$ournal of Banking and Financ~ i3 ~|9891 $ | - | ~ .
Nor~-He~l~d
STOCK MARKET REACTIONS TO THE DEPOSITORY
INSTITUTIONS D E R E G U I A T I O N AND MONETARY CONWROL
ACT O F 1 9 ~
Marcia H. MILLON-CORNETT*
Southern Methodist University, D~las. 7X 75275, USA
Hassan TEHRANIAN*
Boston College, Chestn~ Hill, MA 02167, USA
Received March 1987, final version received March 1988
This paper evaluates the effects of events leading to the passage of the Depository Institufio~
Deregulation and Monetary Control Act of 1980. The evidence suggests that the initial proposal
of the Act by then President Carter, and the final passage of the Act by the House of
Representatives, produced positive abnormal returns to stockholders of large commerc:-tal banks.
Stockholders of small commerda| banks and small savings and loans, on the other hand,
generally experienced negative abnormal returns. Furthermore, when hopes of passage of the Act
faced significant negative (positive) abnormal returns were experienced by stockholders of large
(small) commercial banks.
1. Introduction
As of April 1, 1986, commercial banks have the ability to assign any rate
of interest on their aemand and savings deposits. This loosening of controls
is the final step in a series of steps deregulating the depository institution
industry according to the Depository Institutions Deregulation Monetary
Control (DIDMC) Act of 1980. In this paper, the effect of the DIDMC Act
on returns to stockholders of commercial banks and savings and loans
(S&L's) is examined. Specifically, the abnormal return to stockholders due to
each new piece of information concerning passage of the Act is measured
using a generalized least squares (GLS) estimation.
A rich and diverse body of theoretical and empirical evidence centering on
the economic effects of regulation has developed over the past several years.
Much of this work has utilized the concepts and methods of welfare economics
*The authors would like to thank Andrew Chen, Hassan Espahbodi, James Poterba, Nick
Travlos, Jerry A. Viscione and three anonymous referees for their helpful comments. The authors
also acknowledge the contributions of Afarin Sadrolhoffazi for her research assistance. Any
errors are the responsibility of the authors.
0378-4266/89/$3.50 © 1989, Elsevier Science Publishers B.V. {North-Holland)
82
M,H. Millon-C~eu and H. T , ¢ ~ ,
DIDMC Act 1980
to assess the extent to which regulation affects market relations. In particular, the need for regulation as a solution to raarket failu~ is me~ured with a
comparison of the costs imposed on society by market imperfections which
exist in the absence of regulation and the direct and indirect costs to society
resulting from regulatory reform. !a the case of banking reform, a unique
feature which makes these depository institutions ~ susceptible to market
failure is the contagious nature of bank failure and the potential impact on
the nation's monetary system and financial marketplace. Transaction
accounts issued by depository institutions are the main component of the
economy's money supply, they provide the mechanism for the economy's
system of payments and they play a key role for the implementation of
monetary policy? Evaluation of bank regulation must weigh the costs to
society of a failure of these functions against the costs of regulating the risk
of bank failure.
A problem in measuring the impact of regulatory change is that many
unrelated events occur which aff~t the targeted industry during the period of
regu|$~tory reAeorm.2 Accordingly, changes in profitability due to regulatory
changes are diffic~t to isoiate. Many papers have resolved this problem by
employing financial data to evaluate regulatory change. Schwert (1981) first
suggested the use of market data to measure the effects of regulation. He
argues that the use of financial data is more powerful than other measures
because asset price movements incorporate all relevant information as soon
as it becomes available. Thus, the effect of regulatory change is reflected
in share price movements as soon as the change is first announced or
anticipated. More recently, Schipper and Thompson (1983) evaluate the
impact of a group of merger-related changes on security returns. Fraser,
Richards and Fosberg (1985) examine security :eactions associated with the
authorization of Super NOW accounts. Binder (1985) uses stock returns to
measure the efffects of twenty regulatory changes which occurred since 1887.
Rose (1985) and Bruning and Tehranian (1986) test stock market reactions to
regulatory ch~mges in the motor carrier industry. Finally, Chen and Mer~iile
(1986) use capital market data to examine the divestiture effects pertaining to
deregulation associated with the breakup of AT&T. Similar to these, this
study utilizes market data to evaluate the effect of the DIDMC Act of 1980.
The results of this study indicate that the DIDMC Act of 1980 did produce
significant changes in the value of commercial banks and savings and loans.
Specifically, two announcements concerning the passage of the Act are found
to produce significant positive abnormal returns to stockholders of large
commercial banks and negative abnormal returns to stockholders of small
commercial banks and small S&L's: the initial proposal of the Act by then
1 ~ Cooper ~od Fraser (1986) for further discussion.
2In our study, this would include changes in interest rates, introduction of symbiotic financial
institutions and deepening economic recession, to name a few factors.
M~H, Mii~Y~-Co~els and H. Te&anian~ DIDMC .4ct 1 9 ~
83
President Carter and the final p~sage of the Act by the H o ~ of
Representatives. At one point during the period hopes of passage of the Act
faded. This produced significant negative abnormal returns to stockholders of
large commercial banks and positive abnormal returns to those of small
banks and small S&L's.
