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Journal of Advances in Management ResearchBank asset allocation: the effectiveness of market monitoring

Grace W.Y. Wang Arvind Mahajan Ruby P. KishanArticle information:

To cite this document:Grace W.Y. Wang Arvind Mahajan Ruby P. Kishan , (2013),"Bank asset allocation: the effectiveness ofmarket monitoring", Journal of Advances in Management Research, Vol. 10 Iss 3 pp. 376 - 398Permanent link to this document:http://dx.doi.org/10.1108/JAMR-07-2013-0046

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Bank asset allocation: theeffectiveness of market

monitoringGrace W.Y. Wang

Department of Maritime Administration, Texas A&M University at Galveston,Galveston, Texas, USA

Arvind MahajanDepartment of Finance, Texas A&M University, College Station,

Texas, USA, and

Ruby P. KishanDepartment of Finance and Economics, Texas State University,

San Marcos, Texas, USA

Abstract

Purpose The purpose of this paper is to study the effectiveness of market discipline on banksrisk-taking behavior based on how swiftly banks respond to market information.Design/methodology/approach A simplified incentive model provides the necessary justificationfor two types of market disciplines: first, monitoring by uninsured market participants, and second,risk premium in terms of interest spread required by risk-averse depositors. Panel data regressionis carried out for both surviving and failed US banks for the period 1999:Q4-2007:Q3 to examine therole of market discipline, bank capital, and macroeconomic shocks.Findings The paper finds that banks which failed during 2007:Q4-2010:Q4 suffered from fundamentalweaknesses in their asset quality relative to the surviving banks prior to the crisis.Originality/value The paper focusses on two questions: In what circumstance does market

monitoring exist? And how can market incentives affect banking firms actions? The first questionis studied in a simplified incentive model that provides justification for two types of market discipline.Given that, the effectiveness of market discipline is empirically tested, using the US banking data inthe period leading up to a surge in the number of bank failures in 2007-2010. The papers results showthat failed institutions with large size were relatively less responsive to early warning signalsof declining uninsured deposits and rising deposit spread.

Keywords Banking, Financial instability, Financial institutions

Paper type Research paper

1. IntroductionThe USA experienced bankruptcy of financial institutions and severe market illiquidityduring the financial crisis of 2007-2009. A surge in the number of bank failures, from threein 2007 to 157 in 2010, has caused huge social losses. Studies (Adrian and Shin, 2010;Brunnermeier, 2009; Ivashina and Scharfstein, 2010; Kacperczyk and Schnabl, 2009;Krishnamurthy, 2010; Peck and Shell, 2010; Shleifer and Vishny, 2010; Uhlig, 2010) haveattempted to explain bank distress and failure in terms of funding illiquidity, marketilliquidity, capital inadequacy, exogenous macroeconomic shocks, and a shift in thebanking industry from an originate to hold to an originate and distribute model. Noneof the above studies, however, examines individual bank balance sheets to explorewhether asset allocation characteristics of banks that survived the crisis of 2007-2009differed significantly from those that failed.

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0972-7981.htm

Journal of Advances in ManagementResearchVol. 10 No. 3, 2013pp. 376-398r Emerald Group Publishing Limited0972-7981DOI 10.1108/JAMR-07-2013-0046

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As the recent financial crisis highlights the importance of the risk managementin banking, there are strong interests in the governance mechanism toward bankfailure reduction. Numerous studies have showed that unless the government safetynet is properly priced, banks with distorted incentive tend to take excessive risks.

In this study, we focus on two questions:(1) In what circumstance does market monitoring exist?

(2) How can market incentives affect banking firms actions?

The first question is studied in a simplified incentive model that provides justificationfor two types of market discipline. From the theoretical model, we define necessaryconditions for the existence of depositors discipline. Were asset allocation differencesamong banks evident prior to the crisis? It is probable that, due to these characteristics,some banks were more susceptible than others to adverse shocks. Given that,the effectiveness of market discipline is empirically tested, using the US banking datain the period leading up to a surge in the number of bank failures in 2007-2010.

The paper proceeds as follows. Adequate and relevant past studies are presented inSection 2. Section 3 presents a theoretical model as motivation to support the analysisof both quantity of uninsured deposits and the charge of interest rate premium forevidence of market discipline. In Section 4, we describe the empirical approach andthe data specification. Section 5 reports the major empirical results, and Section 6summarized the conclusions.

2. Literature reviewOur paper is related to two strands of research, including work on the theory ofincentives and the existence and effectiveness of market discipline. It is importantto ascertain that market discipline influences a banks portfolio composition and thecorresponding risks effectively. The incentive approach has been applied extensively tothe banking sector to combat problems associated with asymmetric information for

adverse selection and moral hazard (Giammarino et al., 1993; Mas-Colell et al., 1995;Laffont and Martimort, 2002; Gropp and Vesala, 2004). Giammarino et al. (1993) andGropp and Vesala (2004) are of particular relevance. In both models, banks privateinformation is important in determining the return of risky assets financed. The pay-off ofthe banks portfolio is governed by the resources devoted by the manager of the bank(Giammarino et al., 1993) or the binding no arbitrage condition between depositorsmonitoring and governments bail out (Gropp and Vesala, 2004). However, explicitmarket discipline is missing in both incentive models. We model the behavior of marketmonitoring explicitly and provide justification for two types of market disciplines,serving as a foundation for the early warning signal on banks risk taking in a simplifiedprincipal-agent model.

In addition, our paper is related to the literature on market discipline. Recentexamples of depositors discipline can be found in Park and Peristiani (1998), Goldbergand Hudgins (2002), Kobayashi and Bremer (2005), and Berger and Turk-Ariss (2010).The hypothesis of whether uninsured depositors discipline banks was tested in thesepapers[1]. Depositors may penalize riskier banks by requiring higher interest rates orwithdrawing deposits. The former is a price adjustment of market discipline while thelatter is the quantity adjustment of deposit growth. Park and Peristiani (1998) find thatthrifts with high probability of failure attracted smaller amounts of uninsured deposits.Goldberg and Hudgins (2002) find that the share of uninsured deposits falls prior tobank failures, and failing banks have difficulty attracting uninsured deposits. Berger

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and Turk-Ariss (2010) find significant depositor discipline, but it varies with bankssize and between the USA and the EU. A review of empirical evidence of depositordiscipline can be found in Kobayashi and Bremer (2005). Given various study periodsand the number of institutions considered, the review provides positive results

regarding the existence of depositor discipline.Compared to the existence of monitoring, the effectiveness of market monitoring

remains unclear in the literature. This study, comparing to the empirical literatureabove, is to emphasize the effectiveness of market discipline rather than the existenceof market discipline. The effectiveness of market discipline is not based on whethermarket price is sensitive to information about risk but whether risk-sensitive pricingis sufficient to combat banks risk taking behavior (Ashcraft, 2008). Calomiris andKahn (1991) and Flannery (1994) use demandable deposits as a tool and emphasize thatbank deposits provide a disciplining effect on bank managers. By strengtheningmanagers accountability for performance and depositors responsibility for theirinvestment, Maechler and McDill (2006) find effective market discipline in good banks,but no evidence of it in weak banks. Large deposit withdrawals and high interest

rates are associated with banks risk-taking behavior in the USA. Similar resultsare obtained by Martinez-peria and Schmukler (2001) for Latin American counties,Calomiris and Powell (2001) for Argentina, and Ungan et al. (2008) for Russia.We investigated the effect of depositors discipline on banks asset allocation. Given thechanges of banking regulations and the development of sophisticated financialinstruments since the last savings and loans crisis, the results of this study helpsus better understand how banks respond to possible market discipline. In addition,the degree of bank sensitivity to market discipline can provide an early warning signalto possible occurrences of bank failures.

In the banking model, ex ante market discipline on profit-maximizing banks is througheither: monitoring by uninsured market participants, or risk premium in terms ofinterest spread required by risk-averse depositors. The strategy followed is to examine

empirically how sensitive banks behavior is to market discipline, capital, fundingliquidity, and macroeconomic factors ex post. To extend the existing literature oneffectiveness of market monitoring, this research compares asset allocation of non-failedand failed banks. We are particularly interested in whether, prior to the 2007 crisis,failed banks responded differently from surviving banks in terms of traditional lendingand market trading, to changes in risk associated with bank funding supply, i.e. marketdiscipline and banks capital, and to macroeconomic shocks. It is important to examinebanks asset allocation decisions prior to the crisis in order to send the warning signal offailing banks to the banking regulators and further develop sound regulatory practiceswhich ensure stability of the banking sector.

