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Working Paper Series
An Empirical Test of the Incentive Effects
of Deposit Insurance: The Case of Junk
Bonds at Savings and Loan Associations
Elijah Brewer III and Thomas H. Mondschean
Working Papers Series
Issues in Financial Regulation
Research Department
Federal Reserve Bank of Chicago
September 1991 (W P-91-18)
FEDERAL RESERVE BANK
OF CHICAGO
An Empirical Test of the Incentive Effects of Deposit Insurance:
The Case ofJunk Bonds at Savings and Loan Associations
Elijah Brewer in
Research Department -11thFloor
Federal Reserve Bank ofChicago
230 South LaSalle Street
Chicago, Illinois 60604
(312) 322-5813
and
Thomas H. Mondschean
Department ofEconomics
DePaul University
25 EastJackson Blvd.
Chicago, Illinois 60604
(312) 362-5210
September 1991
W e thank Herbert Baer, George Kaufman, Steven Strongin, Vefa Tarhan, and
the participants of finance seminars at DePaul University and the University
of Notre Dame for valuable comments and suggestions. The research
assistance of Loretta Ardaugh, George Rodriguez, and Gary Sutkin is greatly
appreciated. All views expressed here are those of the authors and are not
necessarily those of the Federal Reserve Bank of Chicago or the Federal
Reserve System.
FRB CHICAGO Working Paper
September 1991, WP-1991-18
1
An Empirical Test of the Incentive Effects ofDeposit Insurance:
The Case ofJunk Bonds at Savings and Loan Associations
Using data for the July 1985-December 1989 period, thispaper analyzes how
diversification into low-grade (junk) bonds affects a savings and loan
association's (S&L) equity returns. First, we report, among other things, that
diversification into junk bond investments appears to have increased the
volatility of S&L equity returns. Moreover, an examination of the risk
premium on large certificates of deposit (CDs) indicates a significantly
positive relationship between the interest rate paid on uninsured CDs and the
volume of junk bonds held. Next, we examine the impact of junk bonds on
equity returns. For an institution with low net worth, greater risk-taking will
increase the value of underpriced, fixed-rate deposit insurance to the S&L and
its equity holder. This should lead to increases in the return on common
stock. However, a well-capitalized institution that increases junk bond
holdings should not experience stock price gains. W e find that thisisthe case
for the sample of S&Ls we studied.
FRB CHICAGO Working Paper
September 1991, WP-1991-18
2
An Empirical Test of the Incentive Effects of Deposit Insurance:
The Case of Junk Bonds at Savings and Loan Associations
Much of the debate concerning the savings and loan (S&L) crisis has focused
on questions regarding the various investments undertaken by S&Ls. The
Financial Institutions Reform, Recovery and Enforcement Act (FIRREA) of
1989 requires, among other things, that S&Ls' existing holdings of corporate
debt securities not of investment grade ("junk" bonds) be divested by July 1,
1994.1 Proponents of this restriction believe that S&Ls should return to their
original purpose and concentrate solely on providing credit to potential and
existing homeowners. They argue that junk bonds are inappropriate
investments for institutions with federal deposit insurance. On the other hand,
others contend thatinvesting injunk bonds may improve the diversification of
an S&L's assets and therefore lead to less risky, healthier institutions. Has
allowing investment in junk bonds contributed to the severity of the S&L
crisis by permitting increased risk-taking by some institutions? Or did
holdings ofjunk bonds actually reduce S&L portfoliorisk through the benefits
of diversification? This is an important empirical question because the
FIRREA restrictions have adversely affected the low-grade bond market by
eliminating a potential source of demand for these securities. It is also
important because much of the large losses of the S&L industry in the 1980's
was borne by the taxpayer.
It may be argued, however, that debates about which assets thrifts should be
allowed to hold are focusing on the wrong questions. There are many types of
risky assets that thrifts are stillpermitted to hold in theirportfolios even after
the passage of FIRREA, e.g., fixed-rate, 30-year mortgage loans. If an
institution wishes to increase its risk exposure, prohibiting junk bond
investment will not prevent it from doing so. Thus, a more relevant policy
question is what factors induce thrifts to take on additional risk. We believe
that by studying the effects of junk bond investment on S&Ls, we can better
understand the motivation for greater S&L risk-taking in general. In the case
of junk bonds, we find empirical support for the view that the existence of
deposit insurance created a moral hazard situation which gave poorly
capitalized institutionsa greater incentive to increase theirriskexposure.
Several recent studies suggest thatpoorly capitalized institutionshave actively
sought to take additional risk. Benston and Koehn (1989) reported that
increased emphasis on riskiernontraditional activitiesresulted in greater stock
FRB CHICAGO Working Paper
September 1991, WP-1991-18
3
return volatility for poorly capitalized S&Ls and lower volatility for healthier
institutions. Brewer (1989) tested the hypothesis that federal deposit
insurance distorts the risk/retum trade-offs for seriously troubled institutions.
He found that shifts in asset composition toward nontraditional activities
resulted in increases in die return on equity for distressed institutions but had
no effecton healthy institutions. This suggests that the shareholders rewarded
risk-shiftingactions thatraise the value of the insurance subsidy.
This paper differs from the previous studies in that we analyze the impact of
S&L junk bond exposure on market risk. Using a sample of 75 S&Ls from
July 1985 to the end of 1989, we report that institutions with a larger share of
junk bonds (as a proportion of their market value of net worth) also had
greater stock market volatility, as measured by the standard deviation of their
stock market returns. This suggests that these institutions did use junk bonds
to increaseratherthan reduce theirrisk exposure.
Next, we examine whether S&Ls with larger shares of junk bonds in their
portfolios paid higher interestrates to depositors. Ifinstitutions holding junk
bonds are perceived by depositors to have a higher probability of failure, then
uninsured depositors would demand a higher risk premium. W e find a
significantly positive relationship between junk bond investments and deposit
interestrates for the 1987-1989 period. Thus, we conclude not only thatjunk
bonds increased S&L market risk but also that institutions which held larger
shares ofjunk bonds were perceived as more risky by uninsured depositors.
If holding junk bonds increases risk for S&Ls, then (1) why would S&Ls
invest in these assets and (2) why were almost all junk bond investments
concentrated in a small number of institutions? An S&L should increase its
investment in junk bonds (or any asset) if the expected marginal benefit of
doing so is greater than its expected marginal cost If the stock market is
operating efficiently, then this should be reflected in stock returns. However,
the existence of deposit insurance alters the risk-return trade-off for some
institutions. IfS&Ls with largejunk bond holdings were also less capitalized
and had a higher probability of failure than other S&Ls, the deposit insurance
option becomes more valuable and the expected gain of larger junk bond
investments would exceed the S&L's expected loss. Thus, the stock market
should reward these S&Ls with higher returns for increasing their riskiness.
However, a well-capitalized institution that increased its holdings of junk
bonds would experience a decline in stock returns, because the expected gain
to the institution would not exceed itsexpected loss. W e test this hypothesis
by dividing our sample of 75 S&Ls into 18 "high" junk bondholders and 57
FRB CHICAGO Working Paper
September 1991, WP-1991-18
4
"low" junk bondholders. W e find a significantly positive relation between
junk bond holdings and stock returns for the "high" junk bond S&Ls and a
significantly negative relation for the "low" junk bondholders. These
empirical results support the theory that the existence of deposit insurance
provides incentives for some institutions to shift their asset compusition
toward riskieractivities.
This paper is divided into five sections. Section one describes the regulations
concerning S&L junk bond investment and presents descriptive data on the
extent of S&L holdings over the sample period. Section two develops the
method used to test the effects of junk bond holdings on stock market risk.
Section three analyzes the effects of S&L junk bond holdings on the cost of
deposit funds. Section four tests the impact ofjunk bond investment on S&L
stock returns. Section five concludes.
