This document presents a theoretical model and empirical analysis of how the size of a firm's banking pool can signal the firm's quality to investors. The model suggests higher quality firms will borrow from fewer banks due to greater monitoring. Empirically, using a sample of European loans, the analysis finds that firms with higher Altman Z-scores, indicating better financial health, tend to borrow from smaller banking pools, supporting the signaling theory. The effect is weaker for larger, more transparent firms. Syndicated loans also mitigate the signaling through stronger coordination.

1. BETTER BORROWERS, FEWER BANKS?
Christophe J. Godlewski
Frédéric Lobez
Jean-Christophe Statnik
Ydriss Ziane
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2. Outline
1. Introduction
2. Literature
3. Model
4. Empirical design
5. Results
6. Discussion
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3. 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)
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4. 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)
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5. 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
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6. 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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7. Model
• Economy
Managers Banks
Investors
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8. Model (cont.)
• Timeline
T=0 T=1 T=2
Private information
on project’s success /
Investment in a failure Project outcome
risky project positive info. => => k : probability x
(size 1) project continuation
negative info =>
strategic default & => 0 : probability (1-x)
assets’ diversion
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9. 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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10. 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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11. 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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12. ,
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 Z-
score
• => X1= working capital / TA; X2= retained earnings / TA;
X3= EBIT / TA; X4= equity / liabilities; X5= sales / TA
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13. ,
Empirical design (cont.)
• Different Z-score measures
Variable Definition Mean Std dev.
Altman (2000) Z score computed on the same
Z score (t) 1.9061 1.4641
fiscal year as the bank loan
Altman (2000) Z score computed on the same
Z score (t, S1) fiscal year as the bank loan including loans 1.9067 1.4767
granted on the first semester of the year only
Altman (2000) Z score computed on t+1 fiscal
Z score (t+1) 2.0886 1.5866
year with respect to the bank loan
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14. ,
Empirical design (cont.)
• Control variables
Logarithm of the loan facility
Loan size amount in USD
Logarithm of the loan maturity in
Loan maturity months
Syndication =1 if loan is syndicated
Term loan =1 if loan is a term loan
Ebit margin EBIT / Operating revenue
Share of 3 largest banks in total
Bank concentration banking assets
Index aggregating creditor rights
Creditor rights (0:poor creditor rights to 4)
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15. ,
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 Model 1 Model 2 Model 3
Z score (t) -0.2824**
(0.1286)
Z score (t, S1) -0.4691***
(0.1444)
Z score (t+1) -0.2708
(0.4023)
N 2474 1184 603
R² 0.3843 0.4313 0.4599
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16. ,
Results (cont)
• Banking pool organization => banking pool size / borrower quality
Variables Model 1a Model 2a Model 3a
Z score (t) -0.9887***
(0.2938)
Z score (t, S1) -1.7015***
(0.4500)
Z score (t+1) -1.3409**
(0.5920)
Z score (t) x Syndication 0.7737**
(0.3024)
Z score (t, S1) x
1.3564***
Syndication
(0.4242)
Z score (t+1) x Syndication 1.2649**
(0.5462)
N 2474 1184 603
R² 0.3787 0.4192 0.4539
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17. ,
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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18. ,
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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19. ,
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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20. ,
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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