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Systemic Risk, Credit Risk, and The Effect of Managerial Style in
Syndicated Bank Loans*
Yu Shan†
Jan 13th, 2018
Abstract
In this study, I investigate the relationship between systemic risks and banks’ credit risk-taking in
syndicated bank loan market, and analyze how idiosyncratic executive-specific effects help explain
the variation in credit risk-taking sensitivity on bank-level systemic risk. I find that systemically
riskier banks originate riskier loans even after controlling for self-selection of lenders by borrowers,
and this relationship is weaker during periods of economic turmoil (recessions or high CATFIN
periods). Using within-loan regressions to remove the demand-side factors, I find that the result is at
least partially driven by supply-side factors. Lead-lag analyses and dynamic panel GMM estimations
help address the reverse causality concerns. Using factor analyses to identify executive and bank
styles, I find that some idiosyncratic bank executive-specific innovation preferences can explain the
variation in credit risk-taking sensitivity on systemic risk, indicating that unobservable executive
personal traits play an important role in affecting banks’ reactions to potential systemic risk crisis.
Also, executive-specific styles are much more important than bank-specific styles in explaining credit
risk-taking sensitivity on systemic risk.
JEL Classifications: G20, G21, G24, G28.
Keywords: Systemic Risk, Credit Risk, Public Guarantee, Syndicated Bank Loans, Manager Style,
Risk-taking, Bank Governance.
* I thank Linda Allen, Armen Hovakimian, Ayako Yasuda, Ayan Bhattacharya, Dexin Zhou, Gayle Delong, Lin Peng,
Michael LaCour-Little, Nahata Rajarishi, Nancy Wallace, Sonali Hazarika, Yildiray Yildirim, and seminar/conference
participants at the Baruch College Brownbag Seminar and Financial Management Association 2017 Annual Meeting
for their insightful suggestions and comments. All errors are my own. † Ph.D. candidate in finance, Bert W. Wasserman Department of Economics and Finance, Zicklin School of Business,
Baruch College, New York, NY 10010, E-mail: [email protected].
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1. Introduction
An important lesson that policymakers and academicians have taken away from the global financial
crisis of 2007-2009 is the importance of distinguishing between individual financial institutions’ risks
in isolation and their systemic risk exposure. Bank regulations that focus exclusively on the individual
institution fail to recognize systemic interrelationships that should form an important component of
macro-prudential regulation. Basel I and Basel II are designed to regulate banks’ risk in isolation, but
ignored the systemic impact of those institutions on the financial system and the real sector of
economy. Basel III capital requirements incorporate this recognition through the imposition of
countercyclical capital buffers and total loss absorbing capacity (TLAC) requirements related to
aggregate market conditions. However, systemic bank regulations are viewed as add-ons and do not
integrate established individual bank regulations to newer systemic risk regulations. If there exists a
direct connection between systemic risk and disaggregated credit risk exposure on an individual bank
basis, and if this connection is ignored, then bank regulations at one dimension could lead to
suboptimal outcomes at the other dimension. For instance, if banks regularly reduce its individual
bank risk exposure when their systemic risk rises, then systemic bank regulations can be less drastic
due to this automatic stabilizer. Alternatively, if banks regularly exacerbate their own bank risk
exposure by exploiting potential moral hazard advantages, systemic risk regulation will be
insufficient to address system-wide crises. In this paper, I build the connection between systemic risk
and individual bank’s risk-taking in the context of syndicated bank loan market.
Credit risk is a major source of risk in bank portfolios. In this study, I examine whether systemically
risky banks endogenously adjust the credit risk they take in syndicated bank loans as either the
individual micro-level or the aggregate macro-level measure of systemic risk changes. Specifically,
I test three alternative responses to systemic risk in the credit risk-taking of syndicated bank loans.
That is, banks may either reduce or increase their credit risk in their loans as their systemic risk
increases, or alternatively, there may be no connection between credit risk and systemic risk.
First, banks may reduce their own risk exposures when a systemic crisis looms in order to pull back
from the brink of insolvency. This automatic stabilizer may reduce systemic risk, thereby mitigating
some of the negative impact. This could be driven by higher charter values (Cordella and Levy-Yeyati,
2003), reducing undiversifiable contagion risk across banks (Freixas, Parigi, and Rochet, 2000; Allen
and Gale, 2001; Diamond and Rajan, 2005; Dell’Ariccia and Ratnovski, 2013; Choi, 2014),
managerial incentives (Schwarcz, 2017), and clawbacks (Allen and Li, 2011). Charter values may
induce banks to control the credit risk in their loan portfolios when systemic crises are more likely to
2
occur. Either implicit or explicit government bail-out guarantees may result in higher charter values
for protected banks due to lower refinancing costs (Keeley, 1990; Gropp, Hakenes, and Schnabel,
2011). Thus, my first hypothesis is:
Hypothesis I: Banks’ credit risk-taking in syndicated bank loans decreases with bank’s systemic risk,
ceteris paribus.
Alternatively, systemically risky banks may exacerbate their credit risk exposure. This behavior could
be incentivized by limitations in market discipline and imperfections in regulatory pricing. A growing
literature has shown that explicit (deposit insurance) and implicit (potential bail-outs) public
guarantees may increase systemically important banks’ risk-taking by reducing market discipline
(Flannery, 1998; Sironi, 2003; Gropp, Vesala, and Vulps, 2006; Acharya, Anginer, and Warburton,
2016) and increasing moral hazard (Kane, 1989; Demirguc-Kunt and Detragiache, 2002; Diamond
and Rajan, 2009; Farhi and Tirole, 2012). First, the deposit insurance premiums in US are not priced to
include actuarially fair risk premiums (Acharya, Santos, Yorulmazer, 2010) and exhibit a procyclicality
feature, which reduce the cost of high risk-taking especially for systemically risk banks (Angier, Demirguc-
Kunt, and Zhu, 2013). Second, the system of capital surcharges for GSIBs is insensitive to banks’ actual
risk-taking (Passmore and Hafften, 2017) and is completely free for non-G-SIBs, which can also be highly
systemically risky. Since the cost is not sufficiently internalized by current regulation, systemically risky
banks will rationally take advantage of it to pursue abnormal returns. Third, due to explicit and implicit
public guarantees, depositors’ and other insured stakeholders are less incentivized to monitor bank,
thereby limiting the risk premium embedded in market rates on the bank’s obligations. Acharya,
Anginer, and Warburton (2016) find that market discipline is less effective in curbing the risk-taking
behavior of systemically important financial institutions. Thus, the above arguments lead me to the
following hypothesis:
Hypothesis II: Banks’ credit risk-taking in syndicated bank loans increases with bank’s systemic risk,
ceteris paribus.
Lastly, some literature also provides evidence showing that the connection between credit risk and
systemic risk could be very weak. Credit risk relates to the idiosyncratic risk a bank is confronting,
which is an internal risk, while systemic risk is the importance of bank to the financial system and
the broader economy, which is a form of externality. Adrian and Brunnermeier (2016) suggest that
there is only a very loose cross-sectional link between institution’s risk/tail risk and its systemic risk
(Adrian and Brunnermeier, 2016). Focusing on bank charter values (rather than credit risk in the loan
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portfolio), Cordella and Yeyati (2003) and Hakenes and Schnabel (2010) argue that the net effect of
systemic risk and public guarantees on bank risk-taking is ambiguous. Gropp, Hakenes, and Schnabel
(2011) shows that there is no evidence that public guarantees increase the protected banks’ risk-taking,
except for banks that have outright public ownership.
This topic is particularly relevant in light of recent regulatory interventions during the 2007-2009
crisis. The explicit and implicit cost of funds from government support may actually be quite high.
First, the injected preferred equity has priority over common equity, which may amplify the losses of
common equity holders in the event of failure, resulting in a reduction in the value of common equity,
and increasing the difficulty of raising equity (Berger, Roman and Sedunov, 2016). If banks’
shareholders are concerned about the negative implications of bailouts, they may require the bank
managers to adjust the credit risk of their loans in order to pull back from the brink of delinquency
and bailout. Second, the supported banks may have to undertake certain social welfare responsibilities
through lending expansion, which may not be an equilibrium choice during economic downturns.
Recent experience with government intervention in the financial system may induce banks to reduce
their credit risk exposure in order to mitigate the bank’s own risk of insolvency, thereby reducing the
likelihood that bailouts will be needed.‡ This paper empirically examines the relationship between
systemic risk and credit risk exposure in bank loan portfolios to address these important public policy
issues.
Using data on syndicated bank loans, this paper investigates whether financial institutions adjust the
credit risk in their loan portfolios risks in response to levels of systemic risks. I measure the credit
risk using borrower distance-to-default at the quarter of loan origination, while controlling for an
array of loan characteristics. I use two complementary measures of systemic risk in this paper. To
measure the macro-level aggregate systemic risk, I utilize CATFIN (Allen, Bali and Tang, 2012),
which is a cross-sectional measure that identifies the overall level of systemic risk in the financial
system at each point in time. This allows me to examine whether high levels of aggregate systemic
risk that are likely to lead to government bailouts or other interventions induce banks to adjust the
credit risk in their loan portfolios upward or downward. To measure the micro-level systemic risk, I
‡ In the wake of the 2007-2009 financial crisis, greater regulatory costs, stricter security, and higher disciplinary
pressure has been imposed on systemically important banks. For example, G-SIBs are subject to higher capital
buffer requirements, Total Loss-Absorbing Capacity (TLAC) standards, resolvability requirements, and higher
supervisory expectations. With all these higher regulatory requirements, systemically important banks may be
at a competitive disadvantage relative to other banks, leading them to reduce their lending to high risk borrowers
that may encourage regulatory scrutiny. For example, regulatory warnings regarding leveraged loans in 2014
induced banks to cut back on lending to high risk borrowers. Alternatively, however, systemically risky banks
may choose to lend to riskier borrowers in order to recoup their higher costs of bank capital.
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utilize Δ𝐶𝑜𝑉𝑎𝑅 (Adrian and Brunnermeier, 2016) to determine the impact of an individual bank’s
insolvency on systemic risk in the overall financial system. The greater the contribution of an
individual bank’s insolvency to market-wide systemic risk, the greater the individual bank’s
imposition of systemic risk onto the macroeconomy. I utilize this measure to determine whether the
bank’s systemic risk exposure impacts its ability to lend to borrowers of differing risk levels.
I find that during periods of non-recession or low macro-level systemic risk, bank systemic risk is
positively related to its credit risk-taking. This indicates that banks may develop a proclivity that
encourages the pursuit of abnormal returns from risk enhancing activities. The result is robust to
several different specifications, which control for loan, borrower, and bank characteristics, as well as
a series of fixed effects and endogeneity controls. This supports Hypothesis II that bank’s credit risk-
taking in syndicated bank loans increases with bank’s systemic risk.
I also find that, when the economy enters a recession or a heightened risk of systemic crisis, the positive
relationship between systemic risk and credit risk-taking is weaker, and the banks with most systemic risk
reduce the credit risk in their loan portfolios the most. This indicates that, during volatile periods, the
heightened risk of insolvency and the growing importance of bank charter value dominate the incentives to
exploit abnormal returns from risk-taking. Thus, banks pull back from the brink of insolvency by reducing
their credit risk, particularly if they have a high exposure to systemic risk. Indeed, during these volatile
periods, my results show that systemically important banks actually maintain high levels of systemic risk
while also reducing their credit risk exposure in order to improve the likelihood that they would benefit
from government bailouts. Since the federal government does not have any explicit, ex-ante commitment
to support a distressed bank, banks do not know in advance whether they will be rescued once they become
financially distressed. The combination of increased systemic risk and reduced loan portfolio credit risk
may improve the likelihood that the bank receives government support. These results are consistent with
the Baker, Bloom, and Davis (2016) policy uncertainty index, which hit a peak after Lehman Brothers failed,
as well as the subsequent reforms codified in the Dodd-Frank Act and the Consumer Protection Act which
explicitly aim to reduce expectations of government shield. Further, consistent with my results, lower credit
risk in the loan portfolio may reduce regulatory capital requirements, thereby reducing the bank’s risk of
failure.
In the baseline regressions, the borrower self-selection and lender-borrower matching is endogenous.
In other words, the probability of initiating a new loan between firm i and bank j is endogenous. It’s
possible that some unobservable borrower characteristics induce borrowing firms to choose to apply
for loans from certain banks, thereby introducing selection bias into the OLS analysis. Therefore, I
5
must control for the probability of originating a loan between firm i and bank j that is independent of
the channel of systemically risky banks selecting borrower with certain default risks. That is, the
lending relationship measure used as a control variable in my baseline regressions is itself endogenous.
In order to reduce this endogeneity, I conduct a two-stage-least-square analysis and employ the
geographic distance and number of banks in the state of the borrower as instruments for observed
lending relationships. In the first stage, I regress the lending relationship variable on geographic
distance and number of banks, and other independent variables. In the second stage, I re-run the
baseline regression using fitted value of lending relationship in all specifications. The results are
consistent with those in the baseline regressions.
The baseline results indicate that systemically risky banks are matched with risky borrowers in the
syndicated bank loan market. However, it is unclear whether high systemic risk leads to high credit risk-
taking or vice versa. To address this, I use a set of lead-lag regressions, using contemporaneous borrower’s
distance-to-default or lender’s Δ𝐶𝑜𝑉𝑎𝑅 as the dependent variables, and test whether they are associated
with lagged lender’s Δ𝐶𝑜𝑉𝑎𝑅 or lagged borrower’s distance-to-default. I find that higher lender’s systemic
risk in quarter t-1 is associated with higher borrower’s credit risk in quarter t, while higher borrower credit
risk in quarter t-1 is not necessarily associated with higher lender’s systemic risk in quarter t. This indicates
that the causality very likely extends from systemic risk to credit risk-taking and not vice versa.
Banks control the credit risk in their loan portfolios by adjusting their credit and underwriting
standards. However, borrowers may also respond to bank risk-taking. For example, riskier borrowers
may be more dependent on stable refinancing sources especially during economic downturns.
Therefore, riskier borrowers may tend to request loans from systemically important banks, which
may be protected by public guarantees, and therefore maybe more likely to survive during periods of
economic turmoil. Thus, it is hard to disentangle the supply-side or demand-side effects empirically.
In this study, I empirically control for the demand effects of lending at systemically important banks
using within-loan analysis. Thus, I test my findings of a direct relationship between systemic risk and
credit risk using a within-loan regression analysis that controls for borrower self-selection lenders
(Chu, Zhang and Zhao, 2017). Taking advantage of the unique feature that a syndicated loan often
has multiple lenders, I examine how the systemic risks of banks that fund the same loan affect their
contributions to the loan, i.e., within-loan estimation, which eliminates any fluctuation on the
demand-side. Consistent with my previous findings, I find systemically risky banks contribute a large
(smaller) portion to risky (safe) loans, and the results are more significant for loans whose risk are
more extreme relative to other loans originated in the same year. This result indicates that my previous
findings are at least partially driven by supply-side factors.
6
Endogeneity can arise from reverse causality, meaning that current values of bank systemic risk can
be affected by credit risk-taking in previous periods. Current values of systemic risk may not be
independent of the credit risk-taking on previous loans, and ignoring the dynamic nature of the
independent and dependent variable relationship may yield biased and inconsistent estimates
(Wintoki, Linck, and Netter, 2012). To alleviate the reverse causality concern, I follow Wintoki,
Linck, and Netter (2012) by utilizing a dynamic panel GMM estimation. There are several advantages
of using dynamic panel GMM method. First, it allows current explanatory variables to be influenced
by previous realizations of dependent variable. Second, it eliminates unobservable heterogeneity by
first differencing all dependent and independent variables. Third, if utilize the combination of
variables from the history as valid instruments to account for simultaneity. The dynamic panel GMM
estimation confirms my pervious findings.
I then analyze how idiosyncratic executive-specific effects help explain the variation in credit risk-
taking sensitivity on bank-level systemic risk. Even though I find a positive relationship between
systemic risk and credit risk-taking, different banks, possibly with different risk-taking cultures, may
react to changes in systemic risks very differently. It is indeed the case. There is a wide variation in
credit risk-taking sensitivity on systemic risk across banks, which raises the question of what factor
is driving this heterogeneous reaction of credit risk-taking to systemic risk changes. Recent literature
has been focusing on the roles of bank executives and their effects on bank risk-taking culture and
extreme risk exposure (Berger, Kick and Schaeck, 2014; Nguyen, Hagendorff and Eshraghi, 2017),
and Hagendorff et. al. (2017) suggest that compensation and various other observable executive
characteristics can only describe a small amount of the variation in banks business model and risk-
taking preference, and they find that much of the variation in bank business policy can be explained
by executive factors (“styles”) that are time-invariant, which helps explain the risk-taking culture in
some banks. Since bank credit risk-taking sensitivity on systemic risk is largely related to banks
business models and how banks are managed (aggressively or conservatively) by the executives, its
variation may also be explained by the unobservable, time-invariant executive fixed effects.
Motivated by the recent work of Hagendorff et. al (2017), I provide evidence that idiosyncratic
executive-specific effects (“styles”) can also help explain a large variation in banks’ credit risk-taking
sensitivity on systemic risk. Focusing on executives including CEOs, CFOs, COOs, and executive
directors and using a connectedness sample method of Abowd, Kramarz, and Margolis (AKM,
thereafter) (1999), I run a series of three-way fixed effects regressions (bank, executives, and year)
to estimate bank and executive fixed effects, and then conduct factor analysis for the executive and
bank fixed effects to extract factors that dominant in explaining pattern across the styles of individual
7
executives and banks. The two dominant factors capture two dimensions of innovations that
executives and banks show preferences: asset-side and liability-side. I find executives’ asset- and
liability-side innovation preferences significantly affect a bank’s credit risk-taking and credit risk-
taking sensitivity on systemic risk, while banks’ innovation preferences don’t. Based on the loadings
of individual executives on the two factors, I assign executive into four types: (1) Asset innovator but
liability traditionalist; (2) Asset and liability innovator; (3) Asset and liability traditionalist; (4) Asset
traditionalist and liability innovator. I find that banks managed by different types of executives exhibit
very different credit risk-taking sensitivities on systemic risk.
The rest of the paper is organized as follows. Section 2 describes the data and sample construction
for the main tests and with-in loan models. Section 3 presents the empirical results, including the
baseline results, a two-stage least squares analysis controlling for borrower self-selection, a lead-lag
analysis, and within-loan estimations. Section 4 provides robustness checks, including a different
version of within-loan regression and a dynamic panel GMM analysis. Section 5 focus on how
idiosyncratic executive-specific effects help explain the variation in credit risk-taking sensitivity on
bank-level systemic risk. Section 6 concludes.
2. Data and Variable Constructions
2.1. Credit Risk: Borrowing Firm Distance-to-default
In this paper, I use the Merton distance-to-default as a measure of borrower default risk. Since credit risk
can also be affected by loan characteristics such as loan amount, maturity, and being secured or not, I
control for an array of loan characteristics. I follow Bharath and Bharath and Shumway (2008) and Crosbie
and Bohn (2003) in calculating Merton’s distance-to-default. The market equity value of a company is
modeled as a call option on the company’s assets:
𝑉𝐸 = 𝑉𝐴𝑒−𝑑𝑇𝑁(𝑑1) − 𝑋𝑒−𝑟𝑇𝑁(𝑑2) + (1 − 𝑒−𝑑𝑇)𝑉𝐴 (1)
𝑑1 =log(
𝑉𝐴𝑋
)+(𝑟+𝑠𝐴
2
2)𝑇
𝑠𝐴; 𝑑2 = 𝑑1 − 𝑠𝐴√𝑇 (2)
where 𝑉𝐸 is the market value of a firm’s equity, which is calculated from the CRSP database as the product
of share price at the end of the quarter and the number of shares outstanding. X is the face value of debt
maturing at time T, which is calculated as debt in current liabilities (COMPUSTAT data item 45) plus one
half of long term debt (COMPUSTAT data item 51). 𝑉𝐴 is the value of the firm’s assets. r is the risk-free
rate, which is defined as the 1-year Treasury Constant Maturity Rate obtained from the Board of Governors
8
of the Federal Reserve system. 𝑠𝐴 is the volatility of the value of assets. I simultaneously solve the above
two equations to find the values of 𝑉𝐴 and 𝑠𝐴. Quarterly Merton’s distance-to-default is finally computed as:
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑡𝑜 𝐷𝑒𝑓𝑎𝑢𝑙𝑡 =log (
𝑉𝐴
𝑋) + (𝑚 −
𝑠𝐴2
2)𝑇
𝑠𝐴√𝑇 (3)
As a robustness check, I also calculate quarterly distance-to-default by first calculating the monthly
distance-to-default and then taking quarterly averages. Both measures generate similar results in the
baseline regressions.
2.2. Systemic Risks
To measure the micro-level systemic risk, I follow the methodology used in Adrian and Brunnermeier
(2016) to generate time-varying Δ𝐶𝑜𝑉𝑎𝑅. First, I run the following quantile regressions in the weekly data
(where j is a financial institution):
𝑋𝑡𝑗
= 𝛼𝑞𝑗
+ 𝛾𝑞𝑗𝑀𝑡−1 + 𝜖𝑞,𝑡
𝑗, (4)
𝑋𝑡𝑠𝑦𝑠𝑡𝑒𝑚|𝑗
= 𝛼𝑞𝑠𝑦𝑠𝑡𝑒𝑚|𝑗
𝑀𝑡−1 + 𝛽𝑞𝑠𝑦𝑠𝑡𝑒𝑚|𝑗
𝑀𝑡−1 + β𝑞𝑠𝑦𝑠𝑡𝑒𝑚|𝑗
𝑋𝑡𝑗
+ 𝜖𝑞,𝑡𝑠𝑦𝑠𝑡𝑒𝑚|𝑗
, (5)
where 𝑋𝑡𝑗 is the weekly return of institution j in week t, 𝑋𝑡
𝑠𝑦𝑠𝑡𝑒𝑚|𝑗 is the financial sector return in week t,
and 𝑀𝑡−1 is a vector of seven systematic state variables in week t, including three-month yield change,
term spread change, TED spread, credit spread change, market return, real estate excess return, equity
volatility. Then I generate the predicted values from these regressions to obtain
𝑉𝑎𝑅𝑞,𝑡𝑗
= �̂�𝑞𝑗
+ 𝛾𝑞𝑗𝑀𝑡−1, (6)
𝐶𝑜𝑉𝑎𝑅𝑞,𝑡𝑗
= �̂�𝑞𝑠𝑦𝑠𝑡𝑒𝑚|𝑗
+ �̂�𝑞𝑠𝑦𝑠𝑡𝑒𝑚|𝑗
𝑀𝑡−1 + �̂�𝑞𝑠𝑦𝑠𝑡𝑒𝑚|𝑗
𝑉𝑎𝑅𝑞,𝑡𝑗
, (7)
Finally, I compute the 𝛥𝐶𝑜𝑉𝑎𝑅𝑞,𝑡𝑗
for each institution:
𝛥𝐶𝑜𝑉𝑎𝑅𝑞,𝑡𝑗
= 𝐶𝑜𝑉𝑎𝑅𝑞,𝑡𝑗
− 𝐶𝑜𝑉𝑎𝑅50,𝑡𝑗
= �̂�𝑠𝑦𝑠𝑡𝑒𝑚|𝑗(𝑉𝑎𝑅𝑞,𝑡𝑗
− 𝑉𝑎𝑅50,𝑡𝑗
) (8)
From these regressions, I get a panel of weekly 𝛥𝐶𝑜𝑉𝑎𝑅𝑞,𝑡𝑗
. I obtain a quarterly time series of 𝛥𝐶𝑜𝑉𝑎𝑅𝑞,𝑡𝑗
by averaging the weekly risk measures within each quarter. Throughout the paper, I use q equals 99%, but
my results are robust to the 95% level.
9
To construct the macro-level systemic risk, CATFIN§, I follow Allen, Bali, and Tang (2012) and first
estimate VaR at the 99% confidence level using three different methodologies - the generalized Pareto
distribution (GPD), the skewed generalized error distribution (SGED) and the non-parametric
estimation method based on the left tail of the actual empirical distribution without any assumptions
about the underlying return distribution. CATFIN is defined as the arithmetic average of the GPD,
SGED and non-parametric VaR measures.
Allen, Bali, and Tang (2012) suggest that the risk of macroeconomic downturns increases when
CATFIN is above some early warning level, where early warning level is determined by using Chicago
Fed National Activity Index (CFNAI) as a benchmark. CFNAI is an index of eighty-five existing
monthly economic indicators. The Federal Reserve Bank of Chicago denotes the three-month moving
average of CFNAI (CFNAI-MA3) value of -0.7 as a turning point indicating economic contraction, and
Allen, Bali, and Tang (2012) show that when CATFIN is above some early warning level, it can
significantly predict lower economic activity (CFNAI-MA3) one month to eight months in advance of
the downturn. Therefore, CATFIN offers an early warning to alert regulators to the risk of economic
recessions. In this paper, I also test whether the credit risk of the banks’ loan portfolio is affected by
whether CATFIN breaches the early warning level. Following Allen, Bali, and Tang (2012), I
construct an early warning dummy, which is equal to 1 if CATFIN is above the early warning level,
and 0 otherwise. For each quarter t, the early warning level is calculated as the median CATFIN using
all observations up to quarter t in which CFNAI-MA3 falls below -0.7. To address possible reverse
causality, I utilize both contemporaneous and lagged measures of systemic risk to examine the
following quarter’s credit risk in the bank’s loan portfolio. A historical monthly CATFIN, CFNAI-
MA3, Early Warning Level, and Warning dummy are presented in the Online Appendix.
