Market Discipline of Subordinated Debt: Empirical Evidence from Japanese Commercial Banks

doi:10.4236/jfrm.2013.22006

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Journal of Financial Risk Management

2013. Vol.2, No.2, 38-42

Published Online June 2013 in SciRes (http://www.scirp.org/journal/jfrm) http://dx.doi.org/10.4236/jfrm.2013.22006

Market Discipline of Subordinated Debt: Empirical

Evidence from Japanese Commercial Banks

Young-Soon Hwang1, Hong-Ghi Min2*

1Busan Development Institute, Busan, Republic of Korea

2Department of Management Science, Korea Advanced Institute of Science and Technology,

Daejeon, Republic of Korea

Email: *[email protected]

Received November 30th, 2012; revised February 24th, 2013; accepted March 2nd, 2013

Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any

medium, provided the original work is properly cited.

We investigate if Subordinated Note and Debenture (SND) holders make banks to take less risk by ana-

lyzing balance sheet data of Japanese commercial banks. The cross-section regression shows that banks

take less risk as the amount of SNDs increase. Specifically, it is shown that the loan risk measure (the ra-

tio of impaired loans to the total loans) and the stock investment risk measure (the invested stocks over

bank capital) have decreased with the increase of SND amounts. These results provide evidence that

SNDs are effective instrument for the market discipline.

Keywords: Market Discipline; Subordinated Debt; Japanese Commercial Banks; Loan Risk; Stock

Investment Risk

Introduction

Concern on the stability of banking system has been on the

center of financial regulators. Because corruption of whole

banking system could start from a bank failure, government

regulatory agencies monitors bank risk and take an action to

restrict excessive risk taking by banks. However, financial in-

novation keeps advancing quickly and financial integration

with the globe makes the regulator difficult to monitor and

control the bank risk only with direct regulation. Market disci-

pline is suggested as a supplement to direct regulation. Either

market information could be used to help the regulator to re-

strict bank risk or market can be designed to work as a super-

visor by itself. This new way of bank supervision, market dis-

cipline, has attracted the more interest since when Basel II sug-

gestion included market discipline as one of the main pillar for

banking supervision system. This BIS (Basel II, 2004)’s new

suggestion emphasizes transparency of bank information, so

that market could evaluate the risk and penalize if the risk is

excessive.

If one knows the role of finance, it is easier to understand

how market disciplines the bank. (Lane, 1992) points out that

finance’s role is to allow agents to maintain temporary imbal-

ances of revenue and cost. Thereby economic agents who have

profitable investment opportunities could start the business

even though they lack sufficient capital. Finance increases

economic efficiency by transforming excess savings to the most

efficient investments. What is important in this process is that

by whom and how it is determined whether the imbalances are

temporary or not. Economists argue that this function is well

done by markets and the resulting resource allocation is optimal.

This is the way market treats possibly unsustainable deficits:

As the probability of default becomes higher, investors require

the more interest rate spread. But if the probability goes beyond

some critical level, then markets deny supplying any additional

funds. This is because the adverse selection problem makes the

probability of reimbursement become too low. The former we

call weak market discipline, and the latter strong market disci-

pline. Strong market discipline is sometimes known as credit

crunch. Summarizing the process, at first, investors increase the

interest rate spread and then stop lending. In this way, market

penalizes high risk debtors.

The framework for market discipline is helpful before we

proceed. (Hamalainen et al., 2005) well organized the previous

researches and presented the framework. Figure 1 is the sum-

mary description of it. We see there are two phases. One is

monitoring phase and the other is influencing phase. In order

for market discipline to be effective, the most essential condi-

tion would be that investors recognize they are at stake, that is,

their invested funds are not guaranteed by government or any-

one. Then investors have an incentive to observe the risk of

bank. Given this condition, if the bank information is transpar-

ent so that investors could evaluate the risk, then investors

would require appropriate interest rate spread for the risk. This

is the monitoring phase. If investors are at stake and could suc-

cessfully monitor the bank risk, then the spread would respond

to the risk level. That is, market monitors the bank risk.

If the spread is proportional to the bank risk, then banks

should respond to the market signal. Increased spread means

increased cost to the bank. Besides that, government regulator

might probably warn for the high risk taking. This explicit and

implicit cost would make the bank to try to reduce its risk. This

is the influencing phase. That is, market influences bank be-

*Corresponding author.

Y.-S. HWANG, H.-G. MIN

Figure 1.

Market Discipline Mechanism (Hamalainen et al., 2005).

havior.

