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Bank Liquidity Mismatch, Strategic Complementarity and Deposit Flows *

Qi Chen Duke University, 100 Fuqua Drive, Durham, NC 27708, United States

Phone: 919-660-7753 / Email: qc2@duke.edu

Itay Goldstein Wharton School, 3620 Locust Walk, Philadelphia, PA 19104, United States

Phone: 215-746-0499 / Email: itayg@wharton.upenn.edu

Zeqiong Huang

Yale University, 165 Whitney Avenue, New Haven, CT 06511, United States Phone: 203-436-9426 / Email: zeqiong.huang@yale.edu

Rahul Vashishtha

Duke University, 100 Fuqua Drive, Durham, NC 27708, United States Phone: 919-660-7755 / Email: rahul.vashishtha@duke.edu

First Draft: May 2019 This Draft: July 2019

Preliminary and do not cite or circulate without permission

Abstract: We find a significantly positive relation between bank liquidity mismatch and the sensitivity of deposit flows to bank performance. The result is driven by uninsured deposits, when banks experience poor performance, and for small and medium sized banks. Banks with more liquidity mismatch are more prone to failure, experience more deposit withdraws, and lending reduction during the Financial Crisis of 2008. Our results support the idea that liquidity creation by banks comes at the cost of fragility in banks’ financial structures that seed the potential for banking instability.

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1. Introduction

One of the key questions in banking literature is whether or to what extent banks runs are

driven by poor fundamental or panic. Banks perform a critical role in the economy by transforming

liquidity. On one hand banks provide liquidity to borrowers by funding long-term, illiquid loans,

and on the other hand they take demand deposits and thus provide liquidity to depositors. While

providing liquidity to borrowers and depositors, banks create liquidity mismatch on their own

balance sheet and make them inherently fragile institutions.1 It creates strategic complementarities

in depositors’ payoff: when a depositor withdraws money, he/she leaves fewer liquid resources

remaining to pay the remaining depositors. Remaining depositors’ payoff would be further reduced

if the bank has to liquidate illiquid assets at a discount. Therefore, depositors wish to withdraw

money more when they expect others to do so. As illustrated in Diamond and Dybvig (1983), this

creates the possibility of pure panic based bank failures in which a large mass of depositors

withdraws money purely because they expect others to do so even though the bank has enough

assets to eventually pay everybody in the long run. Such panic runs are distinct from fundamental

runs (modelled in Chari and Jagannathan, 1988; Jacklin and Bhattacharya, 1988; Allen and Gale,

1998) in which depositors run on an insolvent bank lacking sufficient assets to pay all depositors.

In this study, we empirically examine whether and to what extent panic characterizes

depositor behavior. Separating between panic- and fundamental-based behaviors is critical from a

policy perspective. Several (and arguably quite costly) government initiatives – deposit insurance,

lender of last resort, suspension of convertibility – are predicated on the idea that panic

characterizes many of the bank runs. Given the importance, it is not surprising that we are not the

1 Hanson, Shleifer, Stein, and Vishny (2015) find that over the period 1896 to 2012, deposits have financed 80% of banks’ assets on average with a standard deviation of just 8%.

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first to attempt to tackle this issue. Most prior works address this issue by examining the link

between financial crises and fundamentals.2 These works largely found that crises episodes are

correlated with poor fundamentals and have interpreted these findings as supportive of

fundamental-based runs and against panic-based runs. Goldstein and Pauzner (2005), however,

illustrate that this is a flawed conclusion. Using the global games approach, they link various run

equilibriums to the level of fundamentals. They show that even if crises can be linked to

fundamentals, it can still be the case that they would not have occurred without panic driven

coordination failure amongst depositors. This could occur if low fundamentals trigger depositor

panic, creating an indirect correlation between crises and fundamentals. In this scenario, panic

amplifies the response to fundamentals and generates crises in a level of fundamentals that (absent

coordination problems) could have supported a non-crisis outcome. Therefore, while the prior

empirical works highlight the importance of fundamentals in crises, they speak little about the role

of panic.

Motivated by the insights from Goldstein and Pauzner (2005), we empirically explore the

presence of panic in depositor behavior. Specifically, we examine whether depositors respond more

strongly to fundamentals when strategic complementarities are expected to be stronger. Such

evidence would be indicative of the presence of panic as it implies that depositors react more

strongly to the same level of fundamental news because of their beliefs about actions of other

depositors. This approach has also been used in prior studies to explore the importance of strategic

complementarities in non-banking institutions (Chen, Goldstein, and Jiang, 2010; Schmidt,

Timmerman, and Wermers, 2016; Goldstein, Jiang, and Ng, 2017).

2 Examples of studies include Gorton (1988), Demirguc-Kunt and Detragiahe (1998, 2002), Schumacher (2000), Martinez-Peria and Schmukler (2001), and Calomiris and Mason (2003).

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Our main identification strategy is that depositors of banks that engage in more liquidity

transformation are expected to exhibit greater strategic complementarities and thus more incentive

for panic based running. A key element of our research design is to measure the extent of liquidity

mismatch between banks’ assets and liabilities that creates strategic complementarities. We use the

measure of liquidity transformation developed by Berger and Bouwman (2009) that captures the

extent to which banks employ short-term, liquid funding sources to invest in illiquid, long-term

assets. Their measure CatFat is a summary measure of mismatch in a bank’s liquidity, but not risk

or maturity. We provide a more detailed discussion of the measure in Section 2. The measure

exhibits significant cross-sectional variation across banks, which we exploit to examine whether

depositor response to fundamental shocks varies with the extent of liquidity transformation.

Using a large sample of U.S. commercial banks over the period 1993-2014, we uncover

large differences in depositor sensitivity to fundamental news across banks with different levels of

liquidity transformation. Compared to a bank at the 25th percentile of liquidity transformation, the

uninsured deposit flows of a bank at the 75th percentile are 40% more sensitive to a unit change in

return on equity (ROE). This result is consistent with strategic complementarities significantly

amplifying the response to fundamentals.

A key concern is that this result might reflect differences in persistence of earnings shock

instead of panic. If earnings shocks at banks with greater liquidity transformation are more

persistent, then the depositor response at these banks could be stronger because of the greater

magnitude of the fundamental shock and not because of the amplification caused by strategic

complementarities. Two analyses address this concern. First, we measure the persistence of

earnings shocks at different banks and find that controlling for it makes little difference to our

results. Second, we document that uninsured deposit flows exhibit a concave relation with ROE,

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consistent with theory prediction that strategic complementarity is stronger (depositor’s incentive to

run is higher) when banks’ fundamental is weaker. In fact, our results are almost entirely driven by

below median ROE and we find no differences in depositor responses for above median shocks.

More importantly, such concavity is stronger when the bank engages in more liquidity

transformation, consistent with the channel works through strategic complementarity. Collectively,

these results suggest that there is a significant element of panic in the behavior of depositors not

protected by deposit insurance.

We also explore the behavior of insured depositors. Recent evidence suggests that banks

attempt to deal with the fragility of their uninsured depositor base by actively attracting insured

depositors in times of poor performance (Martin, Puri, and Ufier, 2018; Chen, Goldstein, Huang,

and Vashishtha, 2019). Consistent with these prior results, we find that insured deposit flows are

less sensitive to fundamental shocks at high liquidity transformation banks. The strategy appears

effective, as we do not find any differences in the sensitivity of total deposit flows (both uninsured

and insured) across banks with different levels of liquidity transformation. These findings not only

yield insights into how banks actively manage the fragility of their depositor base but also highlight

the efficacy of deposit insurance in mitigating panic.

Another concern is that the result might reflect banks’ risk; that is, banks with more liquidity

mismatch take on more risk and as a result, depositors are more sensitive to its performance and

respond more strongly. While risk transformation and liquidity transformation are related in some

cases, they do not move perfectly in tandem. 3 For a given level of risk, the amount of liquidity

transformation might vary considerably. We conduct two analyses to address this concern. First, we

3 For example, a bank can securitize loans into asset backed securities and improve its liquidity.

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partition the sample into four groups based on liquidity creation on asset side, and within each

group test whether more liquidity creation on liability side makes uninsured deposits more sensitive

to banks’ performance. The idea is that holding the asset side constant, the result cannot be driven

by asset risk and thus is attributable to strategic complementarity from bank’s liquidity creation

from its liability side. We find that more liquid liability leads to a more sensitive uninsured deposit

base as long as the asset side is not too liquid, and the sensitivity is higher when the asset side is

more illiquid. Second, we also partition observations into four quartiles based on liability liquidity

creation. We find that uninsured deposits are more sensitive to performance when the banks’ assets

are less liquid, and even more so when the liability side is more liquid. These results lend support to

that the results are driven by strategic complementarity but not just asset side risk.

In our final set of analyses, we explore the implications of our above findings on the

presence of panic in depositor behavior on three aggregate banking outcomes: failure rates,

performance during crises, and overall profitability. We find that liquidity mismatch has strong

predictive ability for future bank failure. An interquartile increase in bank’s liquidity mismatch is

associated with a 6% increase in failure chance over the next 3 years. We use the financial crisis of

2008 as a laboratory to observe the performance and response of banks with different levels of

liquidity mismatch during a crisis episode. We find that during the crisis, banks with more liquidity

mismatch exhibit a greater erosion in their deposit base (despite offering higher rates), lower

growth in credit, and higher failure rates. Finally, we analyze profitability and find that greater

liquidity transformation is associated with higher profitability. Collectively, these results enhance

our understanding of the banking business model and highlight that liquidity transformation allows

banks to earn higher profits at the cost of enhanced fragility and failure risk.

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Our paper relates to prior studies that explore the role of fundamentals and panic in bank

runs. Based on a thorough review of the earlier work, Goldstein (2013) concludes that while prior

studies document that runs are correlated with fundamentals, they speak little to the presence and

importance of panic. Among the more recent studies on this topic, Iyer and Puri (2012, 2016) and

Egan et al. (2017) are the most related. Iyer and Puri (2012, 2016) explore depositor responses in

their case study of one bank run in India that arguably was triggered by panic. Egan et al. (2017)

study a sample of the 16 largest US retail banks and find that uninsured deposit elasticity to banks

distress is sufficiently high to make banks very fragile. Unlike our study, however, they do not

separately identify the fragility that results from panic. Furthermore, to the best of our knowledge,

we are the first to provide large sample evidence of the presence of panic based running in

depositors in the commercial banking industry.

Our study is also related to papers that document presence of panic in other settings such as

open-end equity funds (Chen, Goldstein, and Jiang, 2010), money market funds (Schmidt,

Timmerman, and Wermers, 2016), and corporate bond funds (Goldstein, Jiang, and Ng, 2017). We

contribute by providing evidence for banks – an institution that has been frequently at the epicenter

of several financial crises and, not surprisingly, the focal point of government regulations designed

to foster financial stability.

2. Measure of bank liquidity mismatch, sample construction and summary statistics

We obtain most of our bank-level variables from U.S. Call Reports as disseminated by the

Wharton Research Data Services (WRDS). Call reports contain quarterly data on all commercial

banks’ income statements and balance sheets.

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Our sample period is from January 1994 to December 2013. Our bank-quarter observation is

at commercial bank level. To avoid the impact of mergers and acquisitions, we exclude bank-quarter

observations with quarterly asset growth greater than 10%. We also exclude bank quarters with total

assets smaller than 100 million. All variables are winsorized at 1% and 99%. These sample-selection

and cleaning procedures are commonly used in prior work (e.g., Gatev and Strahan, 2006; Acharya

and Mora, 2015).

A key element for our analysis is to measure the degree of liquidity mismatch on banks’

balance sheet. When a bank creates more liquidity, the bank itself has more liquidity mismatch and

becomes more illiquid. Therefore, we measure liquidity mismatch with the liquidity creation measure

in Berger and Bouwman (2009). To calculate their measure, Berger and Bouwman (2009) first

classify each category of activity (both on and off-balance sheet ones) into liquid, semi-liquid, or

illiquid, and then assign a weight accordingly. They then assign a weight of 1/2 to each dollar of

illiquid assets and liquid liabilities, zero to semi-liquid assets and liabilities, and -1/2 to liquid assets

and illiquid liabilities and equities. The idea is that a bank creates liquidity when it transforms liquid

liability into illiquid loans, and destroys liquidity when it uses illiquid funding to purchases liquid

assets. Banks’s liquidity creation is the weighted sum of all the items. For example, a bank would

create a dollar of liquidity if it finances a dollar of commercial and industrial loan (which is classified

as an illiquid asset) by 1 dollar of demand deposit (which is classified as a liquid liability). The bank

would not have created this liquidity had it financed the same loan with equity or long-term debt.