I. Background
Allowing S&Ls to invest in junk bonds changes the efficient risk/retum
frontier available to the S&L. The exact shape of the new frontier depends
both on how junk bonds mix with other assets and how an S&L chooses to
manage these investments. The Gam-St Germain Depository Institutions Act
of 1982 allowed federally chartered S&Ls to invest up to 11 percent of their
assets in junk bonds. In the May 1983 regulation implementing the act, the
Federal Home Loan Bank Board (FHLBB) authorized federally chartered
S&Ls to invest up to (1) 1 percent of their assets in commercial paper and
corporate debt securities and (2) 10 percent of their assets in commercial
loans. The FHLBB classified junk bonds under the category of commercial
loans. At the same time, many state governments enacted statutes that
broadened asset powers for their state-chartered S&Ls. State-chartered S&Ls
were permitted by several states to invest almost unlimited amounts directly
in junk bonds.2 Recently, these junk bond investments have been associated
with some of the largest S&L failures.
Table 1 reports S&L holdings of junk bonds by different classifications from
1985 to 1989. Several points are worth noting. First, from the end of 1985 to
the end of 1988, total holdings of junk bonds by all S&Ls grew from $6.02
billion to $15.34 billion,an increase of over 150 percent in three years. After
1988, however, S&Ls began to reduce and/or write down their holdings of
junk bonds, so thatby yearend 1989, the amount held had declined to $10.68
billion. Second, the vast majority of these securities woe held by a small
FRB CHICAGO Working Paper
September 1991, WP-1991-1%
5
number of institutions. Throughout the sample period, the top SO holders had
over 95 percent of all S&L junk bond holdings. Third, even though these
investments were concentrated in a small number of institutions, these
investments still represented a substantial amount relative to regulatory
capital. For the SO largest holders as a group, the dollar value of junk bonds
exceeded theirregulatory capital since theend of 1986.
Junk bond investments are frequently perceived as relatively risky assets in
the sense that the distribution of returns associated with a single asset of this
kind or even a group of such assets has a large variance: some institutions
will earn generous returns on these investments while others will sufferlow or
negative returns. Recent studies of the junk bond market have verified that,
other things equal,junk bonds are more risky than investment-grade bonds but
less risky than equity. For example. Perry and Taggart (1990) found that the
standard deviation of monthly junk bond returns was greater than that of
investment-grade bonds but lessthan that ofequities. Blume, Keim, and Patel
(1991), found that, from 1977 to 1989, low-grade bonds exhibited more
volatility than equivalent government bonds. They also report that there was
no indication that junk bonds are either overpriced or underpriced, and this
corroborates the findings of a 1988 General Accounting Office study. In
general, junk bonds are less liquid than either Treasury or investment grade
bonds and more liquid than consumer and commercial loans.
The hypothesis that junk bonds cause failure is related to this perceived
riskiness. Given the large variance in returns, institutions which have high
levels of junk bond investments have a higher probability of failure if they
experience an unfavorable series of "draws" from the distribution of returns.
However, this argument does not distinguish between the risk associated with
ajunk bond and the risk associated with a portfolio of assets. The riskiness of
a portfolio-that is,the variance in the return on the entire setofassets held by
an S&L-may decrease when junk bonds are added. Portfolio riskiness also
depends on the covariance among assets. For example, if the returns on a
junk bond tend to be high when the returns on other assets are low (i.e.,
negative covariance), adding thejunk bond will reduce the overall riskiness of
a portfolio.
FRB CHICAGO Working Paper
September 1991, WP-1991-18
6
n . The Relation between Junk Bonds and S&L Market Risk
A. Theoretical Considerations
Do changes in S&L holdings of junk bonds significantly influence S&L
riskiness? W e address this question by examining the relation between die
volatility of S&L stock returns and holdings of junk bonds. The first step in
the development of the model, following Black and Scholes (1973) and Galai
and Masulis (1976), is to relate the volatility of the market return on S&L
equity, o^y, to the volatilityofthe return on S&L assets,cA:
(1)
where (dMV/dA)/(A/MV) is the elasticity of market value of equity with
respect to the value of total assets of a representative S&L. Equation (1)
indicates that the volatility of S&L equity returns is a function of: the
volatility of the asset returns, aA; the change in market value capital with
respect to the change in total assets, 3MV/3A; and the asset-to-capital ratio,
A/MV.
Because we cannot observe all the right hand side variables in equation (1),a
simplified econometric specification of equation (1), following Christie
(1982), can be written as equation (2):
(2)
where a^t is the equity return volatility (o^v) of the ijb S&L in period t;
LEVy is the total asset-to-market value capital ratio of the jib S&L in period
t; isa stochastic errorterm; and the coefficients s0and stare parameters to
be estimated. Since greater leverage increases S&L riskiness, we predict that
Si >0.
Christie (1982) indicates that the volatility of equity returns is affected by a
number of other variables in addition to the asset-capital ratio. One possible
source of influence may be junk bonds. To test the effect of junk bonds on
S&L market risk,define JUNKj ,tobe theratio ofjunk bonds to maiket value
of capital of the iibS&L in period t,so equation (2) becomes:3
FRB CHICAGO Working Paper
September 1991, WP-1991-18
7
o. = s + s LEV. +s^JUNK. +e.
M
0
1
M
2
M
(3)
M
The estimated coefficient, s2,would be positive ifa higher proportion ofjunk
bonds in an S&L's portfolio increased its riskiness. However, this
specification does not control for asset mix. For example, if an S&L
increased holdings of junk bonds by selling off Treasury bills, it would be
unclear whether risk increased because of an increase in junk bonds or a
decrease in less risky Treasury bills. To address the effect of asset mix, we
added several other variables to the model, which isnow rewritten as equation
(4):
O. = s. + s,L E V + s.JUNK. + s.RMORT
M
0
1
M
2
M
3
M
+ S O M O R T . + s.CMORT. + s.ADL.
4
x,l
5
M
6
+ s.DIRECT +S.NONMORT. + e.
M
(4)
In equation (4), we have included residential mortgage loans (RMORT),
commercial mortgage loans (CMORT), other mortgage loans (OMORT),
acquisition and development loans (ADL), real estate direct investment
(DIRECT), non-mortgage loans (NONMORT), and junk bonds (JUNK). To
avoid perfect multicollinearity, one asset category, comprised of cash,
deposits, investment securities and other assets not specified in the equation,
was excluded. All asset variables are divided by market value of capital and
are measured forthe i|hS&L attheend ofperiod t.
Conceptually, if an S&L holds a portfolio of mortgage and non-mortgage
assets of differing degrees of risk, then, as the relative investment in the
different assets changes, the return volatility of the S&L must change.4 The
precise behavior of aiJL as a function of the asset mix will depend on the
variance/covariance structure of the S&L asset returns; changes in asset mix
can either increase or decrease the volatility of equity returns. Three
potentially important sources of risky non-mortgage assets are real estate
direct investments (DIRECT), non-mortgage loans (NONMORT), and junk
bonds (JUNK). Changes in the relative investment in these different assets
might affectthe volatilityofS&L equity returns.
An S&L's riskiness isalso influenced by the composition of itsmortgage loan
portfolio. During the early 1980s, S&Ls were given broader powers to hold
FRB CHICAGO Working Paper
September 1991, WP-1991-18
8
commercial mortgage loans. If S&Ls altered the composition of their
mortgage portfolios (moving, for example, from residential mortgage loans to
commercial mortgage loans), this might have a similar impact on S&L stock
return volatility as would shifts from traditional mortgage assets to
nontraditional non-mortgage assets. Barth and Bradley (1989) find that,
within the mortgage category, insolvent institutions have rapidly increased
their commercial mortgage lending. Barth, Bartholomew, and Labich (1989)
present evidence indicating that acquisition and development loans, which are
loans to finance the purchase of land and the accomplishment of all
improvements required to convert it to developed building lots, have a
positive and statistically significant effect on resolution costs. In our
empirical analysis, four mortgage loan categories are examined: residential
mortgage loans (RMORT), commercial mortgage loans (CMORT),
acquisition and development loans (ADL), and other mortgage assets
(OMORT). W e expect that returns on commercial mortgage loans and
acquisition and development loans would be more volatile than returns on
residential mortgage loans.