Bank deposit is not a good candidate for market discipline

because of implicit government guarantee or explicit deposit

insurance scheme. Depositors have little incentive to monitor

bank risk. So public policy designers proposed the use of low-

priority debt. Because subordinated debt1 holders are prone to

failure to reimbursement, they are very sensitive to banks’ risk

raking. Using the incentive structure, some researchers even

suggested that subordinated note and debenture (SND) issuance

should be mandatory (Hamalainen, 2004).

There were many researches regarding on SND. Most of

them are focusing on the monitoring phase market discipline,

that is, whether SND interest rate spread reflects bank risk.

Most of the results are supporting the effectiveness of SND.

The work of (Jagtiani & Lemieux, 2001) and (Evanoff & Wall,

2002) showed that sub-bond spread contains better information

about bank risk than the credit rating, BIS capital ratio, or gov-

ernment CAMEL rating. These results indicate the informa-

tional advantage of using sub-bond spread data. (Evanoff &

Wall, 2001) argued that sub-debt spread for large size bank

represent bank risk well while that for small size bank represent

liquidity risk. (Goyal, 2005) carefully analyzed bond’s restrict-

tive covenants and found that the previous several unclear dis-

ciplining effect was the result of not considering restrictive

covenants and when controlling the factor SND reflects bank

risk. Differently from the above researches which are using US

bank data, (Sironi, 2003) analyzed with European bank data

and he also showed the same implications. However, still there

is a research that SND does not respond to risk (Krishnan,

Ritchken et al., 2005) argues that after controlling for macro-

economic variables and liquidity, SND yield curve did not re-

flect bank risk.

If sub-debt holders successfully monitor the bank risk, then

the bank would be willing to respond to the sub-debt holders’

interest. The bank will behave toward the more conservative in

order to protect debt holders’ value. This change of bank’s be-

havior ultimately affects the market value of equity or the bal-

ance sheet of the bank. Chen, Robinson et al. (2004) showed

that when a bank issues SND, then it protects bank value thus

affect positively to the stock price (Nier & Baumann, 2006)

showed that SND issuance increases bank capital.

This paper deals with the influencing phase of market disci-

pline. We gathered balance sheet data of Japanese commercial

banks and regressed loan risk measure and stock investment

risk measure on the amount of SND and other explanatory

variables. However the simple regression cannot get reliable

results because the issuance of SND depends on some other

factors. This simultaneity is critical in estimation so we adopted

two-step regression technique.

This paper is organized as follows. Section 2 discusses the

risk preferences which we are using in this paper. Section 3

describes the data and develops estimation model. This section

also provides empirical results. Section 4 concludes the paper.

Risk Preference in the Balance Sheet

Balance sheet contains information on risk preference of the

firm. As a firm becomes the more conservative, it tries to re-

duce high risk assets. By looking into how much risk asset has

been reduced during one year, we infer the risk tolerance of the

firm. Bank balance sheet items consist of three main categories

of assets: Loans, Stocks, and Bonds. There are also off-balance-

sheet (OBS) items such as guarantees and derivative positions.

Bonds are counted as low risk asset comparing with stocks.

OBS items’ risk evaluation is not clear because the derivatives

can be counted as risky asset but it also is being used for hedg-

ing purposes. Thus, among these four categories we focused on

loans and stocks.

Loans are the characteristics of banks. Banks lend money to

borrowers and get interest as a reward. Because it is probable

that borrowers will not pay interest or return principal, banks

suffer default risk. It is impossible to perfectly discriminate

good credit from bad credit. Thus bank assets necessarily con-

tain non-performing loans, which does not generate any return

from loans. We call these impaired loans. The amount of im-

paired loans is presented in balance sheet and we use this in-

formation to catch the risk preference of the bank.

We define the ratio of impaired loans to the total loans as the

loan risk measure. This impaired loan ratio, at first, reflects the

screening ability of the bank examiner. If they are good at

screening bad credit from good credit, then the possible default

loan would not have been issued and the resulting ratio would

be low. This ability is the core of bank competitiveness. An-

other factor which affects the ratio is the risk preference of the

bank. It is possible that the bank is willing to lend to high risk

borrowers for high return, and then defaulted loan would nec-

essarily increase. To the contrary, if the bank is less willing to

take risk, then only very good credits could get loans from the

bank. Usually as the bank become the more conservative, high

risk borrowers such as small and medium sized firms and new

innovative firms get less chance to receive loans from bank. As

a result, the impaired loan ratio would be low. If we assume the

screening ability does not change much during one year horizon,

the change rate of impaired loan ratio could be used as a proxy

for the change of risk preference.

Along with loans, banks also hold stocks and bonds. We fo-

cus on stock investments. Stock investment’s return is more

1A loan (or security) that ranks below other loans (or securities) with regard

to claims on assets or earnings.