We obtain the quarterly bank liquidity creation data from Christa Bouwman’s website. 4

Berger and Bouwman’s (2009) preferred measure is CatFat, which includes the liquidity creation

4 https://sites.google.com/a/tamu.edu/bouwman/data

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from off-balance sheet items, such as unused loan commitments. Following them, we use CatFat per

unit of total gross asset as our main measure of liquidity mismatch. For robustness check, we also use

CatNonFat as an alternative measure which does not consider the liquidity created from off-balance

sheet items. As shown in Table 1, Panel B, these two measures are highly correlated with the

correlation coefficient of 0.97.

Table 1, Panel A presents the summary statistics. The mean of CatFat (deflated by gross total

assets) is 0.32. It exhibits significant cross-sectional variation with the standard deviation at 0.17 and

the interquartile change of 0.22 (from 0.21 at 25th percentile to 0.43 at 75th percentile).

Figure 1 plots the average CatFat for the whole sample during our sample period. It shows the

banking sector has on average created more liquidity from 1994 up to and peaked around the financial

crisis period, followed by a decline to 2013. Figure 1 also shows that the average pattern holds for

banks in different percentile of CatFat and indicates that there is no significant change in the cross-

sectional variations in CatFat. Figure 2 suggests that a significant portion of the sample variations in

CatFat is driven by cross-bank differences and not by over-time differences. Further analysis

(untabulated) confirmed that this is indeed true. The adjusted R-squared from regression CatFat on

quarter fixed effects is 6.7%, and increases to 83.9% after adding in the bank-fixed effects.

Figure 2 plots the average CatFat for the subsamples of banks by total asset size. We classify

banks with asset size smaller than 500 million as small banks, those with more than $3 billion assets

as large banks, and the rest as medium banks, all measured in real 2000 dollars. Consistent with

Berger and Bouwman (2009), large banks create more liquidity per dollar asset than medium and

small banks throughout most of the sample period. Small and medium banks experience an increase

in liquidity creation up to the financial crisis, and all banks experienced decline in liquidity creation

afterwards.

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Table 1 Panel A shows that the average (median) bank performance, as measured by

annualized ROE, is 10.65% (11.7%) with a standard deviation of 11.38%. Table 1, Panel B presents

the pairwise correlation for all variables. The correlation between uninsured deposit flows and lagged

ROE is much higher (at 0.17) than the correlation between insured deposit flows and ROE (at 0.03),

suggesting that uninsured deposit flows are more sensitive to bank performance.

3. Liquidity mismatch and deposit flow-performance sensitivity

3.1 Concavity of deposit flow to performance sensitivity

We start our analysis by estimating a semi-parametric regression of uninsured deposit flows

on bank performance. We follow prior literature and allow a flexible relation between deposit flows

and bank performance.5 Specifically, the regression is specified as

Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈 = 𝛼𝛼 + 𝑓𝑓(𝑃𝑃𝐷𝐷𝑃𝑃𝑓𝑓𝑖𝑖𝑖𝑖−1) + 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑃𝑃𝐶𝐶𝑙𝑙𝑖𝑖𝑖𝑖 + 𝜖𝜖𝑖𝑖𝑖𝑖 (1)

where Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈 is the deposit flows measured as the changes in bank i’s deposit balances over period

t scaled by the beginning of period assets; 𝑃𝑃𝐷𝐷𝑃𝑃𝑓𝑓𝑖𝑖,𝑖𝑖−1 is a measure of bank performance that

depositors observe at the end of quarter t-1.

Following Chen et al (2019), we measure the deposit flows over the two quarters following

the end of quarter t-1 for which bank performance is measured. This is because banks typically file

call reports with a delay of 30 days after the calendar quarter ending (Baderscher et al., 2017) and

because the literature on post-earnings announcement drift suggests that investors respond to

quarterly accounting reports with a delay of up to a quarter following the announcement (Bernard

and Thomas, 1989). We measure deposit flows as the change in deposits over the subsequent two

5See Chevalier and Ellison (1997), Chen, Goldstein, and Jiang (2010) and Goldstein, Jiang and Ng (2017).

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quarters scaled by the beginning of period assets. We also follow Chen et al. (2019) and use return

on equity (ROE) as the primary measure of bank performance.6

The control variables include both bank and quarter fixed effects. In addition, we also include

time varying controls for bank characteristics that are shown to affect deposit flows in prior works

(e.g., Acharya and Mora, 2015). These control variables include (i) capital ratio defined as book value

of capital scaled by total assets (Capital Ratio), (ii) wholesale funding scaled by total assets

(Wholesale Funding), (iii) real estate loan share calculated as the amount of loans secured by real

estate divided by total loans (RealEstate_Loans), and (iv) the logarithm of asset size (Ln(Assets)), all

measured at the end of the quarter t-1. Finally, we control for lagged deposit rate which would also

be expected to affect the deposit flows (Deposit Rate). Ideally, we would like to control for rates

offered on uninsured and insured deposits when modelling these two categories of deposit flows.

However, Call reports do not separately report the interest expenses on insured and uninsured deposits.

We use the core deposit rate to proxy the rates offered on insured deposits and the rate on large time

deposit to proxy the rates on uninsured deposits. We believe this is a reasonable approximation

because core (large time) deposits are most likely to be insured (uninsured).7 We measure these rates

as the quarterly interest expense on the deposits divided by the average quarterly deposits over the

same period.

Figure 3 presents the semi-parametric plot of uninsured deposit flows and lagged ROE for

the full sample. It shows that uninsured deposit flow is positively related to past performance, and

more so when the performance is lower. The concavity indicates the withdrawal pressure from

6 Chen et al (2019) provide a detailed discussion on why ROE is a preferred measure of bank performance than the alternative measures such as ROA or non-performing loans. 7 Until March 31, 2011, core deposits were defined in the Uniform Bank Performance Report (UBPR) User Guide as the sum of demand deposits, all NOW and automatic transfer service (ATS) accounts, money market deposit accounts (MMDAs), other savings deposits, and time deposits under $100,000. As of March 31, 2011, the definition was revised to reflect the permanent increase to FDIC deposit insurance coverage from $100,000 to $250,000 and to exclude insured brokered deposits from core deposits.

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depositors when bank performance is poor is higher than deposit inflows when bank performance is

good. Goldstein et al. (2017) find similar concave relation between fund flows and fund past

performance in open-end corporate bond funds. A casual inspection of Figure 1 suggests that the

concavity starts when the ROE is somewhere in the middle of 10-20%, higher than the average

level in our sample.

We formally test the non-linearity in the flow-performance sensitivity by estimating

Equation (1) below:

Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖 = 𝛽𝛽0𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖−1 + 𝛽𝛽1𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖−1 × 𝐷𝐷(𝑅𝑅𝑅𝑅𝑅𝑅 < 𝑀𝑀𝐷𝐷𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶) + 𝛽𝛽2𝐷𝐷(𝑅𝑅𝑅𝑅𝑅𝑅 < 𝑀𝑀𝐷𝐷𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶) + 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑃𝑃𝐶𝐶𝑙𝑙𝑖𝑖𝑖𝑖

+ 𝜖𝜖𝑖𝑖𝑖𝑖 (1)

where D(𝑅𝑅𝑅𝑅𝑅𝑅 < 𝑀𝑀𝐷𝐷𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶) is an indicator variable that equals 1 (0) if the bank’s ROE in the last

quarter is below (above) the sample median. We cluster standard errors at bank level, which adjusts

for arbitrary forms of correlations between observations for the same bank that might result from

overlapping windows for flow measurement.

Estimates shown in Table 2, Column (1) indicate the flow-performance sensitivity for

uninsured deposits is larger when performance is poorer: the slope estimate is 0.045 for above-

median ROE, 0.076 (0.045+0.031) for below-median ROE, and the difference is statistically

significant at 1% level. Columns (2) and (3) show that similar concave relations also exist in

insured deposits and total deposits, with comparable magnitude.

3.2 Liquidity mismatch and the deposit flow to performance sensitivity

Figure 4 presents the semi-parametric plot of uninsured deposit flows to lagged ROE for the

two subsamples of banks. The solid (dashed) line is for the subsample of banks with above-

(below-) median level of liquidity mismatch. The solid line lies above the dashed line for high

levels of ROE, suggesting that banks with more liquidity mismatch attract more uninsured deposits

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during good times. However, as performance declines, the solid line eventually crosses the dashed

line and lies bellows for low levels of performance. This suggests that for the same decline in

performance, banks with more liquidity mismatch experience stronger outflows in uninsured

deposits than banks with less liquidity mismatch.

We formally test the effect of liquidity mismatch on the flow-performance sensitivity by

estimating the following equation:

Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈 = 𝛽𝛽0𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖−1 + 𝛽𝛽1𝐶𝐶𝑀𝑀𝐶𝐶𝐶𝐶𝑀𝑀𝐶𝐶𝑖𝑖𝑖𝑖−1 ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖−1 + 𝛽𝛽2𝐶𝐶𝑀𝑀𝐶𝐶𝐶𝐶𝑀𝑀𝐶𝐶𝑖𝑖𝑖𝑖−1 +

+𝛽𝛽3𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖−1 ∗ 𝐷𝐷(𝑅𝑅𝑅𝑅𝑅𝑅 < 𝑚𝑚𝐷𝐷𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶) + 𝛽𝛽4𝐶𝐶𝑀𝑀𝐶𝐶𝐶𝐶𝑀𝑀𝐶𝐶𝑖𝑖𝑖𝑖−1 ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖−1 ∗ 𝐷𝐷(𝑅𝑅𝑅𝑅𝑅𝑅 < 𝑚𝑚𝐷𝐷𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶)

+ 𝛽𝛽5𝐶𝐶𝑀𝑀𝐶𝐶𝐶𝐶𝑀𝑀𝐶𝐶𝑖𝑖𝑖𝑖−1𝐷𝐷(𝑅𝑅𝑅𝑅𝑅𝑅 < 𝑚𝑚𝐷𝐷𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶) + 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑃𝑃𝐶𝐶𝑙𝑙𝑖𝑖𝑖𝑖 + 𝜖𝜖𝑖𝑖𝑖𝑖 (2)

where Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈 is the uninsured deposit flows for bank i during period t, and 𝐶𝐶𝑀𝑀𝐶𝐶𝑓𝑓𝑀𝑀𝐶𝐶𝑖𝑖𝑖𝑖−1 is the

measure of banks’ liquidity mismatch.

To start with, to assess the average pattern, we first estimate a reduced form of Eqn. (2)

without including the interaction terms with D(ROE<Median). The result is presented in Column

(1) of Table 3. It shows that liquidity mismatch significantly increases the sensitivity of uninsured

deposit flows to bank performance. The coefficient estimate for ROE is 0.035 (t-stat = 4.932) and

that for the interaction term between ROE and CatFat is 0.103 (t-state = 5.327). These estimates

suggest that an interquartile increase in liquidity mismatch (of 0.22) would increase the sensitivity

of uninsured deposit flows to bank performance by 40%.8 In terms of deposit stability (volatility),

the estimates imply that for the same changes in the fundamental ROE, a bank at 75 percentile level

8This is estimated as (sensitivity at 75th percentile – sensitivity at 25th percentile) / (sensitivity at 25th percentile) = 0.103*(0.43-0.21)/ (0.03335+0.103*0.21)= 40%.

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of liquidity mismatch would experience 96% (=1.4*1.4-1) higher volatility in its uninsured deposit

flows than a bank with 25 percentile level of liquidity mismatch.

Column (2) presents the results from estimating the full version of Eqn. (2). It shows that the

effect of liquidity mismatch is primarily driven when banks experience below median performance.

Specifically, the estimated coefficient for ROE*CatFat is -0.024 and insignificant from zero at

conventional levels, indicating that liquidity mismatch does not affect flow-performance sensitivity

for banks with above median performance. However, the coefficient estimate for

ROE*CatFat*D(ROE<Median) is 0.130 and significant at less than 5% (t-stat of 2.36), indicating

that for banks with below median ROE, liquidity mismatch significantly increases the sensitivity of

uninsured deposit flows to bank performance.

In Columns (3) to (4), we repeat the above analyses with insured deposit flows as the

dependent variable. Unlike uninsured deposits, insured deposits are fully protected in case of bank

failure, and depositors’ payoff is not affected by other depositors’ run behavior. This implies that

the strategic complementarity is not as important a concern for insured deposits, and we should not

expect to observe a positive relation between liquidity mismatch and the insured flows sensitivity to

performance. In addition, prior literature has shown that banks tend to attract insured deposits to

compensate for the outflows in uninsured deposits (Manju et al. 2018). When this is the case, we

may observe a negative coefficient for the interaction term between CatFat and ROE for insured

deposits.