B. Data Sources and Estimation Procedure
The data used in this paper are for 75 S&L organizations whose stocks were
traded cm the New York Stock Exchange, American Stock Exchange, or over
the counter and who filedFederal Home Loan Bank Board (now the Office of
Thrift Supervision) Report of Condition data for each quarter from July, 1985
to December, 1989. A few of the 75 S&Ls were resolved by thriftregulators
prior to the end of the sample period. These failed institutions are included in
the sample period forthe quarters before resolution, and are excluded from the
sample fern the time period after resolution. Stock market data are from
Interactive Data Services, Inc. For multiple S&L holding companies, die
assetsof individual S&L subsidiaries are consolidated to construct thebalance
sheet variables discussed below.5
To obtain our measure of risk, we use the daily stock market data. For each
quarter in the sample period, estimates of the daily average rate of return and
standard deviation of the returns on S&L's equity, o^, were computed using
data covering the three month period ending with the last month of the
quarter. The market value of equity is calculated by multiplying the number
of shares outstanding at the end of each quarter by the price of the S&L's
equity atthe end of thequarter.
FRB CHICAGO Working Paper
September 1991, WP-1991-18
9
The asset-to capital ratio (LEV) is calculated as the ratio of total book value
assets to the market value of capital. All asset variables are from the
Quarterly Reports of Condition filed by all insured savings and loan
associations. The variable R M O R T is computed by dividing residential
mortgage loans by the market value of capital. C M O R T is the ratio of
commercial mortgage loans to the market value of capital. ADL represents
total acquisition and development loans divided by the market value of
capital. The other mortgage asset variable (OMORT) is the sum of
multifamily mortgage loans and mortgage-backed securities divided by the
market value of capital. The real estate direct investment variable (DIRECT)
is calculated by taking the sum of equity securities (except Federal Home
Loan Bank Stock), real estate investments, and investments in service
corporations or subsidiaries and dividing by the market value of capital. The
non-mortgage loan ratio (NONMORT) is the sum of total business and
consumer loans divided by the market value of capital. Finally, JUNK is
measured by taking the amount of S&L junk bonds in each quarter and
dividing by the market value ofcapital.
Equations (3) and (4) are estimated for a pooled cross-section, time series
sample of S&Ls from 1985:3 to 1989:4 to test the relationship between asset
mix and S&L market risk as reflected in the volatility of S&L equity returns.
To control forpossible correlation eitheracross institutions or across time, we
included both cross-sectional and time dummy variables in the specifications
of equations (3) and (4),rewritten here as equations (5)and (6):
r
N
+ 5J U N K . +e.
2
if
(5)
*.«
and
T
N
+ s.3RMORT.M + s.
OMORT. + s.CMORT.
4
i,r
5
ijt
+ s.ADL. + s.DIRECT. + saN ONMORT. +e.
6
FRB CHICAGO Working Paper
September 1991, WP-199118
M
'
i,l
8
if
if
(6)
10
where W t=l for quarter t(t=2,...,T)and 0 otherwise, and Zpl for the Ub S&L
(i=2,...,N) and zero otherwise. The model isalso estimated quarterly from die
beginning of 1987 to the end of 1989 to provide comparisons to tests
conducted laterin thepaper covering a similarperiod.
C. Empirical Results
Results from estimating equations (S) and (6) using ordinary least squares are
reported in Table 2. The estimated values of the parameters represent their
cross-sectional average values.6 The results from equation (S) show a
significant positive relationship between S&L return volatility and junk bond
holdings. This supports the claim that S&Ls with larger proportion of junk
bonds in their portfolios also exhibited higher volatility of stock returns. As
expected, the coefficient on LEV is statistically significant and positively
signed. This finding is consistent with the hypothesis that greater financial
leverage isassociated with more risk-taking.
The second column presents the results from estimating equation (6). Again,
the coefficient on junk bonds is significantly positive at the one percent level.
One other asset category-acquisition and development loans (ADL)~has a
positive and statistically significant coefficient, while the OMORT,
NONMORT, and DIRECT variables have significantly negative coefficients.
The result for ADL is consistent with previous studies which find that
acquisition and development loans have a positive and statistically significant
effecton riskiness. Itisalso worth noting thatthe coefficienton junk bonds is
significantly larger than any other asset coefficient except the coefficient for
ADL. What this implies is that, holding market value and total assets
constant, a portfolio shift from any asset except ADL into junk bonds would
increase stock return volatility. Thus, we conclude from these results that
holdings of junk bonds increased the volatility of S&L stock returns for our
sample of institutionsover the 1985:3-1989:4 period.
The third and fourth columns of Table 2 report the results from estimating
equations (5) and (6) over the period 1987:1 through 1989:4. These results
are similar to those over the entire sample period. In particular, the results
indicate that stock return volatility is positively correlated with JUNK and
ADL. The coefficients on N O N M O R T and DIRECT are negative and
significant. Overall, we find that increased emphasis on junk bonds resulted
in greaterstock return volatility.
FRB CHICAGO Working Paper
September 1991, WP-1991-18
11
One additional test was conducted to examine whether more liberal
regulations on Junk bond investment available to some state chartered
institutions may have resulted in their incurring greater risks than federally
chartered S&Ls. These liberal guidelines have been blamed by federal
regulators for some of the large lossesof failed statechartered S&Ls. Thus, it
is hypothesized that changes in junk braid holdings should have a greater
impact on die stock return volatility of state chartered S&Ls than federally
chartered firms. W e testthispredictionby estimating the following equation:
T
N
+ sxl(JUNK.j)(DUM) + t.j
(7)
where D U M isa binary variable that has a value of one when die observation
corresponds to federally chartered S&Ls and zero otherwise. The coefficient
on the multiplicative dummy variable, (JUNKitXDUM), measures the
increase (or decrease) in the impact of junk bonds on the stock return
volatility of federally chartered S&Ls relative to state chartered S&Ls. The
resultsarepresented inTable 3. The negative coefficienton the multiplicative
dummy variable indicates thatjunk bonds have lessof an impact on die stock
return volatility of federally chartered S&Ls than statechartered institutions.7
The greater range of junk bond authority available to state chartered
institutions may have resulted in their incurring greater risks than federally
chartered associations.
m . The Relationship between Deposit Interest Rates and Junk Bond Investments
In this section, we explore the relationship between the interest rate paid on
large,partially insured certificatesof deposit (deposits in excess of $100,000),
the amount ofjunk bonds relative to market value of S&L net worth, and a set
of variables designed to proxy for factors affecting the risk premiums on S&L
deposits. Following Baer and Brewer (1986), we specify the following
empirical model as equation (8):
FRB CHICAGO Working Paper
September 1991, WP-1991-18
12
RCD
= 8„ + 8,1RTB,t + 8.2 CAP.m + 5,RISK
3
M
*•*0
+ 8. SIZE. + 8.JUNK. + 8.AGROWTH. + i>. ,
4
«,/
s
v
«
v
M
(8)
RCDi<t represents the interest rate paid by the ift S&L in period t on
certificates of deposit with a maturity of six to twelve months and was
obtained from the Quarterly Report of Condition. S&Ls were not required to
submit deposit interestrate data toregulators prior to 1987; hence, our sample
period in this section is from the beginning of 1987 to the third quarter of
1989.8 RTBtis the interest rate on 182 day Treasury bills, measured by the
average yield over each quarter. The RISKit variable is obtained by
multiplying the variance in stock returns in a quarter by the square of the
market value of equity to total assets.^ The variable CAPitisthe ratio of the
market value of common stock to total assets of the ijfc S&L at the end of
quarto’t; SIZE^, represents the natural logarithm of total assets; JUNKj tis
the S&L holdings of junk bonds as defined earlier; AGROWTHj t is the
percentage change in total assets over quarter tfor the iih S&L; and’v^ is a
stochastic errortom.
Since CDs and Treasury bills are close but not perfect substitutes, we expect
the coefficient on RTB to be positive but less than one. S&Ls do not adjust
their C D rates as rapidly as market interest rates change. W e predict the
coefficient on CAP should be negative because a higher capital-asset ratio
implies a lower probability that depositors would suffer a loss.10 The
coefficient on RISK should be positive because an increase in stock market
risk implies that there isa greaterchance thatthe value of an S&L's assets will
fall below the level needed to repay all depositors. We include an asset size
measure as an additional explanatory variable to account for the possibilities
that either purchasers of negotiable CDs view larger S&Ls as having greater
implicit federal deposit insurance than smaller institutions or that the CDs of
larger S&Ls are more liquid. We hypothesize that the coefficient cm the ratio
of junk bonds to market value should be positive. If larger S&L holdings of
junk bonds increase the probability of failure, then uninsured depositors
would demand a higher risk premium. Rapid asset growth was linked by the
now-defunct Federal Home Loan Bank Board both to high likelihoods of
failureand tohigh costs to the deposit insurance fund to resolve those failures.