2.3. Control Variables
I use a set of control variables to control for loan characteristics, borrower characteristics and lender
characteristics. To ensure that outliers do not heavily influence statistical results, I set all observations
higher than the 99th percentile of each variable to that value; all values lower than the 1st percentile of
each variable are similarly winsorized. All variables are defined in the appendix.
The first set of control variables include bank characteristics, such as Bank Total Assets, Bank Capital Ratio,
Return on Equity, Liquidity, Loan Charge-offs, Loan Loss Allowance, and Risk-Weighted Assets. Since
§ I thank Linda Allen and Yi Tang for providing the data on their CATFIN systemic risk measure and Tobias Adrian
and Markus Brunnermeier for making their measure of systemic risk (∆CoVaR) available.
10
Δ𝐶𝑜𝑉𝑎𝑅 is constructed using market data and most public banks are bank holding companies, I measure
bank financial information at the bank holding company level. ln(Bank Total Assets) is defined as the
natural logarithm of bank total assets (in billions); Bank Capital Ratio is defined as the bank’s total capital
over bank’s total assets; Bank ROE is defined as bank net income over book equity; Bank Liquidity is
defined as the sum of cash and available-for-sale securities divided by bank total assets; Loan Charge-offs
is defined as the total charge-offs on loans and leases divided by bank total assets; Loan Loss Allowance is
defined as the total allowance for loan and lease losses divided by bank total assets; Risk-Weighted Assets
is defined as total risk weighted assets divided by bank total assets. Although I include both lead lenders
and participants in all my regressions, I add a lead bank dummy in the within-loan regressions to
account for possible unobservable differences between lead and non-lead banks. I define a bank as a
lead lender if its lend arranger credit variable is “Yes” in Dealscan.
The second set of control variables consists of borrower characteristics. ln(Borrower Total Assets) is
defined as the natural logarithm of total assets (in billions); Tangibility is defined as total property, plant,
and equipment divided by total assets; Leverage is defined as the total debt divided by total assets. I also
include a lending relationship measure, as in Bharath et al. (2007) since the intensity of past lending
relationships can have an impact on the matching between borrowers and lenders. The lending relationship
between borrower i and bank j is defined as the dollar amount of loans to borrower i by bank j in the last 5
years over the total dollar amount of loans by borrower i in last 5 years.
Then I control for an array of loan characteristics. ln(Package Amount) is defined as the natural log of the
package amount, where package amount is measured in millions. ln(Package Maturity) is defined as the
natural log of the maturity of the deal in months. Package maturity is calculated as the value-weighted
average of the facility maturities. ln(No. of Lead Banks) is the natural log of the number of lead lenders in
the deal syndicate.
Finally, I control for macroeconomic conditions. I use quarterly GDP per capita growth rate to measure the
macroeconomic performance. I also generate a recession dummy (Recession) to test the differential effect
of bank systemic risk on credit risk-taking in expansion and recession periods. The recession dummy equals
1 if the month of loan origination is designated by NBER to be a contraction month and 0 if it is designated
to be an expansion month.
2.4. Sample Construction
The sample period of my study spans Q1 1995 to Q4 2013. Banks’ financial data are collected from the
Consolidated Financial Statements for Holding Companies (“FR Y-9C”) available on the Federal Reserve
11
Bank of Chicago website, and all market data are obtained from CRSP. Borrowing firms’ financial data are
collected from Compustat. Syndicated loan data are obtained from Loan Pricing Corporation’s (LPC)
Dealscan loan database. The Dealscan database contains historical information on the terms and conditions
of deals in the global syndicated bank loan market. Borrower financial data are linked to Dealscan using
the Dealscan-Compustat linking data provided by Chava and Roberts (2008, updated in Aug. 2012). All
observations are quarterly.
To construct the sample, I first start with a sample of 219,023 deal packages newly originated between Jan.
1995 and Dec. 2013 from the LPC Dealscan database. Since in this paper I conduct my analysis at the bank
holding company level, I need to first identify the lenders in my sample and their ultimate parent companies.
I utilize the information provided by the Federal Reserve System via its National Information Center (NIC)
database to identify financial institutions acting as lenders in my sample. I do not use the identity variables
for lender and ultimate owner in Dealscan because Dealscan overwrites the ultimate owner of the lenders
after mergers and acquisitions, i.e., the ultimate owner in Dealscan is the ultimate owner at the end of the
merger chain. In this analysis, I must identify the ultimate bank holding company or lending parent at the
time of the issuance of each loan. The NIC database provides detailed information about financial
institutions, including types of institutions, establishment time, ownership information, address changes,
name changes, merger and acquisition history. NIC also provides each financial institution’s RSSD ID, a
unique identifier assigned to each financial institution by the Federal Reserve System. Based on the lender
information provided by Dealscan, including name, location and lending history, I manually find each
lender’s RSSD ID. Using their RSSD ID, which is item RSSD9001 in the Call Report and Y-9C databases,
the lender’s ultimate owner at the time of loan origination is determined by cross-checking the information
contained in Call Report items RSSD ID of Regulatory High Holder 1 (RSSD9348), Financial Higher
Holder ID (RSSD9364), and Financial High Holder Percent of Equity (RSSD9365). The three items provide
the RSSD ID (RSSD9001) of a lender’s ultimate bank holding company during the time when a lead
arranger has a RSSD ID. For a bank that was acquired by another bank and lost its RSSD ID but kept its
lending activity, the acquirer’s RSSD ID was applied to the acquired bank as its new RSSD ID, and the
new ultimate owner can be found from the Call Report and Y-9C items RSSD9348, RSSD9364, and
RSSD9365. The full bank and bank holding companies merger and acquisition history is obtained from the
Federal Bank of Chicago. Using the RSSD ID, I link Dealscan to the Y-9C to obtain bank financial data. I
also link Dealscan to CRSP to collect market data through the PERMCO-RSSD link table provided by the
Federal Reserve Bank of New York. I collect bank characteristics data at the bank holding company level
using Y-9C reports. Using the PERMCO-RSSD link, I merge bank holding companies with their systemic
risk measure, Δ𝐶𝑜𝑉𝑎𝑅. This process reduces my sample to 80,193 packages (140,609 package-lender pairs).
12
Next, I merge borrower characteristics from Compustat with the information on corporate loans in
Dealscan using the linking table provided by Chava and Roberts (2008, updated in Aug. 2012). This
table matches loan facilities from Dealscan with the borrower’s GVKEY identifier in Compustat. Due
to differences in capital structures and financing strategies between financial and non-financial firms,
I exclude loans to financial companies (SIC between 6000 and 6999) from the sample. I also exclude
the utility firms (SIC code falls between 4900 and 4999) because they may have different operating and
reporting environments. This leads to a sample of 10,915 packages (22,595 package-lender pairs), which
include 3,603 unique borrowers, and 214 unique banks, which are owned by 66 unique publicly traded bank
holding companies with Δ𝐶𝑜𝑉𝑎𝑅 data. Since systemic risk Δ𝐶𝑜𝑉𝑎𝑅 is mostly measured at the bank
holding company level, in my subsequent analysis, I run all regressions at the loan-bank holding company
level.
2.5. Summary Statistics for Baseline Regressions
I present the summary statistics in Table 1. There are in total 22,595 lender-package observations in the
baseline regressions. I only use package level data in my baseline regression. The key dependent variable
is borrower’s quarterly distance-to-default, which has a mean of 6.772, a median of 6.002, and a standard
deviation of 4.903. The key independent variables are Δ𝐶𝑜𝑉𝑎𝑅, which has a mean of 5.216, a median of
4.351, and a standard deviation of 2.550, and CATFIN, which has a mean of 2.393, a median of 2.286, and
a standard deviation of 0.926. The average deal amount is US$878 million, with average maturity of 49
months. On average, there are 4.141 lead lenders in each package. The Lending Relationship between
borrower i and lender j, which is defined as the dollar amount of loans to borrower i by bank j in last 5 years
over the total dollar amount of loans by borrower i in last 5 years, has a mean of 0.47, indicating that on
average each bank engaged in 47% of the total amount a typical firm borrowed in the 5 years preceding the
loan origination. Borrowers have an average size of $6.752 billion, with a mean tangibility of 0.310 and
leverage of 0.322. Banks have a mean size of $779 billion, with mean capital ratio of 8.5% and return on
equity of 8.3%.
Table 2 presents the spearman correlation matrix for the variables included in the baseline regressions.
As shown in Table 2, borrower distance-to-default is negatively correlated with both Δ𝐶𝑜𝑉𝑎𝑅 (the
bank-specific measure of systemic risk) and CATFIN (the aggregate measure of systemic risk), with
correlation coefficients of -0.220 and -0.289, respectively. This provides preliminary evidence that
banks with higher levels of systemic risk lend to borrowers with higher credit risk during periods of
high aggregate systemic risk.
13
2.6. Sample Construction for Within-loan Regressions
To investigate whether the relationship between systemic risks and borrower default risk is driven by the
bank size, in Section 3 and Section 4, I apply the within-loan estimations methodology from Chu, Zhang
and Zhao (2017). By adding package (facility) fixed effects, I can remove the impact of the demand-
side factors from the supply-side factors.
The sample of within-loan regressions is constructed differently from the one used in the baseline
regressions. First, since all borrower, loan, and macroeconomic characteristics drop out in a package
or facility fixed effects regression, only lender characteristics are relevant to the within-loan
regressions. Second, to control for an array of lender characteristics, I use a broader set of lender
variables, which includes bank size, capital ratio, return on equity, liquidity, loan charge-offs, loan loss
allowance, and risk-weighted assets, to control for lender characteristics and avoid omitted variable bias.
Bank liquidity is the sum of cash and available-for-sale securities divided by bank total assets. Loan charge-
offs is defined as the total charge-offs on loans and leases divided by bank total assets. Loan loss allowance
is the total allowance for loan and lease losses divided by bank total assets. Risk-weighted assets is the total
risk weighted assets divided by bank total assets. Third, since I employ the bank allocation share variable
in Dealscan to measure bank lending at the individual facility/package level, I put a series of
requirements on this variable to increase its validity. The sample is constructed as follows.
I construct my samples for the within-loan estimation at the package level and facility level,
separately, because the data processing procedures are different between the two levels. I first
construct my sample for within-loan estimations at the facility level. I start with an initial sample of
246,260 loan facilities originated between Jan. 1995 and Dec. 2013. Following Chu, Zhang, and Zhao
(2017), I focus on 182,745 facilities that involve credit lines, term loans, or both in my analysis since
they are the dominant types of bank loans borrowed by non-financial firms. I further require the
facility to have at least two banks as lenders, because the allocation share for a sole-lender loan is
always 100%. This reduces the sample to 152,549 facilities. Based on lenders’ name, city, state, and
dates of their earliest and latest lending activities, I manually search for their RSSD ID through the
National Information Center, and drop all lenders that don’t have an identifiable RSSD ID. Next, I
identify the ultimate holding companies of those banks from the Consolidated Financial Statements
for Holding Companies (FR Y-9C), following the same methodologies introduced in Section 2.4, and
drop lenders that don’t have an identifiable ultimate holding company in quarter t-1, where t is the
quarter of loan origination. This reduces the sample size to 80,041 facilities.
14
DealScan reports a bank’s allocation share in a facility for about 31.78% of all lender-facility pairs.
For each bank in each facility, Dealscan reports the allocation share in percentages. Since I use a
bank’s allocation share to measure bank lending at the individual facility level, I exclude loans
without bank allocation share information or with allocation share greater than 100%, which is
apparently erroneous. Since Δ𝐶𝑜𝑉𝑎𝑅 is estimated at the bank holding company level, for consistency
I also calculate the allocation share at the bank holding company level. For example, if both bank A
and bank B belong to a same bank holding company C, and if bank A and bank B participate in a
same facility with allocation shares of 10% and 20%, respectively, then I will treat this as bank
holding company C contributing 30% to the facility. If the allocation share for A or B is missing or
greater than 100%, then I entirely remove the observations of A, B and C on this loan because they
will give erroneous allocation share at the level of bank holding company C. I also drop all facilities
in which the sum of all lender shares exceeds 110%. I choose 110% to account for rounding and
minor errors). The above treatments lead to a sample of 15,850 facilities.
Then I restrict my sample to loans from banks with non-missing market equity data, systemic risk
data and Y-9C financial data in quarter t and t-1. This leads to a sample of 10,800 facilities, which
involve 148 unique banks. These 148 banks belong to 68 unique bank holding companies. I then
identify the borrowing firms from Compustat using the DealScan-Compustat linking table provided
by Chava and Roberts (2009, updated in Aug. 2012). For loans originated after Aug. 2012, I manually
adjust the changes in the link by comparing borrower companies’ names in the two databases. I
require the borrowing firms to have SIC code and distance-to-default in quarter t-1 and t to be
included in the sample, and I exclude utility firms and financial firms (two-digit SIC code equal to
49 or between 60 and 69). These procedures lead to a sample of 3,797 facilities, which involve 123
unique banks that correspond to 66 unique bank holding companies.
The sample construction at the package level follows similar procedures as that at the facility level
with several differences. First, I only keep the observations of a bank holding company j in a package
k if the bank allocation share data is non-missing for all its subsidiaries in all facilities under this
package. Otherwise, the bank holding company’s observation in package k is entirely removed from
the sample. For example, bank A and bank B belong to a same bank holding company C, and A
contributes 30% only to facility F1, and B precipitates only in facility F2 but its contribution share
data is missing. F1 and F2 are under the same package. In this case, I remove the observation for C
entirely from this package because its total contribution will be biased downward due to the missing
data. Second, the allocation share for bank holding company j in package k is defined as the total
allocation shares of all banks that belong to j and participate in the package k. A bank’s allocation
15
share in a package is its allocation share multiplied by facility amount and then divided by package
amount. Third, the lead lender dummy for bank holding company j in package k is 1 if at least one of
its subsidiaries acts as a lead lender in at least one facility under the package k. The final package
level sample includes 3,081 packages, which involve 123 unique banks that correspond to 66 bank
holding companies.
For both levels, I include the lending relationship measure (Bharath et al., 2007) to control for the intensity
of past lending relationship. The lending relationship between borrower i and bank j is defined as the dollar
amount of loans to borrower i by bank j in last 5 years over the total dollar amount of loans by borrower i
in last 5 years. In my within-loan regressions, the observations are by facility-bank holding company pairs,
while lending relationship is a bank-borrower level measure. In many cases, two or more banks that belong
to the same bank holding company may participate in a same loan. In those cases, I add up their bank level
lending relationship measure to create a bank holding company level measure. The rationale behind is that
the proprietary information obtained from past lending activities are shared among banks that belong to a
same bank holding company.
To account for possible unobservable differences between lead and non-lead banks, I add a lead bank
dummy in the regressions, which is equal to 1 if a bank is the lead bank in the package/facility, and 0
otherwise. I define a bank as a lead lender if its lead lender credit variable is “Yes” in Dealscan. The lead
lender credit variable is by facility-bank pairs, and two or more banks that belong to a same bank holding
company may participate in a same facility but some of them may be lead lenders while the others are not.
To create a bank holding company level lead lender dummy, I assign the dummy a value of 1 if at least one
of its bank acted as a lead lender in the facility.
I present the summary statistics for key variables describing borrower characteristics, bank characteristics,
and loan characteristics in Table 3. Panel A of Table 3 presents the descriptive statistics at the package level
and Panel B of Table 3 presents the descriptive statistics at the facility level. At the package (facility) level,
the borrower distance-to-default has a mean value of 8.181 (8.056) and a standard deviation of 5.073 (5.102).
Δ𝐶𝑜𝑉𝑎𝑅 has a mean value of 4.705 (4.701) and a standard deviation of 2.684 (2.643). On average, a bank
contributes 10.513% (11.013%) of the total package (facility) amount, and the standard deviation of bank
allocation is 10.307% (10.914%). The mean value of Bank Total Assets is 692.547 (706.372) billion and
Bank Capital Ratio has a mean value of 9.2% (9.3%). The average package (facility) amount is 1027.549
(741.714) million, and on average, each package (facility) has 2.304 (2.224) lead banks.
16
3. Empirical Results
3.1. Baseline Results
I first present the baseline results. I estimate the following model:
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑡𝑜 𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑗,𝑘,𝑡
= 𝛼0 + 𝛼1Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 + 𝛼2𝐶𝐴𝑇𝐹𝐼𝑁𝑡 + 𝛼3Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝐶𝐴𝑇𝐹𝐼𝑁𝑡 + 𝛼4Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1
+ 𝛼5𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑡𝑜 𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡−1 + 𝛼6𝐿𝑜𝑎𝑛 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑘,𝑡 + 𝐵𝑎𝑛𝑘 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑗,𝑡
+ 𝛼8𝐵𝑜𝑟𝑟𝑜𝑤𝑒𝑟 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑡 + 𝛼9𝐿𝑒𝑛𝑑𝑖𝑛𝑔 𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑠ℎ𝑖𝑝𝑖,𝑗,𝑡 + 𝛼10𝑀𝑎𝑐𝑟𝑜 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑡 + 𝑌𝑒𝑎𝑟 𝐹𝐸
+ 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐹𝐸 + 𝐵𝑎𝑛𝑘 𝐹𝐸 + 𝜀𝑖,𝑗,𝑘,𝑡 (9)
where subscript i, j, k and t indicate the firm, the bank, the package, and the time (quarter), respectively.
The regressions are run at the package level. The dependent variable, 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑡𝑜 𝐷𝑒𝑓𝑎𝑢𝑙𝑡, is borrower’s
distance-to-default at the quarter of loan origination. The explanatory variables of interest are Δ𝐶𝑜𝑉𝑎𝑅
and CATFIN, as well as their interaction. The vectors of variables Loan Controls, Bank Controls, and
Borrower Controls contain loan, bank, and firm-specific control variables from the quarter of loan
origination.
In all regressions, I include calendar year fixed effects to remove time trends, and industry and bank
fixed effects to remove time-invariant factors that drive matching between borrowers and lenders. I
control for bank size to distinguish the effects of size and systemic risk on borrower-lending choice.
I also control for bank capital ratio and return on equity since undercapitalized or less profitable banks
may refrain from lending to risky borrowers. I include controls for borrower characteristics (asset
tangibility, size, and leverage) to mitigate the impact of demand-side factors that are correlated with
both the firms distance to default and firm’s choice of bank. Loan Controls include the natural
logarithm of deal amount, maturity, and number of lead lenders. To distinguish secured loans from
unsecured loans, I also add a dummy variable, which is equal to 1 if the loan is secured, and 0 otherwise.
Since relationship lending affects lender-borrower matching, I include the lending relationship measure
from Bharath et al. (2007). The lending relationship between borrower i and bank j is defined as the dollar
amount of loans to borrower i by bank j in last 5 years over the total dollar amount of loans by borrower i
in last 5 years. Finally, I add quarterly GDP growth rate to control for macroeconomic conditions.
Table 4 reports the regression results. In Column I and II, I only include the variables of interest and fixed
effects in the regressions. In Column III and IV, I add all other control variables. The results in Column I
to IV show a significant inverse relation between borrower distance-to-default and the bank’s micro-
17
level measure of systemic risk. The coefficients on Δ𝐶𝑜𝑉𝑎𝑅 and lagged Δ𝐶𝑜𝑉𝑎𝑅 in all four columns
are negative and significant at the 1% level. The results show that higher bank systemic risk exposure
is associated with lower borrower distance-to-default, i.e., higher borrower credit risk. That is,
systemically important banks tend to lend to borrowers with high default risk. The coefficients on
both lagged and contemporaneous CATFIN are also negative and significant at the 1% level,
indicating that credit risk in bank loan portfolios is high (i.e., borrowers’ distance-to-default is low)
during periods of high aggregate systemic risk. Economically, taking the coefficients of -0.078 and
-0.452 in Column IV, starting from mean value, one standard deviation increase (2.550) in Δ𝐶𝑜𝑉𝑎𝑅
is associated with a decrease in distance-to-default by 0.199, which a 0.041 standard deviation (4.903)
decrease in borrower’s distance-to-default and a 2.937% decrease from the mean value (6.772) of distance-
to-default; One standard deviation increase (0.926) in CATFIN is associated with a decrease in distance-to-
default by 0.419, which is a 0.085 standard deviation (4.903) decrease in borrower’s distance-to-default
and 6.190% decrease from the mean value (6.772) of distance-to-default. Thus, the empirical results in
Table 4 support Hypothesis II.
To perform a quasi-diff-in-diff analysis, I interact Δ𝐶𝑜𝑉𝑎𝑅 and CATFIN in Column V to investigate
how aggregate systemic risk affects the relation between Δ𝐶𝑜𝑉𝑎𝑅 and borrower distance to default.
The signs, magnitude, and significance of the coefficients on Δ𝐶𝑜𝑉𝑎𝑅 and CATFIN remain. The
coefficient on the interaction term is positive and significant at 1% level, although its magnitude is
much smaller than the coefficients on Δ𝐶𝑜𝑉𝑎𝑅 and CATFIN. Actually, even though the sign of the
interaction term is positive, unless CATFIN is extremely high, the net effect of changes in Δ𝐶𝑜𝑉𝑎𝑅
on borrower distance-to-default is still significantly negative with large economic magnitude. For
example, when CATFIN is at its median level (2.286), one standard deviation increase in Δ𝐶𝑜𝑉𝑎𝑅 is
associated with 0.073 standard deviation decrease in borrower’s distance-to-default, which is an even
larger effect than shown in Column IV. Only when CATFIN is much higher than median values do
banks with high individual levels of systemic risk reduce the credit risk in their loan portfolios. The
aggregate effect of an increase in both Δ𝐶𝑜𝑉𝑎𝑅 and CATFIN is also strong. For example, a
simultaneous one standard deviation increase in both Δ𝐶𝑜𝑉𝑎𝑅 and CATFIN is associated with a 0.135
standard deviation decrease in borrower’s distance-to-default. These results suggest that banks reduce
their individual credit risk exposure when aggregate systemic risk is high, thereby lending to safer
borrowers when the risk of recession increases. That is, the negative relation between Δ𝐶𝑜𝑉𝑎𝑅 and
borrower distance to default is weakened during periods of high CATFIN. As robustness checks, I
perform another two tests. First, in Column VI, I replace CATFIN by a Recession dummy, which is
equal to 1 if the quarter of loan origination is an economic recession quarter, and 0 otherwise. The
18
result is similar as in Column VI. The relationship between Δ𝐶𝑜𝑉𝑎𝑅 and borrower distance to default
is weakened, but still remains. In Column VII, I replace CATFIN by an Early Warn dummy, which
equal to 1 when CATFIN exceeds its early warning level, and 0 otherwise. As shown in Column VII,
the result remains similar as Column VII. Economically, taking the coefficient of -0.195 on Δ𝐶𝑜𝑉𝑎𝑅 in
Column VII, one standard deviation increase in Δ𝐶𝑜𝑉𝑎𝑅 is associated with 0.101 standard deviation
decrease in borrower’s distance-to-default. If the loan is originated during periods when the early warning
level is breached, then one standard deviation increase in Δ𝐶𝑜𝑉𝑎𝑅 is only associated with 0.047 standard
deviation decrease in the borrower’s distance-to-default.
The results in Column V, VI, and VII indicate that, during periods of very high levels of aggregate
systemic risk, recession or when the early warning has been triggered, banks with high individual
levels of systemic risk reduce the credit risk in their loan portfolios. This result is consistent with
some mitigation of moral hazard by systemically important banks during crisis (or imminent crisis)
periods, as these banks attempt to pull back from the brink by reducing the credit risk in their loan
portfolios. This is consistent with Anginer, Demirguc-Kunt, and Zhu (2014), which finds that moral
hazard seems to be dominating during calm periods. Note that the coefficients on bank total assets
remain insignificant in all columns of Table 4. Large banks do not necessarily take on higher credit
risk in their loan portfolios. This result, together with the coefficient on Δ𝐶𝑜𝑉𝑎𝑅, indicates that moral
hazard incentives impact all systemically important banks rather than only big banks.
3.2. 2SLS: Self-selection and Lender-borrower Matching
In the baseline regressions, the borrower self-selection and lender-borrower matching is endogenous.
In other words, the probability of initiating a new loan between firm i and bank j is endogenous. It
could be that some unobservable borrower characteristics induce borrowing firms to choose to apply
for loans from certain banks, thereby introducing selection bias into the OLS analysis. Therefore, I
must control for the probability of originating a loan between firm i and bank j that is independent of
the channel of systemically risky banks selecting borrower with certain default risks. That is, the
lending relationship measure used as a control variable in my baseline regressions is itself endogenous.