Testing the model using US bank-level data, the empirical results suggest that failedinstitutions were relatively less responsive to early warning signals of falling uninsured

deposits and rising deposit spread. The above conclusions are statistically significantfor large size banks in particular. Surviving banks statistically significantly reducedtheir holdings of different types of securities and loans when uninsured deposits fell.In contrast, failed banks did not reduce their holdings of commercial & industrial (C&I)loans and relatively risky short-term debt securities as significantly as their survivingpeers. These results show that failed banks assets, compared to the surviving group, hadhigh risk exposure prior to the crisis. Due to aggressive movement into real-estate loansand failure to respond prudently to market discipline signals, these banks were moresusceptible to adverse shocks.

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3. The model3.1 Depositors monitoringIn this section, we discuss the purpose of monitoring. Depositors take actionsthat make creditable threats to banks by either withdrawing resources from the

bank or requesting a higher interest rate as premium. Either way it will increasebanks operating cost and thus impeding banks profit maximization. If marketprovides enough incentives to encourage market monitoring, agency problems can becontrolled using market information. In model setting, monitoring efforts affectbanks asset allocation in two possible ways. The first-order stochastic dominancesetting reduces the probability of undesirable low rate of return, and thesecond-order stochastic dominance setting lowers the variance of return of banksrisky investment portfolios.

There is a continuum of ex-ante heterogeneous depositors with unit mass in theeconomy. At periodt0, depositors save and deposit in the bank, and the bank choosesits investment portfolio. A representative depositor is an expected utility maximizerwith a Bernoulli utility function over (rD,m), where rDis the rate of return on deposits,

andmis the monitoring effort. Depositors are able to affect and improve the quality ofbanks portfolio implicitly if monitoring effort m is devoted. At the same time, effortsto monitor creates disutility for depositors with the cost, C(m). The disutility functionis increasingC040 and convexC0040 inm withC(0) 0.

Although the returns are affected by m, they are not fully determined by it.The return is stochastic related tom in a manner described by conditional cumulativefunctionG(rL|m) and density function g(rL|m), where rLis the return on banks riskyinvestment. Economic intuition of g40,8m comes from whether depositorssuccessfully influence the composition of banks asset portfolio and the realizedreturns through monitoring and withdrawals. Depositors withdrawals reduce bankscash flow and create a negative impact on banks funding liquidity. Banks may financethe deficit through costly equity capital.

Monitoring effort improves rate of return, rL, in the sense of first-orderstochastic dominance where Pr(rLpr

*L|m) is decreasing with m for any given

rate of return on risky assets, r*L. Note that risk-averse depositors prefer the stochasticdistribution induced by the monitoring effort, where Gm

q

qm

R rLg rL mj drL

p0. At

the same time, monitoring effort will be assumed to shift the distribution ina way of the second-order stochastic dominance by reducing the variance ofthe return on risky assets, where

RGm rL mj drLp0. The depositors problem

is to choose the optimal level of monitoring effort to maximize expected utility inEquation (1):

Max EUD Z r

rb

v rD rL dG rLjm Z rb

r

bv rDrL dG rLjm C m

v rDrL G rLjm rrb

Z rrb

v0 rD drD

drLG rLjm drL

b v rDrL G rLjm rbr

b

Z rbr

v0 rD drD

drLG rLjm drLC m

1

where v is the non-decreasing concave function with v040 and v00p0 representingrisk averse characteristic of depositors. Depositors expected payoffs are governed by

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Equation (1) given the optimal asset allocation determined by a representative bankin the following Section 2.2 rb is the lower bound of the random realized return thatsatisfies the breakeven condition. The stochastic level of rate of return, rL, may take anypossible value in the interval [ r

, r] with positive probability density function g(rL|m)

and the differentiable conditional cumulative distributionG(rL|m). The first term is theexpected payoff if bank earns positive profits. The second term is depositors payoff ifthe bank becomes insolvent. WithbA[0,1], the amount received which is assumed to beproportional to the expected payoff is determined by the FDICs insurance coverage and,at the same time, is based on banks residuals. The second equality is obtained byintegrating by parts. The behavior of a risk averse depositor is governed by interestspread in the following equation[2]:

rD rf 1y rLrf

2

where yA[0,1] is the fraction of deposits insured and rf is the risk free rate. If thedepositor is fully insured, y 1, the return on deposits converges to the risk-free rate,which can be normalized to 1. Thus, for fully insured depositors, return on deposits isequivalent to the risk-free rate. If not fully insured, there exists a probability premium,rLrf, that makes the depositors indifferent between the risk-free rate and risky returnrealized from banks asset allocation. Once depositors make the effort to monitor thebank, they know ex ante the realization of a banks asset allocation. Equation (1) canbe reduced further ifb y 1, i.e. all deposits are fully insured, we have:

MaxEUDv rf Z r

rb

g rLjm drL bv rf

1

Z rrb

g rLjm drL

!C m

v rf Z r

r

g rLjm drL C m

10

The first order condition (FOC) of depositors monitoring effect is obtainedas follows:

qEUD

qm v rf

GmC

0 m o0

Proposition 1. Given that a higher level of monitoring is assumed to reduce thelikelihood of low returns by shifting the distribution of return, i.e. first orderstochastic dominance, Gm

q

qm

RrL

g rLjm drLp0. The FOC is strictly negative,and thus the optimal choice of monitoring effort is zero when deposits arefully insured.

If depositors are fully insured, i.e. y 1, no monitoring effort will be exerted bydepositors to maximize the expected utility. Ifyo1, the FOC is:

qEUD

qm

Z rrb

v rL qg rLjm

qm drL b

Z rbr

v rL qg rLjm

qm drLC

0 m 0

The interior solution to the optimal effort of monitoring,m, is characterized by the FOCabove. The optimal monitoring effort is determined when the marginal gain in the

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mean of the conditional distribution, E[v(rL|m)] R

rLv(rL)g(rL|m)drL, equals tothe marginal cost of monitoring.

3.2. Banks asset allocation. Following Giammarino et al. (1993), we modelprofit-maximizing banks behavior. There is a continuum of identical banks with unit

mass in the economy. A representative bank maximizes the expected return,choosing the optimal combination between risk-free assets, R, with a certain return,rf, and risky assets, L, with a random realized return,rL. The stochastic level of rateof return, rL, may take any possible value in the interval [ r

,r] with positive

probability density function g(rL|m) and the differentiable conditional cumulativedistributionG(rL|m).

Banks asset allocation and the realization of rate of return on banks risky assets areprivate information available only to bank managers. The moral hazard problem arisesfrom a banks unobservable temptation to gamble. Profit of bank is defined as:

ERB

Z rrb

rLLrfRrDD

dG rLjm P 3

where rb is the lower bound of the random realized return that satisfies the breakevencondition, andPis the insurance premium[3]. The first two terms in the integral are thereturn for risky and risk-free assets, respectively. The third term is the cost of depositswith deposit rate rDequals to the risk-free rate for fully insured depositors and probabilitypremium on top of risk-free rate for uninsured depositors which is described in Equation(2). Given that the distribution ofrLconditional onm is first-order stochastic dominance,the monitoring effort has a positive impact on banks expected profits:

rbrDDrfR

L 4

Banks maximization problem needs to incorporate the cash flow constraint.Following Giammarinoet al.(1993), in a given period, cash flow includes net income,

the changes in current assets and current liabilities, and cash raised by issuing debtor stock:

LRPDE 5

where the constraint can be decomposed into the amounts of risky assets, L, risk-freeassets, R, and risk premium paid, P, deposits, D, and equity capital, E. SubstitutingEquations (2) and (5), the banks expected return Equation (3) can be rewritten as:

ERB

Z rrb

rL L 1y D rf RyD g rLjm drLP

Z r

rb

rL L 1y LRPE rf Ry LRPE g rLjm drLP30

The FOC for firms capital structure and asset allocation is obtained as follows:

qERB

qL

Z rrb

yrL yrf

g rLjm drL 0

E rLjm rfG rLjm

wherem* is the optimal level of monitoring determined by depositors.

