypothesis, lead banks would try
to syndicate quality loans, signaling their position as “certifiers of quality loans” for
future loan syndication. They used new information measures to re-examine the factors
affecting decision-making for loan syndication and the percentage of loans syndicated out
to other syndication members. Their empirical findings are consistent with the reputation
hypothesis. They also found that the addition of a performance pricing feature linking a
loan’s prime or LIBOR spread to the subsequent financial performance of the borrower,
as measured by various financial ratios, helps to mitigate information asymmetries within
loan syndicates. As a result, the addition of such a performance pricing feature facilitates
the formation of loan syndicates.
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In both Dennis and Mullineaux (2000) and Panyagometh and Roberts (2003), the
findings confirm the belief that “scale and scope of information asymmetries are relevant
to the ‘salability’ of a debt contract” (Dennis and Mullineaux (2000)). These prior studies
also consistently found that a reputation effect is relevant to both decisions. In other
words, more reputable lead banks are more likely to syndicate, and when they do,
syndicate greater proportions of higher quality loans.
However, neither study investigates precisely how reputation functions at the
theoretical level or what kind of information can be deemed to be associated with a
negative/positive reputation. The information measures in Dennis and Mullineaux (2000)
and Panyagometh and Roberts (2003) did not discriminate between positive and negative
reputation effects, nor did these studies investigate the mechanics of how the other
syndicate members learn about the lead bank from previous syndications, or how a
syndicate member’s information about the lead bank(s) is updated before decision-
making. The theoretical model in our paper addresses the questions, connecting and
explaining past empirical work on a theoretical level. The empirical part of our paper,
together with the previous empirical work of Dennis and Mullineaux (2000) and
Panyagometh and Roberts (2003) can be considered as confirmations of the model.
In addition, furthering the reputation versus exploitation hypothesis developed by
Panyagometh and Roberts (2003) for the syndication market as a whole, we view lead
banks as falling into two groups. The “active” group is defined as lead banks that were
still initiating syndicated loans in 1998 or after, or whose parent firms were still doing so.
The “drop-out” group is defined as lead banks that had previously been in the origination
market for loan syndication but ceased acting as a lead bank in 1998 and after. We
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propose that the “active” group should be consistent with reputation hypothesis, while the
“drop-out” group could be subject to either the exploitation hypothesis or the
overconfidence hypothesis.
Good monitoring ability is usually implicitly assumed in previous work studying
asymmetric information between lead banks and participating members. Hubbard et al.
(2002), among others, pointed out that banks have a cost advantage over non-bank
financial institutions and, therefore, better ability to monitor the financial health of these
relational clients. The reputation hypothesis and the exploitation hypothesis from
Panyagometh and Roberts (2003) implicitly assume that lead banks have sufficient
monitoring ability either to establish their reputation as a certifier of quality loans or to
exploit their inside information about the borrower. However, in reality this might not be
the case. In this paper we also extend previous work by incorporating an overconfidence
hypothesis, which has until now been ignored. Under the overconfidence hypothesis, the
reason lead banks drop out and abdicate their leading roles could be that they over-
estimate their ability to monitor the borrowers. As a result of this overconfidence, more
loans turn out to be “bad” than expected. Consequently, both lead banks and participating
members suffer from more loan defaults than expected. We also conduct tests for both
the exploitation and the overconfidence hypotheses for the “drop-out” group.
In this paper, a repeated Bayesian game with incomplete information for
syndicate participants is constructed for the syndicated loan decision-making process. In
each period, from a syndicate member’s perspective, only one of two possible situations
(states) can occur. In the first situation, the lead bank does not exploit inside information
about the borrower, which is usually a relational client of the lead bank, and the lead bank
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also has adequate ability to monitor the borrower. In the second situation (state) the lead
bank either exploits its inside information about the borrower or is overconfident about
its monitoring ability. The potential syndicate participant does not know which is the
true state, but assigns a probability to each state. In each period, the syndicate
participants learn about the quality of loans in all previous periods, and update their
beliefs about the motives and the monitoring ability of the lead bank by updating the
probability of these two states. By comparing the two payoffs using the updated
probabilities, syndicate members then decide whether to become involved in syndication.
We also introduce two new variables associated with lead bank reputation and
changes in borrower financial status. The first new variable, TOTAL, serves as an
improved measure of the lead bank’s reputation as a loan syndication originator. Dennis
and Mullineaux (2000) used several proxies for the reputation effect: the number of
syndication originations conducted in the pre-sample period by the lead bank syndicating
the loan (REPEAT), and a dummy variable indicating whether the managing agent is a
bank (BANK). REPEAT is calculated based on the number of loan syndications
originated by the lead bank before January 1st, 1987, the pre-sample period before the
regression sample. REPEAT might not be a strong indicator of reputation within the
regression sample, although it does reflect some information from the pre-sample period.
Also it does not discriminate between “good” syndications and “bad” syndications. The
dummy variable BANK can only distinguish between bank and non-bank originators, not
between different bank originators. Panyagometh and Roberts (2003) used AVGVOL as
the measure of lead bank reputation. AVGVOL is the yearly average dollar amount of
syndicated loans previously originated by the lead bank. It still suffered from the same
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problem of not being able to discriminate between “good” and “bad” loan syndications in
the past.
The new variable used in this paper, ZCHAG, serves as an improved measure of
financial health changes experienced by the borrower. Panyagometh and Roberts (2003)
used ZSCORECHG in their robustness test. ZSCORECHG is a dummy variable that
equals 1 if the change in Z score is positive and –1 if the change is negative. As a
dummy variable, ZSCORECHG can exaggerate minor random noise caused by factors
irrelevant to the financial status of the borrower. For example, it is an overstatement to
assume a -0.05% Z score change would have the same effect on syndication decision-
making as a –200% Z score change. Instead, we define our new continuous variable
ZCHAG as the change in a borrower’s Altman’s Z score one year after the loan is closed,
which solves the problem of non-proportionality in the variable.
An improved Z score change will have implications for the possibility of loan
default that are the opposite of those of a deteriorated Z score change ZCHAG can,
therefore, also be considered as a proxy for changes in loan quality. Next, we define
“good” loans as loans whose borrowers end up with unchanged1 or improved Z scores
one year after the loans were initiated, and “bad” loans as loans whose borrowers end up
with deteriorated Z scores one year after loans are initiated.
In this paper, a new and more accurate measurement of the reputation effect of the
lead banks, TOTAL, is used. TOTAL is the number of prior syndications conducted by
the lead bank that turned out to be “good” loans, minus the number that turned out to be
“bad” loans. Compared to reputation proxies used in prior studies, TOTAL is a more
advanced, dynamic measure of syndication reputation as it accumulates over time, which 1 Unchanged Z score is defined here as Z score with change greater than -15%.
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is able to discriminate and, further, reward past “good” loan syndication and penalize past
“bad” loan syndication by the lead bank. As a result, it captures the reputation effect with
more accuracy.
Our model predicts that the decision to syndicate is positively correlated with the
number of “good” loans and negatively correlated with the number of “bad” loans.
Further, lead banks are divided into two groups, “active” lead banks and “drop-out” lead
banks. For the “drop-out” lead bank group, the model implies that in the case of inside
information exploitation by the lead bank, the change of financial status which is
represented by a Z score change after the loan is syndicated should be positively related
to the percentage of the loan that the lead bank kept to itself; in the case of lead bank
overconfidence, the lead banks were not able to tell “bad” loans from “good” loans, in
other words, there should not be a relationship between the Z score change after the loan
is out and the percentage of a loan that the lead bank kept to itself in the overconfidence
case.
The empirical results of this paper confirm the overconfidence hypothesis.