In order to reduce this endogeneity, I employ the geographic distance and number of banks in the
state of the borrower as instruments for observed lending relationships. Geographic distance is
measured as the distance in thousands of kilometers between the location of the firm and the location
of the parent company of the lending bank in the quarter of loan origination. Geographic distance
proxies for information asymmetries and transportation costs (Degryse and Ongena, 2005) which
impede the bank’s ability to monitor a firm’s financial condition. This may reduce the likelihood of
19
a loan origination. The number of banks in the borrowing firm’s state of incorporation is measured
as the number of financial institutions that filed Call Report during the quarter of loan origination.
Both instruments should affect the probability of originating loans between a certain pair of borrower
and lender, but is unlikely to affect how systemically risky banks are matched with borrowers with
differing default probabilities.
In the first stage, I regress the lending relationship variable on geographic distance and number of
banks, and other independent variables. Table 5 reports the regression diagnostics for all
specifications in Table 6, and Table 6 reports the results for the first stage regression. In Table 5, I
first report the Anderson LM test statistic for tests of identification. The null hypothesis tested is that
the instruments and endogenous variable are not correlated and, in addition, that the overidentifying
restrictions are valid. The p-values is close to 0, which strongly rejects the null hypothesis. Then, I
report the Sargan's chi-square statistic, which tests the joint null hypothesis that the excluded
instruments are valid instruments (i.e., uncorrelated with the error term) and correctly excluded from
the estimated equation. The Sargan’s chi-square test statistics are insignificant in all specifications.
This implies that the excluded instruments are valid instruments and correctly excluded from the
estimated equation.
Table 6 shows that the distance coefficient is significantly negative in the lending relationship first
stage regression. The further the borrower is from the lender, the less likely that they will have a
lending relationship. The coefficient on No. of Banks is negative but insignificant. The negative sign
is very intuitive; the more banks operating in the state of the borrower, the less likely that the borrower
and lender will have a lending relationship.
Table 7 reports the results for the second stage regression using Distance to Default as the dependent
variable. I use the fitted value of lending relationship in all specifications. The coefficient estimates
on both 𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 and 𝐶𝐴𝑇𝐹𝐼𝑁𝑡 are negative and statistically significant at the 1% level as in the OLS
results reported in Table 4. Although the lagged systemic risk, 𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1, is less significant than in
Table 4, Table 7 shows that the contemporaneous systemic risks, 𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 and 𝐶𝐴𝑇𝐹𝐼𝑁𝑡 , remains
significant with similar economic magnitude as found in the baseline regressions. Taking the
coefficients of -0.107 and -0.540 in Column IV as an example, starting from mean value, one standard
deviation increase (2.550) in Δ𝐶𝑜𝑉𝑎𝑅 is associated with a decrease in distance-to-default by 0.273, which
a 0.055 standard deviation (4.903) decrease in borrower’s distance-to-default and a 4.030% decrease from
the mean value (6.772) of distance-to-default; one standard deviation increase (0.926) in CATFIN is
associated with a decrease in distance-to-default by 0.500, which is a 0.102 standard deviation (4.903)
20
decrease in borrower’s distance-to-default and 7.380% decrease from the mean value (6.772) of distance-
to-default. Similarly, a one standard deviation increase (0.926) in CATFIN is associated with a decrease in
distance-to-default by 0.500, which is a 0.102 standard deviation (4.903) decrease in borrower’s distance-
to-default and 7.380% decrease from the mean value (6.772) of distance-to-default.
3.3. Lead-lag Analysis
Previous results indicate that systemically risky banks are matched with risky borrowers in the
syndicated bank loan market. However, it is unclear whether the causality extends from systemic risk
to credit risk or vice versa. That is, borrowers may self-select on the basis of the bank’s systemic risk,
particularly if government bailouts may protect bank customers from the impact of bank insolvency.
To address this, I use a set of lead-lag regressions, using contemporaneous borrower’s distance-to-
default or lender’s Δ𝐶𝑜𝑉𝑎𝑅 as the dependent variables, and test whether they are associated with
lagged lender’s Δ𝐶𝑜𝑉𝑎𝑅 or lagged borrower’s distance-to-default. The intuition is that if higher
lagged systemic risk leads the higher borrower credit risk measured by distance-to-default, it suggests
that banks are very likely making endogenous decision on their loan portfolio credit risk-taking
conditioning on their systemic risk. In contrast, if borrower’s higher credit risk leads the bank’s
systemic risk exposure, it suggests that borrowers are endogenously choosing banks.
Table 8 presents the results of the lead-lag analysis. The dependent variable in Column I, II, III, and
IV is 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑡𝑜 𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡, the borrower’s distance-to-default at the quarter of loan origination,
and the dependent variable in Column V, VI, VII, and VII is 𝐶𝑜𝑉𝑎𝑅𝑗,𝑡, bank j’s systemic risk during
the quarter of loan origination. The key variable of interest is 𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 and
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑡𝑜 𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡−1 . In Column I, II, V, and VI, I only include variables of interest as
independent variables, while in Column III, IV, VI, and VII, I include all characteristics of loans,
banks, and borrowers as control variables. From Column I to IV, the coefficients on 𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 are
all significant at 1% level, indicating that lagged bank systemic risk significantly is related to
contemporaneous borrower distance-to-default. Economically, taking the coefficient on 𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1
in Column III, one standard deviation increase in 𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 is associated with 0.063 standard
deviation decrease in borrower’s distance-to-default at loan origination. However, in Column V to
VIII, the coefficients on 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑡𝑜 𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡−1 are significant in three out of four specifications.
This suggest that while higher lender’s systemic risk in quarter t-1 is associated with higher
borrower’s credit risk in quarter t, higher borrower credit risk in quarter t-1 is not necessarily
associated with higher lender’s systemic risk in quarter t. In other words, the borrowers that are riskier
21
in the quarter preceding loan origination do not necessarily borrow from systemically risky banks at
loan origination, and systemically risky banks in the quarter preceding loan origination lend to risky
borrowers at loan origination. Even though this test cannot separate the supply-side effect from the
demand-side, it still provides evidence indicating that systemically risky banks are more likely to
endogenously select risky borrowers, rather than risky borrowers choosing banks with greater
exposure to systemic risk. In the next section, I employ within-loan estimations to remove the impact
of the demand-side factors from the supply-side in order to address these causal effects.
3.4. Within-loan Regressions
Even though previous results show a direct relationship between bank systemic risk and borrower
default risk, the test cannot identify the causal effect. The results can be attributed to supply-side
choice, borrower-side choice, or both. On the supply-side, systemically risky banks may increase
their risk taking by proactively choosing risky borrowers. On the demand-side, due to borrowers’
self-selection, risky borrowers may tend to apply for loans specifically from systemically risky banks
that have a higher likelihood of receiving government support to survive through crisis period. It is
difficult to separate the effect of systemic risk on borrower default risk from demand-side factors. To
resolve this concern, I use the within-loan estimations methodology from Chu, Zhang and Zhao (2017)
to remove the impact of the demand-side factors, so that I can investigate whether the higher credit-
risk taking of systemically risky banks is driven by the supply-side. Taking advantage of the unique
feature that a syndicated loan often has multiple lenders, I examine how the systemic risks of different
banks that fund the same loan impacts their loan allocation share percentages. Because all lenders
lend to the same borrower, this removes demand-side factors from the analysis.
The within-loan estimation methodology takes advantage of the underwriting process of a syndicated
loan. In a syndicated loan, the lead lender (arranger) originates the loan, negotiates the spreads and
terms with the borrower, and attracts other banks to participate. Conditional on the loan demand, a
participant in the syndicated loan can therefore determine its own contribution to funding the total
loan amount. If the previous findings are driven by supply-side choice, then the within-loan regression
should show that, systemically risky banks contribute a larger portion to riskier borrowers, and (or) a
smaller portion to safe borrowers.
I conduct my analysis using multiple methodologies. First, I test banks’ allocation share preference
in risky loan groups versus less risky loan groups, and investigate whether the allocation share
preferences are significantly different between the two groups. In each year for all newly originated
22
loans, I sort their borrower distance-to-default into ten quantiles, with the 1st quantile including the
lowest distance-to-default (most risky) borrowers, and the 10th quantile indicating highest distance-
to-default (least risky) borrowers. Then I construct three risk dummies: 𝑅𝑖𝑠𝑘 𝐷𝑢𝑚𝑚𝑦1 ,
𝑅𝑖𝑠𝑘 𝐷𝑢𝑚𝑚𝑦2 , and 𝑅𝑖𝑠𝑘 𝐷𝑢𝑚𝑚𝑦3 . 𝑅𝑖𝑠𝑘 𝐷𝑢𝑚𝑚𝑦1 equals 1 if the borrower distance-to-default
falls into the 1st, 2nd, 3rd, 4th, and 5th quantiles (risky), and 0 if the borrower distance-to-default falls
into the 6th, 7th, 8th, 9th, and 10th quantiles (less risky). 𝑅𝑖𝑠𝑘 𝐷𝑢𝑚𝑚𝑦2 equals 1 if the borrower
distance-to-default falls into the 1st, 2nd, 3rd, and 4th quantiles (riskier), and 0 if the borrower distance-
to-default falls into the 7th, 8th, 9th, and 10th quantiles (less risky). I drop all observations that fall
into the 5th and 6th quantile due to the potential ambiguous relationship between Δ𝐶𝑜𝑉𝑎𝑅 and bank
share in those groups so that I can focus on borrowers with more extreme credit risk levels, which are
either very high or very low. Similarly, I construct the third risk dummy, 𝑅𝑖𝑠𝑘 𝐷𝑢𝑚𝑚𝑦3, which
focuses on even more extreme credit risk levels. 𝑅𝑖𝑠𝑘 𝐷𝑢𝑚𝑚𝑦3 takes a value of 1 if the borrower
distance-to-default falls into the 1st, 2nd, and 3rd quantiles (very risky), and 0 if the borrower distance-
to-default falls into the 8th, 9th, and 10th quantiles (least risky). I will call the group of firms with
risky dummy being 0 the safe group and the group of firms with risky dummy the risky group.
With the three dummy variables defined, I run the following within-loan regression at both package
and facility levels:
𝐵𝑎𝑛𝑘 𝐴𝑙𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛𝑖,𝑗,𝑘,𝑡
= 𝛼𝑘 + 𝛼1Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 + 𝛼2Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 × 𝑅𝑖𝑠𝑘 𝐷𝑢𝑚𝑚𝑦
+ 𝐿𝑒𝑎𝑑 𝐿𝑒𝑛𝑑𝑒𝑟 𝐷𝑢𝑚𝑚𝑦𝑗,𝑘,𝑡 + 𝐿𝑒𝑛𝑑𝑖𝑛𝑔 𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑠ℎ𝑖𝑝𝑖,𝑗,𝑡 + 𝛼3𝐵𝑎𝑛𝑘 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑗,𝑡−1
+ 𝐵𝑎𝑛𝑘 𝐹𝐸 + 𝜀𝑖,𝑗,𝑘,𝑡 , (10)
where subscript i, j, k, and t indicate the borrowing firm, the bank, the packages/facilities, and the
time (quarter) respectively, and 𝛼𝑘 denotes the package/facility fixed effects. 𝑅𝑖𝑠𝑘 𝐷𝑢𝑚𝑚𝑦 is
𝑅𝑖𝑠𝑘 𝐷𝑢𝑚𝑚𝑦1, 𝑅𝑖𝑠𝑘 𝐷𝑢𝑚𝑚𝑦2, or 𝑅𝑖𝑠𝑘 𝐷𝑢𝑚𝑚𝑦3. The dependent variable is the bank allocation
share in percentages at either package or facility level. 𝐵𝑎𝑛𝑘 𝐹𝐸 denotes bank fixed effects. Since I
use bank fixed effects, I only include banks that have at least five loan observations throughout the
sample period to ensure variation. I also conduct the analysis using either Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 or Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1
to avoid potential simultaneity. I hypothesize that 𝛼1 is negative and 𝛼2 is positive. Note that
𝑅𝑖𝑠𝑘 𝐷𝑢𝑚𝑚𝑦 is included in this regression only through the interaction term, but does not show up
independently, because as one of the borrower characteristics, its direct effect on the bank share has
been absorbed by the package/facility fixed effects. 𝐵𝑎𝑛𝑘 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑗,𝑡−1 includes an array of bank
control variables including the natural logarithm of bank total assets (in millions), bank capital ratio,
23
bank return on equity, bank liquidity, bank loan charge-offs, bank loan loss allowance, and bank risk-
weighted assets. All bank control variables are lagged by one quarter. To account for the possibility
that there might be some funding persistence among frequent bank players and such funding
persistence causes regression error terms to be correlated within banks, I cluster the standard errors
at the bank level.
Table 9 shows the coefficients estimates, with Panel A reporting the results at the package level and
Panel B reporting the results at the facility level. For each panel, Column I, II and III report the
coefficients from the regressions using contemporaneous systemic risk, Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡, and Column IV,
V, and VI report the coefficients from the regressions using one period lagged systemic risk,
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 . I interact bank systemic risk with 𝑅𝑖𝑠𝑘 𝐷𝑢𝑚𝑚𝑦1 in Columns I and IV, with
𝑅𝑖𝑠𝑘 𝐷𝑢𝑚𝑚𝑦2 in Columns II and V, and with 𝑅𝑖𝑠𝑘 𝐷𝑢𝑚𝑚𝑦3 in Columns III and VI. The results in
Panel A shows a negative 𝛼1 , which are statistically significant at 1% or 5% levels in most
specifications. The negative 𝛼1 indicates that systemically risky banks decrease their allocation share
for loans to less risky borrowers. The coefficient of the interaction term, 𝛼2, is significantly positive
at 1% level in all specifications, implying that the relationship between systemic risk and bank
allocation share is significantly different between the risky group and the safe group. 𝛼1 + 𝛼2
represents the relationship between systemic risk and bank allocation share in the risky group, and
we can see that 𝛼1+𝛼2 is always positive in all specifications, which indicates that systemically risky
banks increase their allocation share on risky borrowers.
Economically, taking the coefficients of -0.096 on Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 and 0.204 on the interaction term in
Column I of Panel A as an example, a bank with one standard deviation increase in systemic risk
would decrease contribution to a borrower in the safe group by 0.096 × 2.684 = 0.258% from its mean.
In contrast, for the risky group, a bank with one standard deviation increase in systemic risk would
increase contribution to a borrower in the risky group by (-0.096 + 0.204) × 2.684 = 0.290% from its
mean.
It is also noteworthy that the negative/positive relationship between bank allocation share and
systemic risk is even stronger in borrower groups with even more extreme default risk levels. 𝛼1
decreases from -0.096 in Column I to -0.189 in Column III, with significance levels increase from
10% to 1%. It indicates that on average, systemically risky banks pull back their lending the most on
the most creditworthy borrowers, which provides stronger evidence showing that systemically banks
are less willing to lend to creditworthy borrowers. Meanwhile, 𝛼2 increases from 0.204 in Column I
to 0.387 in Column III, with 1% significance level in all specifications, which indicates that the effects
24
of systemic risk on bank allocation share are even more different between very risky borrowers and
very safe borrowers. The net effect, 𝛼1+𝛼2, increases from -0.096 + 0.204 = 0.108 in Column I to -
0.189 + 0.387 = 0.198 in Column III, meaning that systemically banks increase their lending even
more on riskier borrowers.
I find similar results in Columns IV, V, and VI, where I use one period lagged systemic risk to reduce
simultaneity. The coefficient on Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 stays negative and is significant at 1% significance
level in all three columns. The coefficient on the interaction term is positive and significant at 1%
significance level, implying that the relationship between bank allocation share and systemic risk is
significantly different between the safe group and the risky group. Economically, taking -0.180 and
0.348 in Column VI as an example, on average, a bank with one standard deviation increase in
systemic risk would decrease contribution to a borrower in the safe group by 0.180 × 2.684 = 0.483%
from its mean, and would increase contribution to a borrower in the risky group by (-0.180 + 0.348)
× 2.684 = 0.451%. Panel B of Table 9 reports the within-loan estimation at the facility level. As
shown in Panel B, the results are very similar with Panel A.
4. Robustness Checks
4.1. Robustness Check of Within-loan Regressions
In Section 3.4, a borrower is defined as a risky or safe borrower based on how its distance-to-default
is ranked among all borrowers in the same calendar year. It is possible that during an economic
expansion some borrowers creditworthy in absolute terms but are categorized into the risky group
just because they are riskier relative other borrowers. If banks are screening borrowers based on their
absolute level of credit risk, instead of their relative rank of credit risk among all borrowers in the
year, then my within-loan regression can create misleading results.
As a robustness check, in this section, I rerun the within-loan regressions using the interaction of
systemic risk and absolute value of borrower distance-to-default. Specifically, I look at the following
regression:
𝐵𝑎𝑛𝑘 𝐴𝑙𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛𝑖,𝑗,𝑘,𝑡
= 𝛼𝑘 + 𝛼1Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 + 𝛼2Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 × 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 − 𝑡𝑜 − 𝐷𝑒𝑓𝑎𝑢𝑙𝑡 𝑖,𝑡−1
+ 𝐿𝑒𝑎𝑑 𝐿𝑒𝑛𝑑𝑒𝑟 𝐷𝑢𝑚𝑚𝑦𝑗,𝑘,𝑡 + 𝐿𝑒𝑛𝑑𝑖𝑛𝑔 𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑠ℎ𝑖𝑝𝑖,𝑗,𝑡 + 𝛼3𝐵𝑎𝑛𝑘 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑗,𝑡−1
+ 𝐵𝑎𝑛𝑘 𝐹𝐸 + 𝜀𝑖,𝑗,𝑘,𝑡, (11)
25
where subscripts i, j, k, and t index the borrowing firm, the bank, the loan pacage/facility, and time.
The key variables of interest are Δ𝐶𝑜𝑉𝑎𝑅 and 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 − 𝑡𝑜 − 𝐷𝑒𝑓𝑎𝑢𝑙𝑡. 𝐿𝑒𝑎𝑑 𝐿𝑒𝑛𝑑𝑒𝑟 𝐷𝑢𝑚𝑚𝑦𝑗,𝑘,𝑡
equals 1 if bank j is a lead lender in the package/facility k. 𝐿𝑒𝑛𝑑𝑖𝑛𝑔 𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑠ℎ𝑖𝑝𝑖,𝑗,𝑡 controls for the
intensity of past lending relationships between borrower i and bank j. 𝐵𝑎𝑛𝑘 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 is a vector of bank
characteristics and 𝐵𝑎𝑛𝑘 𝐹𝐸 denotes bank fixed effects.
The aggregate effect of Δ𝐶𝑜𝑉𝑎𝑅 on 𝐵𝑎𝑛𝑘 𝐴𝑙𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛 is 𝛼1 + 𝛼2 × 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 − 𝑡𝑜 − 𝐷𝑒𝑓𝑎𝑢𝑙𝑡 𝑖,𝑡−1.
Therefore, 𝛼1 measures the relationship between Δ𝐶𝑜𝑉𝑎𝑅 and 𝐵𝑎𝑛𝑘 𝐴𝑙𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛 when borrower
distance-to-default is 0 (or very close to 0). In other words, 𝛼1 measures the relationship between
Δ𝐶𝑜𝑉𝑎𝑅 and bank contribution for loans whose borrower is at the edge of bankruptcy. A positive 𝛼1
implies that systemically risky banks increase their lending to very risky borrowers, and a negative
𝛼2 would indicate that this relationship is weaker or even reverted for safe borrowers.
The regression results are presented in Table 10. Panel A of Table 10 reports the package-level results
and Panel B of Table 10 reports the facility-level results. To avoid potential simultaneity, I try both
the contemporaneous and one-period lagged values for the two variables of interest, systemic risk
and borrower distance-to-default. For both panels, I use contemporaneous systemic risk, Δ𝐶𝑜𝑉𝑎𝑅𝑖,𝑡,
in Column I and II, and one-period lagged systemic risk, Δ𝐶𝑜𝑉𝑎𝑅𝑖,𝑡−1, in Column III and IV. In
Column I and III, I interact systemic risk with contemporaneous borrower distance-to-default, and in
Column II and IV, I interact systemic risk with one-period lagged borrower distance-to-default. In
Panel A of Table 10, 𝛼1 is positive and significant at 5% or 1% significance levels in most
specifications, which suggests that for very risky borrowers, bank allocation share increases with
bank systemic risk. 𝛼2 is negative and significant at 1% significance level in all four columns, which
indicates the positive relationship between bank systemic risk and bank allocation share is weaker as
borrower distance-to-default increases, in other words, as the borrower becomes more creditworthy.
Economically, taking 0.171 and -0.024 in Column II as an example, for a borrower whose distance-
to-default is 0 (or close to 0), a bank with one standard deviation increase in Δ𝐶𝑜𝑉𝑎𝑅𝑖,𝑡 would
increase its contribution by 0.171 × 2.684 = 0.459%, suggesting a higher contribution to risky
borrowers. As borrower distance-to-default increases, in other words, as the borrower becomes more
creditworthy, the positive relationship between systemic risk and bank allocation share is weaker.
When borrower distance-to-default reaches 5.952, 𝛼1 + 𝛼2 × 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 − 𝑡𝑜 − 𝐷𝑒𝑓𝑎𝑢𝑙𝑡 𝑖,𝑡−1 is
equal to 0 (0.171 – 0.024 × 5.952 = 0) so bank allocation is unrelated with systemic risk on these
loans. At this point, the relationship between systemic and bank allocation is reverted to a negative
26
relationship. Note that 5.952 is lower than but not far from the sample mean (8.181) or median (7.564)
value of borrower distance-to-default. As borrower distance-to-default further increases to the 75th
percentile, which is about 10.872, a bank with one standard deviation increase in Δ𝐶𝑜𝑉𝑎𝑅𝑖,𝑡 would
decrease its contribution by (0.171 – 0.024 × 10.872) × 2.684 = 0.241%. Panel B of Table 10 reports
the facility-level results which are similar with those at the package-level.
The results in Table 10 echo my results in Section 3.4 and suggest that the positive (negative)
relationship between bank systemic risk and allocation share for risky (safe) borrowers is robust with
both relative and absolute credit risk levels, and is robust with treatment of discrete credit risk
quantiles and continuous borrower distance-to-default levels.
4.2. Robustness Check of Causality Tests: Dynamic Panel GMM Estimation
Endogeneity can arise from reverse causality. For example, current values of systemic risk could be
a function of the credit risk of previous borrowers. Although the lead lag analysis presented in Table
8 addresses this point, in this section I utilize a dynamic panel GMM analysis to alleviate this concern.
I produce dynamic panel GMM estimators following Wintoki, Linck, and Netter (2012). The
estimation consists of four steps: First, I convert my regression equation to a bank-quarter panel
regression. For each bank j in quarter t, I calculate the average borrower distance-to-default of all
loans originated by this bank in this quarter. For this calculation, I don’t put any restriction on whether
the bank acts as a lead lender or non-lead participant. As long as a bank participates in a loan, this
loan is included to the average borrower distance-to-default calculation in the bank’s loan portfolio.
I name this average distance-to-default Bank Portfolio Distance-to-Default. Following the same
methodology, I generate the average borrower characteristics and loan characteristics for each bank
in each quarter. In this way, the regression is converted to a panel regression using quarterly Bank
Portfolio Distance-to-Default as the dependent variable, quarterly systemic risks as main independent
variables, and quarterly bank, average borrower, and average loan characteristics as control variables.
Second, I rewrite the regression equation as a dynamic model, adding three lags of Bank Portfolio
Distance-to-Default ( 𝐵𝑎𝑛𝑘 𝑃𝑜𝑟𝑡𝑓𝑜𝑙𝑖𝑜 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑡𝑜 𝐷𝑒𝑓𝑎𝑢𝑙𝑡 𝑗,𝑡−𝑝 , p=1,2,3) as explanatory
variables. Therefore, the regression equation becomes:
27
𝐵𝑎𝑛𝑘 𝑃𝑜𝑟𝑡𝑓𝑜𝑙𝑖𝑜 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑡𝑜 𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑗,𝑡
= 𝛼0 + 𝛼1 ∑ 𝐵𝑎𝑛𝑘 𝑃𝑜𝑟𝑡𝑓𝑜𝑙𝑖𝑜 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑡𝑜 𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑗,𝑡−𝑝
3
𝑝=1
+ 𝛼2𝐿𝑎𝑔𝑔𝑒𝑑 𝐴𝑣𝑔 𝐷𝑡𝑜𝐷𝑗,𝑡−1
+ 𝛼3Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 + 𝛼4𝐶𝐴𝑇𝐹𝐼𝑁𝑡 + 𝛼5𝐴𝑣𝑔 𝐿𝑜𝑎𝑛 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑡 + 𝛼6𝐵𝑎𝑛𝑘 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑗,𝑡
+ 𝛼7𝐴𝑣𝑔 𝐵𝑜𝑟𝑟𝑜𝑤𝑒𝑟 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑡 + 𝛼8𝐿𝑒𝑛𝑑𝑖𝑛𝑔 𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑠ℎ𝑖𝑝𝑖,𝑗,𝑡 + 𝛼9𝑀𝑎𝑐𝑟𝑜 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑡
+ 𝑌𝑒𝑎𝑟 𝐹𝐸 + 𝜀𝑖,𝑗,𝑡 (12)
Note that 𝐵𝑎𝑛𝑘 𝑃𝑜𝑟𝑡𝑓𝑜𝑙𝑖𝑜 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑡𝑜 𝐷𝑒𝑓𝑎𝑢𝑙𝑡 𝑗,𝑡−p is different from 𝐿𝑎𝑔𝑔𝑒𝑑 𝐴𝑣𝑔 𝐷𝑡𝑜𝐷𝑗,𝑡−1 .
