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Debt Maturity, Risk, and Asymmetric Information

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We test the implications of Flannery's (1986) and Diamond's (1991) models concerning the effects of risk and asymmetric information in determining debt maturity, and we examine the overall importance of informational asymmetries in debt maturity choices. We employ data on over 6,000 commercial loans from 53 large U.S. banks. Our results for low-risk firms are consistent with the predictions of both theoretical models, but our findings for high-risk firms conflict with the predictions of Diamond's model and with much of the empirical literature. Our findings also suggest a strong quantitative role for asymmetric information in explaining debt maturity.
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Finance and Economics Discussion Series
Divisions of Research & Statistics and Monetary Affairs
Federal Reserve Board, Washington, D.C.









Debt Maturity, Risk, and Asymmetric Information
















Allen N. Berger, Marco A. Espinosa-Vega, W. Scott Frame,
and Nathan H. Miller
2004-60

NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS)
are preliminary materials circulated to stimulate discussion and critical comment. The
analysis and conclusions set forth are those of the authors and do not indicate
concurrence by other members of the research staff or the Board of Governors.
References in publications to the Finance and Economics Discussion Series (other than
acknowledgement) should be cleared with the author(s) to protect the tentative character
of these papers.



Debt Maturity, Risk, and Asymmetric Information




Allen N. Berger, Marco A. Espinosa-Vega, W. Scott Frame, and Nathan H. Miller*








Forthcoming, Journal of Finance


Abstract

We test the implications of Flannery’s (1986) and Diamond’s (1991) models concerning the effects of
risk and asymmetric information in determining debt maturity, and we examine the overall importance of
informational asymmetries in debt maturity choices. We employ data on over 6,000 commercial loans
from 53 large U.S. banks. Our results for low-risk firms are consistent with the predictions of both
theoretical models, but our findings for high-risk firms conflict with the predictions of Diamond’s model
and with much of the empirical literature. Our findings also suggest a strong quantitative role for
asymmetric information in explaining debt maturity.
JEL Classification Numbers: G32, G38, G21
Keywords: Debt Maturity, Risk, Asymmetric Information, Banks, Credit Scoring


* Berger is at the Board of Governors of the Federal Reserve System and Wharton Financial Institutions
Center, Espinosa-Vega is at the International Monetary Fund, Frame is at the Federal Reserve Bank of
Atlanta, and Miller is at the University of California at Berkeley. The views expressed do not necessarily
reflect those of the Federal Reserve Board, Federal Reserve Bank of Atlanta, the International Monetary
Fund, or their staffs. The authors thank the editor (Rick Green) and the anonymous referee for very
helpful suggestions that improved the paper. We also thank Mark Flannery and Doug Diamond for their
encouragement and suggestions. We thank as well Bob Avery, Steve Dennis, Giovanni Dell’Ariccia,
Jerry Dwyer, Diana Hancock, Alan Hess, Steve Smith, Phil Strahan, Greg Udell, and participants at the
Financial Intermediation Research Society conference, the ASSA meetings, the Financial Management
Association meetings, the All-Georgia Finance Conference, and the Credit Scoring and Credit Control
meetings for helpful comments and Phil Ostromogolsky for outstanding research assistance.


Please address correspondence to Allen N. Berger, Mail Stop 153, Federal Reserve Board, 20th and C
Streets. NW, Washington, DC 20551, call 202-452-2903, fax 202-452-5295, or email aberger@frb.gov.

Debt Maturity, Risk, and Asymmetric Information

Introduction
Why do firms with long-term projects often borrow on a short-term basis? One answer from the
debt maturity literature emphasizes the importance of risk under conditions of asymmetric information.
Flannery (1986), Diamond (1991), and others provide intuitive models that rely on the volition of low-
risk and high-risk firms with long-term projects choosing different maturities to reduce their financing
costs or liquidity risks. Although other theories of debt maturity focus on the roles of agency costs (e.g.,
Myers (1977), Barnea, Haugen, and Senbet (1980)), taxes (e.g., Brick and Ravid (1985), Lewis (1990)),
and other market imperfections, we concentrate on the role of asymmetric information and how it
interacts with firm risk. The importance of debt maturity has also recently been highlighted in the context
of policy concerns about financial crises and credit availability (e.g., Diamond and Rajan (2001)).
In this paper, we test the empirical predictions of Flannery’s and Diamond’s theoretical models,
and further explore the role of asymmetric information in debt maturity choices. Our data set provides an
advantageous laboratory for these tasks. We match the maturities, risk ratings, and other contract terms
of over 6,000 individual new loans to small businesses in 1997 from the Federal Reserve’s Survey of
Terms of Bank Lending (STBL) with Call Report data on the 53 large U.S. banks that extend these
credits. We also include data from an Atlanta Federal Reserve survey on whether and how these banks
employ small business credit scoring technology (SBCS), which provides our measure of asymmetric
information. Prior research supports the notion that SBCS can be used to reduce informational
asymmetries (Berger, Frame, and Miller (forthcoming)).
We perform two main tests based on regressions of loan maturity on the risk rating of the loan,
use of the SBCS technology, and other bank characteristics and loan contract terms. In Test 1, we
examine whether maturity is an upward-sloping function of the risk rating as predicted by Flannery’s
model versus a nonmonotonic function of the risk rating with the shortest maturities for the lowest and
highest risk ratings as predicted by Diamond’s model. We perform Test 1 using only observations for
banks that do not use the SBCS technology, given that the models predict that the relationships between
debt maturity and firm risk ratings should be strongest when informational asymmetries are greatest. In
Test 2, we examine the effects of reduced informational asymmetries from SBCS on debt maturities for

