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Federal Reserve Bank of Chicago
The Value of Banking Relationships
During a Financial Crisis: Evidence
from Failures of Japanese Banks
Elijah Brewer III, Hesna Genay,
William Curt Hunter and George G. Kaufman
WP 2002-20
The Value of Banking Relationships During a Financial Crisis:
Evidence from Failures of Japanese Banks
Elijah Brewer III
Federal Reserve Bank of Chicago
Hesna Genay
Federal Reserve Bank of Chicago
William Curt Hunter
Federal Reserve Bank of Chicago
George G. Kaufman
Loyola University Chicago
and Federal Reserve Bank of Chicago
March 1, 2002
This version December 31, 2002
Corresponding author: Hesna Genay, Federal Reserve Bank of Chicago, Economic Research, 230 S.
LaSalle Street, Chicago, IL 60604. Phone: (312) 322-5796; fax: (312) 322-2357 hgenay@frbchi.org.
The authors would like to thank the seminar participants at the Federal Reserve Bank of Chicago and
the “Financial Issues in the Pacific Basin Region” Conference for their comments. The views expressed
in the paper are those of the authors and do not necessarily represent the views of the Federal Reserve
System.
1
The Value of Banking Relationships During a Financial Crisis:
Evidence from Failures of Japanese Banks
Previous literature suggests that banking relationships can enhance the value of client firms in
the presence of asymmetric information problems. Hence, severance of banking ties due to a
bank failure can have adverse consequences for the clients of the failed bank. In this paper, we
provide evidence on the value of banking relationships by examining the impact of three large
bank failures in Japan on their clients and the clients of surviving banks. We find that, as in
previous studies, the market value of customers of the failed banks is adversely affected at the
date of the failure announcements. In addition, the effects are related to the financial
characteristics of the client firms and their primary banks. Firms that have greater access to
alternative sources of funding experience a less severe adverse impact from bank failure
announcements. Similarly, clients of banks that are more profitable, better capitalized, and have
lower loan loss reserves suffer less from the failure announcements. However, we also find that
these effects are not significantly different from the effects experienced by all firms in the
economy. That is, the bank failures represent “bad news” for all firms in the economy, not just
for the customers of the failed banks.
2
I.
Introduction
Bank failures are theorized to have adverse consequences for other firms in general, and for
customers of the failed institutions in particular. Firms that are customers of the failed institution
may be adversely affected because, among other things, they may lose an ongoing source of
funding and need to incur the expense of search and providing financial and other information
about themselves to new lenders. Firms that are not customers of the failed bank may be
adversely affected because the failure may signal existing but yet unrecognized problems at other
banks, ignite problems at other banks through spillover or contagion, or foretell adverse
economic conditions for the economy in the region or nationwide. But all firms and bank
customers may not be equally affected by bank problems and failures. The effects may be related
to characteristics of the individual firm and its bank. A number of recent studies have provided
empirical evidence that bank problems and failures adversely affect the market value of a bank’s
corporate borrowers, both in the United States and a number of other countries (Slovin, Sushka,
and Polonchek, 1993; Yamori and Murakami, 1999; Djankov, Jindra, and Klapper, 2001; Bae,
Kang, and Lim, 2002; Ongena, Smith, and Michalsen, forthcoming). This paper contributes to
the literature both by providing evidence on the effects of bank failures on the banks’ loan
customers in Japan and by examining whether the adverse effects on the failed bank’s customers
differ from those of other firms.
In recent years, an extensive literature has developed that examines the costs and benefits of
bank-customer relationships, typically defined as multiple interactions between banks or bank
loan officers and their borrower customers, whereby the bank gathers valuable, often
confidential, information about the client. 1 In the presence of asymmetric information between
1
For recent reviews of the literature, see Boot (2000) and Ongena and Smith (2000a).
3
firms and investors, long-term banking relationships can provide Pareto-improving solutions to
the financing of firms. Close ties between banks and customers can generate information that
would otherwise be not available to investors in public markets; make it possible for banks and
firms to write contracts with features that, among other things, are not feasible or enforceable in
public markets or in one-time transactions; provide the flexibility and the ability to renegotiate
contracts which would allow banks and firms to adjust to unanticipated shocks; allow banks to
better monitor the assets and activities of clients, mitigating agency problems; certify the value
of the firm to outside investors; and enable intertemporal smoothing of contract terms that
enhance the value of contracts.
On the other hand, banking relationships can reduce social welfare by generating perverse
incentives for banks in the enforcement of contracts, provision of follow-up financing, and
financing of high risk projects with positive net present value; increasing monopoly powers of
banks; and isolating both customer firms and their banks from timely market discipline and
corporate governance.
The value of banking relationships is likely to change when the banking system as a whole is
experiencing problems, particularly if there are few alternatives to bank financing. For instance,
the value of an existing ongoing relationship with a healthy bank can be higher during a financial
crisis since firms would have limited financing options from alternative sources. 2 At the same
time, bank failures can forcefully sever or limit valuable banking relationships. Moreover, banks
might make sub-optimal decisions during a financial crisis regarding termination of loan
contracts and allow insolvent firms continue to operate in order to reduce the reported amount of
nonperforming loans on their books or to inflate their reported capital. “Evergreening” of loans
2
Spiegel and Yamori (2000) provide evidence that the market value of firms is more closely tied to the
market value of their main bank during financially turbulent periods.
4
during the savings and loan crisis in the U.S. and repeated restructuring of loans to insolvent
Japanese firms in recent years are some examples of such sub-optimal termination decisions.
Problems in the banking sector can also result in fewer profitable investments by firms that are
highly dependent on bank financing.
A number of papers provide empirical evidence on the costs and benefits of banking
relationships. James (1987), Billett et. al. (1995), and Lummer and McConnell (1989) report a
special role of banks in lowering the cost of capital for firms with limited access to alternative
sources of financing. Petersen and Rajan (1994), Berger and Udell (1995), and Cole (1998) find
the value of banking relationships to small businesses in the U.S. -- which typically face greater
information problems than larger firms and have more limited access to public capital markets -to be particularly important. Several papers present evidence on the value and the nature of
banking relationships in other countries where banks play a greater role in financing of firms
than in the United States. Hall and Weinstein (2000), Hoshi, Kashyap, and Scharfstein (1990 and
1991), Kaplan and Minton (1994), Kang and Shivdasani (1995), Morck and Nakamura (1999),
Morck, Nakamura, and Shivdasani (2000), Peek and Rosengren (2002), and Weinstein and
Yafeh (1998) focus on banking relationships in Japan. Degryse and Van Cayseele (2000),
Detragiache et. al. (2000), Elsas and Krahnen (1998), Foglia et. al. (1998), and Ongena and
Smith (2000b), examine banking relationships in Europe. These studies report that banking
relationship enhance firm value by generating exchange of information that facilitates finance,
provide corporate governance, enable intertemporal smoothing of loan prices, and provide
liquidity insurance to borrowers during periods of financial distress. However, the studies also
present evidence that banking relationships can, at times, involve costs in terms of lower growth
experienced and higher interest rates paid by firms with close banking relationships (Weinstein
5
and Yafeh, 1998) as well as misallocate economic resources by allocating funds to poorly
performing firms (Peek and Rosengren, 2002).
Several other papers focus on the effects of problems or failures of individual banks or
multiple banks on banking relationships. Chiou (1999) reports that Japanese firms that were
customers of Daiwa Bank suffered negative excess returns following the announcement of
Daiwa’s trading scandal in 1995. Gibson (1995 and 1997) shows that investments at bankdependent Japanese firms were lower for firms with lower-rated main banks. Kang and Stulz
(2000) provides evidence that Japanese firms that were more dependent on Japanese bank loans
performed relatively better when their banks were doing well in the 1980s and more poorly when
their banks were performing poorly in the 1990s, after the bubble in asset prices collapsed.
Slovin, Sushka, and Polonchek (SSP, 1993) examine the stock price reactions of client firms
of the Continental Illinois National Bank during its period of economic insolvency leading up to
its bailout by the FDIC in 1984. They find that firms with known lending relationships at
Continental Illinois experienced significantly negative abnormal returns during the banking
firm’s financial difficulties before its resolution, but significant positive returns at the
announcement of the bailout by the FDIC. However, the positive abnormal returns over the
bailout event window were smaller than the aggregate negative abnormal returns over the event
period immediately before the bailout. Hence, on net, clients of Continental experienced
significant negative abnormal returns as a result of the banking firm’s financial distress.
A number of papers extend the SSP approach to bank distress or failure announcements
during financial crises in other countries. Yamori and Murakami (1999) focus on the failure of a
Japanese bank -- Hokkaido Takushoku Bank in 1997 – and find that the customers of the bank
earned negative abnormal returns at the time of the failure announcement. Djankov, Jindra, and
6
Klapper (2001) examine the stock market valuation effect of the insolvency of 31 banking
organizations in East Asia (Indonesia, Korea, and Thailand) during the Asian Crisis on
borrowing firms. They report that a bank’s announcement of insolvency and pending liquidation
led to a significant negative stock market reaction. On the other hand, nationalization
announcements with subsequent recapitalization and new management were associated with
positive abnormal returns.
Bae, Kang, and Lim (2002) examine the durability of bank relationships in Korea during that
country’s financial crisis. They find that bank financial distress was associated with negative
abnormal returns for client firms, and the announcement effects were greater for the bankdependent and financially weak firms of the weakest banks. This suggests that a combination of
bank and firm characteristics determines the impact of bad news about a bank on its customers.