𝐿𝑎𝑔𝑔𝑒𝑑 𝐴𝑣𝑔 𝐷𝑡𝑜𝐷𝑗,𝑡−1 controls for the borrower’s lagged credit risk one quarter before their
borrowing activity, while 𝐵𝑎𝑛𝑘 𝑃𝑜𝑟𝑡𝑓𝑜𝑙𝑖𝑜 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑡𝑜 𝐷𝑒𝑓𝑎𝑢𝑙𝑡 𝑗,𝑡−1 is the average credit risk the
lender actually took in quarter t-1. Third, I first difference all variables, which allows me to control
for unobserved heterogeneity and eliminate potential omitted variable bias. Fourth, I estimate the
model by dynamic panel GMM and use lagged explanatory variables as instruments. As suggested
by Saunders, Schmid, Walter (2016), using lagged variables as instruments for the present values of
these variables controls for potential simultaneity and reverse causality. In addition, this estimation
procedure allows all the explanatory variables to be treated as endogenous.**
Table 11 reports the results of GMM estimation. For space reasons, only the coefficients on systemic
risks, 𝐿𝑎𝑔𝑔𝑒𝑑 𝐴𝑣𝑔 𝐷𝑡𝑜𝐷𝑗,𝑡−1, and interaction terms are reported. The results across all seven columns
confirm a negative contribution of systemic risks to borrower distance-to-default. Columns V and VI
suggest that this result holds regardless of macrolevel systemic risk as both interaction terms are
statistically insignificantly different from zero. Column VII shows that the magnitude of the effect
Δ𝐶𝑜𝑉𝑎𝑅 on dependent variable is reduced to some extent, while the negative contribution still holds.
5. Credit Risk-taking Sensitivity on Systemic Risk – The Effect of
Executive and Bank Innovation Preference and Styles
5.1. Constructing Executive and Bank Innovation Dimensions
**The dynamic GMM test is not without its flaws. All loans used to compute lagged Bank Portfolio Distance-
to-Default are newly originated loans in respective quarters, since it is hard to control for the effect of all existing
loans due to data limitation. Thus, I utilize this as a robustness check of my earlier results
28
Even though I find a positive relationship between systemic risk and credit risk-taking in the previous
sections, different banks, possibly with different risk-taking cultures, may react to changes in
systemic risks very differently. Table 12 shows the estimates for coefficients 𝛼1 and 𝛼3 in equation
(9) for selected US bank holding companies. 𝛼1 is the coefficient on the micro-level systemic risk,
Δ𝐶𝑜𝑉𝑎𝑅 , and 𝛼3 is the coefficient on the interaction of Δ𝐶𝑜𝑉𝑎𝑅 and the recession dummy. 𝛼1
measures a bank’s credit risk-taking sensitivity during normal periods, and 𝛼1 + 𝛼3 measures the
bank’s credit risk-taking sensitivity during recession periods. Table 12 shows that there is a wide
variation in credit risk-taking sensitivity across different US banks in and out of recessions, which
raises the question of what factor is driving this heterogenous reactions of credit risk-taking to
systemic risk changes.
In this section, I provide evidence that some bank executive specific effects, or “styles”, play an
important role in explain banks’ credit risk-taking and credit risk-taking sensitivity on systemic risk.
Recent literature has been focusing on the roles of bank executives and their effects on bank risk-
taking culture and extreme risk exposure, and a growing body of evidence has shown how pay
arrangements and other executive characteristics affect banks’ risk-taking (Berger, Kick and Schaeck,
2014; Nguyen, Hagendorff and Eshraghi, 2017). However, Hagendorff et. al. (2017) suggest that
compensation and various other observable executive characteristics can only describe a small
amount of the variation in banks business model and risk-taking preference, and they find that much
of the variation in bank business policy can be explained by executive factors (“styles”) that are time-
invariant, which helps explain the risk-taking culture in some banks.
Since bank credit risk-taking sensitivity on systemic risk is largely related to banks business models
and how banks are managed (aggressively or conservatively) by the executives, its variation may also
be explained by the unobservable, time-invariant executive fixed effects. In this section, motivated
by the recent work of Hagendorff et. al (2017), I provide evidence that idiosyncratic executive-
specific effects (“styles”) can also help explain a large variation in banks’ credit risk-taking sensitivity
on systemic risk.
I generate executive-specific styles following the method of Hagendorff et. al (2017). Specifically, I
run a series of three-way fixed effects models (with bank, time, and executive fixed effects) using
eight bank business policy variables as dependent variables and a series of bank controls and
macroeconomic controls as independent variables. Executives included are CEOs, CFOs, COOs, and
executive directors, whose position and tenure data are obtained from Execucomp. The executive and
bank fixed effects are disentangled using a sample of banks assembled using the connectedness
29
sampling method of Abowd, Kramarz and Margolis (1999) (AKM, thereafter) and Abowd, Creecy,
and Kramarz (2002). The AKM method allows me to separate bank and executive fixed effects
through a connectedness sample, which includes not only moving executives but also non-movers
who work in banks that have hired at least one mover during the sample period. The executive fixed
effects proxy for some unobservable executive-specific styles or personality that can potentially affect
banks’ business policy models and risk-taking strategies. Similarly, the bank fixed effects capture
some bank-specific time-invariant (or stable) business or risk-taking culture.
Following Liu, Mao, and Tian (2016), I use graph theory to construct the connectedness sample:
starting with an arbitrary mover executive, I find out all banks for which she had ever worked during
the sample period, then find all executives (movers and non-movers) who had ever worked for these
banks during the sample period, and further track all banks they ever work for. Continue this
procedure until all data are exhausted, and these “connected” executives and banks are assembled
into a single “connected” group. Then, I select another arbitrary mover executive that is not assigned
a group, and follow the above procedure again. Follow the procedures until all mover executives have
been assembled into groups. As suggested by Abowd, Creecy, and Kramarz (2002), the AKM method
makes it computationally feasible to estimate executive and bank fixed effects for each group relative
to a within-in group benchmark. To make executive and bank fixed effects directly comparable across
groups, I follow the normalization procedure by Cornelissen (2008): First, I normalize the mean bank
fixed effects for each group to zero and add the group mean bank fixed effects to executive fixed
effects; Second, I subtract the grand mean of executive fixed effects from each executive fixed effect
and then add this grand mean executive fixed effect to the intercept. Then, the three-way fixed effect
regression can be written as:
𝑃𝑗(𝑚,𝑡+1) = 𝐵𝑗(𝑚,𝑡)𝛾 + 𝐸𝑡𝛽 + Σ𝑗=1𝐽 𝐷𝑚,𝑗,𝑡𝜃𝑗 + 𝜙𝑚 + 𝜇𝑡 + 𝜀𝑗,𝑡 (13)
where 𝐷𝑖,𝑗,𝑡 is a dummy variable that is equal to one if executive m works at bank j at time t, and zero
otherwise. Following Hagendorff et. al. (2017), 𝑃𝑗(𝑚,𝑡+1) are a series of bank business policy variables (non-
interest income, loans over assets, MBS, derivatives, lending diversifications, Gap12, loans over deposits,
non-deposit funding) for bank j and time t. Bank policy variables are based on bank balance sheet
characteristics that parsimoniously reflect key choices that bank executives make with respect to the asset
and liability side of a bank’s balance sheet. The bank policy variables are defined in Appendix II. The
dependent variable is explained by bank characteristics 𝐵𝑗(𝑚,𝑡), macroeconomic conditions 𝐸𝑡, bank fixed
effects 𝜃𝑗, manager fixed effects 𝜙𝑚, and time fixed effects 𝜇𝑡. In the first step, the AKM method sweeps
30
out the executive fixed effects by averaging over all executive m’s relationship with business model
variables to obtain:
�̅�𝑚 = �̅�𝑚𝛾 + �̅�𝑚𝛽 + Σ𝑗=1𝐽
�̅�𝑚,𝑗𝜃𝑗 + 𝜙𝑚 + �̅�𝑡 + 𝜀�̅� (14)
where �̅�𝑚 is the executive m’s average business policy across the full sample period. Then demean (13)
with (14) to remove executive fixed effects:
𝑃𝑗(𝑚,𝑡+1) − �̅�𝑖 = 𝛾(𝑋𝑗(𝑚,𝑡) − �̅�𝑖) + 𝛽(𝐸𝑡 − �̅�𝑚) + Σ𝑗=1𝐽
(𝐷𝑚,𝑗,𝑡 − �̅�𝑖,𝑗)𝜃𝑗 + (𝜇𝑡 − �̅�𝑡) + (𝜀𝑖,𝑡 − 𝜀�̅�) (15)
Thus, I can use the movers’ information to identify bank fixed effects since 𝐷𝑚,𝑗,𝑡 − �̅�𝑖,𝑗 ≠ 0 for a mover,
which can be estimated by the least-squares dummy variables (LSDV) method (as used by Bertrand and
Schoar, 2003). Finally, using the estimates in the above regression, I can recover the executive fixed effects
equation:
�̂�𝑚 = �̅�𝑚 − �̅�𝑚�̂� − �̅�𝑚�̂� − Σ𝑗=1𝐽 �̅�𝑚,𝑗𝜃𝑗 (16)
where �̅�𝑡 is often treated as the benchmark in estimating time effects and thereby assumed to be zero.
The three-way fixed effects models assign each individual bank and individual executive styles
(estimated fixed effects) in each of the eight policy variables. Since I’m interested in the estimated
fixed effects, the regression results for the three-way fixed effects are omitted. In order to describe
the commonalities in the styles that banks and executives show across different policy choices, I
conduct a factor analysis to identify the main dimensions of variation in executive styles. Factor
analysis allows me to reduce the correlations amongst the eight business policy variables to a lower
number of common factors. Panel A and Panel D of Table 13 presents the results of factor analysis
for executives and banks, respectively. The analysis extracts two dominant factors (Factor 1 and
Factor 2) that summarize a relevant portion of the variance of the correlation matrix of executive
styles.
Panel B of Table 3 reports the factor loadings of each policy style with respect to the Factor 1 and
Factor 2. The results are qualitatively and quantitatively consistent with Hagendorff et. al (2017).
Overall, Factor 1 and Factor 2 exhibit managerial preferences that deviate from the traditional banking
business model of deposit-taking and loan-making. Factor 1 has a high loading on executive
preferences for non-traditional and innovative forms of income generation and asset allocation. For
example, Factor 1 loads positively on non-interest income, indicating a preference for income
generating innovation. It also loads positively on mortgage-backed securities and derivatives, and
31
loads negatively on loan-to-assets ratio and loan-to-deposit ratio, suggesting a stronger preference for
asset allocation innovation and a lower reliance on traditional loan-making business model. Overall,
an executive that has a higher score on Factor 1 exhibits a stronger preference for asset-side
innovation. Factor 2 loads positively non-deposit funding, which captures a managerial preference
for non-traditional bank liabilities, therefore an executive that has a higher score on Factor 2 exhibits
a stronger preference for liability-side innovation. Thus, I can utilize the scores of each executive on
the two factors to describe an executive’s styles on two dimensions of innovation: asset-side and
liability-side. I define an executive’s score on Factor 1 as the asset-side innovation score, and an
executive’s score on Factor 2 as the liability-side innovation score. Similarly, I can also define bank-
level innovation scores using banks’ score on Factor 1 and Factor 2 in Panel E.
To preliminarily validate that the executive and bank innovation scores on the asset-side and liability-
side are relevant measures for systematic differences in how executives impact banks overall
operations, Table 14 shows a correlation matrix for systemic risk, executive and bank innovation
scores for the period of 1992 to 2013. Table 14 shows that the average executive asset-side innovation
score for all executives working for a bank j in quarter t is highly correlated with bank j’s
contemporaneous and next period bank-level systemic risk. This is consistent with the widely
accepted view that bank non-interest income and innovative assets have a higher contribution to
systemic risk than traditional banking (Brunnermeier et. al., 2012). There is a similar relationship
between systemic risk and executive liability-side innovation, but the relationship is weaker,
indicating that an executive’s asset-side innovation preference is the dominant factor in an executive’s
personal traits that affects her institution’s systemic risk-taking. I also find a similar correlation
pattern between systemic risk and the bank-level innovation scores, but the correlations are lower
than those between systemic risk and executive innovation scores, which is an evidence that in terms
of impact on a bank’s systemic risk-taking, the executive innovation preferences may have a higher
contribution than the institutional innovation culture.
5.2. Credit Risk-taking Sensitivity on Systemic Risk: The Effect of Executive Asset-side and
Liability-side Innovation Dimensions
In this section, I investigate whether executive’s personal traits on asset and liability innovation effect
a bank’s credit risk-taking and credit risk-taking sensitivity on systemic risk. I add the innovation
scores to the credit risk-taking sensitivity on systemic risk model, and interact the scores with bank-
level systemic risk.
32
Table 15 presents the results. In Column II, I interact Δ𝐶𝑜𝑉𝑎𝑅 with 𝑀𝑎𝑛𝑎𝑔𝑒𝑟 𝐴𝑠𝑠𝑒𝑡 𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗,𝑡 ,
which is the average executive asset-side innovation scores for all executives working in bank j in quarter
t. In Column III, I interact Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 with 𝑀𝑎𝑛𝑎𝑔𝑒𝑟 𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗,𝑡, which is the average liability-
side innovation scores for all executives working in bank j in quarter t. In Column IV, I add both interaction
terms into the regression.
The coefficients on executive asset-side innovation score in both Column II and IV are negative and
significant at 1% or 5% level, and the coefficients on the interaction of Δ𝐶𝑜𝑉𝑎𝑅 with asset-side innovation
score are positive and significant at 1% level. There are several implications. First, generally, at normal
state when systemic risk is at its median level (4.351), credit risk-taking increases with executive asset-side
innovation score. A potential explanation is that bank executives that are more innovative on asset
allocation are better at hedging or sourcing credit risk, which enables their bank to take higher credit risk
in the traditional debt instruments (Norden, Buston, and Wagner, 2014). Asset-side innovative executives
are more specialized in utilizing innovative products (in particular structured instruments and credit
derivatives), and the use of the innovative “originate to distribute” products can increase banks’ risk-taking
if the transfer of risks leads to incentive problems at banks. There is evidence that loan sales (Cebenoyan
and Strahan, 2004) and Collateralized Debt Obligations (Franke and Krahnen, 2007) lead to an increase in
lending at banks, potentially to risky borrowers. My result here thus supports the view in that asset-side
innovative executives may participate in riskier syndicated bank loan deals. The second implication from
the results in Table 15 is that, credit risk-taking sensitivity on systemic risk is lower for banks managed by
executives with high asset-side innovation score. This suggests that executives with different asset-side
innovation preferences react to potential systemic risk crisis very differently. Compared with asset-side
traditional executives, asset-side innovating executives are less likely to exploit abnormal returns (induced
by imperfect market discipline) from higher risk-taking in traditional loan-making activities. This is
potentially due to the systemically risky banks’ trade-off between the potential costs from regulatory
scrutiny and requirements such as capital constraints, and the potential benefits that can be obtained from
excessive risk-taking in the traditional syndicated loan market. Note that Factor 1 loads negatively loan-to-
assets ratios and loan-to-deposit ratios, indicating that traditional loans only take a low portion in an asset-
side innovator’s preferred asset portfolio. Thus, compared with the regulatory scrutiny and regulatory risk
pricing, the potential abnormal returns that can be obtained from higher risk-taking in the traditional loan
market is lower, which discourage them from exploiting abnormal returns in the loan market.
Combining the two results above, we can say that banks with asset-side innovators generally take higher
credit risk (this part is independent of systemic risk) but their credit risk sensitivity to systemic risk is low.
This is consistent with the reality that banks with innovators generally focus more on non-interesting
33
generating activities, so that it's more likely that their moral hazard problems and exploit of excessive return
mainly focus on non-traditional business, which lead to lower credit risk sensitivity on systemic risk.
Traditional activities such as syndicated loans are less of an attractive moral hazard opportunity for them.
Instead, banks with traditionalists generally take lower credit risk but they focus their moral hazard
opportunities on more on traditional businesses than innovators, which leads to a higher credit risk
sensitivity on systemic risk.
The coefficients on the executive liability-side innovation, as shown in Columns III and IV in Table
15, is positive and significant at 1% or 5% level. Columns III and IV also show that the coefficients
on the interactions of systemic risk and liability-side innovation are negative and significant at 1%
level. Combing the two results, for banks managed by executives with high liability-side innovation,
their loan credit risk-taking is generally lower but is more sensitive to potential systemic risk crisis.
This reflects both the bright side and dark side of executives’ liability innovation, and can be
explained from the view of bank competition and trade-off between retail and wholesale funding
costs. Executives with high scores on liability innovation dimension are better at utilizing non-deposit
funding, such as wholesale funding, to supplement retail deposits and finance their investment. The
use of innovative liability such as wholesale funding enables banks to avoid the intense competition
on traditional retail deposits and thus lower the cost of bank funding. Literature has shown that the
incentives of banks to invest in risky projects increase with the cost of its funding, and banks with
cheap sources of funding pursue more conservative risk strategies (Allen and Gale, 2000; Hellmann
et al., 2000). Thus, the liability-side innovation provides a bright side in that executives that are more
innovative on the liability are more able to utilize the cheaper wholesale funding to lower their risk-
taking on loans.
However, the negative coefficient on the interaction term reveals that there is also a dark side.
Compared with traditional retail depositors, innovative fund providers such as wholesale financiers
are relatively more sophisticated and more sensitive to idiosyncratic and systemic risks. They can exit
and withdraw their fund much more quickly than retail depositors. In cases of potential systemic risk
crisis (high Δ𝐶𝑜𝑉𝑎𝑅 periods), innovative liability holders can exit and withdraw their funds much
more quickly than retail depositors, which in the short term may sharply increase a bank’s funding
costs, forcing them to conduct more risky projects. This problem would be severer for banks managed
by executives that deviate from the traditional deposit-taking model and are more innovative in her
liability structure.
34
5.3. Alternative Explanation: The Effect of Bank Innovation Dimensions
An alternative explanation for the above results could be that, the managerial asset-side and liability-
side innovation preference measures not only capture the managerial styles, but also capture the bank-
level innovation preferences, or bank-level risk-culture, which may be correlated with managerial
innovation preferences, and maybe it’s the bank-level preferences that are affecting a bank’s credit
risk-taking and credit risk-sensitivity on systemic risk. To answer that question, I also estimate the
bank fixed effects in the three-way fixed effect model, and conduct the same factor analysis for bank
fixed effects, and then extracts two dominant factors that summarize a relevant portion of the variance
of the correlation matrix of bank styles, as specified in Panel D of Table 13. In Panel E of Table 13,
I observe a similar pattern of loadings for Factor 1 and Factor 2. Factor 1 loads positively on
innovative business models of income generation and asset allocation. It loads positively on non-
interest income, MBS, and derivatives, and loads negatively on loan-to-assets ratio and loan-to-
deposit ratio. Therefore, a higher score in Factor 1 is associated with a bank’s higher asset-side
innovation preference. In contrast, Factor 2 loads positively on non-deposit funding, which indicates
an innovation on liability structure. Thus, higher scores on Factor 2 indicate that the bank exhibits a
higher liability-side innovation. I then study how executive and bank innovation dimensions affect a
bank’s credit risk-taking sensitivity on systemic risk.
The results are presented in Table 16. In all columns, I add bank fixed effects so bank-level innovation
dimension variables are omitted. In Column II and IV, I include the interaction of systemic risk and
bank-level innovation dimensions. The coefficients are insignificant. In Column III and V, I include
the interaction of systemic risk and each of the bank-level innovation dimensions, as well as the
interaction of systemic risk and each of the managerial innovation dimensions. The coefficients on
the interaction of systemic risk and bank-level innovation dimensions remain insignificant, while the
coefficients on the interaction of systemic risk and managerial innovation dimensions remain
significant and consistent with previous results. In Column VI, I include the interactions of systemic
risk with both bank-level asset innovation and liability innovation. The coefficients remain
insignificant. In Column VII, I add all interactions. The signs, magnitude, and significance level for
the interaction of systemic risk and managerial innovation dimensions remain the same as those in
the previous section, and the interactions of systemic risk and bank-level innovation dimensions is
marginally significant, and the magnitudes are much smaller.
The results in Table 16 is important. In terms of affecting credit risk-sensitivity on systemic risk,
managerial innovation preferences are much more important and influential than bank-level
35
innovation culture or preferences. This result is consistent with Hagendorff et. al. (2017), who argue
that extreme risk-taking and other unsustainable business models in banking could ultimately be a
‘people problem’ that is rooted in the idiosyncratic preferences of individuals and not easily reined
by regulators and investors.
5.4. The Effect of Managerial Styles
Since both managerial asset-side and liability-side innovation dimensions have important but
different effects on a bank’s credit risk-taking sensitivity on systemic risk, it is then possible to use
the scores on the two dimensions to assign executives to four different profiles: (1) asset innovating
and liability traditional executives are those with score on Factor1 higher than the mean level (0.012)
and score on Factor 2 lower than the mean level (-0.149); (2) asset and liability innovating executives
are those with scores on Factor 1 and Factor2 higher than the mean levels; (3) asset and liability
traditional executives are those with scores on Factor 1 and Factor 2 lower than the mean levels; (4)
asset traditionalist and liability innovators are those with scores on Factor 1 lower than the mean level
and scores on Factor 2 higher than the mean level. These profiles capture the idiosyncratic effects of
different types of executives on bank credit risk-taking sensitivity on systemic risks. Figure 1 presents
the graphical clustering of managerial patterns in styles, and Panel C in Table 3 shows the average
values for Factor1 and Factor 2 for each executive style. Since each type of executives represents a
different combination of asset-side and liability-side innovation preferences, and innovation
preferences on the asset-side and liability-side have different effects on bank’s risk-taking choices, it
is interesting to test how these different combinations of innovation preferences affect a bank’s credit
risk reaction to potential systemic risk crisis.
Table 17 presents the results. In order to eliminate the effect of different types of executives, in Table
17, I only include bank-quarters when there are only one type of executives working for the bank. In
Columns II to V, I use the interaction of systemic risk and each of the four type dummies. The type
dummy for bank j in quarter t is equal to 1 if the bank j only hires this type of executives in quarter t.
The results in Column II to V of Table 17 show that, the credit risk-taking sensitivity on systemic risk
is significantly lower for banks managed by asset innovators who are also liability traditionalists, and
significantly higher for asset traditionalists regardless of their liability innovation types. In Column
VI, I add three type dummies while omit the first dummy, and find similar results. The credit risk-
sensitivity on systemic risk is highest for liability innovators who are asset traditionalists, and lower
for pure traditionalists, and even lower for pure innovators, and then lowest for asset innovators who
are liability traditionalists. In other words, the credit risk-sensitivity on systemic risk is lowest for an
36
executive who prefer innovative assets, which allow then avoid using the traditional loan market to
exploit the excessive returns, and who prefer traditional liability structure, which allows them to avoid
the inefficient and sensitive wholesale financiers in cases of potential systemic risk crisis. Another
important finding from Table 17 is that, an executive’s asset-side innovation preference is more
important in determining a bank’s reaction to systemic risk in the syndicated loan market.
6. Conclusion
This paper investigates whether and how financial institutions adjust their credit risk exposure in their
loan portfolios in response to systemic risks, and study how idiosyncratic executive-specific effects
help explain the variation in this adjustment across banks. First, using a database of syndicated bank
loans, this paper examines whether systemically important banks take higher credit risk in the
syndicated loan market. Using two complementary measures of systemic risk, Δ𝐶𝑜𝑉𝑎𝑅 and CATFIN,
and the borrower’s distance-to-default as a measure for borrower credit risk, I find that systemically
risky banks are matched with borrowers with higher credit risk, implying higher credit risk-taking by
systemically risky banks, and this relationship is weaker during recessions and periods of high systemic
risk. The result is robust to a number of different specifications, which control for loan, borrower, and
bank characteristics, as well as a series of fixed effects. The result also persists after controlling for
borrower self-selection of lenders. A lead-lag analysis suggests that the greater the bank’s systemic
risk exposure, the greater the default risk in the loans it chooses for its loan portfolio. By using a
within-loan regression methodology to remove the impact of demand-side factors, I find that banks
with more systemic risk choose to fund larger portions of loans to borrowers with high levels of credit
risk and lower shares of loans to relatively safe borrowers. A dynamic GMM estimation further
supports these results and alleviates the endogeneity concern. I further find that some idiosyncratic
bank executive-specific innovation preferences, or styles, can explain the variation in credit risk-
taking sensitivity on systemic risk, indicating that some unobservable executive personal traits play
an important role in affecting banks’ reactions to potential systemic risk crisis.
37
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41
Appendix I
Variable Name Definition
Δ𝐶𝑜𝑉𝑎𝑅 Micro-level systemic risk at 1% significance level (Adrian and Brunnermeier, 2016). In all
tables in this paper I’m presenting Δ𝐶𝑜𝑉𝑎𝑅 at 1% significance level but I also used 5%
level as a robustness check and get very similar results.