Estimates in Column (3) show that the coefficient estimate for ROE is 0.094 (t-stat =

12.394). This finding is consistent with findings in prior literature that insured deposit flows are

sensitive to bank performance (e.g., Peria and Schumkler, 2001; Berger and Turk-Ariss, 2015).

However, the interactive term between ROE and CatFat is negative at -0.079 (t-state = -4.024),

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indicating that banks liquidity mismatch is negatively related to the sensitivity of insured deposit

flows to bank performance, suggesting that more mismatched banks manage their insured deposits

to offset the loss of uninsured deposits.

Column (4) shows that the relationship between insured deposit flows and bank

performance is concave (as shown in Table 2), but the degree of concavity does not differ

significantly by banks’ liquidity mismatch. Specifically, the coefficient estimate for the

ROE*D(ROE<median)*CatFat is -0.066 and not statistically significant from zero at conventional

levels. These results are consistent with the interpretation that the effect of liquidity mismatch on

the sensitivity of uninsured depositor flows to bank performance is driven by the strategic

complementarities in the payoffs of uninsured depositors, and not by other uncontrolled bank

characteristics that affect both insured and uninsured deposits similarly.

Columns (5) and (6) use total deposit flows as the dependent variable to assess the net effect

of liquidity mismatch on the stability of banks’ deposit funding. The results shown there indicate

that the mediating effect of insured deposit flows largely offset the flows from uninsured deposits.

As a result, the liquidity mismatch does not have a significant effect on the sensitivity of total

deposits to bank performance. Additional analysis: controlling for persistence

One may be concerned that the difference in flow-performance sensitivity we find reflect

differences in the performance persistence across banks. If banks with higher liquidity mismatch

also have more persistent performance, then depositors will respond more strongly to each unit of

performance. To address this concern, we directly control for the effect of banks’ performance

persistence on flow-performance sensitivity by adding the interactive between a measure of bank

performance persistence and ROE. Specifically, we measure banks’ performance persistence using

16

the coefficient 𝛽𝛽1 estimated from the following AR(1) regression, estimated for each bank-quarter

using the bank’s observations over the previous 12 quarters:

𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖 = 𝛼𝛼0 + 𝛽𝛽1𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖−1 + 𝜀𝜀𝑖𝑖 (3)

Table 3, Panel B presents the results after controlling for persistence. They show that the

coefficients on our main variable of interest, 𝐶𝐶𝑀𝑀𝐶𝐶𝐶𝐶𝑀𝑀𝐶𝐶𝑖𝑖−1 × 𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖−1, remain qualitatively positive

and statistically significant for uninsured deposits.9 In untabulated anaylsis, we also include

interactions of control variables and 𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖−1, and the results are robust. Thus our results are not

driven by potential correlation with these variables.

3.3 The impact of liquidity mismatch during financial crisis and in normal times

We have shown that more liquidity mismatch is associated with a larger deposit flow

performance sensitivity. However, it is not clear whether the impact is different during financial crisis

and normal times. In this section, we first split the sample into two: crisis period and non-crisis period

and examine if the results are similar in the two subsamples. Following Archarya and Mora (2016),

we look at the recent Global Financial Crisis and define it as from 2007Q3 to 2009Q2.

Table 3, Panel C shows how deposit flow performance sensitivity varies in and out of the

Financial Crisis. Column (1) examines the uninsured deposit flow during the Crisis. The coefficient

on ROE it-1 is 0.030, on CatFatit-1 × ROE it-1 is -0.055, and neither is statistically significant. However,

the coefficient on CatFatit-1 is -5.635, and is significant at 5% level. This suggests that during the

crisis, uninsured deposits were indiscriminate about bank’s recent performance; rather, given that the

banking system is in a crisis, they were concerned more about the systematic risk and withdrew more

9 The number of observations in Table 5 is lower than those in Table 3. This is because we estimate Eqn. (5) only for banks that did not experience more than 10% increase in annual assets during the previous 12 quarters to minimize the impact of bank mergers on performance persistence. In untabulated sensitivity analysis, we re-estimate our main specifications using the same sample as in Table 5 and find little change in our results quantitatively.

17

from banks with more liquidity mismatch. Column (4) shows that our main results are robust in non-

crisis period.

3.4 Liquidity creation on liability side and uninsured deposit sensitivity to performance

Another concern is that the result might reflect banks’ risk; that is, banks with more liquidity

mismatch take on more risk and as a result, depositors are more sensitive to its performance and

respond more strongly. While risk transformation and liquidity transformation are related in some

cases, they do not move perfectly in tandem. For a given level of risk, the amount of liquidity

transformation might vary considerably. We conduct two analyses to address this concern. First, we

focus on the liability side and examine whether more liquid liability makes uninsured deposits more

sensitive. This is to hold asset liquidity constant and focus on the strategic complementarity among

depositors, and the results would not be attributable to pure risk of the asset portfolio.

We partition the sample into four based on the level of asset side liquidity creation, and for

each group, we run the following regression:

Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈 = 𝛽𝛽0𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖−1 + 𝛽𝛽1𝐿𝐿𝐶𝐶_𝐿𝐿𝑖𝑖𝑖𝑖−1 ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖−1 + 𝛽𝛽2𝐿𝐿𝐶𝐶_𝐿𝐿𝑖𝑖𝑖𝑖−1 +

+ 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑃𝑃𝐶𝐶𝑙𝑙𝑖𝑖𝑖𝑖 + 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑃𝑃𝐶𝐶𝑙𝑙𝑖𝑖𝑖𝑖 ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖−1 + 𝜖𝜖𝑖𝑖𝑖𝑖

Table 4, Panel A presents the results. Asset side liquidity creation increases from Column

(1) to (4). When asset liquidity creation is in the lowest quartile, the coefficient on 𝐿𝐿𝐶𝐶_𝐿𝐿𝑖𝑖𝑖𝑖−1 ∗

𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖−1 is not significantly different from 0, suggesting little strategic complementarity when asset

is mostly liquid. As banks’ assets become more illiquid, the coefficient on 𝐿𝐿𝐶𝐶_𝐿𝐿𝑖𝑖𝑖𝑖−1 ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖−1 gets

more positive and significant, from 0.210 for the second quartile to 0.302 in the third and 0.408 in

the top quartile. This suggests that when asset is less liquid, strategic complementarity makes

uninsured deposits more sensitive to performance, and more so when the asset side is more illiquid.

18

Table 4, Panel B conducts a similar analysis, but partition the sample based on liability side

liquidity creation. We find that in each quartile, the coefficient on 𝐿𝐿𝐶𝐶_𝐴𝐴𝑖𝑖𝑖𝑖−1 ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖−1 is positive

and significant, suggesting that more asset illiquidity always leads to more sensitive uninsured

deposits. This is consistent with more illiquidity on asset side exposes banks to the risk of selling

assets at a discount and thus generating strategic complementarity among depositors. Furthermore,

the coefficients on 𝐿𝐿𝐶𝐶𝐴𝐴𝑖𝑖𝑖𝑖−1 ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖−1 increase from 0.071 for the lowest quartile to 0.189 in the

highest quartile, suggesting that the impact of asset illiquidity grows larger with liability liquidity.

3.5 Bank size and the impact of liquidity mismatch

Prior literature finds that banks with different size behave quite differently. Relative to small

banks, large banks tend to be subject to more regulatory oversight and are often perceived to be too-

big-to-fail. These differences can in turn affect depositors’ expectations about the likelihood of a

run and their behaviors. To examine whether the impacts of liquidity mismatch on bank deposit

flows vary across different size groups, we estimate an expanded version of Eqn. (2) as follows

Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈 = � 𝐼𝐼(𝑆𝑆𝑀𝑀𝑆𝑆𝐷𝐷)𝐼𝐼(𝑠𝑠𝑖𝑖𝑠𝑠𝑠𝑠)

{𝛽𝛽0+𝐼𝐼(𝐻𝐻𝑀𝑀𝐻𝐻ℎ𝑅𝑅𝑅𝑅𝑅𝑅)𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖−1 + 𝛽𝛽1+𝐼𝐼(𝐻𝐻𝑀𝑀𝐻𝐻ℎ𝑅𝑅𝑅𝑅𝑅𝑅)𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖−1 ∗ 𝐶𝐶𝑀𝑀𝐶𝐶𝐶𝐶𝑀𝑀𝐶𝐶𝑖𝑖𝑖𝑖−1

+𝛽𝛽0−𝐼𝐼(𝐿𝐿𝐶𝐶𝐿𝐿𝑅𝑅𝑅𝑅𝑅𝑅)𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖−1 + 𝛽𝛽1−𝐼𝐼(𝐿𝐿𝐶𝐶𝐿𝐿𝑅𝑅𝑅𝑅𝑅𝑅)𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖−1 ∗ 𝐶𝐶𝑀𝑀𝐶𝐶𝐶𝐶𝑀𝑀𝐶𝐶𝑖𝑖𝑖𝑖−1}+𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑃𝑃𝐶𝐶𝑙𝑙𝑖𝑖𝑖𝑖 + 𝜖𝜖𝑖𝑖𝑖𝑖 (4)

We classify each observation based on asset size: big banks with more than 3 billion assets,

small banks with less than 500 million, and medium banks with assets between 500 million and 3

billion, all measured in real 2000 dollars. I(Size) is the indicator variable that equals 1 for

observations classified as in one of the three size categories. To facilitate interpretation, we also

create two performance indicator variables: I(HighROE) equals 1 for observations with above

median level of ROE and 0 otherwise. Similarly, I(LowROE) equals 1 for observations with below

median level of ROE and 0 otherwise.

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Table 5 presents the results from estimating Eqn. (4). The first four rows show the estimated

coefficients for small banks. Column (1) studies uninsured deposit flows. The coefficient for

I(HighROE)*ROE is 0.031 (t=1.587) and that for I(HighROE)*ROE*CatFat is -0.013 and not

statistically significant at conventional levels. In contrast, the coefficients for I(LowROE)*ROE and

I(LowROE)*ROE*CatFat are 0.030 and 0.116, respectively. Both are statistically positive at less

than 1 percent level. These results indicate that liquidity mismatch increases the sensitivity of

uninsured deposit flows in small banks only when the banks were experiencing poor performance.

The magnitude is similar to that documented for the whole sample as shown in Table 3, Panel A.

Estimates in Column (2) indicate that the flow-performance sensitivity is negatively related

to banks’ liquidity mismatch for poorly performing banks. As a result, when the dependent variable

is the total deposit flows in Column (3), the coefficient for I(LowROE)*ROE*CatFat is 0.049 (t-

stat=1.435), indicating that the net effect of liquidity mismatch on the sensitivity of total deposit

flows to performance is not statistically significant at conventional levels.

For medium banks (about 15% of the whole sample), liquidity mismatch does not affect the

flow-performance sensitivities when banks are performing well. The coefficient estimates for

I(HighROE)*ROE*CatFat are statistically indistinguishable from zero across Columns 1 to 3.

Column 1 further shows that the coefficient estimate for I(LowROE)*ROE*CatFat is positive at

0.097 for uninsured deposits, indicating that medium banks with more liquidity mismatch

experience larger outflows of uninsured deposit during poor performances. However, despite its

large economic magnitude, the coefficient estimate is not statistically significant from zero at

conventional levels. This may be a result of low power due to the smaller sample size for medium

banks.

20

Estimates in the last four rows of Table 5 show that depositors in large banks on average

respond more strongly to poor performance than to good performance, i.e., the concave relation

between deposit flows and bank performance exists in large banks as well. However, liquidity

mismatch has no incremental effects on these sensitivities. This is consistent with the idea that large

banks are too-big-to-fail and are more likely to receive protection from the government support in

times of crisis.

4. Effects of liquidity mismatch on bank performance

In this section, we explore the implications of our above findings on the presence of panic in

depositor behavior. We start by examining the impact of significant deposit outflows on bank

performance, followed by analyses on how liquidity mismatch affects bank failure, performance

during crises, and overall profitability.

4.1 Liquidity mismatch and future bank failure

We have interpreted the positive effect of liquidity mismatch on the uninsured deposit

flows-performance sensitivity as evidence of panic-based run due to coordination failures. In this

section, we conduct several additional analyses to support this interpretation.