The remainder of the paper is organized as follows: section 2 describes
background and the economic consequences of the DIDMC Act of 19~,
section 3 introduces the data, methodology and testable hypotheses concerning returns to stockholders of commercial banks; section 4 presents the
results of the study and section 5 concludes the paper.
2. Major changes introduced by the DIDMC Act of 1980
The introduction and passage of the DIDMC Act of 1980 was the result of
severe pressure on the profitability and market share of some depcsitory
financial institutions. One source of this pressure was the lack of ability of
depository financi.'a! institutions to attract funds in the face of rising interest
rates. Regulation Q limited the maximum rate that financial insti~tions
could pay on deposits. The inability of depository, financial institutions to
offer competitive rates led to an erosion of their deposit base. A second
source of F~e~sure came from the new competition presented by unregulated
financial institutions (i.e., brokerage firms and money market mutual funds).
Many of these firms offered services which competed directly witl~ those
offered by depository financial institutions. Because they were unregulated,
however, these financial institutions could offer more comprehensive and
higher paying services than depository institutions. Unable to compete,
depository financial institutions experienced a significant erosion in both
market share and profitability. Although consumers benefitted with higher
rates and better services, depository institutions faded in importance in the
market for financial services and the monetary system faced potential
collapse. The changes in economic and financial conditions resulted in a
demand for reform of the financial system, and eventually, the passage of the
DIDMC Act of 1980.
The reforms introduced by the DIDMC Act of 1980 are intended to affect
the very nature of the financial ind;Jstry. The Act provides legislation which
improves the degree of equity in the regulation of financial institutions. The
major changes contained in the Act ;nclude uniform reserve requirements for
depository institutions, gradual elimination of Regulation Q, access to
Federal Reserve System services for all depository institutions, authority for
all depository institutions to offer interest bearing transaction accounts
nationwide, greater lending power for savings and loans and mutual savings
banks, increased federal deposit insurance coverage, and a reduction in the
scope of state usury laws (i.e., limits on the interest rates on first mort-
~4
M.H. Mitlon-Cornett and H, Tehe~km, DIDMC Act i ~
?~ages)? The ultimate goal of the Act is to allow all finandal institutions to
=:ompete on an equal level. This is accomplished by allowing for an increased
:amber and variety of services offered and by removing interest rate
::strictions on deposits. Furthermore, the increased ability to compete is
a~tended to improve the profitability of depository institutions, which, in
~ m , assures the continuous and efficient functioning of the monetary system.
From the standpoint of the industry, the DIDMC Act allows depository
institutions to r~ain their prominan:,:¢ as providers of financial services.
Regulatory reform, particularly the abolition of Regulation Q and the
relaxation of service restriction.~ allow depository institutions to compote
with non~epository institutions for service demand. Again, consumers
benefit with improved efficiency in the provision of services and, in addition,
the soundness of the nation's monetary system is preserved. In fact, it has
been argued that post-reform depository institutions have two major advantages over non-depository institutions: location convenience and deposit
insurance.~ The post-reform shift of funds from money market mutual funds
to money market deposit a~ounts suggests that these may be significant
advantages.
One group of financial institutions which greatly benefitted from the
passage of the DIDMC Act is large commercial banks. Deregulation in the
form of the introduction of interest bearing transaction accounts and
dimination of Regulation Q allowed them access to funds that regulation
~ad previously prohibited them from obtaining. With the passage of the
DIDMC Act, large commercial banks were provided with the resources
needed to compete directly with other types of depository institutions (i.e.,
savings and loans), as well as, previously non-regulated financial institutions
(i.e., mutual funds and brokerage houses).
Large savings and loans also benefit from the DIDMC Act. The increased
1ending powers and ability to access funds allow savings and loans to
compete more readily with the previously non-regulated financial institutions.
The benefits accruing to S&L's, however, are offset by the introduction of an
additional source of competition. That is, the DIDMC Act allows commercial banks to compete directly with savings and loans for borrowed funds.
Accordingly, the overall consequences of the DIDMC Act on large S&L's is
somewhat uncertain.
Contrar~ to the impact of the D|DMC Act on large depository institutions, small banks and $&L's received fewer benefits from deregulation. Like
~heir larger counterparts, small banks and S&L's are allowed better access to
~orrowed funds, as well as all other benefits of the DIDMC Act. However,
~e small banks and S&L's are more iikely to incur the costs associated with
3Details of the provisions of the DIDMC Act are obtained from Rose and Fraser (1985),
~ cNeill (1980), and McCord (1980).
~$ee, for example, Cooper and Fraser (1986), ch. 7.
