Journal of Banking and Finance 108 (2019) 105651
Contents lists available at ScienceDirect
Journal of Banking and Finance
journal homepage: www.elsevier.com/locate/jbf
The moderating role of capital on the relationship between bank
liquidity creation and failure risk ✩
Chen Zheng a, Adrian (Wai Kong) Cheung b,∗, Tom Cronje a
a
b
School of Economics, Finance and Property, Curtin University, WA, Australia
College of Business, Governance and Law, Flinders University, Bedford Park Campus, Adelaide, SA 5042, Australia
a r t i c l e
i n f o
Article history:
Received 19 August 2017
Accepted 16 September 2019
Available online 18 September 2019
JEL classification:
G01
G21
G28
Keywords:
Liquidity creation
Bank failure
Bank capital
Liquidity risk
Bank size
Crisis
a b s t r a c t
We examine the role of bank capital in moderating the relationship between bank liquidity creation and
the failure risk in U.S. banks over the period of 2003–2014. We find that, conditional on bank capital,
bank liquidity creation is related to bank failure risk negatively. The negative relationship is moderated
positively (i.e., strengthened) by (changes in) bank capital. This finding is consistent with the view that
banks may strengthen their solvency through increased capital in response to the illiquidity risk associated with liquidity creation; and higher capital enhances the ability of banks to create liquidity. The result
is robust to different estimation methods, and alternative measures of liquidity creation, bank failure risk,
and bank capital. Further analysis shows that the significant and negative effect is more prominent for
small banks, and the impact of bank capital was more pronounced during the recent financial crisis of
20 07–20 09.
© 2019 Elsevier B.V. All rights reserved.
1. Introduction
According to the modern theory of financial intermediation,
liquidity creation is a major function of banks in the economy.
Banks create liquidity on the balance sheet by financing relatively
long-term illiquid assets with relatively short-term liquid liabilities (Bryant, 1980; Diamond and Dybvig, 1983). Banks also create
liquidity by way of off-balance sheet activities, such as providing
standby letters of credit and loan commitments to their customers
(Holmström and Tirole, 1998; Kashyap et al., 2002; Thakor, 2005).
Liquidity creation is risky because it makes banks less liquid
(i.e., banks hold illiquid assets when they provide liquidity to the
external entities), increases the bank’s exposure to risk, and raises
the likelihood and severity of losses associated with having to dispose of illiquid assets to satisfy the liquidity demands of customers
✩
We would like to thank Felix Chan, Robert Durand, Mark Harris, Mostafa Hasan,
Tianpei Luo, Lee Smales, and the participants at the seminars/conferences at Curtin
University, Edith Cowan University, the 2nd Business Doctoral and Emerging Scholars Conference at Perth in 2017, and the FEBS Conference at Rome in 2018, for their
helpful comments and suggestions. Any errors are our own. Cheung acknowledges
financial support from the College of Business, Government and Law, Flinders University.
∗
Corresponding author.
E-mail address: adrian.cheung@flinders.edu.au (A. (Wai Kong) Cheung).
https://doi.org/10.1016/j.jbankfin.2019.105651
0378-4266/© 2019 Elsevier B.V. All rights reserved.
(Allen and Santomero, 1997; Allen and Gale, 2004). In extreme situations, aggregate increases in liquidity demand can result in bank
runs by depositors (Diamond and Dybvig, 1983). Existing literature
indicates that an increase in liquidity creation could result in increased bank illiquidity, and this can be considered a main source
of banking fragility (e.g., Acharya and Naqvi, 2012; Thakor, 2005;
Berger and Bouwman, 2017). Our paper is complementary to this
line of research, showing how the liquidity risk-sharing role of
bank capital can affect the relationship between liquidity creation
and failure risk at individual bank level.
Bank capital is important, since it implicates the survival
probability of banks in two ways. Firstly, it plays a loss-absorption
role because higher bank capital increases the buffers of banks
against shocks to asset values (Repullo, 2004; Von Thadden, 2004).
Secondly, it also serves a role to reduce risk. According to
incentive-based theories, higher bank capital strengthens the
incentive of banks to monitor their relationships with borrowers
(Holmström and Tirole, 1998), or reduces the excessive risk-taking
incentives of banks (Acharya et al., 2016). Prior literature finds
that banks manage or strengthen their capital position actively,
to hedge against perceived risk exposure (Berger et al., 2008;
Distinguin et al., 2013). Higher capital, in turn, favours the ability
of banks to create liquidity, as capital helps absorb the greater risk
associated with liquidity creation (Repullo, 2004). Further, higher
2
C. Zheng, A. (Wai Kong) Cheung and T. Cronje / Journal of Banking and Finance 108 (2019) 105651
capital may incentivize banks to work harder, leading to more
lending and liquidity creation (Donaldson et al., 2018). Hence,
ignoring the roles of bank capital could lead to incomplete and,
sometimes, erroneous conclusions about the relationship between
liquidity creation and bank failure risk. The goal of this paper is to
empirically examine the interaction effect of liquidity creation and
bank capital on the failure risk of individual banks.
By way of preview, our main findings are summarized as follows. First, contrary to what one might initially expect, we find
that liquidity creation is negatively associated with bank failure
risk in the presence of bank capital. Moreover, this negative relationship is moderated positively (i.e., strengthened) by (changes
in) bank capital. This is because banks that create more liquidity, and are then exposed to higher liquidity risk in general, accumulate more capital buffer as an insurance against the illiquidity risk from liquidity creation. The higher capital, consequently,
lowers the probability of bank failure and enhances the ability
of banks to create liquidity. We consider changes in bank capital,
since the incremental changes in bank capital actually capture the
liquidity risk-sharing role. Overall, an increase in bank capital will
give rise to a further increase in liquidity creation, but a further
decrease in bank failure risk, implying that an increase in bank
capital will strengthen the negative relationship between liquidity
creation and bank failure risk. This result supports the findings of
Castiglionesi et al. (2014) that banks may use capital to deal with
undiversifiable liquidity risk that cannot be diversified away by interbank markets. The second main finding is that the negative and
significant relationship between bank liquidity creation and bank
failure risk is mainly applicable to smaller banks, and the impact
of (changes in) bank capital on the relationship between liquidity
creation and bank failure risk is more pronounced during the recent financial crisis period.
The paper related to ours is that of Fungacova et al. (2015) who
find that high liquidity creation significantly increases the probability of bank failure of Russian banks. Their result is different from our result probably due to the following reasons. First,
Fungacova et al. (2015) concentrate on bank failures during the
pre-crisis period (i.e., between 20 0 0 and 2007) when economic
conditions were relatively stable and tranquil, while our study covers not only the pre-crisis period but also the crisis and postcrisis periods, during which stringent capital requirements were
imposed.1 It is noteworthy that these stringent capital requirements can affect bank risk management in a different way and
this endogenous behavior is the main idea on which this paper
is based. Prior research finds that bank risk taking is negatively
related to capital requirements (e.g., Furlong and Keeley, 1989;
Konishi and Yasuda, 2004). Hence, it is expected that the implementation of stricter capital standards during the crisis and
post-crisis periods would substantially reduce bank failure risk.
Second, unlike Berger and Bouwman’s (2009) liquidity creation
measure, which includes both on- and off-balance sheet items,
Fungacova et al. (2015) consider on-balance sheet items only because they argue that off-balance sheet activities are insignificant in Russia. However, in our sample, the amount of off-balance
sheet liquidity creation is much higher than that of on-balance
sheet liquidity creation in the U.S., primarily due to the increase
of unused loan commitments. More importantly, prior studies (e.g.,
Cornett et al., 2011) find that the level of pre-existing unused loan
1
For example, in response to the Global Financial Crisis (GFC), the Basel Committee on Banking Supervision (BCBS) proposed a number of post-crisis regulatory
framework changes (known as Basel III) designed to address inadequacies of bank
risk management. The new capital rules introduced higher capital requirements.
More specifically, Basel III increased the minimum tier 1 risk-based capital ratio
from 4% to 6%, and the common equity component of tier 1 capital from 2% to
4.5%.
commitments was a major contributor to liquidity risk during the
financial crisis. Therefore, it is not surprising that we find a negative relationship between liquidity creation and failure risk because
banks may strengthen or build up their capital buffers in response
to the liquidity risk. The higher capital, consequently lowers the
probability of bank failure.
Interestingly, Fungacova et al. (2015) also find some limited evidence that banks with very low liquidity creation are inclined
to fail. Their explanation is that liquidity creation is one of the
most important roles that banks perform in the economy. Therefore, the inability of banks to perform this function likely signals
trouble. The findings of our paper support this view. Specifically,
we find that banks which create less liquidity are more likely to
fail, since they seem to have too low levels of capital buffers to
absorb the liquidity risk from their lower liquidity creation activities. It is worth mentioning that in addition to the difference in
findings, the research question of our paper differs from that of
their study. Complementing the literature on early warning system
and bank failure prediction models, they include a liquidity creation variable and investigate whether this variable has incremental explanatory power in predicting bank failures. In this study, we
examine the interaction effect of liquidity creation and bank capital
on failure risk of banks. We focus on whether bank capital plays a
moderating role in the relationship between liquidity creation and
bank failure risk.
Our paper contributes to the literature by providing evidence
that the association between liquidity creation and failure risk depends crucially on bank capital. To the best of our knowledge, no
prior study has empirically investigated the role of bank capital in
moderating the relationship between liquidity creation and failure
risk. This is our first contribution. Our findings also have important
policy implications that are particularly relevant today. The extant
literature finds that the Basel III liquidity regulations are correlated with liquidity creation.2 In particular, the new standards may
have the unintended consequences of reducing liquidity creation
and passing on higher costs to the real economy, as banks are required to hold more liquid assets, and are discouraged from holding illiquid loans and liquid deposits (Berger and Sedunov, 2017).
Our second contribution is to complement this literature by showing that lower bank liquidity creation caused by stricter liquidity
regulations may have a potentially adverse impact on the stability of individual banks when taking into consideration the loss absorption role of bank capital. This finding suggests that capital and
liquidity requirements cannot be separated. Policymakers should
consider the liquidity risk-sharing function of bank capital as an
integrated component of bank liquidity management, and evaluate
its effect on the relationship between liquidity creation and failure
risk. The third contribution of our paper is to shed new light on
the one-size-fits-all approach to bank regulations. Our finding that
liquidity creation is associated with bank failure risk negatively in
the presence of bank capital is heavily concentrated among the
usual notion of “community banks” with assets less than $1 million, implies that small banks increase their capital ratios when
they face the higher illiquidity risk associated with liquidity creation. This, in turn, reduces the probability of their default and
2
In the wake of the recent global financial crisis, the Basel Committee on Banking Supervision (BCBS) introduced a number of new micro-prudential measures
designed to make individual banks more resilient to common and idiosyncratic
shocks. This regulatory framework, known as Basel III Accord, not only focused on
minimum capital standards as established under the Basel I and II Accords, but also,
for the first time, imposed stringent liquidity standards – the Liquidity Coverage Ratio (LCR) and the Net Stable Funding Ratio (NSFR) for banks in its signatory nations.
The LCR standard requires banks to maintain unencumbered high-quality liquid assets to survive a severe distress scenario over the short-term horizon (a 30-day period), while the NSFR standard requires banks to finance their medium- and longterm loans with stable sources of funding that are unlikely to run during a crisis.
C. Zheng, A. (Wai Kong) Cheung and T. Cronje / Journal of Banking and Finance 108 (2019) 105651
enables them to create more liquidity. This corroborates the observations of Tran et al. (2016), who show some evidence that the
impact of capital on liquidity creation appears strongest for small
banks. Consistent with DeYoung et al. (2018) who find that imposing new liquidity constraints on small banks is likely to be redundant and expensive, our results lend support to the idea of eliminating (or at least substantially relaxing) the new Basel III binding liquidity constraints from small banks, as they are likely to be
medium liquidity creators, and to self-manage their illiquidity risk
resulting from liquidity creation actively.
The remainder of the paper proceeds as follows: Section 2 discusses the related literature and develops the hypothesis.
Section 3 describes the data and sample, introduces the variable
definitions, and provides summary statistics. Section 4 presents the
baseline regression model and main findings, and shows a battery of robustness checks. Section 5 addresses the endogeneity issue based on an instrumental variable approach and time-dynamic
models. Section 6 concludes.
2. Related literature and hypothesis development
In this section, we first provide a brief review of the literature
to place our paper in context. Our paper relates to three strands of
literature: studies analyzing the relationship between bank capital
and liquidity creation; research exploring the role of bank capital
in failure risk; and papers investigating the relationship between
liquidity creation and failure risk. We then formulate different hypotheses/predictions as to how changes in bank capital moderate
the relationship between bank liquidity creation and failure risk.
2.1. Bank capital and liquidity creation
There are two theories as to how bank capital affects liquidity creation. According to the “financial fragility-crowding out” hypothesis, capital has a negative effect on liquidity creation, because a higher capital ratio makes a bank less fragile, which,
in turn, will decrease the monitoring activity of depositors and
hamper a bank’s ability to create liquidity (Diamond and Rajan, 20 0 0, 20 01), and a higher capital ratio may reduce liquidity
creation through the crowding out of deposits, a key component
of liquidity creation (Gorton and Winton, 2017). In contrast, the
“risk absorption” hypothesis, implies that there is a positive effect of capital on liquidity creation, because capital helps to absorb the illiquidity risks associated with liquidity creation, expands
the risk-bearing capacity of banks and improves the capacity of
banks to create liquidity (Allen and Santomero, 1997; Allen and
Gale, 2004; Bhattacharya and Thakor, 1993; Repullo, 2004;
Von Thadden, 2004). Berger and Bouwman (2009) investigate the
relationship between bank capital and liquidity creation using a
sample of U.S. commercial banks from 1993 to 2003, and find empirical support for both hypotheses. For large banks, which create
by far most of the liquidity, they find a positive relationship, consistent with the “risk absorption” effect, whereas for small banks,
the relationship is negative, consistent with the “financial fragilitycrowding out” effect.
A small but growing set of empirical studies examine the
two-way relationship between capital and liquidity creation,
because bank capital and liquidity might be jointly determined.
For example, Horváth et al. (2014) find that capital and liquidity creation negatively Granger-cause each other for a sample of Czech banks from 20 0 0 to 2010. Along the same line,
Fu et al. (2016) find that there is a significant negative bi-causal
relationship between liquidity creation and regulatory capital
across 14 Asia Pacific economies from 2005 to 2012. On the
contrary, Tran et al. (2016) show a positive bidirectional relationship between liquidity creation and regulatory capital for U.S.
3
banks, after controlling for bank profitability from 1996 to 2013.
Distinguin et al. (2013) also find that bank capital and liquidity
creation can affect each other for the publicly traded U.S. and
European banks during the pre-crisis from 20 0 0 to 2006, but the
signs of the effects depend on bank size.
2.2. Bank capital and failure risk
Turning to the role of bank capital in failure risk, there are
several theories supporting the view that bank capital reduces
the probability of bank failure. One set of theories argues that
capital acts as a cushion to absorb potential losses, owing to
unexpected asset returns (e.g., Repullo, 20 04; Von Thadden, 20 04).
Another set of theories focuses on the incentive effects of capital.
In these models, either capital induces banks to better monitor
their relationship borrowers, thereby, reducing the probability of
default (e.g., Holmström and Tirole, 1998), or it attenuates the
excessive risk-taking incentives of banks (e.g., Acharya et al., 2016).
Collectively, these theories suggest that there is a negative relationship between bank capital and failure risk.3 On the empirical
side, papers that precede the recent financial crisis, such as that of
Cole and Gunther (1995) and Estrella et al. (20 0 0) find that bank
failure risk is driven mainly by low capitalization. Recent papers
exploring the determinants of bank failures during the recent
financial crisis, find that capital is one of the factors explaining
failure (e.g., Cole and White, 2012; Beltratti and Stulz, 2012).
