2. 2
We study the influence of financial institutions’ network (position,
i.e. centrality) on private debt renegotiation (outside of distress)
• Financial contracting literature (design of private debt contracts)
+ social network analysis of credit markets & financial institutions
(information production, experience, reputation, trust)
• Major advantage of private debt contracts = flexibility =>
renegotiation = ex-post remedy to initial contractual
incompleteness (e.g. Hart & Moore 1999)
BACKGROUND
3. 3
• Initial conditions are important drivers of loan renegotiation
• Depend on lenders’ access to timely, relevant, valuable
information; experience, reputation, trust => collaboration &
reciprocity = social capital (e.g. Cagno & Sciubba 2010)
• Syndicated loans market = information & capital networks
• Rely on reputable, trustworthy, experienced (lead) banks =>
enhanced monitoring & screening, signal deal quality, mitigate
information asymmetries & agency costs
• Social network shapes patterns of exchanges => strategic
positions (centrality) = competitive advantage (access / control
over resources)(e.g. Baum et al. 2004)
BACKGROUND
4. 4
• Centrality = access resources => valuable private information =>
better monitoring & screening
• Centrality = constraints on opportunistic behavior => reputation
capital
• Information, experience, trust values of centrality => mitigate
adverse selection & moral hazard of renegotiation => lower costs
=> positive influence on loan renegotiation
• But central lenders are expected to write “better”, more
“complete” initial contracts + Threat of losing reputation by
making “wrong” renegotiation decision => negative influence on
loan renegotiation
BACKGROUND
5. 5
• Focus on Europe because bank based financial system (private
debt = major source of external financing for firms) + legal
environment less protective of creditors (importance of loan
contract design)
• Contribution to literature on private debt renegotiation (e.g.
Nikolaev 2018); role of connections, networking, reputation in
bank lending (e.g. Gatti et al. 2013); social network analysis of
syndicated lending (e.g. Houston et al. 2018) => focus on loan
renegotiation
BACKGROUND
6. 6
• Information on loan amendments, initial credit agreements,
lenders, and borrowers from Bloomberg
• Information on country level data (economy, finance, law) from
World Bank
• 6361 loans issued to 4805 firms involving 238 lenders in 25
European countries between 1999 & 2017
• Relations between lead & participant banks only + overlapping
moving 3-year windows (dynamic structure of syndication
network)
• 3 most common centrality measures = degree, closeness,
betweenness
EMPIRICAL DESIGN
7. 7
• Betweenness = network position (strategic / bridge /
intermediary)
• Closeness = network proximity (depth / close to center)
• Degree = network involvement / size (local neighborhood)
• Maximum centrality => most central member / maximum
informedness
• Renegotiation process = decision or likelihood (1/0), dynamics (#
rounds: 1-12), scope (# amended terms: 1-6)
• => Logit, ordered logit, poisson regressions
• Control for loan, syndicate, borrower, country level variables
EMPIRICAL DESIGN
15. 18
Summary of main findings
• We confirm the hypothesis that central lenders have a positive
influence on the entire renegotiation process
• We do not validate the alternative hypothesis that central lenders
are able to write more “complete” contracts at origination which
are more renegotiation-proof or that the threat of harming their
reputation capital by making the “wrong” renegotiation decision
reduces the lenders’ willingness to enter a renegotiation process
• Access to superior information, greater experience, reputation,
and trust encourages renegotiation
RESULTS
16. 19
Robustness checks at “micro” & “macro” levels
• Micro = specific renegotiation, loan, and syndicate characteristics
• Unique renegotiation, unique loan, first loan
• Small loan, short maturity, no collateral, no covenants, many tranches, few
past issues
• Small syndicates, no league table lender, no relationship, no rating, adding
bank fixed effects, excluding “top dogs”
• Macro = specific country and time characteristics
• Weak rule of law, weak protection of creditors against shareholders in case
of default, low creditors recovery
• Excluding UK, only historical core EZ, only GIIPS, only post crisis periods (US
and EZ)
RESULTS
17. 20
Summary of robustness results
• Main results survive => network-central lenders have a positive
influence on the entire renegotiation process (superior
information, greater experience, reputation, and trust)
• Additional findings:
• Centrality becomes non-significant for renegotiation of
complex deals with multiple tranches
• Legal and institutional environments with weaker protection
of creditors make the value of centrality less relevant for
renegotiation
RESULTS
18. 21
• We provide an empirical link between the social network analysis of credit
markets and the design of loan contracts
• We use a large sample of more than 6 000 loans issued in 25 European countries
• We find that lender’s network-centrality (Betweenness, Closeness, Degree) has a
significant and positive influence on the loan renegotiation process
(renegotiation likelihood, # renegotiation rounds, # amendments)
• Our findings confirm that access to superior information, greater experience,
reputation, and trust encourages renegotiation
FINAL WORDS