each different risk rating. Test 2 allows us to test the implications of the effects of asymmetric
information in both models, and perhaps more important, to examine the quantitative importance of
informational asymmetries in debt maturity generally. A number of empirical papers examine the
relationship between risk ratings and debt maturity (Test 1), although none to our knowledge examine this
relationship using only observations for which informational asymmetries are expected to be the greatest.
Some empirical studies examine the effects of reduced informational asymmetries on debt maturities, but
none to our knowledge examine these effects by risk ratings (Test 2).
Notably, our empirical tests are based on bank loans, rather than public debt securities as in the
theoretical models and most of the empirical literature. The implications of the models are the same in
both contexts – to the extent that value is created by maturity choice, it is similarly created whether the
firm chooses from a menu of contract terms from a bank or from its expectations of market reactions.
By way of preview, the evidence supports the predictions of Flannery’s and Diamond’s models
for low-risk firms – maturity is an upward sloping function of risk ratings (Test 1) and a reduction in
informational asymmetries is associated with increased maturities (Test 2) for these firms.1 These
findings for low-risk firms are also consistent with most of the empirical literature. However, our
evidence for high-risk firms conflicts with the predictions of Diamond’s model and with much of the
extant empirical literature. The most likely explanation for our difference from the literature for high-risk
firms may be our use of bank loans rather than publicly issued debt, as banks may be better able than
public markets to use tools other than short maturities to resolve asymmetric information problems for
high-risk firms (Berlin and Loeys (1988)). We do, however, find that the predictions of Diamond’s
model for high-risk firms appear to hold for one group of small businesses – those without loan
commitments – and we offer some possible explanations for this finding.
Our findings strongly support the quantitative importance of asymmetric information in the debt
maturity decision. The results of Test 2 suggest a very substantial increase in average maturity for low-
risk firms when informational asymmetries are lessened. As well, we find that the results of Test 1 would
be substantially weakened if it were applied to observations for banks using the SBCS technology. Both
findings are consistent with the predictions of the theoretical models. In Flannery’s and Diamond’s
models, asymmetric information causes some firms to choose short maturity because they are less likely

2


than other firms to have problems rolling over their short-term debt either in terms of high interest rates
(in Flannery’s model) or liquidity risk (in Diamond’s model). As shown below, reductions in
informational asymmetries reduce these incentives and increase the average maturity for firms rated as
low risk.
Section I of the paper provides the framework for our tests – delineating the intuition behind
Flannery’s and Diamond’s models, connecting these theoretical models to the data, and motivating the
empirical tests. Section II reviews the relevant empirical literature on debt maturity. Section III outlines
the empirical tests. Section IV furnishes information on how the data samples were compiled, and
Section V discusses the specific variables and their sample statistics. Section VI supplies the main
empirical test results, while Section VII describes additional empirical checks. Section VIII presents
some conclusions. The Technical Appendix formalizes our intuition regarding the effects of reduced
informational asymmetries on debt maturity in Flannery’s and Diamond’s models.
I. Framework for the tests
Flannery’s (1986) and Diamond’s (1991) models are closely related in that they both explain why
risky firms with long-term projects might borrow on a short-term basis in the presence of asymmetric
information. However, they differ in important ways and have some distinct empirical predictions. In
this section, we briefly describe the intuition underlying these theoretical models and show how they may
be tested in the same empirical model.
In both Flannery’s and Diamond’s models, firms have two-period projects about which they have
private information. The projects could be financed either using long-term debt – a two-period security –
or by short-term debt – a succession of two one-period securities. The longer maturity has a higher
interest rate, but some firms may still choose it because of anticipated problems in rolling over short-term
debt. In Diamond’s model, some firms are not offered the option of long-term debt.
In Flannery’s model, two types of firms that are initially observationally equivalent both have
positive net present value (NPV) projects, and also have private information that one type is riskier than
the other. At the end of one period, creditors learn whether projects were upgraded or not; firms with
favorable private information (i.e., low-risk projects) have a higher probability of upgrade than those with
unfavorable information (i.e., high-risk projects). At that time, all firms that initially chose short-term