Specifically, banks with higher capital buffers have more flexibility
to respond to adverse shocks and lower failure risk.
2.3. Bank liquidity creation and failure risk
With respect to the relationship between liquidity creation and
bank failure risk, there are different views in the literature and the
evidence is mixed. One argument put forward by Diamond and
Dybvig (1983), and extended by Allen and Santomero (1997) and
Allen and Gale (2004), stresses that, as liquidity is created by
mismatching long-term assets with short-term liabilities, liquidity
creation exposes banks to illiquidity risk: the risk of having to
dispose of illiquid assets to meet customers’ liquidity demands. For
example, sudden and large withdrawals from demand depositors
and borrowers with credit line facilities can force the bank to
prematurely liquidate many of its assets at fire sale prices and to
fail (Diamond and Rajan, 2011). This view predicts that liquidity
creation leaves banks vulnerable to insolvency – the more the
liquidity creation, the more the likelihood of bank failure. But
another view predicts that liquidity creation is associated with
bank failure negatively for two reasons. First, liquidity creation is a
primary and crucial function of banking institutions. Since liquidity
creation, as a key measure of total bank output, contains information about a bank’s capability to support the macro-economy and
facilitate transactions among economic agents through maturity
transformation, the inability of the bank to manage its balance
sheet and perform a liquidity creation function is likely to be an
early warning signal of trouble (Fungacova et al., 2015). Consistent
with this line of argument, Chatterjee (2018) finds that lower
liquidity creation leads to recessions in the U.S. economy. Second,
Berger et al. (2008) show that banks manage their capital ratios
actively, to hedge against the perceived risk exposure. Specifically, banks may strengthen or build up their capital buffers in
response to the illiquidity risk stemming from liquidity creation
(Distinguin et al., 2013). This viewpoint implies that liquidity
creation, acknowledged as a risk in existing literature, can be
3
See Berger and Bouwman (2013) for extensive review of existing theories that
explain how bank capital could influence a bank’s survival probability.
4
C. Zheng, A. (Wai Kong) Cheung and T. Cronje / Journal of Banking and Finance 108 (2019) 105651
negatively associated with bank failure risk owing to the tendency
of banks to adopt methods, with specific reference to the increase
in capital, to manage/mitigate the risk.
On the whole, existing research has clearly established some
possible connections between liquidity creation and bank capital
(e.g., Berger and Bouwman, 2009; Distinguin et al., 2013), between
liquidity creation and failure risk (e.g., Diamond and Dybvig, 1983),
and between bank capital and failure risk (e.g., Berger and Bouwman, 2013). In particular, theory suggests that bank capital may be
related to liquidity creation positively or negatively, depending on
whether the risk absorption hypothesis or the financial fragilitycrowding out hypothesis prevails. As for the relationship between
liquidity creation and failure risk, theory also suggests that the relationship can be either positive or negative. When it comes to the
relationship between bank capital and failure risk, the consensus
is that the relationship is negative. However, almost no research to
date has empirically analyzed the impact of changes in bank capital on the relationship between liquidity creation and bank failure
risk. The purpose of this paper is to fill this void in the literature.
Considering the abovementioned research findings, it is evident
that there are four (i.e., 2 × 2 × 1) possible predictions/hypotheses
regarding the role of bank capital in determining the relationship
between liquidity creation and bank failure risk. Suppose liquidity
creation is associated positively with failure risk and bank capital
is related positively (negatively) to liquidity creation (failure risk),
the first prediction/hypothesis is that an increase in bank capital
is expected to weaken the positive relationship between liquidity
creation and failure risk because such an increase will give rise to a
further increase (decrease) in liquidity creation (failure risk). Similarly, if liquidity creation is now associated negatively with failure
risk but the positive (negative) relationship of bank capital with
liquidity creation (failure risk) remains intact, the second hypothesis/prediction is that any increase in bank capital could strengthen
the negative relationship between liquidity creation and failure risk
because a further increase in liquidity creation that is induced by
an increase in bank capital will come with a further decrease in
bank failure risk. However, whenever the impact of bank capital
on liquidity creation and failure risk is the same (i.e., negative),
then the third (fourth) prediction/hypothesis is that any increase in
bank capital could strengthen (weaken) the relationship between
liquidity creation and bank failure risk, depending on whether the
relationship is a positive (negative) one. This is because if the impact of bank capital on liquidity creation and failure risk is negative, any increase in bank capital will give rise to a further decrease
in liquidity creation and a further decrease in failure risk, which in
turn strengthens (undermines) the positive (negative) relationship
between liquidity creation and bank failure risk.
3. Sample, data and variables descriptions
3.1. Sample and data
The sample of banks in this paper consists of all FDIC insured
U.S. institutions over the period from 2003: Q1 to 2014: Q4. The
2003 to 2014 time period is unique in that it contains data before,
during and after the largest financial crisis in recent history. It
starts five years before the Global Financial Crisis (GFC) and ends
five years after the GFC, to allow for the long-term effect of this exogenous shock on the relation between bank liquidity creation and
failure risk. The data is obtained from several sources. Quarterly
financial data is sourced from the Federal Deposit Insurance Corporation Statistics on Depository Institutions (FDIC SDI), which contains detailed on- and off-balance sheet information for all FDICinsured institutions. Bank failure information is obtained from the
FDIC’s failed banks list. This study makes use of the publicly available dataset of quarterly bank liquidity creation for U.S. commer-
cial banks over the observation period that was compiled by Allen
N. Berger and Christa Bouwman.4 Macroeconomic data, such as
GDP, Federal funds rate, yield spread and gross private savings, are
taken from the St. Louis Federal Reserve “FRED” public database.
Local market economic and demographic data are sourced from
the Bureau of Economic Analysis (BEA) (e.g., per capita personal
income, total employment and population) and the Federal Reserve
Board website (e.g., Senior Loan Officer Opinion Survey on Bank
Lending Practices (SLOOS)). All the aforementioned data sources
are merged together to construct the dataset for this study.
3.2. Variables
Bank failure risk serves as the dependent variable in this study.
Since the z-score has been extensively used to measure banks’ insolvency risk, we focus primarily on each bank’s z-score, which is
inversely related to the probability of bank insolvency. Specifically,
z-score = (roa + car)/σ (roa), where roa is the return on assets,
car is the ratio of equity to assets, and σ (roa) is the standard deviation of roa. For the derivation of σ (roa), we use the standard
deviation of a bank’s roa over the previous twelve quarters. Intuitively, the measure represents the number of standard deviations
that a bank’s roa must decline from its expected value to become
insolvent once equity is depleted (Roy, 1952). Accordingly, a higher
z-score indicates a lower bank risk. Because the z-score is highly
skewed, we follow the literature (e.g., Laeven and Levine, 2009)
and use the natural logarithm of the z-score as the risk measure.
For brevity, we use the label “z-score” in referring to the natural
logarithm of the z-score in the remainder of the paper.
For the bank liquidity creation, the main independent variable,
we use the measure proposed in the ground-breaking work of
Berger and Bouwman (2009). The BB measure (catfat) is a comprehensive single measure of bank liquidity creation that considers all the bank’s on-balance sheet and off-balance sheet activities.
To summarize briefly, the BB measure is the weighted sum of all
assets, liabilities, equity, and off-balance sheet activities. Since liquidity is created when banks finance illiquid assets (e.g., business
loans) with liquid liabilities (e.g., transaction deposits), a positive
weight of 1/2 is given to both illiquid assets and liquid liabilities.
Thus, transforming $1 of illiquid commercial loan into $1 of liquid
transaction deposit creates $1 of liquidity for the public. Similarly,
since banks destroy liquidity when they use illiquid liabilities (e.g.,
subordinated debt) or equity to finance liquid assets (e.g., cash,
treasury securities), a negative weight of −1/2 is given to liquid
assets, illiquid liabilities, and equity. Thus, taking $1 of liquid asset
from the public and giving the public $1 of illiquid subordinated
debt or equity destroys $1 of liquidity. All semi-liquid assets and
liabilities (e.g., residential real estate loans) are assigned a neutral
weight of zero. Off-balance sheet activities are assigned weights
consistent with those assigned to functionally similar on-balance
sheet activities. As is standard in the bank liquidity creation literature, the BB measure is normalized by gross total assets (GTA)5
so that the measure is comparable across banks, rather than dominated by the largest banks. The BB is calculated as follows:
cat f at = 0.5 × (il l iquid assets + l iquid l iabil ities
+ il l iquid guarantees ) + 0 × (semiliquid assets
+ semil iquid l iabil ities + semiliquid guarantees ) − 0.5 ×
(1)
(liquid assets + illiquid liabilities + equity + liquid guarantees
+ liquid derivatives )
4
We are grateful to Christa Bouwman for providing the bank liquidity creation
data. It is downloadable from Christa Bouwman’s personal website (https://sites.
google.com/a/tamu.edu/bouwman/data).
5
Gross total assets (GTA) equal total assets plus allowances for loan and lease
losses and the allocated risk transfer.
C. Zheng, A. (Wai Kong) Cheung and T. Cronje / Journal of Banking and Finance 108 (2019) 105651
This study controls for CAMELS-type variables because the
CAMELS6 rating is often used by U.S. regulators to evaluate the
safety and soundness of commercial banks. The values of the
CAMELS ratings are confidential and not publicly available, thus,
we use proxy variables to capture the key features of the CAMELS
system. They are: the ratio of equity capital to total assets as a
proxy for capital adequacy (ca); the ratio of all nonperforming
loans to total assets as a proxy for asset quality (aq); the cost-toincome ratio as a proxy for management capability (mc); the ratio
of net income to total equity as a proxy for earnings (roe); the ratio of cash and balances due from depository institutions to total
assets as a proxy for liquidity (liq); and the loans-to-deposits ratio as a proxy for sensitivity to market risk (ltdrt). Furthermore,
this study employs the following ratios as bank controls: the ratio of non-interest income to total income (noniirt) as a measure
of income diversification (DeYoung and Torna, 2013); the Basel I
risk-weighted assets of banks divided by total assets (ristak) as a
measure of credit risk (Berger and Bouwman, 2013); the ratio of
commercial real estate loans to total loans (commre) as a proxy for
commercial real estate investment (Berger and Bouwman, 2013);
and the natural logarithm of bank deposits (lndep) as a measure of
stable liabilities (Gatev and Strahan, 2006).
Turning to the macroeconomic and local market variables, the
Federal funds rate (fedfunds) is used to control for the effect of
monetary policy. This study also employs the spread between 3month U.S. T-Bills and 10-year U.S. Treasuries (spread), the log of
Gross Domestic Product (lngdp) and the log of Gross Private Savings (lngpsave) as macro control variables. Loan demand depends
on regional and nation-wide economic conditions as well as on
individual bank conditions. To control for varying levels of loan
demand, this study employs the Senior Loan Officer Opinion Survey on Bank Lending Practices (sloos). The sloos data is available
from 1982: Q2 onwards, and provides quarter-by-quarter national
level reports of how strong the loan demand was based on observations of senior loan officers at the application desks. It includes information such as the net percentage of domestic banks
reporting stronger demand for auto loans, credit card loans, government mortgage loans, commercial and industrial (C&I) loans,
etc. The Herfindhal-Hirschman Index (hhi_dep) is used to measure
the level of competition for deposits among banks in local markets.
Per capita personal income (lnperinc), total employment (lnemploy)
and total population (lnpop) are used as measures of local economic and demographic conditions. The definitions and abbreviations used for the main variables are contained in Appendix 1.
3.3. Descriptive statistics
Panel A of Table 1 contains summary statistics for the variables
used in the analysis. The mean value of z-score is 2.799, similar to
the z-score value in Imbierowicz and Rauch (2014) who find that
the average z-score for the sample of all U.S. commercial banks
during the period 1998–2010 is 3.434. The average bank liquidity
creation scaled by gross total assets (catfat) is 0.311, which is generally comparable to those in Berger et al. (2017b). For example,
the mean value of catfat is 0.230 in their study. The mean value
of capital adequacy (ca) and asset quality (aq) indicates that the
sample banks have strong capital positions and higher quality of
assets.
Panel B provides Pearson correlation coefficients across dependent variables and key independent variables. Contrary to our expectations, the bank liquidity creation variable, catfat, is correlated
to bank failure risk significantly and positively, as measured by the
6
Each acronym of CAMELS stands for capital adequacy; asset quality; management; earnings; liquidity; and sensitivity to market risk.
5
z-score and faildummyq. However, these correlations give a general
idea only of the bivariate relations, and do not control for other
factors (e.g., bank capital) in a rigorous fashion as shown in our
regression analyses below. The data confirms the negative relation
between changes in bank capital (࢞ca) and bank failure risk, proxied by the z-score and faildummyq, indicating that equity acts as
a buffer against the probability of bank failure. These results suggest that ignoring bank capital may lead to erroneous conclusions
about the effects of bank liquidity creation on failure risk.
Panel C reports the number and percentage of bank failures by
year in our sample from 2003 to 2014. As shown, failures of the
U.S. banks were relatively infrequent between 20 03 and 20 07, but
increased dramatically in the second half of 2008. Indeed, U.S. regulators shut down 25 banks in 2008 versus only 10 banks during
the entire 20 03–20 07 period. Bank failure remained at elevated
levels: 140 in 2009, 157 in 2010, and 92 in 2011; before declining
to 51 in 2012, 24 in 2013, and 18 in 2014. The Panel D univariate
test shows that failed banks differ strongly from non-failed banks.
We find that failed banks on average create more liquidity than
non-failed banks (catfat). These results are in line with our correlation findings in Panel B. The evidence here further reiterates that
ignoring the interactions between bank liquidity creation and bank
capital may lead to flawed conclusions about the impact of liquidity creation on failure risk. As expected, and consistent with previous empirical evidence (e.g., Cole and White, 2012), failed banks on
average have lower capital ratios (ca), but higher costs-to-income
(mc) and loans-to-deposits ratios (ltdrt). They have a larger fraction
of commercial real estate loans (commre) and nonperforming loans
(aq). Not surprisingly, failed banks have a negative average return
on equity (roe), and a high level of credit risk exposure (ristak). Interestingly, the reported liquidity ratio (liq) is higher for the failed
banks. This finding is consistent with Cleary and Hebb (2016) who
find that troubled banks may build up liquidity in anticipation of
future distress.
4. Multivariate analysis
4.1. Empirical model and main results
We specify the following empirical model to examine the interaction effect of bank capital and liquidity creation on the failure
risk of individual banks:
Bank Failure Riski, t
= β0 + β1 Liquidity Creationi,t−1
+ β2 Liquidity Creationi,t−1 × Bank Capitali,t
+β3 Bank Capitali,t + β4 Bank Characteristicsi,t−1
+ β5 Macroeconomic Characteristicsi,t−1
+ β6 Local Market Characteristicsi,t−1
+ T ime, State and Bank F ixed E f f ects + εi,t
(2)
In this equation, the dependent variable is Bank Failure Riski,t ,
measured by a bank’s z-score, referring to the failure risk of bank i
in the quarter t. Liquidity Creationi,t -1 is proxied by the BB measure
(catfat). Since capital ratios in subsequent years are usually highly
correlated, we take the first difference of this variable (࢞Bank
Capitali, t = Bank Capitali, t − Bank Capitali,t-1 ) to measure the incremental changes in bank capital that capture the liquidity-risk sharing role, which is the variable we are actually interested in. Given a
relationship between changes in liquidity creation and bank failure
risk (i.e., β 1 ), the interaction term of Liquidity Creation and ࢞Bank
Capital is used to test for the four hypotheses/predictions. Further, we control for bank-specific characteristics,7 macroeconomic
conditions and local market influences. Among the controls, we
7
We include bank capital (ca) as a control variable to capture any effects other
than the liquidity-risk sharing role of incremental changes in bank capital (࢞ca).
6
C. Zheng, A. (Wai Kong) Cheung and T. Cronje / Journal of Banking and Finance 108 (2019) 105651
Table 1
Descriptive statistics.