Moll. M~ilon-Co~nett and H. T e b _ r ~ . DIDMC Act f ~
85
increased competiton. With the injection of competition presented by the
DIDMC Act, the weakest financial institutions ~H ~ driven out of the
competitive market. The faiiur,:of a large bank or ~I,, howc~.c~, is likely to
have a significant impact on the economy and, therefore, these ~ ~ n s
will be spared from failure by government intervention. The ~'ailure of
depository instituions on the other hand, is less s i g , , ~ t
from the
perspective of the financial system. The increased ~ d c n c e of fa~ure, wh~h
naturally accompanies increased competition, therefore, is more l~ely to
occur in smaller depository instituions. Accordingly, sinai| ~ n k s and S&L's
are the least likely to experience long run benefits as a result of the passage
of the DIDMC Act.
Given the potential impact on banks and savings and loarD~ described
above it is hypothesized in this paper that the passage of the DIDMC Act of
1980 should produce a measurable impact on the returns to the stockholders
of these institutions. In particular, it is hypothesized that the stockholders of
large commercial banks experience wealth increazes, while stockholders of
small commercial banks and savings and loans experienced wealth decreases
as a result of the passage of the Act. Furthermore, the offsetting effects of the
Act to large savings and loans are hypothesized to produce no effect on the
wealth of the shareholders. In the next sections of this paper, the effect of the
Act on stockholder returns is examined.
3. Data, methodology and hypotheses
3.1. Data
The data used in the analysis are daily stock pri~s for commercial banks
and savings and loans for the period September, 1978 through April, 1980.
To be included in the sample, a commercial bank must be listed on the New
York Stock Exchange (NYSE), the American Stock Exchange (ASE) or the
Over-the-Counter Market (OTC) during the entire regulatory changc period.
Daily return data was collected from the Center for Research in Security
Prices (CRSP) data tapes for the NYSE and ASE listed corporations. For
banks and S&L's listed on the OTC exchange, the stock price history was
collected from the O TC Daily Stock Price Record. Where necessary, prices
were adjusted for stock splits, stock dividends and cash dividends when
transforming the price data into a return series. In order to isolate the effect
of the DIDMC Act on large versus small depository institutions, the sample
set was subdivided into four subsets: large commercial banks, small commercial banks, large S&L's and small S&L's. In general, only the largest f'mancial
institutions are listed on NYSE or ASE, while smaller financial institutions
will be listed on the OTC. Accordingly, the groups of large banks and S&L's
are those listed on the NYSE or ASE, while the groups of small banks and
86
M,H, Miiion-Corr~ett and H, T e h r ~ ,
DIDMC Ac~ ! ~
Table 1
Event descriptions,
Date
Even: number
~ption
2-22-79
1
Carter adminstration announces consideration of fundamental
~ a ~ e s in banking regulation.
5-23-79
2
C~ter proposes regulation to Cocgress to phase out interest
rate ceilings on all consumer d e p o t s ,
9-16-79
3
Announ~t that Congress may not include legflizmion of
interest,beawing checking in b~mking bill,
9-25-79
4
Senate Banking Committee approves bill phasing out interest
rate ceilings,
11-1-79
5
Senate approves bill.
12-3-79
6
Hopes fade for banking bill passase in the House.
1-25~80
7
House Banking Subc~tamittee considers changes in bill.
3-6-80
8
Senate and House conferees work to complete action on bill.
g28-80
9
House ai~ro~es bill; Carter signs bill into law?
"Two b u s i ~ days after House passage, Carter signed the Act into law. Event 9 captures the
effect of both of these events.
S&L's are those listed on the OTC. This process resulted in a sample set
consisting of 42 large banks, 200 small banks, 12 large S&Us and 18 small
S&L's. The average deposit size (in millions of dollars) at the end of 1979 for
the four subgroups is $11,989 (standard deviation=$18,1C4), $1,261 (standard
deviation=S1,157;, $2,297 (standard deviation=S2,340) and $486 (standard
deviation = $ 2 3 1 ) , respectively.
The tests performed on the sample require identification of dates on which
major new information about the regulation (DIDMC Act) became publicly
available. News items pertaining to changes in regulation are identified by
examining the New York Times Index, the Wall Street Journal Index, trade
journals in banking and textbooks on bank regulation. Three time periods
are examined from these sources. The year preceding the formal proposal of
the regulation to Congress is checked for any information. Significant
information during the period of enactment is then identified. Finally, a
period of one year following the final announcement of passage (this would
be the signing of the Act into law) is checked for any significant information
concerning the regulation. This process produced nine events in which
important information about passage of the regulation was announced. Table
1 |ists these nine events and the date on which they occurred. 5
5Many other announcements, besides the nine listed in table 1, were made concerning the
DIDMC Act. Only those announcements referring to major changes in the reform, stumbling
blocks to passage or passage by a key group are analyzed in this paper.
M.H. Mffl~n-CorTtet~ ~ d H. T e h r ~ ,
DIDMC Act t9(i0
87
3.2. M e t h o d o l o g y
This analysis adopts an event-study methodology to determine the
expected effect of regulatory reform on famre pro~ts. Binder {~985~, Hughes
and Ricks (1984), Rose (1985) and $chipper and Thompson (1~3) have
carried the theoretical development of event study methods a step beyond
those developed by Brown and Warner (1980). ~
papers emp[oy a
simultaneous equations multivariate regression approach based on ZeRncr's
(1962) seemingly unrelated regression model. The system of ~uations
explicitly conditions the return-generating process on the occurrence or nonoccurrence of an event. This is accomplished by appending zero-one dummy
variables to market model equations. The variable is set equal to one if an
event occurred, and e q u a l to zero if not. m e c o d ~ e n t s multiplying the
event dummy variables measure the event's impact on stock returns.