Further, our empirical results shows that, in general, the “active” group has syndicated
out more “good” loans to participating members than “bad” loans, signaling that it
chooses not to exploit inside information and that it has good monitoring ability, which
serves as further confirmation of our model. Therefore, the reputation effect, studied in
Dennis and Mullineaux (2000) and Panyagometh and Roberts (2003), showing that lead
banks as a whole syndicated out more higher quality loans, is consistent with the
numerical domination of the “active” group over the “drop-out” group. Further, our
9
model clarifies the differences between the two groups concerning the performance
record of syndicated loans they originated.
Part two of this paper describes the repeated Bayesian game with incomplete
information; part three addresses the testable hypothesis of the model; part four describes
the data and addresses the methodology used in the empirical test of the hypotheses and
discusses the tests and results; part five shows the results of robustness tests; part six
reports our conclusions.
II. Model
As state earlier, a borrower’s long-term relationship with the lead bank(s) could
supply the lead bank(s) with some inside information about the borrower that is not
revealed to the public. As a result, when deciding whether to take part in a syndicated
loan, and taking into account all the public information about the lead bank and the
borrower, participating members will have concerns about the lead bank’s real motivation
for originating the loan as well as its true ability to monitor the borrower. Lead banks’
real motivation could be to certify a good quality loan in order to establish a reputation
which will facilitate further originations of syndicated loans, or to try to help out a
troubled long-term relational borrower before the borrower’s financial difficulty is
revealed to the public; or the lead bank’s own financial health might be deteriorating (as
yet unknown to the public) and the up-front fee from originating a syndicated loan
becomes more important than building a reputation for future deals. On the other hand,
even when the lead bank’s motivation is to certify quality loans, its true ability to monitor
10
the borrower might not be as good as claimed, and participating members still could
suffer from this.
Assuming good monitoring ability on its part, if the motivation of the lead bank is
to certify good quality loans for future deals, the loan to be syndicated is expected to be
“good.” There might be cases where loans turn out to be “bad”, but the expected quality
of loans should be “good.” Also assuming good monitoring ability on the part of the lead
bank, if its motivation is to exploit inside information about the borrower, the expected
quality of the loan is “bad.” If the lead bank does not have good monitoring ability, and
if this is obvious to them, then the members would expect the loan to be “bad.” As a
result, potential participating members will not sign on to the loan syndication due to the
lead bank’s lack of monitoring ability. However, if the participating members are overly
optimistic about the lead bank’s monitoring ability, they might take part in the loan
syndication, and are more likely to find out afterwards that it is a “bad” loan.
In this section, we model the above reputation, exploitation and overconfidence
effects using a repeated Bayesian game model with incomplete information for syndicate
participants. If we consider the lead bank(s) in a syndicated loan as a single player and
the participating members as another player, for the purposes of the game there is one
lead bank and one participating member in each period. Both the lead bank and
participating member are risk-neutral. The participating member exists for an infinite
period, while the lead bank’s time horizon is unknown to the participating member.
There are two states in the game. State one ( refers to the state in which the lead bank
has good monitoring ability and will not exploit inside information about the borrower;
the expected quality of loans in state one is “good”. State two ( is refers to the state in
)1s
)2s
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which the lead bank will exploit inside information about the borrower or/and is
overconfident about its ability to monitor the borrower; the expected quality of loans in
state two is “bad”. If the motive of the lead bank is to exploit inside information, then its
time horizon is finite. In other words, it knows that it will only play a finite number of
loan syndication games because it knows that eventually the quality of its loans will be
revealed and participating members will update their estimations of the relative
probabilities of state one in period t ( )tα and two in period t )1( tα− accordingly, and at
some point will decide not to participate due to the low probability assigned to state one.
If the motive of the lead bank is to certify quality loans, consistent with the reputation
motive, its time horizon is infinite, which means it will originate an infinite number of
syndicated loans. The participating member does not know which state is the true state
, but does know the following prior: )(s
=s
tα
1l
−−−∈−−
;1.,];1,0[.,
2
1
t
t
probwithsprobwiths
αα
The lead bank may or may not know the true state, although it believes it does. This is
not important in the current setting as our model focuses on the participating member’s
perspective as long as a certain constraint about the lead bank’s belief is satisfied. We
will discuss this constraint below.
In the beginning of each period, the syndicate member learns about the loan
quality in the last period. As a result, it adjusts its belief to incorporate the news
about quality of the last syndicated loan originated by the lead bank. If the last
syndicated loan originated by the lead bank is a “good” loan, the syndicate member will
re-assign tα as follows 1 += −tt αα where is positive. Conversely, if the last the 1l
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syndicated loan is revealed to be a “bad” loan, syndicate member will re-assign tα as
follows: 21 l+= −tt αα where is negative. 2l
)p
)(∂ i
From the participating member’s perspective, the payoff has the following layout:
[CHART I GOES ABOUT HERE]
Where e is the expected payoff for the lead bank if state one occurs; f is the expected
payoff for the participating bank if state one occurs; g is the expected payoff for the lead
bank if state two occurs; h is the expected payoff for the participating member if state two
occurs.
In this model, h<0, e>0, f>0 and ∂××−+×∂××+= SpiSpag )1( , which can be
positive or negative.
The variable, e, the expected payoff for the lead bank if state one occurs, should
always be positive; f, the expected payoff for the participating bank if state one occurs,
should always be positive as well; h, the expected payoff for the participating member if
state two occurs, should be negative.
The variable, g, is the payoff for the lead bank if state two occurs. The value of g
depends on the fee for originating the loan ( , the actual probability of the borrower
repaying the loan ( , the size of the loan ( , the percentage of the loan kept by the
lead bank , the loan spread ( , and the possibility of loan default . In the case
of the overconfidence or the exploitation effect, the actual probability of loan default
is higher than its counterpart in state one, where the lead bank has good
monitoring ability and its motive is to certify quality loans for future deals. In the case of
)a
)S
) )1( p−
)1( p−
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overconfidence in state two, the lead bank does not know ex ante the actual probability of
loan default ( , although it believes it does. As a result of its overconfidence, the
lead bank cannot discriminate between “bad” and “good” loans and, therefore, the
percentage of a loan kept by the lead bank (
)1 p−
)∂ should not be correlated with the loan’s
quality. In the case of information exploitation in state two, however, the lead bank
knows ex ante the actual probability of loan default as a result of exploiting inside
information. In an effort to avoid heavy expected loan losses, the lead bank will keep
more “good” loans and fewer “bad” loans for itself while still benefiting from syndication
up-front fees.
)1( − tα
There are two explanations for the motivation of the lead bank in state two. Under
the poor monitoring ability scenario, the lead bank is overconfident about its monitoring
ability. In this case, g could be negative. If state two refers to the state where the lead
bank wants to exploit inside information, g should be positive; it also implies that g is
greater than e and, therefore, there is an incentive for the lead bank to exploit inside
information.
The fact that the lead bank is willing to take the lead role implies that the payoff
for the lead bank to syndicate is greater or equal to the payoff for not syndicating, as
follows:
0>=×+× t ge α , where tα is the probability the lead bank assigns to state one in
period t.
From the lead bank’s perspective, in the case where the lead bank will not exploit
its inside information about the borrower, the lead bank assigns a probability )(β of 1 to
state one. In the case where it will exploit its inside information, the lead bank assigns a
14
probability )(β of 1 to state two as the true state. In the case where the lead bank is
overconfident in its own monitoring ability, it assigns a probability )(β of 1 to state one
as the true state, reflecting its overconfidence about its monitoring ability. In other
words, the lead bank mistakenly thinks that the true state is state one when in reality it is
not.
=α
In order for participating members to decide to become involved in the loan
syndication, the payoff of taking part, hf tt ×−+× )1( αα , should be greater or equal to
the payoff of not taking part, 0. In other words, if )/( hfht −−≥α then the participating
members will opt for the syndication, if they will decide not to
participate in the syndication.
