Full text of Economic Review (Federal Reserve Bank of San Francisco) : 1994, Number 2

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Economic
Review
Federal Reserve Bank
of San Francisco
1994

Mark E. Levonian

Chan Huh and
Sun Bae Kim

Elizabeth S. Laderrnan

Number 2

The Persistence of Bank Profits:
- What the Stock Market Implies
Fmancial Regulation and Banking Sector
Performance: A Comparison of Bad Loan
Problems in Japan and Korea
Wealth Effects of Bank Holding Company
Securities Issuance and Loan Growth
under the Risk-Based Capital Requirements

Table of Contents

The Persistence of Bank Profits: What the Stock Market Implies .............................. . 3
Mark E. Levoniam

Financial Regulation and Banking Sector Performance: A Comparison of
Bad Loan Problems in Japan and K orea.......................

18

Chan Huh and Sun Bae Kim

Wealth Effects of Bank Holding Company Securities Issuance and Loan Growth
under the Risk-Based Capital Requirements .............................................. .
Elizabeth S. Laderman

30

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The Persistence of Bank Profits:
What the Stock Market Implies

I. INTRODUCTION

Mark E. Levonian
I am grateful to Elizabeth Laderman, Philip Lowe, Jonathan Neuberger, David Pyle, Sherrill Shaffer, Paula Worthington, and seminar participants at the University of
Melbourne for helpful suggestions; I am also grateful to
Jennifer Soller for competent research assistance.

This paper examines the speed with which abnormal
economic profits vanish in the U.S. banking industry. A
model is developed to infer expected speeds of profit
adjustment from stock market and financial accounting
data, deriving the rate of adjustment that is most consistent with observed cross-sectional relationships between
bank stock prices and profitability. The model allows
for the possibility that reported accounting income may
be a biased and noisy signal of economic profit. Estimation is performed using generalized nonlinear least
squares on a pooled series of cross sections. The results
indicate that the expected rate of adjustment tends to
be significantly greater than zero, although smaller than
adjustment speeds found in studies of nonbank firms.
The estimated speed of adjustment for negative profits is
greater than for positive profits;for banks with highprofit
rates, the adjustment speed is near zero, implying that
supernormal profits are very long-lived.

This paper examines the expected path of bank profits over
time, with an emphasis on the persistence of abnormal
profits at the individual bank level. A method is developed
to infer the persistence of economic profits from stock
market and financial accounting data for a cross section of
banking firms. Specifically, a rate of profit adjustment is
derived that is most consistent with the observed crosssectional relationship between stock prices and profitability, with slower implied rates of adjustment indicating that
the market believes bank profits are more persistent.
Banking presents an interesting case, since government
regulatory policies shelter the industry from outside forces
to some extent. These policies are introduced for various
reasons related to the stability of the financial system, but
as an unintended side effect they may tend to discourage
vigorous competition as well. On the other hand, regulators generally recognize that competition yields both static
and dynamic efficiency benefits, and therefore attempt to
encourage a degree of interbank competition that stops
short .of causing financial instability.1 If these opposing
strains within bank regulation have the net effect of weakening competitive forces relative to other industries, bank
profits should reflect that fact, and abnormal bank profits
should be more persistent.
Using a sample of U.S. banks, I find that stock market
investors implicitly believe that competitive forces operate
in banking, as profits do tend toward zero over time. However, the implied rates of adjustment are slow, suggesting
that nonzero economic profits tend to be quite persistent.
The results. are conditional on the model of stock valuation',
if the stock price model is valid and the stock market
efficiently reflects information, the implied adjustment
speeds can be taken as important and valuable information
about industry dynamics. Although I apply these techniques specifically to banking, they should be applicable to
other industries as well.
Section II discusses the reasons that profits might not
always be zero, and why adjustment toward zero profit
equilibrium might take time. Section III describes a few
1. For example, current regulations require the denial of bank applications for mergers, acquisitions, or certain other activities if approval
would substantially reduce competition.