Panel A: Summary statistics
(1)
Variables
N
(2)
Mean
(3)
Sd
(4)
Min
(5)
P25
(6)
P50
(7)
P75
(8)
Max
z-score
faildummyq
faildummyy
catfat
ca
࢞ca
aq
mc
roe
liq
ltdrt
noniirt
ristak
commre
lndep
fedfunds
spread
lngdp
lngpsave
crisisdummy
sloos
hhi_dep
lnperinc
lnemploy
lnpop
2.799
0.001
0.005
0.311
0.108
6.95e-06
0.003
0.784
0.047
0.065
0.772
0.852
0.689
0.248
11.795
1.604
2.036
9.581
7.961
0.214
0.402
0.476
10.450
10.693
11.296
0.414
0.032
0.073
0.178
0.036
0.051
0.005
0.169
0.077
0.062
0.201
1.744
0.132
0.153
1.170
1.869
1.140
0.115
0.181
0.410
25.330
0.299
0.263
1.939
1.831
1.734
0.000
0.000
−0.155
0.049
−0.171
−0.001
0.479
−0.363
0.008
0.240
−5.052
0.342
0.001
9.865
0.070
−0.512
9.339
7.702
0.000
−60.400
0.027
9.898
7.351
7.849
2.502
0.000
0.000
0.196
0.086
−0.024
0.000
0.689
0.021
0.026
0.645
0.289
0.604
0.129
10.972
0.130
1.529
9.502
7.787
0.000
−16.700
0.250
10.267
9.207
9.933
2.774
0.000
0.000
0.322
0.100
−3.33e-06
0.001
0.761
0.046
0.042
0.788
0.578
0.699
0.233
11.638
0.510
2.249
9.587
7.922
0.000
1.400
0.402
10.432
10.223
10.885
3.084
0.000
0.000
0.435
0.120
0.024
0.004
0.842
0.084
0.079
0.910
1.041
0.782
0.344
12.407
2.940
2.875
9.667
8.137
0.000
19.600
0.620
10.611
12.138
12.625
3.902
1.000
1.000
0.718
0.272
0.171
0.031
1.637
0.230
0.343
1.262
11.379
0.976
0.691
16.064
5.260
3.578
9.786
8.230
1.000
45.500
1.000
11.243
15.496
16.088
296,538
297,610
73,690
297,607
297,607
297,609
297,609
297,566
297,607
297,607
297,607
297,472
297,609
297,607
297,609
297,609
297,607
297,607
297,607
297,610
297,607
297,607
297,508
297,508
297,508
Panel B: Correlation matrix of key variables
z-score
faildummyq
catfat
࢞ca
z-score
faildummyq
catfat
࢞ca
1.000
−0.042∗ ∗ ∗
−0.162∗ ∗ ∗
0.082∗ ∗ ∗
1.000
0.009∗ ∗ ∗
−0.046∗ ∗ ∗
1.000
0.025∗ ∗ ∗
1.000
Panel C: Number of failed banks by year from 2003 to 2014
Year
Bank count
Number of failed banks
Percentage of failed banks
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
All
7074
6977
6882
6797
6717
6593
6393
6171
5902
5751
5548
5337
76,142
3
4
0
0
3
25
140
157
92
51
24
18
517
0.04%
0.06%
0.00%
0.00%
0.04%
0.38%
2.19%
2.54%
1.56%
0.89%
0.43%
0.34%
0.68%
Panel D: Univariate test
Variables
Non-failed banks
Failed banks
Mean difference
t-statistics for equal means
catfat
࢞ca
ca
aq
mc
roe
liq
ltdrt
noniirt
ristak
commre
lndep
0.311
−0.001
0.108
0.003
0.784
0.047
0.065
0.772
0.852
0.689
0.248
11.795
0.363
−0.015
0.055
0.005
1.239
−0.274
0.091
0.801
−0.024
0.776
0.352
11.921
−0.052∗ ∗ ∗
0.014∗ ∗ ∗
0.053∗ ∗ ∗
−0.002∗ ∗ ∗
−0.455∗ ∗ ∗
0.321∗ ∗ ∗
−0.026∗ ∗ ∗
−0.029∗
0.876∗ ∗ ∗
−0.087∗ ∗ ∗
−0.104∗ ∗ ∗
−0.126
−5.05
25.24
26.05
−5.50
−46.91
72.80
−7.34
−2.47
8.70
−11.47
−11.78
−1.86
Note: There are four panels in this table. Panel A presents summary statistics for all variables used in our models. Panel B reports Pearson correlation
matrix of key variables. Panel C lists the number of and percentage of failed banks by year in our sample. Panel D provides univariate test results on
means for the sample of failed and non-failed banks. ∗ , ∗ ∗ , and ∗ ∗ ∗ represent significance at the 10%, 5% and 1% level, respectively. See Appendix 1 for
variable definitions.
C. Zheng, A. (Wai Kong) Cheung and T. Cronje / Journal of Banking and Finance 108 (2019) 105651
7
Table 2
Baseline OLS regression.
Variables
catfat
࢞ca
catfat × ࢞ca
Bank-specific characteristics:
ca
aq
mc
roe
liq
ltdrt
noniirt
ristak
commre
lndep
Macroeconomic characteristics:
fedfunds
spread
lngdp
lngpsave
Local economic characteristics:
hhi_dep
lnpop
sloos
lnperinc
lnemploy
Constant
Year_quarter FE
Bank FE
State FE
Observations
Adjusted R2
Dependent variable: z-score
(1)
(2)
(3)
(4)
0.096∗ ∗ ∗
(0.01)
10.913∗ ∗ ∗
(0.21)
5.565∗ ∗ ∗
(0.54)
0.067∗ ∗ ∗
(0.01)
9.767∗ ∗ ∗
(0.21)
7.019∗ ∗ ∗
(0.52)
0.018∗
(0.01)
8.617∗ ∗ ∗
(0.17)
6.553∗ ∗ ∗
(0.40)
0.019∗ ∗
(0.01)
8.609∗ ∗ ∗
(0.17)
6.579∗ ∗ ∗
(0.40)
8.734∗ ∗ ∗
(0.05)
−3.762∗ ∗ ∗
(0.08)
−0.543∗ ∗ ∗
(0.01)
−1.008∗ ∗ ∗
(0.04)
0.009
(0.01)
−0.135∗ ∗ ∗
(0.01)
0.005∗ ∗ ∗
(0.00)
−0.101∗ ∗ ∗
(0.02)
0.014∗ ∗ ∗
(0.00)
0.001
(0.00)
8.076∗ ∗ ∗
(0.05)
−3.843∗ ∗ ∗
(0.08)
−0.416∗ ∗ ∗
(0.01)
−0.567∗ ∗ ∗
(0.03)
0.004
(0.01)
−0.035∗ ∗ ∗
(0.01)
0.004∗ ∗ ∗
(0.00)
−0.056∗ ∗ ∗
(0.01)
0.012∗ ∗ ∗
(0.00)
0.001∗ ∗
(0.00)
8.226∗ ∗ ∗
(0.04)
−0.010
(0.03)
−0.098∗ ∗ ∗
(0.01)
0.975∗ ∗ ∗
(0.01)
−0.046∗ ∗ ∗
(0.01)
0.006
(0.01)
0.001∗ ∗ ∗
(0.00)
−0.024∗ ∗ ∗
(0.01)
−0.005∗ ∗ ∗
(0.00)
0.000
(0.00)
8.228∗ ∗ ∗
(0.04)
−0.008
(0.03)
−0.098∗ ∗ ∗
(0.01)
0.975∗ ∗ ∗
(0.01)
−0.047∗ ∗ ∗
(0.01)
0.006
(0.01)
0.001∗ ∗ ∗
(0.00)
−0.024∗ ∗ ∗
(0.01)
−0.005∗ ∗ ∗
(0.00)
0.000
(0.00)
0.054∗ ∗ ∗
(0.00)
0.054∗ ∗ ∗
(0.00)
−0.356∗ ∗ ∗
(0.01)
0.158∗ ∗ ∗
(0.01)
0.048∗ ∗ ∗
(0.00)
0.052∗ ∗ ∗
(0.00)
−0.198∗ ∗ ∗
(0.01)
0.205∗ ∗ ∗
(0.01)
−0.082
(0.07)
0.052∗
(0.03)
0.659
(0.65)
0.043
(0.18)
−0.002
(0.00)
0.061∗ ∗ ∗
(0.01)
−0.000
(0.00)
0.232∗ ∗ ∗
(0.01)
−0.068∗ ∗ ∗
(0.01)
1.891∗ ∗ ∗
(0.06)
No
Yes
No
220,188
0.462
−0.005∗ ∗ ∗
(0.00)
0.012∗ ∗ ∗
(0.00)
0.000∗ ∗ ∗
(0.00)
0.050∗ ∗ ∗
(0.00)
−0.019∗ ∗ ∗
(0.00)
1.801∗ ∗ ∗
(0.04)
No
No
Yes
220,188
0.671
−0.001∗
(0.00)
−0.002
(0.00)
−0.001∗ ∗
(0.00)
0.016∗ ∗ ∗
(0.00)
0.001
(0.00)
−5.336
(7.01)
Yes
Yes
Yes
220,188
0.959
1.703∗ ∗ ∗
(0.01)
Yes
Yes
Yes
220,260
0. 959
Note: This table presents the results of multivariate OLS regression model (i.e., Eq. (2)) analysing the interactive effects of bank capital and liquidity creation
on the insolvency risk of individual banks. Bank liquidity creation is proxied by the BB measure, i.e., catfat. Change in bank capital (࢞cai,t ) is the absolute
change from the year t-1 to t of bank i’s equity-to-total assets ratio. z-score measures the distance from insolvency. A higher z-score value indicates that a
bank is less likely to default. The variable descriptions are in Appendix 1. Regressions include different fixed effects (FE) estimations (Year_quarter FE, State
FE and Bank FE). Columns (1)–(3) include bank controls, macroeconomic controls and local economic controls; Column (4) includes bank controls only.
Robust standard errors clustered by bank are in parentheses. ∗ , ∗ ∗ , and ∗ ∗ ∗ represent significance at the 10%, 5% and 1% level, respectively.
also include bank, year-quarter and state fixed effects. Justification
for the inclusion of bank fixed effects is derived from the argument that unobserved, time-invariant bank-level heterogeneity exists. The inclusion of year-quarter fixed effects captures changes
specific to individual year-quarters. We also include state fixed effects to control for the influences of unknown time-invariant differences across states. Standard errors are clustered at bank level
to account for cross sectional dependence.
Table 2 presents the regression results. As shown, the coefficient of the Liquidity Creation variable (catfat) is positive and sig-
nificant across all specifications, indicating increasing liquidity creation results in higher z-score and reduced riskiness. Consistent
with our second prediction/hypothesis, we find a positive and significant coefficient for interaction terms between liquidity creation
and changes in bank capital. This suggests that given the incremental increases in bank capital, a high level of liquidity creation
is associated with a high z-score value, which indicates a low level
of bank failure risk. In other words, any increase in bank capital strengthens the negative relationship between liquidity creation and bank failure risk. Further, the coefficient of changes in
8
C. Zheng, A. (Wai Kong) Cheung and T. Cronje / Journal of Banking and Finance 108 (2019) 105651
bank capital (࢞ca) is statistically significant and related positively
to z-score, implying that high changes in capital ratio are associated with low levels of insolvency risk. All in all, the result verifies
the importance of the liquidity-risk sharing role of bank capital.
Specifically, bank capital acts as a buffer to absorb the higher level
of illiquidity risk that comes with high levels of liquidity creation.
More capital, in turn, reduces the probability of bank failure and
enables banks to create more liquidity. Therefore, the negative relationship between liquidity creation and failure risk is moderated
positively (i.e., strengthened) by (changes in) bank capital. This evidence is in line with the “risk absorption” hypothesis in the literature (see Section 2.1).
4.2. Robustness checks
We check the robustness of these main findings in terms of (1)
different bank size groups; (2) crisis vs. non-crisis periods; (3) alternative measures of bank failure risk; (4) alternative measures of
bank liquidity creation; (5) different bank capital measures; and
(6) the role of managerial ability. Our results remain unchanged
and provide qualitatively similar conclusions.
4.2.1. Size effect
We re-estimate the OLS regression model Eq. (2) using subsamples of banks based on different size cut-offs. Four different sets of
cut-offs are used. First, following the methodology of Berger and
Bouwman (2009), the sample is split into large banks (GTA exceeding $3 billion), medium banks (GTA between $1 billion and $3 billion), and small banks (GTA up to $1 billion). Second, we use alternative cut-offs ($5 billion and $10 billion, respectively) separating
medium and large banks while the small bank definition remains
at the $1 billion cut-off. Third, we run regressions categorizing all
banks as either small or large using a cut-off of $10 billion GTA.
Finally, very large banks may be considered too-big-to-fail (TBTF),
and in the event of distress, they tend to receive government support. To make sure that our large bank results are not overly influenced by TBTF banks, we re-run our $10 billion cut-off analysis
while excluding these banks. Following the 2010 Dodd-Frank Act,
we define TBTF banks as those with GTA exceeding $50 billion.
As shown in Table 3, the coefficient of catfat is positive and
statistically significant for small banks while it is insignificant for
medium and large banks, indicating that the positive relation between liquidity creation and z-score is more prominent for small
banks. Table 3 also shows that the coefficient of the interaction
terms catfat × ࢞ca remains positive and statistically significant in
all subsamples of small banks, but it is statistically insignificant,
or only weakly significant, for medium and large banks. This is
not surprising, given that access to external funds is limited for
small banks. Since small banks face greater information asymmetry which makes it costly for them to access the interbank market,
they have strong incentives to hoard capital in order to avoid financing constraints and costly default. Thus, small banks may increase their capital ratios when they face increasing illiquidity resulting from liquidity creation (Distinguin et al., 2013). This would,
in turn, reduce the probability of bank failure and enhance the
ability of banks to create liquidity. In contrast, large banks tend to
have lower capital ratios. Large banks can more easily access funding from national or international capital markets and incur lower
costs when raising new equity on short notice. Large banks may
use their diversification advantage to operate with greater leverage
and lower capital ratios, and (for the largest, most inter-connected
financial firms) may have access to explicit and implicit government protection (“too-big-to-fail” policy). Therefore, they may underestimate liquidity risk and be less likely to strengthen their capital accordingly.
4.2.2. Crisis vs. non-crisis periods
During the GFC, banks tended to hold a large capital buffer to
strengthen their solvency and to better assume losses arising from
forced early liquidation of illiquid assets and mortgage backed securities at fire sale prices, so a bank capital buffer played a more
prominent role in alleviating the illiquidity risk from liquidity creation during the crisis period than during the non-crisis period.
We examine how the effect of Liquidity Creation on Bank Failure
Risk varied with the changes in bank capital during the crisis and
non-crisis periods using a triple-interaction term in the following
regression model:
Bank F ailure Riski, t
= β0 + β1 Liquidity Creationi,t−1
+ β2 Bank C apitali,t + β3C risisdummyt
+ β4 Liquidity C reationi,t−1 × C risisdummyt
+β5 Liquidity Creationi,t−1 × Bank Capitali,t
+ β6Crisisdummyt × Bank Capitali,t
(3)
+ β7 Liquidity Creationi,t−1 × Bank C apitali,t × C risisdummyt
+ β8 Bank Characteristicsi,t−1
+ β9 Macroeconomic Characteristicsi,t−1
+ β10 Local Market Characteristicsi,t−1
+ T ime, State and Bank F ixed E f f ects + εi,t
The triple interaction term, Liquidity Creation × ࢞Bank Capital × Crisisdummy, is our main variable of interest. Recalling that
a positive coefficient of Liquidity Creation implies that increased
liquidity creation is associated with a higher z-score value, i.e., a
lower probability of bank failure, the positive sign on the triple
interaction term would suggest that the marginal effect of Liquidity Creation on Bank Failure Risk is stronger for banks with higher
incremental increases in capital during the financial crisis period
compared with the non-crisis period. Following existing literature
(e.g., Berger and Bouwman, 2013), we assigned the crisisdummy
variable a value of one from the third quarter of 2007 to the fourth
quarter of 2009, and zero otherwise. Table 4 contains the crisis regression results.