Y.-S. HWANG, H.-G. MIN

volatile than that of bonds, thus risk-averse bank prefer to hold

more bonds and less stocks. This tendency could be used to

infer the risk preference of the bank. We defined stock invest-

ment ratio as the amount of stocks invested over the amount of

bank capital. As a denominator we used bank capital because

default risk depends on the bank capital amount. The capital

works as a cushion for the possible asset loss. If the cushion is

very small, then investors regard the bank in a very risky state

because when small losses in stock investment happen to occur,

the bank would soon default. To the contrary, if the cushion is

large, then large stock holding is not so much risky.

Empirical Analysis

Data and Estimation Model

To see the market discipline effect of subordinated note and

debenture, we compared balance sheets of the two successive

periods of 85 Japanese commercial banks. For each bank, we

collected 5 balance sheets from fiscal year 2000 to 20042. We

gathered the data from the Bank Scope™ database. Table 1

shows descriptive statistics of the data. The 85 banks in Japan

are chosen by criteria that it must be listed in securities market

and not the bank holding company itself. In addition, there

should be no major M & As during the data period.

For the two measures of risk reduction, reduction of impaired

loan ratio and reduction of investment stock ratio, we defined

the following continuous change rate.





log tt

RiskRisk 



. (1)

If market discipline is effective, it should prevent excessive

risk taking behavior of banks. To prove this process, we mod-

eled one year horizon starting from year 0 to year 1. If the ini-

tial risk level in year 0 is already very low, then risk reduction

would be also very low. But if the initial risk level is very high,

Table 1.

Descriptive statistics.

Variable Number of

observation Mean Standard

deviation Min. Max.

Non-Performing-Loans/

Total Loans 416 7.26 2.67 1.95 19.00

Reduction

rate of- 330 −0.05 0.23 −1.01 0.55

Investment Stock/

Bank Capital 397 5.15 7.65 0.11 153.16

Reduction

rate of- 314 −0.02 0.37 −1.82 3.65

Return On

Equity 425 −2.58 23.11 −357.11 17.20

Equity/Asset 424 0.05 0.01 0.02 0.17

Asset 425 2831 2595 339 20,139

SND/Total Liability 425 0.006 0.007 0.000 0.046

Deposit/Total Liability 425 0.96 0.45 0.12 9.81

Data source: Bank Scope™ database.

then risk reduction would be also high. However, the speed of

risk reduction would be greater if market discipline is strong

because market discipline penalizes the risky banks. Thus we

model that risk reduction depends on the initial level of risk and

the degree of market discipline.

Because our primary concern is the role of subordinated debt

holders, we used SND ratio as market discipline variable. In

addition, we included deposit ratio to see monitoring effect by

depositors.

01 020345

γγγγγγ εRRiskROESNDR DR X



 

 (2)

Risk0: The initial risk level

ROE0: The initial return on average equity

SNDR: Total issued Subordinated Note and Debenture over

total liability of the bank

DR: Deposit liability over total liability of the bank

X: Other explanatory variables

Profitability, ROE, is included in Equation (2) because the

adequate risk level might depend on the return of the bank. If

the profitability is high, then the risk reduction may be less

required or high profitability is the result of high risk taking. To

control for this effect, we included ROE. However, because the

dependent variable also includes return component in its calcu-

lation, for example, high non-performing loans do contempo-

raneously decrease net income and high investment stock hold-

ing would mean high return on average, to avoid complexity

from estimation, we used initial level of ROE, not current level

of ROE.

The regression of Equation (2) is subject to the endogenous

bias because the issuance of SND is related with the risk of the

bank. For example, if a bank suffers a loss repeatedly, then its

capital becomes smaller. The low level of capital makes the

bank a risky one and large depositors may flip to other secure

banks. Furthermore, the cost of external funding becomes

higher: it means that it should pay additional spread for its risk.

These bad terms again are the cause of the low profitability of

the bank. In order to avoid this vicious cycle, banks should

maintain the necessary level of capital. However, because sup-

plying additional equity capital costs much, banks prefer other

ways such as issuing subordinated debt as well as retaining

earnings. Even though SND is the kind of debt, not the equity,

BIS calculation allows one some portion of SND to be counted

as equity. So, banks prefer to issue SND when they think their

capital is low.

Because the SND amount is not exogenous, Equation (2)

cannot be reliably estimated. To circumvent this, we used two-

step approach. Firstly, we forecast SND ratio, and then sec-

ondly, we estimate Equation (2) using the forecasted SND ratio,

not the actual variable.