Ongena, Smith, and Michalsen (forthcoming) examine impact of bank distress announcements in
Norway on bank client firms. The authors find that the impact of these announcements on bank
client firms were small and temporary, and did not statistically differ from their impact on
unrelated firms. The authors also find that more liquid firms—as measured by access to unused
bank funds and equity issues prior to the banking crisis—had higher abnormal returns.
We add to this literature in this paper by examining the impact of the failure of three large
Japanese banks in 1997 and 1998 on the market valuation of nonfinancial firms. Following
Slovin, Sushka, and Polonchek (1993) and others, we estimate the impact of the failure
announcements on the market valuation of the client firms of the failed banks. We extend the
analysis, however, by also estimating the impact of the failure announcements on all firms
including the clients of surviving banks. With the exception of Ongena, Smith, and Michaelsen
(forthcoming), previous studies have not analyzed this aspect of bank financial distress. By also
7
examining the stock valuation of the failure announcements for firms that did not have
relationships with the failed institutions, we can identify any differences in the effects on clients
and non-clients of the failed banks. This is particularly important when the distress or failure
announcements occur in the midst of an on-going financial crisis, and therefore, can have strong
implications for the viability of surviving banks and their relationships with client firms. In
addition, we relate the estimated abnormal returns for both sets of nonfinancial firms to variables
that capture the value of banking relationships. Prior studies suggest that the value of banking
relationships should depend on the characteristics of firms and their banks. The stronger the
financial health of a firm, the more alternatives it has to existing bank financing. Hence, we
would expect firms with greater access to alternative sources of funding to have a less adverse
reaction to the failure announcements. Similarly, relationships maintained by banks in relatively
good financial condition are expected to last longer. Hence the impact of the failure
announcements should be less negative for the clients of healthier banks.
We find that, as in previous studies, the market value of customers of the failed banks are
adversely affected at the date of the failure announcements. In addition, the effects are related to
the financial characteristics of the client firms and their banks. For nonfinancial firms that have a
more valuable banking relationship, the less severe is the adverse impact. Moreover, consistent
with expectations, the impact of the announcements are positively correlated with the financial
condition of firms’ primary bank. However, we find that these effects are not significantly
different from the effects experienced by all firms in the economy. That is, the bank failures
represent “bad news” for all firms in the economy, not only the customers of the failed banks.
Our analysis focuses on an economy that is bank-dependent and in the midst of an extended
financial crisis. Nevertheless, to the extent that these results for Japan are representative, they
8
raise questions regarding the total impact of bank failures on their clients and the rest of the
economy.
The next section of this paper describes how bank failures can potentially influence the stock
market value of bank borrowers and other firms. The third section describes the data and
methodology. The empirical results for the effects of the bank failures on their loan customers
and other firms is reported in section four. Robustness checks of our main results are presented
in section five. The final section summarizes the findings and offers conclusions. The Appendix
provides a brief overview of the events leading up to the three failures.
II. The Impact of the Failures
We examine the market response at the failure announcements of three important Japanese
banks in 1997 and 1998—Hokkaido Takushoku Bank on November 17, 1997, the Long-Term
Credit Bank of Japan (LTCB) on October 23, 1998, and the Nippon Credit Bank (NCB) on
December 13, 1998. 3 A defining characteristic of all these three failures was that the magnitude
of bad loans and valuation losses previously disclosed by the failed institutions had been
significantly understated. Thus, the banks concealed the true extent of their problems. The
release of this new information might call into questions the availability of funds for client firms,
especially for those experiencing financial distress and/or those that use bank loan agreements as
a major source of liquidity and certification of value. Second, the failures might also have
signaled a regulatory shift to increased probability of bank closures in the future, particularly for
the riskier banks (Brewer et. al., forthcoming; Spiegel and Yamori, 2000a). In either of these
cases, if banking relationships enhance the value of bank clients, we would expect clients of both
announcing and surviving banks to be adversely affected by the failures.
3
These failures are described in greater detail in the Appendix.
9
Third, the three failures revealed a significant change in the institutional and government
support structure of Japanese financial institutions. Previously, , weak or troubled institutions
could rely on implicit and explicit government support, capital injections and new loans from
financially or otherwise affiliated companies, or “rescue mergers” with a stronger institution.
The unwillingness of other banks to provide support and thus, permit these banks to fail suggests
that the financial distress might extend beyond the failed bank and adversely affect the whole
economy. Thus, a bank failure could have implications for the availability of bank credit for a
nonfinancial firm irrespective of the identity of its lending bank.Fourth, failed banks in Japan
generally are not closed and put into receivership. Two of our three failed banks were
nationalized and kept in operation. The third bank was taken over by several other banks. If these
changes cause the “new” banks to provide their loan customers with less favorable terms than
the old banks, then the stock market valuation effects should be similar to those observed in
Slovin, Sushka, and Polonchek (1993). On the other hand, if the nationalizations are perceived
by the financial market as an attempt by the Japanese government to ensure that the client firms
have continued assess to credit on the same basis, the stock market reactions’ of clients of the
nationalized banks should be non-negative.
It is also possible that the bank failures have no impact on the valuation of their clients, if it
was common knowledge that the three banks were experiencing severe problems prior to their
failures. If the failures were fully anticipated by investors and already priced in the stock prices
of bank clients, we would expect no significant reaction to the failure announcements. However,
previous papers by Brewer et. al. (forthcoming) and Spiegel and Yamori (2000a) show that these
failures had a significant adverse impact on the market valuation of surviving banks, indicating
that the events were not fully anticipated.
10
Lastly, previous studies suggest that the value of banking relationships is related to the
ability of firms to access alternative sources of funding, the degree of information asymmetry
between firms and investors, the future investment opportunities of firms, their profitability, and
other firm characteristics. If the Japanese bank failures changed the value of banking
relationships, we would expect the magnitude of the impact of these failures to be correlated to
firm characteristics that enhance the value of the relationships. In particular, we would expect
firms that are heavily dependent on their existing banks and have few alternatives to existing
relationships to be more adversely affected by bank failure announcements. On the other hand,
firms that are clients of relatively healthy banks should suffer less from these announcements. A
relationship with a bank in good financial condition is less likely to be threatened by the failure
of another bank; hence for firms whose primary bank is relatively healthy, the failure
announcements should have a less adverse effect.
III. Data and Methodology
Our empirical analysis is conducted in two parts. In the first part, we estimate the responses
of industrial firms to the three bank failures. We compare the responses of firms that were clients
of the three failed banks to the responses of a control set of firms that were clients of the
surviving banks.
Our methodology closely follows the event study methodology used in previous papers
examining the response of stock prices to changes in the regulatory environment and
announcements. Specifically, the daily stock returns of firms are examined to identify any
abnormal performance on or around the announcement of the three failure events. The impact of
the events is measured by estimating a standard multivariate regression model, similar to that
used by Binder (1988), Brewer et. al., forthcoming, Karafiath, Mynatt, and Smith (1991),
11
Malatesta (1986), Millon-Cornett and Tehranian (1990), and Schipper and Thompson (1983),
among others. The model takes the following form:
Rit = αi + βi Rmt +
+1
∑ γ ik Dk + εit ,
(1)
k =−1
where Rit is the stock return of firm i on day t; α i is the intercept coefficient for firm i; Rmt is the
market index for day t; β i is the market risk coefficient for firm i; Dk is a binary variable that
equals 1 if day t is equal to the event day or window k ( k ∈ [ −1, +1] ), zero otherwise; γ ik is the
event coefficient for firm i; and ε it is a random error. Equation (1) is estimated as a system of
separate equations for the individual firms in the sample using seemingly unrelated regressions,
which permit the impact of the events examined and the variance of the residuals to vary across
firms. The estimated parameters γ ik capture any daily intercept shifts on event day (window) k
and provide an estimate of abnormal (excess or unexpected) returns associated with the failure
announcement on day (window) k.
The announcement dates of the three failures were obtained through a search of the Wall
Street Journal, Reuters news wire, Newscast news service, and the Knight Ridder business wire.
These include news articles from Japanese and other international news sources. All dates are
Japanese dates. If the failure announcement was made during a trading day in Japan, that date is
used as the event day [0]. If an announcement was made after the market was closed or over the
weekend, we use the next trading date as event date. 4 For the Long-Term Credit Bank we used
the date of the first news stories that cited official government sources that the bank was in
imminent danger of being nationalized. Daily stock prices and returns were obtained from the
4
Consequently, the event dates for LTCB (October 19, 1998) and NCB (December 14, 1998) differ from
the announcement dates.
12
University of Rhode Island’s Pacific Basin Capital Markets Research Center (PACAP) 1999
database. Market returns are measured by the TOPIX index, which includes seasoned shares of
over 1,000 major companies (First Section) traded on the Tokyo Stock Exchange, and were
obtained from PACAP.
The values of the parameters in equation (1) are estimated daily over a sufficiently long
observation period before and after each event date to obtain meaningful results, but short
enough not to be affected by the other events examined in the study. The length of the sample
periods is from 198 trading days before the first event date to 10 days after the last event dateand
conforms closely to those used in previous studies (e.g., MacKinlay, 1997; Smirlock and
Kaufold, 1987). However, because the two events in 1998 are reasonably close to each other,
we use a common estimation period for these two events. To reduce the effects of specific events
on subsequent events in the common estimation period, equation (1) is modified for these two
failures so as to permit a shift in both the intercept (α) and the market index coefficient (β) after
the first failure in each estimation period as follows (Binder and Norton, 1999):
Rit = αi + βi Rmt + α i P + βi PRmt + ∑
e
+1
∑ γ ik ,e Dk ,e + εit ,
(1’)
k = −1
where e is the number of events in 1998 (e =2), and P is a binary variable that identifies postevent periods; i.e., P is equal to 1 after the LTCB failure, zero otherwise.