This extended market model is used to measure the expected returns for
securities; the 'abnormal" return associated with a regulatory event is the
residual from this model. This implies a stochastic return generating process
of the following form for a firm j that is possibly affected by regulatory
events: 6
N
R i ' = ,~i + ~jR=, ~ x'/_ ci~Dk,
+ #i:
where
(I)
R~ = the Txl time series vector of returns on the jth firm's stock;
R,~ = the Tx! time series vector of returns on the CRSP equally weighted
market porU~lio;
~i = an intercept coefficient;
fli = a measure of systematic risk for the jth firm;
cik = the K x l vector of the effect of the K regulatory changes on the jth f'wm
(K = 9 in this study);
D~,, = a T x K matrix of dummy variables with one column for each regulatory announcement. The dummy variable equals one if the kth event
occurred and zero otherwise;~ and,
/~j, = the Txl time series vector of error terms which are assumed to be
serially independent, independent of the return on the market and the
regulatory announcement variable and normally, identically
distributed.
The introduction of the dummy variable allows for the isolation of the
abnormal return during the day in which information is released. Estimates
6We also perform the analysis using the capital asset pricing model (i.e., the return on the
risk-free asset is included as a variable). In no instance are the results changed.
7Depending on what time during the trading day the announcement is made, either the
publication day or the day before might be the relevant announcement day. gince the exact time
of the announcement is unknown, the announcement period is three trading days, t = I, t =0,
t = + 1, relative to the published announcement.
88
M.H. Millon-Cornett and h. Tehranian. DIDMC Act 1980
of the coefficients (c~k's) are similar to residual returns obtained from the
market model.
The benefit of this approach is that it provides a framework for testing a
wide range of hypotheses and offers several advantages in making efficient
use of variable data. As is pointed out in Schipper and Thompson (1983), the
concern with efficiency is the result of the following three characteristics
associated with policy event studies:
(1) multiple announcements of information during the period of regulatory
change;
(2) high cross-sectional correlation in the security return residuals of affected
banks due to the clustering of event dates for all banks, as well as,
common industry effects; and
(3) relatively small sample size•
Another benefit with this approach is t~at t-statistics on tke regression
coefficients can be used to test the signific,'~ce of the estimated abnormal
returns.
Following Theil (1971), the regressions in eq. (1) can be generalized as:
(2)
R = X T + E, where
R=
,
X
X=
,
X=[!
R,,, D:,]
P,
CIK
~j
O~j
Note that the model defined in eq. (2) differs from a set of individual firm
regressions in that it allows fo~' the disturbance variance across equations
(firm~). It also assumes that, across firms, the contemporaneous covariances
of the disturbances E ( u ~ . u . ) can be non-zero. To test the joint hypotheses in
the multivariate regression model a chi-squared test defined by Theil (1971)
and employed by Schipper and Thompson (1983) is used.
Given the assumption of constant cross-sectional covariance matrix of
residuals, an alternative to the seeming]ty unrelated regression model is a
M.H. Millon-Cornett and H. Tehranian, DIDMC Act 1980
89
portfolio interpretation of coefficients estimated frc~rn the pooled time-series
cross-sectional regressions, where the weights in this portfolio are constant
through time. Accordingly, eq. (3) defines the return generating process for
an equally-weighted portfolio of sample firms,
| N
N
j
k
(3)
3.3. Hypothesis testing in the multivariate regression model
The standard hypotheses about average abnormal returns, as well as more
general hyp~.,,thesescan be tested within the framework of eq. (3). Specifically,
two hypotheses are formulated and tested.
Hypothesis 1: The average excess return for each type of depository institution during the announcement period, ~, equals zero.
Hypothesis 2: The announcement period, k, had no differential effect on
different types of depository institutions.
4. Empirical results
4.1. Preliminary results
4.1.1. Large banks
Table 2 presents the average abnormal returns and related tests for each of
the nine events, across the 42 large commercial banks and 200 small banks.
Column one lists the event. Columns 2 through 5 present results for large
commercial banks. Specifically, the second column contains the average
abnormal return (AR) for each event estimated from a single portfolio
regression of the form given by eq. (3). Column three identifies the t-statistic
associated with each event's average abnormal return. Column four lists the
Z-values associated with the average abnormal returns. These Z-values are
constructed fi'om standardized coefficients of individual firms and are based
on the assumption that disturbances arc cross-sectionally independent [see
Pateil (1976)]. Finally, the fifth column depicts the number of positive/
negative average abnormal returns for each event.