In our first analysis, we examine whether deposit outflows adversely affect bank future

performance and more importantly, whether the effect is stronger for banks with more liquidity

mismatch. We do so by estimating the following regression:

𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖+1 = 𝛽𝛽1𝑅𝑅𝑂𝑂𝐶𝐶𝑓𝑓𝑙𝑙𝐶𝐶𝐿𝐿𝑖𝑖𝑖𝑖 + 𝛽𝛽2𝑅𝑅𝑂𝑂𝐶𝐶𝑓𝑓𝑙𝑙𝐶𝐶𝐿𝐿𝑖𝑖𝑖𝑖 ∗ 𝐶𝐶𝑀𝑀𝐶𝐶𝐶𝐶𝑀𝑀𝐶𝐶𝑖𝑖𝑖𝑖 + �𝛾𝛾𝑗𝑗𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖−𝑗𝑗

4

𝑗𝑗=0

+ 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑃𝑃𝐶𝐶𝑙𝑙𝐶𝐶 + 𝜖𝜖𝑖𝑖𝑖𝑖, (6)

21

where 𝑅𝑅𝑂𝑂𝐶𝐶𝑓𝑓𝑙𝑙𝐶𝐶𝐿𝐿𝑖𝑖𝑖𝑖 is a dummy variable that equals 1 if the total deposit outflows in the previous

quarter is more than 2.5% of the total assets, and 0 otherwise.10

Table 6 presents the results from estimating Eqn. (6). To facilitate interpretation, we first

present the result in Column (1) using a binary version of the CatFat, i.e., a dummy variable that

equals 1 (0) when CatFat is above (below) sample median. Estimates in Column (1) show a bank

with above median CatFat would experience a 0.938% (=-0.165-0.773) decline in its quarterly ROE

following an extreme deposit outflows in the previous two quarters, whereas a bank with below

median CatFat would experience only 0.165% decline. The difference is significant at less than

10% level. Column 2 uses the continuous version of CatFat. The coefficient estimate for

Outflow*CatFat is -1.149, and significant at less than the 5% level.

At the same time, estimates in Table 6 also shows that in normal times with no extreme

deposit outflows, banks with more liquidity mismatch have higher profitability. The coefficient

estimate for D(CatFat>median) in Column (1) indicates that the average ROE for banks with above

median liquidity mismatch is 0.296% higher than that for banks with below median liquidity

mismatch. The difference is highly significant (at less than 1% level) and relative to the average

quarterly ROE for our sample banks (2.66%=10.65%/4, from Table 1 Panel B), represents a fairly

large economic magnitude as well. Similar inferences can be drawn from Column 2 as well when

we use the continuous version of CatFat. The positive relation between CatFat and bank

profitability is consistent with the Berger and Bouwman (2009) who document a positive

association between CatFat and the stock market valuations for the subsample of publicly traded

10 2.5% of total assets is at the 11th percentile of total deposit flows. The average cash to total assets is 5.1%.

22

banks. These results are consistent with the idea banks with more liquidity mismatch also create

more socially desirable liquidity for the economy.

Next, we examine whether banks with more liquidity mismatch are more likely to fail by

estimating a logit model as bellows:

𝐵𝐵𝑀𝑀𝐶𝐶𝐵𝐵𝐶𝐶𝑀𝑀𝑀𝑀𝑙𝑙𝑂𝑂𝑃𝑃𝐷𝐷𝑖𝑖,[𝑖𝑖,𝑖𝑖+𝑗𝑗] = 𝛾𝛾1𝐶𝐶𝑀𝑀𝐶𝐶𝐶𝐶𝑀𝑀𝐶𝐶𝑖𝑖𝑖𝑖−1 + 𝛾𝛾2𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖−1 + 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑃𝑃𝐶𝐶𝑙𝑙𝐶𝐶 + 𝜖𝜖𝑖𝑖𝑖𝑖, (7)

where 𝐵𝐵𝑀𝑀𝐶𝐶𝐵𝐵𝐶𝐶𝑀𝑀𝑀𝑀𝑙𝑙𝑂𝑂𝑃𝑃𝐷𝐷𝑖𝑖,[𝑖𝑖,𝑖𝑖+𝑗𝑗] is an indicator variable that equals 1 if bank i appears on FDIC’s failed

bank list during the period of year t to t+j where j={1,2,3}. We estimate Eqn. (7) using observations

from non-crisis period. To avoid double counting and underestimating the standard errors, we use

non-overlapping years, so that a failed bank will only appear in our sample once in the estimation.

For example, when the dependent variable is failure within the next two year (i.e., [t,t+2]), we use

observations from the first quarter of odd years, such as 1995Q1, 1997Q1.

Table 7 presents the results on predicting bank failure. To ease interpretation, we present the

marginal effect. Column (1) examines failure within the next year and does not find any significant

relation between bank liquidity mismatch and immediate failure. However, when we examine future

failures in Column (2) and (3), we find that liquidity mismatch is significantly positively associated

with bank failure in the future. The estimate from Column 2 suggests that an interquartile increase in

bank’s liquidity mismatch would increase the chance of bank failure by 0.06 basis point, representing

an increase of 6% over the average failure rate (of 1.07 basis point). The effect is stronger during

financial crisis. An interquartile increase in liquidity mismatch will increase the chance of a bank

failure during the crisis period by 0.57 basis point, a 22% increase over the base rate of 2.18% failure

rate during the crisis. This result is supportive of the interpretation that liquidity mismatch can lead

to panic-based run that can turn an otherwise solvent bank insolvent. To see this, notice that while

ROE is significantly negatively associated with bank failures in normal times (the coefficient estimate

23

for ROE is significantly negative in columns 1 to 3), it does not have any predictive power for bank

failure during the crisis. Yet, the ability of CatFat to predict bank failure is much stronger during the

financial crisis than during normal time.

Table 7 also shows that the interest rate that bank offered on core deposits are also positively

related to the probability of future failure. This is consistent with the prediction in Goldstein and

Pauzner (2005) and Vives (2014) that higher interest rate offered on deposits can increase the strategic

complementarities among demand depositors and lead to a higher ex ante probability of failure. In

addition, bank transparency is positively related to future failure as well. This is consistent with more

transparency hurts banks’ ability to create safe, money-like securities and increases its probability of

failure.

4.2 Performance in financial crisis

We next examine how liquidity mismatch affects banks’ performance during the financial

crisis of 2008 by estimating the following equation:

Yit = 𝛽𝛽0𝐶𝐶𝑀𝑀𝐶𝐶𝐶𝐶𝑀𝑀𝐶𝐶𝑖𝑖𝑖𝑖−1 + 𝛽𝛽1𝐶𝐶𝑀𝑀𝐶𝐶𝐶𝐶𝑀𝑀𝐶𝐶𝑖𝑖𝑖𝑖−1 ∗ 𝐶𝐶𝑃𝑃𝑀𝑀𝐶𝐶𝑀𝑀𝐶𝐶𝑖𝑖 + 𝛽𝛽2𝐶𝐶𝑃𝑃𝑀𝑀𝐶𝐶𝑀𝑀𝐶𝐶𝑖𝑖 + 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑃𝑃𝐶𝐶𝑙𝑙𝑖𝑖𝑖𝑖 + 𝜖𝜖𝑖𝑖𝑖𝑖 (7)

where 𝑌𝑌𝑖𝑖𝑖𝑖 is the outcome variables for bank i at time t. We examine three categories of outcomes:

deposit flows, deposit rates, and loan decisions. 𝐶𝐶𝑃𝑃𝑀𝑀𝐶𝐶𝑀𝑀𝐶𝐶𝑖𝑖 is an indicator variable for the financial

crisis period of 2007Q3 to 2009Q2.

Column (1) examines the behavior of uninsured deposit flows. We find that a significant

positive coefficient estimate for CatFatit-1 and a negative coefficient estimate for the interaction

term between CatFatit-1 and Crisis, both are highly statistically significant. This suggests that banks

with more liquidity mismatch had a larger growth in uninsured deposit during non-crisis period, but

during the crisis, they experience a larger decrease in uninsured deposits. Column (2) uses the

insured deposit flows as the dependent variable and shows that a positive coefficient on CatFatit-1 ×

24

Crisis at 3.414 (t-stat = 6.577), suggesting some degree of substitution between insured and

uninsured deposits. Estimates in Column (3), which examines total deposit flows, show that during

the crisis, banks with more liquidity mismatch still lose more deposits, as evidenced by the negative

coefficient on CatFatit-1 × Crisis of -2.550 (t-stat=-4.045).

Columns (4) to (7) examine how deposit rates vary with crisis. We study the rates on four

types of deposits: transaction, saving, small time deposits and large time deposits. The deposits in

the first three categories are more likely to be within FDIC insurance limit while the large time

deposits are more likely to be uninsured. For the average bank in our sample, 25% of its deposits

are in the form of transaction deposits, 30% in savings deposits that pays the average rate of 1.81%,

and 28% in small time deposit and 17% in large time deposits, both paying on average 3.6% deposit

rate. As expected, the deposit rates on transaction and saving deposits are much lower than those on

time deposits.

In constructing the CatFat measure, Berger and Bouwman (2009) classify both transaction

and saving deposits as liquid liabilities because they can be withdrawn without any penalty. All

time deposits regardless of maturity are classified semi-liquid liabilities as they can be withdrawn

with some penalty. A bank that finances long-term illiquid loan with more liquid liabilities creates

more liquidity for the economy and is more liquidity mismatched. Thus, we expect CatFat to be

positively correlated with the percentage of deposits in transactions and savings deposit and

negatively correlated with time deposits. This is indeed the case. The correlation coefficients with

both small and large time deposits are negative, at -0.32 and -0.02, respectively. Interestingly, while

CatFat is positively correlate with the sum of transaction and savings deposit (at 0.28), it is driven

by the positive correlation with the savings deposit. The correlation coefficient between CatFat and

25

savings deposit is 0.34 and that between CatFat and transaction deposit is -0.11. Therefore, it

appears that savings deposits play a significant role in banks’ ability to create liquidity.

Column (4) to (7) show that during the non-crisis period, there is no significant relation

between liquidity mismatch and deposit rates except the rates on savings deposit. However, the

coefficient estimates for the interaction term between CatFat and Crisis are all significantly positive

for all rates, indicating that during the crisis period, banks with more liquidity mismatch had to

offer higher rates to attract deposits.

We next examine how liquidity mismatch affects banks’ credit decisions during the crisis

period. Columns (8) shows the result from using growth in banks’ loan balances as the dependent

variable. It documents a negative coefficient for CatFat*Crisis, suggesting a negative effect of crisis

on banks’ loan growth for more liquidity mismatch banks. The effect however is not statistically

significant at conventional levels. Columns (9) and (10) use growth in in banks’ commitment and in

total credit (which is the sum of growth in loan and commitment) as the dependent variables. In

both columns, the coefficients for CatFat*Crisis are significantly negative, indicating that banks

subject to liquidity mismatch risk reduce their credit growth more during the crisis period. The

economic magnitude of the effect is significant. The average annualized growth in commitment is

0.96%, whereas a 0.22 increase in CatFat (corresponding to the change in CatFat from 25th

percentile to 75th percentile) would lower growth in commitment during the crisis period by 1.3%.

In untabulated analysis, we further break the crisis period into two subperiods: crisis 1,

defined as the period between 2007Q3 and 2008Q2 and crisis 2, defined as the period between

2008Q3 and 2009Q2. We continue to find significant negative coefficients for both CatFat*Crisis 1

and CatFat*Crisis 2 when the dependent variable is either growth in commitment and growth in

total credit. However, when we examine growth in loan, we find a significantly positive coefficient

26

for CatFat*Crisis 1 but a negative coefficient for CatFat*Crisis 2. These results suggest that

borrowers draw down their commitment from banks during the first stage of the crisis, consistent

with the findings from Ivashina and Scharfstein (2010) and Acharya and Mora (2015).

5. Additional analyses

5.1 Alternative liquidity mismatch measures

In this section, we examine whether our results are robust to two alternative measures of

liquidity mismatch. The first is CATNONFAT, which differs from CatFat is that it does not consider

the impact from banks’ off-balance sheet commitment. The second alternative measure is LMIRisk,

constructed based on the Liquidity Mismatch Index from Bai et al. (2018). 11 We follow Bai et al.

(2018) and its online appendix and construct LMI for a sample of commercial banks. Specifically,

𝐿𝐿𝑀𝑀𝐼𝐼𝑖𝑖𝑖𝑖 for bank i at time t is computed as the net of the asset and liability liquidities: 𝐿𝐿𝑀𝑀𝐼𝐼𝑖𝑖𝑖𝑖 =

∑ 𝜆𝜆𝑖𝑖,𝑎𝑎𝑘𝑘 𝑀𝑀𝑖𝑖,𝑘𝑘𝑖𝑖

𝑘𝑘 + ∑ 𝜆𝜆𝑖𝑖,𝑙𝑙𝑘𝑘′ 𝑙𝑙𝑖𝑖,𝑘𝑘′𝑖𝑖

𝑘𝑘 .where 𝜆𝜆𝑖𝑖,𝑎𝑎𝑘𝑘 is time varying asset liquidity factor that was backed out

from haircuts in repo market ; 𝜆𝜆𝑖𝑖,𝑙𝑙𝑘𝑘′ is the liability liquidity factor that calculated recursively using

the maturity and liquidity cost. Thus, LMI measures the mismatch between the market liquidity of a

bank’s assets and the funding liquidity of liabilities. LMI_Risk is calculated as max (𝐿𝐿𝑀𝑀𝐼𝐼𝑖𝑖𝑖𝑖 −

𝐿𝐿𝑀𝑀𝐼𝐼𝑖𝑖𝑖𝑖1𝜎𝜎, 0). It measures the exposure of a bank to a 1σ unfavorable change in both market and funding

liquidity conditions. It is based on a stress scenario under which all claimants on the firm are assumed

to act under the terms of their contract to extract the maximum liquidity possible, and the firm reacts

by maximizing the liquidity it can raise from its assets. LMI measure differs from Berger and

11 Bai et al. (2017)’s LMI index is constructed for bank holding companies. We take their parameters and calculate the LMI index at commercial bank level, using data from call report. A priori, it is not clear whether depositors make withdrawal decisions based on the health of the top bank holding company or of the subsidiary commercial bank alone. We use commercial bank level specification because the insured deposits data are not available from Y9-C reports filed by bank holding companies.