We examine the individual firms’ estimated daily abnormal returns— γ ik —for each event
for two groups of firms-- the clients of failed banks and the control group of the clients of
surviving banks. Following Gibson (1995 and 1997) and Yamori and Murakami (1999), we
identify the clients of the three failed banks from the Autumn 1997 and Autumn 1998 issues of
the Japan Company Handbook (JCH), which identify the banks used by each company. Firms
13
are identified as clients of a failed bank if the failed bank appears anywhere on the bank list,
irrespective of its rank. Because there were two failures in 1998, some firms are identified as
clients of both the LTCB and NCB. All other firms included in the 1999 PACCAP database are
identified as the clients of the surviving banks and are grouped in the control sample. Our sample
for the failure of Hokkaido Takushoku Bank in 1997 includes 70 firms identified as clients of the
failed bank and 1,214 firms identified as clients of surviving banks. For the failures in 1998 the
sample includes 197 firms that were clients of LTCB only, 60 firms that were clients of NCB
only, 29 firms that were clients of both LTCB and NCB, and 926 firms that were clients of the
surviving banks. To ensure that the estimates of parameters in equations (1) and (1’) are based on
reliable data, we exclude from our sample any firm that did not have daily stock returns for at
least one-half of the estimation period.
If the failures of the three banks severed or limited valuable banking relationships and had
unanticipated negative implications for the value of the firms, we would expect the abnormal
returns of client firms during the event window to be negative and statistically significant. If the
events revealed no new information or were considered irrelevant by the shareholders of firms,
the abnormal returns would be statistically indistinguishable from zero. To distinguish among the
two scenarios, we test the hypothesis H 10 , that the cross-sectional average of individual abnormal
returns for the clients of the failed banks is equal to zero for each event, e, i.e.,
1 N1
H : ∑ γ i, e = 0
N1 i=1
1
0
where N1 is the number of clients of the failed bank.
14
We also conduct similar tests for the clients of surviving banks in the sample to determine if
the failures had a significant impact on the stock market valuations of these firms. That is, we
test the hypothesis:
H 02 :
1
N2
N2
∑γ
j =1
j, e
=0
where N 2 is the number of firms that were clients of surviving banks.
To determine whether the abnormal returns of the failed-bank clients are the same as those of
the clients of surviving banks, we test the hypothesis that the average abnormal return for the
clients of the failed banks equals the average abnormal return of the clients of the surviving
banks. That is, we test the hypothesis:
H03 :
1 N1
1 N2
γ
=
γj
∑i N ∑
N1 i=1
j
=
1
2
In addition to examine the robustness of the results, we also examine the cross-sectional
median of abnormal retur ns and test the hypothesis that the number of firms with negative
abnormal returns is equal to 50 percent of each sample against the alternative hypothesis that the
number of firms with negative abnormal returns comprise more than 50 percent of the sample. A
rejection of the null hypothesis for clients of the failed banks with a greater number of negative
abnormal returns than positive ones would be consistent with the hypothesis that the failures
resulted in the severance of valuable banking relationships. A rejection of this hypothesis for
client firms of surviving banks with a greater number of negative abnormal returns than positive
ones would be consistent with the hypothesis that the failures had negative spill-over effects on
the remainder of the economy, or revealed adverse information about the surviving banks and/or
their clients.
15
Lastly, we test the hypothesis that the clients of the failed and surviving banks belong to
populations with the same distribution using the Mann-Whitney- Wilcoxon test. A rejection of
this hypothesis that the median of the abnormal returns are the same for the two populations
would be consistent with the notion that the failures had a different impact on the clients of the
failed banks than on the rest of the economy.
In the second part of our analysis, we examine whether the individual abnormal returns
estimated in equation (1’) are related to the financial characteristics of firms and their banks. To
do this, we pool the time series observations of the abnormal returns for the three-day event
window [-1, +1] for each firm, γ [− 1,1],i across all three events in one equation. Hence, the final
sample can include up to three observations for each firm: one measuring the firm’s abnormal
returns at the failure of announcement of Hokkaido Takushoku Bank, and one for the failure
announcement of LTCB and the third for the failure announcement of NCB. We then relate
these abnormal returns to variables that capture the value of banking relationships as follows:
γ [ −1,+ 1], i = α + +δ CLi + φCONDi + ψ Bi + λ (CLi x CONDi ) + θ (CLi x Bi ) +∑ ϑ j DINDj + µ i ,
(2)
j
where CLi is a binary variable that identifies the clients of the failed banks and is equal to one if
firm i is a client of the failed bank, zero otherwise; CONDi is a variable that describes the
financial condition of firm i at the time of the event; and Bi is a vector of variables that measure
the financial characteristics of firm i’s primary bank. The interaction terms (CL x COND and CL
x B) are included to examine whether the abnormal returns of clients of failed banks are more
sensitive to firm and bank characteristics than the abnormal returns of the clients of surviving
16
banks. Six industry binary variables (DIND) are included in equation (2) to account for
unobserved industry “fixed effects.”5
We estimate equation (2) using ordinary least squares with White’s (1980) adjustment for
heteroskedasticity. The vector of firm characteristics, COND, includes firm size as measured by
the log of total assets; firm age; future profit opportunities as measured by the ratio of market
value of assets to book value of assets (Tobin’s Q), and four alternative measures of the
financial condition of firms: the ratio of loans to total assets (LOANS/TA); the ratio of book
value of equity to total assets (EQUITY/TA); the average return on assets over the previous five
years (ROA); and a measure of liquidity—the ratio of cash and securities to total assets. 6
Asset size serves as a proxy for the potential information asymmetries faced by firms when
seeking external financing (Petersen and Rajan, 1994). Larger firms are likely to be better known
among market participants and tend to have easier access to external financing. 7 Hence we would
expect stock returns of larger firms to be less adversely affected by the bank failures. We include
firm age because previous research (Petersen and Rajan, 1994) suggests that older firms that
have a more established reputation tend to have easier access to external financing and hence be
less adversely affected by bank failures. A high ratio of market to book value of assets-- Tobin’s
Q—suggests more growth opportunities. Barclay and Smith (1997) find that firms with more
growth opportunities have greater financing choices. Hence, we expect that firms with more
growth opportunities should be less affected by the loss of a banking relationship. To allow for
5
For a discussion of the existence of “other effects” in pooled cross-sectional time-series analysis see
Balestra and Nerlove (1966).
6
As an alternative to ROA, we also included return on equity in equation (2). The results were similar to
those with ROA, and hence are not reported in the paper.
7
The correlation between asset size and access to external financing is likely to be stronger in Japan
where some of the eligibility requirements for issuing corporate bonds on the capital market are based
on firm size.
17
nonlinear as well as linear relationships between abnormal returns and AGE and TOBQ, we also
specify their squared terms-- AGE2 and TOBQ 2 .
The ratio of loans (both from banks and other financial intermediaries) to total assets captures
the extent to which firms rely on intermediated credit for external funding. Firms with a greater
amount of intermediated credit in Japan are also likely to be more bank dependent and thus, less
able to find new external sources of financing. We expect that the abnormal returns should be
negatively correlated with the ratio of loans to total assets.
The capitalization ratio measures firm leverage. Higher leveraged firms are perceived as
more risky. In addition, given the adverse selection problem associated with external financing, a
highly levered nonfinancial firm may face higher interest costs and/or other fees to replace an
existing banking relationship or obtain another external monitor after the failure of the bank with
which it has a relationship. Thus, the capitalization ratio should be positively correlated with
nonfinancial firms’ abnormal returns.
We also capture firm performance with return on assets (net income divided by book value of
total assets) averaged over the five years prior to the failures. More profitable firms should have
more financing options. We expect that firms with greater profitability should be less negatively
affected by the loss of a banking relationship or bank financial distress in general.
The ratio of cash plus investment securities to total assets measures the firm’s liquidity or
amount of internal funds available to the firm. Firms with relatively more internal funds should
be less dependent on external financing, and, therefore, less affected by bank failures. Because
the above financial condition variables are highly correlated, we specify only one at a time in
estimating equation (2).
18
The vector Bi includes variables that measure the financial health of each firm’s primary
bank – identified as the first bank listed in the Japan Company Handbook : three accounting
measures of condition, a market measure of the bank’s financial health, and a measure of bank
size..