According to our hypothesis, the greater is the growth in assets, the more a
S&L would have to pay on CDs to attract funds and to compensate uninsured
depositors forincreased exposure toriskof failure.
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September 1991, WP-1991-1B
13
The results from estimating equation (8) are presented in Table 4. The results
indicate that all the coefficients are significantly different from zero. As
expected, die CD rate is positively related to the Treasury bill rate and
negatively related to both the capital-asset ratio and asset size. Both
coefficients on die RISK and JUNK variables are significantly positive,
indicating that depositors demanded higher interest premiums to compensate
for bearing additional risk. Moreover, institutions which had larger holdings
ofjunk bonds paid an additional risk premium over institutionswith the same
market risk but smaller holdings of junk bonds. Finally, the results show a
significant positive relationship between S&L CD rates and asset growth,
supporting the concerns of many that institutions growing rapidly are paying
higher rates to increase their deposits. These results are consistent with
previous studies that found a risk premium in interestrates paid on large CDs
[see, forexample, Baer and Brewer (1986), Hannan and Hanweck (1987), and
James (1990)]. Thus, we conclude that institutionswhich had larger shares of
junk bonds in their portfolios were perceived as more risky by uninsured
depositors.
IV. The Impact ofJunk Bond Investments on S&L Stock Returns
A. Theoretical Considerations
In this section, we examine the effects of junk bond holdings on the stock
returns of savings and loan associations. We have already shown that S&Ls
with higher proportions of junk bonds were perceived as riskier by both
stockholders and depositors. S&Ls with large holdings of junk bonds were
also less capitalized than those with small holdings were. Figure 1 compares
the aggregate generally accepted accounting principles (GAAP) capital-toasset ratios for the 18 S&Ls in the sample classified as "high” junk
bondholders with those far die 57 S&Ls classifiedas "low" junk bondholders.
To be considered a "high”junk bondholder, an S&L must have ranked among
the top 50 junk bondholders at the beginning of the sample period. For every
quarter between 1986 and 1989, the capital-asset ratio for the "high" junk
bond group was lower than the "low" junk bond group.11
The impact ofjunk bond investments on S&L stock returns may differacross
firms because underpriced deposit insurance may be more valuable to poorly
capitalized S&Ls than to others. Merton (1977) and Buser, Chen and Kane
(1981) show that providing deposit guarantees at less than their market value
subsidizes S&Ls. The value of this subsidy equals the difference between the
FRB CHICAGO Working Paper
September 1991, WP-1991-18
14
Figure 1
G A A P net worth ratios
percent
cost of risky and riskless (guaranteed) deposit claims less the premium
charged for insurance. Access to future deposit guarantees, under these
circumstances, isan asset of the S&L. The value of this asset is equal to the
present value of the stream of subsidies the S&L expects to receive. It
increases in value, ceteris paribus,when either the S&L's leverage and/or the
volatility of die returns on its underlying assets (and o, t) increase. Thus,
because insurance premiums are not a function of risk exposure, the
shareholders of S&Ls with the largest exposure to the junk bond market,
ceteris paribus, obtain more net benefits from deposit insurance than those
with the smallestexposure to thejunk bond market.
Increased risk taking, however, may adversely affect S&L stock returns of
well-capitalized institutions. First, regulators are highly concerned about
maintaining S&L safety and soundness. Consequently, regulators impose
solvency standards on S&Ls by setting capital adequacy requirements and
implicitly defining upper bounds on acceptable probabilities of ruin. Such
policies effectively impose a (risk of ruin) constraint on an S&L's portfolio
FRB CHICAGO Working Paper
September 1991, WP-1991-18
15
choice in return-risk space. Assuming that tiiis constraint is viewed as
binding, an increase in volatility due to largerjunk bond holdings serves to
reduce the opportunity set of acceptable S&L portfolios, forces itaway from
its optimal unregulated portfolio, and lowers die market value of the S&L.
Alternatively, an S&L may stay with higher rideportfolios only by improving
its capital adequacy. However, increasing capital through cutting dividends,
retaining earnings, or issuing new stock may be costly to existing
stockholders. As noted by Buser, Chen and Kane (1981), capital
requirements, S&L activity/portfolio restrictions, as well as S&L
examinations, can be thought of as taxes imposed by regulators which create
deadweight losses to the value of the S&L. Moreover, to the extent that an
S&L has a valuable charter, the value of the firm will fall with an increase in
volatility.
To examine the impact ofjunk bonds on S&L stock returns, we estimated the
following pooled cross-section, time series regression,based on James (1990),
which isderived in the appendix:
N
R E TM= «0 + y
^
N
a.z.+
Y,
B ZMRET,t
j i
JL d *f i
•*1
(9)
where ft is the stock market beta coefficient of the igh S&L (i = 1.....N),
MRET, is the return on the market portfolio, Jwtkitis the iih S&L's holdings
ofjunk bonds in period t,MV^tis die market value of capital of the ilb S&L
in period t,TA^tis the book value of total assets of the ilb S&L in period t,
and (oitisa stochastic error term. To control for the possible impact of other
S&L-specific factors on stock returns, individual S&L dummy variables, Zj's,
are included in the regression equation. In addition, individual S&L dummy
variables multiplied by the return cm the value-weighted market portfolio,
ZjMRETt,are included toallow the market betas to vary cross-secdonaUy.
Much of the concern about S&L junk bond holdings has to do with S&Ls
gambling the institutions' assets on investments with large but high-risk
payoffs. In order to examine this issue, the S&Ls in this study are divided
into two groups according to their junk bond exposure. Those stock
associations thatare among the top SO junk bondholders are classifiedas high-
FRB CHICAGO Working Paper
September 19911WP-1991-18
16
junk bond holders. Out of our sample of 75 S&L holding companies, 18 were
among the top 50 junk bondholders. The remaining 57 S&L holding
companies in the sample are classifiedas low-junk bondholders. Equation (9)
is estimated separately for each group of S&Ls. Table 5 shows the average
totalassets for the S&Ls in each group over the sample period 1985:3-1989:4.
Average total assets for the low-junk bond group of S&Ls is $4,700 million
and the high-junk bond group is$7,048 million.
The fact that institutions with large holdings of junk bonds also had lower
than average capital might explain the rapid growth of junk bond holdings
from 1986 to 1988. Theory suggests that an S&L would increase holdings of
junk bonds if the expected marginal benefit to the institution of doing so
exceeds itsexpected marginal cost. Ifstock markets are operating efficiently,
then such institutions should receive higher stock returns. However, the
existence of deposit insurance alters the risk-return trade-off for some
institutions. Shareholders of S&Ls with high capital-asset ratios would be
bearing all of the risk of junk bond investments, but shareholders of S&Ls
with low capital-asset ratios would share the risk with the deposit insurer. In
the extreme case of an institution that ismarket value insolvent but kept open
by regulatory forbearance, the shareholders are bearing no additional risk
from increased S&L risk-taking. Thus, stock returns of such institutions
should actually increase as these institutionsacquire more riskyassets.
If, in the absence of deposit insurance, the expected marginal benefit of
increased junk bond holdings is less than expected marginal cost, then we
should observe a negative relationship between the stock returns of wellcapitalized, "low” junk bond institutions and their junk bond holdings.