CATFIN Macro-level systemic risk. (Allen, Bali and Tang, 2012)
ln(Bank Allocation) Natural logarithm of a bank’s allocation share in percentages of a lead or participant bank.
ln(Package Amount) Natural log of the deal size. Amount is in millions. Source: DealScan
ln(Maturity) Natural log of the maturity of the deal in months. Deal maturity is the weighted average of
the facility maturities. Source: DealScan
ln(No. of Lead Banks) Natural log of the number of lead lenders in the deal syndicate. Source: DealScan
ln(Facility Amount) Natural log of the facility size. Amount is in millions. Source: DealScan
ln(Facility Maturity) Natural log of the maturity of the facility in months. Source: DealScan
ln(No. of Participants) Natural log of the number of participating lenders in the facility syndicate. Source: DealScan
Secured Dummy An indicator variable that takes a value of one if the facility is secured. Source: DealScan
ln(Bank Total Assets) Natural log of the total assets of the lender at quarter of loan origination. Source: Y-9c
Bank Capital Ratio Total capital of the lender over total assets of the lender. Source: Y-9c
Bank Return on Equity Lenders return on book equity. Source: Y-9c
Bank Liquidity The sum of cash and available-for-sale securities divided by bank total assets. Source: Y-9C
Bank Loan Charge-offs The total charge-offs on loans and leases divided by bank total assets. Source: Y-9C
Bank Loan Loss Allowance The total allowance for loan and lease losses divided by bank total assets. Source: Y-9C
Bank Risk-Weighted Assets The total risk weighted assets divided by bank total assets.
GDP Growth Quarterly GDP per capita growth rate. Source: Bureau of Economic Analysis
Early Warn An indicator variable that takes a value of one if CATFIN exceeds the early warning threshold
of 35.1855%.
Recession An indicator variable that takes a value of one if the quarter right before loan origination is
a recession period. Source: National Bureau of Economic Research
Distance-to-default The expected distance-to-default of the borrowers following Bharath and Shumway (2004),
Crosbie and Bohn (2003) and Drucker and Puri (2009).
Borrower Asset Volatility The one-year asset volatility calculated using KMV/Merton method.
Borrower Total Assets Borrower book assets. Source: Compustat
Borrower Leverage The ratio of the book value of total long- and short-term debt to the book value of total assets.
Borrower Tangibility The ratio of net plant, property, and equipment (NPPE) to total assets.
Lending Relationship Borrowed from Bharath el al. (2007), this variable measures the lending relationship between
borrower and lender in a specific year. Specifically, the lending relationship between
borrower i and bank j in year t is defined as:
𝐿𝑒𝑛𝑑𝑖𝑛𝑔 𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑠ℎ𝑖𝑝𝑖,𝑗,𝑡 =$ 𝐴𝑚𝑜𝑢𝑛𝑡 𝑜𝑓 𝑙𝑜𝑎𝑛𝑠 𝑡𝑜 𝑏𝑜𝑟𝑟𝑜𝑤𝑒𝑟 𝑖 𝑏𝑦 𝑏𝑎𝑛𝑘 𝑗 𝑖𝑛 𝑙𝑎𝑠𝑡 5 𝑦𝑒𝑎𝑟𝑠
𝑇𝑜𝑡𝑎𝑙 $ 𝑎𝑚𝑜𝑢𝑛𝑡 𝑜𝑓 𝑙𝑜𝑎𝑛𝑠 𝑏𝑦 𝑏𝑜𝑟𝑟𝑜𝑤𝑒𝑟 𝑖 𝑖𝑛 𝑙𝑎𝑠𝑡 5 𝑦𝑒𝑎𝑟𝑠
42
Appendix II
Following Hagendorff et. al. (2017), Panel A presents the eight bank business model variables used for the
three-way fixed effect regressions. Panel B lists the bank control variables and macroeconomic variables.
Bank-level data are from form FR Y-9C of the Consolidated Financial Statements published by the Board
of Governors of the Federal Reserve System with references to data mnemonics displayed. State-level
coincident indices are from the Federal Reserve Bank of Philadelphia.
Variable Name Definition
Panel A: Bank Business Policy Variable
Non-interest income Non-interest income (bhck4079) over the sum of interest income (bhck4107) and non-interest
income (bhck4079) (%)
Loans Total loans (bhck2122) over total assets (bhck2170) (%)
MBS Before 2009: Private-label mortgage backed securities (bhck1709 + bhck1733 + bhck1713 +
bhck1736 + bhck3536) over total assets (%);
After 2009: Private-label mortgage backed securities (bhckg308 + bhckk146 + bhckg320 +
bhckk154 + bhckg311 + bhckk149 + bhckg323 + bhckk157 + bhckg381 + bhkk198) over total
assets (%)
Derivatives Gross notional amount of derivative contracts held for trading (log of 1 + gross notional amounts
on contracts on interest rate (bhcka126), foreign exchange (bhcka127), equity derivatives
(bhck8723), and others (bhck8724)) over total assets (%)
Lending diversification 1–Herfindahl index of the shares of real estate (bhck1410), commercial and industrial (bhck1763 +
bhck1764), consumer (bhck1975) and other loans out of total loans.
Gap12 Liabilities repricing or maturing within 12 months (bhck3197) minus assets repricing or maturing
within 12 months (bhck3296 + bhck3298) divided by total asset (%)
Loans/Deposits Total loans over total deposits (bhdm6631 + bhdm6636 + bhfn6631 + bhfn6636) (%)
Non-deposit funding 1 – (deposits over total liabilities (bhck2948)) (%)
Panel B: Bank Control Variable and Macroeconomic Variables
Size Log of total assets (in 2000 $)
Equity Total equity (bhck3210) over total assets (%)
Market to book Log of the ratio of the market to book value of equity
Core deposits 1 – (total time deposits of $100,000 or more (bhcb2604) over total deposits (%)
Productivity Total assets over full-time employees (bhck4150) ($ millions)
Economy 12-month average of the monthly coincident index at the state level
43
Table 1
Summary Statistics
Table 1 reports summary statistics of the sample of 10,915 packages (22,595 package-lender pairs) borrowed by 3,603
firms from 214 banks, which are owned by 66 bank holding companies. All loans are originated between Q1 1995 and
Q4 2013. Syndicated loan data is obtained from Loan Pricing Corporation’s (LPC) Dealscan loan database. Bank
characteristics data are collected from the Consolidated Financial Statements for Holding Companies (“FR Y-9C”)
available on the Federal Reserve Bank of Chicago website. Borrower balance sheet data are obtained from Compustat.
The sample presented is used in the baseline regressions presented in Table 3. The number of observations (N) indicates
the sample on which the summary statistics are based. N is by package-lender, where lender is identified at the bank
holding company level. ln(Deal Amount) is the natural logarithm of the package size (in millions). ln(Maturity) is the
natural log of the maturity of the package (in months). Maturity of the package is calculated as the value-weighted
average maturity of all facilities in the package. ln(Number of Leads) is the natural logarithm of the number of lead
lenders in the deal syndicate. A bank is defined as a lead lender if its lend arranger credit variable in Dealscan is “Yes”.
Secured is a dummy variable that takes a value of 1 if at least one facility in the package is secured, and 0 otherwise.
𝑙𝑛(𝐵𝑜𝑟𝑟𝑜𝑤𝑒𝑟𝑇𝑜𝑡𝑎𝑙𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡) is defined as the natural logarithm of borrower’s total assets (in billions);
𝐵𝑜𝑟𝑟𝑜𝑤𝑒𝑟𝑇𝑎𝑛𝑔𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑖,𝑡 is defined as total property, plant, and equipment divided by total assets;
𝐵𝑜𝑟𝑟𝑜𝑤𝑒𝑟𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖,𝑡 is defined as borrower’s total debt divided by total assets. ∆CoVaR is the time-varying
microlevel systemic risk calculated following in Adrian and Brunnermeier (2016). 𝑙𝑛(𝐵𝑎𝑛𝑘𝑇𝑜𝑡𝑎𝑙𝐴𝑠𝑠𝑒𝑡𝑠𝑗,𝑡) is
the natural logarithm of the total assets (BHCK2170) of the lender at the quarter of loan origination. Bank Capital
Ratio is lender’s total book equity (BHCK3210) over total assets (BHCK2170). Capital Ratio is in decimal. Bank Return
on Equity is lender’s net income (BHCK4340) over book equity (BHCK3210). Distance-to-default is the expected
distance-to-default calculated using method from Bharath and Shumway (2004), Crosbie and Bohn (2003) and Drucker
and Puri (2009). Lending relationship between borrower i and bank j is defined as the dollar amount of loans to borrower
i by bank j in last 5 years over the total dollar amount of loans by borrower i in last 5 years. CATFIN is the macro-level
systemic risk borrowed from Allen, Bali, and Tang (2012). 𝐺𝐷𝑃𝐺𝑟𝑜𝑤𝑡ℎ𝑡 is quarterly growth rate of US quarterly GDP
per capita.
44
Variable N Mean Median Std. P25 P75
Loan Characteristics
Deal Amount ($million) 22,595 878.017 400.000 1,555.896 150.000 1,000.000
ln(Deal Amount) 22,595 19.677 19.807 1.504 18.826 20.723
Maturity (in months) 22,595 49.480 59.891 20.530 36.000 60.000
ln(Maturity) 22,595 3.775 4.093 0.566 3.584 4.094
Number of Lead Banks 22,595 4.141 4.000 2.716 2.000 5.000
Secured 22,595 0.576 1.000 0.494 0.000 1.000
Lending Relationship 22,595 0.471 0.482 0.435 0.000 1.000
Borrower Characteristics
Distance-to-default 22,595 6.772 6.002 4.903 3.433 9.184
Tangibility 22,595 0.310 0.243 0.233 0.126 0.446
Borrower Total Assets ($billion) 22,595 6.752 1.828 14.621 0.610 5.777
Leverage 22,595 0.322 0.298 0.194 0.188 0.426
Bank Characteristics
∆CoVaR 22,595 5.216 4.351 2.550 3.432 6.302
Bank Total Assets ($billion) 22,595 778.874 427.849 748.184 167.830 1317.591
Capital Ratio 22,595 0.085 0.083 0.016 0.074 0.094
Return on Equity 22,595 0.083 0.078 0.052 0.041 0.115
Macroeconomic Conditions
CATFIN 22,595 2.393 2.286 0.926 1.644 2.968
GDP Growth (quarterly) 22,595 2.662 2.730 1.565 1.650 4.080
45
Table 2
Correlation Matrix
Table 2 reports the spearman correlation matrix for the sample of 10,915 packages (22,595 package-lender pairs) originated between Q1 1995 and Q4 2013. The sample is
used in the baseline regressions presented in Table 3. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)
(1) Borrower Distance to Default 1.000
(2) ∆CoVaR -0.220 1.000
(3) CATFIN -0.289 0.582 1.000
(4) Package Amount 0.204 0.035 -0.074 1.000
(5) Package Maturity 0.145 -0.097 -0.189 0.218 1.000
(6) Number of Lead Banks 0.198 -0.018 -0.100 0.640 0.197 1.000
(7) Secure Dummy -0.357 -0.017 0.008 -0.279 0.157 -0.219 1.000
(8) Bank Total Assets 0.141 0.233 -0.076 0.303 0.132 0.077 -0.075 1.000
(9) Bank Capital Ratio 0.158 -0.154 -0.152 0.068 0.155 0.092 0.044 0.170 1.000
(10) Bank Return on Equity 0.005 -0.059 -0.099 -0.082 -0.014 0.007 -0.020 -0.289 -0.254 1.000
(11) Borrower Tangibility -0.110 0.025 0.021 0.071 0.004 0.032 -0.022 -0.044 -0.086 0.046 1.000
(12) Borrower Total Assets 0.209 0.046 -0.075 0.840 0.047 0.543 -0.410 0.362 0.068 -0.108 0.100 1.000
(13) Borrower Leverage -0.371 0.044 0.012 0.192 0.212 0.106 0.245 -0.029 -0.102 0.044 0.172 0.061 1.000
(14) Lending Relationship 0.079 0.048 -0.080 0.125 0.002 0.008 -0.102 0.274 0.078 -0.122 -0.001 0.163 -0.017 1.000
(15) GDP Growth -0.007 -0.083 -0.032 -0.081 0.031 -0.006 -0.018 -0.362 -0.354 0.299 0.053 -0.145 0.070 -0.110 1.000
46
Table 3
Summary Statistics: Within-loan Regressions
Table 3 reports the summary statistics for the within-loan regressions in equation (11). Because the regressions are run
at both package and facility level, the summary statistics are reported at both levels, with Panel A reporting at the
package level and Panel B reporting at the facility level. Since all borrower, loan, and macroeconomic characteristics
drop out in a within-loan regression, I only report loan characteristics (at both levels) in this table. A borrower doesn’t
have to have data on characteristics except for their distance-to-default and two-digit SIC code to be included in this
regression.
47
Panel A: Package Level
Variable N Mean Median Std. P25 P75
Borrower Characteristics
Merton Distance-to-default 13004 8.181 7.564 5.073 4.758 10.872
Bank Characteristics
∆CoVaR 13004 4.705 3.893 2.684 3.067 5.323
Bank Allocation (in percent) 13004 10.513 7.779 10.307 4.300 13.000
Bank Total Assets ($billion) 13004 692.547 307.786 733.975 103.110 1228.625
ln(Bank Total Assets) 13004 19.595 19.522 1.353 18.438 20.917
Bank Capital Ratio 13004 0.092 0.090 0.017 0.081 0.101
Bank Return on Equity 13004 0.068 0.057 0.051 0.033 0.099
Bank Liquidity 13004 0.213 0.197 0.106 0.150 0.239
Bank Loan Charge-off 13004 0.004 0.002 0.004 0.001 0.005
Bank Loan Loss Allowed 13004 0.011 0.010 0.006 0.007 0.014
Bank Risk-weighted Assets 13004 0.760 0.776 0.156 0.658 0.855
Lead Lender Dummy 13004 0.265 0.000 0.441 0.000 1.000
Lending Relationship 13004 0.412 0.063 0.558 0.000 0.791
Loan Characteristics
Package Amount ($million) 13004 1027.549 500.000 1608.502 250.000 1200.000
Package Maturity (in months) 13004 50.970 60.000 13.402 37.846 60.000
Number of Lead Banks 13004 2.304 2.000 2.199 1.000 2.000
Number of Participants 13004 15.194 14.000 9.114 9.000 20.000
Secure Dummy 10949 0.424 0.000 0.494 0.000 1.000
Panel B: Facility Level
Variable N Mean Median Std. P25 P75
Borrower Characteristics
Merton Distance-to-default 16275 8.056 7.363 5.102 4.684 10.684
Bank Characteristics
∆CoVaR 16275 4.701 3.917 2.643 3.076 5.332
Bank Allocation (in percent) 16275 11.013 8.000 10.914 4.665 13.333
Bank Total Assets ($billion) 16275 706.372 308.913 745.355 104.265 1251.046
ln(Bank Total Assets) 16275 19.611 19.534 1.361 18.451 20.927
Bank Capital Ratio 16275 0.093 0.091 0.017 0.081 0.103
Bank Return on Equity 16275 0.068 0.058 0.051 0.032 0.099
Bank Liquidity 16275 0.211 0.195 0.104 0.150 0.236
Bank Loan Charge-off 16275 0.004 0.003 0.004 0.001 0.005
Bank Loan Loss Allowed 16275 0.011 0.010 0.006 0.007 0.014
Bank Risk-weighted Assets 16275 0.759 0.776 0.155 0.650 0.855
Lead Lender Dummy 16275 0.278 0.000 0.448 0.000 1.000
Lending Relationship 16275 0.331 0.000 0.398 0.000 0.692
Loan Characteristics
Facility Amount ($million) 16275 741.714 400.000 1094.778 175.000 850.000
Facility Maturity (in months) 16232 53.370 60.000 12.772 48.000 60.000
Number of Lead Banks 16275 2.389 2.000 2.224 1.000 3.000
Number of Participants 16275 15.602 14.000 9.502 9.000 21.000
Secure Dummy 13657 0.487 0.000 0.500 0.000 1.000
48
Table 4
Systemic Risk and Credit Risk: Fixed Effects Regressions
Table 4 reports the coefficient estimates from the following fixed effects regression:
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑡𝑜𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑗,𝑘,𝑡
= 𝛼0 + 𝛼1Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 + 𝛼2𝐶𝐴𝑇𝐹𝐼𝑁𝑡 + 𝛼3Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝐶𝐴𝑇𝐹𝐼𝑁𝑡 + 𝛼4Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1
+ 𝛼5𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑡𝑜𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡−1 + 𝛼6𝐿𝑜𝑎𝑛𝐶𝑜𝑛𝑡𝑟𝑜l𝑠𝑘,𝑡 + 𝐵𝑎𝑛𝑘𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑗,𝑡 + 𝛼8𝐵𝑜𝑟𝑟𝑜𝑤𝑒𝑟𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑡
+ 𝛼9𝐿𝑒𝑛𝑑𝑖𝑛𝑔𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑠ℎ𝑖𝑝𝑖,𝑗,𝑡 + 𝛼10𝑀𝑎𝑐𝑟𝑜𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑡 + 𝑌𝑒𝑎𝑟𝐹𝐸 + 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝐹𝐸 + 𝐵𝑎𝑛𝑘𝐹𝐸
+ 𝜀𝑖,𝑗,𝑘,𝑡 ,(9)
where subscript i, j, k, and t indicate the borrowing firm, the bank, the package, and the time (quarter), respectively. The
regressions are run at the package level and observations are by package-lender pairs, where lenders include both lead
lenders and non-lead participants, and are identified at the bank holding company level. The dependent variable,
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑡𝑜𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡, is borrower’s distance-to-default at the quarter of loan origination. Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 and 𝐶𝐴𝑇𝐹𝐼𝑁𝑡
are contemporaneous systemic risks at the quarter of loan origination. Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 and 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑡𝑜𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡−1 are
one quarter lagged bank systemic risk and borrower distance-to-default. The vectors of variables Loan Controls, Bank
Controls, and Borrower Controls contain loan, bank, and firm-specific control variables from the quarter of loan
origination. 𝑅𝑒𝑐𝑒𝑠𝑠𝑖𝑜𝑛𝑡 is a dummy variable, which is equal to 1 if the loan is originated at an economic recession
quarter defined by National Bureau of Economic Research, and 0 otherwise. 𝐸𝑎𝑟𝑙𝑦𝑊𝑎𝑟𝑛𝑡 is a dummy variable, which
is equal to 1 when CATFIN exceeds its early warning level at the quarter of loan origination, and 0 otherwise. For each
quarter t, the early warning level is calculated as the median CATFIN using all observations up to quarter t in which the
three-month moving average Chicago Fed National Activity Index (CFNAI-MA3) falls below -0.7. ln(Deal Amount) is
the natural logarithm of the package size (in millions). ln(Maturity) is the natural log of the maturity of the package (in
months), where deal maturity is the weighted average maturity of all facilities in the package. ln(Number of Leads) is
the natural logarithm of the number of lead lenders in the deal syndicate. Secured is a dummy variable that takes a value
of 1 if at least one facility in the package is secured, and 0 otherwise. 𝑙𝑛(𝐵𝑎𝑛𝑘𝑇𝑜𝑡𝑎𝑙𝐴𝑠𝑠𝑒𝑡𝑠𝑗,𝑡) is the natural logarithm
of the total assets of the lender at the quarter of loan origination. 𝐵𝑎𝑛𝑘𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑅𝑎𝑡𝑖𝑜𝑗,𝑡 is defined as the bank’s total
capital over total assets. 𝐵𝑎𝑛𝑘𝑅𝑒𝑡𝑢𝑟𝑛𝑜𝑛𝐸𝑞𝑢𝑖𝑡𝑦𝑗,𝑡 is defined as bank net income over book equity.
𝑙𝑛(𝐵𝑜𝑟𝑟𝑜𝑤𝑒𝑟𝑇𝑜𝑡𝑎𝑙𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡) is defined as the natural logarithm of borrower’s total assets (in billions);
𝐵𝑜𝑟𝑟𝑜𝑤𝑒𝑟𝑇𝑎𝑛𝑔𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑖,𝑡 is defined as total property, plant, and equipment divided by total assets;
𝐵𝑜𝑟𝑟𝑜𝑤𝑒𝑟𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖,𝑡 is defined as borrower’s total debt divided by total assets. 𝐿𝑒𝑛𝑑𝑖𝑛𝑔𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑠ℎ𝑖𝑝𝑖,𝑗,𝑡 is a
lending relationship measure borrowed from Bharath et al. (2007). The lending relationship between borrower i and
bank j is defined as the dollar amount of loans to borrower i by bank j in last 5 years over the total dollar amount of
loans by borrower i in last 5 years. 𝐺𝐷𝑃𝐺𝑟𝑜𝑤𝑡ℎ𝑡 is quarterly growth rate of US quarterly GDP per capita. Standard
errors are in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
49
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑡𝑜𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑗,𝑘,𝑡
I
II
III
IV
V
VI
VII
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 -0.146∗∗∗ -0.077∗∗∗ -0.144∗∗∗ -0.078∗∗∗ -0.203∗∗∗ -0.200∗∗∗ -0.195∗∗∗
(0.005) (0.006) (0.006) (0.008) (0.015) (0.008) (0.011)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 -0.029∗∗∗ -0.070∗∗∗ -0.031∗∗∗ -0.071∗∗∗ -0.050∗∗∗ -0.010 -0.020∗∗
(0.006) (0.006) (0.008) (0.008) (0.009) (0.008) (0.008)
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑡𝑜𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡−1 0.965∗∗∗ 0.966∗∗∗ 0.953∗∗∗ 0.953∗∗∗ 0.954∗∗∗ 0.954∗∗∗ 0.954∗∗∗
(0.002) (0.002) (0.003) (0.003) (0.003) (0.003) (0.003)
𝐶𝐴𝑇𝐹𝐼𝑁𝑡 -0.469∗∗∗ -0.452∗∗∗ -0.613∗∗∗
(0.023) (0.030) (0.034)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝐶𝐴𝑇𝐹𝐼𝑁𝑡 0.027∗∗∗
(0.003)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝑅𝑒𝑐𝑒𝑠𝑠𝑖𝑜𝑛𝑡 0.103∗∗∗
(0.011)
𝑅𝑒𝑐𝑒𝑠𝑠𝑖𝑜𝑛𝑡 -1.037∗∗∗
(0.125)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝐸𝑎𝑟𝑙𝑦𝑊𝑎𝑟𝑛𝑡 0.105∗∗∗
(0.012)
𝐸𝑎𝑟𝑙𝑦𝑊𝑎𝑟𝑛𝑡 -1.180∗∗∗
(0.083)
𝑙𝑛(𝐷𝑒𝑎𝑙𝐴𝑚𝑜𝑢𝑛𝑡)𝑘,𝑡 0.002 0.004 0.003 0.001 0.006
(0.018) (0.017) (0.017) (0.018) (0.017)
𝑙𝑛(𝑀𝑎𝑡𝑢𝑟𝑖𝑡𝑦)𝑘,𝑡 0.045∗∗ 0.038∗ 0.043∗ 0.052∗∗ 0.051∗∗
(0.023) (0.023) (0.023) (0.023) (0.023)
𝑙𝑛(𝑁𝑢𝑚𝑏𝑒𝑟𝑜𝑓𝐿𝑒𝑎𝑑𝑠)𝑘,𝑡 0.083∗∗∗ 0.079∗∗∗ 0.080∗∗∗ 0.084∗∗∗ 0.075∗∗∗
(0.022) (0.022) (0.022) (0.022) (0.022)
𝑆𝑒𝑐𝑢𝑟𝑒𝑑𝑘,𝑡 0.010 0.005 0.003 0.007 0.005
(0.027) (0.027) (0.027) (0.027) (0.027)
𝑙𝑛(𝐵𝑎𝑛𝑘𝑇𝑜𝑡𝑎𝑙𝐴𝑠𝑠𝑒𝑡𝑠)𝑗,𝑡 0.019 0.026 0.020 0.010 0.011
(0.047) (0.047) (0.047) (0.047) (0.047)
𝐵𝑎𝑛𝑘𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑅𝑎𝑡𝑖𝑜𝑗,𝑡 0.128 -0.765 -0.816 -0.281 -0.043
(1.336) (1.331) (1.328) (1.332) (1.329)
𝐵𝑎𝑛𝑘𝑅𝑒𝑡𝑢𝑟𝑛𝑜𝑛𝐸𝑞𝑢𝑖𝑡𝑦𝑗,𝑡 0.151 0.264 0.547∗∗ 0.584∗∗ 0.663∗∗∗
(0.254) (0.253) (0.254) (0.257) (0.256)
𝐵𝑜𝑟𝑟𝑜𝑤𝑒𝑟𝑇𝑎𝑛𝑔𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑗,𝑡 -0.228∗∗∗ -0.246∗∗∗ -0.246∗∗∗ -0.229∗∗∗ -0.237∗∗∗
(0.055) (0.055) (0.055) (0.055) (0.055)
𝐵𝑜𝑟𝑟𝑜𝑤𝑒𝑟𝑆𝑖𝑧𝑒𝑖,𝑡 -0.005 -0.007 -0.007 -0.005 -0.008
(0.014) (0.014) (0.014) (0.014) (0.014)
𝐵𝑜𝑟𝑟𝑜𝑤𝑒𝑟𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖,𝑡 -0.563∗∗∗ -0.561∗∗∗ -0.564∗∗∗ -0.562∗∗∗ -0.548∗∗∗
(0.069) (0.068) (0.068) (0.068) (0.068)
𝐿𝑒𝑛𝑑𝑖𝑛𝑔𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑠ℎ𝑖𝑝𝑖,𝑗,𝑡 -0.013 -0.013 -0.015 -0.013 -0.016
(0.027) (0.026) (0.026) (0.026) (0.026)
𝐺𝐷𝑃𝐺𝑟𝑜𝑤𝑡ℎ𝑡 0.054∗∗∗ 0.062∗∗∗ 0.109∗∗∗ 0.113∗∗∗
(0.018) (0.018) (0.019) (0.019)
Observations 35219 35219 22595 22595 22595 22595 22595
R2 0.901 0.902 0.893 0.894 0.894 0.893 0.894
Adjusted R2 0.901 0.902 0.892 0.893 0.894 0.893 0.893
Industry fixed effects Yes Yes Yes Yes Yes Yes Yes
Lender fixed effects Yes Yes Yes Yes Yes Yes Yes
Year fixed effects Yes Yes Yes Yes Yes Yes Yes
50
Table 5
Two-Stage Least Square Regressions: Underidentification Test and Exogeneity Test
Table 5 reports the Anderson LM test statistic for tests of identification and Sargan statistics for test of exogeneity
for the 2SLS regressions. The null hypothesis for the test of identification is that the instruments and endogenous
variables are not correlated and, in addition, that the overidentifying restrictions are valid. Sargan’s chi-square statistic
tests the joint null hypothesis that the excluded instruments are valid instruments (i.e., uncorrelated with the error
term) and correctly excluded from the estimated equation. Each column corresponds to the column of same number
in Table 5 and Table 6.