As shown in table 2, three events produce significant average abnormal
returns to stockholders of large banks during the period of passage of the
DIDMC Act: two positive (5-23-79: the initial announcement of the bill, and
3-28-80: House approves hi!|) and one negative (12-3-79: hopes fade for bill
passage). Investors reacted in a significant manner with respect to these three
90
M.H. Millon-Cornettand H. Tehranian,D I D M C Act 1980
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M.H. Millon-Cornett and H. Tehranian. DIDMC Act 1980
91
announcements. For the remaining six events, no significant investor reaction
was identified.
With respect to event 2 (5-23-79: Carter proposes regulation to Congress
to phase out interest rate c~ilings on all consumer deposits), table 2 indicates
that the average abnormal return is 0.68 percent. Using the t-statistic of 3.18,
the null hypothesis of no announcement effect can be rejected at the 0.05
level of significance. A similar conclusion is made with respect to event 9
(3-28-80: House approves bill). The average abnormal return reported for
this event is 4.94 percent with a corresponding t-statistic of 7.87. Thus, the
null hypothesis of no regulatory effect can be rejected. On the contrary, event
6 (12-3-79: hopes fade for banking bill passage in the House) produces an
average abnormal return equal to -0.48 percent. Using the t-statistic of
-1.76, the null hypothesis of no announcement effect can be rejected at the
0.10 level of significance.
The average abnormal returns for each day are examined in order to
determine whether the statistically significant test results for these events are
driven by a small portion of the sample. A binomial test on the signs of the
average abnormal return is conducted. Based on the binomial test, and a
significance level of 0.05, 26 commercial banks with positive (negative)
abnormal returns are required to reject the null hypothesis of no abnormal
return. For events 2 and 9 the binomial test suports the earlier results. The
number of positive/negative returns experienced on these two event days are
33/9 and 32/10, respectively. For event six, however, the negative abnormal
return does not appear to be representative of the full sample. The number of
positive/negative returns experienced on this event day is 24/18.
The significance of event 9 (passage of the Act by the House) merits
further discussion. Very often, the succession of events leading up to the
passage of a bill or legislation by Congress is certain. In these cases, passage
by the House of Representatives, following passage by the Senate, conveys no
new information and acceptance of the null hypothesis of no excess return
would result. This, however, was not the case with the DIDMC Act of 1980.
Rather, successful passage of this Act was in question throughout the
legislative process. To illustrate the uncertainty associated with this bill, two
versions of a banking reform bill were sent to a conference committee in the
House, both of which were approved by the Senate, but the two contained
substantial differences. For a long time, the committee was unable to agree
on one compromise bill. During this period financial institution regulatory
agencies (the Federal Reserve Board, the Federal Home Loan Bank Board
and the National Credit Union Administration) had allowed depository
financial institutions to offer interest-bearing transaction accounts such as
~utomatic transfer from savings to checking, share drafts and remote service
units. The U.S. Court of Appeals for the District of Columbia Circuit ruled
that these agencies had exceeded their authority in doing so and gave
92
M.H. Millon-Cornett and H. Tet,:ranian, DIDMC Act 1980
Congress until March 31, 1980 to pass le~lation that would give the
financial regulatory agencies appropriate authori~'y. As this deadline neared it
appeared as though no compromise could be reached, but on March 28,
1980 the House passed the DIDMC Act. The final bill that emerged was
quite different from either bill that had been sent to the conference
committee. In fact some provisions in the final bill had never been discussed
by either the House of the Senate.8 This type of uncertainty associated with
the passage of the DIDMC Act results in more significant events than is
typically associated wtih the passage of a piece of legislation. Accordingly,
event 9 produces significant abnormal returns.
Comparing the results using the Z-values with those using the t-statistic,
table 2 shows that greater significance is obtained with the Z-values. For
example, for event 6, the Z-test produces a value of -2.76 which is
significant at the 0.05 level. The greater significance of the Z-value is
consistent with a bias against the null hypothesis implied by observed
positive cross-dependencies. As a result, more emphasis is placed on the
t-statistic.
Theoretically, however, a problem with t-tests is that they provide very
crude measures of significance for the average parameter estimates. That is,
the extent to which the disturbances of the individual regressions covary with
one another is not considered.9 T-tests also influence the significance of
each of the individual coeffiecients because they ignore time series variability
of re:~duals. A more appropriate test is a joint test which incorporates such
cross-dependencies. Such a test is considered in section 4.2.
4.1.2. Small banks
Columns 6 through 9 of table 2 present the event announcement returns to
stockholders of small banks. As in the case of large banks, the same three
events produce significant average abnormal returns during the period of
passage of the DIDMC Act. However, in contrast to the large banks, two
events were negative and one was positive. The average abnormal returns for
events 2 and 9 are -0.52% and -1.67% with corresponding t-statistics of
-2.72 and -3.72 respectively. On the other hand, event 6 produced an
average abnorm.al return of 0.39% with the t-statistic of 2.31. Thus the null
hypothesis of no regulatory effect can be rejected for all three events at the
0.05 level of significance. A binomial test of the number of positive and
negative abnormal returns requires that 112 banks in this sample experience
a positive (negative) abnormal return to reject the null hypothesis at the 0.05
level. The number of positive/negative abnormal returns for events 2, 9 and 6
8See Cooper and Fraser (1986, p. 113) fo~."further discussion.