)h−/( fht −<α
If the previous syndication deal is revealed ex post to have been a “good” loan, a
larger probability will be assigned to state one in the next game. If it is revealed to be a
“bad” loan ex post, a larger probability will be assigned to state two in the next game. In
other words, this is a repeated game, and following each round the participants update the
probabilities of each of the two states occurring in the next round.
The following is a numerical example in which two scenarios can be discussed.
Consider the following payoff schedule for participating banks
[CHART II GOES ABOUT HERE]
Scenario 1: 0 (when the participant agent knows with certainty that state two will
occur)
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If participant agents know ex ante that state two will occur, the Bayesian game
degenerates into a Strategic game. Obviously, in the Nash-Equilibrium for such a game,
no syndication will occur, as indicated in Chart III.
[CHART III GOES ABOUT HERE]
Scenario 2: Assume a series of three potential games as follows:
Assuming , , and the probability that state one occurs in the first
period, from the syndicate member’s perspective,
1.01 =l 1.02 −=l
9.01 =α ; at the beginning of game two
it is learned that the Z score of the borrower from game one has deteriorated, therefore,
the expectation that state one will occur, i.e., 2α is re-evaluated, as
8.01.0 =9.02 −=l12 += αα , in game two. In game three, the participant agent learns
that the Z score of the borrower from game two has worsened again. Therefore, α is re-
assigned as 7.01.08.0223 =−=l+= αα in game three.
In game one
9.0=α
Given this probability distribution, the payoff for participant agents to take part is:
6.0)3(1.019.0 =−×+×
The payoff for both lead banks and participant agents if no syndication occurs is
001.009.0 =×+×
06.0 >
16
Therefore, the Nash-Equilibrium in game one is for both parties to participate in the
syndication.
In game two
8.0=α
Given this probability distribution, the payoff for both parties when syndication occurs is:
2.0)3(2.018.0 =−×+×
The payoff for both parties if no syndication occurs is
002.008.0 =×+×
02.0 >
Therefore, the Nash-Equilibrium in game two is for both the lead bank and the participant
agents to participate in the syndication. As a result, in game two syndication occurs.
In game three
7.0=α
Given this probability distribution, the payoff for participant agents to participate is:
2.0)3(3.017.0 −=−×+×
The payoff for them not to participate is
003.007.0 =×+×
02.0 <−
Therefore, the Nash Equilibria in game three are those cases where no syndication
occurs. That is to say, no syndication will occur in game three because the probability
that state one will occur is too low. In this scenario, the lowest α participants can tolerate
in order to decide to take part in the syndication is when the payoff of participating equals
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0, the payoff of not participating. That is when 0)1( =×−+× hf αα , i.e.,
4/3))3(1/(3)/()( =−−=−−= hfhα .
III. Testable Hypotheses
Empirically, it is not easy to calibrate the parameters in our game theory models.
However, if we consider a Z score change as a signal to participant agents of a change in
loan default probability, we are able to test our model using Z score change as an
information measure.
The following are testable hypotheses consistent with the model above.
Hypothesis One (positive versus negative reputation effect)
The larger the number of lead roles a bank played in the past where borrowers
had unchanged or improved Z scores, the more likely a loan is to be syndicated. The
larger the number of lead roles played in past deals where borrowers have deteriorated Z
scores, the less likely the decision to syndicate.
The more positive experiences a lead bank has, the more likely it is, from the
participant agents’ point of view, that the lead bank is trying to build up its reputation as a
syndication originator for future deals and has good monitoring ability. In other words, it
is more likely that state one, where the expected payoff for the participant agents is
positive, is the true state. Therefore, the more likely it will be that the participant agents
are willing to opt into the loan syndication.
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Hypothesis Two (reputation effect)
The “active” lead banks may have signaled to the market through the previous
syndicated loans they have originated that they have good monitoring ability and will not
exploit syndicate members for future originations.
Assuming our theoretical model is successfully confirmed, the next question will
be what is the reason for those lead banks to drop out, overconfidence or information
exploitation?
Hypotheses One and Two are the market dynamics of identifying “drop-out” lead
banks, that is to say, those lead banks which are unable to attract further syndication
participants and are forced out of the market. Hypotheses Three and Four explain ex post
the reason behind dropping out according to the reputation/exploitation and poor
monitoring ability studies respectively.
Hypothesis Three (exploitation effect)
If the main reason for lead banks to drop out is that they exploited participating
members before, they must have syndicated more deteriorated loans (where borrowers
ended up with decreased Z scores) than “good” loans (where borrowers ended up with
unchanged or improved Z scores). As a result, this provides a signal to participant
agents that these lead banks knew ex ante which were “good” loans and tried to keep
more “good” than “bad” loans to themselves.
Hypothesis Four (overconfidence effect)
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If the main reason for lead banks to drop out is that they over-estimated their
monitoring ability, then they must have been unable to differentiate ex ante between
“good” loans and “bad” loans. In other words, the coefficient of Z score changes for the
“drop-out” group would not significantly affect the percentage of the loan syndicated out
in the over-confidence case.
IV. Data and empirical tests
The LPC DealScan database covering loan observations in the period 1987-1999
is used in these empirical tests. LPC provides market information on syndicated loans,
non-syndicated loans, and private placements obtained through the Security Exchange
Commission or directly from banks. Our version of the LPC database contains
approximately 66,000 loan facilities. LPC tries to confirm loan transactions filed by the
SEC with senior managers at the lending institutions and reports loans as “fully
confirmed”, “partially confirmed” or, “unconfirmed”. We start by selecting all non-
privately placed, “fully confirmed” loan transactions, which gives us a sample of 46,600
observations. We then exclude observations lacking information on the lead banks’
stake, collateralization and facility size. We end up with a sample of 14,180 loan
transactions of which 7,678 (54%) are syndicated loans.
[TABLE I. GOES ABOUT HERE]
Table I provides some descriptive statistics on the sample for the period of 1987
to 1999. Panel A of Table I shows proportions for the discrete variables. Of the full
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sample 12,356 (87%) of the loans are secured, while 5,854 (82.75%) of the syndicated
loans are secured. Secured loans greatly outnumber unsecured loans, especially for non-
syndicated loans. Among syndicated loans, 48.75% of the borrowers have a senior,
unsecured debt rating, compared to only 9.74% for non-syndicated loans, confirming that
borrower transparency facilitates loan syndication.
Panel B of Table I show descriptive statistics for continuous variables. The mean
(49 months) and median (48 months) maturities of the syndicated loans are higher than
those of the non-syndicated sample, indicating that syndicated loans have longer
maturities on average than non-syndicated loans. The mean and median dollar values of
syndicated loans are about 15 times larger than those of non-syndicated loans, indicating
that as expected, syndicated loans are larger than non-syndicated loans.
[TABLE II. GOES ABOUT HERE]
Panel A of Table II reports the estimation of Pearson correlation coefficients for
our descriptive variables for syndicated and non-syndicated loans over 1987-1999. The
largest correlation is between Facsize and Bondrate, indicating that borrowers with a
bond rating are able to borrow larger amounts than borrowers without bond ratings.
Compared to non-banks, banks tend to originate loans with larger facility sizes, with
shorter maturities, and with performance pricing features, and tend to lend to rated
borrowers. Moreover, loans with performance pricing features tend to be unsecured,
made to rated borrowers, of shorter maturity, and of smaller size.
Panel B reports the estimation of Pearson correlation coefficients for the sample
with the variable TOTAL over 1987-1999, the number of prior syndications conducted
21
by the lead bank that turned out to be “good” loans minus the number that turned out to
be “bad” loans. Also the highest correlation for this sample is between Facsize and
Bondrate - 0.31455. In general, the results are similar to those in Panel A for both non-
syndicated and syndicated loans transactions, except that performance pricing features
tends to be included in syndicated loans of longer maturity. Lead banks with a good
reputation as a syndicated loan originator, which are more likely to be banks as opposed
to non-banks, tend to originate syndicated loans with longer maturities.