I II III IV V VI VII
Underidentification Test (Anderson’s LM Statistic)
33.326 33.203
29.124
29.029
28.867
29.112
29.296
Chi-square P-value 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Sargan Statistic
(Instrument Exogeneity Test)
0.006
0.012
0.438
0.434
0.509
0.386
0.329
Chi-square P-value 0.936 0.913 0.501 0.510 0.476 0.534 0.566
51
Table 6
Two-Stage Least Square Regressions: First Stage
Table 6 reports the coefficients estimates from the first stage of the two-stage lease square regressions. The dependent
variable is 𝐿𝑒𝑛𝑑𝑖𝑛𝑔𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑠ℎ𝑖𝑝𝑖,𝑗,𝑡, defined as the dollar amount of loans to borrower i by bank j in last 5 years over
the total dollar amount of loans by borrower i in last 5 years. The two instrument variables are geographic distance and
number of banks in the state of the borrower. Geographic distance measured as the distance in thousand kilometers
between the location of the firm and the location of the financial top holder of the lending bank at the quarter of loan
origination. The number of banks is measured as the number of financial institutions that filed Call Report during the
quarter of loan origination in the borrower’s state. Each column in Table 5 corresponds to the column with same number
in Table 6, which presents the second stage of the two-stage least square regressions. Standard errors are in parentheses.
*, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
52
𝐿𝑒𝑛𝑑𝑖𝑛𝑔𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑠ℎ𝑖𝑝𝑖,𝑗,𝑡
I
II
III
IV
V
VI
VII
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑖,𝑗 -0.031∗∗∗ -0.030∗∗∗ -0.029∗∗∗ -0.029∗∗∗ -0.029∗∗∗ -0.029∗∗∗ -0.029∗∗∗
(0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006)
𝑁𝑜. 𝑜𝑓𝐵𝑎𝑛𝑘𝑠𝑖 -0.037 -0.036 -0.022 -0.022 -0.023 -0.023 -0.023
(0.025) (0.025) (0.025) (0.025) (0.025) (0.025) (0.025)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 -0.005 -0.004 -0.005 -0.004 -0.009 -0.004 -0.011∗
(0.004) (0.005) (0.004) (0.005) (0.010) (0.005) (0.006)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 0.007∗∗ 0.007∗ 0.006∗ 0.006∗ 0.005 0.006 0.006∗
(0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004)
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑡𝑜𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡−1 0.000 0.000 -0.000 -0.000 -0.000 -0.000 -0.000
(0.001) (0.001) (0.002) (0.002) (0.002) (0.002) (0.002)
𝐶𝐴𝑇𝐹𝐼𝑁𝑡 -0.007 -0.005 -0.017
(0.013) (0.013) (0.023)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝐶𝐴𝑇𝐹𝐼𝑁𝑡 0.002
(0.003)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝑅𝑒𝑐𝑒𝑠𝑠𝑖𝑜𝑛𝑡 -0.005
(0.009)
𝑅𝑒𝑐𝑒𝑠𝑠𝑖𝑜𝑛𝑡 0.078
(0.073)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝐸𝑎𝑟𝑙𝑦𝑊𝑎𝑟𝑛𝑡 0.008
(0.007)
𝐸𝑎𝑟𝑙𝑦𝑊𝑎𝑟𝑛𝑡 -0.025
(0.046)
𝑙𝑛(𝐷𝑒𝑎𝑙𝐴𝑚𝑜𝑢𝑛𝑡)𝑘,𝑡 0.029∗∗∗ 0.029∗∗∗ 0.029∗∗∗ 0.029∗∗∗ 0.029∗∗∗
(0.010) (0.010) (0.010) (0.010) (0.010)
𝑙𝑛(𝑀𝑎𝑡𝑢𝑟𝑖𝑡𝑦)𝑘,𝑡 -0.027∗∗ -0.027∗∗ -0.027∗∗ -0.028∗∗ -0.027∗∗
(0.012) (0.012) (0.012) (0.012) (0.012)
𝑙𝑛(𝑁𝑢𝑚𝑏𝑒𝑟𝑜𝑓𝐿𝑒𝑎𝑑𝑠)𝑘,𝑡 -0.123∗∗∗ -0.123∗∗∗ -0.123∗∗∗ -0.123∗∗∗ -0.123∗∗∗
(0.012) (0.012) (0.012) (0.012) (0.012)
𝑆𝑒𝑐𝑢𝑟𝑒𝑑𝑘,𝑡 -0.023 -0.023 -0.023 -0.023 -0.023
(0.014) (0.014) (0.014) (0.014) (0.014)
𝐵𝑜𝑟𝑟𝑜𝑤𝑒𝑟𝑇𝑎𝑛𝑔𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑗,𝑡 -0.020 -0.020 -0.020 -0.021 -0.020
(0.028) (0.028) (0.028) (0.028) (0.028)
𝐵𝑜𝑟𝑟𝑜𝑤𝑒𝑟𝑆𝑖𝑧𝑒𝑖,𝑡 0.022∗∗∗ 0.021∗∗∗ 0.021∗∗∗ 0.022∗∗∗ 0.022∗∗∗
(0.008) (0.008) (0.008) (0.008) (0.008)
𝐵𝑜𝑟𝑟𝑜𝑤𝑒𝑟𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖,𝑡 0.027 0.028 0.027 0.026 0.026
(0.036) (0.036) (0.036) (0.036) (0.036)
𝑙𝑛(𝐵𝑎𝑛𝑘𝑇𝑜𝑡𝑎𝑙𝐴𝑠𝑠𝑒𝑡𝑠)𝑗,𝑡 -0.052∗∗ -0.052∗∗ -0.053∗∗ -0.052∗∗ -0.054∗∗
(0.026) (0.026) (0.026) (0.026) (0.026)
𝐵𝑎𝑛𝑘𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑅𝑎𝑡𝑖𝑜𝑗,𝑡 -0.294 -0.297 -0.292 -0.288 -0.303
(0.648) (0.648) (0.648) (0.649) (0.648)
𝐵𝑎𝑛𝑘𝑅𝑒𝑡𝑢𝑟𝑛𝑜𝑛𝐸𝑞𝑢𝑖𝑡𝑦𝑗,𝑡 -0.170 -0.168 -0.163 -0.181 -0.149
(0.123) (0.124) (0.124) (0.125) (0.125)
𝐺𝐷𝑃𝐺𝑟𝑜𝑤𝑡ℎ𝑡 0.003 0.003 0.004 0.005 0.002
(0.009) (0.009) (0.009) (0.009) (0.009)
Observations 4909 4909 4909 4909 4909 4909 4909
𝑅2 0.110 0.110 0.135 0.135 0.135 0.135 0.136
Adjusted 𝑅2 0.093 0.093 0.116 0.116 0.116 0.116 0.116
Industry fixed effects Yes Yes Yes Yes Yes Yes Yes
Lender fixed effects Yes Yes Yes Yes Yes Yes Yes
Year fixed effects Yes Yes Yes Yes Yes Yes Yes
53
Table 7
Two-Stage Least Square Regressions: Second Stage
Table 7 reports the coefficients estimates from the second stage of the two-stage lease square regressions. The dependent
variable is 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑡𝑜𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡 , the borrower’s distance-to-default at the quarter of loan origination.
𝐿𝑒𝑛𝑑𝑖𝑛𝑔𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑠ℎ𝑖𝑝𝑖,𝑗,𝑡 is the fitted value from the first stage regression presented in Table 6. All other variables are
defined the same as Table 4, and their definition can be found in appendix. Standard errors are in parentheses. *, **, and
*** indicate significance at the 10%, 5%, and 1% levels, respectively.
54
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑡𝑜𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑗,𝑘,𝑡
I
II
III
IV
V
VI
VII
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 -0.192∗∗∗ -0.101∗∗∗ -0.195∗∗∗ -0.107∗∗∗ -0.200∗∗∗ -0.199∗∗∗ -0.137∗∗∗
(0.021) (0.023) (0.022) (0.023) (0.047) (0.023) (0.032)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 -0.018 -0.037∗∗ -0.014 -0.033∗ -0.048∗∗∗ -0.016 -0.030∗
(0.018) (0.017) (0.018) (0.018) (0.018) (0.019) (0.018)
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑡𝑜𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡−1 0.949∗∗∗ 0.952∗∗∗ 0.937∗∗∗ 0.940∗∗∗ 0.941∗∗∗ 0.939∗∗∗ 0.940∗∗∗
(0.007) (0.007) (0.008) (0.007) (0.007) (0.008) (0.007)
𝐶𝐴𝑇𝐹𝐼𝑁𝑡 -0.555∗∗∗ -0.540∗∗∗ -0.748∗∗∗
(0.061) (0.062) (0.109)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝐶𝐴𝑇𝐹𝐼𝑁𝑡 0.033∗∗
(0.014)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝑅𝑒𝑐𝑒𝑠𝑠𝑖𝑜𝑛𝑡 0.059
(0.042)
𝑅𝑒𝑐𝑒𝑠𝑠𝑖𝑜𝑛𝑡 -0.788∗∗
(0.358)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝐸𝑎𝑟𝑙𝑦𝑊𝑎𝑟𝑛𝑡 0.015
(0.035)
𝐸𝑎𝑟𝑙𝑦𝑊𝑎𝑟𝑛𝑡 -0.745∗∗∗
(0.219)
𝑙𝑛(𝐷𝑒𝑎𝑙𝐴𝑚𝑜𝑢𝑛𝑡)𝑘,𝑡 -0.070 -0.062 -0.060 -0.073 -0.059
(0.053) (0.052) (0.051) (0.053) (0.052)
𝑙𝑛(𝑀𝑎𝑡𝑢𝑟𝑖𝑡𝑦)𝑘,𝑡 0.178∗∗∗ 0.156∗∗ 0.155∗∗ 0.193∗∗∗ 0.173∗∗∗
(0.063) (0.061) (0.061) (0.063) (0.062)
𝑙𝑛(𝑁𝑢𝑚𝑏𝑒𝑟𝑜𝑓𝐿𝑒𝑎𝑑𝑠)𝑘,𝑡 0.388∗∗∗ 0.366∗∗∗ 0.360∗∗∗ 0.396∗∗∗ 0.372∗∗∗
(0.124) (0.121) (0.121) (0.125) (0.122)
𝑆𝑒𝑐𝑢𝑟𝑒𝑑𝑘,𝑡 -0.018 -0.010 -0.012 -0.021 -0.024
(0.071) (0.069) (0.069) (0.071) (0.070)
𝑙𝑛(𝐵𝑎𝑛𝑘𝑇𝑜𝑡𝑎𝑙𝐴𝑠𝑠𝑒𝑡𝑠)𝑗,𝑡 -0.029 0.020 0.004 -0.038 0.003
(0.132) (0.129) (0.129) (0.133) (0.132)
𝐵𝑎𝑛𝑘𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑅𝑎𝑡𝑖𝑜𝑗,𝑡 0.205 -0.211 -0.139 -0.244 0.490
(3.095) (3.022) (3.006) (3.120) (3.059)
𝐵𝑎𝑛𝑘𝑅𝑒𝑡𝑢𝑟𝑛𝑜𝑛𝐸𝑞𝑢𝑖𝑡𝑦𝑗,𝑡 -0.062 0.174 0.251 0.125 0.036
(0.600) (0.586) (0.583) (0.612) (0.598)
𝐵𝑜𝑟𝑟𝑜𝑤𝑒𝑟𝑇𝑎𝑛𝑔𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑗,𝑡 -0.356∗∗∗ -0.384∗∗∗ -0.383∗∗∗ -0.359∗∗∗ -0.373∗∗∗
(0.136) (0.133) (0.132) (0.137) (0.134)
𝐵𝑜𝑟𝑟𝑜𝑤𝑒𝑟𝑆𝑖𝑧𝑒𝑖,𝑡 -0.087∗∗ -0.089∗∗ -0.088∗∗ -0.088∗∗ -0.092∗∗
(0.042) (0.041) (0.041) (0.043) (0.042)
𝐵𝑜𝑟𝑟𝑜𝑤𝑒𝑟𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖,𝑡 -0.650∗∗∗ -0.633∗∗∗ -0.635∗∗∗ -0.636∗∗∗ -0.634∗∗∗
(0.172) (0.168) (0.167) (0.174) (0.170)
𝐿𝑒𝑛𝑑𝑖𝑛𝑔𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑠ℎ𝑖𝑝𝑖,𝑗,𝑡 2.299∗∗∗ 2.163∗∗∗ 2.413∗∗∗ 2.260∗∗∗ 2.215∗∗ 2.477∗∗∗ 2.348∗∗∗
(0.817) (0.800) (0.889) (0.869) (0.867) (0.896) (0.876)
𝐺𝐷𝑃𝐺𝑟𝑜𝑤𝑡ℎ𝑡 0.141∗∗∗ 0.105∗∗ 0.123∗∗∗ 0.189∗∗∗
(0.044) (0.043) (0.043) (0.044)
Observations 4909 4909 4909 4909 4909 4909 4909
R 2 0.845 0.852 0.844 0.851 0.853 0.842 0.848
Adjusted R 2 0.842 0.849 0.841 0.848 0.850 0.838 0.844
Industry fixed effects Yes Yes Yes Yes Yes Yes Yes
Lender fixed effects Yes Yes Yes Yes Yes Yes Yes
Year fixed effects Yes Yes Yes Yes Yes Yes Yes
55
Table 8
Lead-lag Effect Analysis
Table 8 reports the coefficients estimates from the lead-lag effect regressions. The dependent variable in Column I, II,
III, and IV is 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑡𝑜𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡, the borrower’s distance-to-default at the quarter of loan origination, and the
dependent variable in Column V, VI, VII, and VII is 𝐶𝑜𝑉𝑎𝑅𝑗,𝑡, bank’s systemic risk at the quarter of loan origination.
Standard errors are in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑡𝑜𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡 Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡
I
II
III
IV
V
VI
VII
VIII
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 -0.113∗∗∗ -0.085∗∗∗ -0.121∗∗∗ -0.093∗∗∗ 0.559∗∗∗ 0.373∗∗∗ 0.586∗∗∗ 0.403∗∗∗
(0.007) (0.009) (0.007) (0.009) (0.008) (0.009) (0.008) (0.009)
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑡𝑜𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡−1 0.965∗∗∗ 0.965∗∗∗ 0.946∗∗∗ 0.945∗∗∗ -0.001 0.004 0.001 0.006∗∗
(0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003)
𝐶𝐴𝑇𝐹𝐼𝑁𝑡 -0.175∗∗∗ -0.176∗∗∗ 1.170∗∗∗ 1.166∗∗∗
(0.027) (0.027) (0.030) (0.029)
𝑙𝑛(𝐷𝑒𝑎𝑙𝐴𝑚𝑜𝑢𝑛𝑡)𝑘,𝑡 0.021 0.023 -0.008 -0.019
(0.017) (0.017) (0.019) (0.018)
𝑙𝑛(𝑀𝑎𝑡𝑢𝑟𝑖𝑡𝑦)𝑘,𝑡 0.051∗∗ 0.045∗ -0.097∗∗∗ -0.054∗∗
(0.023) (0.023) (0.026) (0.025)
𝑙𝑛(𝑁𝑢𝑚𝑏𝑒𝑟𝑜𝑓𝐿𝑒𝑎𝑑𝑠)𝑘,𝑡 0.096∗∗∗ 0.093∗∗∗ -0.043∗ -0.025
(0.023) (0.023) (0.025) (0.024)
𝑆𝑒𝑐𝑢𝑟𝑒𝑑𝑘,𝑡 0.001 0.005 0.036 0.005
(0.027) (0.027) (0.030) (0.029)
𝑙𝑛(𝐵𝑎𝑛𝑘𝑇𝑜𝑡𝑎𝑙𝐴𝑠𝑠𝑒𝑡𝑠)𝑗,𝑡 -0.034 -0.029 0.305∗∗∗ 0.276∗∗∗
(0.049) (0.048) (0.054) (0.052)
𝐵𝑎𝑛𝑘𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑅𝑎𝑡𝑖𝑜𝑗,𝑡 1.369 1.061 -0.803∗∗∗ -8.764∗∗∗
(1.263) (1.263) (1.397) (1.350)
𝐵𝑎𝑛𝑘𝑅𝑒𝑡𝑢𝑟𝑛𝑜𝑛𝐸𝑞𝑢𝑖𝑡𝑦𝑗,𝑡 1.213∗∗∗ 1.287∗∗∗ -5.169∗∗∗ -5.660∗∗∗
(0.246) (0.246) (0.272) (0.263)
𝐵𝑜𝑟𝑟𝑜𝑤𝑒𝑟𝑇𝑎𝑛𝑔𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑗,𝑡 -0.132∗∗ -0.134∗∗ -0.008 0.008
(0.056) (0.056) (0.062) (0.059)
𝐵𝑜𝑟𝑟𝑜𝑤𝑒𝑟𝑆𝑖𝑧𝑒𝑖,𝑡 -0.026∗ -0.025∗ 0.033∗∗ 0.025∗
(0.013) (0.013) (0.015) (0.014)
𝐵𝑜𝑟𝑟𝑜𝑤𝑒𝑟𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖,𝑡 -0.843∗∗∗ -0.847∗∗∗ 0.072 0.097
(0.064) (0.064) (0.071) (0.069)
𝐿𝑒𝑛𝑑𝑖𝑛𝑔𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑠ℎ𝑖𝑝𝑖,𝑗,𝑡 0.008 0.006 -0.039 -0.029
(0.027) (0.027) (0.030) (0.029)
𝐺𝐷𝑃𝐺𝑟𝑜𝑤𝑡ℎ𝑡 0.092∗∗∗ 0.086∗∗∗ -0.461∗∗∗ -0.427∗∗∗
(0.020) (0.020) (0.022) (0.022)
Observations 22464 22464 22464 22464 22464 22464 22464 22464
R 2 0.889 0.889 0.890 0.890 0.622 0.647 0.637 0.662
Adjusted R 2 0.888 0.889 0.890 0.890 0.620 0.645 0.636 0.660
Industry fixed effects Yes Yes Yes Yes Yes Yes Yes Yes
Lender fixed effects Yes Yes Yes Yes Yes Yes Yes Yes
Year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes
56
Table 9
Within-loan Regressions
Table 9 reports the coefficients estimate from the within-loan regressions:
𝐵𝑎𝑛𝑘𝐴𝑙𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛𝑖,𝑗,𝑘,𝑡 = 𝛼𝑘 + 𝛼1Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 + 𝛼2Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 × 𝑅𝑖𝑠𝑘𝐷𝑢𝑚𝑚𝑦 + 𝐿𝑒𝑎𝑑𝐿𝑒𝑛𝑑𝑒𝑟𝐷𝑢𝑚𝑚𝑦𝑗,𝑘,𝑡 +
𝐿𝑒𝑛𝑑𝑖𝑛𝑔𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑠ℎ𝑖𝑝𝑖,𝑗,𝑡 + 𝛼3𝐵𝑎𝑛𝑘𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑗,𝑡−1 +𝐵𝑎𝑛𝑘𝐹𝐸 + 𝜀𝑖,𝑗,𝑘,𝑡,(10)
Panel A reports the package level regressions, and Panel B reports the facility level regressions. In each panel, Columns
I, II, and III report the coefficients of regressions using contemporaneous systemic risk, while Columns IV, V, VI report
the coefficients of regression using lagged systemic risk. Subscript i, j, k, and t indicate the borrowing firm, the bank,
the packages/facilities, and the time (quarter) respectively, and 𝛼𝑘 denotes the package/facility fixed effects. Standard
errors are clustered by bank. The dependent variable in all regressions is the bank allocation share in percentages at
either package or facility level. 𝑅𝑖𝑠𝑘𝐷𝑢𝑚𝑚𝑦 denotes how extreme a borrower’s default risk is compared with
other borrowers in the same year. It can be 𝑅𝑖𝑠𝑘𝐷𝑢𝑚𝑚𝑦1 , 𝑅𝑖𝑠𝑘𝐷𝑢𝑚𝑚𝑦2 , or 𝑅𝑖𝑠𝑘𝐷𝑢𝑚𝑚𝑦3 . 𝑅𝑖𝑠𝑘𝐷𝑢𝑚𝑚𝑦1
equals 1 if the borrower’s distance-to-default falls into the 1st, 2nd, 3rd, 4th, and 5th quantiles (risky), and 0 if it falls into
the 6th, 7th, 8th, 9th, and 10th quantiles (less risky). 𝑅𝑖𝑠𝑘𝐷𝑢𝑚𝑚𝑦2 equals 1 if the borrower’s distance-to-default falls into
the 1st, 2nd, 3rd, and 4th quantiles (risky), and 0 if it falls into the 7th, 8th, 9th, and 10th quantiles (less risky). 𝑅𝑖𝑠𝑘𝐷𝑢𝑚𝑚𝑦3
equals 1 if the borrower’s distance-to-default falls into the 1st, 2nd, and 3rd quantiles (risky), and 0 if it falls into the 8th,
9th, and 10th quantiles (less risky). 𝑅𝑖𝑠𝑘𝐷𝑢𝑚𝑚𝑦2 and 𝑅𝑖𝑠𝑘𝐷𝑢𝑚𝑚𝑦3 only focus on borrowers whose default risks are
either very high or very low. Columns I and IV interact Δ𝐶𝑜𝑉𝑎𝑅 with 𝑅𝑖𝑠𝑘𝐷𝑢𝑚𝑚𝑦1, Columns II and V interact
Δ𝐶𝑜𝑉𝑎𝑅 with 𝑅𝑖𝑠𝑘𝐷𝑢𝑚𝑚𝑦2 , and Columns III and VI interact Δ𝐶𝑜𝑉𝑎𝑅 with 𝑅𝑖𝑠𝑘𝐷𝑢𝑚𝑚𝑦3 .
𝐿𝑒𝑎𝑑𝐿𝑒𝑛𝑑𝑒𝑟𝐷𝑢𝑚𝑚𝑦𝑗,𝑘,𝑡 equals 1 if bank j is a lead lender in the package/facility k. I define a bank as a lead lender if
its lender credit variable is “Yes” in Dealscan. 𝐿𝑒𝑛𝑑𝑖𝑛𝑔𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑠ℎ𝑖𝑝𝑖,𝑗,𝑡 controls for the intensity of past lending
relationships between borrower i and bank j. It is defined as the dollar amount of loans to borrower i by bank j in the
last 5 years over the total dollar amount of loans by borrower i in last 5 years. 𝐵𝑎𝑛𝑘𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑗,𝑡−1 includes an array of
bank control variables including the natural logarithm of bank total assets (in millions), bank capital ratio, bank
return on equity, bank liquidity, bank loan charge-offs, bank loan loss allowance, and bank risk-weighted assets.