27

Bouwman (2009) in the weight assigned is dynamic and incorporate marked liquidity conditions,

while Berger and Bouwman (2009) use fixed weight.

Table 1, Panel B shows that LIM and LMI_Risk are highly correlated with CatFat, indicating

that they all reflect the degree of liquidity mismatch in a given bank. However, the LMI measure is

designed to capture market wide liquidity mismatch, and therefore its variation is primarily driven by

over time changes in market condition and less by cross-bank differences. This can be seen from

Figure 3 in the Appendix where we plot the summary statistics for LMI risk over time. Further

analyses (untabulated) show that quarterly fixed effects alone would explain 92% of the variations in

LMI where bank fixed effects would explain only 7%.

Table 9 Panel A show that our results are robust to using these two alternative measures.

Column 1 shows that the sensitivity of uninsured deposit flows to performance is significantly higher

in banks with more liquidity mismatch as measured by CatNonFat. The coefficient estimate for ROE*

CatNonFat is 0.198 (t-stat = 8.98). Column 2 shows that CatNonFat does not appear to affect the

sensitivity of insured deposit flows to bank performance. Column 4-6 use LMI_Risk as the liquidity

mismatch measure and find similar results.

5.2 Alternative performance measures

In our final set of robustness tests presented in Panel B of Table 9, we explore the sensitivity of

our results to two alternative performance measures: return on assets (ROA) and non-performing

loans (NPL). The results using these measures are qualitatively similar to those using ROE.

Specifically, Panel A shows that the sensitivity of uninsured deposit to ROA is increasing in liquidity

28

mismatch measured as CatFat. Panel B shows uninsured deposit flows is negatively associated with

banks’ non-performing loans, and more so for banks with more liquidity mismatch.

6. Conclusion

In this paper we examine the relation between liquidity mismatch on bank’s balance sheet

and depositors’ response to bank performance. We find that the sensitivity of deposit flows to bank

performance is much higher for banks with more liquidity mismatch on their balance sheet. The

result is driven by uninsured deposits, when banks experience poor performance, and for small and

medium sized banks. We also find a positive association between bank’s liquidity mismatch and

their future failure. Banks with more liquidity mismatch at the beginning of the financial crisis are

much more likely to fail during the financial crisis of 2008, regardless of their profitability. Banks

with more liquidity mismatch also experience more deposit withdraws and lending reduction during

the Financial Crisis of 2008. Our results are consistent with the theoretical prediction of Goldstein

and Pauzner (2005) where liquidity mismatch can generate strategic complementarities in

depositors’ payoff and induce panic-based runs due to coordination failure among depositors. While

they emphasize the importance of fundamental in bank runs, they also support the idea that liquidity

creation by banks comes at the cost of fragility in banks’ financial structures that seed the potential

for banking instability.

29

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32

Figure 1: Over time changes in CatFat

Panel A: Full sample

Panel B: Subsample by bank size

33

Figure 2: Sensitivity of uninsured deposits to bank ROE for the full sample

Panel A: Full sample

Panel B: Subsamples by Catfat

-20

24

Uni

nsur

ed D

epos

it Fl

ow

-10 0 10 20 30ROE

-20

24

Uni

nsur

ed D

epos

it Fl

ow

-10 0 10 20 30ROE

Below Median Catfat Above Median Catfat

34

Table 1. Summary statistics

This table presents summary statistics for the main regression variables. These statistics are calculated over the regression sample. To avoid the impact of mergers and acquisitions, we exclude bank-quarter observations with quarterly asset growth greater than 10%. We also exclude bank quarters with total assets smaller than 100 million. See the Appendix for variable definitions.

Panel A: Summary statistics

N Mean Std. Dev. p10 p25 Median p75 p90 CatFat 241889 0.32 0.17 0.10 0.21 0.32 0.43 0.53 CatNonFat 241889 0.25 0.14 0.07 0.16 0.26 0.35 0.43 LMI 152557 0.62 0.14 0.44 0.56 0.64 0.70 0.76 LMIRisk 152557 -0.09 0.09 -0.16 -0.13 -0.09 -0.03 -0.02 ROE 241889 10.65 11.38 2.35 7.41 11.70 16.04 20.75 ROA 241889 1.01 0.96 0.23 0.73 1.12 1.47 1.86 Capital_Ratio 241889 0.10 0.03 0.07 0.08 0.09 0.11 0.13 Wholesale_Funding 241889 0.20 0.11 0.08 0.13 0.19 0.26 0.34 RealEstate_Loans 241889 0.68 0.17 0.45 0.58 0.71 0.81 0.88 Ln(Assets) 241889 12.62 1.10 11.66 11.88 12.32 12.99 13.92 Crisis 241889 0.11 0.32 0.00 0.00 0.00 0.00 1.00 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑈𝑈 241889 1.88 10.18 -7.62 -2.15 2.09 6.77 12.45 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝐼𝐼 241889 3.00 9.55 -5.02 -1.58 1.45 5.30 11.70 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑇𝑇𝑇𝑇𝑖𝑖𝑎𝑎𝑙𝑙 241889 4.88 10.67 -6.77 -1.41 4.00 10.16 17.37 Transaction Deposit Rate 240261 1.26 1.00 0.18 0.41 1.01 1.98 2.67 Saving Deposit Rate 241274 1.81 1.22 0.33 0.72 1.58 2.84 3.52 Small Time Deposit Rate 231298 3.56 1.61 1.28 2.22 3.59 5.00 5.64 Large Time Deposit Rate 231460 3.55 1.66 1.25 2.13 3.56 5.01 5.72 Persistence 171820 0.18 0.39 -0.30 -0.10 0.15 0.44 0.69 StDev_ROE 233717 5.68 11.02 1.19 1.80 2.97 5.53 11.40

35

Panel B: Pairwise correlation

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

1 CatFat it-1 1.00 0.97 -0.19 -0.09 0.03 -0.28 -0.06 0.01 0.28 0.05 0.04 0.04 0.03 0.10 0.03 0.04 0.12 0.04

2 CatNonFat it-1 0.97 1.00 -0.15 -0.11 -0.01 -0.29 -0.07 0.08 0.18 0.04 0.02 0.03 0.01 0.07 -0.01 0.00 0.11 0.07

3 LMI it-1 -0.19 -0.15 1.00 0.52 -0.04 0.17 -0.31 0.01 -0.26 -0.62 0.05 -0.12 -0.22 -0.23 -0.32 -0.31 -0.10 0.06

4 LMIRisk it-1 -0.09 -0.11 0.52 1.00 0.13 -0.02 -0.09 -0.07 -0.09 -0.43 0.15 -0.11 0.09 0.13 0.09 0.10 -0.03 -0.05

5 ROE it-1 0.03 -0.01 -0.04 0.13 1.00 0.05 -0.11 -0.15 0.00 -0.01 0.16 0.06 0.13 0.16 0.16 0.16 -0.05 -0.28

6 Capital_Ratio it-1 -0.28 -0.29 0.17 -0.02 0.05 1.00 -0.19 -0.07 -0.10 -0.04 0.03 -0.01 -0.10 -0.10 -0.13 -0.12 -0.08 -0.16

7 Wholesale_Funding it-1 -0.06 -0.07 -0.31 -0.09 -0.11 -0.19 1.00 0.01 0.19 0.12 -0.05 0.06 0.10 0.11 0.14 0.12 0.05 0.08

8 RealEstate_Loans it-1 0.01 0.08 0.01 -0.07 -0.15 -0.07 0.01 1.00 -0.05 0.01 -0.06 -0.01 -0.05 -0.06 -0.06 -0.04 0.07 0.10

9 Ln(Assets) it-1 0.28 0.18 -0.26 -0.09 0.00 -0.10 0.19 -0.05 1.00 0.01 0.02 0.01 0.04 -0.07 -0.03 -0.04 0.11 0.02

10 Crisis it 0.05 0.04 -0.62 -0.43 -0.01 -0.04 0.12 0.01 0.01 1.00 -0.11 0.15 0.18 0.24 0.42 0.39 0.09 -0.07

11 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈 0.04 0.02 0.05 0.15 0.16 0.03 -0.05 -0.06 0.02 -0.11 1.00 -0.54 0.03 0.04 -0.02 0.00 -0.06 -0.07

12 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝐼𝐼 0.04 0.03 -0.12 -0.11 0.06 -0.01 0.06 -0.01 0.01 0.15 -0.54 1.00 0.06 0.08 0.12 0.11 0.07 -0.09

13 Trans. Deposit Rateit 0.03 0.01 -0.22 0.09 0.13 -0.10 0.10 -0.05 0.04 0.18 0.03 0.06 1.00 0.50 0.53 0.51 0.03 -0.09

14 Saving Deposit Rateit 0.10 0.07 -0.23 0.13 0.16 -0.10 0.11 -0.06 -0.07 0.24 0.04 0.08 0.50 1.00 0.74 0.74 0.04 -0.13

15 Small Time Dep Rateit 0.03 -0.01 -0.32 0.09 0.16 -0.13 0.14 -0.06 -0.03 0.42 -0.02 0.12 0.53 0.74 1.00 0.89 0.08 -0.16

16 Large Time Dep Rateit 0.04 0.00 -0.31 0.10 0.16 -0.12 0.12 -0.04 -0.04 0.39 0.00 0.11 0.51 0.74 0.89 1.00 0.07 -0.17

17 Persistence it-1 0.12 0.11 -0.10 -0.03 -0.05 -0.08 0.05 0.07 0.11 0.09 -0.06 0.07 0.03 0.04 0.08 0.07 1.00 -0.02

18 StDev_ROE it-1 0.04 0.07 0.06 -0.05 -0.28 -0.16 0.08 0.10 0.02 -0.07 -0.07 -0.09 -0.09 -0.13 -0.16 -0.17 -0.02 1.00

36

Table 2. Sensitivity of deposit flows to bank performance

This table presents OLS estimates of Equation (1). The dependent variable is in Column (1) to (3) is respectively, the change in the uninsured, insured, and total deposits scaled by the beginning value of total assets. D(ROE<Median) is a dummy variable that equals 1 (0) when the bank’s ROEit-1 is below the sample median. All regressions include bank- and quarter-fixed effects. T-statistics, reported in parentheses, are based on standard error estimates clustered at the bank level. Statistical significance (two-sided) at the 10%, 5%, and 1% level is denoted by *, **, and ***, respectively.

VARIABLES (1) (2) (3) Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝐼𝐼 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑇𝑇𝑇𝑇𝑖𝑖𝑎𝑎𝑙𝑙 ROE it-1 0.045*** 0.023*** 0.070***

(5.835) (3.159) (6.687) ROE it-1×D(ROE<Median) 0.031*** 0.055*** 0.084***

(3.619) (6.597) (7.256)

D(ROE<Median) -0.725*** -1.045*** -1.758*** (-5.769) (-8.744) (-10.465)

Large Time Deposit Rateit-1 -0.013 -0.028 (-0.438) (-0.706)

Core Deposit Rateit-1 0.503*** 0.893*** (7.045) (8.680) Capital_Ratio it-1 34.055*** 27.243*** 63.464***

(17.617) (13.157) (19.925) Wholesale_Funding it-1 6.090*** 9.657*** 16.008***

(10.349) (15.161) (17.818) RealEstate_Loans it-1 -1.529*** -1.888*** -3.306***

(-3.917) (-4.551) (-5.185) Ln(Size)it-1 -3.124*** -2.920*** -6.223***

(-22.909) (-20.480) (-26.868)

Observations 257,161 257,240 256,714 R-squared 0.338 0.334 0.206

37

Table 3. Liquidity mismatch and sensitivity of deposit flows to bank performance

This table presents OLS estimates of Equations (2) and (3). The dependent variables are changes in the uninsured, insured, and total deposits scaled by the beginning value of total assets. D(ROEit-1<Median) is an indicator variable that equals 1(0) if the ROEit-1 is above sample median. All regressions include bank- and quarter-fixed effects. T-statistics, reported in parentheses, are based on standard error estimates clustered at the bank level. Statistical significance (two-sided) at the 10%, 5%, and 1% level is denoted by *, **, and ***, respectively.