For the first-step regression, we included three main deter-

minants of SND issuance. The first one is Equity/Asset. Low

level of equity capital is the cause of issuing SND. Thus, high

E/A would negatively affect SND ratio. The second is the log

of Asset. Because the risk of SND is very high, SND holders

require high interest spread. If the spread becomes too high,

then it loses the benefit of issuing SND. Thus actually, very

large and relatively secure banks could only issue SND at ac-

ceptable price. Asset size would positively affect SND ratio.

The third is the initial SND issuance amount. This is included

to control for some persistence in SND amount. The other two

explanatory variables of the second-step regression, initial risk

2New law entitled “The Financial Instruments and Exchange Act” was

promulgated on June 2006 which caused huge change in financial system

and transactions. Since this might have caused serious regime shift in Japa-

nese commercial banking, we keep our database to 2004.

Y.-S. HWANG, H.-G. MIN

level and initial ROE, are also included. The second-step re-

gression is Equation (4). SND ratio is substituted by predicted

SNDR of Equation (3).

01 0203

4506

ββ ββ

βln ββ

SNDRRiskROEEA

Asset SNDRDR

 





(3)

01 02034

561

γγγγ γln

γγε

RiskROEE AAsset

SNDR DR



 





(4)

Our data has four times of period by 85 cross-sectional data.

Using pooled data could mislead the result, because macroeco-

nomic condition differs across time and it affects all the banks’

business condition at the same time. To resolve this problem we

introduced time dummy D, not explicitly using appropriate

macroeconomic data. This we call it Fixed Effect Model (FEM).

There is another model which assumes random time difference.

This we call it Random Effect Model (REM). We estimated

both model and then tests which model is appropriate by

Hausman test statistic.

Reduction of Impaired Loan Ratio

The first measure of risk preference we used in estimation is

impaired loan ratio, which is the amount of non-performing

loans divided by the total loans. As mentioned earlier the more

risk-averse bank wants to restrict loans only to the good credit

low risk borrowers. High risk borrowers could only get loans

from aggressive banks. Thus change of impaired loans has in-

formation on the preference on the risk taking of the banks.

We primarily concern on the effects of sub-debts on this risk

taking preference and Equation (3) and Equation (4) is the

proper estimation model for our purposes. Table 2 shows the

estimation results. For the both first step and second step re-

gression, Hausman statistic does not reject the null hypothesis

Table 2.

Reduction of impaired loan ratio.

Dep. Variable SND ratio Reduction of Impaired

Loan Ratio

Estimation Model REM FEM REM FEM

Initial Risk Level −0.0001* −0.0001** 0.0317*** 0.0314***

Initial ROE 0.0000 0.0000 0.0000 0.0000

E/A −0.1127*** −0.1113*** 3.5742*** 3.5959***

Log Asset 0.0010*** 0.0009*** 0.0160 0.0155

Initial SND ratio 0.7192*** 0.7249*** - -

Predicted SND ratio - - 6.4821*** 6.5566***

Deposit Ratio −0.0003 −0.0003 −0.0617***−0.0616***

adj. R2 - 0.82 - 0.44

Degree of freedom 325 322 323 320

Hausman Statistic 2(6) 1.96 0.35

a) ***Significant at 1% level (**5%; *10%); b) Constants are not reported; c) Null

Hypothesis: Random Effect Model.

of random effect model. However, fixed effect model estima

tion results are not much different from that of REM. The esti-

mation result is robust.

The first step regression, which dependent variable is SND

ratio, shows that two major determinants of SND issuance, low

capital ratio and big asset size, had expected signs and signify-

cances. Banks issued SND when they lack equity capital and

large asset size banks issued the more SNDs. Besides these two,

we controlled initial level of SND ratio and other variables

which were used in second step regression. The predicted value

of SND ratio was used as an input to the next regression.

The second-step regression result confirms market discipline

effect of subordinated debt. Impaired loan ratio reduced more

as the amount of sub-debt. This is the influencing phase of

market discipline. High capital ratio also decreased loan risk. In

other words, as the capital ratio become small, the more risk the

bank is willing to take. This was typically the case of U.S. S &

L bank failures (Kane, 1987). Their capital level was very small

but the regulator did not leave out their license hoping that they

will survive. The result was the excessive risk taking because

they have less to lose. As a result, their balance sheets become

worsen and the whole banking system became unstable. This

estimation result is consistent with this moral hazard effect of

low capital level. Deposit ratio was also significant. This is

possibly because of the moral hazard of deposit insurance

scheme. Deposits are guaranteed up to some amount and they

need not monitor bank risk. As a result banks could take exces-

sive risk without penalty.