In order to include the variable ZCHAG, we start with the data set of loan
transactions over the period of 1987 to 1999 in the previous section and select only those
facilities whose Z score at close and one year after are both available in the
COMPUSTAT database’s borrower information. We end up with a sample of 1,982 loan
facilities of which 1,100 (55%) are syndicated. The absence of the information needed
to calculate Z score changes is the main reason for loss of observations. Of the 1,100
facilities, we define the “active” group as lead banks that were still initiating syndicated
loans in 1998 or after, or whose parent firms were still doing so. The “drop-out” group is
defined as lead banks that had previously been in the origination market for loan
syndication but ceased acting as a lead bank in 1998 and after. The reason for choosing
1998 as the cutoff year is that the version of the DealScan database used in this paper
covers data up to 1999, and some companies do not syndicate loans every year. So we
allow two years of non-origination to define a “drop-out” lead bank. In the “active” lead
banks group, we exclude syndicated loans where lead banks just started to syndicate in
1998, the cutoff year.
[TABLE III GOES ABOUT HERE]
22
Descriptive statistics for ZCHAG are provided in Table III. Panel A of Table III
shows proportions for discrete variables. In the “active” group the proportion of loans
with performance pricing, and loans to firms with bond ratings is higher than in the
“drop-out” group, while the proportion of secured loans is smaller. In the “active” group,
about 92% of loan transactions are originated by banks, as compared to 84% in the “drop-
out” group.
Panel B of Table III provides descriptive statistics for the continuous variables.
“Full sample” refers to all syndicated loans and non-syndicated loans with calculable
change in Z score one year after the loans are closed. It is the sample used in the first
state truncated regression. Starting from the “full sample”, we select all the syndicated
loans and divide them into the “active” and “drop-out” groups according to the
definitions stated earlier. The mean size of loans for the “active” group is slightly larger
than that of the “drop-out” group. Moreover, the mean and median size of both the
“active” group and the “drop-out” sample are much larger than those of the full sample,
indicating that in general syndicated loans are much larger than the average facility size
of non-syndicated loans. The mean maturity of the “active” and “drop-out” group is
longer than the average maturity of the full sample, implying that syndicated loans have
longer maturities than non-syndicated loans.
Examining the means of TOTAL reveals that, on average, lead banks in the
“active” group syndicated more “good” loans and fewer “bad” loans than those in the
“drop-out” group. This constitutes preliminary evidence that the “drop-out” group is
revealed ex post as less successful in originating “good” loans in general than the “active”
23
group and is penalized for its reputation as a “bad” syndicated loan originator. For more
precise tests, we turn to our regressions.
We follow the two-step procedure first introduced by Cragg (1971) and employed
by numerous researchers such as Dennis and Mullineaux (2000) and Panyagometh and
Roberts (2002). The first stage uses a Logit model to estimate the decision variable that
equals one if the loan is syndicated and zero otherwise. The decision is affected by
different factors such as the borrower’s degree of transparency as represented by
performance pricing linking the loan spread to borrower performance2, characteristics of
the loan such as secured or unsecured status, size and maturity, and the ability of
syndicate members to control the credit risk of the loan. Most importantly, as implied by
our model, the decision depends on the characteristics of the lead bank,3 such as how
many previously syndicated deals display improved/deteriorated Z scores. The degree of
transparency of the borrower is represented by whether the borrower has a ticker or bond
rating. Unlike Dennis and Mullineaux (2000) and Panyagometh and Roberts (2002),
where the percentage of the loans syndicated out is studied for syndicated loans as a
whole, we select all syndicated loan transactions in the first stage, then divide them into
“active” and “drop-out” groups. After that, we study factors affecting the percentage of
the loans syndicated out to syndication members using a regression model truncated
above 100%. By comparing the results for the two groups, we will be able to identify
essential differences between the two groups as well as the reason behind the “drop-out”
2 There are two types of performance pricing, the first is when the loan’s spread is linked to certain of the borrower’s financial ratios such as interest coverage, debt to tangible net worth, fixed charge coverage, etc, and the second one is when the loan’s spread is linked to the borrower’s credit rating. 3 Following Lee and Mullineaux (2001), the banks responsible for the creation of the syndicate re referred to as the “lead banks,” “arrangers,” or “lead managers.” In the DealScan database if these roles cannot be identified, then the role “agent” is used to recognize the lead banks.
24
group falling out of the loan syndication origination market. To avoid colinearity, and
also to improve robustness, we report the results with various specifications.
[TABLE IV. GOES ABOUT HERE]
Table IV reports the result of estimations for the decision to syndicate over the
period of 1987-1999. The factors affecting the decision to syndicate a loan include the
characteristics of the borrower, maturity, security, size, the inclusion of performance
pricing features, and the characteristics of the lead banks. In addition, we add TOTAL to
determine how the decision is affected by the positive or negative reputation of the lead
bank as a syndication originator. Consistent with the findings of Dennis and Mullineaux
(2000) and Panyagometh and Roberts (2002), we find similar effects of originator’s
reputation, existence of a bond rating, existence of a ticker, facility size, and the maturity
of loans. A loan is more likely to be syndicated if the originator is a bank, the borrower
has a senior unsecured debt rating, the maturity of the loan is longer, or the loan is larger.
Consistent with Panyagometh and Roberts (2002), the results in Table IV confirm that the
coefficient of Pfprice variable is positive, which means performance pricing features help
to control agency problems and, as a result makes a loan more likely to be syndicated,
although at a marginal level of statistical significance.
TOTAL is the number of transactions to date conducted with the lead bank
syndicating “good” loans minus the number of transactions conducted with the lead bank
syndicating “bad” loans. The positive coefficient of TOTAL at the 5 percent significance
25
level shows, consistent with Hypothesis One that the more a bank has played lead roles
where the loans are shown to be “good” (where the Z scores of the borrower are
unchanged or improved one year after the loan is finalized) in the past, the more likely is
the loan is to be syndicated. Conversely, it shows that the more a bank has played lead
roles where the loans are shown to be “bad” (where the Z scores of the borrowers are
deteriorated one year after the loan was finalized) in the past, the more likely it is the loan
will not be syndicated. Previous studies found that the more syndication originations are
conducted in the pre-sample period by the lead bank, as in Dennis and Mullineaux
(2000); or the larger the yearly average dollar amount of syndicated loans previously
originated by the lead bank, as in Panyagometh and Roberts (2002), the more likely a
loan is to be syndicated. However, neither of the two studies differentiated between
“good” and “bad” loans, and, therefore, neither can be considered as rigorous
confirmations of our model. However, our model can explain both of the results by
considering them as reflecting the numerical domination of “active” lead banks over
“drop-out” lead banks.
Having confirmed the mechanics of lead banks dropping out, the next question is
what was the reason these lead banks failed to meet the expectations of the market and
consequently dropped out, overconfidence about their monitoring ability or inside
information exploitation? In order to address this question, the second stage of our
empirical work studies the percentage of loans syndicated out by the “active” lead bank
group and by the “drop-out” lead bank group.
A new variable ZCHAG is introduced to test our hypotheses regarding to the
motivation of the dropping-out of the lead banks. ZCHAG is the change in the
26
borrower’s Altman’s Z score one year after the loan is closed. Our model predicts that, in
the case of inside information exploitation by lead banks, “drop-out” lead banks will have
syndicated out more “bad” loans than “good” loans to participating members. Therefore,
the coefficient of ZCHAG for the “drop-out” group should be negative with statistical
significance. As a result, it provides a signal to participant agents ex post that these lead
banks knew ex ante, which loans were “good” loans and tried to keep more “good” and
fewer “bad” loans.
Our model also predicts that, in the case of lead bank over-confidence, “drop-out”
lead banks were not able to differentiate between “good” loans and “bad” loans ex ante.