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Bank assallocatio


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Proposition 2. The interior solution of banks optimal choice of risky assets existsonly ifrL rf, which violates the assumption that rL4rf. IfrL4rf, given Equation (2),then rD4rf. The optimal decision of a representative bank is to take on risk.Correspondingly, depositors require a higher deposit interest rate based on banks

risk taking behavior.Without an explicit setup of shareholders, equity capital is governed by the

cash flow constraint in Equation (5). Once the return on asset allocation is realized,gross reserves of the bank must be sufficient to fulfill the obligation of paying backdepositors. Then, the equity holder is paid with dividends if the earning exceedsobligation at period t1. z is the fraction of a banks project financed internallythrough shareholders equity. The ex post rate of return on equity capital is inEquation (6)[4]:

re Z 1

E

Z rrb

rLL rfR

rDD

dG rLjm

6

4. The methodology and data4.1 Data specificationTo explore if some banks were more susceptible than others to adverse shocks,we obtain quarterly bank-level data from the FDIC for federally insured commercialbanks and saving institutions. We examine banks balance sheets in the periodleading up to the financial crisis over the period of 1999-2007. The number of bankfailures was three in 2007, 25 in 2008, 140 in 2009, to 157 in the year of 2010.The starting point of the latest recession in the USA, 2007:Q4, defined by the NationalBureau of Economic Research (NBER) identifies the surge of bank failures.Thus, two mutually exclusive periods are chosen: 1999:Q4-2007:Q3 and 2007:

Q4-2010:Q4. We first distinguish banks that survived in period 2 from those thatfailed. Period 2 covers the Troubled Asset Relief Program (TARP) signed into lawin October, 2008. Hence, the definition of failed vs survived banks needs a carefuldistinction. To be classified as failed banks, the given institutions have failurestatus with a press release about the institutions closure issued by the FDIC.Failed institutions did not include the institutions that were assisted by FDIC inmerging with another institution. We then examine empirically how sensitivebanks respond to market discipline, using pre-2007 crisis asset allocation of bothsurviving and failed banks. Panel data regression is carried out for US banks for theperiod 1999:Q4-2007:Q3 to examine the role of bank capital, market discipline, andmacroeconomic shocks.

When total loans or assets had non-positive values, we drop that bank from the

sample in that quarter. To adjust for outliers, we winsorize observations that lie abovethe 99th percentile or below the 1st percentile for each variable used in the regression.After applying these filters, our sample contains 200,531 bank-quarter observations.There were 320 failed banks between 2007:Q4-2010:Q4.

Table I presents the summary statistics of key variables. Failed banks have a lowgovernment security ratio, high real estate loans, and total loan to asset ratioscompared to the non-failed banks. Failed banks also have a relatively low level ofcapital asset ratio and high level of uninsured deposits, all indicators of high defaultrisk, all else equal.

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4.2 MethodologyWe consider the one-way error component dynamic panel regression model withthe fixed effect where miis assumed to be a fixed parameter to be estimated and therandom effect where miBiid(0,s

2m) can be assumed randomly:

Yitf Xit; b vit mi

where mi is the unobservable time-invariant individual effect and vit is therandom disturbance. The fixed effects model is an appropriate specification ifthe subject to study is a specific set of i banks or banks cross n states or mgeographic regions, using dummy variables for (i1) banks, (n1) states or (m1)geographic areas. Inference is conditional on the particular banks, states, or areas thatare observed.

However, the fixed effect least squares suffer from the loss of degrees of freedomby estimating extra parameters. In addition, the time-invariant variables treated asdummies create multicollinearity in the regression package (Baltagi, 2001). Sincetime-invariant dummy variables may be wiped out through mean transformation underthe fixed effect estimation, we do not introduce failure dummy as an independentvariable. Instead, we use the interactive term of balance sheet and macro variables withthefailure dummy to measure different responses of surviving vs failed banks. We controlfor the interactive terms, multiplying the dummy variable for failure by the independentvariables if applied.

To investigate changes in asset profiles due to changes in funding supply, aggregatedemand shocks, credit market conditions, and, more importantly, market discipline,

A. Full sample B. Surviving C. Failed D. Mean difference

(n 9,740) (n 9,471) (n 269)Debts 22.88395 23.13304 14.44608 8.686958 (0.1382026)***MBS 6.406698 6.437757 5.354548 1.083209 (0.0826893)***Govts 17.63395 17.81641 11.44454 6.371871 (0.118701)***Real_loan 41.83807 41.44003 55.34008 13.90005 (.2133227)***C/I_loan 9.544038 9.51086 10.66948 1.158615 (0.1031938)***Loan/asset 63.05822 62.79997 71.81864 9.018672 (0.1694714)***Coredep 68.12508 68.30087 62.16193 6.138939 (0.1625785)***UninsTime 40.83645 40.54189 50.82828 10.2864 (0.2144329)***I_spread 3.498728 3.666844 2.187538 5.854382 (0.6715649)***Capital 11.57967 11.5873 11.32123 0.2660758 (0.0945009)***Asset 11.67217 11.65992 12.08796 0.4280463 (0.0168698)***

Notes: The Table reports mean statistics of key variables in the study. The summary statisticsfor all institutions in the sample, surviving banks, and failed banks are listed in columns A, B,and C, respectively. Column D presents the mean difference t-test with the standard errors listed

in the parentheses. n is the number of institutions in each sub-sample. All securities werescaled by total assets. As the column shows, failed banks have lower government security to assetratio, relatively high real estate loan and C/I loan ratios, and higher total loan to asset ratiosrelative to their surviving counterparts. Failed banks also have relatively low level of coredeposits and capital asset ratios, all indicators of high default risk, ceteris paribus. Descriptivestatistics cover the sample period of 1999:Q4-2007:Q3. ***Significant at 1 percent levelSource:The FDIC

TableSummary statist

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we employ the following fixed-effects model:

Assetitb1Core Depositi t1 b2Core Depositi t1 failureb3Mktdisti t1

b4Mktdisti t1 failureb5Capitali t1 b6Capitali t1 failureb7growAsseti t1

b8growAsseti t1 failureX

j

gjDfedfund tj X

j

cjDfedfund tj failure

X

j

djgdp tj X

j

Zjgdp tj failuremivit; i 1; 2;. . . ; N:t 1; 2;. . . ; T:j 0 4

7

where dependent variable Asset refers to securities and loans of bank iat time t.The former represents market trading activities while the latter represents traditionallending activity. We use three type of securities short-term debt securities (Debt),mortgage backed securities (MBS), and government securities (Govts) and three loancategories real-estate loans (Real_loan), commercial and industrial loans (C/I_loan),

and total loans as dependent variables[5]. Securities and loans are scaled by totalassets. Investment in mortgage-backed securities and C&I loans are presumed to berisky and would increase the likelihood of bank failures according to the bankregulators. The comparative of dependent variables as various asset allocations aredescribed in detail in Appendix and Figures 1 and 2.

Debts (total short-term debt security to total assets): as these include assets heldin trading accounts we anticipate a positive correlation to bank liquidity.

MBS(mortgage-bank securities to total assets): as stated in the literature, beforethis crisis MBS were viewed as gilt-edge assets. On the other hand, MBS is of longduration exposing the holder to interest rate risk and heavy losses if rates increase.Therefore, the relation of market discipline to this factor is uncertain.

Govts (US government securities to total assets): due to these highly liquid

government securities owned we anticipate market discipline will encourage banks tohold more government securities.

Real_loan (real estate loans to total assets): prior to the housing asset bubblebursting in the time period of this study loans secured by real estate were consideredto be safe, secured by a mortgage on a consumers primary residence. Therefore,we expect effective market discipline would increase banks holding of this asset.However, similar to MBS, this asset is becoming a risky asset during this crisis.The relation of market discipline to this factor is uncertain.

C/I_loan (commercial and industrial loans to total assets): these assets are incomeproducing properties focussing on financing commercial and industrial large andmedium firms. They are sensitive to economic downturns and hence are positivelyassociated with banks risky portfolios. Effective market discipline may reduce banksincentive to involve in risk-taking and reduce the holding of this asset.

Loan/asset (Total loans to total assets): compared to short-term securities, highpercentage of loans may increase the chance of asset mismatching. Thus, we anticipateeffective market discipline to discourage banks loan to asset ratios.

According to Equation (7), a banks asset allocation is determined by previous behaviorof asset choices, movement of market discipline, bank-specific fundamental factors, andthe development of macroeconomic indicators. We apply dynamic panel fixed effect modelwith a quarterly frequency to capture the unobservable individual effect and theautoregressive terms regarding the previous choices of banks asset portfolios. To avoid

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13.1

15.9

15.0

18.4

15.1

20.2

8.9

15.6

9.4

15.0

0

5

10

15

20

Percentage

> 50 bill

Uninsured Deposits to Total Assets

11.312.0

12.812.5

12.112.012.7

13.9

14.914.8

15.8

8.4 8.18.3

7.4 7.2

9.9

11.4

12.4

9.5 9.18.7

0

5

10

15

Percentage

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Notes:The shares of uninsured time deposits to total assets and to total deposits are shown.