The three accounting measures of bank financial condition are the capitalization ratio
(Bank’s Equity/TA), the ratio of loan loss reserves to total loans (Bank’s Loan Loss Reserves /
Total Loans), and return on assets averaged over the previous five years (Bank ROA). Equity
capital and profits offer banks a buffer against adverse shocks. Hence, more profitable and better
capitalized banks are more likely to survive in the long term, enhancing the value of relationships
they maintain. Consequently, we would expect the clients of these banks to suffer less from the
adverse consequences of bank failure announcements. On the other hand, greater loan loss
reserves may indicate sub-par loan portfolios, and hence lack of bank longevity. Therefore, we
would expect the clients of banks with larger loan loss reserves to suffer more from the failure
announcements. As with firm characteristics, we introduce these variables one at a time to avoid
multicollinearity problems.In addition to the accounting variables, the model includes a market
measure of each bank’s financial health: the bank’s stock market reaction to each failure as
measured by its abnormal returns over the [-1, +1] event window and estimated within the
framework of equation (1’). Brewer et. al. (forthcoming) find that the failures of Hokkaido
Takushoku Bank, LTCB, and NCB had, on average, a significant negative impact on the market
valuation of surviving Japanese banks. Moreover, at the bank level, these effects were correlated
with the financial condition of the surviving banks: healthier banks suffered significantly less
from the failure announcements. These results indicate that the abnormal returns of banks at the
announcement of each failure provide a market measure of bank longevity. Therefore, we would
19
expect the abnormal returns of bank clients to be positively correlated with those of their primary
bank.
Bank size is also likely to be positively correlated with the value of relationships. Larger
banks tend to be more diversified, and hence, are able to better withstand adverse shocks.
Moreover, regulators may implement explicit or implicit ”too-big-to-fail” policies that insulate
larger banks from market forces and prolong their life. In addition to enhanced longevity, larger
banks may be able to offer a wider scope of services to their clients, increasing their value in a
relationship. Therefore, we would expect to observe a positive correlation between the impact of
bank failure announcements on firms’ stock returns and the size of their primary bank.All
variables on the financial condition and other characteristics of firms and banks were obtained
from the PACAP 1999 database and are measured as of the end of the fiscal year prior to each
failure.
If the failure events had a significant impact on the stock market valuation of the firms which
was systematically related to financial characteristics of firms and their banks, we would expect
the coefficients φ , λ , ψ, and θ in equation (2) to be significantly different from zero. Hence, we
test the hypotheses
H 04 : φ + λ = 0
and
H 05 : ψ + θ = 0
for the clients of the failed banks, and
H 06 : λ = 0
and
20
H 07 : ψ = 0
for the clients of the surviving banks.
To determine whether the relationship between abnormal returns and financial characteristics
of firms differed systematically across clients of failed and surviving banks, we also test the
hypotheses:
H 08 : φ = 0
and
H 09 : θ = 0
IV.
Empirical results
Table 1 provides estimates of abnormal returns for several portfolios of bank customers at the
announcement dates of the three bank failures. Estimates reported are the mean and median of
the individual equations of each firm. Separate results are reported for bank customers that are
clients of one of the three failed banks and clients of one or more of the surviving banks. For the
LTCB and NCB failures, we also report results for a portfolio of bank customers that list both
failed banks as their primary banks. Thus, there are five different failed bank client portfolios
and three surviving bank client portfolios. Table 1 also provides test statistics for three
hypotheses for all three failure events: 1) that the abnormal returns for the portfolio of client
firms equal to zero for each event ( H 04 through H 07 ); 2) that the portfolio abnormal returns of
failed bank clients are equal to that of surviving bank clients ( H 08 and H 09 ); and 3) that 50
percent of the failed banks’ client firms have negative abnormal returns on and around each of
the three events. The first three columns of table 1 report the results of the estimated abnormal
21
returns of individual firms for days [-1], [0], and [+1] of each event window, respectively. The
fourth column reports the average abnormal returns for the [-1, +1] window.
Of the 20 estimated abnormal mean returns of the failed bank clients (four event windows for
five different failed bank client portfolios), 15 (75 percent) have the expected negative signs, but
only 8 are statistically significant. Do these effects significantly different from those of clients of
surviving banks? The results in table 1 suggest that they do not. Of the 12 estimated abnormal
mean returns of the surviving bank clients, 11(92 percent) have the expected negative sign and 9
are statistically significant.
The results using the median abnormal returns are similar to those using the the mean
returns. For example, the median abnormal return over the three-day [-1, +1] window was
negative for each portfolio, but significant only for the failures of LTCB and NCB. Once again,
we could find no evidence that the impact of the failures was greater for the clients of the failed
banks. To determine whether client firms with negative abnormal returns statistically
outnumbered those with positive returns, we computed the proportion of positive abnormal
returns minus 0.5 divided by the standard deviation of a binomial distribution (the “sign test”).
For the [-1, +1] window, the sign test indicates that the number of client firms with negative
abnormal returns exceeded those with positive returns in 4 of the 5 cases for failed bank sample
of firms and in all three cases for the clients of surviving banks.
The statistics in the rows labeled “T-test for equality of means” test the hypothesis that the
impact of the announcements was equal across the two different client portfolios. We can reject
the equality of the average abnormal returns and their distribution for the clients of failed and
surviving banks only in 8 of the 40 cases. This collective evidence strongly suggests that bank
failures have meaningful adverse effects on the stock market valuation of surviving as well as
22
failed bank client firms. Thus, our results suggest that bank failures serve as bad news for all
firms in the economy, not just those of failed banks. In part this may reflect the fact that the
whole banking sector in Japan was experiencing financial distress during the 1990s. This makes
it more likely that bank dependence is costly for all firms regardless of the identity of their
primary bank (Kang and Stulz, 2000).
Cross-section tests of the relationship between firms financial characteristics and abnormal
returns
Failure announcements need not have equal effects on all bank client firms. Indeed, theory
suggests that the announcement effects should be related to the financial and other characteristics
of both the firms and their banks. In this section we explore this relationship. Table 2 provides
summary statistics for the variables that we use in estimating the cross-section regression
equation. For the Hokkaido Takushoku Bank, there appears to be no statistically significant
differences in size, dependence on intermediated debt, profitability, liquidity, and age, between
firms that are clients of the failed bank and those that are clients of surviving banks. However,
client firms have less capital and fewer future profit opportunities (as measured by Tobin’s Q)
than clients of other banks.
On the other hand, there are more significant differences between the characteristics of
clients of LTCB and NCB, and clients of surviving banks. As indicated in panel B of table 2,
failed bank client firms are larger, less capitalized, more dependent on intermediated debt, and
are less profitable and less liquid. There are, however, no significant differences between these
firms in terms of future profit opportunities and age. A comparison of the clients of LTCB and
NCB separately (“Clients of LTCB only, “Clients of NCB only,” and “Clients of both banks”)
23
indicates that, except for size, the significant differences persist in these smaller client groups as
well.
Regressions of abnormal returns on client firms financial characteristics
The cross-section regression results are reported in table 3. 8 The dependent variable is
abnormal returns for each firm as computed in the previous section. The four panels in the table
report the results of estimating equation (2) with one of the four alternative measures of the
financial condition of firms (Loans/TA, Equity/TA, ROA, and Liquidity). Each panel, in turn,
contains three sets of three columns, results for each of the bank characteristics: Equity/TA, Loan
Loss Reserves/Total Loans, and ROA. Within each set, columns one and two report the
coefficient estimates for client firms of failed and surviving banks, respectively. Column three
reports the significance levels for the test that the coefficients for clients of failed and surviving
banks are equal.
If bank failure adversely affects valuable relationships, we should expect variables positively
correlated with information problems, and hence bank dependence, to be negatively correlated
with abnormal returns. Furthermore, we would expect the correlation to be stronger for the
clients of failed banks.
The results in table 3 are broadly consistent with the prediction that firms for which existing
banking relationships are more valuable suffer more at announcement from the failure of their
bank. Clients of failed banks that relied more on intermediated debt, those that were less
profitable, or less capitalized had significantly more negative reactions to the failure
announcements.
8
Industry binary variables are not included in table 3. The results are available from the authors upon
request.
24
Similarly, client firms of surviving banks for which existing banking relationships are likely
to be more valuable experienced more negative abnormal returns at announcement of the three
bank failures. In particular, firms that relied more heavily on intermediated debt, those that had
lower capital ratios, lower ROA, and lower liquidity had significantly more negative abnormal
returns. These results are consistent with the hypothesis that bank failures threaten the viability
of valuable banking relationships at all banks.
The only instance where we can reject the equality of coefficients on these firm
characteristics for the clients of failed and surviving banks is for firm profitability. The
correlation between abnormal returns and ROA of firms is stronger for the clients of failed
banks. Hence, the results show little support for the prediction that the relationship between
abnormal returns and financial characteristics is stronger for the clients of failed banks.
The coefficients on the variables {Firm Size, Firm Age, and Firm’s Tobin Q} indicate that
firm size is positively and significantly correlated with the abnormal returns of the failed bank
clients in all models. Therefore, consistent with our predictions, larger clients suffered less from
the failure of their banks. Moreover, we can reject the hypothesis that the correlation between
size and abnormal returns for the clients of failed and surviving banks is equal in all of these
models. The magnitudes of the coefficients on firm size for the two groups indicate that the
abnormal returns of the failed-bank clients are two to three times as large as those of the
surviving-bank clients. These results suggest that clients of failed banks that had greater access
to external financing experienced less severe stock market reactions to the failure announcements
than the clients of surviving banks with similar access.
The sign and magnitude of coefficients for AGE and its squared term in table 3 indicate that
older firms suffered less from the failure announcements than younger firms, consistent with our
25
expectations. 9 In most cases, the coefficient on AGE is negative and significant and the
coefficient on AGE2 is positive and significant. When one calculates the marginal effect of age
on abnormal returns, the age at which the relationship between abnormal returns and age turns
from negative to positive ranges between 14 and 44 years. Hence, for relatively young firms (less
than 44 years old at most), abnormal returns are negatively correlated with age. However, for
mature firms (more than 44 years old), abnormal returns are positively correlated with age. Since
the sample mean for age is about 55 years, for most of the firms in the sample, the net impact of
AGE is positive.