Panel A: Main results

(1) (2) (3) (4) (5) (6) Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝐼𝐼 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝐼𝐼 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑇𝑇𝑇𝑇𝑖𝑖𝑎𝑎𝑙𝑙 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑇𝑇𝑇𝑇𝑖𝑖𝑎𝑎𝑙𝑙 ROE it-1 0.035*** 0.041** 0.094*** 0.022 0.128*** 0.063*** (4.932) (2.277) (12.394) (1.292) (13.615) (2.597) ROE it-1×CatFatit-1 0.103*** -0.024 -0.079*** -0.031 0.032 -0.048 (5.327) (-0.504) (-4.024) (-0.726) (1.299) (-0.778) CatFatit-1 4.924*** 7.496*** 9.030*** 8.343*** 14.121*** 16.029*** (10.738) (8.573) (18.695) (10.401) (20.731) (13.476) ROE it-1× D(ROE<Median) -0.006 0.084*** 0.076*** (-0.299) (4.097) (2.763) D(ROE<Median) 0.175 -1.260*** -1.071*** (0.597) (-4.631) (-2.794) ROE it-1× D(ROE<Median) × CatFatit-1 0.130** -0.066 0.065 (2.361) (-1.276) (0.931) D(ROE<Median) × CatFatit-1 -2.961*** 0.597 -2.391** (-3.663) (0.833) (-2.329) Large Time Deposit Rateit-1 -0.016 -0.017 -0.051 -0.055 (-0.491) (-0.521) (-1.178) (-1.260) Core Deposit Rateit-1 0.615*** 0.604*** 1.132*** 1.122*** (8.360) (8.220) (10.670) (10.592) Capital_Ratio it-1 40.634*** 40.562*** 33.862*** 32.570*** 76.363*** 75.198*** (19.469) (19.076) (16.505) (15.575) (23.653) (22.781) Wholesale_Funding it-1 7.754*** 7.746*** 12.388*** 12.369*** 20.567*** 20.549*** (11.978) (11.972) (18.558) (18.574) (22.375) (22.412) RealEstate_Loans it-1 -1.328*** -1.303*** -2.017*** -1.982*** -3.257*** -3.199*** (-3.107) (-3.050) (-4.624) (-4.567) (-4.819) (-4.750) Ln(Size)it-1 -3.355*** -3.355*** -3.189*** -3.214*** -6.676*** -6.701*** (-24.780) (-24.746) (-24.047) (-24.253) (-30.439) (-30.536) Observations 233,942 233,942 234,058 234,058 233,748 233,748 R-squared 0.336 0.337 0.363 0.363 0.216 0.217

38

Panel B: Controlling for performance persistence

This table presents ordinary least-squares estimates of Equation (2) after controlling for the effect of performance persistence. We measure Persistence as the AR(1) coefficient from a time-series regression of banks’ ROE on its lagged value over the 12-quarters Columns (1) to (3) present the results for uninsured deposit flows, insured deposit flows and total deposit flows, with Persistence and its interaction with ROE included in the regressor. Column The Appendix contains detailed descriptions for the independent variables. All regressions include bank- and quarter-fixed effects. T-statistics, reported in parentheses, are based on standard error estimates clustered at the bank level. Statistical significance (two-sided) at the 10%, 5%, and 1% level is denoted by *, **, and ***, respectively.

(1) (2) (3) Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝐼𝐼 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑇𝑇𝑇𝑇𝑖𝑖𝑎𝑎𝑙𝑙 ROEit-1 0.025*** 0.083*** 0.107***

(2.903) (9.411) (10.244) ROEit-1 × CatFat it-1 0.092*** -0.049** 0.045

(4.003) (-2.212) (1.644) CatFat it-1 4.713*** 8.784*** 13.672***

(9.066) (16.035) (18.187) Persistenceit-1× ROE it-1 0.023*** -0.012** 0.012* (3.744) (-2.058) (1.762) Persistenceit-1 -0.572*** 0.282*** -0.307**

(-5.661) (2.850) (-2.524) Large Time Deposit Rateit-1 -0.014 -0.065 (-0.349) (-1.177) Core Deposit Rateit-1 0.679*** 1.239*** (7.694) (10.039) Capital_Ratio it-1 46.624*** 33.143*** 81.692***

(18.560) (14.112) (21.618) Wholesale_Funding it-1 8.921*** 13.686*** 22.905***

(11.191) (17.835) (21.419) RealEstate_Loans it-1 -1.004** -1.469*** -2.386***

(-2.097) (-2.848) (-3.060) Ln(Assets) it-1 -3.753*** -3.246*** -7.150***

(-20.341) (-18.700) (-24.188) Bank FE Yes Yes Yes Qtr FE Yes Yes Yes Observations 166,270 166,342 166,226 Adj. R-squared 0.373 0.407 0.219

39

Panel C: Robustness to crisis period

(1) (2) (3) (4) (5) (6) Crisis Period (2007Q3 – 2009Q2) Non-Crisis period Dependent Variable Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝐼𝐼 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑇𝑇𝑇𝑇𝑖𝑖𝑎𝑎𝑙𝑙 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝐼𝐼 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑇𝑇𝑇𝑇𝑖𝑖𝑎𝑎𝑙𝑙 ROE it-1 0.030 0.058** 0.088*** 0.036*** 0.086*** 0.121*** (1.063) (2.440) (3.199) (4.996) (10.972) (12.226) ROE it-1 * Catfatit-1 -0.055 -0.104* -0.156** 0.106*** -0.033* 0.079*** (-0.787) (-1.707) (-2.316) (5.412) (-1.662) (3.024) Catfatit-1 -5.635** 11.582*** 5.294* 5.472*** 8.473*** 14.111***

(-1.975) (4.278) (1.741) (11.754) (16.577) (19.482) Large time deposit rate 0.081 -0.085 0.008 -0.052

(0.498) (-0.515) (0.233) (-1.142) Core Deposit Rateit-1 -1.095*** 0.518 0.562*** 1.113***

(-3.795) (1.472) (7.208) (9.552) Capital_Ratio it-1 102.439*** 69.288*** 178.698*** 42.710*** 32.147*** 76.824***

(7.005) (5.743) (11.451) (19.628) (14.803) (22.058) Wholesale_Funding it-1 14.360*** 58.245*** 74.740*** 8.273*** 11.591*** 20.245***

(2.882) (15.191) (17.272) (12.686) (17.009) (20.679) RealEstate_Loans it-1 -2.696 -0.073 -2.453 -1.232*** -2.024*** -3.153***

(-0.568) (-0.020) (-0.513) (-2.913) (-4.448) (-4.547) In(Assets) it-1 -27.899*** -23.603*** -51.703*** -2.891*** -3.414*** -6.421*** (-11.361) (-10.981) (-14.179) (-21.828) (-23.808) (-28.208) Observations 27,336 27,331 27,306 206,606 206,727 206,442 Adj. R-squared 0.596 0.625 0.414 0.280 0.315 0.233

40

Table 4. Decompose Asset side and Liability Side Liquidity

This table presents ordinary least-squares estimates of Equation (3). Columns (1) to (4) present the results for uninsured deposit flows, with control variables and their interaction with ROE included. The Appendix contains detailed descriptions for the independent variables. All regressions include bank- and quarter-fixed effects. T-statistics, reported in parentheses, are based on standard error estimates clustered at the bank level. Statistical significance (two-sided) at the 10%, 5%, and 1% level is denoted by *, **, and ***, respectively.

Panel A: Partition based on liquidity creation on aside side

LC_A quartile (low to high) 1 2 3 4 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈 ROEit-1 -0.188* -0.039 0.022 -0.029

(-1.794) (-0.486) (0.353) (-0.452) ROEit-1 × LC_L it-1 -0.101 0.210* 0.302** 0.348***

(-0.612) (1.675) (2.440) (4.676) LC_L it-1 -15.267*** -17.862*** -11.988*** -13.760***

(-4.092) (-5.903) (-3.946) (-4.598) Large Time Deposit Rateit-1 27.779*** 43.105*** 39.533*** 32.114*** (4.879) (6.979) (6.923) (5.956) Core Deposit Rateit-1 3.177 1.830 3.327* 1.794 (1.383) (1.065) (1.793) (1.119) Capital_Ratio it-1 -1.495 -2.071** -1.327 -0.990

(-1.438) (-2.142) (-1.258) (-0.758) Wholesale_Funding it-1 -3.294*** -3.674*** -3.555*** -5.514***

(-9.139) (-13.641) (-11.534) (-15.758) RealEstate_Loans it-1 -0.188* -0.039 0.022 -0.029

(-1.794) (-0.486) (0.353) (-0.452) Ln(Assets) it-1 -0.101 0.210* 0.302** 0.348***

(-0.612) (1.675) (2.440) (4.676) Interactive controls (*ROE) Yes Yes Yes Yes Bank FE Yes Yes Yes Yes Qtr FE Yes Yes Yes Yes Observations 57,487 57,708 58,777 59,964 Adj. R-squared 0.263 0.335 0.403 0.453

41

Panel C: Partition based on liquidity creation on liability side

LC_L quartile (low to high) 1 2 3 4 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈 ROEit-1 0.046 0.001 -0.022 -0.027

(0.826) (0.012) (-0.295) (-0.297) ROEit-1 × LC_A it-1 0.071* 0.161*** 0.199*** 0.189***

(1.799) (2.826) (3.612) (3.591) LC_A it-1 13.651*** 13.495*** 13.778*** 14.338***

(11.058) (10.824) (10.731) (11.489) Large Time Deposit Rateit-1 19.616*** 43.490*** 61.666*** 70.952*** (4.675) (8.510) (11.468) (10.227) Capital_Ratio it-1 -4.225*** 5.538*** 11.619*** 20.102***

(-2.983) (3.135) (5.983) (9.270) Wholesale_Funding it-1 0.356 -2.678*** -1.979* -3.342***

(0.313) (-2.674) (-1.887) (-2.919) RealEstate_Loans it-1 -4.061*** -3.653*** -3.796*** -4.430***

(-11.675) (-12.525) (-14.568) (-13.592) Ln(Assets) it-1 0.046 0.001 -0.022 -0.027

(0.826) (0.012) (-0.295) (-0.297) Interactive controls (*ROE) Yes Yes Yes Yes Bank FE Yes Yes Yes Yes Qtr FE Yes Yes Yes Yes Observations 58,563 58,324 58,439 58,610 Adj. R-squared 0.445 0.416 0.376 0.306

42

Table 5. Liquidity mismatch and deposit flows by bank size

This table presents OLS estimates of a modified version of Equation (3). Small, Medium and Large are indicator variables based on bank asset size at the beginning of period t. I(.) is an indicator function that equals 1 (0) if the argument is true (false). All regressions include the control variables, and bank- and quarter-fixed effects. T-statistics, reported in parentheses, are based on standard error estimates clustered at the bank level. Statistical significance (two-sided) at the 10%, 5%, and 1% level is at the 10%, 5%, and 1% level is denoted by *, **, and ***, respectively.

(1) (2) (3) VARIABLES Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝐼𝐼 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑇𝑇𝑇𝑇𝑖𝑖𝑎𝑎𝑙𝑙 Small * I(HighROEit-1) * ROEit-1 0.031 0.023 0.055**

(1.587) (1.233) (2.049) Small * I(HighROEit-1) * ROE it-1 * CatFat it-1 -0.013 -0.044 -0.052

(-0.245) (-0.899) (-0.729) Small * I(LowROE it-1) * ROE it-1 0.030*** 0.102*** 0.132***

(3.020) (9.100) (10.444) Small * I(LowROE it-1) * ROE it-1 * CatFat it-1 0.116*** -0.068** 0.049

(4.083) (-2.241) (1.435)

Medium * I(HighROEit-1) * ROEit-1 0.080* 0.018 0.093 (1.827) (0.412) (1.566)

Medium * I(HighROE it-1) * ROE it-1 * CatFat it-1 -0.006 0.040 0.055 (-0.053) (0.406) (0.384)

Medium * I(LowROE it-1) * ROE it-1 0.039 0.111*** 0.148*** (1.436) (4.399) (4.842)

Medium * I(LowROE it-1) * ROE it-1 * CatFat it-1 0.097 -0.138** -0.035 (1.484) (-2.290) (-0.467)

Large * I(HighROEit-1) * ROEit-1 0.021 0.065 0.075 (0.318) (0.870) (0.831)

Large * I(HighROE it-1) * ROE it-1 * CatFat it-1 -0.058 -0.150 -0.215 (-0.441) (-1.017) (-1.186)

Large * I(LowROE it-1) * ROE it-1 0.098** 0.105** 0.187*** (2.143) (2.304) (3.345)

Large * I(LowROE it-1) * ROE it-1 * CatFat it-1 -0.026 -0.184 -0.162 (-0.234) (-1.589) (-1.141)

Controls included Yes Yes Yes Bank FE Yes Yes Yes Qtr FE Yes Yes Yes Observations 233,942 234,058 233,748 R-squared 0.338 0.364 0.219

43

Table 6. Liquidity mismatch and bank performance

This table explores the effect of liquidity mismatch on bank performance. The dependent variable is return on equity (ROEit+1) for period t+1. Outflowit is a dummy variable that equals 1 if the bank experienced a total deposit outflows equal or larger than 2.5% of its total assets in period t, and 0 otherwise. D(CatFatit>median) is a dummy variable for whether the bank’s CatFatit is above or below sample median. The Appendix contains detailed descriptions for the independent variables. T-statistics, reported in parentheses, are based on standard error estimates clustered at the bank level. Statistical significance (two-sided) at the 10%, 5%, and 1% level is denoted by *, **, and ***, respectively.