However, because the "high" junk bondholders also have less capital, we
expect the stock market returns of these institutions to be higher if they
acquire more junk bonds since the value of the deposit insurance option,
which is capitalized into the market value of the stock, increases with
additional risk-taking. By dividing the sample into "high” and "low" junk
bond S&Ls, we can test this "forbearance” hypothesis. Thus, in equation (9)
we expect the sign of 6j to be positive for "high” junk bondholders and
negative for "low junk bondholders." If returns are inversely related to the
capital-asset ratio, then the sign of 62, by construction, should have the
opposite sign of 0i-
FRB CHICAGO Working Paper
September 1991, WP-1991-18
17
B. Data Sources and Empirical Procedure
The data sources for the stock prices, market values, total book value of
assets,and junk bond holdings are described in section n. The common stock
returns over a quarter are calculated by compounding weekly common stock
returns within a quarter. The stock market portfolio used to compute M R E T
in this study is the value-weighted portfolio (NYSE and AMEX) obtained
from the Center farResearch in Security Prices (CRSP) data base. A measure
of the total returns on junk bonds isconstructed from a high yield bond index
obtained from Merrill-Lynch.1^ Our methodology involves first estimating,
for each group of S&Ls over the period 1987:1 through 1989:4, the
relationship between an S&L's stock return
and the market return
(MRET,) and the return on the high yield bond index (RHYBONDJ. Second,
equation (9) isestimated foreach group of S&Ls using ordinary leastsquares.
C. Empirical Results
Table 6 contains the results from the estimation of the stock return equations.
Part A shows estimates of a two-factor market model which relates the return
on S&L stock to die market returns on both common stocks and high yield
bonds. The results in Part A of Table 6 indicate that stock returns of both
high- and low-junk bondholders are sensitive to changes injunk bond returns.
Junk bond returns are shown to be positively related to S&L stock returns;
however, the coefficient difference between high- and low-junk bond S&Ls is
not statisticallysignificant.
As discussed earlier, an increase injunk bond holdings can either increase or
decrease S&L stock returns, depending on the value of the deposit insurance
option relative to the value of die S&L charter. An S&L's charter value can
be divided into three categories. The first is the value of business
relationships built over time. Kane and Malkiel (1965) argue that
longstanding customer banking relationships have value because they lower
the information and contracting costs associated with doing business. The
reduction in the cost of servicing longstanding customers is available only to
the servicing S&L and isa source of profitable future business opportunities.
The second source is monopoly rents that may accrue to S&Ls from
branching laws and other regulations that restrict competition. The third
source of the charter’s value is the ability of depository institutions to borrow
on a collateralized basis from the Federal Home Loan Banks. These factors
taken together could cause S&L stock returns to decrease with an increase in
the volume ofjunk bonds.
FRB CHICAGO Working Paper
September 1991, WP-1991-18
18
Part B of Table 6 presents the results of estimating equation (9). The first
column of results indicates, for high-junk bond institutions, a statistically
significant and positive relationship between stock returns and changes in
junk bond investments, controlling for movements in the returns on junk
bonds. The second column shows, for the sample of S&Ls with small junk
bond holdings, a statistically significant but negative relationship between
S&L stock returns and changes in junk bond investments relative to market
capital. This is consistent with the notion that deposit insurance is more
valuable to institutions with largejunk bond exposure relative to capital, but
less valuable to institutions with low junk bond exposure. In the latter case,
capital adequacy considerations and other charter value concerns impose costs
on S&Ls thatlower theirmarket value.
Finally, <t>j (= 6 2 / 6 1 as defined in the appendix) is negative and statistically
significant only for the sample of S&Ls with low exposure to the junk bond
market. This result is consistent with the work of Galai and Masulis (1976)
which predicts that the sensitivityof common stock returns with respect to the
return on the underlying assets of the firm varies inversely with the firm's
capital-to-asset ratio. However, Brickley and James (1986) argue that
common stock returns of distressed financial institutions do not necessarily
vary inversely with a firm's capital-to-asset ratio because decreases in this
ratioincrease the riskborne by the federal deposit insurer,raising the value of
access to insurance. The insignificant <|>icoefficient for the S&Ls with the
largest exposure to the junk bond market is consistent with the latter
prediction.
V. Summary
In this paper, we firstexamine whether the financial markets view S&Ls with
relatively large exposure tojunk bonds as more risky than S&Ls with smaller
exposure to junk bonds. We test this hypothesis using data on S&L stock
returns and interest rates paid on large CDs. W e find that equity return
volatility appears to be positively related to the proportion ofjunk bonds held
in S&L portfolio. In addition, we find evidence that C D holders demand
higher rates when junk bond holdings increase relative to market value of
equity.
Given thatlargerjunk bond holdings increase S&L risk, we attempt toexplain
why junk bond holdings are concentrated among a small number of
institutions and why these holdings grew so rapidly in the 1986-1988 period.
FRB CHICAGO Working Paper
September 1991. WP-1991-18
19
Because of the low capital-asset ratios of the largejunk bondholders, we test
the "forbearance" hypothesis by dividing die sample of institutions into two
groups based on theirjunk bond holdings, and examine die relation between
their stock returns and their holdings of junk bonds. W e find that the stock
returns of S&Ls who have relatively largejunk bond portfolios are positively
related to changes in junk bond holdings. The stock returns of other S&Ls,
however, are negatively related to changes in junk bond holdings relative to
their capital. These results support the notion that the stock returns of S&Ls
on the "edge" respond to volatility increases as if deposit insurance is a
valuable subsidy. Access to deposit insurance is not as valuable for other
types of S&Ls (that is, those with litde, if any, exposure to the junk bond
market).
The resultsof thisstudy should not be construed as support forthe decision by
Congress to force S&Ls to exit the junk bond market by 1994. Rather, we
argue that regulatory forbearance allowed S&Ls to take on excessive risk in
many ways, including die purchase of junk bonds. Forbearance, in effect,
rewards S&Ls for taking additional risks, since it induces a positive
correlation between stock market returns and holdings of risky assets.
Closing the junk bond market to S&Ls will not prevent S&Ls from taking
more risk because there are many ways far depository institutions to acquire
assets which are at least as risky as junk bonds. Legislative action which
attacks excessive risk-taking by prohibiting institutions from acquiring
particular classes of risky assets is attacking the symptoms of the disease
instead of its causes and is doomed to fail. If the incentives to increase risk
are there, then value-maximizing institutions will find a way to circumvent
regulations and increase risk. The solution is to adopt policies that eliminate
incentives forinstitutionswith low capital to increase theirriskexposure.
FRB CHICAGO Working Paper
September 1991. WP-1991-18
20
Appendix
This appendix presents a formal derivation of equation (9) in the text by
modelling changes in market value ofequity. W e adapt the procedure used by
James (1990) toanalyze the effectof LDC debt on bank stock returns. Define
the market value of equity of the iih S&L as MVj and the market value of the
ilh S&L's total assets as A r Assume that the returns on S&L assets are
lognormally distributed with a constant instantaneous variance of a \ In
addition, assume that the S&L pays no dividends and that S&L deposits have
promised payments of Xj due in x periods. Then the change in the value of
total assets through time can be described by the following stochastic
differentialequation:
dA
— - = a.dt + aeb,
A
*
»
A.
(A.1)
where cq is the instantaneous expected return on the assets and dz is a
standard Gauss-Wiener process.
The market value of equity of the i|tiS&L reflects the value of the asset and
of time. That is,
MV=F(A.,t).
(A2)
Given the distributional assumption on A;, we have, by Ito'sLemma, that the
change in the market value of equity over time satisfies the stochastic
differentialequation:
d M V - F dA. + ^ F
i
i *
2
(dA)2 + F^dt,
11
1
2
(A.3)
where
(A.4)
FRB CHICAGO Working Paper
September 1991, W P1991-18
21
Substituting (A.4) into (A.3)and dividingby MVj results in
MV.
t
MV.
L
iJ
+\f
C4 —
dA .1
--F.
V
1___
dM V
2 ti MV.
1-
i
dt.
LMV.iJ
dt + F 2
(A.5)
Assuming the last two terms in equation (A.S) can be captured by a constant
Xj,the rateofreturn on theequity can be written as
r a <.i[dAl
i
1 MV. A.
L <JL iJ
RET = \+F ,
I
I
(A.6)
Next, partition S&L total assets into two categories: junk bonds and "other
assets". Let,
A.i tV. + Junk.,
I
(A.7)
where V, die market value of the S&L's other assets and Junk4is the market
value of the S&L's junk bond portfolio. Substituting into equation (A.6)
yields the following expression:
m i r^.i
Junk'
«
% d(Junk.)