Consistent with our predictions, clients of both the failed and surviving banks that had more
future opportunities were less severely affected by the failure announcements, but this effect was
declining in the level of TOBQ.
Also consistent with our expectations, we find that clients of healthier banks suffered less
from the failure announcements. For the clients of surviving banks, the accounting measures of
bank health were the significant determinants of cross sectional variation in firms’ abnormal
returns. Clients of banks that were better capitalized and more profitable, as well as the clients of
banks that has lower loan loss reserves experienced less negative abnormal returns at the failure
announcements. On the other hand, for the clients of failed banks, the market measure of bank
health appears to be the most important. The more negative the stock market reaction of the
primary bank of a client of the failed bank was, the more negative was the reaction of the firm.
The size of firms’ primary bank appears to play no significant role in explaining the crosssectional differences in the abnormal returns of bank clients.
Overall, the results in table 3 show support for the hypothesis that the abnormal returns of
9
For ease of reading, the coefficients for AGE 2 in table 3 are normalized by dividing them by 100.
26
firms at the announcement of the three bank failures are correlated with the characteristics of
both the firms and their primary bank. Moreover, the directions of these correlations are
consistent with our predictions. However, table 3 offers little evidence that the relationship
between firm and bank characteristics and abnormal returns is stronger for the clients of failed
banks relative to the clients of surviving banks. The three failures had more severe adverse
impacts on the valuations of all firms for which existing banking relationships were more
valuable, regardless of whether their banks failed or survived.
V.
Robustness Checks
We checked the robustness of our results under an alternative definition of a bank client
and alternative specifications of the baseline model. In this section we briefly discuss the results
of these robustness checks.
An Alternative Definition of a Client
We altered the empirical definition of a bank client so that a firm is classified as a client
of a failed bank only if the bank appeared as the first bank in the list published by the Japan
Company Handbook. One direct impact of this, more conservative, definition of a client was a
significant reduction in the number of firms identified as clients of the three failed banks from a
total of 327 firms in the baseline model to 30 firms under the new definition. Nevertheless, our
qualitative results remained the same. 10 The failure announcements were again associated with a
decline in the share prices of the clients of both the failed and surviving banks and the
differences between the two groups were again statistically insignificant. For both groups of
firms, the stock market reactions were correlated with firm characteristics in the same manner as
reported in table 3.
10
To conserve space, we do not report the results with the alternative definition of a client in the paper.
However, they are available from the authors upon request.
27
Selection bias
a. Potential Reverse Causality
Next, we focused on the issue of potential reverse causality, or endogenous self-selection
between banks and client firms. It is possible that the negative impact of the three failure
announcements on the share prices of bank clients that we report in the paper reflects not the
value of banking relationships, but the information revealed by the failures on the
creditworthiness of bank clients. If the banks failed because of poor underwriting standards—so
poor banks are caused by poor client firms--, then it is difficult to interpret our results as
evidence of changes in the value of banking relationships. There is no panacea for dealing with
this potential problem. Previous papers have addressed it by either examining the impact of bank
distress that does not result in outright failure (e.g. deterioration in bank capital or downgrades of
banks’ credit rating) or by excluding insolvent clients from the analysis. While these procedures
exclude the some of the potential source of reverse causality, they do not completely remove it.
In this paper, we take an alternative approach. Specifically, we assume that most of the potential
causality arises from endogenous self-selection between banks and firms based on the financial
characteristics of banks and their clients. That is, weak firms seek out weak banks. In that case,
client firms of failed banks would be in worse financial condition than those of surviving banks.
We can explicitly model this self-selection in a two-equation treatment model as follows:
γ [−1, +1] = f ( COND,Firm Size, CL,Bank's Reaction to the Failure, DIND)
CL* = g (COND ,Firm Size,Firm Age, (Firm Age) 2 , Firm's Tobin Q,
(Firm's Tobin Q) 2 ,Bank Size, Bank's Equity/TA, DIND)
(3)
1 if CL* > 0
CL =
0 otherwise
28
where the variables are defined as before, and the error terms in the models of γ and CL* are
bivariate normal with mean zero and covariance matrix:
σ
ρ
ρ
.
1
Hence, we assume that whether a firm is identified as a client of one of the three failed
banks (CL=1) is the outcome of an unobserved variable CL* that is a function of firm and bank
characteristics. We also assume that the abnormal returns of firms are related to their financial
condition, size, industry, and an indicator variable for identifying the clients of failed banks.
Note that the analysis of the correlation between abnormal returns of firms and their financial
characteristics in equation (3) does not differentiate between the clients of failed and surviving
banks. Instead, the relationship between these variables is analyzed by pooling all firms together,
and the only differentiation for the clients of failed banks appears in the coefficient for the
indicator variable for these firms, CL. However, firm (and bank) characteristics influence the
matching between firms and the failed banks. Table 4 reports the summary of results from the
maximum likelihood estimation of equation (3). Specifically, we report the coefficients on the
variables of interest under both the OLS estimation of the γ model in equation (3) and the
coefficients from the maximum likelihood estimation. We also report results for the hypothesis
that the correlation between the error terms in the models of γ and CL* is zero ( ρ = 0 ). In all
four models we can reject the hypothesis that the selection and the excess returns models are
independent. Hence, self-selection appears to play a role in the response of firms to the bank
failures and how these responses relate to financial characteristics. Moreover, unlike the results
in table 3, the coefficient on the indicator variable for the clients of failed banks is negative and
significant in all four of the maximum likelihood models. Therefore, the clients of failed banks,
29
on average, had more negative abnormal returns than the clients of surviving banks. However,
the maximum likelihood estimates of the coefficients on financial condition of firms are not very
different from those obtained with a similarly specified OLS model. In both models, firms with
fewer intermediated funds, higher capital, higher profits, higher liquidity, and larger firms suffer
less from the adverse impact of the failure announcements. In addition, in most cases, the clients
of banks with higher abnormal returns at the time of the announcements have higher abnormal
returns. In summary, even when firms and banks are allowed to sort themselves based on their
characteristics, firms’ abnormal returns are related to firm and bank financial characteristics in
the direction we predict. Therefore, to the extent that reverse causality arises from self-selection
of the form modeled in equation (3), it does not appear to affect the results reported in table 3.
b. Sorting by client firms
Our analysis may also be subject to another form of selection bias. It is possible that
particular clients of the failed banks anticipated the insolvency of their bank and, whenever
possible or profitable, severed their relationship prior to the failure announcements. If this type
of selection bias existed in our data, it is likely that firms that had the most access to alternative
sources of funding terminated their relationship with the failing banks. In that case, the results
from our baseline model would overestimate the impact of the failures on the clients of the failed
banks and how the impact relates to firm characteristics. To explore the potential impact of this
type of selection bias on our results, we examined the list of banks reported by a random sample
of firms two and three years prior to the announcements. Banking relationships of this particular
sample of firms were very stable. Indeed, for each firm in the sample, the banks listed three
years prior to the failure announcements were the same as those listed just before the failure
30
announcements. Hence, if we were to use information three years prior to the failures to identify
bank clients, it is unlikely that our results would change significantly.
Endogenous Financial Characteristics
It is also possible that, contrary to the assumptions of our baseline model, financial
characteristics of banks and their clients are endogenously determined. If so, the coefficient
estimates from our baseline model would be inconsistent. To correct for this potential problem,
we assume that firms’ financial characteristics are correlated with firm size, age, investment
opportunities, and industry. We also assume that the abnormal returns of firms’ primary banks
are correlated with bank size and capitalization. These two endogenously determined variables,
along with firm size and industry, are then correlated with firms’ abnormal returns. We use
instrumental variables to estimate this model as:
γ = f (COND,Firm Size, Bank's Reaction to the Failure, DIND)
COND = g (Firm Size, Firm Age, (Firm Age)2 ,
Firm's Tobin Q, (Firm's Tobin Q) 2 , DIND)
Bank's Reaction to the Failure = h(Bank Size, Bank's Equity/TA)
(4)
Table 5 shows a summary of the results from the estimation of equation (4) separately for
the clients of failed and surviving banks. The table also reports estimates from an OLS
estimation of the abnormal returns as modeled in equation (4) and the statistic for the Hausman
specification test. For the clients of surviving banks, we can reject the hypothesis that the OLS
equation is misspecified. Hence, for these firms, the characteristics of firms and their banks are
important in determining their financial condition and the reaction of their banks to the failure
announcements. However, the OLS model appears to be well specified for the clients of failed
banks. Moreover, in most of the models, the coefficient estimates obtained from the IV
estimation are similar to those obtained from the OLS estimation. Hence, endogenously
31
determined financial characteristics do not appear to affect our main results, particularly for the
clients of failed banks.
Access to Foreign Funds
It is possible that we do not find significantly different effects from the failure
announcements for the clients of failed banks because these firms had greater access to foreign
funds – either through intermediaries or capital markets- than the clients of surviving banks and
that the foreign funds replaced those obtained from the failed banks. To check for this
possibility, we looked to see if any firm listed a foreign bank in its Reference List of primary
banks. None of the firms in our sample did. So, access to funds from foreign banks is not likely
to explain our results. We also checked to see if firms in our sample had access to foreign capital
markets by examining the exchanges in which their stock traded. For the sample firms listed the
First Section of the Tokyo Stock Exchange – the largest, and hence the most likely, firms to have
foreign listings – only 77 firms (5.8%) had their stock listed in exchanges outside of Japan. Of
these 77 firms, only 41 (3.1% of the total sample) had their stock listed in more than one foreign
stock exchange. Once again, access to foreign sources of funds does not appear to be a source of
our main results. Moreover, firms that have access to foreign stock markets are significantly
larger than those that do not. We control for access to foreign markets in our analysis with the
Firm Size variable.