3


debt must roll it over at a new interest rate and incur additional transactions costs.
In this model, if transactions costs are sufficiently high, a separating equilibrium may exist in
which firms with favorable private information issue short-term debt at a relatively low interest rate and
roll it over, and those with unfavorable private information issue long-term debt at a relatively high rate.
Firms with unfavorable private information are willing to pay the high rate on long-term debt to avoid
expected costs in rolling over short-term debt – the transactions costs plus a relatively high probability of
paying a high rate in the second period. Firms with favorable private information, in contrast, face a
lower probability of a high rate in the second period and so are willing to bear the transactions costs to
obtain the lower rate on short-term debt in the first period. In equilibrium, creditors can infer some of
what was initially firm private information and use it in assigning risk ratings – assigning lower risk
ratings to firms that choose short-term debt and higher risk ratings to those that choose long-term debt.
As a result, debt maturity is predicted to be positively related to risk ratings. While we refer to this
prediction as arising from Flannery’s model, it is also consistent with related signaling models that do not
rely on the presence of transactions costs (e.g., Kale and Noe (1990), Titman (1992)).
Diamond’s model differs from Flannery’s in that firms are not initially observationally equivalent
and not all projects have positive NPVs. Firms have private information that their projects have positive
or negative NPV. Creditors do not observe whether projects have positive or negative NPV, but are able
to assign initial risk ratings based on other observational differences. No additional transactions costs are
required for financing via short-term debt. As in Flannery’s model, creditors learn whether projects were
upgraded at the end of one period. Because some of the projects have negative NPV, creditors may refuse
to roll over short-term debt at the end of one period, creating liquidity risk for firms with short-term debt.
In Diamond’s model, firms with favorable private information (i.e., positive NPV projects) and
sufficiently low risk ratings may choose short-term debt at relatively low interest rates because of a high
likelihood of being able to roll over their debt. Those with favorable private information and intermediate
risk ratings may choose long-term debt at a higher rate to reduce their greater liquidity risk of being
unable to roll over short-term debt after one period. Firms with unfavorable private information (i.e.,
negative NPV projects) and either low or intermediate risk ratings may mimic the actions of firms with
favorable private information – otherwise, they may be identified by creditors as having negative NPV

4


projects and be denied credit. Thus, all firms rated as low-risk borrow short-term and all those rated as
intermediate-risk borrow long-term, whether their private information is favorable or unfavorable.
Firms that are initially rated as high-risk in Diamond’s model may be refused the option of long-
term debt because of a high probability of a negative NPV project. This is consistent with the debt
contracting literature, in which the most restrictive contract terms are often used with the high-risk
borrowers under conditions of asymmetric information (e.g., Berlin and Loeys (1988), Berlin and Mester
(1993), Carey, Prowse, Rea, and Udell (1993)). However, if creditors can obtain sufficiently high returns
from liquidation after the end of the first period, they may offer short-term debt to firms with projects
rated as high-risk. Thus, Diamond’s model predicts debt maturity to be a nonmonotonic function of the
risk ratings, with firms rated as low-risk and high-risk having short-term debt and firms rated as
intermediate-risk having long-term debt.
As discussed above, in Test 1, we examine whether maturity is an upward-sloping function of the
risk rating as predicted by Flannery’s model versus the nonmonotonic function predicted by Diamond’s
model. Thus, we test both theoretical models using the same empirical model. We argue that the use of
the risk rating at the time the debt is issued gives appropriate tests of both theories. In Flannery’s model,
creditors draw inferences from debt maturity choices, and their risk ratings reflect some of what was
initially private information of the firms. In Diamond’s model, creditors’ risk ratings reflect only the
initial assessments based on observable differences because no private information is revealed by
maturity choice. Thus, both theories have testable empirical implications for the relationship between
maturity and risk ratings at the time the credits are issued when evaluated under their own assumptions.
As noted earlier, in Test 2, we examine the effects of reduced informational asymmetries from
SBCS on debt maturities for each different risk rating as predicted by Flannery’s and Diamond’s models
and further explore the quantitative impact of asymmetric information within the context of these models.
Both models would predict an increase in average maturity for firms rated as low-risk if informational
asymmetries are reduced. In Flannery’s model, this occurs because the benefits to a low-risk firm from
distinguishing itself via costly signaling from riskier firms are lessened as transparency is improved. That
is, low-risk firms need not bear the transactions costs of rolling over short-term debt if they are no longer
in danger of being pooled with high-risk firms. In Diamond’s model, the removal of asymmetric