9The binomial tests on the sign of the firm's coefficients are also inappropriate if they rely on
independence of the parameter estimates.
M.tt. Milion-Cornett and H. Tehranian, DIDMC Act 1980
93
are 79/121, 51/149 and 139/61, respectively. Thus, the abnormal returns are
not dn~:en by a small portion of the sample.
4.1.3. Large and small savings and loans
Table 3 shows the percentage average abnormal returns to stockholders of
large and small savings and loans. The table is similar in format to table 2.
Two interesting results emerge from this table. First, only one event (9:
House approves bill) produces significant negative abnormal returns for large
S&L's at the 0.10 level. In addition, the number of positive/negative
abnormal returns is 2/10 for this sample suggesting that this result is not
drieen by a few firms. Second, the act had much more of a negative impact
on small S&L's than either large S&L's or small banks. Indeed, events 2, 8,
and 9 produced significant negative percentage abnormal returns to stockholders at the 0.05 level. The AR% for these events are -1.77, -1.52 and
-1.95 with corresponding t-statistics of 2.12, 2.78 and 2.24 respectively.
Furthermore, the result is confirmed by a binomial test of positive and
negative abnormal returns. For events 2, 8 and 9, the number of postive
negative abnormal returns is 5/13, 3/15 and 2/16, respectively. For this
subgroup 13 postive (negative) returns are needed to reject the null hypothesis of no abnormal return.
4.2. Test of Hypothesis 1: The average excess return for each type of
depository institution during the announcement period k equals zero.
This hypothesis is analyzed using the multivariate regression model
(seemingly unrelated regression) and tested using the chi-squared statistic
defined by Theii (1971, pp. 313-314). Table 4 reports results of the test of
hypothesis 1 for different types of banks using two different statistics
corresponding to two different specifications of the covariance matrix of
residuals.
Column one in table 4 presents the event. Column two contains the full
covariance matrix for large banks which uses the maximum likelihood
estimates, requiring inversion of the full covariance matrix. Column three
shows the diagonal covariance matrix for large banks which uses only the
diagonal (own variance) elements in the covariance matrix of residuals. If the
true covariance matrix is diagonal these two tests are asymptotically equal.
Otherwise, the cross interdependence among firms tends to overstate the chisquared statistic.
As indicated in table 4, for large banks the chi-squared associated with the
full covariance matrix for events 2, 6 and 9 are 5.26, 4.43, and 9.57,
respectively. Similarly, using the diagonal covariance matrix for these three
events chi-squared values are 12.32, 10,18, and 21.16 respectively. Both
M.H. M#lon-Cornettand H. Tehranian,D I D M C Act 1980
94
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M.H. Millon-Cornett and H. Tehranian, DIDMC Act 1980
95
Table 4
Test o( hypothesis that the average excess returns during the announcement period k
equals zero?
X2-statistics
Large banks, N = 42
X2-statistics
Small banks,
Event
Full covariance Diagonal covariance
matrix
matrix
Full covari~,nce Diagonal covariance
matrix
matrix
1
2
3
4
5
6
7
8
9
1.35
5.26 b
0.22
1.48
0.69
4.43 b
0.78
0.86
9.57 b
0.55
4.87 b
1.14
3.01
1.06
4.21 b
2.33
2.96
6.46 b
2.81
12.32 b
0.98
3.20
2.53
10.18b
1.74
2.45
21.16 b
N = 200
0.96
9.36 b
1.88
3.92
1.63
6.27 b
3.64
3.81
11.44 b
"The time period covered is September 1978 through April 1980. The chi-squar~
statistic has one degree of freedom.
bSignificant at the 0.05 level.
specifications allow for the rejection of the null hypothesis of no average
excess return during the announcement period (k = 2, 6, and 9.) ~°
Notice in column 3 of table 4 that the diagonal covariance matrix
produces much larger chi-squared values than the full covariance matrix.
This finding implies that interdependencies across large banks are important
and, therefore, the multivariate regression model is the more appropriate test
of significance. The ranking of significance in table 2 is very similar to that
reported in table 4, with the exception of event 6 which now becomes
significant at the 0.05 level.
Columns 4 and 5 in table 4 present the full covariance matrix and
diagonal covariance matrix for small banks. The X2-statistics associated with
the full covariance matrix for events 2, 6 and 9 are 4.87, 4.21 and 6.46
respectively. Similarly, using diagonal covariance matrix for these events
X2-statistics are 9.36, 6.27 and 11.44. Thus the null hypotheses of no average
excess return can be rejected under both specifications for all three events.
Table 5 presents results of the test of hypothesis 1 for different types of
savings and loans using the methodology described above. Table 5 is set up
in the same manner as table 4.
As shown i~ columns 2 and 3 of table 5, for the sample of large S&L's
neither specification allows for the rejection of the null hypotheses for any
event. In contrast, for the sample of sm~il S&L's, the null hypotheses of no
I°We also test the hypothesis by applying the F-test defined by Rap (1973) and used by
Binder (1985). The F-statistics were smaller but still significant at the 0.05 level for events 2 and
9, and not significant for event 6.