We observed that, on average, the institutions with less than five billion assets fund 13.1 to

15.1 percent of assets with uninsured deposits. On the other hand, for those with more than

five billion assets fund 8.7 to 9.4 percent of assets with uninsured deposits. Similar to

Maechler and McDill (2006), the figure shows that larger institutions have access to more

flexible funding channels

Source:FDIC. 1999:Q4-2009:Q3 quarterly data with all commercial and saving s institutions

Uninsured Deposits to Total Deposits

> 5 bill


12/24

5.8

11.011.4

5.4

11.212.1

6.2

11.2

12.9

7.1

11.2

12.6

7.3

11.2

12.4

7.4

11.6

13.0

6.6

11.8

14.1

5.9

12.2

15.6

6.0

12.7

15.2

7.6

12.3

15.3

7.9

11.7

16.5

5.6

11.5

14.5

5.1

11.6

15.8

6.0

10.9

17.2

6.6

10.6

16.6

6.5

10.7

16.6

6.2

11.3

18.2

5.3

11.5

21.0

4.4

11.6

23.8

4.3

10.9

20.5

5.4

8.5

19.8

5.35.5

22.2

0

5

10

15

20

25

(a)

(b)

Percentage

oftotalassets

Uninsured time deposits

36.4

10.0

37.5

10.2

38.2

10.2

39.2

9.8

39.7

9.5

43.1

8.9

44.8

9.0

45.9

9.0

46.5

9.2

47.7

9.3

47.7

8.8

44.9

11.9

46.5

11.8

49.0

11.9

51.5

11.6

53.8

11.1

57.4

9.9

60.4

9.4

62.7

9.3

64.4

9.3

64.6

9.2

62.9

8.4

0

20

40

60

Percentage

oftotalas

sets

Real Estate Loans

Notes:panel (a) presents the ratios of mortgage-based securities and equity capital to total

assets, and Panel (b) shows real estate loans and C/I loans to total assets

Source:FDIC. 1999:Q4-2009:Q3 quarterly data with commercial banks

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

1999

2000

2001

2009

2002

2003

2004

2005

2006

2007

2008

FailedSurvived

FailedSurvived

MBS Equality capital

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

C/I Loans

Figure 2.Asset allocation betweensurvived and failed banks

386

JAMR10,3


13/24

endogeneity, we include four lags of dependent variables and macro indicators asregressors. Following Maechler and McDill (2006), we also use a lag of balance-sheet andincome-statement variables to account for the delay of public information.

Based on the market discipline literature, we use two proxies for the market discipline

variable (Mktdist) the uninsured time deposits to total assets ratio (UninsTime)and deposit interest rate spread (i_spread). Banks risk taking is highly related to theamount of uninsured debt (Calomiris, 1999). Similar to Demirguc-Kunt and Huizinga(2004), i_spreadis computed as the ratio of interest expense to interest-bearing debt minusthe three month T-Bill rate. If the quantity or the price measure of market disciplineis effective, rising interest rate spread or declining uninsured deposits should limita banks risk taking activities. Thus, we expect to observe a movement away fromrisky loans into government securities. Otherwise, we may observe zombie banks thatfailed to respond depositors discipline allocate assets aggressively to overcome thepossibility of default.

Our measure ofCore Deposits is the total dollar amount of all deposits in depositaccounts of $100,000 or less. Capital is the equity capital to total assets ratio. Large

levels of equity capital to asset ratio and core deposits imply abundant funding supply.Therefore, we expect to find a positive relationship between funding supply variablesand a banks capacity to lend and securitize. growAsset is the grow of real totalassets[6]. Since large banks are able to reduce idiosyncratic risk through diversificationand enjoy conjectural too-big-to-fail guarantees, we expect a positive relationshipbetween growAssetand loans and risky security holdings.

Following Bernanke and Blinder (1993), change in the federal funds rate is usedas a measure of monetary policy. Following Maechler and McDill (2006), the realGDP growth rate represents the overall macroeconomic conditions. We expect to finda negative effect of monetary policy and a positive effect of GDP growth on loans andrisky security holdings.

Two mutually exclusive periods are chosen: 1999:Q4-2007:Q3 and 2007:Q4-2010:Q4

in the study. We first distinguish banks that survived in period 2 from those that failed.Then we give a failure dummy variable value one for failed banks in period 1, and zerootherwise, to captures the heterogeneity in banks asset portfolios prior to the financialcrisis. Instead of constructing a perceived estimate for the failure probabilityof financial institutions, we use ex post actual failure records provided by the FDIC tostudy the effects of market discipline on banks. The interaction of balance sheet andmacro variables with the failure dummy variable measures the differential responsesof surviving vs failed banks. These interactions are the key variables of interest.

5. The findingsTable II reports the main results with uninsured deposit ratio as a proxy for marketdiscipline. The coefficients on uninsured deposits for non-failed banks are positive and

significant for all models except model (2). When uninsured time deposits drop, we findthat surviving banks reduce holdings of all types of loans real estate loans, C/I loans,and total loans to asset ratios while increase relatively safe short-term MBS beforethe financial crisis in 2007. Failed banks, however, are not as sensitive as survivingbanks in responding to market discipline. We observe a trade-off for failed banksin substituting real estate loans to C/I loans. That is shown in the significant negativecoefficients on the interactive term, UninsTimet1 failure, in columns (5) and (6).

In Table III, we find that quantity adjusted market discipline, uninsured time deposits,performs better than the price adjusted discipline, the growth of the interest spread,

38

Bank assallocatio


14/24

(1)

(2)

(3)

(4)

(5)

(6)

(t-value)

Debts

MBS

Govts

Real_loa

n

C/I_

loan

Loan/asset

Coredept

1

0.0

420(24.6

6)***

0.0

547(28.5

1)**

*

0.0

403(20.0

7)***

0.2

728(87.5

0)***

0.0

342(19.3

8)***

0.3

107(102.0

4)***

Coredept

1

failure

0.0

260(2.8

1)***

0.0

228(2.1

9)**

0.0

122(1.1

2)

0.1

246(7.38

)***

0.0

546(5.7

1)***

0.0

142(0.8

6)

UninsTimet

1

0.0

272(11.8

9)***

0.0

429(16.6

8)**

*

0.0

263(9.7

5)***

0.5

110(122.2

0)***

0.0

491(20.7

5)***

0.5

596(137.0

1)***

UninsTimet

1

failure0.0

019(0.1

8)

0.0

270(2.2

9)**

0.0

030(0.2

4)

0.1

020(5.32

)***

0.1

067(9.8

3)***

0.1

096(5.8

5)***

Capitalt

1

0.0

1877(6.1

5)***

0.0

858(24.9

8)**

*0.0

406(11.2

8)***

0.1

474(26.4

2)***

0.0

461(14.6

0)***

0.1

169(21.4

6)***

Capitalt

1

failure

0.0

172(0.9

5)

0.0

070(0.3

4)

0.0

630(2.9

6)***

0.0

815(2.46

)**

0.0

828(4.4

2)***

0.0

119(0.3

7)

growAssett

1

0.0

064(1.1

3)

0.0

004(0.0

6)

0.0

040(0.6

0)

0.0

839(8.08

)***

0.0

033(0.5

7)

0.0

731(7.2

1)***

growAssett

1

failure

0.7

818(1.0

0)

0.6

837(0.7

8)

0.6

331(0.6

9)

12.1

329(8.50)***

0.7

4080(0.9

2)

12.6

113(9.0

5)***

P4 0Dfedfund

0.5

797(10.7

4)***

0.0

095(0.1

6)

0.0

237(0.3

7)

16.7

464(169.6

4)***

0.8

882(15.8

9)***

16.7

332(173.5

5)***

P4 0Dfedfund

failure

0.2

726(0.8

8)

1.5

70(4.4

9)***

2.3

555(6.4

3)***

10.4

059(18.3

1)***

1.6

681(5.1

8)***

5.4

242(9.7

7)***

P4 0Dgdp

0.3

917(17.6

5)***

0.1

560(6.2

4)***

0.2

7613(10.5

6)***

1.8

075(44

.55)***0.2

468(10.7

4)***

2.6

085(65.8

2)***

P4 0Dgdp

failure

0.1

231(0.9

2)

0.4

139(2.7

5)***

0.2

626(1.6

6)

1.2

219(4.99)***

0.1

126(0.8

1)

0.3

153(1.3

2)

R2

0.9

576

0.2

670

0.7

893

0.3

60

0.0

659

0.6

433

n

8,8

98

8,8

98

8,8

98

8,8

98

8,8

98

8,89

8

Observations

200,5

31

200,5

31

200,5

31

200,5

31

200,5

31

200,53

1

Notes:Allmodelsalsoincludeaconstantandfourlagsofthed

ependentvariables.t-valuesareinparentheses.