5


information would turn some firms into transparent, low-risk firms with positive NPV projects and others
into transparent, high-risk firms with negative NPV projects. The former set of firms should be
indifferent to short-term versus long-term debt, since the liquidity risk issue is resolved. Assuming that
some choose long-term debt, the average maturity for low-risk firms would increase relative to the case of
asymmetric information in which all firms rated as low-risk choose short-term debt. The latter set of
firms that are revealed to have negative NPV projects would be denied credit and so would have no effect
on the observed relationship between maturity and risk ratings.
Test 2 also addresses a potential shortcoming of Test 1 both here and in the empirical literature
that the observed relationship between debt maturity and risk ratings may reflect other factors. In
particular, there may be a problem if risk ratsings are assigned in part on the basis of the risks associated
with the amount of time that the funds are tied up, as opposed to the credit risks of the firms. Test 2
examines different maturities for a given risk rating, minimizing the effects of this potential problem.
II. Empirical literature review
This section first reviews the empirical evidence regarding debt maturity under conditions of
asymmetric information. We focus on the relationship between maturity and risk ratings and the extent to
which this relationship may be attributed to informational asymmetries as predicted by Flannery’s and
Diamond’s models. We do not discuss findings with regard to other theories of debt maturity, such as
agency costs and taxes. We then discuss how our empirical analysis differs from this literature.
A. Tests of Flannery’s and Diamond’s models
Several studies examine the relationship between risk ratings and firm debt maturity structure, or
the stock of debt that has been built up over time to test the predictions of Diamond’s model. Barclay and
Smith (1995) find that among publicly traded industrial firms with bond ratings, those with higher bond
ratings tend to use more short-term debt and those with lower bond ratings tend to have more long-term
debt. Those without bond ratings generally have more short-term debt. If one interprets firms with high
bond ratings as low-risk, firms with low bond ratings as intermediate-risk, and unrated firms as high-risk,
then their results as a whole may be considered to be consistent with Diamond’s predicted nonmonotonic
relationship. Subsequent studies by Stohs and Mauer (1996) using bond ratings for publicly traded
industrial firms and Scherr and Hulbert (2001) using an accounting measure for risk ratings (Altman Z-

6


Score) for small businesses also find evidence of a nonmonotonic relationship between firm risk ratings
and debt maturity structure. Johnson (2003) studies nonfinancial traded firms and uses three different
types of risk ratings, two based on accounting data (firm size and earnings volatility), and one based on
whether the firm’s debt is investment grade. Johnson’s accounting indicators have the nonmonotonic
relationship with the debt maturity structure, but the indicator for investment grade debt is negatively
related to the proportion of short-term debt, which may be considered to be contrary to the predictions of
Diamond’s model, under which low-risk firms would have short-term debt.
These studies do not use the relationship between risk ratings and maturity to test the predictions
of Flannery’s model, although some inferences might be drawn using our framework for Test 1 discussed
above. The nonmonotonic relationships in Barclay and Smith (1995), Stohs and Mauer (1996), and
Johnson (2003) using bond ratings may be considered to be consistent with the predictions of Flannery’s
model for low-risk firms, but not for high-risk firms. The relationships using accounting measures for
risk ratings in Scherr and Hulbert (2001) and Johnson (2003) do not have implications for Flannery’s
model. The risk rating in Flannery’s model is based at least in part on the revelation of private
information by firm maturity choice. Although bond ratings may reflect such a revelation, accounting
measures cannot.
It is unclear, however, how well these empirical studies of debt maturity structure test the
theoretical models. Flannery’s and Diamond’s models deal with the maturity of new debt issues at the
time of origination, not the remaining time on the stock of old contracts. The use of the maturity structure
does not distinguish between, for example, a newly issued one-year bond and a 30-year bond with one
year remaining – both contribute to the stock of one-year securities in the debt maturity structure. In
addition, the debt maturity structure may reflect decisions made at different historical points in time when
risk ratings and asymmetric information may have differed significantly from the sample period.2
Several studies avoid the potential problems with the use of maturity structure and focus on the
maturity of new debt issues. Mitchell (1993), Guedes and Opler (1996), and Ortiz-Molina and Penas
(2004) estimate the relationship between the maturity of new debt issues and risk ratings, although these
studies do not use specifications that a

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