Asset size was not significant. There are two possible expla-

nations. One is the lack of Too-Big-To-Fail problem. Actually,

in Japan, there was a major bank exit in 1997/1998. Since we

used data after 1999, TBTF problem might be weakened. The

other is that bank size does not lead to aggressive loans lending.

There is a tendency that small banks have strength at relation-

ship lending to SMEs and large banks have strength at arms’

length lending to large well-known firms. Thus large banks

tend to reduce loans to firms such as high risk SMEs. This

might be the cause of insignificance of asset size on loan risk.

TBTF moral hazard is not present in loans data.

Reduction of Investment Stock Ratio

The second measure of risk preference we used is stock in-

vestment ratio, which is the amount of invested stocks divided

by the amount of bank capital. The dynamic change of this ratio

provides information on the risk preference of the bank. The

more conservative bank prefers lower stock investment and

thus the high level of this ratio in the previous year would make

the higher reduction in this ratio during the next year. Because

we want to see the market discipline effect of subordinated debt,

we regress the reduction of this ratio on the amount of SND. If

the presence of SND makes the bank the more risk averse, then

the stock investment ratio would reduce the more as the amount

of SND. The estimation methodology is the same as in section

3.2.

Table 3 shows the estimation results. Hausman statistic

shows that random effect model cannot be rejected. However,

REM result is not much different from fixed effect model esti-

mation. The result is robust.

The regression of SND ratio is almost the same as in Table 2.

The slight difference comes because of the availability of the

stock investment data. The two samples, Table 2 and Table 3,

Y.-S. HWANG, H.-G. MIN

Table 3.

Reduction of investment stock ratio.

Dep. Variable SND ratio Reduction of

Investment Stock Ratio

Estimation Model REM FEM REM FEM

Initial Risk Level −0.0000 −0.0000 0.0564*** 0.0530***

Initial ROE 0.0000 0.0000 −0.0018 −0.0020*

E/A −0.1135*** −0.1128*** 11.0756*** 10.5251***

Log Asset 0.0011*** 0.0011*** −0.0930** −0.0884***

Initial SND ratio 0.7065*** 0.7140*** - -

Predicted SND ratio - - 12.3068*** 11.8429***

Deposit Ratio -0.0003 -0.0003 0.0063 0.0074

adj. R2 - 0.82 - 0.12

Degree of freedom 314 311 306 303

Hausman Statistic 2(6) 2.13 1.97

a) ***Significant at 1% level (**5%; *10%); b) Constants are not reported; c) Null

Hypothesis: Random Effect Model.

are not exactly the same. However, we confirm that low capital

ratio and big asset size increases the issuance of subordinated

debt.

The second regression, the reduction of investment stock ra-

tio, confirms market discipline effect of SND. The presence of

sub-debt holders makes the bank reduce stock holdings the

more. Bank capital ratio also has disciplining effect. If the eq-

uity capital is large, then the bank tends to become risk averse.

This is consistent with the moral hazard theory. If the equity

capital is low, then the expected value of risk taking become

large because the loss is limited only to the equity capital.

These two results are the same as in Table 2. Subordinated debt

and equity capital has market discipline effect.

Differently from the loan risk estimation, deposit ratio and

asset size has inconsistent significance. Deposit ratio was in-

significant. Deposit insurance’s moral hazard effect is not pre-

sent here. We infer that depositors’ disciplining effect is not so

large comparing to other effects such as equity capital or SNDs

and that is the cause of this statistical inconsistency. Asset size

is negatively significant. This reflects that asset size increases

risk taking, that is, TBTF moral hazard.

Conclusion

This paper uses Japanese commercial bank balance sheet

data to empirically show market discipline effect of subordi-

nated note and debentures. The discipline framework we used

was got from literatures. It recognizes two phases: Monitoring

phase and influencing phase. While the majority of literatures

are dealing with the monitoring phase, that is, whether sub-debt

interest rate spread reflects bank risk or not, that which deals

with influencing phase are not many. One of the reasons is that

modeling influencing phase is not so easy because it requires

modeling bank behavior. We offered an estimation model for

this purpose and circumvented the simultaneous bias by using

the predicted value of SND ratios.

Estimation results supported SND’s market discipline effect.

The more sub-debt and the more bank equity make the bank

reduce excessive risk taking. This is consistent with monitor

theory and moral hazard theory. This empirical work contrib-

utes to existing literatures on sub-debt holders. Sub-debt not

only contains information on bank risk, but also influences

banks to behave toward less risk-taking. Policy makers who are

devising a sound banking supervision system using sub-debt

instruments might require much empirical evidences. This pa-

per would contribute as one empirical work.

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