(1) (2) VARIABLES ROEi,t+1 ROEi,t+1

Outflowit -0.165 -0.051 (-1.045) (-0.326) Outflowit * D(CatFatit>median) -0.773* (-1.661) Outflowit *CatFatit -1.149** (-2.479) D(CatFatit>median) 0.296*** (4.938) CatFatit 3.521*** (7.748) Ln(Assets)it -1.767*** -1.765*** (-11.772) (-11.787) Capital_Ratio it+1 -45.272*** -43.794*** (-17.816) (-17.084) Wholesale_Funding it+1 -4.207*** -3.686*** (-5.751) (-4.954) ROEit 0.226*** 0.225*** (33.071) (32.966) ROEit-1 0.177*** 0.176*** (28.038) (27.884) ROEit-2 0.094*** 0.093*** (16.321) (16.169) ROEit-3 0.171*** 0.170*** (27.365) (27.223) ROEit-4 -0.003 -0.004 (-0.637) (-0.790) Observations 172,325 172,325 R-squared 0.584 0.584 Bank FE Yes Yes Qtr FE Yes Yes

44

Table 7. Liquidity mismatch and bank failures

This table shows the result of logit regression that explores the association between liquidity mismatch and bank failure. In Columns (1) to (3), the dependent variable is an indicator variable that the bank fails within the next 1, 2, and 3 years during the non-crisis period, respectively. In Column (4), the dependent variable is an indicator variable that the bank failed during the financial crisis period. The Appendix contains detailed descriptions for the independent variables. T-statistics, reported in parentheses, are based on standard error estimates clustered at the bank level. Statistical significance (two-sided) at the 10%, 5%, and 1% level is denoted by *, **, and ***, respectively.

(1) (2) (3) (4)

Dependent variable Failure in near year

Failure in next two

years

Failure in next three

year

Failure during crisis

CatFat it 0.001 0.003** 0.004*** 0.026***

(1.215) (2.290) (2.823) (2.956) ROEit -0.000*** -0.000*** -0.000*** 0.000

(-2.734) (-2.916) (-3.410) (1.150) Core Deposit Rateit -0.000 -0.000 0.000 0.010***

(-0.015) (-0.419) (1.580) (4.546) Large Time Deposit Rate it -0.000 -0.000 -0.000 0.002

(-0.962) (-1.288) (-1.286) (1.492) Capital Ratioit -0.055*** -0.052*** -0.090*** 0.049

(-3.395) (-2.805) (-4.921) (1.269) Wholesale_Funding it 0.002 0.005*** 0.008*** 0.036***

(1.385) (2.650) (3.580) (3.180) RealEstate_Loans it -0.001 -0.001 0.003** 0.038***

(-0.907) (-0.645) (2.114) (4.066) In(Assets) it 0.000* 0.000* 0.000 0.005***

(1.925) (1.879) (1.392) (4.939) Observations 50,339 23,618 17,975 3,388 Pseudo. R-squared 0.261 0.295 0.387 0.211

45

Table 8. Effects of liquidity mismatch during crisis

This table presents ordinary least-squares estimates of Equation (6). The dependent variable is changes in the balance of total loans in Columns (1), the changes in the balance of total commitments in Columns (2), the changes in the sum of loans and commitment in Column (3), and changes in the balances of liquid assets in Columns (4). All dependent variables are scaled by lagged total assets. The Appendix contains detailed descriptions for the independent variables. All regressions include bank- and quarter-fixed effects. T-statistics, reported in parentheses, are based on standard error estimates clustered at the bank level. Statistical significance (two-sided) at the 10%, 5%, and 1% level is denoted by *, **, and ***, respectively.

Dependent Variable

Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝐼𝐼 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑇𝑇𝑇𝑇𝑖𝑖𝑎𝑎𝑙𝑙 Trans

Deposits rate

Savings Deposits

rate

Small Time

Deposits rate

Large Time

Deposits Δ𝑙𝑙𝐶𝐶𝑀𝑀𝐶𝐶 Δ𝐶𝐶𝐶𝐶𝑚𝑚𝑚𝑚𝑀𝑀𝐶𝐶𝑚𝑚 Δ𝐶𝐶𝑃𝑃𝐷𝐷𝑀𝑀𝑀𝑀𝐶𝐶 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Catfatit-1 7.940*** 8.605*** 16.545*** 0.062 0.500*** -0.049 0.030 19.079*** 1.776*** 20.855*** (18.876) (20.221) (24.905) (1.099) (11.162) (-1.268) (0.627) (30.800) (7.426) (29.045) Catfatit-1 × Crisis -5.964*** 3.414*** -2.550*** 0.130** 0.256*** 0.083** 0.198*** -0.794 -5.913*** -6.908*** (-10.377) (6.577) (-4.045) (2.483) (6.278) (2.036) (4.727) (-1.338) (-19.818) (-9.353) Capital_Ratio it-1 44.201*** 33.223*** 77.424*** -0.507* -0.731*** -1.456*** -1.058*** 27.674*** 5.284*** 33.013*** (20.123) (15.223) (21.483) (-1.837) (-3.393) (-7.084) (-4.822) (8.957) (4.788) (8.785) Wholesale_Funding it-1 8.681*** 11.477*** 20.158*** -0.181** -0.224*** 0.149** 0.350*** 0.864 1.192*** 1.878* (13.402) (16.888) (20.762) (-2.370) (-3.752) (2.471) (5.483) (1.055) (3.585) (1.858) RealEstate_Loans

it-1 -1.005** -2.318*** -3.323*** -0.128** 0.026 -0.165*** -0.039 -0.914 -2.523*** -3.449*** (-2.318) (-5.214) (-4.739) (-2.185) (0.516) (-3.672) (-0.753) (-1.403) (-10.294) (-4.326) In(Assets) it-1 -3.284*** -3.350*** -6.634*** 0.213*** 0.083*** 0.095*** 0.067*** -4.039*** -1.287*** -5.286*** (-24.684) (-24.676) (-30.189) (11.948) (5.695) (7.603) (4.786) (-23.221) (-17.221) (-24.483) Capital_Ratio it-

1× Crisis -1.853 -2.522 -4.374 -0.782*** 0.123 0.852*** 0.515** 3.229 -5.370*** -2.475 (-0.519) (-0.760) (-1.038) (-2.625) (0.466) (3.571) (2.038) (0.899) (-3.192) (-0.577) Wholesale_Funding it-1× Crisis -1.881* 7.246*** 5.365*** 0.088 0.423*** 0.173** 0.130** 0.946 -3.325*** -2.350* (-1.736) (8.065) (4.916) (1.050) (5.804) (2.436) (1.971) (0.935) (-7.162) (-1.887)

46

RealEstate_Loans

it-1× Crisis -3.587*** 2.015*** -1.573** 0.132** 0.203*** 0.315*** 0.290*** -0.877 -3.605*** -4.337*** (-5.957) (3.852) (-2.244) (2.506) (4.404) (6.708) (6.894) (-1.435) (-11.926) (-5.875) In(Assets) it-1× Crisis 0.110 0.011 0.122 0.009 -0.019*** -0.039*** -0.035*** -0.249*** -0.411*** -0.666***

(1.368) (0.149) (1.308) (1.084) (-3.178) (-5.370) (-4.911) (-2.843) (-8.732) (-5.927)

Bank fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Quarter fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 241,889 241,889 241,889 240,261 241,275 231,299 231,460 241,889 241,889 241,889 Adj. R-squared 0.329 0.353 0.203 0.735 0.881 0.936 0.908 0.296 0.120 0.288

47

Table 9. Sensitivity and robustness

Panel A of this table explores the robustness of our main results to use of two alternative liquidity mismatch measures: CatNonFat in Columns (1) to (3) and LMIRisk in Column (4) to (6). Panel B explores the robustness to two alternative bank performance measures– ROA (return on assets) in Column 1-3 and Non-performing Loan (NPL) in Columns (4)-(6). The Appendix contains detailed descriptions for the independent variables. All regressions include bank- and quarter-fixed effects. T-statistics, reported in parentheses, are based on standard error estimates clustered at the bank level. Statistical significance (two-sided) at the 10%, 5%, and 1% level is denoted by *, **, and ***, respectively.

Panel A: Robustness to alternative liquidity mismatch measures

(1) (2) (3) (4) (5) (6) Mismatch measure CatNonFat LMIRisk Dependent variable Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝐼𝐼 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑇𝑇𝑇𝑇𝑖𝑖𝑎𝑎𝑙𝑙 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝐼𝐼 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑇𝑇𝑇𝑇𝑖𝑖𝑎𝑎𝑙𝑙 ROEit-1 0.024*** 0.074*** 0.102*** 0.057*** 0.089*** 0.147*** (3.359) (10.177) (11.322) (12.047) (16.850) (24.656) ROE it-1 * LiqMismatchit-1 0.170*** -0.018 0.140*** 0.161*** -0.177*** -0.011 (7.747) (-0.826) (5.078) (4.491) (-4.661) (-0.296) CatNonFat it-1 4.410*** 9.014*** 13.845*** -34.321*** 46.157*** 12.727*** (8.400) (16.317) (17.827) (-14.689) (13.193) (4.905) Controls Yes Yes Yes Yes Yes Yes Observations 233,942 234,058 233,748 166,107 166,211 165,867 Adj. R-squared 0.313 0.340 0.188 0.385 0.432 0.195

Panel B: Robustness to alternative performance measures

(1) (2) (3) (4) (5) (6) Performance measure ROA NPL Dependent variable Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝐼𝐼 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑇𝑇𝑇𝑇𝑖𝑖𝑎𝑎𝑙𝑙 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝐼𝐼 Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑇𝑇𝑇𝑇𝑖𝑖𝑎𝑎𝑙𝑙 BankPerfit-1 0.391*** 0.932*** 0.391*** -0.097*** -0.370*** -0.481***

(4.658) (11.144) (4.658) (-2.818) (-9.837) (-9.198) BankPerf it-1 * CatFatit-1 1.118*** -0.700*** 1.118*** -0.842*** -0.228** -1.024***

(4.750) (-3.251) (4.750) (-8.722) (-2.226) (-7.252) CatFatit-1 3.965*** 8.002*** 3.965*** 6.402*** 7.301*** 13.860***

(8.082) (16.287) (8.082) (14.967) (17.583) (21.931) Controls Yes Yes Yes Yes Yes Yes Observations 233,942 234,058 233,748 233,942 234,058 233,748 Adj. R-squared 0.337 0.363 0.217 0.337 0.366 0.223

48

Appendix: Variable definitions

Variables Definitions

CatFat

The preferred measure of Bank liquidity creation per unit of gross total assets, by Berger and Bouwman (2009) and downloaded from https://sites.google.com/a/tamu.edu/bouwman/data. Step 1: Classify all bank activities (asset, liability, and off-balance-sheet) as liquid, semi-liquid, or illiquid based on product category. Step 2: Assign weights to the activities classified in Step 1. Illiquid assets, liquid liabilities get ½, Liquid assets, illiquid liabilities and equities get -1/2. Certain loans and liabilities are classified as semi-liquid and get 0. Step 3: Combine bank activities as classified in Step 1 and as weighted in Step 2 to construct our liquidity creation measure.

CatNonFat The same as CatFat, but does not include off-balance sheet items.