All bank control variables are lagged by one quarter. 𝐵𝑎𝑛𝑘𝐹𝐸 denotes bank fixed effects. Standard errors are in
parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
57
Panel A: Package Level 𝐵𝑎𝑛𝑘𝐴𝑙𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛𝑖,𝑗,𝑘,𝑡
I II III IV V VI
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 -0.096∗ -0.122∗∗ -0.189∗∗∗
(0.050) (0.059) (0.060) Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 -0.129∗∗ -0.133∗∗ -0.180∗∗
(0.050) (0.058) (0.067)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝑅𝑖𝑠𝑘𝐷𝑢𝑚𝑚𝑦1 0.204∗∗∗
(0.053) Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝑅𝑖𝑠𝑘𝐷𝑢𝑚𝑚𝑦2 0.245∗∗∗
(0.063) Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝑅𝑖𝑠𝑘𝐷𝑢𝑚𝑚𝑦3 0.387∗∗∗
(0.078) Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 × 𝑅𝑖𝑠𝑘𝐷𝑢𝑚𝑚𝑦1 0.204∗∗∗
(0.053) Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 × 𝑅𝑖𝑠𝑘𝐷𝑢𝑚𝑚𝑦2 0.227∗∗∗
(0.061) Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 × 𝑅𝑖𝑠𝑘𝐷𝑢𝑚𝑚𝑦3 0.348∗∗∗
(0.074)
𝐿𝑒𝑎𝑑𝐿𝑒𝑛𝑑𝑒𝑟𝐷𝑢𝑚𝑚𝑦𝑗,𝑘,𝑡 5.909∗∗∗ 6.137∗∗∗ 6.399∗∗∗ 5.908∗∗∗ 6.136∗∗∗ 6.401∗∗∗
(0.451) (0.436) (0.513) (0.451) (0.438) (0.515)
𝐿𝑒𝑛𝑑𝑖𝑛𝑔𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑠ℎ𝑖𝑝𝑖,𝑗,𝑡 0.709∗∗∗ 0.637∗∗∗ 0.772∗∗∗ 0.705∗∗∗ 0.633∗∗∗ 0.766∗∗∗
(0.105) (0.115) (0.096) (0.104) (0.114) (0.096)
ln(𝐵𝑎𝑛𝑘𝑇𝑜𝑡𝑎𝑙𝐴𝑠𝑠𝑒𝑡𝑠)𝑗,𝑡−1 0.570∗∗ 0.654∗∗ 0.723∗∗ 0.572∗∗ 0.662∗∗ 0.735∗∗
(0.280) (0.280) (0.334) (0.279) (0.279) (0.333)
𝐵𝑎𝑛𝑘𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑅𝑎𝑡𝑖𝑜𝑗,𝑡−1 -3.785 -4.343 -3.041 -3.774 -4.449 -3.197
(4.380) (4.478) (5.675) (4.453) (4.496) (5.761)
𝐵𝑎𝑛𝑘𝑅𝑒𝑡𝑢𝑟𝑛𝑜𝑛𝐸𝑞𝑢𝑖𝑡𝑦𝑗,𝑡−1 -0.696 0.639 0.464 -0.826 0.507 0.361
(1.316) (1.433) (1.770) (1.308) (1.420) (1.810)
𝐵𝑎𝑛𝑘𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦𝑗,𝑡−1 -2.752∗ -3.260∗ -2.785 -2.676 -3.216∗ -2.773
(1.591) (1.835) (2.186) (1.600) (1.846) (2.198)
𝐵𝑎𝑛𝑘𝐿𝑜𝑎𝑛𝐶ℎ𝑎𝑟𝑔𝑒 − 𝑂𝑓𝑓𝑠𝑗,𝑡−1 7.419 31.068 49.596 9.400 33.031 52.783
(29.020) (29.913) (33.254) (28.611) (29.510) (33.243)
𝐵𝑎𝑛𝑘𝐿𝑜𝑎𝑛𝐿𝑜𝑠𝑠𝐴𝑙𝑙𝑜𝑤𝑎𝑛𝑐𝑒𝑗,𝑡−1 1.236 -0.308 1.337 2.826 0.734 1.528
(23.152) (24.910) (30.013) (23.078) (25.021) (30.085)
𝐵𝑎𝑛𝑘𝑅𝑖𝑠𝑘 −𝑊𝑒𝑖𝑔ℎ𝑡𝑒𝑑𝐴𝑠𝑠𝑒𝑡𝑠𝑗,𝑡−1 3.235∗ 3.571∗ 4.505∗ 3.222∗ 3.548∗ 4.492∗
(1.614) (1.910) (2.267) (1.641) (1.931) (2.287)
Observations 12647 10065 7513 12647 10065 7513
R 2 0.855 0.853 0.850 0.855 0.853 0.850
Adjusted R 2 0.821 0.818 0.812 0.821 0.818 0.812
Package fixed effects Yes Yes Yes Yes Yes Yes
Lender fixed effects Yes Yes Yes Yes Yes Yes
58
Panel B: Facility Level 𝐵𝑎𝑛𝑘𝐴𝑙𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛𝑖,𝑗,𝑘,𝑡
I II III IV V VI
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 -0.153∗∗∗ -0.151∗∗∗ -0.218∗∗∗
(0.047) (0.055) (0.069) Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 -0.176∗∗∗ -0.183∗∗∗ -0.217∗∗
(0.051) (0.063) (0.082)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝑅𝑖𝑠𝑘𝐷𝑢𝑚𝑚𝑦1 0.212∗∗∗
(0.045) Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝑅𝑖𝑠𝑘𝐷𝑢𝑚𝑚𝑦2 0.268∗∗∗
(0.054) Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝑅𝑖𝑠𝑘𝐷𝑢𝑚𝑚𝑦3 0.340∗∗∗
(0.067) Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 × 𝑅𝑖𝑠𝑘𝐷𝑢𝑚𝑚𝑦1 0.202∗∗∗
(0.041) Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 × 𝑅𝑖𝑠𝑘𝐷𝑢𝑚𝑚𝑦2 0.254∗∗∗
(0.049) Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 × 𝑅𝑖𝑠𝑘𝐷𝑢𝑚𝑚𝑦3 0.284∗∗∗
(0.057)
𝐿𝑒𝑎𝑑𝐿𝑒𝑛𝑑𝑒𝑟𝐷𝑢𝑚𝑚𝑦𝑗,𝑘,𝑡 6.065∗∗∗ 6.275∗∗∗ 6.513∗∗∗ 6.064∗∗∗ 6.275∗∗∗ 6.514∗∗∗
(0.436) (0.432) (0.472) (0.436) (0.433) (0.474)
𝐿𝑒𝑛𝑑𝑖𝑛𝑔𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑠ℎ𝑖𝑝𝑖,𝑗,𝑡 0.944∗∗∗ 0.887∗∗∗ 0.872∗∗∗ 0.940∗∗∗ 0.883∗∗∗ 0.863∗∗∗
(0.108) (0.102) (0.146) (0.108) (0.100) (0.146)
ln(𝐵𝑎𝑛𝑘𝑇𝑜𝑡𝑎𝑙𝐴𝑠𝑠𝑒𝑡𝑠)𝑗,𝑡−1 0.181 0.311 0.261 0.189 0.319 0.273
(0.322) (0.308) (0.324) (0.322) (0.308) (0.322)
𝐵𝑎𝑛𝑘𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑅𝑎𝑡𝑖𝑜𝑗,𝑡−1 -11.684∗∗ -12.397∗ -15.468∗∗ -11.504∗∗ -12.374∗ -15.210∗∗
(5.694) (6.425) (6.872) (5.714) (6.354) (6.856)
𝐵𝑎𝑛𝑘𝑅𝑒𝑡𝑢𝑟𝑛𝑜𝑛𝐸𝑞𝑢𝑖𝑡𝑦𝑗,𝑡−1 -2.127 -1.380 -1.504 -2.262 -1.563 -1.578
(1.767) (1.800) (2.211) (1.777) (1.813) (2.265)
𝐵𝑎𝑛𝑘𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦𝑗,𝑡−1 -3.581∗ -3.380∗ -3.044 -3.505∗ -3.293 -2.982
(1.847) (1.954) (2.212) (1.838) (1.962) (2.243)
𝐵𝑎𝑛𝑘𝐿𝑜𝑎𝑛𝐶ℎ𝑎𝑟𝑔𝑒 − 𝑂𝑓𝑓𝑠𝑗,𝑡−1 9.996 32.834 48.991 14.141 36.790 56.327∗
(28.346) (29.436) (32.808) (27.758) (28.913) (31.239)
𝐵𝑎𝑛𝑘𝐿𝑜𝑎𝑛𝐿𝑜𝑠𝑠𝐴𝑙𝑙𝑜𝑤𝑎𝑛𝑐𝑒𝑗,𝑡−1 5.139 7.437 18.002 5.762 9.442 18.053
(28.904) (32.261) (35.329) (28.921) (32.118) (35.152)
𝐵𝑎𝑛𝑘𝑅𝑖𝑠𝑘 −𝑊𝑒𝑖𝑔ℎ𝑡𝑒𝑑𝐴𝑠𝑠𝑒𝑡𝑠𝑗,𝑡−1 4.378∗∗ 5.008∗∗ 7.022∗∗∗ 4.375∗∗ 4.993∗∗ 7.034∗∗∗
(1.777) (1.984) (2.270) (1.798) (2.014) (2.302)
Observations 15785 12556 9383 15785 12556 9383
R 2 0.843 0.844 0.839 0.843 0.844 0.839
Adjusted R 2 0.807 0.807 0.800 0.807 0.807 0.800
Facility fixed effects Yes Yes Yes Yes Yes Yes
Lender fixed effects Yes Yes Yes Yes Yes Yes
59
Table 10
Within-loan Regressions: Robustness Check
Table 10 reports the coefficients estimate from the within-loan regressions:
𝐵𝑎𝑛𝑘𝐴𝑙𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛𝑖,𝑗,𝑘,𝑡
= 𝛼𝑘 + 𝛼1Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 + 𝛼2Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 × 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 − 𝑡𝑜 − 𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡−1 + 𝐿𝑒𝑎𝑑𝐿𝑒𝑛𝑑𝑒𝑟𝐷𝑢𝑚𝑚𝑦𝑗,𝑘,𝑡
+ 𝐿𝑒𝑛𝑑𝑖𝑛𝑔𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑠ℎ𝑖𝑝𝑖,𝑗,𝑡 + 𝛼3𝐵𝑎𝑛𝑘𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑗,𝑡−1 +𝐵𝑎𝑛𝑘𝐹𝐸 + 𝜀𝑖,𝑗,𝑘,𝑡,(11)
where subscripts i, j, k, and t indicate the borrowing firm, the bank, the packages/facilities, and the time (quarter)
respectively, and 𝛼𝑘 denotes the package/facility fixed effects. The dependent variable in all regressions is the bank
allocation share in percentages at either package or facility level. Panel A reports the package level regressions, and
Panel B reports the facility level regressions. In each panel, Columns I and II report the coefficients of regressions using
contemporaneous systemic risk, Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡, while Columns III and IV report the coefficients of regression using lagged
systemic risk, Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1. In Columns I and III, I interact Δ𝐶𝑜𝑉𝑎𝑅 with contemporaneous borrower distance-to-
default, 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 − 𝑡𝑜 − 𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡
, while in Columns II and IV, I interact Δ𝐶𝑜𝑉𝑎𝑅 with 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 − 𝑡𝑜 −
𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡−1
. 𝐿𝑒𝑎𝑑𝐿𝑒𝑛𝑑𝑒𝑟𝐷𝑢𝑚𝑚𝑦𝑗,𝑘,𝑡 equals 1 if bank j is a lead lender in the package/facility k. A bank is defined
as a lead lender if its lender credit variable is “Yes” in Dealscan. 𝐿𝑒𝑛𝑑𝑖𝑛𝑔𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑠ℎ𝑖𝑝𝑖,𝑗,𝑡 controls for the intensity
of past lending relationships between borrower i and bank j. It is defined as the dollar amount of loans to borrower i by
bank j in the last 5 years over the total dollar amount of loans by borrower i in last 5 years. 𝐵𝑎𝑛𝑘𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑗,𝑡−1 includes
an array of bank control variables including the natural logarithm of bank total assets (in millions), bank capital
ratio, bank return on equity, bank liquidity, bank loan charge-offs, bank loan loss allowance, and bank risk-
weighted assets. All bank control variables are lagged by one quarter. 𝐵𝑎𝑛𝑘𝐹𝐸 denotes bank fixed effects.
Standard errors are clustered by bank and are shown in parentheses. *, **, and *** indicate significance at the 10%, 5%,
and 1% levels, respectively.
60
Panel A: Package Level 𝐵𝑎𝑛𝑘𝐴𝑙𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛𝑖,𝑗,𝑘,𝑡
I II III IV
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 0.125∗∗∗ 0.171∗∗∗
(0.026) (0.030)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 0.064∗ 0.078∗∗
(0.033) (0.033)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 − 𝑡𝑜 − 𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡 -0.021∗∗∗
(0.005) Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 − 𝑡𝑜 − 𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡−1 -0.024∗∗∗
(0.005) Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 × 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 − 𝑡𝑜 − 𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡 -0.022∗∗∗
(0.006) Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 × 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 − 𝑡𝑜 − 𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡−1 -0.023∗∗∗
(0.005)
𝐿𝑒𝑎𝑑𝐿𝑒𝑛𝑑𝑒𝑟𝐷𝑢𝑚𝑚𝑦𝑗,𝑘,𝑡 5.914∗∗∗ 5.915∗∗∗ 5.915∗∗∗ 5.917∗∗∗
(0.452) (0.452) (0.452) (0.452)
𝐿𝑒𝑛𝑑𝑖𝑛𝑔𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑠ℎ𝑖𝑝𝑖,𝑗,𝑡 0.710∗∗∗ 0.714∗∗∗ 0.705∗∗∗ 0.706∗∗∗
(0.105) (0.105) (0.103) (0.103)
ln(𝐵𝑎𝑛𝑘𝑇𝑜𝑡𝑎𝑙𝐴𝑠𝑠𝑒𝑡𝑠)𝑗,𝑡−1 0.589∗∗ 0.594∗∗ 0.585∗∗ 0.585∗∗
(0.278) (0.276) (0.278) (0.276)
𝐵𝑎𝑛𝑘𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑅𝑎𝑡𝑖𝑜𝑗,𝑡−1 -3.571 -3.561 -3.354 -3.470
(4.242) (4.230) (4.272) (4.233)
𝐵𝑎𝑛𝑘𝑅𝑒𝑡𝑢𝑟𝑛𝑜𝑛𝐸𝑞𝑢𝑖𝑡𝑦𝑗,𝑡−1 -0.704 -0.792 -0.876 -0.985
(1.249) (1.263) (1.254) (1.255)
𝐵𝑎𝑛𝑘𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦𝑗,𝑡−1 -2.878∗ -2.966∗ -2.685∗ -2.721∗
(1.527) (1.514) (1.543) (1.538)
𝐵𝑎𝑛𝑘𝐿𝑜𝑎𝑛𝐶ℎ𝑎𝑟𝑔𝑒 − 𝑂𝑓𝑓𝑠𝑗,𝑡−1 6.116 6.451 12.241 11.693
(29.829) (30.116) (28.913) (29.293)
𝐵𝑎𝑛𝑘𝐿𝑜𝑎𝑛𝐿𝑜𝑠𝑠𝐴𝑙𝑙𝑜𝑤𝑎𝑛𝑐𝑒𝑗,𝑡−1 3.440 2.170 4.444 4.614
(23.746) (24.336) (23.620) (24.088)
𝐵𝑎𝑛𝑘𝑅𝑖𝑠𝑘 −𝑊𝑒𝑖𝑔ℎ𝑡𝑒𝑑𝐴𝑠𝑠𝑒𝑡𝑠𝑗,𝑡−1 3.099∗ 3.013∗ 3.202∗ 3.125∗
(1.573) (1.559) (1.600) (1.600)
Observations 12647 12647 12647 12647
R 2 0.855 0.855 0.855 0.855
Adjusted R 2 0.821 0.821 0.821 0.821
Package fixed effects Yes Yes Yes Yes
Lender fixed effects Yes Yes Yes Yes
61
Panel B: Facility Level 𝐵𝑎𝑛𝑘𝐴𝑙𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛𝑖,𝑗,𝑘,𝑡
I II III IV
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 0.061∗ 0.112∗∗∗
(0.036) (0.040)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 0.009 0.027
(0.039) (0.039)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 − 𝑡𝑜 − 𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡 -0.018∗∗∗
(0.004) Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 − 𝑡𝑜 − 𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡−1 -0.022∗∗∗
(0.004) Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 × 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 − 𝑡𝑜 − 𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡 -0.019∗∗∗
(0.006) Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 × 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 − 𝑡𝑜 − 𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡−1 -0.022∗∗∗
(0.006)
𝐿𝑒𝑎𝑑𝐿𝑒𝑛𝑑𝑒𝑟𝐷𝑢𝑚𝑚𝑦𝑗,𝑘,𝑡 6.070∗∗∗ 6.071∗∗∗ 6.071∗∗∗ 6.072∗∗∗
(0.437) (0.437) (0.437) (0.436)
𝐿𝑒𝑛𝑑𝑖𝑛𝑔𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑠ℎ𝑖𝑝𝑖,𝑗,𝑡 0.940∗∗∗ 0.945∗∗∗ 0.934∗∗∗ 0.936∗∗∗
(0.106) (0.106) (0.107) (0.106)
ln(𝐵𝑎𝑛𝑘𝑇𝑜𝑡𝑎𝑙𝐴𝑠𝑠𝑒𝑡𝑠)𝑗,𝑡−1 0.203 0.211 0.203 0.205
(0.321) (0.321) (0.322) (0.322)
𝐵𝑎𝑛𝑘𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑅𝑎𝑡𝑖𝑜𝑗,𝑡−1 -11.480∗∗ -11.466∗∗ -11.141∗∗ -11.234∗∗
(5.528) (5.484) (5.511) (5.435)
𝐵𝑎𝑛𝑘𝑅𝑒𝑡𝑢𝑟𝑛𝑜𝑛𝐸𝑞𝑢𝑖𝑡𝑦𝑗,𝑡−1 -2.136 -2.195 -2.271 -2.361
(1.712) (1.710) (1.734) (1.723)
𝐵𝑎𝑛𝑘𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦𝑗,𝑡−1 -3.712∗∗ -3.800∗∗ -3.530∗ -3.569∗
(1.793) (1.788) (1.788) (1.789)
𝐵𝑎𝑛𝑘𝐿𝑜𝑎𝑛𝐶ℎ𝑎𝑟𝑔𝑒 − 𝑂𝑓𝑓𝑠𝑗,𝑡−1 9.042 9.416 16.804 16.405
(28.962) (29.210) (28.052) (28.274)
𝐵𝑎𝑛𝑘𝐿𝑜𝑎𝑛𝐿𝑜𝑠𝑠𝐴𝑙𝑙𝑜𝑤𝑎𝑛𝑐𝑒𝑗,𝑡−1 7.622 6.492 7.720 7.806
(29.299) (29.945) (29.405) (29.900)
𝐵𝑎𝑛𝑘𝑅𝑖𝑠𝑘 −𝑊𝑒𝑖𝑔ℎ𝑡𝑒𝑑𝐴𝑠𝑠𝑒𝑡𝑠𝑗,𝑡−1 4.227∗∗ 4.130∗∗ 4.334∗∗ 4.257∗∗
(1.745) (1.733) (1.759) (1.759)
Observations 15785 15785 15785 15785
R 2 0.843 0.843 0.843 0.843
Adjusted R 2 0.807 0.807 0.807 0.807
Facility fixed effects Yes Yes Yes Yes
Lender fixed effects Yes Yes Yes Yes
62
Table 11
Dynamic Panel GMM Regressions
Table 11 reports the results from dynamic panel GMM regressions. The dependent variable,
𝐵𝑎𝑛𝑘𝑃𝑜𝑟𝑡𝑓𝑜𝑙𝑖𝑜𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 − 𝑡𝑜 − 𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑗,𝑡, is the equal-weighted average borrower distance-to-default for all
loans originated by bank j in quarter t. 𝐿𝑎𝑔𝑔𝑒𝑑𝐴𝑣𝑔𝐷𝑡𝑜𝐷𝑗,𝑡−1 is the equal-weighted average distance-to-default in
quarter t-1 of borrowers who borrowed from bank j in quarter t. For space reasons, only the coefficients on systemic
risks, 𝐿𝑎𝑔𝑔𝑒𝑑𝐴𝑣𝑔𝐷𝑡𝑜𝐷𝑗,𝑡−1, and interaction terms are reported. The estimation of the dynamic panel GMM estimator
consists of four steps: First, I convert my regression equation to a bank-quarter panel regression. For each bank j
in quarter t, I calculate the average borrower distance-to-default of all loans originated by this bank in this quarter.
For this calculation, I don’t put any restriction on whether the bank act as a lead lender or non-lead participant.
As long as a bank participates in a loan, this loan is included to the average borrower distance-to-default
calculation. I name this average distance-to-default Bank Portfolio Distance-to-Default. Following the same
methodology, I generate the average borrower characteristics and loan characteristics for each bank in each
quarter. In this way, the regression is converted to a panel regression using quarterly Bank Portfolio Distance-to-
Default as the dependent variable, quarterly systemic risks as the main independent variables, and quarterly bank,
average borrower, and average loan characteristics as control variables. Second, I rewrite the regression equation
as a dynamic model, adding three lags of Bank Portfolio Distance-to-Default (𝐵𝑎𝑛𝑘𝑃𝑜𝑟𝑡𝑓𝑜𝑙𝑖𝑜𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 − 𝑡𝑜 −
𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑗,𝑡−𝑝, p=1,2,3) as explanatory variables. Note that 𝐵𝑎𝑛𝑘𝑃𝑜𝑟𝑡𝑓𝑜𝑙𝑖𝑜𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 − 𝑡𝑜 − 𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑗,𝑡−1is
different from 𝐿𝑎𝑔𝑔𝑒𝑑𝐴𝑣𝑔𝐷𝑡𝑜𝐷𝑗,𝑡−1. 𝐿𝑎𝑔𝑔𝑒𝑑𝐴𝑣𝑔𝐷𝑡𝑜𝐷𝑗,𝑡−1 controls for the borrower’s lagged credit risk one
quarter before their borrowing activity, while 𝐵𝑎𝑛𝑘𝑃𝑜𝑟𝑡𝑓𝑜𝑙𝑖𝑜𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 − 𝑡𝑜 − 𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑗,𝑡−1 is the average
credit risk the lender actually took in quarter t-1. Third, I first difference all variables, which allows me to control
for unobserved heterogeneity and eliminate potential omitted variable bias. Fourth, I estimate the model by
dynamic panel GMM and use lagged (t-3 to t-8) explanatory variables as instruments. As suggested by Saunders,
Schmid, Walter (2016), using lagged variables as instruments for the present values of these variables controls
for potential simultaneity and reverse causality. In addition, this estimation procedure allows all the explanatory
variables to be treated as endogenous. AR(1) and AR(2) are tests for first-order and second-order serial correlation in
the first differenced residuals with the null hypothesis of no serial correlation. Standard errors are in parentheses. *, **,
and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
63
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑡𝑜𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑗,𝑘,𝑡
I II III IV V VI VII
𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 -0.174∗∗∗ -0.142∗∗∗ -0.193∗∗∗ -0.146∗∗∗ -0.293∗∗ -0.229∗∗ -0.372∗∗∗
(0.023) (0.022) (0.062) (0.043) (0.125) (0.103) (0.088)
𝐿𝑎𝑔𝑔𝑒𝑑𝐴𝑣𝑔𝐷𝑡𝑜𝐷𝑗,𝑡−1 0.897∗∗∗ 0.869∗∗∗ 0.931∗∗∗ 0.864∗∗∗ 0.904∗∗∗ 0.882∗∗∗ 0.976∗∗∗
(0.059) (0.054) (0.096) (0.102) (0.103) (0.079) (0.098)
𝐶𝐴𝑇𝐹𝐼𝑁𝑡 -0.161∗∗∗ -0.361∗∗∗ -0.513∗∗∗
(0.042) (0.095) (0.158) 𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝐶𝐴𝑇𝐹𝐼𝑁𝑡 0.034
(0.031) 𝑅𝑒𝑐𝑒𝑠𝑠𝑖𝑜𝑛𝑡 -1.533∗∗
(0.713)
𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝑅𝑒𝑐𝑒𝑠𝑠𝑖𝑜𝑛𝑡 0.217∗∗
(0.084)
𝐸𝑎𝑟𝑙𝑦𝑊𝑎𝑟𝑛𝑡 -0.729
(0.497) 𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝐸𝑎𝑟𝑙𝑦𝑊𝑎𝑟𝑛𝑡 0.061
(0.101)
Observations 1550 1550 1505 1505 1505 1505 1505
Controls Variables No No Yes Yes Yes Yes Yes
Year fixed effects Yes Yes Yes Yes Yes Yes Yes
P-value of AR(1) test 0.001 0.001 0.002 0.007 0.010 0.004 0.001
P-value of AR(2) test 0.940 0.943 0.320 0.943 0.811 0.401 0.565
64
Table 12
Credit Risk Sensitivity on Systemic Risk: Regressions by Single Banks
Table 12 reports the estimates for coefficients 𝛼1 and 𝛼3 in equation (9), where 𝛼3 is coefficient for the interaction of
Δ𝐶𝑜𝑉𝑎𝑅 and the recession dummy. 𝛼1 measures a bank’s credit risk-taking sensitivity during normal periods, while
𝛼1 + 𝛼3 measures the bank’s credit risk-taking sensitivity during recession periods.