As shown in Panel A of Table 4, the coefficient of the triple interaction terms, catfat × ࢞ca × crisisdummy, is large, positive, and
highly statistically significant and economically non-trivial across
all specifications. These findings suggest that during the crisis period, bank liquidity creation is related to bank failure risk negatively for banks with high incremental increases in bank capital.
Furthermore, the negative and statistically significant coefficient of
the interaction terms, catfat × crisisdummy, indicates that, in the
absence of any changes in bank capital, bank liquidity creation is
the major contributor to bank failure risk during the crisis period
and, thus, supports our preliminary results in Section 3. Finally,
consistent with the main results in Table 2, the coefficient of the
Liquidity Creation variable (catfat) is positive and statistically significant in all specifications, and the coefficient of the interaction
terms of catfat with ࢞ca remains positive and statistically significant, suggesting that bank liquidity creation affects a bank’s z-score
(the probability of bank failure) positively (negatively). This relationship is strengthened by incremental increases in bank capital.
Findings here suggest that the interaction effect of liquidity creation and bank capital on failure risk is more pronounced during
the GFC, supporting the view that the effects of capital on survival could be even stronger during crises, and banks with relatively high capital ratios are in better shape during these times
(Berger and Bouwman, 2013).
The 20 07–20 09 financial crisis and subsequent government assistance (e.g., TARP capital bailout) affected the banking sector in
many aspects. This raises a potential concern that our results may
be driven by the crisis and post-crisis data. To mitigate this concern, we use pre-crisis data only to validate the robustness of the
main results. The results, reported in Panel B of Table 4, show that
C. Zheng, A. (Wai Kong) Cheung and T. Cronje / Journal of Banking and Finance 108 (2019) 105651
9
Table 3
Results of OLS estimates sorted by bank size.
Panel A: $1 billion and $3 billion size cutoff
VARIABLES
catfat
࢞ca
catfat × ࢞ca
Baseline Controls
Year_quarter FE
Bank FE
State FE
Observations
Adjusted R2
Dependent variable: z-score
(1)
(2)
(3)
Small banks
0.021∗ ∗
(0.01)
8.765∗ ∗ ∗
(0.16)
6.654∗ ∗ ∗
(0.38)
Yes
Yes
Yes
Yes
202,791
0.965
Medium banks
0.026
(0.05)
8.394∗ ∗ ∗
(1.29)
6.027∗ ∗
(2.90)
Yes
Yes
Yes
Yes
10,905
0.910
Large banks
0.075
(0.05)
10.724∗ ∗ ∗
(0.88)
−0.763
(1.47)
Yes
Yes
Yes
Yes
6492
0.930
Dependent variable: z-score
(1)
Small banks
(2)
Medium banks
(3)
Large banks
0.021∗ ∗
(0.01)
8.765∗ ∗ ∗
(0.16)
6.654∗ ∗ ∗
(0.38)
Yes
Yes
Yes
Yes
202,791
0.965
0.040
(0.05)
8.344∗ ∗ ∗
(1.13)
5.652∗ ∗
(2.42)
Yes
Yes
Yes
Yes
12,882
0.901
0.073
(0.06)
11.621∗ ∗ ∗
(0.89)
−1.985
(1.61)
Yes
Yes
Yes
Yes
4515
0.952
Dependent Variable: Z-score
(1)
Small banks
(2)
Medium banks
(3)
Large banks
0.021∗ ∗
(0.01)
8.765∗ ∗ ∗
(0.16)
6.654∗ ∗ ∗
(0.38)
Yes
Yes
Yes
Yes
202,791
0.965
0.067
(0.04)
8.930∗ ∗ ∗
(1.02)
4.000∗
(2.21)
Yes
Yes
Yes
Yes
14,448
0.907
0.010
(0.05)
11.487∗ ∗
(1.16)
−1.118
(2.04)
Yes
Yes
Yes
Yes
2949
0.954
Panel B: $1 billion and $5 billion size cutoff
Variables
catfat
࢞ca
catfat × ࢞ca
Baseline Controls
Year_quarter FE
Bank FE
State FE
Observations
Adjusted R2
Panel C: $1 billion and $10 billion size cutoff
Variables
catfat
࢞ca
catfat × ࢞ca
Baseline Controls
Year_quarter FE
Bank FE
State FE
Observations
Adjusted R2
Panel D: $10 billion size cutoff
Variables
catfat
࢞ca
catfat × ࢞ca
Baseline Controls
Year_quarter FE
Bank FE
State FE
Observations
Adjusted R2
Dependent variable: z-score
(1)
Small banks
(2)
Large banks with TBTF banks∗
(3)
Large banks without TBTF banks
0.018∗
(0.01)
8.597∗ ∗ ∗
(0.17)
6.669∗ ∗ ∗
(0.40)
Yes
Yes
Yes
Yes
217,239
0.960
0.010
(0.05)
11.487∗ ∗ ∗
(1.16)
−1.118
(2.04)
Yes
Yes
Yes
Yes
2949
0.954
0.041
(0.07)
12.004∗ ∗ ∗
(1.41)
−1.007
(2.69)
Yes
Yes
Yes
Yes
1809
0.961
Note: This table presents the results of OLS model (i.e., Eq. (2)) examining the interactive effects of bank capital and liquidity creation on the insolvency risk
of individual banks across three subsamples. Bank liquidity creation is proxied by the BB measure, i.e., catfat. Change in Bank Capital (࢞cai,t ) is the absolute
change from the year t-1 to t of bank i’s equity-to-total assets ratio. z-score measures the distance from insolvency. A higher z-score value indicates that a
bank is less likely to default. We sort the sample banks into large, medium and small banks based on different size cut-offs across Panels A–D. The variable
descriptions are in Appendix 1. For brevity, we report only specifications that include all the control variables and all fixed effects in this table. Robust
standard errors clustered by bank are in parentheses. ∗ , ∗ ∗ , and ∗ ∗ ∗ represent significance at the 10%, 5% and 1% level, respectively. ∗ TBTF banks = “too-bigto-fail” banks.
10
C. Zheng, A. (Wai Kong) Cheung and T. Cronje / Journal of Banking and Finance 108 (2019) 105651
Table 4
Results of OLS estimates during the crisis and non-crisis periods.
Panel A: The OLS regression results during the crisis and non-crisis periods
Dependent variable: z-score
Variables
(1)
catfat
࢞ca
catfat × ࢞ca
crisisdummy
catfat × crisisdummy
crisisdummy × ࢞ca
catfat × ࢞ca × crisisdummy
Bank Controls
Other Baseline Controls
Year_quarter FE
Bank FE
State FE
Observations
Adjusted R2
0.063∗ ∗ ∗
(0.01)
12.155∗ ∗ ∗
(0.24)
3.396∗ ∗ ∗
(0.62)
−0.054∗ ∗ ∗
(0.00)
−0.027∗ ∗ ∗
(0.01)
−3.809∗ ∗ ∗
(0.35)
6.594∗ ∗ ∗
(0.98)
Yes
Yes
No
Yes
No
220,188
0.468
Panel B: The OLS regression results using pre-crisis data only
Dependent variable: z-score
Variables
(1)
catfat
࢞ca
catfat × ࢞ca
Baseline Controls
Year_quarter FE
Bank FE
State FE
Observations
Adjusted R2
∗∗∗
−0.171
(0.03)
8.564∗ ∗ ∗
(0.27)
5.019∗ ∗ ∗
(0.68)
Yes
No
Yes
No
86,744
0.535
(2)
(3)
(4)
0.057∗ ∗ ∗
(0.01)
11.284∗ ∗ ∗
(0.23)
4.150∗ ∗ ∗
(0.60)
−0.061∗ ∗ ∗
(0.00)
−0.031∗ ∗ ∗
(0.00)
−4.603∗ ∗ ∗
(0.36)
8.055∗ ∗ ∗
(0.99)
Yes
Yes
No
No
Yes
220,188
0.676
0.021∗ ∗
(0.01)
8.798∗ ∗ ∗
(0.18)
5.298∗ ∗ ∗
(0.44)
−0.032
(0.12)
−0.019∗ ∗ ∗
(0.00)
−0.531∗ ∗
(0.26)
3.759∗ ∗ ∗
(0.72)
Yes
Yes
Yes
Yes
Yes
220,188
0.960
0.022∗ ∗
(0.01)
8.790∗ ∗ ∗
(0.18)
5.323∗ ∗ ∗
(0.44)
−0.013∗ ∗ ∗
(0.00)
−0.019∗ ∗ ∗
(0.00)
−0.533∗ ∗
(0.26)
3.759∗ ∗ ∗
(0.72)
Yes
No
Yes
Yes
Yes
220,260
0.960
(2)
(3)
0.005
(0.01)
6.522∗ ∗ ∗
(0.25)
8.881∗ ∗ ∗
(0.63)
Yes
No
No
Yes
86,744
0.746
−0.057∗ ∗ ∗
(0.02)
8.327∗ ∗ ∗
(0.22)
5.167∗ ∗ ∗
(0.52)
Yes
Yes
Yes
Yes
86,744
0.972
Note: Panel A of this table presents the results of multivariate OLS regression model (i.e., Eq. (3)) examining the interactive effects of bank capital and liquidity creation
on the insolvency risk of individual banks during the crisis and non-crisis periods. Bank liquidity creation is proxied by the BB measure, i.e., catfat. Change in Bank
Capital (࢞cai,t ) is the absolute change from the year t-1 to t of bank i’s equity-to-total assets ratio. z-score measures the distance from insolvency. A higher z-score
value indicates that a bank is less likely to default. crisisdummy is a dummy variable equal to one during 20 07:Q3-20 09:Q4, and zero otherwise. The key explanatory
variable is the triple interaction term catfat × ࢞ca × crisisdummy. Panel B presents the results based on the pre-crisis data only to address the concern that our results
may be driven by the crisis period and beyond. The variable descriptions are in Appendix 1. Robust standard errors clustered by bank are in parentheses. ∗ , ∗ ∗ , and ∗ ∗ ∗
represent significance at the 10%, 5% and 1% level, respectively.
the coefficient of the interaction terms, catfat × ࢞ca, is still positive
and statistically significant at the 1% level, even when the crisis
and post-crisis periods are excluded, alleviating the concern.
4.2.3. Alternative measures of bank failure risk
While we focus on examining the z-score of individual banks as
a measure of a bank’s insolvency risk, the results are robust to using alternative bank failure risk measures. In this section, we use a
binary performance variable as a proxy for bank failure risk. First,
we use quarterly data to predict whether a bank will fail in the
next 1, 2 and 3 quarters (short-term horizon), and in the next 8,
12 and 20 quarters (long-term horizon) after a specific financial
report date (faildummyq). If failure occurs, it is flagged as “bad”
and is assigned the value of one. Otherwise, it is flagged as “good”
and is assigned the value of zero. Second, we use end-of-year data
to predict whether a bank will fail sometime in the next 1, 2, 3
and 5 years after a specific financial report date (faildummyy). Note
that these two measures are related to bank failure risk positively
while z-score is related to bank failure risk negatively. Results in
Panels A and B, Table 5, corroborate those obtained in Table 2, and
do not change our main conclusion that incremental increases in
bank capital strengthen the negative relationship between liquidity
creation and failure risk. Across all specifications, the Liquidity Creation variable (catfat) enters a negative and statistically significant
coefficient, suggesting liquidity creation is related to bank failure
risk negatively. The coefficient of the interaction term, catfat × ࢞ca,
is again statistically significant and negative, regardless of whether
the bank failure risk dummy is measured in terms of faildummyq
or faildummyy.
In Panels A and B, we estimate the regression model using OLS
specification, thereby, imposing a linear probability model on the
binary choice dependent variable, rather than the more commonlyused logit form.8 Nonetheless, to check the robustness of our re-
8
We do so for three reasons. First, the logit model suffers incidental parameter bias and inconsistent estimation problems if fixed effects are included. That is,
the inclusion of fixed effects in a logit specification would cause the number of
parameters to grow with the number of observations, meaning that the parameter
estimates cannot converge to their true value as the sample size increases, yielding
biased parameter and standard error estimates (Berger et al., 2017a). Second, our focus is to find out what variables are useful in explaining bank failure, rather than in
forecasting the predicted values of bank failure. As such, the issue of whether the
predicted values may go beyond zero and one is not our concern. Third, the key
variable of interest is the interaction term between changes in bank capital and liquidity creation. In linear regressions, any interaction effect is fully captured by the
C. Zheng, A. (Wai Kong) Cheung and T. Cronje / Journal of Banking and Finance 108 (2019) 105651
11
Table 5
Alternative measures of bank failure risk.
Panel A: Quarterly data for bank failure risk dummy
Dependent variable: faildummyq
Variables
(1) Next 1 quarter
(2) Next 2 quarters
catfat
࢞ca
catfat × ࢞ca
Baseline Controls
Year_quarter FE
Bank FE
State FE
Observations
Adjusted R2
−0.008∗ ∗ ∗
(0.00)
−0.210∗ ∗ ∗
(0.03)
−0.169∗ ∗ ∗
(0.06)
Yes
Yes
Yes
Yes
221,159
0.086
−0.012∗ ∗ ∗
(0.00)
−0.295∗ ∗ ∗
(0.03)
−0.205∗ ∗ ∗
(0.07)
Yes
Yes
Yes
Yes
221,159
0.129
(3) Next 3 quarters
(4) Next 8 quarters
(5) Next 12 quarters
(6) Next 20 quarters
−0.012∗ ∗ ∗
(0.00)
−0.295∗ ∗ ∗
(0.03)
−0.205∗ ∗ ∗
(0.07)
Yes
Yes
Yes
Yes
221,159
0.129
−0.094∗ ∗ ∗
(0.01)
−0.855∗ ∗ ∗
(0.08)
−1.751∗ ∗ ∗
(0.22)
Yes
Yes
Yes
Yes
221,159
0.398
−0.105∗ ∗ ∗
(0.01)
−0.729∗ ∗ ∗
(0.08)
−1.459∗ ∗ ∗
(0.22)
Yes
Yes
Yes
Yes
221,159
0.514
−0.074∗ ∗ ∗
(0.01)
−0.423∗ ∗ ∗
(0.07)
−0.707∗ ∗ ∗
(0.18)
Yes
Yes
Yes
Yes
221,159
0.741
Panel B: End-of-year data for bank failure risk dummy
Dependent variable: faildummyy
Variables
(1) next 1 year
(2) next 2 years
catfat
࢞ca
catfat × ࢞ca
Baseline Controls
Year_quarter FE
Bank FE
State FE
Observations
Adjusted R2
−0.049∗ ∗ ∗
(0.01)
−1.090∗ ∗ ∗
(0.11)
−0.493∗
(0.28)
Yes
Yes
Yes
Yes
73,233
0.284
−0.087∗ ∗ ∗
(0.01)
−1.299∗ ∗ ∗
(0.14)
−1.642∗ ∗ ∗
(0.38)
Yes
Yes
Yes
Yes
73,233
0.419
Panel C: Results of nonlinear (logit) model
Dependent variable: faildummyq
Variables
(1) All banks
(2) Small banks
catfat
࢞ca
catfat × ࢞ca
Baseline Controls
Year_quarter FE
Bank FE
State FE
Observations
Pseudo R2
∗∗∗
−0.007
(0.00)
−0.305∗ ∗ ∗
(0.03)
−0.145∗
(0.09)
Yes
No
No
No
221,159
0.586
∗∗∗
−0.006
(0.00)
−0.281∗ ∗ ∗
(0.03)
−0.146∗
(0.09)
Yes
No
No
No
203,657
0.593
(3) next 3 years
(4) next 5 years
−0.102∗ ∗ ∗
(0.02)
−1.197∗ ∗ ∗
(0.13)
−1.420∗ ∗ ∗
(0.37)
Yes
Yes
Yes
Yes
73,233
0.532
−0.070∗ ∗ ∗
(0.01)
−0.680∗ ∗ ∗
(0.09)
−0.636∗ ∗ ∗
(0.29)
Yes
Yes
Yes
Yes
73,233
0.761
(3) Medium banks
(4) Large banks
−0.015
(0.01)
−0.515∗ ∗
(0.22)
−0.482
(0.52)
Yes
No
No
No
10,993
0.560
−0.024∗ ∗
(0.01)
−0.526∗ ∗ ∗
(0.16)
0.275
(0.38)
Yes
No
No
No
6509
0.602
Note: This table presents the results of multivariate regression model (i.e., Eq. (2)) in which the dependent variable is alternative measures of Bank Failure Risk. In Panel
A, we use quarterly data to predict whether a bank will fail in the next 1, 2 and 3 quarters (short-term horizon), and in the next 8, 12 and 20 quarters (long-term
horizon) after a specific financial report date (faildummyq). If failure occurs, it is flagged as “bad” and is assigned the binary value of one. Otherwise, it is flagged as
“good” and is assigned the binary value of zero. In Panel B, we use end-of-year data to predict whether a bank will fail sometime in the next 1, 2, 3 and 5 years
after a specific financial report date (faildummyy). Panel C uses a logit specification instead of OLS and shows marginal effects for all banks and for different sizes of
banks. Large banks, medium banks, and small banks are banks with more than $3 billion, between $3 billion and $1 billon, and less than $1 billion gross total asset
(GTA), respectively. Bank liquidity creation is proxied by the BB measure, i.e., catfat. Change in bank capital (࢞cai,t ) is the absolute change from the year t-1 to t of
bank i’s equity-to-total assets ratio. The variable descriptions are in Appendix 1. Robust standard errors clustered by bank are in parentheses. ∗ , ∗ ∗ , and ∗ ∗ ∗ represent
significance at the 10%, 5% and 1% level, respectively.