Therefore, the coefficient of the Z score change for the “drop-out” group would not
significantly affect the percentage of the loan syndicated out to participating members.
Conversely, “active” lead banks must have shown good monitoring ability. In other
words, they syndicated more “good” loans (with unchanged or improved Z scores) than
“deteriorated” loans (with decreased Z scores) as signals that they knew which were
“safer” loans and were willing to keep more risky loans to themselves than safe ones.
Table V reports the results of the estimation of proportions of loans syndicated
out for the “active” lead bank group. Table VI shows the results for the “drop-out” lead
bank group. The coefficient of ZCHAG for the “active” group is significantly positive,
implying good monitoring ability and non-exploitation of inside information by the lead
banks ex ante. The coefficient of ZCHAG for the “drop-out” group is insignificant,
implying lead bank overconfidence in its monitoring ability.
[TABLE V. GOES ABOUT HERE]
[TABLE VI. GOES ABOUT HERE]
27
V. Robustness Checks
The use of Z score changes as a proxy for private information can be subject to
the criticism raised by Saunders (1999) as follows: First, the out-of-sample performance
ability, referring to the ability of a model to predict outcomes from new data rather than
from data used in the model estimation, is a well-known concern. Second is the
applicability of the model since the coefficient estimation of the Z score uses U.S. data,
while we sometimes apply the Z score estimation to firms from other countries. Third,
the coefficient of the Z score is estimated to discriminate only between two extreme
behaviors of borrowers: default and non-default. In the real world borrowers exhibit
various degrees of financial difficulty. If a borrower faces difficulties, lenders usually
categorize them into different credit tiers, rather than just classifying them into simply
default or non-default categories. Fourth, Z score ignores important hard-to-quantify
factors, such as the long-term reputation of the borrower, which could play a critical role
in borrowers’ decision-making in potential loan default situations.
To address these criticisms, we re-examine our results by replacing the change in
Altman’s Z score (ZCHAG) with an alternative measure of the subsequent discovery of
lead banks’ possession of private information on the borrower’s financial health: bond
rating changes before the expiration of the loan (NOTCHAG) following Panyagometh
and Roberts (2003). In their paper, they used NOTCHAG without dividing samples into
“active” and “drop-out” groups. NOTCHAG, a proxy for changes in borrower financial
health ex post, is the number of notch(es) a borrower’s S&P senior debt rating changes as
observed in DealScan before the maturity of the loan. The first major drawback of
28
NOTCHAG is that debt ratings tend to react only to dramatic financial changes, which
might ignore some less dramatic change in the financial status of borrowers. The second
major drawback of using NOTCHAG is the reduction in sample size due to the
unavailability of S&P senior debt ratings for some borrowers. For this reason, we end up
with a sampleof 1,452 observations, of which 749 (52%) are syndicated loans for the
estimation of the loan syndication decision-making. Further we split the syndicated loans
into two groups: an “active” group with 672 observations, and a “drop-out” group with
only 67 observations. We then define the variable TOTAL2 as the number of previous
syndicated loan deals originated by the lead bank where the borrowers’ S&P senior debt
ratings before the loans expired were unchanged or upgraded minus the number of deals
where they were downgraded. Then we rerun our regressions in Table IV. Examining
Table VII, the results for the NOTCHAG sample, reveals that the coefficient of our new
measure of changes in borrower financial health, NOTCHAG, is positive and strongly
significant in the regression. As this is exactly what we find using ZCHAG in Table IV,
we conclude that our original finding there is robust: the decision to syndicate is
significantly related to the past experience of the lead bank.
Then we return to the NOTCHAG sample and divide the loans into “active” and
“drop-out” groups according to the same standard used for the ZCHAG sample. In the
“drop-out” group we also exclude 35 loans where the borrower has been merged or
acquired. Finally we rerun our regressions in Table V and VI replacing ZCHAG with
NOTCHAG. Table VIII and Table IX display the results for NOTCHAG and reveal that
the coefficient of our new measure of change in borrower financial health, NOTCHAG,
is positive and strongly significant in the “active” group and insignificant in the “drop-
29
out” group. As this is exactly what we find using ZCHAG in Table V and VI, we
conclude that the overconfidence effect, as an explanation for lead banks to drop out is
robust.
[TABLE VII GOES ABOUT HERE]
[TABLE VIII GOES ABOUT HERE]
[TABLE IX GOES ABOUT HERE]
Part VI. Conclusions
This paper focuses on an increasingly important form of corporate debt financing
- loan syndication. Advancing upon prior empirical research on adverse selection and
asymmetric information leading to conflicts between lead banks and participating banks
in loan syndications, we construct a Bayesian game model in which syndicate members
have insufficient information about lead banks to address the mechanics of updating
information about lead banks during a series of loan syndications. The conflicts arise
from uncertainty from the participating members’ perspective, about the lead banks’
motivation to originate and the lead banks’ true monitoring ability. In each period
syndicate participants learn about the quality of loans originated by the lead bank in all
previous periods, and update their beliefs about the motive and the monitoring ability of
the lead bank by updating the probability of the two states. As a result of this updating,
the market decides which lead banks will be unable to originate further syndicated loans.
The model helps to explain the reputation and exploitation effects in Dennis and
Mullineaux (2000) and Panyagometh and Roberts (2003).
30
In addition to modeling reputation and exploitation effects that have been
addressed empirically before, this model also incorporates another possible explanation
for lead banks’ dropping out: overconfidence in their own ability to monitor borrowers.
This study is the first to propose that lead banks’ overconfidence in their own monitoring
ability could be an alternative reason for them to drop out of the syndicate origination
market, besides exploiting participating banks in lending syndications.
Building upon information measures in previous studies, a more precise
reputation measure -- the number of previous “good” loans minus the number of previous
“bad” loans originated by the lead banks -- is designed to examine our theoretical model.
That is, loans are divided into “good” loans and “bad” loans according to the borrowers’
Z score change one year after the loans are closed. We find that the total number of
“good” loans originated in the past by the lead banks (positive reputation effect) is
positively correlated with loan syndication decision making, while the total number of
“bad” loans originated in the past by the lead banks (negative reputation effect) is
negatively correlated with loan syndication decision-making. This finding is consistent
with the negative/positive reputation effect hypothesis implied by our model.
Further, based on whether they are still active in the loan syndication origination
market, we further divide lead banks into two groups: “active” and “drop-out,” to identify
essential differences between the two groups and the reasons for dropouts in the second
group.
Building upon Panyagometh and Roberts’ (2003) reputation and exploitation
hypotheses for lead banks as a whole, we propose, based on our model, that the actions of
the “active” group should be consistent with the reputation hypothesis, while the actions
31
of the “drop-out” group could be motivated by either the exploitation hypothesis or the
overconfidence hypothesis. Testable hypotheses that can discriminate between
exploitation and overconfidence hypotheses are proposed in our paper. Our Empirical
tests confirm that the actions of the “active” group are consistent with the reputation
effect and the actions of the “drop-out” group are consistent with the overconfidence
hypothesis.
Viewed broadly, our results reinforce the importance of reputation and monitoring
ability in aligning the interests of lead banks and syndicate participants.