LMI_Risk

𝐿𝐿𝑀𝑀𝐼𝐼_𝑅𝑅𝐼𝐼𝐶𝐶𝐵𝐵𝑖𝑖𝑖𝑖 = max (𝐿𝐿𝑀𝑀𝐼𝐼𝑖𝑖𝑖𝑖 − 𝐿𝐿𝑀𝑀𝐼𝐼𝑖𝑖𝑖𝑖1𝜎𝜎 , 0). The liquidity risk of a bank is the exposure of that bank to a 1σ unfavorable change in both market and funding liquidity conditions. 𝐿𝐿𝑀𝑀𝐼𝐼𝑖𝑖𝑖𝑖 is constructed following Bai et al. (2018) and its online appendix for a sample of commercial banks. Specifically, 𝐿𝐿𝑀𝑀𝐼𝐼𝑖𝑖𝑖𝑖 for bank i at time t is computed as the net of the asset and liability liquidities: 𝐿𝐿𝑀𝑀𝐼𝐼𝑖𝑖𝑖𝑖 = ∑ 𝜆𝜆𝑖𝑖,𝑎𝑎𝑘𝑘 𝑀𝑀𝑖𝑖,𝑘𝑘

𝑖𝑖𝑘𝑘 + ∑ 𝜆𝜆𝑖𝑖,𝑙𝑙𝑘𝑘′ 𝑙𝑙𝑖𝑖,𝑘𝑘′

𝑖𝑖𝑘𝑘 .where 𝜆𝜆𝑖𝑖,𝑎𝑎𝑘𝑘 is time varying asset

liquidity factor that was backed out from haircuts in repo market ; 𝜆𝜆𝑖𝑖,𝑙𝑙𝑘𝑘′ is the liability liquidity factor that calculated recursively using the maturity and liquidity cost. The parameters on liquidity factors are from Bai et al. (2018).

Commercial Loan Commercial and industrial loan (RCFD1766). Scaled accordingly. RealEstate_Loans Loans secured by real estate (RCFD1410). Scaled accordingly

ROE I,t-1 Annualized ROE (in %) in quarter t-1, calculated as net income (RIAD4300, adjust year-to-date reporting to within quarter) divided by beginning equity (RCFD3210).

StDev_ROE i,t-1 Standard deviation of ROE measured over 12 rolling quarters (from Quarter 𝐶𝐶 − 12 to 𝐶𝐶 −1).

Capital_Ratio Total equity (RCFD3210) divided by total assets (RCFD2170).

Wholesale_Funding

Wholesale funds are the sum of following: large-time deposits (RCON2604), deposits booked in foreign offices (RCFN2200), subordinated debt and debentures (RCFD3200), gross federal funds purchased and repos [RCFD2800, or (RCONB993+RCFDB995 from 2002q1)], other borrowed money (RCFD3190). Scaled by total assets.

RealEstate_Loan Loans secured by real estate (RCFD1410) scaled by total loans. Ln(Assets) Log of total assets (RCFD2170).

𝛥𝛥𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝐼𝐼

Annualized growth rate in insured deposits as a percentage of lagged assets in quarter 𝐶𝐶 and 𝐶𝐶 + 1. (in %): (𝐼𝐼𝐶𝐶𝐶𝐶𝑂𝑂𝑃𝑃𝐷𝐷𝑀𝑀 𝐷𝐷𝐷𝐷𝑝𝑝𝐶𝐶𝐶𝐶𝑀𝑀𝐶𝐶𝐶𝐶𝑖𝑖,𝑖𝑖+1 − 𝐼𝐼𝐶𝐶𝐶𝐶𝑂𝑂𝑃𝑃𝐷𝐷𝑀𝑀 𝐷𝐷𝐷𝐷𝑝𝑝𝐶𝐶𝐶𝐶𝑀𝑀𝐶𝐶𝐶𝐶𝑖𝑖,𝑖𝑖−1)/𝐴𝐴𝐶𝐶𝐶𝐶𝐷𝐷𝐶𝐶𝑖𝑖,𝑖𝑖−1 ∗ 200%. Insured deposits are accounts of $100,000 or less. After 2006Q2, it includes retirement accounts of $250,000 or less. From 2009Q3, reporting thresholds on non-retirement deposits increased from $100,000 to $250,000. Insured deposits: RCON2702 (before 2006Q2); RCONF049 + RCONF045 (from 2006Q2).

𝛥𝛥𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈

Annualized growth rate in uninsured deposits as a percentage of lagged assets (in %) in quarter 𝐶𝐶 and 𝐶𝐶 + 1. Uninsured deposit is calculated as deposits (RCFD2200) – insured deposits.

𝛥𝛥𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑇𝑇𝑇𝑇𝑖𝑖𝑎𝑎𝑙𝑙 Sum of Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝐼𝐼 and Δ𝐷𝐷𝐷𝐷𝑝𝑝𝑖𝑖𝑖𝑖𝑈𝑈

Transaction Deposit Ratei,t

Annualized average interest rate (in %) over the two quarters 𝐶𝐶, 𝐶𝐶 + 1 on core deposits: (𝑇𝑇𝑃𝑃𝑀𝑀𝐶𝐶𝐶𝐶𝑀𝑀𝑇𝑇𝐶𝐶𝑀𝑀𝐶𝐶𝐶𝐶 𝑀𝑀𝐷𝐷𝑝𝑝𝐶𝐶𝐶𝐶𝑀𝑀𝐶𝐶 𝑀𝑀𝐶𝐶𝐶𝐶𝐷𝐷𝑃𝑃𝐷𝐷𝐶𝐶𝐶𝐶 𝐷𝐷𝑒𝑒𝑝𝑝𝐷𝐷𝐶𝐶𝐶𝐶𝐷𝐷 𝑀𝑀𝐶𝐶 𝑄𝑄𝐶𝐶𝑃𝑃 𝐶𝐶 𝑀𝑀𝐶𝐶𝑀𝑀 𝐶𝐶 + 1)/(𝐴𝐴𝐴𝐴𝐻𝐻.𝑇𝑇𝑃𝑃𝑀𝑀𝐶𝐶𝐶𝐶𝑀𝑀𝑇𝑇𝐶𝐶𝑀𝑀𝐶𝐶𝐶𝐶 𝑀𝑀𝐷𝐷𝑝𝑝𝐶𝐶𝐶𝐶𝑀𝑀𝐶𝐶 𝑏𝑏𝑀𝑀𝑙𝑙𝑀𝑀𝐶𝐶𝑇𝑇𝐷𝐷 𝑀𝑀𝐶𝐶 𝑄𝑄𝐶𝐶𝑃𝑃 𝐶𝐶 𝑀𝑀𝐶𝐶𝑀𝑀 𝐶𝐶 + 1) ) ∗ 400%) . Average transaction deposits: RCON3485

49

Saving Deposit Ratei,t

Annualized average interest rate (in %) over the two quarters 𝐶𝐶, 𝐶𝐶 + 1 on savings deposits: (𝐶𝐶𝑀𝑀𝐴𝐴𝑀𝑀𝐶𝐶𝐻𝐻 𝑀𝑀𝐷𝐷𝑝𝑝𝐶𝐶𝐶𝐶𝑀𝑀𝐶𝐶 𝑀𝑀𝐶𝐶𝐶𝐶𝐷𝐷𝑃𝑃𝐷𝐷𝐶𝐶𝐶𝐶 𝐷𝐷𝑒𝑒𝑝𝑝𝐷𝐷𝐶𝐶𝐶𝐶𝐷𝐷 𝑀𝑀𝐶𝐶 𝑄𝑄𝐶𝐶𝑃𝑃 𝐶𝐶 𝑀𝑀𝐶𝐶𝑀𝑀 𝐶𝐶 + 1)/(𝐴𝐴𝐴𝐴𝐻𝐻. 𝐶𝐶𝑀𝑀𝐴𝐴𝑀𝑀𝐶𝐶𝐻𝐻 𝑀𝑀𝐷𝐷𝑝𝑝𝐶𝐶𝐶𝐶𝑀𝑀𝐶𝐶 𝑏𝑏𝑀𝑀𝑙𝑙𝑀𝑀𝐶𝐶𝑇𝑇𝐷𝐷 𝑀𝑀𝐶𝐶 𝑄𝑄𝐶𝐶𝑃𝑃 𝐶𝐶 𝑀𝑀𝐶𝐶𝑀𝑀 𝐶𝐶 + 1) ) ∗ 400%) . Saving: RCONB563 (RCON3486 + RCON3487 before 2001Q1)

Small Time Deposit Ratei,t

Annualized average interest rate (in %) over the two quarters 𝐶𝐶, 𝐶𝐶 + 1 on savings deposits: (𝐶𝐶𝑚𝑚𝑀𝑀𝑙𝑙𝑙𝑙 𝐶𝐶𝑀𝑀𝑚𝑚𝐷𝐷 𝑀𝑀𝐷𝐷𝑝𝑝𝐶𝐶𝐶𝐶𝑀𝑀𝐶𝐶 𝑀𝑀𝐶𝐶𝐶𝐶𝐷𝐷𝑃𝑃𝐷𝐷𝐶𝐶𝐶𝐶 𝐷𝐷𝑒𝑒𝑝𝑝𝐷𝐷𝐶𝐶𝐶𝐶𝐷𝐷 𝑀𝑀𝐶𝐶 𝑄𝑄𝐶𝐶𝑃𝑃 𝐶𝐶 𝑀𝑀𝐶𝐶𝑀𝑀 𝐶𝐶 + 1)/(𝐴𝐴𝐴𝐴𝐻𝐻. 𝐶𝐶𝑚𝑚𝑀𝑀𝑙𝑙𝑙𝑙 𝐶𝐶𝑀𝑀𝑚𝑚𝐷𝐷 𝑏𝑏𝑀𝑀𝑙𝑙𝑀𝑀𝐶𝐶𝑇𝑇𝐷𝐷 𝑀𝑀𝐶𝐶 𝑄𝑄𝐶𝐶𝑃𝑃 𝐶𝐶 𝑀𝑀𝐶𝐶𝑀𝑀 𝐶𝐶 + 1) ) ∗ 400%) . Average mall time deposits: RCONA529 (RCON3469 before 1997Q1).

Large Time Deposit Ratei,t

Annualized average interest rate (in %) over the two quarters 𝐶𝐶, 𝐶𝐶 + 1 on savings deposits: (𝑙𝑙𝑀𝑀𝑃𝑃𝐻𝐻𝐷𝐷𝐶𝐶 𝐶𝐶𝑀𝑀𝑚𝑚𝐷𝐷𝑀𝑀𝐷𝐷𝑝𝑝𝐶𝐶𝐶𝐶𝑀𝑀𝐶𝐶 𝑀𝑀𝐶𝐶𝐶𝐶𝐷𝐷𝑃𝑃𝐷𝐷𝐶𝐶𝐶𝐶 𝐷𝐷𝑒𝑒𝑝𝑝𝐷𝐷𝐶𝐶𝐶𝐶𝐷𝐷 𝑀𝑀𝐶𝐶 𝑄𝑄𝐶𝐶𝑃𝑃 𝐶𝐶 𝑀𝑀𝐶𝐶𝑀𝑀 𝐶𝐶 + 1)/(𝐴𝐴𝐴𝐴𝐻𝐻. 𝑙𝑙𝑀𝑀𝑃𝑃𝐻𝐻𝐷𝐷 𝐶𝐶𝑀𝑀𝑚𝑚𝐷𝐷 𝑀𝑀𝐷𝐷𝑝𝑝𝐶𝐶𝐶𝐶𝑀𝑀𝐶𝐶 𝑏𝑏𝑀𝑀𝑙𝑙𝑀𝑀𝐶𝐶𝑇𝑇𝐷𝐷 𝑀𝑀𝐶𝐶 𝑄𝑄𝐶𝐶𝑃𝑃 𝐶𝐶 𝑀𝑀𝐶𝐶𝑀𝑀 𝐶𝐶 + 1) ) ∗ 400%) .

Core deposit Ratei,t Core deposits include transaction, saving, and small time deposits, and core deposit rate is the average interest rate paid on the three.

Failure in n year An indicator variable that equals 1 if the bank fails in the next n years, measured as 1 if the bank is on the FDIC failed banks list on https://www.fdic.gov/bank/individual/failed/

StDev_ROE i,t-1 Standard deviation of ROE measured over 12 rolling quarters (from Quarter 𝐶𝐶 − 12 to 𝐶𝐶 −1).

NPL i,t-1 The percentage of non-performing loan (RCFD1403+RCFD1407) to total loan.

ROA i,t-1 Annualized ROA (in %) in quarter t-1, calculated as net income (RIAD4300, adjust year-to-date reporting to within quarter) divided by beginning assets.

Persistence it-1

We measure banks’ performance persistence using the coefficient β1 estimated from the following AR(1) regression, estimated for each bank-quarter using the bank’s observations over the previous 12 quarters: 𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖 = 𝛼𝛼0 + β1𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖−1 + 𝜖𝜖