sults, we use the logit regression framework that includes all the
control variables from the baseline OLS specification. However,
fixed effects are excluded to avoid incidental parameter bias. Following the methodology developed by Norton et al. (2004), we
compute marginal effects and standard errors for the nonlinear
model to ensure correct inferences, and report the results in Panel
C. Column (1) shows that for the full sample, the marginal effect of
coefficient on the interaction term. However, this does not carry over in nonlinear
models such as the logit model. The literature on the interpretation of interaction
term coefficients in logit (i.e. non-linear) regression estimations tells us that the
statistical significance of the coefficient as well as its sign and magnitude cannot be
interpreted in the same way as the coefficient of a linear regression (Norton et al.,
2004). Instead, the direction of influence, as well as the significance of the interaction term, might vary across different observations.
the interaction term, catfat × ࢞ca, remains negative and significant,
albeit much smaller in magnitude. In Columns (2)–(4), we sort the
sample banks into large, medium, and small banks, and again find
that the interaction effect of bank liquidity creation and changes in
bank capital is significant for small banks, but insignificant in the
medium and large bank subsamples.
4.2.4. Alternative measures of bank liquidity creation
In this section, we use alternative measures of liquidity creation
to check the robustness of our results presented earlier. First, we
create yearly-deciles of liquidity creation by recoding the BB measure (catfat) each year in ten categories labelled from 1 (lowest
decile) to 10 (highest decile). The results in Panel A of Table 6 show
positive, statistically significant coefficients of catfatdecile and the
12
C. Zheng, A. (Wai Kong) Cheung and T. Cronje / Journal of Banking and Finance 108 (2019) 105651
Table 6
Alternative measures of bank liquidity creation.
Panel A: Yearly-deciles of liquidity creation
Dependent variable: z-score
Variables
(1)
(2)
catfatdecile
࢞ca
catfatdecile × ࢞ca
Bank Controls
Other Controls
Year_quarter FE
Bank FE
State FE
Observations
Adjusted R2
0.001∗ ∗ ∗
(0.00)
8.556∗ ∗ ∗
(0.16)
0.373∗ ∗ ∗
(0.02)
Yes
Yes
Yes
Yes
Yes
220,188
0.959
0.001∗ ∗ ∗
(0.00)
8.548∗ ∗ ∗
(0.16)
0.374∗ ∗ ∗
(0.02)
Yes
No
Yes
Yes
Yes
220,260
0.959
Panel B: Sub-components of liquidity creation
Dependent variable: z-score
Variables
(1)
(2)
(3)
(4)
∗∗∗
lc_offbal
0.257
(0.02)
−0.133∗ ∗ ∗
(0.02)
lc_a
0.074∗ ∗ ∗
(0.01)
lc_l
−0.022∗ ∗
(0.01)
8.672∗ ∗ ∗
(0.16)
lc_onbal
10.411∗ ∗ ∗
(0.14)
2.001
(1.62)
࢞ca
lc_offbal × ࢞ca
10.061∗ ∗ ∗
(0.10)
8.893∗ ∗ ∗
(0.22)
6.299∗ ∗ ∗
(0.44)
lc_a × ࢞ca
10.664∗ ∗ ∗
(1.01)
lc_l × ࢞ca
8.316∗ ∗ ∗
(0.45)
Yes
Yes
Yes
Yes
220,188
0.960
lc_onbal × ࢞ca
Baseline Controls
Year_quarter FE
Bank FE
State FE
Observations
Adjusted R2
Yes
Yes
Yes
Yes
220,188
0.959
Yes
Yes
Yes
Yes
220,188
0.959
Panel C: High, medium and low liquidity creators
Dependent variable: z-score
5th and 95th percentile
(1) High liqu- (2) Medium liq- (3) Low liquVariables
idity creators
uidity creators idity creators
catfat
࢞ca
catfat × ࢞ca
Baseline
Controls
Year_quarter FE
Bank FE
State FE
Observations
Adjusted R2
Yes
Yes
Yes
Yes
220,188
0.959
15th and 85th percentile
(4) High liqu- (5) Medium liq- (6) Low liquuidity creators idity creators
idity creators
25th and 75th percentile
(7) High liqu- (8) Medium liqidity creators
uidity creators
(9) Low liquidity
creators
−0.001
(0.04)
12.289∗ ∗ ∗
(2.50)
−3.714
(3.65)
Yes
0.036∗ ∗ ∗
(0.01)
9.096∗ ∗ ∗
(0.15)
6.037∗ ∗ ∗
(0.41)
Yes
−0.050
(0.06)
9.144∗ ∗ ∗
(0.52)
24.570∗ ∗ ∗
(5.15)
Yes
0.036
(0.02)
11.836∗ ∗ ∗
(1.00)
−2.113
(1.69)
Yes
0.044∗ ∗ ∗
(0.01)
8.879∗ ∗ ∗
(0.22)
7.233∗ ∗ ∗
(0.63)
Yes
−0.018
(0.03)
8.461∗ ∗ ∗
(0.26)
16.094∗ ∗ ∗
(2.03)
Yes
0.036∗
(0.02)
10.987∗ ∗ ∗
(0.66)
−0.074
(1.20)
Yes
0.062∗ ∗ ∗
(0.01)
8.686∗ ∗ ∗
(0.34)
8.136∗ ∗ ∗
(1.00)
Yes
−0.029
(0.02)
8.260∗ ∗ ∗
(0.23)
13.812∗ ∗ ∗
(1.36)
Yes
Yes
Yes
Yes
11,275
0.952
Yes
Yes
Yes
198,377
0.960
Yes
Yes
Yes
10,536
0.973
Yes
Yes
Yes
33,763
0.952
Yes
Yes
Yes
154,311
0.961
Yes
Yes
Yes
32,114
0.973
Yes
Yes
Yes
56,123
0.952
Yes
Yes
Yes
110,208
0.961
Yes
Yes
Yes
53,857
0.973
Note: Panel A of this Table presents coefficient estimates from regressions of z-score on the interaction term between yearly-deciles of liquidity creation and changes in
bank capital. We create yearly-deciles of liquidity creation, i.e., catfatdecile, by recoding the BB measure into categorical ones. Panel B presents coefficient estimates from
regressions of z-score on the interaction terms between components of liquidity creation measures, which are off-balance sheet liquidity creation (lc_offbal) in Column (1),
asset-side liquidity creation (lc_a) in Column (2), liability-side liquidity creation (lc_l) in Column (3), and on-balance sheet liquidity creation (lc_onbal) in Column (4), and
changes in bank capital. Panel C of this table reports coefficient estimates from regressions of z-score on the interaction term between bank liquidity creation and changes
in bank capital by liquidity creation levels. In Columns (1)–(3), high liquidity creators are those banks in the 95th percentile, medium liquidity creators are banks between
the 95th and the 5th percentile, and low liquidity creators are banks in the 5th percentile of the BB measure. In Columns (4)–(6), high liquidity category includes banks in
the 85th percentile, medium liquidity category includes banks between the 85th and the 15th percentile, and low liquidity category includes banks in the 15th percentile
of the BB measure. In Columns (7)–(9), high liquidity creators are those banks in the 75th percentile, medium liquidity creators are banks between the 75th and the
25th percentile, and low liquidity creators are banks in the 25th percentile of the BB measure. Bank liquidity creation is proxied by the BB measure, i.e., catfat. Change in
bank capital (࢞cai,t ) is the absolute change from the year t-1 to t of bank i’s equity-to-total assets ratio. z-score measures the distance from insolvency. A higher z-score
value indicates that a bank is less likely to default. The variable descriptions are in Appendix 1. Robust standard errors clustered by bank are in parentheses. ∗ , ∗ ∗ , and ∗ ∗ ∗
represent significance at the 10%, 5% and 1% level, respectively.
C. Zheng, A. (Wai Kong) Cheung and T. Cronje / Journal of Banking and Finance 108 (2019) 105651
13
Table 7
Alternative measures of bank capital.
Variables
catfat
࢞ca
catfat × ࢞ca
Bank Controls
Other Controls
Year_quarter FE
Bank FE
State FE
Observations
Adjusted R2
Dependent variable: z-score
(1)
(2)
Tier 1 risk-based
Tier 1 risk-based
(3)
Total risk-based
(4)
Total risk-based
(5)
Leverage
(6)
Leverage
0.077∗ ∗ ∗
(0.01)
2.523∗ ∗ ∗
(0.07)
7.596∗ ∗ ∗
(0.20)
Yes
Yes
Yes
Yes
Yes
220,188
0.938
0.074∗ ∗ ∗
(0.01)
2.451∗ ∗ ∗
(0.07)
7.221∗ ∗ ∗
(0.20)
Yes
Yes
Yes
Yes
Yes
220,188
0.937
0.075∗ ∗ ∗
(0.01)
2.444∗ ∗ ∗
(0.07)
7.230∗ ∗ ∗
(0.20)
Yes
No
Yes
Yes
Yes
220,260
0.937
−0.010
(0.01)
7.262∗ ∗ ∗
(0.18)
6.472∗ ∗ ∗
(0.44)
Yes
Yes
Yes
Yes
Yes
220,188
0.944
−0.009
(0.01)
7.240∗ ∗ ∗
(0.18)
6.516∗ ∗ ∗
(0.44)
Yes
No
Yes
Yes
Yes
220,260
0.944
0.078∗ ∗ ∗
(0.01)
2.517∗ ∗ ∗
(0.07)
7.605∗ ∗ ∗
(0.20)
Yes
No
Yes
Yes
Yes
220,260
0.938
Note: This table presents coefficient estimates from regressions of z-score on the interaction term between bank liquidity creation and changes in bank capital by
replacing the equity-to-total assets ratio with three traditional regulatory capital ratios. They are the total risk-based capital ratio, the tier 1 risk-based capital ratio,
and the leverage ratio. The total risk-based capital ratio is measured as core capital (tier 1) plus supplementary capital (tier 2) over risk-weighted assets. The tier 1
risk-based capital ratio includes only core capital in the numerator, which is divided by risk-weighted assets. The leverage ratio is calculated by dividing core capital
(tier 1) by total average assets rather than risk-weighted assets. Bank liquidity creation is proxied by the BB measure, i.e., catfat. Change in bank capital (࢞cai,t ) is the
absolute change from the year t-1 to t of bank i’s three regulatory capital ratios, respectively. z-score measures the distance from insolvency. A higher z-score value
indicates that a bank is less likely to default. The variable descriptions are in Appendix 1. Robust standard errors clustered by bank are in parentheses. ∗ , ∗ ∗ , and ∗ ∗ ∗
represent significance at the 10%, 5% and 1% level, respectively.
interaction term catfatdecile × ࢞ca, and, thus, are congruent with
our main findings. Second, we use four sub-components of liquidity creation: off-balance sheet-side (lc_offbal), asset-side (lc_a), and
liability-side (lc_l) liquidity creation, and on-balance sheet liquidity creation (lc_onbal); to check whether our main results still hold
in different components of bank liquidity creation. Panel B displays
the results from a regression of z-score on the interaction terms between components of liquidity creation and changes in bank capital. The coefficient of the liability-side liquidity creation component is positive and statistically significant. Further, the coefficient
of the interaction term remains statistically significant and positive for the liability-side liquidity creation component. This suggests that liability-side liquidity creation is the driving force behind
our findings.
Third, we examine whether the interaction effect of bank liquidity creation and changes in bank capital varies with the liquidity creation levels of banks. We divide banks into “high liquidity
creators”, “medium liquidity creators” and “low liquidity creators”
subsamples. Following the categorization of high and low liquidity
creators in Díaz and Huang (2017), we define high, medium, and
low liquidity creators as those in the top 25% (75th percentile), between top 25% (75th percentile) and bottom 25% (25th percentile),
and bottom 25% (25th percentile) of the BB measure (catfat) each
year, respectively. We also use two alternative cut-offs, i.e., 15% and
85%; and 5% and 95%, to examine the sensitivity of our results to
an alternative bank classification method. As the results in Panel
C of Table 6 show, the coefficient of the Liquidity Creation variable
(catfat) is insignificant for the categories of high and low liquidity creators, but it is positive and highly significant for medium
liquidity creators. More importantly, we find that the coefficient
of the interaction term, catfat × ࢞ca, is again statistically significant and positive for medium liquidity creators, and the effect is
comparable in magnitude to that shown in Table 2. This result
suggests that the interaction effect on failure risk between bank
liquidity creation and changes in bank capital is even more pronounced for medium liquidity creators. One possible explanation
for the result is that medium liquidity creation banks are likely to
be those in the small bank subsample, because most of the sample banks fall into the category of medium liquidity creators, while
Table 3 shows that the majority of the banks in our sample are
small banks. These banks may strengthen their capital buffer to ab-
sorb the illiquidity risk associated with liquidity creation. Increased
capital buffer would, in turn, reduce the probability of bank failure
and improve banks’ ability to create liquidity.
4.2.5. Alternative measures of bank capital
In our main analysis, we use the ratio of equity capital to total
assets (ca), as our measure of bank capital. To examine whether
our results are robust to an alternative definition of bank capital, we also measure bank capital using three traditional regulatory
capital ratios: the total risk-based capital ratio, the tier 1 risk-based
capital ratio, and the leverage ratio. The total risk-based capital ratio is measured as core capital (tier 1) plus supplementary capital
(tier 2) over risk-weighted assets. The tier 1 risk-based capital ratio includes only core capital in the numerator, which is divided
by risk-weighted assets. The leverage ratio is calculated by dividing core capital (tier 1) by total average assets, rather than riskweighted assets.
The empirical results where the ratio of equity capital to total assets is replaced by regulatory capital ratios are presented in
Table 7. Table 7 shows that the coefficients of catfat and the interaction term, catfat × ࢞ca, are positive and statistically significant
at the 1% level in four of the six specifications, while their magnitudes are similar to those in the baseline findings in Table 2.