Thetablereportsthemainresultswith

uninsuredtimedepositratio

usedasaproxyformarketdisciplineinthemodelinEquation(7).Thevaluesincolumns(1)through(6)arethecoefficientsofthe

factorsfrom

themodelwith

uninsureddepositratiorepresenting

thefactormarketdisciplinevariable(Mktdist)andwherethedependentv

ariableisthe

assetlistedatthetopofthe

column.

Thevaluesinparenthesisaretherespectivet-ratios.Theestimated

coefficientontheinteractionofthefailuredummy

variablewiththevariableU

ninsured(b

4)forfailedbanksisnegativeandstatisticallysignificantwhichindicatesthatfailedbankssawadeclineinthe

quantityofuninsureddepos

itsrelativetosurvivingbankspriort

otheirfailure.

**,*

**Significantat5a

nd1percentlevels,respectively

Source:TheFDIC

Table II.Banks asset allocationwith quantity adjustedmarket discipline

388

JAMR10,3


15/24

(1)

(2)

(3)

(4)

(5)

(6)

(t-value)

Debts

MBS

Govts

Real_loan

C/I_

loan

Loan/asset

Coredept

1

0.0327(21.6

0)***

0.0

400(23.4

5)***

0.0

313(17.5

5)***

0.0

969(33.67

)***

0.0

173(11.0

2)***

0.1

181(41.6

7)***

Coredept

1

failure

0.0329(4.5

4)***

0.0

172(2.1

1)**

0.0

049(0.5

7)

0.0

420(3.05)***

0.0

060(0.8

0)

0.0

739(5.4

5)***

DI_spreadt

1

0.0005(0.8

1)

0.0

030(4.0

9)***

0.0

008(1.0

2)

0.0

014(1.1

2)

0.0

001(0.1

6)

0.0

015(1.2

2)

DI_spreadt

1

failur

0.0114(0.1

9)

0.1

710(2.5

8)***

0.0

290(0.4

2)

0.0

814(0.7

3)

0.0

873(1.4

3)

0.1

995(1.8

1)*

Capitalt

1

0.0258(8.6

1)***0.0

747(22.1

6)***

0.0

473(13.4

0)***

0.0

159(2.7

9)***

0.0

334(10.7

8)***

0.0

272(4.8

4)***

Capitalt

1

failure

0.0084(0.4

9)

0.0

047(0.2

5)

0.0

539(2.6

6)***

0.1

212(3.71)***

0.0

384(2.1

5)**

0.1

123(3.4

9)***

growAssett

1

0.0064(1.1

2)

0.0

003(0.0

5)

0.0

040(0.6

0)

0.0

845(7.8

1)***

0.0

033(0.5

6)

0.0

738(6.9

3)***

growAssett

1

failure0.8123(1.0

4)

0.7

271(0.8

3)

0.6

609(0.7

2)

12.8

337(8.64)***

0.7

966(0.9

8)

13.1

382(8.9

9)***

P4 0Dfedfund

0.2812(5.8

8)***0.4

618(8.5

7)***

0.2

648(4.7

0)***

22.3

542(245.6

5)***

1.4

265(28.7

8)***

22.8

721(255.2

7)***

P4 0Dfedfund

failure0.1182(0.4

4)

1.3

202(4.4

1)***

2.1

861(6.9

8)***

14.2

920(28.26

)***

3.1

200(11.3

3)***

6.2

174(12.4

8)***

P4 0Dgdp

0.3238(15.1

0)***

0.2

631(10.8

9)***

0.2

105(8.3

3)***

3.0

860(75.63)***0.3

699(16.6

4)***

4.0

090(99.7

9)***

P4 0Dgdp

failure

0.1611(1.2

5)

0.3

915(2.7

1)***

0.2

244(1.4

8)

2.2

283(9.12)***

0.4

382(3.2

9)***

0.6

086(2.5

3)**

R2(overall)

0.9

574

0.2

643

0.7

890

0.3

162

0.0

959

0.6

235

n

8

,898

8,8

98

8,8

98

8,8

98

8,8

98

8,89

8

Observations

200

,531

200,5

31

200,5

31

200,5

31

200,5

31

200,53

1


ependentvariables.t-valuesareinpa

rentheses.

Thetablereportsthemain

resultswith

thedepositinterestratespreadusedasaproxyformarketdisciplin

einmodelinEquation(7).Thevalues

incolumns(1)through(6)arethecoef

ficientsofthe

factorsfrom

themodelwith

theinterestratespreadrepresenting

thefactormarketdisciplinevariable(I_s

pread)andwherethedependentvariableis

theassetlistedatthetopofthecolumn.T

hevaluesinparenthesisa

retherespectivet-ratios.Theestimate

dcoefficientontheinteractionofthefailuredummy

variablewiththevariableinterestratespreadI_spread(b

4)forfailedbanksispositiveandstatisticallysignificantfortheassetclassrepresented

bytotalloans

whichindicatesthatfailedb

ankssawrisinginterestratesspread

priortofailingforthisclassofassets.

*,*

*,*

**Significantat10,

5and1p

ercentlevels,

respectively

Source:TheFDIC

Table IBanks asset allocati

with price adjustmarket discipl

38

Bank assallocatio


16/24

in identifying failed banks from their surviving peers. While most of the estimatedcoefficients on the interaction terms, l_spreadt1 failur (b4), are insignificant, we findthat failed banks are more eagerly increase the loan to asset ratio and less interestedin holding short-term MBS than surviving banks when experiencing an increase volatility

of interest spread.To focus on a subset of the larger banks, we report the number of active and inactive

institutions based on the charter type and the size of assets in Table IV. Among 272inactive commercial banks and 51 inactive savings institutions in 2007:Q4-2010:Q4, 55of failed institutions are classified as large with more than one billion assets. 201institutions 176 commercial banks and 25 savings institutions are medium sizewith assets greater than $100 million but less than $1 billion. 56 commercial banks and

Total assetsSmall Medium Large

YearA.

o$100 millionB.

$100 million-$1 billionC.

41 billion Total(a) Active commercial banks categorized by size of assets1999 4,837 3,081 397 8,3152000 4,837 3,081 397 8,3152001 4,486 3,195 399 8,0802002 4,168 3,315 405 7,8882003 3,912 3,434 424 7,7702004 3,655 3,531 445 7,6312005 3,459 3,592 475 7,5262006 3,245 3,662 494 7,4012007 3,066 3,705 513 7,2842008 2,787 3,789 511 7,0872009 2,528 3,798 514 6,840

2010 2,328 3,693 509 6,5302011 2,143 3,633 514 6,290

Total assetsSmall Medium Large

A.o$100 million B. $100 million-$1billion C. 41 billion Total(b) Inactive institutions categorized by size and Charter class in 2007:Q3-2010:Q4Charter class N 12 30 11 53

NM 42 119 24 185SM 2 27 5 34SA 1 2 0 3SB 10 23 15 48

Total 67 201 55 323

Note: Panel (a) in the table presents the number of active commercial banks across the period of 1999-

2011. Numbers are based on the December 31 status. In Panel (b), we reported the institutions thatfailed in 2007:Q4-2010:Q4 based on the charter type and the size of assets. There are 272 inactivecommercial banks and 51 inactive savings institutions. Charter class N, NM, and SM are nationaland state chartered commercial banks supervised by the Office of the Comptroller of the Currency, theFederal Reserve, and the FDIC, respectively; SA and SB indicate savings associations and savingsbanks supervised by the Office of Thrift Supervision and the FDIC, correspondingly. With assets morethan 1 billion, 55 failed institutions are classified as large, while 201 institutions are medium size, and67 banks are classified as small with less than $100 million in total assetsSource: The FDIC classification codes are available at: www2.fdic.gov/hsob/SelectRpt.asp?EntryTyp30

Table IV.Number of active andinactive institutions bysize and Charter class

390

JAMR10,3


17/24

11 savings institutions are classified as small with less than $100 million in totalassets. In addition, if using real assets adjusted by the CPI (1982-1984 100), onaverage, there are 463 large banks with real assets above 95th percentile and 512 smallbanks with less than or equal to 5th percentile in the sample.

