Financial institution network and the certification value of bank loans

1. Financial institution network and
the certification value of bank loans
Christophe J. Godlewski
UHA & EM Strasbourg
Bulat Sanditov
Telecom EM
AFFI Conference 2015, Cergy-Pontoise

2. Take away
2
• Financial institutions network and reputation
• Certification value of bank loans
• European syndicated loans (2001-11)
• Social network analysis + event study methodologies
• Presence of central and reputable lenders increase
borrower’s stock market reaction to a loan announcement
• Stronger effect when informational frictions are important
• Effect vanishes away during severe distruption in the
functioning of financial markets

3. Background & motivations
3
• Banks produce private information on borrowers (Diamond
1984…)
• Bank loans bear a certification value => AR > 0 for
borrower’s stock around the date of bank loan
announcement (James 1987…)
• Maintaining reputation for diligent screening & monitoring
=> mitigate informational frictions & agency problems
• Syndicated loans market (4.7 trln $, 2014): lead bank
reputation is crucial (Ross 2010…)
• Lead bank = structure deal, negotiate loan terms, organize
syndicate
• Reputable leader => enhance monitoring, attract
participants, signal quality, reduce agency costs…

4. Background & motivations (cont.)
4
• Lender reputation  trust & reciprocity = critical forms of
social capital (Song 2009) driven by social networks (Cagno
& Sciubba 2010)
• Social network features of syndicated lending market =
information & capital networks (Baum et al. 2003, 2004)
• Repeated interactions => trust & reciprocity => solve
informational frictions => mitigate agency problems
• => important for firms seeking external financing
(Brander et al. 202, Wang & Wang 2012)
• => affect pricing and structure of bank loan agreements
(Cai 2009, Godlewski et al. 2012, Gatti et al. 2013)

5. Aim & contributions
5
• Do banks’ network/reputation affect certification value of
bank loans?
1. Impact of bank network/reputation on certification value
of bank loan => borrower AR / event study methodology
2. Social network metrics (Centrality centrality) to proxy
reputation => richer / comprehensive measure
3. European focus => bank private debt = main source of
external financing for companies

6. Empirical design | Data
6
• Loan and syndicate characteristics : Bloomberg
• Amount, spread, maturity, announcement date…
• Number of lenders, roles (titles)…
• Borrower characteristics : Factset
• Balance sheet & stock market information
• Country characteristics : GFDD (WB) + Djankov et al. (2007)
• European non-financial companies (24 countries)
• January 2001 – June 2011
• 254 companies / 465 loans / 906 lenders

7. Empirical design | SNA methodology
7
• Network = collection of nodes & links
• Banks’ participation in syndicated loans = affiliation
network
• => bipartite network with 2 types of nodes = actors
(banks) linked with events (deals)
• Projection of bipartite network
• => links between lead and participant banks
• => overlapping moving 3 years windows (Baum et al.
2003…)
• 3 classifications of leaders:
• Mandated arranger or Lead arranger (1)
• + Lead manager, Book runner, Book manager… (2)
• + Co / Joint, Managers… (3)

8. Empirical design | SNA methodology (cont.)
8
(a)
(b)
1 2 3 4 5 6 7 8 9 10
A B C
Lenders
Loans
11
D
1
2
3
4
5
6
7
8
9
10
11

9. Empirical design | SNA methodology (cont.)
9
• Leaders social network metrics => focus on Centrality
centrality
• => how well leader is positioned within a network
• => control over the flow of information/capital
• => interaction, reciprocity, trust => social capital =>
proxy of reputation
• Formally = number of the shortest paths between all pairs
of lenders in a network, which pass through a lender,
deflated by the number of alternative shortest paths
• Compute average, median and interquartile of Centrality
centrality by syndicate
• => 3 measures of centrality + 3 classifications of leaders
= 9 measures of network/reputation

10. Empirical design | Event study methodology
10
• Multi-event and multi-country setting
• Modified market model 𝐴𝑅𝑖 = 𝑅𝑖 − 𝑅 𝑚 (Fuller et al. 2002)
• Use local-currency national market indexes (Campbell et al.
2010)
• Bank loan announcement date = event date (day 0)
• Excluding contaminated events
• Compute three-day period CAR (-1,1)
• Multivariate analysis relies on OLS (robust s.e. clustered at
loan level) :
𝐶𝐴𝑅 −1, 1 = 𝛼 + 𝛽 × 𝐿𝑒𝑛𝑑𝑒𝑟𝑠 𝑐𝑒𝑛𝑡𝑟𝑎𝑙𝑖𝑡𝑦
+ 𝛾 × 𝐿𝑜𝑎𝑛 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠 + 𝜃 × 𝐵𝑜𝑟𝑟𝑜𝑤𝑒𝑟 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠 + 𝜗
× 𝐶𝑜𝑢𝑛𝑡𝑟𝑦 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠 + 𝜀

11. Results | (some) Descriptive statistics
11
0
2
4
6
8
10
12
14
16
18
20
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Percentage
Year
0.0000
0.0050
0.0100
0.0150
0.0200
0.0250
0.0300
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Centrality
Year
avg Betweenness (1) med Betweenness (1) iqr Betweenness (1)
LoansSyndicate
centrality

12. Results | (more) Descriptive statistics
12
Variable Mean SD Median
Y = CAR (-1,1) 0.0544 0.0776 -0.0035
avg. Centrality (1) 0.0185 0.0133 0.0173
med. Centrality (1) 0.0100 0.0144 0.0046
iqr. Centrality (1) 0.0224 0.0181 0.0208
Loan amount (mln $) 1300.0000 1900.0000 729.0000
Maturity (y) 5.5236 3.3988 5.0000
Term loan 0.5125 0.4999 1.0000
Secured 0.2022 0.4017 0.0000
Covenants 0.1639 0.3702 0.0000
Syndicate (n) 19.4737 23.0466 13.0000
Tranches (n) 2.2484 2.4971 2.0000
League table 0.4865 0.4999 0.0000
First loan 0.5899 0.4919 1.0000
Loanvariables
Centrality
variables

13. Results | (more) Descriptive statistics
13
Variable Mean SD Median
Rating 0.3287 0.4698 0.0000
Sales (mln $) 12200.0000 24400.0000 5440.0000
Debt ratio 0.3225 0.1741 0.3240
Ebitda margin -1.5600 94.2942 0.1140
Stock market 0.9289 0.4273 0.8945
Private credit 1.3470 0.4557 1.2992
French law 0.5297 0.4992 1.0000
German law 0.1405 0.3475 0.0000
Creditor rights 2.1323 1.3658 2.0000
Bank Z score 14.5010 6.6430 13.8325
Bank concentration 0.6806 0.1665 0.6558
Crisis 0.2022 0.4017 0.0000
Countryvariables
Borrower
variables

14. Results | Main regression results
14
Variable avg. Centrality med. Centrality iqr. Centrality
1 2 3 4 5 6 7 8 9
Baseline results (loan & syndicate var.)
Centrality 2.0545 2.1096 2.4473 2.3471 2.7250 2.7259 0.3337 -0.0505 -0.6432
With firm characteristics
Centrality 1.0409 0.8847 0.8788 0.4168 0.3827 0.2190 0.2902 0.3967 0.3133
With country characteristics
Centrality 2.8043 2.2539 2.5384 2.5948 2.4133 2.5824 0.9349 -0.4434 -0.8280
OLS regressions, Y = CAR(-1,1), robust s.e. clustered at loan level
Controls = loan currency, purpose, year; borrower industry, country
Bold coef. = significant at 10% min. (*)

15. Results | Interaction terms
15
Variable
Small
loan
Short
maturity
Secured Covenants
Small
syndicate
League table
avg Centrality (1) 0.6244 2.1705 2.0629 3.8361 -3.9506 3.5214
avg. Centrality (1) x
Variable
2.2794 -0.4267 -0.0232 -9.8643 6.5792 -3.6920
Variable
Low
sales
Low debt
Low
profit
avg Centrality (1) -0.2916 0.4310 1.0274
avg. Centrality (1) x
Variable
1.8076 1.0551 0.0530
Variable
Low
stock
market
Low private
credit
Low bank
z score
High bank
concentration
Weak
creditor
rights
Crisis
avg Centrality (1) 2.0932 4.5575 10.0746 3.3854 0.7350 3.2628
avg. Centrality (1) x
Variable
1.9213 -3.4674 -12.8608 -2.0184 5.1910 -4.3345
Ibid.
Interaction variable = dummy (use of sample median for cont. Variables)

16. Conclusion
16
• Syndicate centrality / reputation matter for certification
value of bank loans in Europe
• Presence of central / reputable leaders increase stock
market reaction (AR) to a loan announcement
• Impact on AR reinforced when informational frictions are
important but effect vanishes away during financial crisis of
2008
• Contribution to recent literature on the role of reputation
and networks in financial intermediation
• Important for the development of credit markets, especially
in Europe
• Limits = potential endogeneity in matching of borrowers
and lenders