Hence, our main results are reinforced.
4.2.6. The role of managerial ability
Andreou et al. (2016) investigate the impact of managerial ability on bank liquidity creation and bank risk-taking behavior as
prior literature has revealed that managerial ability is an important
factor in terms of influencing firm performance (Demerjian et al.,
2012). They find that higher ability managers create more liquidity and take more risk, yet during times of financial crisis, higher
ability bank managers reduce liquidity creation as a way to deleverage their balance sheets. It is thus worth considering whether
our main results persist in the presence of managerial ability, and
whether they are driven/moderated by high ability managers.9 Following Andreou et al. (2016), we estimate managerial ability measure in two steps. The first step is to compute bank profit efficiency
score using stochastic frontier analysis (SFA). Since this measure
9
We are grateful to an anonymous reviewer for suggesting this point.
14
C. Zheng, A. (Wai Kong) Cheung and T. Cronje / Journal of Banking and Finance 108 (2019) 105651
Table 8
Results of OLS estimates for models with managerial ability variable.
Panel A: Baseline OLS regression in the presence of managerial ability
Dependent variable: z-score
Variables
(1)
(2)
(3)
catfat
࢞ca
catfat × ࢞ca
ma
0.142∗ ∗ ∗
(0.02)
11.626∗ ∗ ∗
(0.26)
4.050∗ ∗ ∗
(0.67)
−2.235∗ ∗ ∗
(0.43)
0.142∗ ∗ ∗
(0.02)
11.637∗ ∗ ∗
(0.26)
4.028∗ ∗ ∗
(0.67)
−2.221∗ ∗ ∗
(0.43)
0.036∗ ∗ ∗
(0.01)
9.497∗ ∗ ∗
(0.18)
5.045∗ ∗ ∗
(0.45)
−2.000∗ ∗ ∗
(0.25)
madecile
(4)
(5)
(6)
(7)
(8)
0.141∗ ∗ ∗
(0.02)
11.636∗ ∗ ∗
(0.26)
4.054∗ ∗ ∗
(0.67)
0.142∗ ∗ ∗
(0.02)
11.648∗ ∗ ∗
(0.26)
4.031∗ ∗ ∗
(0.67)
0.037∗ ∗ ∗
(0.01)
9.484∗ ∗ ∗
(0.18)
5.056∗ ∗ ∗
(0.45)
0.073∗ ∗ ∗
(0.02)
10.040∗ ∗ ∗
(0.24)
4.816∗ ∗ ∗
(0.69)
0.973
(0.73)
0.073∗ ∗ ∗
(0.02)
10.040∗ ∗ ∗
(0.24)
4.817∗ ∗ ∗
(0.69)
−0.003∗ ∗ ∗
(0.00)
−0.003∗ ∗ ∗
(0.00)
−0.001∗ ∗ ∗
(0.00)
crisisdummy
0.083
(0.20)
−0.548
(0.34)
ma × crisisdummy
madecile × crisisdummy
Bank-specific characteristics:
ca
8.870∗ ∗ ∗
(0.07)
aq
−3.600∗ ∗ ∗
(0.10)
mc
−0.655∗ ∗ ∗
(0.01)
roe
−1.235∗ ∗ ∗
(0.05)
liq
−0.022
(0.02)
ltdrt
−0.198∗ ∗ ∗
(0.01)
noniirt
0.006∗ ∗ ∗
(0.00)
ristak
−0.071∗ ∗ ∗
(0.02)
commre
0.016∗ ∗ ∗
(0.01)
lndep
0.001
(0.00)
Macroeconomic characteristics:
fedfunds
0.054∗ ∗ ∗
(0.00)
spread
0.056∗ ∗ ∗
(0.00)
lngdp
−0.260∗ ∗ ∗
(0.01)
lngpsave
0.075∗ ∗ ∗
(0.01)
Local economic characteristics:
hhi_dep
−0.002
(0.00)
lnpop
0.058∗ ∗ ∗
(0.01)
sloos
−0.000∗ ∗ ∗
(0.00)
lnperinc
0.220∗ ∗ ∗
(0.01)
lnemploy
−0.066∗ ∗ ∗
(0.01)
Constant
1.880∗ ∗ ∗
(0.07)
Year_quarter FE
No
Bank FE
Yes
State FE
No
Observations
154,882
2
0.435
Adjusted R
0.001
(0.00)
0.084
(0.20)
−0.000∗ (0.00)
8.874∗ ∗ ∗
(0.07)
−3.600∗ ∗ ∗
(0.10)
−0.656∗ ∗ ∗
(0.01)
−1.236∗ ∗ ∗
(0.05)
−0.022
(0.02)
−0.198∗ ∗ ∗
(0.01)
0.006∗ ∗ ∗
(0.00)
−0.072∗ ∗ ∗
(0.02)
0.016∗ ∗ ∗
(0.01)
0.001
(0.00)
8.437∗ ∗ ∗
(0.06)
−0.024
(0.03)
−0.115∗ ∗ ∗
(0.01)
0.939∗ ∗ ∗
(0.02)
−0.053∗ ∗ ∗
(0.01)
−0.021∗ ∗ ∗
(0.01)
0.001∗ ∗ ∗
(0.00)
0.057∗ ∗ ∗
(0.01)
−0.001
(0.00)
−0.000
(0.00)
8.872∗ ∗ ∗
(0.07)
−3.602∗ ∗ ∗
(0.10)
−0.654∗ ∗ ∗
(0.01)
−1.235∗ ∗ ∗
(0.05)
−0.023
(0.02)
−0.199∗ ∗ ∗
(0.01)
0.006∗ ∗ ∗
(0.00)
−0.070∗ ∗ ∗
(0.02)
0.016∗ ∗ ∗
(0.01)
0.001
(0.00)
8.876∗ ∗ ∗
(0.07)
−3.601∗ ∗ ∗
(0.10)
−0.655∗ ∗ ∗
(0.01)
−1.235∗ ∗ ∗
(0.05)
−0.023
(0.02)
−0.198∗ ∗ ∗
(0.01)
0.006∗ ∗ ∗
(0.00)
−0.071∗ ∗ ∗
(0.02)
0.016∗ ∗ ∗
(0.01)
0.001
(0.00)
8.437∗ ∗ ∗
(0.06)
−0.024
(0.03)
−0.117∗ ∗ ∗
(0.01)
0.937∗ ∗ ∗
(0.02)
−0.051∗ ∗ ∗
(0.01)
−0.021∗ ∗ ∗
(0.01)
0.001∗ ∗ ∗
(0.00)
0.056∗ ∗ ∗
(0.01)
−0.001
(0.00)
−0.000
(0.00)
8.564∗ ∗ ∗
(0.11)
−0.030
(0.04)
−0.133∗ ∗ ∗
(0.01)
0.871∗ ∗ ∗
(0.02)
−0.050∗ ∗ ∗
(0.02)
−0.019
(0.01)
0.001∗ ∗ ∗
(0.00)
0.023
(0.02)
−0.004
(0.00)
0.000
(0.00)
8.564∗ ∗ ∗
(0.16)
−0.030
(0.04)
−0.133∗ ∗ ∗
(0.01)
0.871∗ ∗ ∗
(0.02)
−0.050∗ ∗ ∗
(0.02)
−0.019
(0.01)
0.001∗ ∗ ∗
(0.00)
0.023
(0.02)
−0.004
(0.00)
0.000
(0.00)
0.054∗ ∗ ∗
(0.00)
0.056∗ ∗ ∗
(0.00)
−0.262∗ ∗ ∗
(0.01)
0.075∗ ∗ ∗
(0.01)
−0.092
(0.07)
0.064∗
(0.03)
1.147∗
(0.62)
0.185
(0.18)
0.054∗ ∗ ∗
(0.00)
0.056∗ ∗ ∗
(0.00)
−0.260∗ ∗ ∗
(0.01)
0.074∗ ∗ ∗
(0.01)
0.054∗ ∗ ∗
(0.00)
0.056∗ ∗ ∗
(0.00)
−0.261∗ ∗ ∗
(0.01)
0.075∗ ∗ ∗
(0.01)
−0.092
(0.07)
0.064∗
(0.03)
1.154∗
(0.62)
0.176
(0.18)
0.020
(0.02)
0.086∗
(0.05)
1.579
(1.12)
0.278
(0.25)
0.019
(0.02)
0.086∗
(0.05)
1.575
(1.12)
0.278
(0.25)
−0.002
(0.00)
0.057∗ ∗ ∗
(0.01)
−0.000∗ ∗ ∗
(0.00)
0.221∗ ∗ ∗
(0.01)
−0.065∗ ∗ ∗
(0.01)
1.804∗ ∗ ∗
(0.07)
No
Yes
Yes
154,882
0.435
−0.001
(0.00)
−0.005
(0.00)
−0.001∗ ∗
(0.00)
0.013∗ ∗ ∗
(0.00)
0.004
(0.00)
−11.402∗
(6.76)
Yes
Yes
Yes
154,882
0.962
−0.002
(0.00)
0.058∗ ∗ ∗
(0.01)
−0.000∗ ∗ ∗
(0.00)
0.220∗ ∗ ∗
(0.01)
−0.065∗ ∗ ∗
(0.01)
1.892∗ ∗ ∗
(0.07)
No
Yes
No
154,882
0.435
−0.002
(0.00)
0.057∗ ∗ ∗
(0.01)
−0.000∗ ∗ ∗
(0.00)
0.221∗ ∗ ∗
(0.01)
−0.065∗ ∗ ∗
(0.01)
1.814∗ ∗ ∗
(0.07)
No
Yes
Yes
154,882
0.435
−0.001
(0.00)
−0.005
(0.00)
−0.001∗ ∗
(0.00)
0.013∗ ∗ ∗
(0.00)
0.004
(0.00)
−11.387∗
(6.77)
Yes
Yes
Yes
154,882
0.962
−0.000
(0.00)
−0.005
(0.00)
−0.002∗ ∗
(0.00)
0.012∗ ∗
(0.01)
0.005
(0.00)
−16.443
(12.38)
Yes
Yes
Yes
86,161
0.961
−0.000
(0.00)
−0.005
(0.00)
−0.002∗ ∗
(0.00)
0.012∗ ∗ ∗
(0.01)
0.005
(0.00)
−16.405
(12.37)
Yes
Yes
Yes
86,161
0.961
(continued on next page)
C. Zheng, A. (Wai Kong) Cheung and T. Cronje / Journal of Banking and Finance 108 (2019) 105651
15
Table 8
(continued)
Panel B: Baseline OLS regression in the presence of managerial ability by bank size
Dependent variable: z-score
(1)
(2)
(3)
(4)
Variables
catfat
Small banks
0.056∗ ∗ ∗
(0.01)
9.722∗ ∗ ∗
(0.18)
4.962∗ ∗ ∗
(0.44)
−1.474∗ ∗ ∗
(0.23)
࢞ca
catfat × ࢞ca
ma
Medium banks
−0.009
(0.05)
9.735∗ ∗ ∗
(1.38)
3.876
(3.15)
−4.769∗ ∗ ∗
(1.33)
Large banks
0.044
(0.07)
10.961∗ ∗ ∗
(1.02)
0.435
(2.20)
−6.657∗ ∗ ∗
(1.68)
madecile
Year_quarter FE
Bank FE
State FE
Observations
Adjusted R2
Yes
Yes
Yes
138,588
0.970
Yes
Yes
Yes
10,319
0.915
Yes
Yes
Yes
5975
0.932
(5)
(6)
Small banks
0.058∗ ∗ ∗
(0.01)
9.712∗ ∗ ∗
(0.18)
4.966∗ ∗ ∗
(0.44)
Medium banks
−0.007
(0.05)
9.728∗ ∗ ∗
(1.38)
3.887
(3.17)
Large banks
0.055
(0.07)
10.958∗ ∗ ∗
(1.06)
0.533
(2.27)
−0.001∗ ∗ ∗
(0.00)
Yes
Yes
Yes
138,588
0.970
−0.004∗ ∗ ∗
(0.00)
Yes
Yes
Yes
10,319
0.915
−0.004∗ ∗ ∗
(0.00)
Yes
Yes
Yes
5975
0.931
Panel C: Interaction effect of liquidity creation, bank capital and managerial ability on failure risk
Dependent variable: z-score
Variables
(1)
(2)
(3)
(4)
∗∗∗
catfat
0.144
(0.02)
11.736∗ ∗ ∗
(0.26)
3.642∗ ∗ ∗
(0.66)
−5.184∗ ∗ ∗
(0.99)
࢞ca
catfat × ࢞ca
ma
∗∗∗
0.144
(0.02)
11.747∗ ∗ ∗
(0.26)
3.621∗ ∗ ∗
(0.66)
−5.161∗ ∗ ∗
(0.99)
∗∗∗
0.037
(0.01)
9.529∗ ∗ ∗
(0.17)
4.890∗ ∗ ∗
(0.44)
−0.480
(0.56)
madecile
−3.625∗ ∗ ∗
(0.76)
ma × ࢞ca
−3.599∗ ∗ ∗
(0.75)
8.061∗ ∗ ∗
(2.73)
8.037∗ ∗ ∗
(2.74)
3.872∗
(2.06)
3.798∗
(2.06)
No
Yes
No
154,882
0.435
No
Yes
Yes
154,882
0.436
0.092
(0.02)
13.638∗ ∗ ∗
(0.50)
0.983
(1.33)
0.093
(0.02)
13.633∗ ∗ ∗
(0.50)
1.010
(1.33)
0.054∗ ∗ ∗
(0.01)
9.817∗ ∗ ∗
(0.33)
4.703∗ ∗ ∗
(0.93)
−0.006∗ ∗ ∗
(0.00)
−0.006∗ ∗ ∗
(0.00)
−0.000
(0.00)
−0.340∗ ∗ ∗
(0.08)
−0.337∗ ∗ ∗
(0.08)
−0.058
(0.05)
0.009∗ ∗ ∗
(0.00)
0.009∗ ∗ ∗
(0.00)
−0.003∗ ∗
(0.00)
0.495∗ ∗
(0.22)
No
Yes
No
154,882
0.435
0.486∗ ∗
(0.22)
No
Yes
Yes
154,882
0.435
0.055
(0.15)
Yes
Yes
Yes
154,882
0.962
0.341
(1.50)
catfat × madecile × ࢞ca
Year_quarter FE
Bank FE
State FE
Observations
Adjusted R2
(6)
∗∗∗
−4.568∗ ∗ ∗
(1.67)
catfat × madecile
catfat × ma × ࢞ca
(5)
−1.129∗ ∗
(0.53)
madecile × ࢞ca
catfat × ma
∗∗∗
Yes
Yes
Yes
154,882
0.962
Panel D: Interaction effect of liquidity creation, bank capital and managerial ability on failure risk – high, medium and low liquidity creators
Dependent variable: z-score
5th and 95th percentile
15th and 85th percentile
25th and 75th percentile
(9)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Variables
catfat
࢞ca
catfat × ࢞ca
ma
catfat × ma
ma × ࢞ca
High
liquidity
creators
0.113
(0.09)
17.816∗ ∗ ∗
(3.33)
−18.066∗ ∗
(8.75)
4.604
(4.66)
−19.348
(11.81)
−16.282∗ ∗ ∗
(5.64)
Medium
liquidity
creators
0.047∗ ∗ ∗
(0.01)
9.655∗ ∗ ∗
(0.17)
4.180∗ ∗ ∗
(0.43)
−0.256
(0.50)
−0.749
(1.32)
1.343∗
(0.73)
Low
liquidity
creators
−0.133
(0.10)
4.578
(2.89)
2.103
(7.78)
−1.529
(3.88)
−14.028
(10.02)
−5.808
(4.45)
High
liquidity
creators
0.049
(0.03)
11.781∗ ∗ ∗
(1.21)
−2.075
(3.35)
−0.267
(1.82)
−5.382
(5.10)
−5.763∗ ∗
(2.65)
Medium
liquidity
creators
0.057∗ ∗ ∗
(0.01)
9.944∗ ∗ ∗
(0.18)
3.614∗ ∗ ∗
(0.46)
0.138
(0.60)
−1.084
(1.62)
0.152
(1.04)
Low
liquidity
creators
−0.043
(0.04)
5.352∗ ∗ ∗
(1.12)
6.695∗ ∗
(3.05)
−1.666
(1.89)
−7.900∗
(4.75)
−6.520∗ ∗ ∗
(2.21)
High
liquidity
creators
0.036
(0.02)
10.935∗ ∗ ∗
(0.76)
0.894
(2.06)
−0.177
(1.28)
−2.287
(3.52)
−3.923∗ ∗
(2.00)
Medium
liquidity
creators
0.063∗ ∗ ∗
(0.02)
10.017∗ ∗ ∗
(0.22)
3.444∗ ∗ ∗
(0.55)
1.286
(0.82)
−3.986∗
(2.26)
0.339
(1.98)
(continued
Low
liquidity
creators
−0.011
(0.03)
7.773∗ ∗ ∗
(0.67)
3.733∗
(1.93)
−1.020
(1.35)
−7.046∗ ∗
(3.48)
−3.157∗ ∗
(1.61)
on next page)
16
C. Zheng, A. (Wai Kong) Cheung and T. Cronje / Journal of Banking and Finance 108 (2019) 105651
Table 8
(continued)
Panel C: High, medium and low liquidity creators
Dependent variable: z-score
5th and 95th percentile
catfat × ma × ࢞ca
Year_quarter FE
Bank FE
State FE
Observations
Adjusted R2
42.178∗ ∗ ∗
(15.28)
Yes
Yes
Yes
7731
0.965
−1.106
(2.07)
Yes
Yes
Yes
139,168
0.967
15th and 85th percentile
−4.395
(11.62)
Yes
Yes
Yes
7983
0.961
15.935∗ ∗
(7.52)
Yes
Yes
Yes
23,616
0.964
3.483
(3.09)
Yes
Yes
Yes
108,007
0.970
25th and 75th percentile
2.033
(5.94)
Yes
Yes
Yes
23,259
0.961
10.780∗
(5.70)
Yes
Yes
Yes
39,383
0.965
5.640
(5.68)
Yes
Yes
Yes
77,018
0.971
−3.134
(4.48)
Yes
Yes
Yes
38,481
0.962
Note: This table presents the OLS regression results in the presence of managerial ability. In Panel A, Columns (1)–(3), managerial ability (ma) is included
as an additional variable in our baseline OLS regression model. Following Andreou et al. (2016), we use Stochastic Frontier Analysis (SFA) to obtain bank
profit efficiency score, and regress the score on a set of bank-specific variables. The residual from this regression is part of efficiency score that cannot be
explained by bank-specific factors and hence should be attributed to management ability. We also create yearly-deciles of managerial ability (madecile) by
recoding managerial ability measure (ma) each year into ten categorical ones labelled from 1 (lowest decile) to 10 (highest decile) in Panel A, Columns (4)–(6).