RSSD ID Name 𝜶𝟏 𝜶𝟑
3587146 BANK OF NEW YORK MELLON CORPORATION, THE -0.8075 0.5444
1199611 NORTHERN TRUST CORPORATION -0.6599 0.9219
1119794 U.S. BANCORP -0.5555 0.4678
1068294 BANK ONE CORPORATION -0.4345 0.5565
1068762 MELLON FINANCIAL CORPORATION -0.4312 0.5588
1113514 FLEETBOSTON FINANCIAL CORPORATION -0.4136 0.5251
1069778 PNC FINANCIAL SERVICES GROUP, INC., THE -0.4126 0.4209
1068025 KEYCORP -0.4112 0.3281
1070345 FIFTH THIRD BANCORP -0.3796 0.0214
1199844 COMERICA INCORPORATED -0.2985 0.1329
1073551 WACHOVIA CORPORATION -0.2878 0.1362
1039502 JPMORGAN CHASE & CO. -0.2854 0.0514
1120754 WELLS FARGO & COMPANY -0.2383 0.1793
1131787 SUNTRUST BANKS, INC. -0.2303 0.1421
1073757 BANK OF AMERICA CORPORATION -0.1906 0.0780
1951350 CITIGROUP INC. -0.1774 0.0857
1069125 NATIONAL CITY CORPORATION -0.0990 0.0137
65
Table 13
Bank Executives Innovation Dimensions and Styles – Factor Analysis
Panel A and Panel D show the factor analysis for manager and bank fixed effects. The manager and bank fixed effects
are estimated from a set of three-way fixed effects regressions (executives, bank, and year fixed effects) which use a
connectedness sample constructed based on Abowd, Kramarz, and Margolis (1999) that includes all banks that have
employed at least one manager who has worked for two or more banks during the sampling period of 1992 -2013:
𝑃𝑗(𝑚,𝑡+1) = 𝐵𝑗(𝑚,𝑡)𝛾 + 𝐸𝑡𝛽 + Σ𝑗=1𝐽 𝐷𝑚,𝑗,𝑡𝜃𝑗 + 𝜙𝑚 + 𝜇𝑡 + 𝜀𝑗,𝑡 (13)
where 𝑃𝑗(𝑚,𝑡+1) are eight business policy variables (non-interest income, loans over assets, MBS, derivatives, lending
diversifications, Gap12, loans over deposits, non-deposit funding) following Hagendorff et al. (2017) for bank j at time
t+1. Definition of business policy variables are listed in Appendix II. Executives included are CEOs, CFOs, COOs, and
executive directors. Position and tenure data are obtained from Execucomp. The dependent variable is explained by
bank characteristics 𝐵𝑗(𝑚,𝑡), macroeconomic conditions 𝐸𝑡−1, bank fixed effects 𝜃𝑗, manager fixed effects 𝜙𝑚, and time
fixed effects 𝜇𝑡. The regression results are omitted, and estimated bank and manager fixed effects are utilized to conduct
factor analysis. Panel B (Panel E) extracts four major factors that summarizes the correlation matrix of managerial (bank)
styles, and based on factors’ loadings on the business model variables, I define an executive’s (a bank’s) score on Factor
1 as the asset-side innovation dimension score, and an executive’s (a bank’s) score on Factor 2 as the liability-side
innovation dimension score. Panel C (Panel F) shows four manager (bank) styles: (1) Asset Innovators are executives
(banks) with score on Factor1 higher than the mean level (0.012) and score on Factor 2 lower than the mean level (-
0.149); (2) Asset and Liability Innovators are executives (banks) with scores on Factor 1 and Factor2 both higher than
the mean levels; (3) Traditionalists are executives (banks) with scores on Factor 1 and Factor 2 lower than the mean
levels; (4) Liability Innovators are those executives (banks) with scores on Factor 1 lower than the mean level and scores
on Factor 2 higher than the mean level. The number of executives (banks) for each type are included in the parenthesis.
66
Panel A: Factor Analysis – Executive Fixed Effects
Factor Eigenvalue Difference Proportion Cumulative
Factor1 3.91806 2.43493 0.6988 0.6988
Factor2 1.48313 1.19896 0.2645 0.9633
Factor3 0.28417 0.08931 0.0507 1.014
Factor4 0.19486 0.13989 0.0348 1.0487
Panel B: Factor Loadings on Manager Fixed Effects
Variable Factor1 Factor2 Factor3 Factor4
Non-interest income 0.8382 0.3239 -0.2053 0.0577
Loans -0.9371 -0.1502 0.1326 0.1015
MBS 0.7556 0.0463 -0.0058 -0.2273
Derivatives 0.8718 -0.0785 0.1615 0.1846
Lending Diversification 0.7602 0.0465 0.3115 0.1300
Gap12 -0.1735 0.3591 -0.2458 0.2685
Loans/Deposits -0.6270 0.6639 0.1963 -0.0177
Non-deposit funding 0.0732 0.8806 0.0486 -0.0785
Panel C: Average Factor Loadings, by Executive Style
Factor 1 (Asset
Innovation Dimension)
Factor 2 (Liability
Innovation Dimension)
Asset Innovator (79) 0.7329 -0.3559
Asset and Liability
Innovator (113) 1.0624 0.2237
Traditionalist (156) -0.6219 -0.3919
Liability Innovator (79) -0.9586 0.0054
67
Panel D: Factor Analysis – Bank Fixed Effects
Factor Eigenvalue Difference Proportion Cumulative
Factor1 2.6757 1.7168 0.6658 0.6658
Factor2 0.9589 0.3982 0.2386 0.9045
Factor3 0.5607 0.1883 0.1395 1.0440
Factor4 0.3724 0.3448 0.0927 1.1367
Panel E: Factor Loadings on Bank Fixed Effects
Variable Factor1 Factor2 Factor3 Factor4
Non-interest income 0.7124 0.1372 -0.211 0.3175
Loans -0.8774 -0.2048 -0.0236 0.2255
MBS 0.6518 0.1669 -0.3193 -0.1829
Derivatives 0.5916 -0.1165 0.3425 0.1054
Lending Diversification 0.6002 -0.2281 0.2531 0.2555
Gap12 -0.174 0.3251 -0.3369 0.2933
Loans/Deposits -0.4639 0.4666 0.2351 0.1361
Non-deposit funding 0.1336 0.6937 0.252 -0.0795
Panel F: Average Factor Loadings, by Bank Style
Factor 1 (Asset
Innovation Dimension)
Factor 2 (Liability
Innovation Dimension)
Asset Innovating Bank
(25) 0.5385 -0.4280
Asset and Liability
Innovation Bank (18) 0.8483 0.6589
Traditional Bank (19) -0.5318 -0.5427
Liability Innovating Bank
(23) -0.8099 0.3978
68
Figure 1: Clustering of Executive Styles
The figure presents the graphical clustering of managerial patterns in styles. Using factor analysis, I extract two factors (Factor1 and
Factor2) that summarize a relevant portion of the correlation matrix of managerial styles. The styles are derived from a k-median
clustering algorithm. The managers types are defined as: (1) Asset Innovators are executives with score on Factor1 higher
than the mean level (0.012) and score on Factor 2 lower than the mean level (-0.149); (2) Asset and Liability Innovators
are executives with scores on Factor 1 and Factor2 both higher than the mean levels; (3) Traditionalists are executives
with scores on Factor 1 and Factor 2 lower than the mean levels; (4) Liability Innovators are those executives with scores
on Factor 1 lower than the mean level and scores on Factor 2 higher than the mean level.
-2-1
01
23
Score
s for
facto
r 1
-1 -.5 0 .5 1Scores for factor 2
Asset Innovator Asset and Liability Innovator
Traditionalist Liability Innovator
69
Table 14
Systemic Risk-taking and Manager Style – Correlation Matrix
Table 14 reports the correlation matrix for systemic risk, executive and bank innovation dimensions for the period of
1992 to 2013. Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡99 (Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡
95) is the bank-level systemic risk for bank j in quarter t estimated at the 99% (95%)
quantile. 𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑣𝑒𝐴𝑠𝑠𝑒𝑡𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗,𝑡 is the equal average executive asset-side innovation dimension scores (scores on
Factor 1) for all executives working for bank j in quarter t. 𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑣𝑒𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗,𝑡 is the equal average
executive liability-side innovation dimension scores (scores on Factor 2) for all executives working for bank j in quarter
t. 𝐵𝑎𝑛𝑘𝐴𝑠𝑠𝑒𝑡𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗 is bank j’s asset-side time-invariant innovation score (score on Factor 1).
𝐵𝑎𝑛𝑘𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗 is bank j’s liability-side time-invariant innovation score (score on Factor 2).
(1) (2) (3) (4) (5) (6) (7) (8)
(1) Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡99 1
(2) Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡95 0.8517 1
(3) Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡+199 0.8534 0.7336 1
(4) Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡+195 0.7275 0.8627 0.8543 1
(5) 𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑣𝑒𝐴𝑠𝑠𝑒𝑡𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗,𝑡 0.3690 0.3942 0.3714 0.3958 1
(6) 𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑣𝑒𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗,𝑡 0.0642 0.0624 0.0635 0.0611 0.2166 1
(7) 𝐵𝑎𝑛𝑘𝐴𝑠𝑠𝑒𝑡𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗,𝑡 0.1580 0.1258 0.1605 0.1285 0.0452 0.0420 1
(8) 𝐵𝑎𝑛𝑘𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗,𝑡 -0.0089 -0.0408 -0.011 -0.0430 0.0189 -0.2033 -0.0744 1
70
Table 15
Credit Risk-taking Sensitivity on Systemic Risk – The Effect of Manager Asset Innovation and Liability
Innovation
Table 15 reports the coefficient estimates from the following fixed effects regression for the connectedness sample:
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑡𝑜𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑗,𝑘,𝑡
= 𝛼0 + 𝛼1Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 + 𝛼3Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑣𝑒𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑆𝑐𝑜𝑟𝑒𝑗,𝑡 + 𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑣𝑒𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑆𝑐𝑜𝑟𝑒𝑗,𝑡
+ 𝛼4Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 + 𝛼5𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑡𝑜𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡−1 + 𝛼3Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝑅𝑒𝑐𝑒𝑠𝑠𝑖𝑜𝑛𝑡 + 𝛼2𝑅𝑒𝑐𝑒𝑠𝑠𝑖𝑜𝑛𝑡
+ 𝛼6𝐿𝑜𝑎𝑛𝐶𝑜𝑛𝑡𝑟𝑜l𝑠𝑘,𝑡 + 𝐵𝑎𝑛𝑘𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑗,𝑡 + 𝛼8𝐵𝑜𝑟𝑟𝑜𝑤𝑒𝑟𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑡 + 𝛼9𝐿𝑒𝑛𝑑𝑖𝑛𝑔𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑠ℎ𝑖𝑝𝑖,𝑗,𝑡
+ 𝛼10𝑀𝑎𝑐𝑟𝑜𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑡 + 𝑌𝑒𝑎𝑟𝐹𝐸 + 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝐹𝐸 + 𝐵𝑎𝑛𝑘𝐹𝐸 + 𝜀𝑖,𝑗,𝑘,𝑡 ,(9)
where subscript i, j, k, and t indicate the borrowing firm, the bank, the package, and the time (quarter), respectively. The
regressions are run at the package level and observations are by package-lender pairs, where lenders include both lead
lenders and non-lead participants, and are identified at the bank holding company level. The dependent variable,
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑡𝑜𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡 , is borrower’s distance-to-default in the quarter of loan origination.
𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑣𝑒𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑆𝑐𝑜𝑟𝑒𝑗,𝑡 is either 𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑣𝑒𝐴𝑠𝑠𝑒𝑡𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗,𝑡 or 𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑣𝑒𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗,𝑡 .
𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑣𝑒𝐴𝑠𝑠𝑒𝑡𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗,𝑡 is the equal average executive asset-side innovation dimension scores (scores on Factor
1) for all executives working for bank j in quarter t. 𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑣𝑒𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗,𝑡 is the equal average executive
liability-side innovation dimension scores (scores on Factor 2) for all executives working for bank j in quarter t. The
vectors of variables Loan Controls, Bank Controls, and Borrower Controls contain loan, bank, and firm-specific
control variables from the quarter of loan origination. 𝑅𝑒𝑐𝑒𝑠𝑠𝑖𝑜𝑛𝑡 is a dummy variable, which is equal to 1 if the
loan is originated at an economic recession quarter defined by National Bureau of Economic Research, and 0 otherwise.
Standard errors are clustered by bank and shown in parentheses. *, **, and *** indicate significance at the 10%, 5%,
and 1% levels, respectively.
71
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑡𝑜𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑗,𝑘,𝑡
I II III IV
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 -0.217∗∗∗ -0.251∗∗∗ -0.217∗∗∗ -0.287∗∗∗
(0.008) (0.012) (0.008) (0.014)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑣𝑒𝐴𝑠𝑠𝑒𝑡𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗,𝑡 0.031∗∗∗ 0.065∗∗∗
(0.009) (0.010)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑣𝑒𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗,𝑡 -0.041∗∗∗ -0.095∗∗∗
(0.013) (0.016)
𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑣𝑒𝐴𝑠𝑠𝑒𝑡𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗,𝑡 -0.200∗∗ -0.295∗∗∗
(0.099) (0.103)
𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑣𝑒𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗,𝑡 0.224∗∗ 0.515∗∗∗
(0.107) (0.117)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 -0.021∗∗∗ -0.022∗∗∗ -0.021∗∗∗ -0.022∗∗∗
(0.006) (0.006) (0.006) (0.006)
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑡𝑜𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡−1 0.953∗∗∗ 0.953∗∗∗ 0.953∗∗∗ 0.953∗∗∗
(0.005) (0.005) (0.005) (0.005)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝑅𝑒𝑐𝑒𝑠𝑠𝑖𝑜𝑛𝑡 0.113∗∗∗ 0.111∗∗∗ 0.110∗∗∗ 0.105∗∗∗
(0.014) (0.014) (0.014) (0.014)
𝑅𝑒𝑐𝑒𝑠𝑠𝑖𝑜𝑛𝑡 -0.890∗∗∗ -0.866∗∗∗ -0.866∗∗∗ -0.793∗∗∗
(0.129) (0.130) (0.129) (0.130)
𝑙𝑛(𝐷𝑒𝑎𝑙𝐴𝑚𝑜𝑢𝑛𝑡)𝑘,𝑡 -0.000 -0.001 -0.000 -0.001
(0.016) (0.016) (0.016) (0.016)
𝑙𝑛(𝑀𝑎𝑡𝑢𝑟𝑖𝑡𝑦)𝑘,𝑡 0.057∗∗∗ 0.057∗∗∗ 0.056∗∗∗ 0.055∗∗∗
(0.020) (0.020) (0.020) (0.020)
𝑙𝑛(𝑁𝑢𝑚𝑏𝑒𝑟𝑜𝑓𝐿𝑒𝑎𝑑𝑠)𝑘,𝑡 0.068∗∗∗ 0.068∗∗∗ 0.069∗∗∗ 0.069∗∗∗
(0.019) (0.019) (0.019) (0.019)
𝑆𝑒𝑐𝑢𝑟𝑒𝑑𝑘,𝑡 -0.014 -0.015 -0.015 -0.015
(0.028) (0.028) (0.028) (0.028)
𝑙𝑛(𝐵𝑎𝑛𝑘𝑇𝑜𝑡𝑎𝑙𝐴𝑠𝑠𝑒𝑡𝑠)𝑗,𝑡 -0.026 -0.035 -0.020 -0.050
(0.044) (0.047) (0.045) (0.047)
𝐵𝑎𝑛𝑘𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑅𝑎𝑡𝑖𝑜𝑗,𝑡 1.344 1.170 1.558 1.301
(1.312) (1.318) (1.337) (1.336)
𝐵𝑎𝑛𝑘𝑅𝑒𝑡𝑢𝑟𝑛𝑜𝑛𝐸𝑞𝑢𝑖𝑡𝑦𝑗,𝑡 0.593∗∗ 0.613∗∗∗ 0.612∗∗∗ 0.702∗∗∗
(0.237) (0.237) (0.238) (0.237)
𝐵𝑜𝑟𝑟𝑜𝑤𝑒𝑟𝑇𝑎𝑛𝑔𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑗,𝑡 -0.187∗∗∗ -0.186∗∗∗ -0.186∗∗∗ -0.185∗∗∗
(0.050) (0.050) (0.050) (0.050)
𝐵𝑜𝑟𝑟𝑜𝑤𝑒𝑟𝑆𝑖𝑧𝑒𝑖,𝑡 -0.002 -0.001 -0.002 -0.002
(0.012) (0.012) (0.012) (0.012)
𝐵𝑜𝑟𝑟𝑜𝑤𝑒𝑟𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖,𝑡 -0.693∗∗∗ -0.690∗∗∗ -0.694∗∗∗ -0.690∗∗∗
(0.094) (0.094) (0.094) (0.094)
𝐿𝑒𝑛𝑑𝑖𝑛𝑔𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑠ℎ𝑖𝑝𝑖,𝑗,𝑡 -0.016 -0.017 -0.015 -0.017
(0.026) (0.026) (0.026) (0.026)
𝐺𝐷𝑃𝐺𝑟𝑜𝑤𝑡ℎ𝑡 0.163∗∗∗ 0.161∗∗∗ 0.164∗∗∗ 0.159∗∗∗
(0.017) (0.017) (0.017) (0.017)
Observations 24098 24098 24098 24098
R 2 0.896 0.896 0.896 0.896
Adjusted R 2 0.896 0.896 0.896 0.896
Industry fixed effects Yes Yes Yes Yes
Lender fixed effects Yes Yes Yes Yes
Year fixed effects Yes Yes Yes Yes
72
Table 16
Credit Risk-taking Sensitivity on Systemic Risk – The Effect of Bank Asset Innovation Score and Liability
Innovation Score
Table 16 presents the fixed effects regressions on borrower distance-to-default for the connectedness sample. Both bank
innovation scores and executive innovation scores are included in the regressions. 𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑣𝑒𝐴𝑠𝑠𝑒𝑡𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗,𝑡 is the
equal average executive asset-side innovation dimension scores (scores on Factor 1) for all executives working for bank
j in quarter t. 𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑣𝑒𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗,𝑡 is the equal average executive liability-side innovation dimension scores
(scores on Factor 2) for all executives working for bank j in quarter t. 𝐵𝑎𝑛𝑘𝐴𝑠𝑠𝑒𝑡𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗 is bank j’s asset-side time-
invariant innovation score (score on Factor 1). 𝐵𝑎𝑛𝑘𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗 is bank j’s liability-side time-invariant
innovation score (score on Factor 2). Control variables include loan, bank, firm-specific, and macroeconomic control
variables in the quarter of loan origination. Standard errors are clustered by bank and shown in parentheses. *, **,
and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑡𝑜𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑗,𝑘,𝑡
I II III IV V VI VII
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 -0.217∗∗∗ -0.213∗∗∗ -0.247∗∗∗ -0.218∗∗∗ -0.217∗∗∗ -0.213∗∗∗ -0.306∗∗∗
(0.008) (0.009) (0.013) (0.008) (0.008) (0.009) (0.016)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝐵𝑎𝑛𝑘𝐴𝑠𝑠𝑒𝑡𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗 -0.008 -0.009 -0.008 0.014∗
(0.006) (0.006) (0.006) (0.007)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝐵𝑎𝑛𝑘𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗 0.027 0.015 0.027 -0.034∗
(0.018) (0.018) (0.018) (0.019)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑣𝑒𝐴𝑠𝑠𝑒𝑡𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗,𝑡 0.032∗∗∗ 0.075∗∗∗
(0.009) (0.011)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑣𝑒𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗,𝑡 -0.039∗∗∗ -0.122∗∗∗
(0.013) (0.020)
𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑣𝑒𝐴𝑠𝑠𝑒𝑡𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗,𝑡 -0.190∗ -0.347∗∗∗
(0.099) (0.105)
𝐸𝑥𝑒𝑐𝑢𝑡𝑖𝑣𝑒𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑗,𝑡 0.208∗ 0.680∗∗∗
(0.109) (0.136)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 -0.021∗∗∗ -0.021∗∗∗ -0.022∗∗∗ -0.021∗∗∗ -0.021∗∗∗ -0.021∗∗∗ -0.022∗∗∗
(0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006)
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑡𝑜𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡−1 0.953∗∗∗ 0.953∗∗∗ 0.953∗∗∗ 0.953∗∗∗ 0.953∗∗∗ 0.953∗∗∗ 0.953∗∗∗
(0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝑅𝑒𝑐𝑒𝑠𝑠𝑖𝑜𝑛𝑡 0.113∗∗∗ 0.112∗∗∗ 0.111∗∗∗ 0.112∗∗∗ 0.110∗∗∗ 0.111∗∗∗ 0.105∗∗∗
(0.014) (0.014) (0.014) (0.014) (0.014) (0.014) (0.014)
𝑅𝑒𝑐𝑒𝑠𝑠𝑖𝑜𝑛𝑡 -0.890∗∗∗ -0.887∗∗∗ -0.862∗∗∗ -0.882∗∗∗ -0.863∗∗∗ -0.879∗∗∗ -0.788∗∗∗
(0.129) (0.129) (0.130) (0.130) (0.130) (0.130) (0.130)
Observations 24098 24098 24098 24098 24098 24098 24098
R 2 0.896 0.896 0.896 0.896 0.896 0.896 0.896
Adjusted R 2 0.896 0.896 0.896 0.896 0.896 0.896 0.896
Industry fixed effects Yes Yes Yes Yes Yes Yes Yes
Lender fixed effects Yes Yes Yes Yes Yes Yes Yes
Year fixed effects Yes Yes Yes Yes Yes Yes Yes
Control Variables Yes Yes Yes Yes Yes Yes Yes
74
Table 17
Credit Risk-taking Sensitivity on Systemic Risk - How Do Manager Styles Matter?
Table 17 reports the coefficient estimates from the baseline regression adding interaction of Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 with manager
style dummy as defined in Panel C in Table 1. The managers types are defined as: (1) Asset Innovators are executives
(banks) with score on Factor1 higher than the mean level (0.012) and score on Factor 2 lower than the mean level (-
0.149); (2) Asset and Liability Innovators are executives (banks) with scores on Factor 1 and Factor2 both higher than
the mean levels; (3) Traditionalists are executives (banks) with scores on Factor 1 and Factor 2 lower than the mean
levels; (4) Liability Innovators are those executives (banks) with scores on Factor 1 lower than the mean level and scores
on Factor 2 higher than the mean level.
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑡𝑜𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑗,𝑘,𝑡
I II III IV V VI
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 -0.243∗∗∗ -0.255∗∗∗ -0.238∗∗∗ -0.241∗∗∗ -0.240∗∗∗ -0.224∗∗∗
(0.009) (0.010) (0.011) (0.009) (0.009) (0.011)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝐴𝑠𝑠𝑒𝑡𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑜𝑟𝑗,𝑡 0.030∗∗∗
(0.010) Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝐴𝑠𝑠𝑒𝑡𝑎𝑛𝑑𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑜𝑟𝑗,𝑡 -0.008 -0.023∗∗
(0.009) (0.010)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝑇𝑟𝑎𝑑𝑡𝑖𝑜𝑛𝑎𝑙𝑖𝑠𝑡𝑗,𝑡 -0.079∗∗∗ -0.099∗∗∗
(0.025) (0.026)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑜𝑟𝑗,𝑡 -0.093∗∗∗ -0.110∗∗∗
(0.032) (0.032)
𝐴𝑠𝑠𝑒𝑡𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑜𝑟𝑗,𝑡 -0.231∗∗∗
(0.073) 𝐴𝑠𝑠𝑒𝑡𝑎𝑛𝑑𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑜𝑟𝑗,𝑡 0.115 0.204∗∗∗
(0.072) (0.074)
𝑇𝑟𝑎𝑑𝑡𝑖𝑜𝑛𝑎𝑙𝑖𝑠𝑡𝑗,𝑡 0.337∗∗∗ 0.009
(0.130) (0.324)
𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑜𝑟𝑗,𝑡 0.275∗ 0.002
(0.159) (0.334)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡−1 -0.019∗∗∗ -0.019∗∗∗ -0.019∗∗∗ -0.020∗∗∗ -0.019∗∗∗ -0.021∗∗∗
(0.006) (0.006) (0.006) (0.006) (0.006) (0.006)
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑡𝑜𝐷𝑒𝑓𝑎𝑢𝑙𝑡𝑖,𝑡−1 0.953∗∗∗ 0.953∗∗∗ 0.953∗∗∗ 0.954∗∗∗ 0.953∗∗∗ 0.954∗∗∗
(0.006) (0.006) (0.006) (0.006) (0.006) (0.006)
Δ𝐶𝑜𝑉𝑎𝑅𝑗,𝑡 × 𝑅𝑒𝑐𝑒𝑠𝑠𝑖𝑜𝑛𝑡 0.115∗∗∗ 0.112∗∗∗ 0.115∗∗∗ 0.114∗∗∗ 0.109∗∗∗ 0.106∗∗∗
(0.016) (0.016) (0.016) (0.016) (0.016) (0.016)
𝑅𝑒𝑐𝑒𝑠𝑠𝑖𝑜𝑛𝑡 -0.836∗∗∗ -0.803∗∗∗ -0.833∗∗∗ -0.819∗∗∗ -0.771∗∗∗ -0.736∗∗∗
(0.155) (0.155) (0.155) (0.155) (0.156) (0.156)
Observations 20819 20819 20819 20819 20819 20819
R 2 0.896 0.896 0.896 0.896 0.896 0.896
Adjusted R 2 0.896 0.896 0.896 0.896 0.896 0.896
Industry fixed effects Yes Yes Yes Yes Yes Yes
Lender fixed effects Yes Yes Yes Yes Yes Yes
Year fixed effects Yes Yes Yes Yes Yes Yes
Control Variables Yes Yes Yes Yes Yes Yes
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