VI.
Conclusions
Bank failures are theorized to have adverse consequences for other firms, particularly if
these firms are clients of the failed institutions. A number of recent studies have provided
empirical evidence that bank problems and failures adversely affect the market value of a bank’s
corporate bank borrowers, both in the United States and a number of other countries. This paper
32
contributes to the literature both by providing evidence on the effects of bank failures on the
banks’ loan customers in another country—Japan—and by examining whether the adverse
effects on the failed bank’s customers differ from those on the clients of surviving banks.
We examine the stock market reaction of over 1,000 Japanese firms to the failure of
announcements of three large banks--the Hokkaido Takushoku Bank in 1997, the Long-Term
Credit Bank of Japan and the Nippon Credit Bank in 1998. We find that, as in previous studies,
the market value of customers of the failed banks is adversely affected at the date of the failure
announcements. In addition, the effects are related to the financial characteristics of the client
firms and their banks. Firms that have greater access to alternative sources of funding or have an
existing relationship with a relatively healthy bank experience a less severe adverse impact from
bank failure announcements. However, we also find that these effects are not significantly
different from the effects experienced by all firms in the economy. That is, the bank failures
represent “bad news” for all firms in the economy, not just for the customers of the failed banks.
It should be noted that our results may be specific to Japanese bank failures in the 1990s.
Nevertheless, they raise an interesting question regarding the impact of other announcements of
bank distress examined in previous papers on the rest of the economy.
In the future, we plan to explore the long-term impact of bank failures on their clients. In
particular, it is possible that even though there were no significant differences in the immediate
impact of the failures on the clients of surviving and failed banks, the failures might have
affected the long-term behavior of firms (e.g. their investments) differently.
Finally, our results should be interpreted with caution when formulating regulatory
policy. Our results suggest that the impact of bank failures extend beyond those firms directly
connected to the failed institutions. However, past banking crises in the U.S. and other countries
33
have shown that delaying recognition of bank losses and regulatory forbearance impose large
costs on the economy. Therefore, it appears more prudent to mitigate the short-term adverse
impact of bank failures by expanding the alternative sources of funding through structural
changes, rather than by delaying the closure of insolvent institutions.
34
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of San Francisco PB00-01 (2000a).
Spiegel, Mark M. and Nobuyoshi Yamori. “Financial Turbulence and the Japanese Main Bank
Relationship,” Pacific Basin working paper series, Federal Reserve Bank of San Francisco PB0004 (2000b).
Weinstein, David E. and Yishay Yafeh (1998) “On the Costs of a Bank-Centered Financial System:
Evidence from the Changing Main Bank Relationships in Japan” Journal of Finance 53, 635–
672.
Yamori, Nobuyoshi and Akinobu Murakami (1999) “Does bank relationship have an economic value?
The effect of main bank failure on client firms,” Economics Letters 65, 115–120.
37
APPENDIX – THE THREE FAILURES
Hokkaido Takushoku Bank (November 17, 1997)
Hokkaido Takushoku Bank was the smallest so-called “city” bank, but one of the largest
20 commercial banks in Japan, with more than ¥9.5 trillion in assets. 11 On November 17, 1997,
the bank announced that, due to its difficulties in raising funds, it would transfer its operations in
the Hokkaido region in northern Japan to the North Pacific Bank. Its operations outside of
Hokkaido were eventually sold to Chuo Trust and Banking Co. The bank’s bad loans were sold
to the Deposit Insurance Corporation (DIC), and the Bank of Japan extended emergency loans to
the bank during the transition period to provide liquidity to meet deposit outflows. The problems
of the bank were well-known, and its closure followed an aborted government-sanctioned merger
attempt with the nearby Hokkaido Bank. 12
Long-Term Credit Bank of Japan (October 23, 1998)
LTCB was one of the largest banks in Japan and was widely perceived to be in serious
financial trouble prior to its failure. Despite an injection of capital from the government in March
1998, its debt was downgraded several times and its share price dropped sharply. A merger
attempt with Sumitomo Trust Bank, a large bank in stronger financial condition, failed in the
summer of 1998. On October 19, 1998, news reports indicated that the newly-established
Financial Supervisory Agency (FSA) had informed LTCB earlier in the day that the bank was
insolvent on a market-value basis as of the end of September, when it was last inspected. 13 The
reports also indicated that LTCB was expected to be nationalized later in the week, when
11
Japanese banks are generally divided into four broad categories—city, trust, long-term credit, and
regional—according to both size and type of business. Historically, the four types of banks have
differed in their size, composition of assets and loans, customer base, funding sources, and regulatory
requirements and treatment. Long-term credit and city banks were the larger banks and trust banks the
most specialized. See Genay (1998) for a discussion of some of the differences in the operations of city,
regional, long-term credit, and trust banks.
12
News articles reported that depositors began to withdraw funds from the bank after it was announced
that the planned merger with Hokkaido Bank would not happen. News reports also noted that many of
the large stakeholders, e.g., the life insurance companies, refused to inject additional funds into the
bank’s capital base in the weeks leading up to its closure. The bank’s share price, which was ¥222 at the
beginning of 1997, had dropped to ¥65 the day before the failure announcement on November 17, 1997.
The day after the announcement, shareholders could only receive ¥5 per share.
13
The Financial Supervisory Agency, which assumed supervisory responsibilities for financial
institutions from the Ministry of Finance, was established on June 22, 1998.
38
recently adopted banking legislation would take effect. 14 Four days later on October 23, 1998,
LTCB applied for nationalization. The government announced that it would guarantee all
obligations of LTCB, the DIC would purchase the bank's shares (last traded at ¥2), and the Bank
of Japan would provide financial aid to LTCB as necessary to maintain liquidity in financial
markets. According to the FSA report, at the end of September, the bank had total assets of ¥24
trillion and ¥160 billion in book-value capital. It also reported ¥500 billion, or three times its
book value capital, of unrealized losses on its securities portfolio and other problem assets
totaling ¥4.62 trillion, or 19 percent of total assets and roughly 30 times its capital. 15
F. Nippon Credit Bank (December 14, 1998)
The semi-annual public financial statements issued by all Japanese banks on November
24, 1998 for the six months ending September 30 showed that another large long-term credit
bank—the Nippon Credit Bank (NCB), with assets of ¥7.7 trillion as of September 1998—had
significant amounts of problem loans and that its earnings had deteriorated significantly since
March 1998. However, the bank stated that it was still solvent. On December 9, 1998, it was
announced that NCB was abandoning its previously announced merger with and Chuo Trust and
Banking Co. The abandoned merger was perceived as a sign of further problems at NCB. Shortly
thereafter, news reports indicated that the FSA’s examination of the bank showed that as of
March 31, 1998, contrary to what NCB had reported, the bank had a capital deficit of ¥94.4
billion and was insolvent. On December 12, the government urged Nippon Credit to apply for
nationalization, which it did on the next business day—December 14. The government provided
assurances that the repayment of all of NCB's obligations would be satisfied in full and on time
and that the Bank of Japan would provide loans to ensure the liquidity of the markets. The Bank
injected some ¥80 billion into NCB to avoid having it default on its liabilities.
14
A package of eight bills was approved by the parliament on October 12, 1998 aimed at resolving the
bad loans of Japanese banks and dealing with the failure of financial institutions. The legislation
allowed for recapitalization of banks with public funds and created the Financial Reconstruction
Commission (FRC), to, among other duties, administer nationalized insolvent institutions.
15
After the nationalization, the good assets of the bank were eventually sold to a consortium led by
Ripplewood Holdings LLC in the U.S., which paid ¥1 billion for the bank and injected additional ¥120
billion in capital. The new bank also received ¥240 billion of public capital from the Financial
Reconstruction Commission in March 2000.
39
Table 1. Estimated of abnormal returns for failed and surviving bank client firms
This table reports statistics for the distribution of abnormal returns for the clients of the three
failed banks and the control group. For each firm, excess return at event date k is the coefficient γ ik in the
following model, estimated by seemingly unrelated regression:
R
it
= α
i
+ β iRm
t
+
+ 1
∑ γ
k = − 1
ik
D
k
+ ε
it ,
For the 1998 failures, the above market model is expanded to allow for post-failure shifts in both the
alpha and market beta coefficients. The rows labeled “Mean” report the cross-sectional average of excess
returns for the appropriate sample and test whether the mean excess return is significantly different from
zero. The rows labeled “Median” report the median excess returns for the relevant sample and the
significance level for the one-sided sign test H0 : median =0 and Ha : median < 0. The two rows labeled
“Wilcoxon test” and “T-test for equality of means” report tests for the equality of the distributions of
excess returns for clients of the failed banks and the clients of surviving banks. The rows labeled
“Wilcoxon test” reports the z-statistic and its significance level for the hypothesis that the failed bank
clients and other bank clients are from populations with the same distribution. The rows labeled “T-test
for equality of means” report the t-statistic for the equality of means across the two samples and its
significance level. ‘***’, ‘**’, and ‘*’ indicate statistical significance at the 1%, 5%, and 10% levels,
respectively.