Columns (7) and (8) of Panel A show the results during the financial crisis period. We split the sample into small, medium, and large banks, and perform
our analyses separately for these three sets of banks in Panel B. Small banks have GTA up to $1 billion, medium banks have GTA exceeding $1 billion and up
to $3 billon, and large banks have GTA exceeding $3 billion. To examine whether our main results are driven/moderated by managerial ability, we include a
triple interaction term (catfat × ma × ࢞ca and catfat × madecile × ࢞ca) in the regression model in Panel C, and further separate banks into high, medium and
low liquidity creators by their liquidity creation levels in Panel D. In Columns (1)–(3) of Panel D, high liquidity creators are those banks in the 95th percentile,
medium liquidity creators are banks between the 95th and the 5th percentile, and low liquidity creators are banks in the 5th percentile of the BB measure.
In Columns (4)–(6), high liquidity category includes banks in the 85th percentile, medium liquidity category includes banks between the 85th and the 15th
percentile, and low liquidity category includes banks in the 15th percentile of the BB measure. In Columns (7)–(9), high liquidity creators are those banks in
the 75th percentile, medium liquidity creators are banks between the 75th and the 25th percentile, and low liquidity creators are banks in the 25th percentile
of the BB measure. Bank liquidity creation is proxied by the BB measure, i.e., catfat. Change in bank capital (࢞cai,t ) is the absolute change from the year t-1 to
t of bank i’s equity-to-total assets ratio. z-score measures the distance from insolvency. A higher z-score value indicates that a bank is less likely to default. The
variable descriptions are in Appendix 1. Regressions include different fixed effects (FE) estimations (Year_quarter FE, State FE and Bank FE). Robust standard
errors clustered by bank are in parentheses. ∗ , ∗ ∗ , and ∗ ∗ ∗ represent significance at the 10%, 5% and 1% level, respectively.
captures both bank-specific and manager-specific efficiency drivers,
the second step is to disentangle managerial ability from bankspecific effects. To this end, we derive a measure of managerial
ability as the residual from a Tobit regression of bank profit efficiency score on five bank-specific characteristics (i.e., bank size,
number of full-time equivalent employees, bank age, leverage, and
free cash flow) and year_quarter fixed effects. The sample for the
managerial ability regressions contains 154,882 bank-year_quarter
observations, representing a reduction of approximately 30%. Sample attrition in these regressions is primarily due to additional data
requirements for constructing the managerial ability variables.
The results of this analysis are presented in Table 8. First, managerial ability (ma) is included as an additional variable in our
baseline OLS regression model (see Panel A, Columns (1)–(3)). We
also create yearly-deciles of managerial ability (madecile) by recoding the managerial ability measure (ma) each year in ten categories labelled from 1 (lowest decile) to 10 (highest decile) (see
Panel A, Columns (4)–(6)). Despite the smaller sample size, the
coefficient of the interaction term, catfat × ࢞ca, remains positive
and statistically significant. Furthermore, the magnitude of the estimate is comparable to that reported in Table 2. Perhaps most
notably, we find that the estimated coefficients of the managerial
ability variables (ma and madecile) are related to a bank’s z_score
negatively, suggesting that more able managers can better manage, and in fact do take more risk, which is in line with the
findings of Andreou et al. (2016). Second, two additional checks
are conducted to see whether the empirical results for the whole
sample period also hold during the financial crisis. We follow
Andreou et al. (2016) and include the liquidity creation measure
(catfat) for the crisis and post-crisis periods from 2007 to 2014;
a bank’s pre-crisis managerial ability measures (ma and madecile)
estimated in 2006; a crisis indicator variable (crisisdummy); and
the interaction term between the indicator variable for the crisis period and the managerial ability measures (ma × crisisdummy
and madecile × crisisdummy). As can be clearly seen from the two
checks in Columns (7) and (8) of Panel A, the coefficient of our
main variable of interest (catfat × ࢞ca) remains statistically significant and positive, although the effect of managerial ability becomes insignificant.
Third, the sample is split into small, medium, and large banks,
with separate analyses for these three sets of banks in Panel B.
The small banks have GTA up to $1 billion, medium banks have
GTA exceeding $1 billion and up to $3 billon, and large banks have
GTA exceeding $3 billion. The results are qualitatively similar to
the findings presented in Panel A of Table 3 and do not change
our main conclusion that the impact of change in capital is the
strongest for small banks. As expected, the coefficients of catfat
and catfat × ࢞ca are positive and statistically significant for small
banks whereas they are insignificant for medium and large banks.
Again, we find a negative and statistically significant relation between managerial ability (ma and madecile) and a bank’s z_score,
which is inversely related to bank risk taking. Fourth, to examine
whether our main results are driven/moderated by managerial
ability, we include a triple interaction term (catfat × ma × ࢞ca and
catfat × madecile × ࢞ca) in the regression model. As can be seen
from Panel C, the coefficient of the interaction term is positive
but only weakly significant in four specifications that include
bank fixed effects or bank and state fixed effects (see Columns
(1), (2), (4) and (5)). However, the coefficient is not statistically
significant in the regression model with time fixed effects (see
Columns (3) and (6)), suggesting that managerial ability is largely
a temporal effect (i.e., temporal change in ma and madecile) rather
than a cross sectional effect (i.e., cross sectional heterogeneity in
ma and madecile). Further, we separate banks into high, medium
and low liquidity creators by their liquidity creation levels. As
shown in Panel D, the coefficient of the triple interaction term
catfat × ma × ࢞ca is large, positive, and statistically significant
for high liquidity creators only. Overall, our main results remain
qualitatively unchanged if we include managerial ability as an
additional variable in our regression. However, we find limited
evidence that the results are driven/moderated by managerial
ability.
C. Zheng, A. (Wai Kong) Cheung and T. Cronje / Journal of Banking and Finance 108 (2019) 105651
17
Table 9
Interaction effect of liquidity creation and bank capital on failure risk in a 2SLS setting.
Panel A: The 2SLS regression results for all banks
Variables
First stage
Dependent variable: Bank Liquidity
Creation (catfat) (1)
Second stage
Dependent variable:
z-score (2)
0.011∗
(0.01)
8.406∗ ∗ ∗
(0.26)
6.556∗ ∗ ∗
(0.64)
catfat
−0.086∗ ∗ ∗
(0.01)
−0.602∗ ∗ ∗
(0.03)
0.977∗ ∗ ∗
࢞ca
catfat × ࢞ca
catfat_average
Baseline
Controls
Year_quarter
FE
Bank FE
State FE
Observations
Adjusted R2
First Stage
F-Test of
Excluded
Instrument
Hausman
Endogeneity
Test
(0.00)
Yes
Yes
Yes
Yes
Yes
Yes
220,188
0.987
F-stat = 2.0e + 05, p-value = 0.000
Yes
Yes
220,188
0.923
F-stat = 396.84, p-value = 0.000
Panel B: The 2SLS regression results by bank size
Dependent variable: z-score
$1 billion and $3 billion size cutoff
Variables
(1) Small banks
(2) Medium banks
catfat
࢞ca
catfat × ࢞ca
Baseline Controls
Year_quarter FE
Bank FE
State FE
Observations
Adjusted R2
0.013∗
(0.01)
8.468∗ ∗ ∗
(0.25)
6.590∗ ∗ ∗
(0.63)
Yes
Yes
Yes
Yes
202,698
0.931
0.023
(0.03)
8.432∗ ∗ ∗
(1.41)
9.805∗ ∗ ∗
(2.81)
Yes
Yes
Yes
Yes
10,489
0.909
(3) Large banks
$1 billion and $5 billion size cutoff
(4) Small banks
(5) Medium banks
(6) Large banks
−0.039
(0.07)
11.191∗ ∗ ∗
(0.98)
0.079
(1.12)
Yes
Yes
Yes
Yes
6350
0.909
0.013∗
(0.01)
8.468∗ ∗ ∗
(0.25)
6.590∗ ∗ ∗
(0.63)
Yes
Yes
Yes
Yes
202,698
0.931
−0.067
(0.06)
11.313∗ ∗ ∗
(0.76)
−0.348
(1.32)
Yes
Yes
Yes
Yes
4419
0.911
0.033
(0.02)
8.559∗ ∗ ∗
(1.26)
8.545∗ ∗ ∗
(2.19)
Yes
Yes
Yes
Yes
12,480
0.873
Note: This table presents the 2SLS regression results. The instrument variable for the BB measure is the eight-quarter lagged average values of bank liquidity creation (catfat_average). Panel A reports the results for the entire sample. In Column (1), the first-stage estimation is shown, using catfat_average
as the instrument to obtain the predicted value of liquidity creation. In Column (2), we use the predicted value of liquidity creation from the first-stage
to estimate the relationship between liquidity creation and failure risk. Panel B reports the results for different sizes of banks. In Columns (1)–(3),
small banks, medium banks, and large banks are banks with less than $1 billion, between $1 billion and $3 billion, and more than $3 billion GTA. In
Columns (4)–(6), small banks, medium banks, and large banks are banks with less than $1 billion, between $1 billion and $5 billion, and more than $5
billion GTA. Bank liquidity creation is proxied by the BB measure, i.e., catfat. Change in bank capital (࢞cai,t ) is the absolute change from the year t-1
to t of bank i’s equity-to-total assets ratio. Z-score measures the distance from insolvency. A higher z-score value indicates that a bank is less likely to
default. The variable descriptions are in Appendix 1. Robust standard errors clustered by bank are in parentheses. ∗ , ∗ ∗ , and ∗ ∗ ∗ represent significance
at the 10%, 5% and 1% level, respectively.
5. Controlling for endogeneity
5.1. Instrumental variable (IV) or two stage least squares method
A potential endogeneity issue clouds the interpretation of our
main results. For example, distressed banks may be aware of
their unsound operations and may adjust their liquidity creation.
To address this reverse-causality problem, our first identification
strategy is an instrumental variable (IV) approach. In particular,
eight-quarter lagged average values of bank liquidity creation (catfat_average) are used as the instrumental variable, since lagged
values are more likely to reflect earlier bank decisions and may
not affect the contemporaneous failure risk directly. The use of an
eight-quarter average, rather than a single lagged quarter value,
may reduce the effect of short-term fluctuations and problems
with the use of accounting data (Berger and Bouwman, 2009).
Identification of the IV model requires a strong correlation between the instrument and the endogenous variable. It is reasonable to expect that the average of eight-quarter lagged bank liquidity creation is highly correlated with the contemporaneous bank
liquidity creation. For the instrument to be valid it should not be
affected by the dependent variable, and not affect the dependent
variable except through the endogenous variable. It is logically impossible for the failure risk of the bank to affect past bank liquidity creation. Also, it is unlikely that the eight-quarter lagged
average values of liquidity creation affect a bank’s failure risk directly, except through their effect on contemporaneous bank liquidity creation. We find that in the first stage of our 2SLS estimations the eight-quarter lagged average values of liquidity creation
(catfat_average) is positively and statistically significantly related to
18
C. Zheng, A. (Wai Kong) Cheung and T. Cronje / Journal of Banking and Finance 108 (2019) 105651
Table 10
Interaction effect of liquidity creation and bank capital on failure risk using time-dynamic models.