1 MV. V. + F l MV.
L iJL iJ
L iJL Junk.< J
RET. = X.+ F,
I
I
(A.8)
The expression d(.funk^/Junk^ represents the totalreturn on the itb S&L's junk
bond portfolio. Since we do not know the composition of each S&L's junk
bond portfolio, we assume the total returns for the portfolio can be
approximated using the total returns for an index of junk bonds. Also let
M O R E T approximate dVj/Vj. Then equation (A.9) becomes:
R E T = X. + F,
I
i
1
Junk'
v< 1
i
M O R E T + F,
RHYBOND,
l
MV.
MV.
L
<J
(A.9)
where M O R E T is a proxy for the return on the S&L's other assets and
R H Y B O N D is an index of the total return on junk bonds. In the empirical
analysis, we assume thatthe returns on other S&L assets are unconelated with
FRB CHICAGO Working Paper
September 1991, WP -1991-18
22
the return on junk bonds. The return on the NYSE index (MRET) isused as a
proxy forthereturn on other S&L assets.
To account for cross-sectional differences among S&Ls in theirsensitivity to
general stock market movements, equation (A.9) isrewritten as
N
Junk.
___i
RET.i= X.<+ T
B1Z.MRET+
F1
,
RHYBOND,
M *
<
MV.f .
1*1
(A.10)
where P, is the stock market beta coefficient of the ijh S&L (i=l,...,N) and Z i
= 1 forthe ilbS&L (i=l,...,N) and zero otherwise.
The Fj expression measures the responsiveness of market value of equity to a
change in the value of S&L assets. Itcan be shown thatthe Ftis,
( A M)
where
d x = lln (A / X .)
+ (r+ of/2) x]/o. v/T ,
(A.12)
with r= risk-freerate of interestand N(.) isthe cumulative normal distribution
function.
Galai and Masulis (1976) show thatN(d]) varies inversely with the capital-toasset ratio. Following James' specification, we assume that a linear
approximation forN(dj) can be written as
N(dx)= 1 + *,
(A.13)
where 4>]isa parameter, with theprediction that4>i< 0.13
FRB CHICAGO Working Paper
September 1991, WP-1991-18
23
Substituting (A.13) into (A.10), adding a stochastic error term and dummy
variables to control for "other” cross-sectional effects, and rearranging results
in
N
RET4
. ,1=
Junk.
___M
M V.
N
a0„ +A Y
a| Z1+ ^
V r*
p Z 4.M R E T ,t
md
Junk.
RHYBONDt +
if.
02 ___y R H Y B O N D , + co. ,
TA.
•f
(A. 14)
J
where 62 = 0i4>i and co^t is a stochastic error term. Equation (A.14) is
reported as equation (9) in the text
FRB CHICAGO Working Paper
September 1991. WP-1991-18
24
FOOTNOTES
^Noninvestment grade securities may be transferred to a bolding company affiliate or (for
mutuals) to a separately capitalized subsidiary.
2Califomia, Connecticut, H onda, Louisiana, Ohio, Texas, and Utah were the states with more
lenient guidelines for state chartered S&Ls.
3ft is worth noting that our specification for the junk bond variable is equivalent to dividing junk
bonds by total assets and multiplying by leverage. Thus, leverage is implicitly interacted with the
ratio o f junk bonds to total assets in this specification. W e thank an anonymous referee for
clarifying this point
^One might expect that stock return volatility may be positively related to growth since many
S&Ls suffering large losses also were growing rapidly during this period. W e did not include this
variable for two reasons. First, Brewer (1989) found no statistically significant relationship
between growth in liabilities and stock returns. Second, we believe that growth is a consequence
rather then a cause o f S&L risk taking, since more rapid deposit growth enables an institution to
acquire more risky assets. In this section, we choose to focus on the relationship between stock
return volatility and asset choice.
^For each o f the holding companies included, the S&Ls were the major activity o f the holding
company in terms o f assets. The mean ratio o f S&L assets to total holding company assets was 96
percent over the sample period. Other holding company activity included real property
management, housing development, brokerage services, insurance products, data processing
services, corporate debt and equity services, and real estate appraisal services. Assets for the
holding companies were obtained from Moody's Banking and Finance M anual, various years.
^Two additional tests were performed. First, using White's test for heteroskedasticity, we were
unable to reject the null hypothesis of homoskedasticity. Second, we estimated the equations
using Fuller's and Battese's (1974) error components model and the results were qualitatively
similar to those reported.
^The simple correlation coefficient between the junk bond-maiket value ratio and the charter
dummy variable was *0.09, which was significantly different from zero at the one percent level.
®Data for the fourth quarter o f 1989 were not available.
^This adjustment has been used by several other researchers including Marcus and Shaked
(1984).
l^ F or empirical evidence supporting this hypothesis see Pozdena (1991) and Gendreau (1991).
^ * Similar results are obtained when tangible accounting principle capital-to-asset ratios are
compared.
l^ T h e junk bond index started October 31, 1986. The simple correlation coefficient between
MRET and RHYBOND was 0.26 and was not statistically significanat from zero.
^3|n other words, a one percent decrease in total assets, octeris paribus, has a much larger
proportional change on the market value o f equity when the capital asset ratio is lower. However,
for market value insolvent institutions kept open by regulatory forbearance, a decrease in total
assets, ceteris paribus, would also increase the value o f the deposit insurance option. Thus, the
sign o f
becomes ambigious when M V gets close to zero.
FRB CHICAGO Working Paper
September 1991, WP-1991-18
25
REFERENCES
Baer, Herbert and Elijah Brewer. "Uninsured Deposits as a Source of Market
Discipline: Some New Evidence." Economic Perspectives, Federal Reserve
Bank of Chicago (September/Octbber 1986), pp. 23-31.
Barth, James R., Philip F. Bartholomew, and Carol Labich. "Moral Hazard
and the Thrift Crisis: An Analysis of 1988 Resolutions." Proceedings of a
Conference on Bank Structure and Competition, Federal Reserve Bank of
Chicago, 1990
Barth, James R., and Michael G. Bradley. "Thrift Deregulation and Federal
Deposit Insurance." Journal of Financial Services Research 2(September
1989), pp. 231-259.
Benston, George J. and Michael F. Koehn.
"Capital Dissipation,
Deregulation, and the Insolvency of Thrifts." Unpublished papa (December
1989).
Black, F. and Scholes, M. "The Pricing of Options and Corporate Liabilities."
Journal ofPolitical Economy 81(May/Iune 1973), pp. 637-659.
Blume, Marshall E., Donald B. Keim, and Sandeep A. Patel. "Returns and
Volatility of Low-Grade Bonds." Journal of Finance 46(March 1991), pp.
49-74.
Brewer, Elijah m. "The Impact of Deposit Insurance on S&L Shareholders'
Risk/Retum Trade-offs." Working Paper Series, Federal Reserve Bank of
Chicago (December 1989).
Brickley, James A. and Christopher M. James. "Access to Deposit Insurance,
Insolvency Rules, and the Stock Returns of Financial Institutions." Journal of
Financial Economics 16(July 1986), pp. 345-371.
Buser, Stephen A., Andrew H. Chen, and Edward J. Kane. "Federal Deposit
Insurance, Regulatory Policy and Optimal Bank Capital." Journal ofFinance
36(March 1981), pp. 51-60.
Christie, A. A.
"The Stochastic Behavior of Common Stock Variance."
Journal ofFinancial Economics 10(December 1982), pp. 407-432.
Fuller, W. A. and G. E. Battese, "Estimation of Linear Models with CrossedError Structure," Journal ofEconometrics 2(May 1974), pp. 67-78.
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September 1991, WP-1991-J8
26
Galai, D. and R. W. Masulis. "The Option Pricing Model and die Risk Factor
of Stock." Journal of Financial Economics 3(January/March 1976), pp. 5381.
Gendreau, Brian.
"U.S. Deposit Insurance Reform.”
World Financial
Markets, Morgan Guaranty Trust Co. (January 25,1991).
General Accounting Office. "High Yield Bonds: Nature of the Market and
Effect on Federally Insured Institutions." Washington: Government Printing
Office (May 1988).