In Table V, if we narrow the sample to only large banks with total real assetsgreater than 95th percentile, the response to quantity adjustment of marketdiscipline identified different asset allocation for surviving banks from those failedlarge institutions. If the government provides guarantees of repayment to largeuninsured creditors of the largest banks, the argument of too-big-to-fail mayenhance banks risk-taking and mitigate the effect of market discipline. Largecommercial banks may not be as sensitive as small size banks to market discipline.Thus, we anticipate that the distinctions in asset allocation among large failedbanks and large survived banks will be limited. However, what we observe isthat when the uninsured time deposits ratio decreases, the interactive variable,UninsTimet1 failur (b4), that identifies the different asset allocation betweensurvived large banks with their failed peers is mostly significant models (2), (3), (5),

and (6). When uninsured time deposits drop, failed banks intended to increaseholding of risky assets such as MBS, C/I loans, and loan to asset ratio. That is knownas banks moral hazard. Other the other hand, survived large institutions respondto market discipline as expected by reducing loans and increasing short-termsecurities and government securities. Hence, market discipline can successfullyprovide an early warning signal in bank failure prediction. If a large size bank failsto adjust the movement of uninsured deposits in allocating asset portfolios, thelikelihood of failure may increase.

Unlike large size banks in Table V, we find market discipline effective in combatingall small banks risk-taking moral hazard problem in Table VI. Without the protectionof the government safety net uninsured creditors in smaller banks are more sensitiveto market information. For small banks that have total real assets less than or equal to

5th percentile, both failed and surviving groups reacted correspondingly to changesin uninsured time deposits. After evaluating ex post asset allocation, we findsignificant positive coefficients ofUninsTimet1 failure(b4) in real estate loans, C/Iloans and total loans to assets ratios in columns (4) to (6) and negatively significantcoefficients in columns (1) to (3). The results implied that failed banks with relativelysmall size behaved even more conservative and prudent comparing to the survivingpeers. Thus, future research is needed to explore the failure of small size banks.

What we learn from the empirical exercise is that all small size banks respondaccordingly to market information in changing their asset portfolios to survive.Larger banks that failed to adjust the asset allocation timely could eventually fail.Thus, effective market discipline in distinguishing survived and failed banks is onlyobserved for banks with total real assets greater than 95th percentile. Similar

conclusions can be found in the thrift industry[7].

6. Summary and conclusionsThis paper investigates the existence and the effectiveness of market discipline.An incentive model emphasizes in which circumstance market monitoring in terms ofuninsured deposits and interest rate spread may exist. The empirical hypotheses examinewhether market discipline could serve as an early warning signal for bank failures. In theenvironment with asymmetric information, conflicts in incentive structures appear to exist.Greenspan (2002) provided an annotation for the problem in the banking system: The

39

Bank assallocatio


18/24

(1)

(2)

(3)

(4)

(5)

(6)

(t-value)

Debts

MBS

Govts

Real_loan

C/I_

loan

Loan/asset

Coredept

1

0.0

097(1.6

3)

0.0

037(0.5

4)

0.0

008(0.1

3)

0.2

234(19.92)***

0.0

346(6.8

5)***

0.2

320(16.8

9)***

Coredept

1

failure

0.0

274(0.7

8)

0.0

608(1.5

1)

0.0

064(0.1

7)

0.2

203(3.32

)***

0.1

407(4.7

0)***

0.0

502(0.6

2)

UninsTimet

1

0.0

129(1.1

2)

0.0

391(2.9

6)***

0.0

037(0.2

9)

0.4

030(18.54)***

0.0

891(9.1

0)***

0.5

287(19.8

6)***

UninsTimet

1

failure

0.0

234(0.5

3)

0.1

702(3.3

6)***

0.0

968(1.9

9)**

0.0

646(0.77)

0.0

964(2.5

6)***

0.3

493(3.4

2)***

Capitalt

1

0.0

339(2.5

3)**

0.0

839(5.4

9)***

0.1

392(9.4

8)***

0.0

936(3.71

)***

0.1

579(13.9

0)***

0.0

946(3.0

6)***

Capitalt

1

failure

0.2

168(1.7

1)*

0.4

162(2.8

8)***

0.1

371(0.9

9)

0.2

854(1.20

)

0.6

225(5.8

0)***

1.0

990(3.7

6)***

growAssett

1

1.2

212(2.4

8)**

0.0

270(0.0

5)

0.4

529(0.8

4)

6.2

4(6.73)***

2.0

523(4.9

1)***

6.8

966(6.0

7)***

growAssett

1

failure

16.3

994(3.9

6)***

11.4

110(2.4

3)**

11.5

214(2.5

5)**

23.1

914(2.99)***

1.3

453(0.3

9)

19.9

580(2.1

0)**

P4 0Dfedfund

1.0

426(4.3

5)***

0.2

837(1.0

4)

0.8

151(3.1

0)***

15.0

894(33.41)***

0.6

596(3.2

4)***

17.6

725(31.9

5)***

P4 0Dfedfund

failure

3.3

469(3.0

8)***

0.5

757(0.4

6)

2.4

290(2.0

4)**

1.4

611(0.71

)

1.1

439(1.2

4)

2.2

503(0.9

0)

P4 0Dgdp

0.2

425(2.3

6)**

0.6

568(5.5

9)***

0.7

673(6.8

0)***

1.1

996(6.19)***

0.2

621(3.0

0)***

2.8

292(11.9

2)***

P4 0Dgdp

failure

0.2

374(0.4

5)

1.4

622(2.4

0)**

1.0

352(1.7

7)*

1.9

250(1.92)*

0.9

873(2.1

8)**

1.9

978(1.6

3)

R2

(overall)

0.9

500

0.5

523

0.7

819

0.4

125

0.1

218

0.4

544

n

463

463

463

463

463

46

3

Observations

7,9

76

7,9

76

7,9

76

7,9

76

7,9

76

7,97

6



rentheses.


resultswith

uninsuredtimedepositstoto

talassetsratiousedasaproxyformarketdisciplineinthemodelinEquation

(7).Giventhatwefocusontheperiod

leadingtothe

recentfinancialcrisisfrom1

999:Q4to2007:Q3,

theamountoftim

edepositsexceed$100,0

00areclassifiedasuninsured.

Commercialbanksw

ithrealtotal

assetsabove95thpercentile

aresubjectsoftheregression.

Thev

aluesincolumns(1)through(6)arethecoefficientsofthefactorsfromth

emodelwith

uninsuredtimedepositratio

representingthefactormarketdiscip

linevariable(Mktdist)andwherethed

ependentvariableistheassetlistedat

thetopofthe

column.

Thevaluesinparen

thesisaretherespectivet-ratios.*,**,*

**Significantat10,

5,and1percent

levels,respectively

Source:TheFDIC

Table V.Large commercial banksasset allocation withuninsured time deposits asmarket discipline

392

JAMR10,3


19/24

(1)

(2)

(3)

(4)

(5)

(6)

(t-value)

Debts

MBS

Govts

Real_loan

C/I_

loan

Loan/asset

Coredept

1

0.0

568(6.4

4)***

0.0

191(2.3

1)**

0.0

862(8.2

8)***

0.2

219(19.57)***

0.0

674(11.4

0)***

0.3

940(25.7

3)***

Coredept

1

failure

0.1

149(1.5

5)

0.0

398(0.5

7)

0.2

026(2.3

2)**

0.3

650(3.83)***

0.2

340(4.7

2)***

0.3

926(3.0

5)***

UninsTimet

1

0.0

469(3.1

7)***

0.0

314(2.2

7)**

0.1

022(5.8

6)***

0.3

203(16.86)***

0.1

215(12.2

8)***

0.5

479(21.3

5)***

UninsTimet

1

failure

0.4

292(2.7

3)***

0.2

856(1.9

4)*

0.6

826(3.6

8)***

0.7

854(3.88)***

0.2

202(2.0

9)**

0.8

761(3.2

1)***

Capitalt

1

0.0

133(0.8

2)

0.1

433(9.4

9)**

*

0.1

409(7.4

0)***

0.1

312(6.32)***

0.0

801(7.4

1)***

0.2

989(10.6

7)***

Capitalt

1

failure

0.2

772(1.9

6)*

0.4

366(3.2

9)**

*

0.1

506(0.9

0)

0.6

789(3.72)***

0.1

945(2.0

5)**

0.7

671(3.1

2)***

growAssett

1

15.2

067(11.1

0)***

2.5

537(1.9

9)**

11.1

457(6.9

0)***

7.4

713(4

.24)***

5.1

394(5.6

0)***

23.7

475(9.9

8)***

growAssett

1

failure

17.9

765(1.4

8)