M.H. Millon-Cornett and H. Tehranian. DIDMC Act 1980
96
Table 5
Test of hypothesis that the average excess returns during the announcement period k
equals zero."
12-statistics
Large S&L, N - 12
lZ-statistics
Small S&L, N = 18
Event
Full covanance Diagonal covariance
matrix
matrix
Full covariance Diagonal covariance
matrix
matrix
1
2
3
4
5
6
7
8
9
2.71
IA3
0.76
0.82
2.11
0.94
1.84
1.05
2.64
0.36
4.44 b
0.21
1.20
1.27
0.72
0.68
4.21 b
4.68 b
2.97
1.68
0.82
0.93
2.75
1.01
2.11
1.24
3.14
0.49
5.96 ~
0.25
1.27
1.36
0.80
0.74
5.78 b
6.01 b
"The time period covered is September 1978 through April 1980. The chi-squared
statistic has one degree of freedom.
bSignificant a'. the 0.05 level.
average excess return can be rejected under both the ful! and diagonal
covariance matrices for events 2, 8 and 9.
4°3. Test of Hypothesis 2: The announceme~t period k, had no differential
effect on different types of depository institutions
Of particular interest to this study is a test of the hypotheses that the
economic impact of the Act was the same for a portfolio of each type of
depository institution during the announcement period. A formal test for
significant differences in the impact of the Act on different types of banks
was performed using the techniq,)e employed by James (1983) and Fraser et
al. (1985). This technique, which allows for the potential of contemporaneous
correlations among the excess returns, uses a paired difference t-test based on
the standardized excess returns fro~ each portfolio. Table 6 presents the
percentage mean difference in excess returns during the announcement k
(2,6,9) for different types of depository institutions. The percentage mean
difference in excess returns between the large banks and small banks (column
2) for events 2, 6 arid 9 are 1.20, - 0 . 8 7 and 6.62 with corresponding tstatistics of 3.53, - 2 . 6 8 and 4.91 respectively. The t-statistics are significant
for all three events at the 0.05 level. These results are inconsistent with our
hypothesis that the two groups react the same, on average, to the announcement period k. In column 3, it can be seen that identical quantitative and
M.H.
Millon-Cornett and H. Tehranian, DIDMC Act 1980
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98
M.H. MiUon-Cornett and H. Tehranian, DIDMC Act 1980
qualitative results and conclusions are derived in comparing excess returns
between large banks and small savings and loans. In addition, as shown in
column 4 of this table, there are no significant differences between the
percentage mean difference in excess returns of large savings and lo~ns and
small savings and loans. The same is true in comparing small banks and
small SkL's (column 5). As reported in column 6, the percentage mean
difference in excess returns for event 9 (House approves bill), between large
banks and large S&L's is 6.81, which is statistically significant at the 5
percent level. However, there are no significant differences for events 2 and 6.
Finally, there is no clear pattern between large S&L's and small banks.
During the announcement of event 2, the percentage mean difference in
excess returns is 1.11 with a t-statistic of 2.08, where during the announcment
of event 9 the mean difference is -0.20 with a corresponding t-statistic of
- 1.86 which is significant at the 10 percent level.
These results suggest that the large banks benefitted from the enactment of
DIDMCA at the expense of small banks and S&L's. Deregulation associated
with the passage of this reform provided both banks and S&L's with the
ability to compete with non-depository institutions for funds. Given the
potential impact of a failure of a large bank or S&L on the financial system,
for these depository institutions the increased competition was w'thout fear
of increased incidence of failure. Accordingly, large depository institutions
benefitted from the increased competition. On the other hand, the cost
associated with the increase in competition is borne by the smaller banks
and S&L's which are less likely to be protected in the event of failure. Thus,
the average abnormal return for large commercial banks is statistically larger
than that for both small banks and small S&L's.
Furthermore, although the DIDMC Act gave large S&L's the ability to
compete with non-depository institutions, it also introduced commercial
banks as a direct source of competition. Thus, the benefits accruing to large
S&L's are tempered by the costs of competing with banks and the average
abnormal return for large banks is statistically greater than that for large
S&L's.
5. Conclusion
This paper examines the effect of a series of announcements leading up to
the passage of the DIDMC Act of 1980 on retuias to s~ockholders of
commercial banks and savings and loans. The conditions of depository
institutions prior to the passage of the Act found them unable to compete
with unregulated, non-depository institutions. Passage of the DIDMC Act
was intended to improve implementation of monetary policy and the degree
of equity in the regulation of the finandal services industry. Passage was the
result of several changes in the market for financial services (including the
M.H. Millon-Cornett and H. Tehranian, DIDMC Act 1980
99
introduction of non-regulated, non-depository financial institutions and
advances in electronic technology), as well as changes in economic conditions
(increased interest rates).