Panel A. Hokkaido Takushoku Bank failure (November 17, 1997)
Event window
-1
0
+1
[-1, +1]
Hokkaido Takushoku Bank client firms (N= 70)
Mean
1.002**
-0.733
-0.606
0.170
Median
0.517
-0.588*
-0.858**
-0.330
Surviving banks client firms (N= 1214)
Mean
0.377***
-0.650***
-0.183
-0.115**
Median
0.380
-0.629***
-0.266***
-0.034
Tests for client effects
Wilcoxon test
-1.24
0.25
1.33
0.12
T-test for equality of means
-1.64
-0.17
0.90
-0.57
40
Table 1. Estimated of abnormal returns for failed and surviving bank client firms (cont'd)
Panel B. LTCB failure (October 23, 1998)
Event windows
-1
0
+1
LTCB client firms (N=197)
-2.288***
-0.471*
-1.115***
Mean
-2.288***
-0.539**
-1.203***
Median
LTCB and NCB client firms (N=29)
-3.877***
0.862
-0.159
Mean
-3.502***
0.451
-0.592
Median
Surviving banks client firms (N= 926)
-2.142***
-0.611***
-0.913***
Mean
-2.061***
-0.728***
-1.013***
Median
Tests for client effects, LTCB clients only
0.87
-1.02
1.25
Wilcoxon test
0.51
-0.59
0.43
T-test for equality of means
Tests for client effects, both LTCB and NCB clients
2.23**
-2.60***
-0.31
Wilcoxon test
2.53**
-2.36**
-1.20
T-test for equality of means
[-1, +1]
-1.324***
-1.156***
-1.042
-1.744*
-1.226***
-1.185***
0.38
0.85
0.30
-0.49
Panel C. NCB failure (December 14, 1998)
Event windows
-1
0
+1
NCB client firms (N=60)
-0.163
0.118
-1.345***
Mean
0.279
-0.216
-0.532**
Median
LTCB and NCB client firms (N=29)
0.460
-0.698
-1.228**
Mean
0.501
-0.207
-1.328***
Median
Surviving banks client firms (N=926)
-0.355***
-0.214
-0.454***
Mean
-0.312***
-0.212**
-0.329***
Median
Tests for client effects, NCB clients only
-1.21
-0.25
1.78*
Wilcoxon test
-0.47
-0.60
2.17**
T-test for equality of means
Tests for client effects, both LTCB and NCB clients
-1.79*
0.46
2.74***
Wilcoxon test
1.40
0.61
1.35
T-test for equality of means
[-1, +1]
-0.497**
-0.424**
-0.522
-0.364**
-0.342***
-0.237***
1.15
0.78
0.94
0.64
41
Table 2. Summary statistics of financial characteristics
for failed and surviving bank client firms
This table presents financial characteristics of failed and surviving bank client firms at the end of March
of the each failure year. Failed bank clients are defined as firms that have Hokkaido Takushoku Bank,
LTCB or NCB anywhere on the References list. Tobin’s Q is the ratio of firm market value (market value
of equity plus total assets minus book value of equity) to total assets. ROA is net income divided by total
assets, and ROE is net income divided by book value of equity. In the column labeled “mean,” ‘***’,
‘**’, and ‘*’ indicate statistical differences in the mean values of the variables for failed and surviving
bank client firms at the 1%, 5%, and 10% levels, respectively.
Panel A. Hokkaido Takushoku Bank failure (November 17, 1997)
Mean
St. Dev
Min
Max
All firms
Nonclients
Clients
Total Assets (trillion yen)
0.27
0.65
0.27
0.67
0.14
0.18
0.00
0.00
0.01
11.18
11.18
1.04
All firms
Nonclients
Clients
Equity / Total Assets (%)
42.40
20.01
42.58
20.11
39.34***
18.20
-48.37
-48.37
2.83
94.54
94.54
77.61
All firms
Nonclients
Clients
Loans / TA (%)
20.05
17.81
20.04
17.90
20.15
16.41
0.00
0.00
0.00
130.82
130.82
66.25
All firms
Nonclients
Clients
ROA (five-year average, %)
1.19
2.31
1.19
2.32
1.03
2.05
-21.29
-21.29
-7.97
11.32
11.32
8.52
All firms
Nonclients
Clients
ROE (five-year average, %)
1.88
7.24
1.90
7.19
1.45
8.11
-53.27
-53.27
-49.09
15.91
15.91
13.00
(Cash and Securities) / Total Assets (%)
16.01
10.93
0.22
16.03
10.96
0.22
15.68
10.45
1.96
75.00
75.00
55.78
All firms
Nonclients
Clients
All firms
Nonclients
Clients
All firms
Nonclients
Clients
Tobin’s Q
1.30
1.31
1.20***
0.40
0.40
0.23
0.59
0.59
0.84
5.60
5.60
1.87
Age (years)
55.84
16.58
55.91
16.70
54.74
14.31
9.00
9.00
17.00
116.00
116.00
83.00
42
Table 2. Summary statistics of financial characteristics
for failed and surviving bank client firms (cont'd)
Panel B. LTCB and NCB failures (October 23, 1998 and December 14, 1998)
Mean
St. Dev.
Min.
Max.
All firms
Nonclients
Clients
LTCB clients only
NCB clients only
Clients of both banks
Total Assets (trillion yen)
0.279
0.667
0.259
0.648
0.344*
0.719
0.386*
0.823
0.231
0.378
0.290
0.418
0.005
0.005
0.005
0.005
0.012
0.032
10.839
10.839
7.025
7.025
1.892
1.904
All firms
Nonclients
Clients
LTCB clients only
NCB clients only
Clients of both banks
Equity / Total Assets (%)
43.63
20.62
45.49
20.61
37.59***
19.49
39.45***
19.53
34.54***
17.64
31.22***
21.25
1.50
2.50
1.50
2.96
6.27
1.50
94.07
94.07
93.45
88.75
93.45
82.06
All firms
Nonclients
Clients
LTCB clients only
NCB clients only
Clients of both banks
Loans / TA (%)
20.12
18.37
17.74
17.32
27.83***
19.56
26.51***
18.99
27.77***
19.78
36.99***
21.13
0.00
0.00
0.00
0.00
0.00
0.00
83.41
78.09
83.41
83.41
76.58
73.06
All firms
Nonclients
Clients
LTCB clients only
NCB clients only
Clients of both banks
ROA (five-year average, %)
1.16
2.46
1.24
2.57
0.88**
2.04
1.10
1.90
0.54
2.30
0.08*
2.17
-32.86
-32.86
-10.69
-5.45
-10.69
-5.64
10.69
10.69
7.01
7.01
5.29
5.23
All firms
Nonclients
Clients
LTCB clients only
NCB clients only
Clients of both banks
ROE (five-year average, %)
1.57
7.63
1.80
7.54
0.84*
7.91
1.57
7.05
0.77
7.28
-3.93***
12.22
-61.49
-61.49
-49.39
-49.39
-31.57
-39.96
15.05
15.05
11.21
11.21
11.05
7.22
(Cash and Securities) / Total Assets (%)
All firms
15.45
10.79
0.09
Nonclients
16.41
11.16
0.31
Clients
12.33***
8.80
0.09
LTCB clients only
12.74***
9.01
0.09
NCB clients only
11.39***
7.97
1.30
Clients of both banks
11.51*
9.11
0.81
74.68
74.68
59.21
59.21
46.30
42.44
43
Table 2. Summary statistics of financial characteristics
for failed and surviving bank client firms (cont'd)
Panel B. LTCB and NCB failures (October 23, 1998 and December 14, 1998)
Mean
St. Dev.
Min.
Max.
Tobin’s Q
All firms
Nonclients
Clients
LTCB clients only
NCB clients only
Clients of both banks
1.06
1.07
1.04
1.06
0.98
1.06
0.45
0.48
0.35
0.39
0.20
0.30
0.40
0.40
0.41
0.41
0.68
0.66
6.58
6.58
3.12
3.12
1.90
1.85
10.00
11.00
10.00
10.00
22.00
37.00
117.00
117.00
113.00
113.00
82.00
109.00
Age (years)
All firms
Nonclients
Clients
LTCB clients only
NCB clients only
Clients of both banks
56.61
56.39
57.30
57.47
56.32
58.17
17.01
16.96
17.18
18.63
11.40
17.30
44
Table 3. Cross-section relationship between abnormal returns
and client firms’ financial characteristics
This table presents estimates of the correlation between abnormal returns and selected measures of client
firms’ financial condition modeled as:
γ [ −1,+ 1], i = α + φ CONDi + ψ X i + δ CLi + λ (CLi x CONDi ) + θ (CLi x X i ) + ∑ ϑ j DINDj + µ it ,
j
where the financial condition variables (COND) employed are: 1) asset size; 2) the ratio of bank loans to
total assets; 3) the ratio of book-value equity to total assets; 4) the ratio of net income to total assets (or
book-value of equity); and 5) the ratio of cash plus investment security to total assets. CLi is a binary
variable that identifies the clients of the failed banks and is equal to one if firm i is a client of the failed
bank, zero otherwise. The X variables are age and the ratio of market value of assets to book value of
assets (TOBQ). We also include the square of these variables. The coefficient estimates of COND and X
for client firms of failed banks are (φ + λ) and (ψ + θ), respectively. The coefficient estimates of COND
and X for client firms of surviving banks are (φ) and (ψ), respectively. The model also includes indicator
variables for industries, which are not reported below. The number of observations in each regression is
3,708; of these, 3,323 relate to the clients of surviving banks and, 385 relate to the clients of failed banks.
