Presentation is loading. Please wait.

Presentation is loading. Please wait.

BETTER BORROWERS, FEWER BANKS? Christophe J. Godlewski Frédéric Lobez Jean-Christophe Statnik Ydriss Ziane 1.

Similar presentations

Presentation on theme: "BETTER BORROWERS, FEWER BANKS? Christophe J. Godlewski Frédéric Lobez Jean-Christophe Statnik Ydriss Ziane 1."— Presentation transcript:

1 BETTER BORROWERS, FEWER BANKS? Christophe J. Godlewski Frédéric Lobez Jean-Christophe Statnik Ydriss Ziane 1

2 Outline 1.Introduction 2.Literature 3.Model 4.Empirical design 5.Results 6.Discussion 2

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) 3

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) 4

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 5

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) 6

7 Model Economy 7 Banks Investors Managers

8 Model (cont.) Timeline 8 T=0T=1T=2 Investment in a risky project (size 1) Private information on project’s success / failure  positive info. => project continuation  negative info => strategic default & assets’ diversion Project outcome => k : probability x => 0 : probability (1-x)

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 9

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 10

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 period 11

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 12,

13 Empirical design (cont.) Different Z-score measures 13, VariableDefinitionMeanStd dev. Z score (t) Altman (2000) Z score computed on the same fiscal year as the bank loan Z score (t, S1) Altman (2000) Z score computed on the same fiscal year as the bank loan including loans granted on the first semester of the year only Z score (t+1) Altman (2000) Z score computed on t+1 fiscal year with respect to the bank loan

14 Empirical design (cont.) Control variables 14, Loan size Logarithm of the loan facility amount in USD Loan maturity Logarithm of the loan maturity in months Syndication=1 if loan is syndicated Term loan=1 if loan is a term loan Ebit marginEBIT / Operating revenue Bank concentration Share of 3 largest banks in total banking assets Creditor rights Index aggregating creditor rights (0:poor creditor rights to 4)

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 15, VariablesModel 1Model 2Model 3 Z score (t) ** (0.1286) Z score (t, S1) *** (0.1444) Z score (t+1) (0.4023) N R²

16 Results (cont) Banking pool organization => banking pool size / borrower quality 16, VariablesModel 1aModel 2aModel 3a Z score (t) *** (0.2938) Z score (t, S1) *** (0.4500) Z score (t+1) ** (0.5920) Z score (t) x Syndication0.7737** (0.3024) Z score (t, S1) x Syndication *** (0.4242) Z score (t+1) x Syndication1.2649** (0.5462) N R²

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 17,

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 European firms [firm’s default defined by rating category and default probability provided by Amadeus] => similar results 18,

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 19,

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) 20,

Download ppt "BETTER BORROWERS, FEWER BANKS? Christophe J. Godlewski Frédéric Lobez Jean-Christophe Statnik Ydriss Ziane 1."

Similar presentations

Ads by Google