BETTER BORROWERS, FEWER BANKS?
Christophe J. Godlewski
Frédéric Lobez
Jean-Christophe Statnik
Ydriss Ziane
1
Outline
1.
2.
3.
4.
5.
6.
Introduction
Literature
Model
Empirical design
Results
Discussion
2
Introduction
• Multiple bank relationships = common and
significant economic phenomenon
• European firm has more than 5 bank relationships
• Various (theoretical & empirical) arguments to
explain multiple banking / optimal number of banks
• Monitoring / hold-up problem / external financing
sources diversification / limit bank liquidity risk…
• This article: novel theoretical explanation based on
signaling + empirical validation (Europe)
3
Literature
•
•
•
•
What drives the optimal number of banks ?
Benefits / costs of an exclusive bank relationship
=> Multiple banking can lead to …
[-] duplication of transaction costs + free riding in
monitoring (Diamond 1984)
• [-] dissemination of strategic information to
competitors (Yosha 1995)
• [-] less flexibility in loan terms setting (Dewatripont &
Maskin 1995)
4
Literature (cont.)
• [+] mitigate the hold-up problem (Sharpe 1990,
Rajan 1992)
• [+] reduce liquidity risk (Detragiache et al. 2000)
• Multiple banking = pool of banks with different
structures
• => + / - homogenous depending on relative power of
some pool’s members among others
• Banking pools structure related to borrower quality /
information asymmetry / agency costs / coordination
5
Literature (cont.)
• Multiple banking => weak monitoring / increases
early project liquidation risk (Bolton & Scharfstein
1996)
• => smaller / concentrated pool => better monitoring
(Elsas et al. 2004, Brunner & Krahnen 2008)
• => bank syndicate => mitigate coordination and
moral hazard problems
• Negative relationship between syndicate size and
borrower quality (Lee & Mullineaux 2004, Sufi 2007)
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Model
• Economy
Managers
Banks
Investors
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Model (cont.)
• Timeline
T=0
Investment in a
risky project
(size 1)
T=1
Private information
on project’s success /
failure
 positive info. =>
project continuation
 negative info =>
strategic default &
assets’ diversion
T=2
Project outcome
=> k : probability x
=> 0 : probability (1-x)
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Model (cont.)
•
•
•
•
•
•
•
•
Firm’s financial structure
Investment financed by n potential banks
=> n : observable by other investors
=> μ(n) : monitoring by n banks
Manager’s utility function
2 components
=> firm’s market value : V(x)
=> strategic default value
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Model (cont.)
• Proposition
• The number of banks in the pool = credible signal of firm’s
quality
• Signalling equilibrium => size of the banking pool = decreasing
with the quality of the firm
• Intuition
• Signaling cost => greater monitoring by banks
• Good quality firm’s manager is less sensitive to a tighter
monitoring than a bad quality firm’s manager
• => Spence condition
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Empirical design
• Data
• Information on banking pools’ size + loan terms =>
Dealscan (Reuters)
• Information on firms => Amadeus (Bureau Van Dijk)
• Information on country level data => Beck et al.
(2007) + Djankov et al. (2007)
• 3303 bank loans to 616 firms from 19 European
countries over the 1999-2006 period
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,
Empirical design (cont.)
• Dependant variable = Number of lenders in the banking
pool (mean = 8.79 / std dev. = 8.52)
• Main explanatory variable = empirical proxy for the
borrower quality signal
• => use of bankruptcy / business risk indicator = Altman Zscore
• => X1= working capital / TA; X2= retained earnings / TA;
X3= EBIT / TA; X4= equity / liabilities; X5= sales / TA
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,
Empirical design (cont.)
• Different Z-score measures
Variable
Definition
Mean
Std dev.
Z score (t)
Altman (2000) Z score computed on the same
fiscal year as the bank loan
1.9061
1.4641
Altman (2000) Z score computed on the same
Z score (t, S1) fiscal year as the bank loan including loans
granted on the first semester of the year only
1.9067
1.4767
Altman (2000) Z score computed on t+1 fiscal
year with respect to the bank loan
2.0886
1.5866
Z score (t+1)
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,
Empirical design (cont.)
• Control variables
Bank concentration
Logarithm of the loan facility
amount in USD
Logarithm of the loan maturity in
months
=1 if loan is syndicated
=1 if loan is a term loan
EBIT / Operating revenue
Share of 3 largest banks in total
banking assets
Creditor rights
Index aggregating creditor rights
(0:poor creditor rights to 4)
Loan size
Loan maturity
Syndication
Term loan
Ebit margin
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,
Results
• Borrower quality => banking pool size (= Number of lenders)
• OLS with standard errors clustered at borrower level / sector + year
dummies / coefficient for main variables displayed only
Variables
Z score (t)
Model 1
-0.2824**
(0.1286)
Z score (t, S1)
Model 2
-0.4691***
(0.1444)
Z score (t+1)
N
R²
Model 3
2474
0.3843
1184
0.4313
-0.2708
(0.4023)
603
0.4599
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,
Results (cont)
• Banking pool organization => banking pool size / borrower quality
Variables
Z score (t)
Model 1a
-0.9887***
(0.2938)
Z score (t, S1)
Model 2a
-1.7015***
(0.4500)
Z score (t+1)
Z score (t) x Syndication
Model 3a
-1.3409**
(0.5920)
0.7737**
(0.3024)
Z score (t, S1) x
Syndication
1.3564***
(0.4242)
Z score (t+1) x Syndication
N
R²
2474
0.3787
1184
0.4192
1.2649**
(0.5462)
603
0.4539
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,
Results (cont)
• Robustness checks
• Regressions by firm and loan size
• => large firms / loans = less information asymmetry
between firm and investors
• => banking pool structure less informative
• Split sample according to medians (TA & loan size)
• => coefficient for Z score / interaction term remains
negative / positive but becomes weaker for large
firms or large loans
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,
Results (cont)
• Use of alternative European Z Score
• Z scores as above computed with different
coefficients of the Z function
• => re-estimation of the scoring function using same
variables as Altman but on a sample of 365 000
European firms
• [firm’s default defined by rating category and default
probability provided by Amadeus]
• => similar results
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,
Discussion
• Alternative theoretical foundations for the existence of
banking pools
• => signaling equilibrium model where firms voluntary
limit asset substitution through smaller banking pool
(better monitoring)
• Theoretical prediction = better firms borrow from fewer
banks
• Empirical validation on a sample of more than 3000
loans to 600 European borrowers
• Use of Altman Z score to measure firm quality
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,
Discussion (cont.)
• Reduced size of the banking pool funding a loan to
better quality borrower
• => banking pool structure = signal of borrower quality
• Signal less important when
• => coordination, hierarchy, and organization of the pool
are stronger (syndication)
• => less information asymmetry between firm and
investors (large firms and loans)
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