Panel A: The system GMM regression results
Dependent variable: z-score
$1 billion and $3 billion size cutoff
Variables
(1) All banks
(2) Small banks
(3) Medium banks
z-scoret-1
catfat
࢞ca
catfat × ࢞ca
Baseline Controls
Observations
Sargan/Hansen Test
p-value
∗∗∗
0.478
(0.04)
−0.880
(0.71)
7.594∗ ∗
(3.53)
35.596∗ ∗
(14.55)
Yes
220,158
31.15
0.150
∗∗∗
0.507
(0.04)
−0.714
(0.71)
7.614∗ ∗
(3.54)
38.500∗ ∗
(15.11)
Yes
202,596
21.87
0.587
∗∗∗
0.636
(0.03)
−0.835
(1.02)
11.026
(10.04)
22.022
(22.80)
Yes
10,402
14.55
0.933
Panel B: The dynamic OLS regression results for all banks
Dependent variable: z-score
Variables
(1)
(2)
catfatt-1
8.617∗ ∗ ∗
(0.17)
6.553∗ ∗ ∗
(0.40)
8.977∗ ∗ ∗
(0.17)
Yes
Yes
Yes
Yes
220,188
0.959
Yes
Yes
Yes
Yes
145,537
0.954
Panel C: The dynamic OLS regression results for small banks
Dependent variable: z-score
Variables
(1)
(2)
8.765∗ ∗ ∗
(0.16)
6.654∗ ∗ ∗
(0.38)
0.005
(0.01)
9.158∗ ∗ ∗
(0.21)
4.712∗ ∗ ∗
(0.57)
Yes
Yes
Yes
Yes
72,059
0.947
(3)
9.157∗ ∗ ∗
(0.16)
0.023∗ ∗
(0.01)
9.347∗ ∗ ∗
(0.22)
5.261∗ ∗ ∗
(0.43)
catfatt-2 × ࢞ca
catfatt-3 × ࢞ca
Baseline Controls
Year_quarter FE
Bank FE
State FE
Observations
Adjusted R2
(3)
0.003∗
(0.01)
catfatt-3
catfatt-1 × ࢞ca
0.603∗ ∗ ∗
(0.06)
−2.383
(1.87)
22.985∗ ∗ ∗
(6.12)
−14.036
(13.96)
Yes
4410
25.01
0.405
0.021∗ ∗
(0.01)
catfatt-2
࢞ca
0.665
(0.04)
−0.326
(1.50)
10.350
(11.79)
16.448
(29.04)
Yes
12,404
10.21
0.994
(7) Large banks
5.241∗ ∗ ∗
(0.44)
catfatt-3 × ࢞ca
catfatt-1
0.507
(0.04)
−0.714
(0.71)
7.614∗ ∗
(3.54)
38.500∗ ∗
(15.11)
Yes
202,596
21.87
0.587
∗∗∗
−0.007
(0.01)
catfatt-2 × ࢞ca
Baseline Controls
Year_quarter FE
Bank FE
State FE
Observations
Adjusted R2
0.662
(0.05)
−1.353
(1.40)
14.500∗
(8.25)
13.074
(19.83)
Yes
6330
15.85
0.894
∗∗∗
0.018
(0.01)
catfatt-3
catfatt-1 × ࢞ca
∗∗∗
$1 billion and $5 billion size cutoff
(5) Small banks
(6) Medium banks
∗
catfatt-2
࢞ca
(4) Large banks
Yes
Yes
Yes
Yes
202,791
0.965
Yes
Yes
Yes
Yes
133,816
0.961
4.813∗ ∗ ∗
(0.60)
Yes
Yes
Yes
Yes
66,207
0.956
(continued on next page)
C. Zheng, A. (Wai Kong) Cheung and T. Cronje / Journal of Banking and Finance 108 (2019) 105651
19
Table 10
(continued)
Panel D: The dynamic OLS regression results for medium banks
Dependent variable: z-score
Variables
(1)
(2)
catfatt-1
0.005
(0.04)
catfatt-2
catfatt-3
࢞ca
catfatt-1 × ࢞ca
8.394∗ ∗ ∗
(1.29)
6.027∗ ∗
(2.90)
8.957∗ ∗ ∗
(1.25)
catfatt-3 × ࢞ca
Yes
Yes
Yes
Yes
10,905
0.910
Yes
Yes
Yes
Yes
6715
0.954
Panel E: The dynamic OLS regression results for large banks
Dependent variable: z-score
Variables
(1)
(2)
catfatt-1
10.724∗ ∗ ∗
(0.88)
−0.763
(1.47)
catfatt-2 × ࢞ca
11.597∗ ∗ ∗
(0.89)
−0.034
(0.06)
10.812∗ ∗ ∗
(1.36)
−1.553
(1.83)
catfatt-3 × ࢞ca
Baseline Controls
Year_quarter FE
Bank FE
State FE
Observations
Adjusted R2
(3)
−0.016
(0.05)
catfatt-3
catfatt-1 × ࢞ca
8.159∗ ∗
(3.60)
Yes
Yes
Yes
Yes
3247
0.936
0.075
(0.05)
catfatt-2
࢞ca
0.023
(0.04)
8.574∗ ∗ ∗
(1.64)
7.494∗ ∗ ∗
(2.85)
catfatt-2 × ࢞ca
Baseline Controls
Year_quarter FE
Bank FE
State FE
Observations
Adjusted R2
(3)
0.026
(0.05)
Yes
Yes
Yes
Yes
6492
0.930
Yes
Yes
Yes
Yes
4129
0.940
−1.428
(2.51)
Yes
Yes
Yes
Yes
2014
0.934
Note: This table reports the estimation results of two time-dynamic models. The first one is a dynamic two-step system GMM panel estimator as proposed by
Arellano and Bover (1995) and Blundell and Bond (1998) with Windmeijier’s (2005) finite sample correction (see Panel A). The null hypothesis of the Sargan
test (or Hansen test) is that the instruments used are not correlated with residuals (over-identifying restrictions). The second one is a dynamic OLS regression
results by including the one-, two-, and three-period lagged liquidity creation and their interaction terms (see Panels B-E). Panel B reports the results for the
entire sample. Panels C-E report the results for different sizes of banks. Small banks, medium banks, and large banks are banks with less than $1 billion,
between $1 billion and $3 billion, and more than $3 billion GTA. Bank liquidity creation is proxied by the BB measure, i.e., catfat. Change in bank capital
(࢞cai,t ) is the absolute change from the year t-1 to t of bank i’s equity-to-total assets ratio. z-score measures the distance from insolvency. A higher z-score
value indicates that a bank is less likely to default. z-scoret -1 is the lagged value of the dependent variable in GMM estimator. The variable descriptions are in
Appendix 1. Robust standard errors clustered by bank are in parentheses. ∗ , ∗ ∗ , and ∗ ∗ ∗ represent significance at the 10%, 5% and 1% level, respectively.
the contemporaneous bank liquidity creation (see Column (1) of
Panel A, Table 9). These results affirm that the selected instrument
is closely related to the endogenous variable, the BB measure, and
statistical tests validate the strength and relevance of the instrument variable.
For all banks, the results for the second-stage regressions are
reported in Column (2) of Panel A, Table 9. The positive and statistically significant coefficient of catfat × ࢞ca by and large confirms
our earlier results, easing concerns of endogeneity bias. In other
words, incremental increases in bank capital strengthen the negative relationship between liquidity creation and failure risk. Panel B
displays the two second-stage regression results for different sizes
of banks.10 In Columns (1)–(3), small banks, medium banks, and
large banks are banks with less than $1 billion, between $1 billion
and $3 billion, and more than $3 billion GTA. In Columns (4)–(6),
small banks, medium banks, and large banks are banks with less
than $1 billion, between $1 billion and $5 billion, and more than
$5 billion GTA. The results clearly show that the coefficients of catfat and catfat × ࢞ca remain positive and statistically significant for
small banks. Thus, the findings on bank size are consistent with
the OLS findings. All in all, most of our earlier results hold up in
10
We omit reporting the first stage results by bank size from our 2SLS to save on
space, but these are available upon request.
20
C. Zheng, A. (Wai Kong) Cheung and T. Cronje / Journal of Banking and Finance 108 (2019) 105651
our instrumental variable estimation, and the analysis broadly confirms our main results.
5.2. Time-dynamic models11
To tackle the endogeneity issue and account for the dynamic
properties of our panel, our second identification strategy is the
generalized method of moments (GMM) estimation technique. This
methodology was advanced by Arellano and Bond (1991), and further developed by Arellano and Bover (1995) and Blundell and
Bond (1998). The fact that bank failure risk, liquidity creation and
bank capital tend to be interrelated over time prescribes the use of
a dynamic model. We rely on the system GMM,12 which has better finite sample properties in terms of bias and root mean squared
error than that of the difference GMM, by using a system of two
equations in level and in first difference. Further, GMM estimators
have one- and two-step variants. We use the two-step estimator
because it is asymptotically more efficient relative to the first step
estimator, especially for the system GMM. However, the two-step
estimates of the standard errors tend to be severely downward biased, thus we use the Windmeijer’s (2005) finite sample correction to report standard errors. As this approach leads to a relatively
high number of internal instruments, we use the collapse option as
proposed by Roodman (2009). We report the Sargan/Hansen test of
over-identifying restrictions, where the null hypothesis is that the
instruments used are appropriate. The results of these statistical
tests confirm the validity of the instruments used in our model.
Panel A, of Table 10, shows the results using the system GMM
estimator. Our results from Column (1) document a substantial,
positive, and highly significant interaction effect of liquidity creation and changes in bank capital, catfat × ࢞ca, on a bank’s z-score,
supporting the view that the negative relationship between liquidity creation and bank insolvency risk is moderated positively by
changes in bank capital. We also report the GMM regressions by
bank size. Results in Columns (2)–(7) show that the reported positive coefficient of catfat × ࢞ca on a bank’s z-score is statistically
significant only for small banks, while this coefficient is not significant for the medium and large bank subsamples. Hence, these
results corroborate those obtained with the OLS results.
Following the methodology of Berger and Sedunov (2017), our
third identification strategy is to analyze the dynamic interaction
effect of liquidity creation and bank capital on bank failure risk
by including one-, two-, and three-period lagged liquidity creation
variables along with their interactions with changes in bank capital. Panel B of Table 10 presents the dynamic OLS regression results
for all banks. Panels C-E present the results for small, medium and
large banks, respectively. We find that the coefficients of one-, two, and three-period lagged interaction terms, catfat × ࢞ca, are positive and statistically significant for small and medium banks, but
the coefficients of lagged catfat are significant for small bank subsamples only, confirming that the interaction effect is more pronounced for small banks.
6. Conclusions
We argue that the liquidity risk-sharing role of bank capital in
moderating the relationship between liquidity creation and failure risk is underexplored. Given the coming joint regulatory capital and liquidity constraints set forth in Basel III, it is important
for bank regulators and policymakers to understand this role. In
this paper, we seek to fill the void in the literature by examining
empirically how change in the capital ratio interacts with liquidity
11
We thank the reviewer for suggesting this time-dynamic technique.
We use the “xtabond2” procedure in Stata for the system GMM estimates.
Please refer to Roodman (2009) for the estimation procedure in detail.
12
creation in affecting the failure risk of individual banks. To examine this interaction, we use a dataset of all FDIC insured U.S. commercial banks spanning the period from 2003:Q1 to 2014:Q4. The
main findings are as follows. Firstly, we find a fairly robust and significant negative relationship between liquidity creation and bank
failure risk. Further, this negative relationship is moderated positively (i.e., strengthened) by bank capital. This evidence is consistent with the liquidity risk-sharing role of bank capital. Specifically,
banks that create more liquidity and are then exposed to higher
liquidity risk may find it optimal to strengthen/increase their capital buffer as a cushion to absorb the illiquidity risk resulting from
bank liquidity creation. Higher levels of bank capital would, in
turn, reduce the probability of bank failure and improve banks’
ability to create liquidity. In other words, an increase in bank capital will give rise to a further increase in liquidity creation, but a
further decrease in bank failure risk, implying that an increase in
bank capital will strengthen the negative relationship between liquidity creation and bank failure risk. The implication of this finding is that it is of extreme importance to consider the use of bank
capital as a risk-sharing device because banks may actively manage their capital ratios in response to the perceived risk exposure.
Secondly, the moderating effect of bank capital on the relationship
between liquidity creation and failure risk is more prominent for
small banks. This result is important but not surprising, since it is
generally accepted that small banks have limited access to external
funds and, thus, they have a strong precautionary motive for holding more capital buffer as a hedge against adverse liquidity shocks.
As a consequence of the bigger capital cushion, the failure risk will
be lower for small banks. Finally, we also find that the interactive
effects of bank capital and liquidity creation on failure risk, were
more pronounced during the recent financial crisis period. This implies that bank capital may have been especially important in mitigating liquidity risk associated with liquidity creation during the
crisis period.
This study has important policy implications for governments
and policy authorities as it provides novel insights for the design of
prudential regulation and supervision of banks. The financial crisis
of 20 07–20 09 has prompted the Basel Committee on Banking Supervision (BCBS) to introduce a new regulatory framework, known
as Basel III, to strengthen the capital and liquidity risk management of banks. As discussed in the Introduction, Basel III liquidity
requirements, i.e., liquidity coverage ratio (LCR) and the net stable
funding ratio (NSFR), also constrain a bank’s capability to create
liquidity unnecessarily, as they require banks to hold more liquid
assets and maintain a sustainable maturity structure of assets and
liabilities (Berger and Sedunov, 2017). Complementing this strand
of literature, our work shows that lower levels of liquidity creation caused by stringent Basel III liquidity regulations may trigger a possibly unintended consequence in that they increase the
risk of bank failure due to inadequate capital buffers. The findings in this paper suggest that capital and liquidity requirements
cannot be separated. Policymakers should consider the liquidityrisk sharing function of bank capital as an integrated component
of bank liquidity management, and evaluate its effect on the relationship between liquidity creation and bank failure risk. Furthermore, the results clearly indicate that one size does not fit all when
it comes to capital and liquidity regulation. Our findings suggest
that small banks could be exempted from having to comply with
the new Basel III potentially binding liquidity constraints, as they
strengthen their capital base actively when they are exposed to increasing illiquidity due to liquidity creation, and they are important liquidity creators (i.e., they are likely to create medium levels of liquidity). Capital buffer has the desired effect in reducing
bank risk. On the contrary, it appears that large banks may underestimate liquidity risk and maintain low capital ratios, possibly because they are more likely to receive government funding support
C. Zheng, A. (Wai Kong) Cheung and T. Cronje / Journal of Banking and Finance 108 (2019) 105651
due to their “too-big-to-fail” position, or because they have broader
access to external financing, or because they may use their diversification advantage to operate with lower capital ratios. These potential reasons why large banks maintain low capital buffers are
interesting and important topics. More research in this area may
be helpful.
Supplementary materials
Supplementary material associated with this article can be
found, in the online version, at doi:10.1016/j.jbankfin.2019.105651.
Appendix 1. Variable definitions
Variable
Definition
Panel A: Bank liquidity creation and bank failure risk variables
catfat
z-score
faildummyq
faildummyy
Dollar amount of “catfat” liquidity creation normalized by
gross total assets. The “catfat” measures the liquidity
created on and off the balance sheet, following
Berger and Bouwman (2009).
z-score has been widely used in the recent literature as a
measure of bank risk, which equals the return on assets
plus the capital asset ratio, divided by the standard
deviation of the return on assets. z-score measures a
bank’s distance to insolvency. Accordingly, it is inversely
related to the probability of default. It is recommendable
to use its natural logarithm because of its high skewness
(e.g., Laeven and Levine, 2009).
A binary performance variable is used to indicate
whether a bank fails in the next quarter after a specific
financial report date. If failure occurs, it is flagged as
“bad” and is assigned the binary value of one. Otherwise,
it is flagged as “good” and is assigned the binary value of
zero.
A binary performance variable is used to indicate
whether a bank fails in the next year after a specific
financial report date. If failure occurs, it is flagged as
“bad” and is assigned the binary value of one. Otherwise,
it is flagged as “good” and is assigned the binary value of
zero.
Panel B: Bank-specific variables
ca
aq
mc
roe
liq
ltdrt
noniirt
ristak
commre
lndep
The ratio of equity capital to total assets
The ratio of all nonperforming loans (all loans 90 days
past due plus all loans charged off) to total assets
The cost-to-income ratio
The ratio of net income to total equity
The ratio of cash and balances due from depository
institutions to total assets
The loans-to-deposits ratio
The ratio of non-interest income to total income
The bank’s Basel I risk-weighted assets divided by total
asset
The commercial real estate loans divided by total loans
The natural logarithm of total bank deposits
Panel C: Macroeconomic variables
fedfunds
spread
lngdp
lngpsave
crisisdummy
The Federal funds rate
The spread between 3-month US T-Bills and 10-year US
Treasuries
Natural logarithm of Gross Domestic Product
Natural logarithm of Gross Private Savings of all US
households
A dummy variable that equals one from the third quarter
of 2007 to the fourth quarter of 2009 and zero otherwise
Panel D: Local market variables
sloos
hhi_dep
lnperinc
lnemploy
lnpop
Net percentage of domestic banks reporting stronger
demand for auto loans, credit card loans, government
mortgage loans, and C&I loans
Bank-level HHI of deposit concentration for the local
markets in which the bank is operating
Natural logarithm of per capita personal income in a
county
Natural logarithm of total employment in a county
Natural logarithm of total population in a county
21
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