32
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36
Chart I
αState 2 with prob 1-α
State 1 with prob
Lead Bank Lead Bank To Syndicate Not to Syndicate To Syndicate Not to Syndicate
Syndication
occurs (g, h)
No syndication occurs (0,0)
No syndication occurs (0,0)
No syndication occurs (0,0)
Syndication
occurs (e, f)
No syndication occurs (0,0)
No syndication occurs (0, 0)
No syndication occurs (0,0)
Not to Participate
Participate
Participate
Not to Participate
37
CHART II
αState 2 with prob 1-α
State 1 with prob
Lead Bank Lead Bank To Syndicate Not to Syndicate To Syndicate Not to Syndicate
Syndication
occurs (g, -3)
No syndication occurs (0,0)
No syndication No syndication
Syndication
occurs (1, 1)
No syndication occurs (0,0)
No syndication occurs (0, 0)
No syndication occurs (0,0)
Not to Participate
ParticipateParticipate
Not to Participate
occurs (0,0)
occurs (0,0)
38
CHART III
Lead Bank
Syndicate Not to Syndicate
No Syndication
Occurs (N-E)
No Syndication
Occurs (N-E)
No Syndication
Occurs (N-E)
Participate Participant agent
Not Participate
\
39
CHART IV
Lead Bank
Syndicate Not to Syndicate
Syndication Occur (N-E) (1,1)
Participate
Participant agent
Not Participate
40
Table I. Descriptive Statistics for the Sample
Variable Sample Size
Full Sample
Syndicated Loan
Non-syndicated Loan
Panel A: Descriptive statistics for discrete variables Secured Secured 12356(87.14%) 5854(82.75%) 6502(91.50%) Not-secured 1824(12.86%) 1220(17.25%) 604(8.50%) Bank Bank 12110(85.40%) 6284(88.83%) 5826(81.99%) Non-bank 2070(14.60%) 790(11.17%) 1280(18.01%) Pfprice With Performance Pricing
1145(8.07%) 627(8.86%) 518(7.29%)
Without Performance Pricing
13035(91.93%) 6447(91.14%) 6588(92.71%)
Bond Rating With Bond Rating 3577(25.23%) 2885(40.78%) 692(9.74%) Without Bond Rating 10603(74.77%) 4189(59.22%) 6414(90.26%) Ticker With Ticker 7837(55.27%) 4022(56.86%) 3815(53.69%) Without Ticker 6343(44.73%) 3052(43.14%) 3291(46.31%)
Panel B: Descriptive statistics for continuous variables mean/median
(min /max)
mean/median (min /max)
mean/median (min /max)
Maturity 14180 41.89/36 (0/408)
49.46/48 (0/366)
34.36/24 (0/408)
Facisize 14180 68.78/15 (0.005/8600)
129.69/50 (0.18/8600)
8.14/5 (0.005/1000)
Note. SECURED is a dummy equal to one if the loan is collateralized and zero otherwise; BANK is a dummy equal to one if the managing agent has a bank charter and zero otherwise; Pfprice is a dummy that equals to 1 if there is performance pricing and 0 otherwise; BONDRATE is a dummy equal to one if the borrower has a senior, unsecured debt rating and zero otherwise; TICKER is a dummy equal to one if the borrower is listed on the NYSE, AMEX or NASDAQ and zero otherwise; MATURITY is the maturity of the loan (in months); FACSIZE is the million dollar value size of the loan facility; Of the 14180 loan transactions in the sample of bank and non-bank facilities over the period of 1987-1999, 7074 (50%) are syndicated.
41
Table II. Correlation of Parameter Estimates
Panel A: For non-syndicated and syndicated loan transactions over the period 1987-1999
Bondrate Secured Bank Maturity Facsize TOTAL Pfprice
Bondrate 1 . 0 0 0 0 0
Secured -0.13672 1.00000
Bank 0 . 0 7 9 6 4 -0.12484 1.00000
Maturity 0 . 1 2 7 1 4 0.04477 -0.04760 1 . 0 0 0 0 0
Facsize 0 . 2 2 1 6 1 -0.15622 0.06118 0 . 0 7 5 0 4 1.00000
Pfprice 0.03586 -0.02993 0.01330 -0.00261 -0.00043 1.00000
Panel B: For syndicated loan transactions over the period 1987-1999 Bondrate 1 . 0 0 0 0 0
Secured -0.14531 1.00000
Bank 0 . 1 0 4 7 9 -0.14407 1.00000
Maturity 0 . 1 4 1 3 5 0.05718 -0.09405 1 . 0 0 0 0 0
Facsize 0 . 3 1 4 5 5 -0.14453 0.05725 0 . 1 0 1 1 7 1.00000
TOTAL 0 . 1 6 7 6 0 -0.16166 0.14110 -0.01110 0.14786 1.00000
Pfprice 0 . 0 2 9 4 0 -0.00084 0.03352 0 . 0 1 4 2 5 -0.00800 0.08063 1.00000
42
Table III. Descriptive Statistics for the ZCHAG Sample
Variable Sample Size
Full Sample
“active” Group
“drop-out” Group
Panel A: Descriptive statistics for discrete variables Bank Bank 1794(90.51) 857(92.25) 117(84.17) Non-bank 188(9.49) 72(7.75) 22(15.83) Bond Rating With Bond Rating 649(32.74) 483(51.99) 47(33.81) Without Bond Rating 1333(67.26) 446(48.01) 92(66.19) Pfprice With Performance Pricing
149(7.52) 87(9.36) 5(3.60)
Without Performance Pricing
1833(92.48) 842(90.64) 134(96.40)
Secured Secured 1572(79.31) 697(75.03) 120(86.33) Not-secured 410(20.69) 232(24.97) 19(13.67)
Panel B: Descriptive statistics for continuous variables mean/median
(min /max)
mean/median (min /max)
mean/median (min /max)
Facisize 1982 75.03/17.50 (0.05/5000)
134.33/60 (0.45/5000)
100.88/30 (1/2100)
Maturity 1982 42.38/36 (1/366)
48.82/46 (3/366)
47.30/47 (2/108)
TOTAL 1100 4.88/2 (-4/45)
5.46/2 (-4/45)
1.5179856/1 (-1/13)
ZCHAG 1982 0.71/-0.001 (-28.69/647.05)
0.02/-0.01 (-28.69/31.45)
0.46/0.03 (-2.2/18.2)
Note. SECURED is a dummy equal to one if the loan is collateralized and zero otherwise; BANK is a dummy equal to one if the managing agent has a bank charter and zero otherwise; Pfprice is a dummy that equals to 1 if there is the performance pricing and 0 otherwise; BONDRATE is a dummy equal to one if the borrower has a senior, unsecured debt rating and zero otherwise; TICKER is a dummy equal to one if the borrower is listed on the NYSE, AMEX or NASDAQ and zero otherwise; MATURITY is the maturity of the loan (in months); FACSIZE is the million dollar value size of the loan facility; TOTAL is the number of repeat transactions conducted with the lead bank syndicating “good” loans in the presample period with unchanged or improved Z score of the borrower minus the number of repeat transactions conducted with the lead bank syndicating “bad” loans with deteriorated Z score of the borrower. Of the 1982 loan transactions in the ZSCRORECHAG sample, 1100 (55%) are syndicated loans. The “active group” contains 929 loans. The “drop-out” group” contains 139 loans.
43
Table IV.