Hannan, Timothy H. and Gerald A. Hanweck. "Bank Insolvency Risk and the
Market for Large Certificates of Deposit," Journal of Money, Credit, and
Banking 20(May 1988), pp.203-211.
James, Christopher. "Heterogeneous Creditors and the Market Value of Bank
LDC Loan Portfolios." Journal of Monetary Economics 25(June 1990), pp.
325-346.
James, Christopher. "The Use of Loan Sales and Standby Letters of Credit by
Commercial Banks." Journal of Monetary Economics 22(November 1988),
pp.395-422.
Kane, Edward J. The Gathering Crisis in Deposit Insurance. MIT Press,
Camridge, MA, 1985.
Kane, Edward J and B. G. Malkiel. "Bank Portfolio Allocation, Deposit
Variability, and the Availability Doctrine." Quarterly Journal of Economics
(February 1965), pp. 113-134.
Marcus, Alan and Israel Shaked. "The Relationship Between Accounting
Measures and Prospective Probabilities of Insolvency: An Application to the
Banking Industry." Financial Review 19(March 1984), pp. 67-83.
Merton, Robert C. "Analytical Derivation of the Cost of Deposit Insurance
and Loan Guarantees: An Application of Modem Option Pricing Theory."
Journal ofBanking and Finance l(June 1977), pp. 3-11.
Perry, Kevin J. and Robert A. Taggart, Jr. "Development of the Junk Market
and Its Role in Portfolio Management and Corporate Finance," in Edward I.
Altman, ed., The High-Yield Debt Market: Investment Performance and
Economic Impact. Homewood, 111: Dow Jones-Irwin, 1990.
Pozdena, Randall. "Recapitalizing the Banking System.” FRBSF Weekly
Letter, Federal Reserve Bank of San Francisco (March 8,1991).
FRB CHICAGO Working Paper
September 1991, WP-1991-18
27
Table 1
Junk bond holdings at savings & loan associations-l985-1989
All Savings and Loans
Y e a r: Q TR
-High"
-Low-
Junk Bond $&L$
Junk Bond SSLs
50 Largest Holders
in sample
in sample
Total Junk
Percent of
Total Junk
Percent of
Total Junk
Percent of
Total Junk
Percent
Bond Holdings
GAAP Capital
Bond Holdings
GAAP Capital
Bond Holdings
GAAP Capital
Bond holdings
GAAP Capital
1 9 8 5 :4
6022.7
16.3
5919.5
122.0
3881.6
123.2
284.3
1 9 8 6 :2
6829.7
17.4
6747.6
135.6
4743.4
112.1
178.5
1.9
1 9 8 6 :4
8096.2
18.4
7971.6
141.9
5632.6
108.1
123.1
1.2
3.8
1 9 8 7 :2
10625.4
23.0
10437.1
113.1
7601.8
125.6
160.3
1.3
1 9 8 7 :4
12493.2
29.8
12271.8
128.4
9169.7
151.5
136.3
1.1
5.4
1 9 8 8 :2
13497.9
36.2
13193.0
144.9
9457.4
155.3
688.1
1 9 8 8 :4
15341.8
28.6
14845.4
141.7
10426.9
173.9
1023.9
7.5
1 9 8 9 :2
13424.7
26.8
12846.7
134.1
8491.0
146.7
1073.5
7.1
1 9 8 9 :4
10675.5
33.6
10316.5
296.8
6064.5
183.8
516.0
4.4
Notes: Data are from Quarterly Reports of Condition filed with the Office of Thrift Supervision. Junk bond holdings are expressed in millions of dollars, and as a
percentage of net worth measured using generally accepted accounting principles (GAAP).
Table 2
The Impact of asset mix on S&L stock return volatility
(All S&Ls)
a.is *
0
+
O. ~ j +
is
•
Y s. W 4 Y c . Z. + s . L £ V + s J U N K + t
Av
0/
Yr
0,r
A- v
i«2
•
Av
lb
i
I
M
2
M
M
W» + J \ c 0>Z . i + t 1tLEVM + s2 JUNK.M + s%
RM0RTM + *OMORT
2
4
M
A2
+ XCMORT
«fs.ADL. + 1.DIRECT + xNONMORT + £
5
if
%
is
1
M
•
M M
where o^t equals the standard deviation of the i|fc S&L's stock retoms in quarter t, Wt is a time
dummy variable, Zj is an S&L dummy variable, LEVj , is the ratio of total assets-to-market capital,
RMORTj t , CMORTi t, ADLj t, OMORT^, DIRECTi t , NONMORTj t , a id JUNKj t are ratios to
market value of capital of residential mortgage loans, of commercial mortgage loans, of acquisition
and development loans, of other mortgage loans, of direct real estate investments, of nonmortgage
loans, and of junk bonds, respectively. Coefficient estimates of time and cross-sectional dummy
variables are not reported but are available upon request from the authors.
Variable
Intercept
LEV
(JUNK)
85:3- 89:4
Parameter
Parameter
Estimate
Estimate
2.6417
(7.306)***
0.0041
(21.939)***
0.0306
(4.819)***
2.6004
(7.991)***
0.0082
(2.354)***
0.0330
(3.465)***
-0.0023
(-0.479)
•0.0096
(-2.305)**
0.0091
(1.256)
0.1100
(8.102)***
•0.0658
(-4.506)***
-0.0076
(-1.720)*
3.1987
(7.099)***
0.0039
(19.177)***
0.0368
(4.988)***
3.2007
(8.069)***
0.0068
(1.669)*
0.0447
(4.084)***
0.0029
(0.503)
-0.0072
(-1.515)
0.0017
(0.214)
0.1389
(8.544)***
-0.0837
(-4.511)***
-0.0080
(-1.621)*
0.6050
22.017
1277
0.6803
28.430
1277
0.6637
20.481
858
0.7398
27.201
858
(RMORT)
(OMORT)
(CMORT)
(ADL)
(DIRECT)
(NONMORT)
Adj. R-Sq
F-Stat:
N-
87:1 - 89:4
Parameter
Parameter
Estimate
Estimate
T-Statistics in parentheses are starred if coefficients are significantly different from zero at the tO f),
5(**), and
percent levels.
Table 3
The impact of asset mix on S&L stock return volatility
Federal ve. State restriction of Junk bond holdings
(All S&Ls)
Estimated Equation:
T
ma
N
VMW* +
M
XPVm +«*,
where o^( equals the standard deviation of the i& SAL'S stock returns in quarter t, W, is a time
dummy variable, Zjis an SAL dummy variable, LEVi t is the ratio of total assets-to-maiket capital,
JUNKj?t is the ratio of junk boods-to-maritet value of capital, and DUM is a binay dummy
variable taking on the value of one for federally chartered SALs, aero otherwise. Coefficient
estimates of time and cross-sectional dmtmy variables are not reported but are available upon
request from the authors.
65:3 - 89:4
Parameter
Estimate
Variable
Intercept
87:1 - 89:4
Parameter
Estimate
2.6499
(7.357)***
3.2101
(7.153)***
0.0041
(21.980)***
0.0039
(19.237)***
0.0553
(5.501)***
0.0618
(5.206)***
(JUNK)(DUM)
-0.0385
(-3.162)***
•0.0385
(-2.681)***
Adj. R-Sq
F-Stat:
N-
0.6080
22.055
1277
0.6664
20.452
858
LEV
(JUNK)
Dum is a binary variable taking on the value of one for federally chartered S&Ls, zero otherwise. T-statistics in
parentheses are starred if coefficients are significantly different from zero at the 1(***) percent level.
Junk bond coefficients
85:3 •89:4
Charter-type
Parameter
Estimate
87:1 •89:4
Parameter
Estimate
Parameter
Estimates
Parameter
Estimates
State
0.0553
0.0480
0.0618
0.0674
Federal
0.0168
(5.501)***
0.0213
(4.151)***
0.0233
(5.206)***
0.0263
(5.085)***
Numbers in parentheses beneath the federally chartered S&L junk bond coefficients are the corresponding
t-statistics. Ail t-statistics are significantly different from zero at the
percent level.