‘***’, ‘**’, and ‘*’ indicate statistical significance at the 1%, 5%, and 10% levels, respectively. The
columns labeled DIFF provide asterisks to indicate statistical significance between the coefficients for the
clients of failed and surviving banks.
45
Firm Characteristic – Loans/TA
CLIENTS
OF FAILED
BANKS
CLIENTS OF
SURVIVING
BANKS
DIFF
CLIENTS CLIENTS OF
OF FAILED SURVIVING
BANKS
BANKS
DIFF
CLIENTS
OF FAILED
BANKS
CLIENTS OF
SURVIVING
BANKS
Intercept
-3.014
-1.067
-2.468
-0.141
-2.631
-0.462
Firm’s Loans/TA
-0.018***
-0.014***
-0.019***
-0.014***
-0.018***
-0.014***
Firm Size
0.316***
0.116***
0.315***
0.117***
0.316***
0.115***
Firm Age
-0.005
-0.006***
-0.005
-0.005**
-0.005
-0.005***
(Firm Age)2
0.012*
0.010***
0.012*
0.010***
0.012*
0.010***
Firm’s Tobin’s Q
2.548***
1.305***
2.575***
1.312***
2.557***
1.299***
-0.617***
-0.194***
-0.626**
-0.196***
-0.621**
-0.193***
0.048
0.073**
-0.018
-0.049**
0.025
0.080**
0.064
-0.022
0.130*
-0.004
(Firm’s Tobin’s
Q)2
Bank’s
Equity/TA
Bank’s Loan
Loss Reserves/
Total Loans
**
**
Bank ROA
Bank Size
0.077
-0.002
Bank’s Reaction
to the Failure
0.132*
-0.002
F-Statistic
17.38***
*
0.056
-0.036
0.127*
-0.007
17.50***
*
DIFF
**
*
17.26***
46
Firm Characteristic – Equity/TA
CLIENTS
OF FAILED
BANKS
CLIENTS OF
SURVIVING
BANKS
DIFF
CLIENTS CLIENTS OF
OF FAILED SURVIVING
BANKS
BANKS
DIFF
CLIENTS
OF FAILED
BANKS
CLIENTS OF
SURVIVING
BANKS
Intercept
-4.023
-2.000
-3.780
-1.361
-3.223
-1.000
Firm’s
Equity/TA
0.011*
0.010***
0.011*
0.010***
0.003*
0.002***
Firm Size
0.330***
0.135***
0.330***
0.136***
0.330***
0.135***
Firm Age
-0.005
-0.005**
-0.004
-0.005**
-0.005
-0.005**
(Firm Age)2
0.013*
0.010***
0.014*
0.010***
0.014*
0.009***
Firm’s Tobin’s Q
2.681***
1.405***
2.712***
1.410***
2.689***
1.398***
-0.639**
-0.216***
-0.650**
-0.216***
-0.644**
-0.214***
0.056
0.076**
-0.020
-0.053***
0.032
0.085
0.078
0.002
0.140**
-0.002
(Firm’s Tobin’s
Q)2
Bank’s
Equity/TA
Bank’s Loan
Loss Reserves/
Total Loans
*
*
Bank ROA
Bank Size
0.093
0.023
Bank’s Reaction
to the Failure
0.142**
0.001
F-Statistic
15.86***
**
0.070
-0.013
0.137**
-0.005
16.02***
**
DIFF
15.75***
47
Firm Characteristic – ROA
CLIENTS
OF FAILED
BANKS
CLIENTS OF
SURVIVING
BANKS
Intercept
-3.520
-1.540
Firm ROA
0.187***
0.075***
Firm Size
0.333***
0.115***
Firm Age
-0.006
(Firm Age)2
Firm’s Tobin’s Q
(Firm’s Tobin’s
Q)2
Bank’s
Equity/TA
Bank’s Loan
Loss Reserves/
Total Loans
DIFF
CLIENTS CLIENTS OF
OF FAILED SURVIVING
BANKS
BANKS
-2.832
-0.504
**
0.185***
0.074***
**
0.332***
0.116***
-0.007***
-0.006
0.016**
0.010***
1.726*
DIFF
CLIENTS OF
SURVIVING
BANKS
DIFF
-3.001
-0.891
0.186***
0.075***
**
0.333***
0.114***
**
-0.007***
-0.006
-0.007***
0.016**
0.010***
0.016**
0.010***
1.185***
1.764*
1.192***
1.735*
1.178***
-0.444*
-0.182***
-0.455*
-0.183***
-0.447*
-0.180***
0.065
0.078***
-0.020
-0.056***
0.037
0.087***
0.088
-0.004
0.139**
-0.001
**
**
Bank ROA
Bank Size
0.106
0.017
Bank’s Reaction
to the Failure
0.142**
0.001
F-Statistic
CLIENTS
OF FAILED
BANKS
15.78***
*
0.079
-0.021
0.136*
-0.005
16.02***
*
*
0.139**
48
Firm Characteristic – Liquidity
CLIENTS
OF FAILED
BANKS
CLIENTS OF
SURVIVING
BANKS
DIFF
CLIENTS CLIENTS OF
OF FAILED SURVIVING
BANKS
BANKS
DIFF
CLIENTS
OF FAILED
BANKS
CLIENTS OF
SURVIVING
BANKS
Intercept
-2.983
-1.840**
-2.320
-0.877
-2.525
-1.256
Firm Liquidity
0.002
0.008***
0.002
0.008***
0.002
0.008***
Firm Size
0.291***
0.109***
0.290***
0.110***
0.291***
0.109***
Firm Age
-0.008
-0.008***
-0.008
-0.007***
-0.008
-0.007***
(Firm Age)2
0.014*
0.009***
0.014**
0.009***
0.014**
0.009***
Firm’s Tobin’s Q
2.276**
1.351***
2.305**
1.354***
2.281**
1.344***
-0.491*
-0.196***
-0.501*
-0.197***
-0.494*
-0.195***
0.057
0.070**
-0.023
-0.054**
0.035
0.080**
0.045
0.003
0.146**
-0.001
(Firm’s Tobin’s
Q)2
Bank’s
Equity/TA
Bank’s Loan
Loss Reserves/
Total Loans
*
*
Bank ROA
Bank Size
0.061
0.022
Bank’s Reaction
to the Failure
0.148**
0.001
F-Statistic
15.07***
**
0.036
-0.013
0.142**
-0.005
15.24***
**
DIFF
*
**
14.99***
49
Table 4. Treatment Model of the Relationship between the Abnormal Returns of Firms, Financial Characteristics of Firms
and Their Primary Bank
Treatment Model
Model 1
Firm’s Loans/TA
-0.012***
Firm Size
0.259***
Bank Abnormal Returns
0.051***
Client Indicator
-1.480***
Intercept
0.374**
2
24.84***
χ for ρ = 0
Model 2
Firm’s Equity/TA
Firm Size
Bank Abnormal Returns
Client Indicator
Intercept
χ 2 for ρ = 0
Model 3
Firm ROA
Firm Size
Bank Abnormal Returns
Client Indicator
Intercept
χ 2 for ρ = 0
Model 4
Firm Liquidity
Firm Size
Bank Abnormal Returns
Client Indicator
Intercept
χ 2 for ρ = 0
OLS
-0.016***
0.228***
0.069***
-0.115
0.343**
0.007***
0.278***
0.055***
-1.583***
-0.209
26.23***
0.010***
0.252***
0.074***
-0.155
-0.437**
0.108***
0.249***
0.051***
1.610***
0.010
22.89***
0.113***
0.214***
0.070***
-0.187*
-0.089
0.006**
0.253***
0.054***
-1.593***
0.068
20.94***
0.011***
0.221***
0.073***
-0.176*
-0.114
50
Table 5. Instrumental Variables Estimation of the Relationship between the Abnormal Returns of Firms, Financial Characteristics of
Firms and Their Primary Bank
Clients of the Failed Banks (N=546)
Hausman
IV
OLS
Test χ 2
Firm’s Loans/TA
Firm Size
Bank’s Reaction to the Failure
-0.051**
0.395***
0.460
-0.021***
0.336***
0.144**
Firm’s Equity/TA
Firm Size
Bank’s Reaction to the Failure
0.011
0.409**
0.551**
Firm ROA
Firm Size
Bank’s Reaction to the Failure
Firm Liquidity
Firm Size
Bank’s Reaction to the Failure
Clients of the Surviving Banks (N=3,000)
Hausman
IV
OLS
Test χ 2
6.82
-0.028***
0.162***
0.621***
-0.015***
0.190*** 72.59***
0.060***
0.012**
0.367**
0.161**
2.38
0.014**
0.192***
0.649***
0.009***
0.214*** 72.55***
0.064***
0.429***
0.376***
0.530**
0.229***
0.321***
0.161**
5.97
0.260***
0.141**
0.546***
0.103***
0.177*** 76.57***
0.060***
0.156***
0.418***
0.107
0.010
0.317***
0.163**
9.24
0.083***
0.189***
0.597***
0.011***
0.186*** 71.48***
0.063***
51
Working Paper Series
A series of research studies on regional economic issues relating to the Seventh Federal
Reserve District, and on financial and economic topics.
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Jonathan Hao, William C. Hunter and Won Keun Yang
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6
Working Paper Series (continued)
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The Value of Banking Relationships During a Financial Crisis:
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WP-02-20
7
@FedFRASER