Estimates of Models for the Decision to Syndicate a Loan
Variable All Variables
Specification I
Specification II
Specification III
Specification IV
Specification V
Intercept -1.5362 ∗ ∗∗
(0.1637) -1.2251 ∗ ∗∗
(0.1526) -1.2052 ∗ ∗∗
(0.1278) -1.5420 ∗∗∗
(0.1634) -0.3942 ∗ ∗∗
(0.1302) -1.2500 ∗ ∗∗
(0.1526)
Facsize 0.0357 ∗∗∗
(0.0006) 0.0378 ∗ ∗∗
(0.0004) 0.0372 ∗ ∗∗
(0.0005) 0.0363 ∗∗∗
(0.0006) 0.0357 ∗ ∗∗
(0.0005)
Maturity 0.0060 ∗∗∗
(0.0011) 0.0064 ∗ ∗∗
(0.0011) 0.0060 ∗∗∗
(0.0011) 0.0072 ∗ ∗∗
(0.0010)
Bank 0.0633 (0.1170)
0.0200 (0.1153)
0.0565 (0.1153)
0.1000 (0.1158)
-0.1795 ∗ (0.1019)
-0.0184 (0.1157)
Bondrate 0.4215 ∗∗∗
(0.0919) 0.4276 ∗∗∗
(0.0916) 1.1078 ∗ ∗∗
(0.0712) 0.4379 ∗ ∗∗
(0.0912)
Pfprice 0.0390 (0.1421)
0.0256 (0.1393)
0.0324 (0.1401) 0.1991 ∗
(0.1214) 0.0375 (0.1408)
TOTAL 0.0133 ∗ ∗
(0.0059) 0.0133 ∗∗
(0.0059) 0.0145 ∗ ∗∗
(0.0059) 0.0353 ∗ ∗∗
(0.0048) 0.0123 ∗∗
(0.0059)
Secured 0.3301 ∗∗∗
(0.1037) 0.3587 ∗ ∗∗
(0.1022) 0.3321 ∗∗∗
(0.1032) -0.0664 (0.0793)
0.3657 ∗ ∗∗
(0.1021) Model4
Significance 1181.1*** 1128.58*** 1150.28*** 1176.36*** 458.88*** 1151.28***
# of observations 1982 1982 1982 1982 1982 1982
Note: Bondrate is a dummy that equals to 1 if the borrower has a senior, unsecured debt rating and 0 otherwise. Secured is a dummy that equals to 1 if the loan is secured by collateral and 0 otherwise; Bank is a dummy that equals to 1 if the lead bank has a bank charter and 0 otherwise; Maturity is the maturity of the loan in months; Facsize is the million dollar value of a loan facility size in million; TOTAL is the number of previous syndicated loan deals originated by the lead bank that is upgraded minus the number of deals. Pfprice is a dummy that equals to 1 if there is the performance pricing and 0 otherwise; Standard errors are in parentheses. * Significant at the .10 level. ** Significant at the .05 level. *** Significant at the .01 level.
4 The likelihood ratio )( LRλ is used to examine model significance by testing the null hypothesis that the coefficients of all
explanatory variables equal to 0. The null hypothesis is rejected if )( JLR χλ ≥ , where J is the number of degrees of freedom,
which is equal to the number of explanatory variables estimated.
44
Table V
Estimates of the Percentage of Syndicated of a loan
For “active” lead banks
Variable All Variable
Specification I
Specification II
Specification III
Specification IV
Specification V
Intercept 1.1634***
(0.0657) 1.1864*** (0.0573)
0.8888*** (0.0308)
1.1747*** (0.0656)
1.1942*** (0.0573)
1.1671*** (0.0657)
Facsize 0.00003
(0.00005) 0.00003 (0.00004)
0.00003 (0.00005)
0.00006 (0.00004)
0.00005 (0.00004)
0.00003 (0.00005)
Maturity 0.0006 (0.0005)
0.0007 (0.0005) 0.0007
(0.0005) 0.0007 (0.0005)
0.0006 (0.0005)
Bank -
0.3218***
(0.0590) -0.3272***
(0.0584) -0.3018***
(0.0585) -0.3068*** (0.0578)
-0.3200*** (0.0590)
Bondrate 0.0644**
(0.0282) 0.0633** (0.0282)
0.0420 (0.0282) 0.0664**
(0.0282)
Pfprice 0.0728
(0.0471) 0.0734 (0.0471)
0.0665 (0.0478)
0.0776* (0.0473)
0.0781* (0.0473)
Secured 0.0241
(0.0312) 0.0496 (0.0314)
0.0204 (0.0313) 0.0250
(0.0313) ZCHAG 0.0132**
(0.0061) 0.0134** (0.0061)
0.0113** (0.0061)
0.0128** (0.0061)
0.0131** (0.0061)
0.0133** (0.0061)
Model Significance5 43.91*** 42.72*** 10.96* 38.1*** 37.7*** 40.9***
# of observations 929 929 929 929 929 929
Note: Truncated (from above at 100%) regression estimates of the percentage of a loan being syndicated. Facsize is a million dollar value of a loan facility size (in million); Maturity is the maturity of the loan (in months); Bank is a dummy that equals to 1 if the managing agent has a bank charter and 0 otherwise; Bondrate is the variable equals to 1 if there is any senior debt rating for this borrower; Pfprice is a dummy that equals to 1 if there is the performance pricing and 0 otherwise; Secured equals to 1 if the loan is secured and 0 otherwise; ZCHAG is the Z score change one year after the facility is closed. * Significant at the .10 level. ** Significant at the .05 level. *** Significant at the .01 level.
5 The likelihood ratio )( LRλ is used to examine model significance by testing the null hypothesis that the coefficients of all
explanatory variables equal to 0. The null hypothesis is rejected if )( JLR χλ ≥ , where J is the number of degrees of freedom,
which is equal to the number of explanatory variables estimated.
45
Table VI
Estimates of the Percentage of Syndicated of a loan
For “drop-out” lead banks
Variable All Variable
Specification I
Specification II
Specification III
Specification IV
Specification V
Intercept 1.2216*** (0.0924)
1.2405*** (0.0277)
0.8689*** (0.0764)
1.2117*** (0.0944)
1.2336*** (0.0340)
1.2144*** (0.0899)
Facsize 0.0003** (0.0002)
0.0003** (0.0001)
0.0003** (0.0002)
0.0003** (0.0001)
0.0003** (0.0001)
0.0003** (0.0002)
Maturity -0.0022* (0.0013)
-0.0022* (0.0013) -0.0022*
(0.0013) -0.0022* (0.0013)
-0.0019 (0.0013)
Bank -
0.2864*** (0.0785)
-0.2885*** (0.0778) -0.2881***
(0.0781) -0.2907*** (0.0772)
-0.2750*** (0.0779)
Bondrate -0.0387 (0.0688)
-0.0398 (0.0686)
-0.0571 (0.0694) -0.0404
(0.0690) Pfprice 0.1594
(0.1448) 0.1577 (0.1447)
0.0847 (0.1478)
0.1609 (0.1446)
0.1590 (0.1445)
Secured 0.0201 (0.0883) 0.0466
(0.0901) 0.0234 (0.0880) 0.0153
(0.0887) ZCHAG -0.0141
(0.0133) -0.0138 (0.0133)
-0.0114 (0.0136)
-0.0136 (0.0133)
-0.0133 (0.0133)
-0.0109 (0.0131)
Model Significance6 19.66*** 16.90*** 5.04 17.00*** 17.3*** 17.10***
# of observations 167 167 167 167 167 167
Note: Truncated (from above at 100%) regression estimates of the percentage of a loan being syndicated. Facsize is a million dollar value of a loan facility size (in million); Maturity is the maturity of the loan (in months); Bank is a dummy that equals to 1 if the managing agent has a bank charter and 0 otherwise; Pfprice is a dummy that equals to 1 if there is the performance pricing and 0 otherwise; Secured equals to 1 if the loan is secured and 0 otherwise; ZCHAG is the Z score change one year after the facility is closed. * Significant at the .10 level. ** Significant at the .05 level. *** Significant at the .01 level.
6 The likelihood ratio )( LRλ is used to examine model significance by testing the null hypothesis that the coefficients of all
explanatory variables equal to 0. The null hypothesis is rejected if )( JLR χλ ≥ , where J is the number of degrees of freedom,
which is equal to the number of explanatory variables estimated.