20.1

905(1.7

8)*

17.0

903(1.1

9)

38.3

512(2.45)**

27.1

918(3.3

4)***

33.7

694(1.6

0)

P4 0Dfedfund

0.2

353(0.7

8)

0.0

337(0.1

2)

0.2

865(0.8

0)

8.9

430(22.96)***

1.8

061(8.9

0)***

9.4

778(18.0

2)***

P4 0Dfedfund

failure

0.5

703(0.1

8)

7.2

499(2.4

9)**

1.1

584(0.3

2)

3.9

994(1.00)

0.3

039(0.1

5)

7.9

283(1.4

7)

P4 0Dgdp

0.5

091(3.8

8)***

0.5

038(4.1

0)***

0.3

003(1.9

4)*

1.4

034(8

.31)***

0.4

476(5.0

9)***

3.3

967(14.9

0)***

P4 0Dgdp

failure

0.9

389(0.7

6)

0.7

020(0.6

1)

1.0

319(0.7

1)

2.7

858(1.76)*

0.0

254(0.0

3)

2.5

113(1.1

8)

R2

0.9

422

0.0

545

0.7

373

0.155

0

0.0

324

0.3

254

n

512

512

512

512

512

51

2

Observations

8,2

92

8,2

92

8,2

92

8,2

92

8,2

92

8,29

2



rentheses.


resultswith

uninsuredtimedepositstoto

talassetsratiousedasaproxyformarketdisciplineinthemodelinEquation

(7).Giventhatwefocusontheperiod

leadingtothe

recentfinancialcrisisfrom1

999:Q4to2007:Q3,

theamountoftim

edepositsexceed$100,0

00areclassif

iedasuninsured.

Commercialbanksw

ithrealtotal

assetslessthanorequalto5thpercentilearesubjectsoftheregress

ion.

Thevaluesincolumns(1)through

(6)arethecoefficientsofthefactorsfr

omthemodel

withuninsuredtimedeposit

ratiorepresentingthefactor-marketd

isciplinevariable(Mktdist)andwherethedependentvariableistheassetliste

datthetopof

thecolumn.

Thevaluesinp

arenthesisaretherespectivet-ratios.*,*

*,*

**Significantat10,

5,and1percentlevels,respectively

Source:TheFDIC

Table VSmall commercial ban

asset allocation wuninsured time deposits

market discipli

39

Bank assallocatio


20/24

market discipline to control risks that insured depositors would otherwise have imposedon banks and thrifts has been weakened. Relieved of that discipline, banks and thriftsnaturally feel less inhibited from taking on more risk than they would otherwise assume[8].

In the banking model we provide justification for the effective market discipline by

demanding higher interest rates or by withdrawing uninsured deposits. We thenexamine empirically how sensitive banks respond to market discipline, using pre-2007crisis asset allocation of both surviving and failed banks defined in post-crisis period.Panel data regression is carried out for US banks for the period 1999:Q4-2007:Q3to examine the role of bank capital, market discipline, and macroeconomic shocks.We find that banks which failed during 2007:Q4-2010:Q4 suffered from fundamentalweaknesses in their asset quality relative to the surviving banks prior to the crisis.Our results show that failed institutions with total real assets greater than 95thpercentile are relatively less responsive to the market discipline such as fallinguninsured deposits and rising deposit spread. In this case, the policy suggestion to theFederal Reserve (the Fed) is to implement relative intense supervision in banks assetchoices whenever banks fail to respond rationally to market monitoring. Since

survived banks behave differently in asset choices when facing market monitoringthan failing banks, more government intervention is needed for those that are notsensitive to market information. Thus, the effective market discipline provides theearly warning signal to the Fed to screen healthy vs poor-performed banks.

Notes

1. Another source of market discipline may come from shareholders. If shareholders risktaking incentive outweighs potential loss of charter value, the moral hazard problem mayarise. The tighter capital regulation may suppress incentive for risk taking by puttingshareholders equity at risk (Park and Peristiani, 2007). Likewise, empirical evidenceof holding capital buffers can be found in Nier and Baumann (2006). The effectiveness ofmarket discipline is reduced when generous social safety net is in place.

2. Gropp and Vesala (2004), in a study of deposit insurance, used a similar risk premium condition.3. Since this study is neither to price an explicit deposit insurance system nor to design it based on

institutional environment, we take the risk-sensitive insurance premium as given. The literaturefollowing this research stream includes Chan et al. (1992), Demirguc-Kunt and Kane (2002),Gropp and Vesala (2004), and for a comprehensive summary, see Demirguc-Kunt et al.(2008).

4. The same setup can be found in Giammarino et al.(1993) in a study of incentive structuresbetween a manager and banks shareholders.

5. See FDIC web site for the construction of Mortgage back security, and short-term debtsecurity holdings.

6. Similar to Maechler and McDill (2006), we computed three proxies real assets, the naturallog of total real assets, and the percentage growth rate of real assets to control for the size

of the bank.7. Empirical results for thrift industry are available based on request from the authors.

8. The Testimony of Chairman Alan Greenspan, Paragraph 7.

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http://www.emeraldinsight.com/action/showLinks?isi=A1992JL44300011http://www.emeraldinsight.com/action/showLinks?isi=A1992JL44300011http://www.emeraldinsight.com/action/showLinks?crossref=10.1257%2Fjep.23.1.77&isi=000263886700004http://www.emeraldinsight.com/action/showLinks?crossref=10.1257%2Fjep.23.1.77&isi=000263886700004http://www.emeraldinsight.com/action/showLinks?crossref=10.1016%2FS0378-4266%2899%2900028-X&isi=000082638500004http://www.emeraldinsight.com/action/showLinks?crossref=10.1016%2FS0378-4266%2899%2900028-X&isi=000082638500004http://www.emeraldinsight.com/action/showLinks?isi=A1991FP88800007http://www.emeraldinsight.com/action/showLinks?crossref=10.1111%2Fj.1540-6261.1992.tb03984.x&isi=A1992HL27800008http://www.emeraldinsight.com/action/showLinks?crossref=10.1016%2Fj.jmoneco.2003.04.001&isi=000220031300008http://www.emeraldinsight.com/action/showLinks?crossref=10.1016%2Fj.jmoneco.2003.04.001&isi=000220031300008http://www.emeraldinsight.com/action/showLinks?crossref=10.1257%2F0895330027319&isi=000175919900009http://www.emeraldinsight.com/action/showLinks?crossref=10.1257%2F0895330027319&isi=000175919900009http://www.emeraldinsight.com/action/showLinks?crossref=10.1257%2F0895330027319&isi=000175919900009http://www.emeraldinsight.com/action/showLinks?crossref=10.7551%2Fmitpress%2F9780262042543.001.0001http://www.emeraldinsight.com/action/showLinks?crossref=10.7551%2Fmitpress%2F9780262042543.001.0001http://www.emeraldinsight.com/action/showLinks?crossref=10.7551%2Fmitpress%2F9780262042543.001.0001http://www.emeraldinsight.com/action/showLinks?isi=A1994NA54200020http://www.emeraldinsight.com/action/showLinks?isi=A1994NA54200020http://www.emeraldinsight.com/action/showLinks?crossref=10.1111%2Fj.1540-6261.1993.tb04766.x&isi=A1993MB51200018http://www.emeraldinsight.com/action/showLinks?crossref=10.1016%2FS0304-405X%2801%2900096-4&isi=000173433100005http://www.emeraldinsight.com/action/showLinks?crossref=10.1093%2Frof%2F8.4.571http://www.emeraldinsight.com/action/showLinks?crossref=10.1016%2Fj.jfineco.2009.12.001&isi=000279188600003http://www.emeraldinsight.com/action/showLinks?crossref=10.1016%2Fj.jfineco.2009.12.001&isi=000279188600003http://www.emeraldinsight.com/action/showLinks?crossref=10.2139%2Fssrn.1507266http://www.emeraldinsight.com/action/showLinks?crossref=10.1257%2Fjep.24.1.3&isi=000275435100001http://www.emeraldinsight.com/action/showLinks?crossref=10.1257%2Fjep.24.1.3&isi=000275435100001http://www.emeraldinsight.com/action/showLinks?crossref=10.1016%2Fj.jbankfin.2005.07.010&isi=000238889800004http://www.emeraldinsight.com/action/showLinks?crossref=10.1016%2Fj.jbankfin.2005.07.010&isi=000238889800004http://www.emeraldinsight.com/action/showLinks?crossref=10.1016%2Fj.jbankfin.2005.07.010&isi=000238889800004http://www.emeraldinsight.com/action/showLinks?crossref=10.1111%2F0022-1082.00354&isi=000169490800008http://www.emeraldinsight.com/action/showLinks?crossref=10.1016%2FS0304-405X%2801%2900096-4&isi=000173433100005http://www.emeraldinsight.com/action/showLinks?isi=A1992JL44300011http://www.emeraldinsight.com/action/showLinks?isi=A1994NA54200020http://www.emeraldinsight.com/action/showLinks?crossref=10.1016%2Fj.jbankfin.2005.07.010&isi=000238889800004http://www.emeraldinsight.com/action/showLinks?crossref=10.1016%2Fj.jbankfin.2005.07.010&isi=000238889800004http://www.emeraldinsight.com/action/showLinks?crossref=10.1016%2Fj.jfineco.2009.12.001&isi=000279188600003http://www.emeraldinsight.com/action/showLinks?crossref=10.1016%2Fj.jfineco.2009.12.001&isi=000279188600003http://www.emeraldinsight.com/action/showLinks?crossref=10.1257%2F0895330027319&isi=000175919900009http://www.emeraldinsight.com/action/showLinks?isi=A1991FP88800007http://www.emeraldinsight.com/action/showLinks?crossref=10.1257%2Fjep.23.1.77&isi=000263886700004http://www.emeraldinsight.com/action/showLinks?crossref=10.1111%2Fj.1540-6261.1992.tb03984.x&isi=A1992HL27800008http://www.emeraldinsight.com/action/showLinks?crossref=10.1257%2Fjep.23.1.77&isi=000263886700004http://www.emeraldinsight.com/action/showLinks?crossref=10.2139%2Fssrn.1507266http://www.emeraldinsight.com/action/showLinks?crossref=10.1111%2Fj.1540-6261.1993.tb04766.x&isi=A1993MB51200018http://www.emeraldinsight.com/action/showLinks?crossref=10.1111%2F0022-1082.00354&isi=000169490800008http://www.emeraldinsight.com/action/showLinks?crossref=10.7551%2Fmitpress%2F9780262042543.001.0001http://www.emeraldinsight.com/action/showLinks?crossref=10.7551%2Fmitpress%2F9780262042543.001.0001http://www.emeraldinsight.com/action/showLinks?crossref=10.1093%2Frof%2F8.4.571http://www.emeraldinsight.com/action/showLinks?crossref=10.1016%2Fj.jmoneco.2003.04.001&isi=000220031300008http://www.emeraldinsight.com/action/showLinks?crossref=10.1016%2Fj.jmoneco.2003.04.001&isi=000220031300008http://www.emeraldinsight.com/action/showLinks?crossref=10.1016%2FS0378-4266%2899%2900028-X&isi=000082638500004http://www.emeraldinsight.com/action/showLinks?crossref=10.1257%2Fjep.24.1.3&isi=000275435100001http://www.emeraldinsight.com/action/showLinks?crossref=10.1016%2FS0378-4266%2899%2900028-X&isi=000082638500004http://www.emeraldinsight.com/action/showLinks?crossref=10.1257%2Fjep.24.1.3&isi=000275435100001