Table 4
A pooled cross-section time series examination of the
relationship between the Interest rate paid on CDs with maturities
greater than six months and the characteristics of the S&L
Estimated Equation:
1987:1 -1989:3
R CD ijt = 60+ 5 ,1RTBt• + h2 CAPi/ + h3RISK tj +85/ZE
4 i/ + h5JU N K i/
+ S A G R O W T II
6
ijt+ \ ) tjt
where RCDj t equals the interest paid on large CDs with a maturity between 6 and 12 months o f
the ith S&L in quaiter t, RTBt is the 182-day Treasury bill rate, C A P jt is the ratio o f market
capital-to-assets, RISKj t is the adjusted variance in stock returns, SIZEj t is the natural logarithm
o f total assets, JUNKj t is the ratio o f junk bonds-to-market value o f capital, and AGROWTHj t is
the percentage change in total assets.
Parameter
Estimate
Intercept
2.4690
(12.138)***
R TB
0.8066
(59.647)***
CAP
-1.0195
(-3.001)***
RISK
0.3532
(2.331)**
JU N K
0.0045
(2.312)**
SIZE
-0.0317
(-2.608)**
A G R O W TH
Adj. R-Sq
F-Stat:
N -
0.4947
(2.143)**
0.8315
638.449
776
Tha estimates are generated for the period 1987:1 to 1989:3 because of data
availability. Fourth quarter 1989 data were not available. T-statistics in
parentheses are starred if coefficients are significantly different from zero at the
5(**) and 1(***) percent levels.
Table 5
Savings and loan organizations
Low-Junk Bondholders
Ahmanson H.F. and Co.
Altus Bank F.S.B. (Alabama)
American Savings Bank F.S.B. (New York)
Ameriwest Financial Corp.
Atlantic Financial Federal
Bankers First Corp.
Buckeye Financial Corp.
Calfed, Inc.
C F S Financial Corp.
Citadel Holding Corp.
Citizens Savings Financial Corp.
Coast Federal Savings and Loan Association
Collective Federal Savings Bank
Columbia First Federal Savings and Loan Association
Comfed Savings Bank (Lowell)
Crossland Savings F.S.B. (New York)
D and N Savings Bank F.S.B.
Downey Savings and Loan Association
Financial Corp. of America
First Federal of Michigan (Detroit)
First Federal Savings and Loan Association
of Fort Myers (Florida)
First Indiana Corp.
First corp Inc.
Fortune Financial Group Inc.
Gienfed Inc.
Golden West Financial Corp.(Delaware)
Great Western Financial Corp.
Hawthorne Financial Corp.
Heart Federal Savings and Loan Association
Home Federal Savings Bank
Home Owners Federal Savings and Loan Association
Average
Asset Size
(in$1000's)
30,966,285
2,516,735
4,257,123
2.072.109
6,340,020
1,106,621
1,204,078
21,952,827
1,033,184
3,896,797
3,123,631
1,188,942
1,791,089
1,930,784
1,366,644
12,966,772
1,870,513
3,296,356
32,625,711
11,698,644
817,527
1,053,012
700,138
2,742,964
20,018,974
14,553.048
27,586,241
841,382
729,155
282,947
2.799.110
Table 5 (cont.)
Low-Junk Bondholders
Landmark Land Inc.
Landmark Savings Association (Pennsylvania)
Mercury Savings and Loan Association
Metropolitan Federal Savings and Loan Association
Metropolitan Financial Corp.
Mid-State Federal Savings and Loan Association
Nafco Financial Group Inc.
Numerica Financial Corp.
Old Stone Corp.
Pacific First Financial Corp.
Pioneer Federal Savings and Loan Association
Pioneer Savings Bank
Ponce Federal Bank F.S.B.
Poughkeepsie Savings Bank F.S.B.
Prudential Financial Services
Security Capital Corp. (Delaware)
South Eastern Savings and Loan Association of
Charlotte (North Carolina)
Southmark Corp.
Valley Federal Savings and Loan Association
of Van Nuys (California)
Virginia First Savings Bank F.S.B.
Washington Federal Savings and Loan Association
Wesco Financial Corp.
Western Capital Investment
Western Federal Savings Bank PR
Western Savings and Loan Association
York Financial Corp.
Average
Asset Size
(in $1000's)
1,880,827
1,635,030
2,308,080
1,119,425
2,163,200
885,966
1,545,312
964,641
4,019,481
4,591,559
517,753
2,046,960
1,048,691
1,450,013
795,174
2,228,524
452,279
3,084,502
2,972,382
463,006
1,734,864
347,415
3,459,170
541,701
5,620,376
712,656
Table 5 (corn.)
High-Junk Bondholders
American Continental Corp.
American Savings and Loan Association of Florida
Boston Five Cents Savings Bank
Centrust Savings Bank
Cityfed Financial Corp.
Coast Savings and Loan Association
Columbia Savings and Loan Association
Commonwealth Savings and Loan Association (Florida)
Dime Savings Bank of New York F.S.B.
Far West Financial Corp.
Financial Corp. of Santa Barbara
Germania Bank A Federal Savings Bank
Gibraltar Financial Corp. of California
Great American First Savings Bank (San Diego)
Home Federal Savings and Loan Association
of San Diego (California)
Imperial Corp. of America (Delaware)
Northeast Savings F.A.
Sooner Federal Savings and Loan Association
Average
Asset Size
(in$1000's)
4,021,889
2,803,049
2,092,630
7,603,975
10,146,035
10,886,183
9,384,621
1,361,745
10,698,985
3,661,383
4,367,618
723,125
12,587,135
14,206,294
13,729,680
10,315,176
6,512,365
1,754,911
Notes: Data are an average of quarterly values from 1985:3 to 1989:4 from the Reports of
Condition filed with the Office of Thrift Supervision. These values are for the S&L only and
not for the entire holding company.
Table 6
Stock returns equations
A.
The effects of high-yield returns on the common stock returns of
S&Ls
Estimated Equation:
1987:1 -1989:4
R ETi
t + PrH
uRHYBOND'
,+v«/
,f= &rO+ nrMM R E T •
where RETj t equals the return on the
S&L’s stock in quarter t, MRETt is the return on the stock
market portfolio, and RH YBONDt is the market return on the junk bond portfolio.
Variable
Intercept
High-junk
bond S&Ls
Parameter
Estimate
Low-junk
bona S&Ls
Parameter
Estimate
-0.1546
(-9.366)***
-0.0986
(-11.120)***
M RET
0.8476
(5.665)***
0.9927
(12.021)***
RHYBOND
2.7633
(5.043)***
2.0009
(6.744)***
Adj. R-Sq
F-Stat:
N -
0.2800
39.301
198
0.2739
125.303
660
T-statistics in parentheses are starred if coefficients are significantly different
from ze ro at the 1 (***) percent level.
Table 6 (cont.)
B.
A pooled cross-section time series examination of the relationship
between S&L stock returns and junk bond holdings
Estimated Equation:
1987:1
*
J*
-
1989:4
\Junk 1
jjfh H n o N D ,
M
M
L
t /J
rJu n k 1
[RHYBOND f
‘•■ k ?
where RET|'| equals the return o n tb e itb S&L's stock in quaiter t, Z jis » S&L dummy variable,
MRETt is the return on the stock maiket portfolio, RHYBONDt is the market return on the Junk
bond portfolio, M V ^ is the maiket value o f the S&L’s stock, Junk^x is the book value o f the S&L’s
junk bond portfolio, and TA^t equals total assets o f the S&L.
High-junk
bond S&Ls
Parameter
Estimate
Variable
Intercept
rduokj
l MV j
-0.144
(-1.313)
rhybond
[JUQk]
Adj. R -Sq
F-Stat:
N -
RHYBOND
Low-junk
bong S&Ls
Parameter
Estimate
-0.083
(-2.242)**
0.174
(3.207)***
-0.308
(-3.430)***
5.221
(0.716)
87.832
(2.606)***
0.281
3.079
198
0.277
3.197
660
Coefficient estimates of the market betas and cross-sectional dummy variables
are not reported but are available upon request from the authors. T-statistics
in parentheses are starred if coefficients are significantly different from zero at
the 5(**) and 1(***) percent levels.
@FedFRASER