46
Table VII
Estimates of Models for the Decision to Syndicate a Loan for the NOTCHAG sample
Variable All Variables
Specification I
Specification II
Specification III
Specification IV
Specification V
Intercept 0.1099* (0.2134)
0.3885** (0.1870)
-0.0426 (0.1437)
0.0807 (0.2094)
0.8530*** (0.1859)
0.3712** (0.2008)
Facsize 0.0047*** (0.0006)
0.0048*** (0.0006)
0.0050*** (0.0005)
0.0049*** (0.0006) 0.0048***
(0.0006) Maturity 0.0069***
(0.0017) 0.0062*** (0.0017)
0.0069*** (0.0017)
0.0081*** (0.0016)
Bank 0.1929 (0.1293)
0.1896 (0.1289)
0.2276* (0.1275)
0.3944*** (0.1261)
0.2163* (0.1236)
0.1897 (0.1289)
Ticker 0.0749 (0.0987) 0.0761
(0.0961) 0.0670
(0.0936) 0.0229
(0.0968) Pfprice 0.3790**
(0.1657) 0.3630** (0.1644)
0.3761** (0.1653) 0.2928**
(0.1575) 0.3643** (0.1645)
TOTAL2 0.0344*** (0.0064)
0.0342*** (0.0063)
0.0339*** (0.0064) 0.0377***
(0.0060) 0.0342*** (0.0063)
Secured -0.2192 (0.1442)
-0.1198 (0.1387) -0.1410
(0.1389) -0.7091*** (0.1278)
-0.1164 (0.1395)
Model7 Significance 240.4*** 223.36*** 237.22*** 192.02*** 139.46*** 223.48***
# of observations 1452 1452 1452 1452 1452 1452
Note: Facsize is the dollar value of a loan facility in millions; Maturity is the maturity of the loan in months; Bank is a dummy that equals 1 if the lead bank has a bank charter and 0 otherwise; Ticker is a dummy equal to one if the borrower is listed on the NYSE, AMEX or NASDAQ and zero otherwise; Pfprice is a dummy that equals 1 if there is performance pricing and 0 otherwise; TOTAL2 is the number of previous syndicated loan deals the lead bank has originated where the borrower’s S&P senior debt rating is unchanged or upgraded minus the number of deals where the rating is downgraded; Secured is a dummy that equals 1 if the loan is secured by collateral and 0 otherwise. Standard errors are in parentheses.
* Significant at the .10 level. ** Significant at the .05 level. *** Significant at the .01 level.
7 The likelihood ratio )( LRλ is used to examine model significance by testing the null hypothesis that the coefficients of all
explanatory variables equal 0. The null hypothesis is rejected if )( JLR χλ ≥ , where J is the number of degrees of freedom, which
is equal to the number of explanatory variables estimated.
47
Table VIII
Estimates of the Percentage of a Loan Syndicated Out
For “active” lead banks, using NOTCHAG sample
Variable All Variables
Specification I
Specification II
Specification III
Specification IV
Specification V
Intercept 1.1120*** (0.0583)
1.1135*** (0.0524)
0.9873*** (0.0302)
1.0912*** (0.0542)
1.0950*** (0.0488)
1.1092*** (0.0580)
Facsize 7.75E-6 (0.00002)
7.48E-6 (0.00002)
5.70E-6 (0.00002)
6.68E-6 (0.00002)
5.98E-6 (0.00002)
7.91E-6 (0.00002)
Maturity 0.0002 (0.0006)
0.0002 (0.0006) 0.0003
(0.0006) 0.0003
(0.0006) 0.0002
(0.0006)
Bank -0.1347*** (0.0487)
-0.1352*** (0.0478) -0.1349***
(0.0489) -0.1365*** (0.0479)
-0.1348*** (0.0487)
Ticker -0.0261 (0.0267)
-0.0263 (0.0265)
-0.0263 (0.0266) -0.0262
(0.0267)
Pfprice -0.0160 (0.0390)
-0.0161 (0.0389)
-0.0178 (0.0389)
-0.0161 (0.0390)
-0.0165 (0.0390)
Secured 0.0019 (0.0274) 0.0171
(0.0263) 0.0046
(0.0273) 0.0025 (0.0273)
NOTCHAG 0.0072*** (0.0027)
0.0072*** (0.0027)
0.0077*** (0.0027)
0.0073*** (0.0027)
0.0073*** (0.0027)
0.0072*** (0.0027)
Model Significance8 17.62*** 17.62**** 9.78* 16.83**** 16.85*** 17.44****
# of observations 687 687 687 687 687 687
Note: Truncated (above 100%) regression estimates of the percentage of a loan being syndicated. Facsize is the dollar value of a loan facility (in millions); Maturity is the maturity of the loan (in months); Bank is a dummy that equals 1 if the managing agent has a bank charter and 0 otherwise; Ticker is a dummy variable that equals 1 if the borrower is publicly listed on any stock market and 0 otherwise. Pfprice is a dummy that equals 1 if there is performance pricing and 0 otherwise; Secured equals 1 if the loan is secured and 0 otherwise; NOTCHAG is the number of notch(es) the borrower’s S&P senior debt rating changed before the maturity of the loan. * Significant at the .10 level. ** Significant at the .05 level. *** Significant at the .025 level. **** Significant at the .01 level.
8 The likelihood ratio )( LRλ is used to examine model significance by testing the null hypothesis that the coefficients of all
explanatory variables equal 0. The null hypothesis is rejected if )(JLR χλ ≥ , where J is the number of degrees of freedom, which
is equal to the number of explanatory variables estimated.
48
Table IX
Estimates of the Percentage of a Loan Syndicated Out
For “drop-out” lead banks, using NOTCHAG sample
Variable All Variables
Specification I
Specification II
Specification III
Specification IV
Specification V
Intercept 1.2648 (0.3061)
1.4334*** (0.2437)
0.7536*** (0.2130)
1.5285*** (0.2614)
1.6019*** (0.2142)
1.2610*** (0.3057)
Facsize 0.0003 (0.0003)
0.0002 (0.0002)
0.0003 (0.0003)
0.0002 (0.0004)
0.0002 (0.0003)
0.0003 (0.0003)
Maturity -0.0063*** (0.0020)
-0.0062*** (0.0020) -0.0065***
(0.0021) -0.0065*** (0.0021)
-0.0062*** (0.0020)
Bank -0.2384 (0.2129)
-0.2475 (0.2152) -0.3105
(0.2154) -0.3112 (0.2163)
-0.2414 (0.2109)
Ticker 0.1753 (0.1196)
0.1435 (0.1149)
0.2301* (0.1264) 0.1753
(0.1196) Pfprice -0.0231
(0.1484) -0.0262 (0.1486)
0.0068 (0.1617)
-0.0209 (0.1533)
-0.0226 (0.1531)
Secured 0.1478 (0.1657) 0.1466
(0.1717) 0.0786
(0.1638) 0.1485 (0.1654)
NOTCHAG -0.0012 (0.0137)
-0.0038 (0.0134)
0.0072 (0.0143)
-0.00007 (0.0142)
-0.0017 (0.0138)
-0.0013 (0.0136)
Model Significance9 14.98*** 14.22*** 4.56 12.94** 12.85*** 14.96***
# of observations 62 62 62 62 62 62
Note: Truncated (above 100%) regression estimates of the percentage of a loan syndicated. Facsize is the dollar value of a loan facility (in millions); Maturity is the maturity of the loan (in months); Bank is a dummy that equals 1 if the managing agent has a bank charter and 0 otherwise; Ticker is a dummy variable that equals 1 if the borrower is publicly listed on any stock market and 0 otherwise. Pfprice is a dummy variable that equals to 1 if there is the performance pricing and 0 otherwise; Secured equals 1 if the loan is secured and 0 otherwise; NOTCHAG is the number of notch(es) the borrower’s S&P senior debt rating changed before the maturity of the loan. * Significant at the .10 level. ** Significant at the .05 level. *** Significant at the .025 level. **** Significant at the .01 level.
9 The likelihood ratio )( LRλ is used to examine model significance by testing the null hypothesis that the coefficients of all
explanatory variables equal 0. The null hypothesis is rejected if )( JLR χλ ≥ , where J is the number of degrees of freedom, which
is equal to the number of explanatory variables estimated.
49
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