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Nier, E. and Baumann, U. (2006), Market discipline, disclosure and moral hazard in banking,Journal of Financial Intermediation, Vol. 15 No. 3, pp. 332-361.

Park, S. and Peristiani, S. (1998), Market discipline by thrift depositors, Journal of Money,Credit and Banking,Vol. 30 No. 3, pp. 347-364.

Park, S. and Peristiani, S. (2007), Are bank shareholders enemies of regulators ora potential source of market discipline?, Journal of Banking & Finance, Vol. 31 No. 8,pp. 2493-2515.

Peck, J. and Shell, K. (2010), Could making banks hold only liquid assets induce bank runs?,Journal of Monetary Economics, Vol. 57 No. 4, pp. 420-427.

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Uhlig, H. (2010), A model of a systemic bank run, Journal of Monetary Economics, Vol. 57 No. 1,pp. 78-96.

Ungan, E.S., Caner, S. and Ozyildirim, S. (2008), Depositors assessment of bank riskiness inthe Russian federation, Journal of Financial Services Research, Vol. 33 No. 2, pp. 77-101.

Further reading

Gilbert, A.R., Meyer, A.P. and Vaughan, M.D. (2001), The use of market information in banksupervision: interest rates on large time deposits, working paper, Federal Reserve Bankof St. Louis, St. Louis, February.

Goldberg, L. and Hudgins, S. (1996), Response of uninsured depositors to impending S&Lfailures: evidence of depositor discipline, The Quarterly Review of Economics and

Finance, Vol. 36 No. 3, pp. 311-325.

396

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Appendix

About the authors

Dr Grace W.Y. Wang is an Assistant Professor in Maritime Administration at the Texas A&M

University at Galveston. Her research includes port efficiency and concessions, policy

implications of the global banking crises, deposit insurance, and the early warning systems in

predicting banking failures. Her articles have appeared in journals such as Maritime Policy

and Management, International Journal of Financial Services Management,Journal of Risk and

Financial Management,Business Quest,EconModels,Ekonomi-tek, and International Journal of

Commerce and Management.

Variables Description Details

Asset Total assets The sum of all assets owned by the institution including cash,loans, securities, bank premises and other assets. This total doesnot include off-balance-sheet accounts

Debts Total debtsecurities

Total debt securities, both domestic and foreign at amortized costand fair value, excluding non-accrual debt securities. For TFRreporters this item includes securities held in trading accounts

MBS Mortgage-backed securities

Mortgage-backed securities include:1. US Government agency and corporation issued obligations or

guaranteed certificates of participation in pools of residentialmortgages.

2. US Government agency and corporation obligations andcollateralized mortgage obligations issued by the Federal NationalMortgage Association and the Federal Home Loan MortgageCorporation (including real estate mortgage investment conduits -REMICS).

3. Other domestic debt private securities (i.e. non-governmentissued or guaranteed) and certificates of participation in poolsof residential mortgages.

4. Other domestic debt securities that are privately issued andcollateralized mortgage obligations (including REMICS).Effective March 1994, the full implementation of FASB 115 statesthat a portion of banks mortgage-backed securities portfolio isnow reported based upon fair (market) values; previously, allmortgage-backed securities not held in trading accounts werereported at either amortized cost or lower of cost or market

Govts US Governmentsecurities

Total US Treasury securities plus US Government agency andcorporation obligations. Beginning January 1, 1994, this item

consists of both securities designated as held-to-maturity, reportedat amortized cost, and securities designated as available-for-sale,reported at fair market value. Includes US Government issued orguaranteed mortgage-backed securities. Includes securities held intrading accounts for TFR Reporters

Real_Loan All real estateloans

Loans secured primarily by real estate, whether originated by thebank or purchased

C/I_Loan Commercial andindustrial loans

Commercial and industrial loans. Excludes all loans secured by realestate, loans to individuals, loans to depository institutions andforeign governments, loans to states and political subdivisions andlease financing receivables

Loan Total loans andleases

Total loans and lease financing receivables, net of unearned income

Table ADefinition of t

comparatidependent variab

39

Bank assallocatio


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Dr Arvind Mahajan is the Texas A&M University System Regents Professor, holder of the

Lamar Savings Professorship and is Professor of Finance at the Texas A&M University. He is

the Director of the Aggies on Wall Street Program, the MS (Finance) Program, and has served

as the Associate Director of the Center for International Business Studies as well as of the

federally funded Center for International Business Education and Research. He has served onthe faculties of many universities as Honorary Visiting Professor including the Indian Institute

of Technology, Delhi (2003-2008), the Johannes Kepler Universitat, Linz, Austria (1992-2004)

as well as the Group Ecole Superieure de Commerce, Rennes and the Group Ecole Superieure

de Commerce, Dijon, both in France.

Dr Ruby P. Kishan received her PhD in Economics from Texas A&M University in 1986.

She is currently the Professor of Economics at the Texas State University, San Marcos. Her main

research interests lie in the areas of macro and monetary economics. She has published scholarly

articles in prestigious journals such as, Journal of Money, Credit, and Banking; Journal of

Banking and Finance; and Journal of Macroeconomics, among others.

To purchase reprints of this article please e-mail: [email protected] visit our web site for further details: www.emeraldinsight.com/reprints

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Bank Asset Allocation The

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