Empirical tests using a joint return-generating model are employed as an
alternative to the traditional regression analysis. The benefit of this method is
that it allows for the consideration of any cross-sectional dependencies which
may exist across firms and which may affect test statistics and results.
Several conclusions are drawn from the empirical tests. First, two events
during the announcement period produced positive significant abnormal
returns to stockholders of large commercial banks: the initial proposal of the
Act by then President Carter (event 2) and the passage of the Act by the
House of Representatives (event 9). A third event, hopes fade for banking bill
passage in the House (event 6), produced significant negative abnormal
returns to these stockholders. Second, results for returns to stockholders of
small commercial banks and small savings and loans are just the opposite.
That is the initial proposal of the Act (event 2) and the House passage of the
Act (event 3) produced significant negative abnormal returns to stockholders,
while when hopes for passage faded (event 6), stockholders of small banks
and S&L's experienced positive abnormal stock returns. Third, stockholders
of large S&L's experienced no significant abnormal return increases or
decreases during the entire period of legislative review. The significance of
these results prevails regardless of the assumptions and the use of crosssectional dependencies in residuals. When data are used most efficiently
evidence allows for the rejection of the null hypothesis of zero excess returns
for the average bank and small S&L in the sample.
Finally, the impact of the passage of the DIDMC Act differed across types
of depository institutions. Specifically, large banks were found to experience
significantly larger abnormal excess returns than small banks and small
S&L's when the Act w~s first proposed (event 2). In addition, when House
passage was announced (event 9), large banks experienced significantly higher
excess returns than small banks and all S&L~s.
The examination of stock returns is used to detcm~line the impact of
regulatory reform on firms in the E'egulated industry. The results document
that the impact of the DIDMC Act was not consistent throughout the
industry. Specifically, large banks benefitted from the increased competition
introduced. In addition, small banks and S&L's incurred the costs associated
with the reform. Finally, large S&L's were unaffected by the deregulation.
References
Binder, J.J., 1985, Measuring the effects of regulation with stock price data, Rand Journal of
Economics !6, 167-183.
Brown, SJ. and J.B. Warner, 1980, Measuring security price performance, Journal of Financial
-Economics 8, 208-258.
100
M.H. MiUon-Cornett and H. Tehranian, DIDMC Act 1980
Bruning, E.R. and H. Tehranian, 1988, Stock market reaction to motor carrier regulatory
reform, International Journal of Transport Economics 15, no. 1, 7-27.
Cargill, T. and G. Garcia, 1982, Financial deregulation and monetary control (Hoover
Institution Press, Stanford, CA).
Chen, A.H. and LJ. Merville, 1986, An analysis of divestiture effects resulting from deregulation,
Journal of Finance 41, 997-1010.
Cooper, K. and D.R. Fraser, 1986, Banking deregulation and the new competition in financial
services (Ballinger, Cambridge, MA).
Fraser, D.R., R.M. Richards and R.H. Fosberg, 1985, A note on deposit rate deregulation, super
News, and bank security returns, Journal of Banking and Finance 9, 585-595.
Hughes, J.S. and W.E. Ricks, 1984, Accounting for retail land sales analysis of a mandate
change, Journal of Accounting and Economics 6, 101-132.
James, C., 1983, An analysis of intra-industry differences in the effect of regulation: The case of
deposit rate ceilings, Journal of Monetary Economics 12, 417-432.
Kareken, J.H., 1986, Federal bank regulatory policy: A description and some observations,
Journal of Businesss 59, 348.
Kaufman, G., 1981, The Depository Institutions Deregulation and Monetary Controls Act of
198~. What has Congress wrought?, Journal of the Midwest Finance Assocation, 20--35.
McCord, T., 1980, The Depository Institutions Deregulation and Monetary Control Act of 1980,
Issues in Bank Regulation 3, 3-7.
McNeil, C.R., 1980, The Depository Institutions Deregulation and Monetary Control Act of
1980, Federal Reserve Bulletin, June, 444-453.
Muss& M., 1986, Safety and soundness as an objective of regulation of depository institutions:
Comment on Kareken, Journal of Business 59, 97-118.
Patell, J., 1976, Corporate forecasts of earnings per share and stock price behavior: Empirical
tests, Journal of Accounting Research 14, 246-276.
Rao, C.R., 1973, Linear statistical inference and its applications, 2nd ed. (Wiley, New York).
Rose, N.L., 1985, The incidence of regulatory rents in the motor carrier industry, Rand Journal
of Economics 16, 299-318.
Rose, P.S. and D.R. Fraser, 1985, Financial Institutions, 2nd ed. (Business Publication, Piano,
TX).
Schipper, K. and R. Thompson, 1983, The impact of merger-related regulations on the
shareholders of acquiring firms, Journal of Accounting Research 21, 184-221.
Schwert, G.W., 1981, Using financial data to measure effect of regulation, Journal of Law and
Economics 25, 121-145.
Theil, H., 1971, Principles of econometrics (Wiley, New York).
Zellner, A., 1962, An efficient method of estimating seemingly unrelated regressions and tests for
aggregation bias, Journal of the American Statistical Association 57, 348-368.