4

FRBSF ECONOMIC REVIEW 1994, NUMBER 2

previous studies of price and profit persistence. Persistent
profits and their effect on market value are modeled in
Section IV. Section V resolves some issues related to
the use of accounting profit data. Section VI develops the
estimation framework, Section VII describes the data set
used in the paper, and Section VIII presents the basic
estimation results. The possibility of asymmetric profit
adjustment is addressed in Section IX. Section X considers
questions raised by the existence of imperfectly priced
deposit insurance. Section XI summarizes the paper and
suggests directions for possible extensions and future
research.

Il.

ABNORMAL PROFITS
AND THEIR PERSISTENCE

Basic, textbook microeconomic theory asserts that economic profits are zero in perfectly competitive equilibrium:
Profits are just sufficient to provide a normal risk-adjusted
return on capital. But the notion of zero profit equilibrium
embodies an inherently static view of markets and competition; any realistic depiction of dynamic competition must
allow for the possibility that profits in a competitive market
might diverge from zero, if only temporarily. A positive
difference between the return on equity capital and the
opportunity cost of investing that capital can be called a
"positive spread."
A firm earns positive spreads through either luck or skill.
The firm might benefit from unanticipated exogenous
shocks that affect demand or production functions, shocks
that collectively constitute "luck." Among these might be
poor decisions by competitors that enhance the firm's competitive position (forexample, that make the firm's products
relatively more attractive) without any action by the firm
itself. Beyondthese external forces, a likelycharacteristic of
competitive markets is that producers strive to make opportunities to earn positive spreads. Positive spreads might
be created through cost-reducing process innovations that
cannot be copied immediately by other producers, or
through product differentiation that confers a degree of
market power enabling producers to sell at prices above
marginal cost. In either case, the consequent benefits may
exceed the costs of innovating or differentiating. Since most
firms continuously attempt to create positive spreads, at any
point in time it is likely that some firms will have succeeded
at least temporarily, creating some degree of dispersion of
profit rates within an industry.
Some sources of positive spreads are intrinsically temporary, and decay naturally over time, all else equal. Other
spreads are eliminated through competition. Several types
of competitive forces might be expected to drive firms'
profits back toward zero and eliminate short-run diver-

gences from zero profit equilibrium. One likely mechanism is entry: Positive spreads encourage the introduction
of new capacity, either by existing competitors or through
actual entry by new competitors. Competitors attempt to
duplicate the advantages of successful firms through imitation of product or process innovations. In some cases the
threat of entry-or a demonstration that the market is
contestable-might be sufficient to eliminate positive
spreads with no actual entry occurring. If several firms
have nonzero spreads arising from identical or similar
sources, interfirm rivalry might provide a second mechanism to dissipate excess profits. Yet another route for
adjustment is migration of demand to substitute products
(which may themselves migrate in product characteristic
space to become closer substitutes to highly profitable
products). Finally, if markets for factors of production are
not perfectly competitive, factor prices might change to
allow suppliers to capture part of the rents inherent in the
positive spreads; for example, wages might rise.
Some combination of these adjustment mechanisms is
likely, but full adjustment probably takes time. Various
factors affect the speed of adjustment. These factors may
be classified as either structural characteristics of banking markets, or aspects of the conduct of competitors in
those markets. Among the more important structural characteristics is the cost of acquiring and using information:
the cost to existing and potential competitors of observing
relevant data about products, technology, and prices, and
then analyzing or making sense of those data to formulate
strategy. Other important structural characteristics relate
to the speed with which producers and consumers can respond to new information. The cost of many of these adjustments may be convex as a function of the size of the
adjustment per period, and therefore lower if adjustment
takes place slowly over time. Examples include the cost of
adding appropriate capacity (acquiring technology, building facilities, hiring or training specialized staff), the cost
of altering characteristics of products or their pricing, and
the cost to customers of switching to a different producer.
In addition to structural characteristics, the conduct of
various players may also matter a great deal. For example,
firms with positive spreads may act to obscure vital information about aspects of the market or may take other steps
to raise the cost of entry. Finally, government regulatory or
other policies may either inhibit or encourage adjustment
in certain industries.
The more significant the impediments to competitive
adjustment are in a particular market or industry, the
slower the adjustment will be, and the further that market
or industry will depart from the perfectly competitive
norm. The persistence of positive economic profits, or the
extent to which nonzero profits in one period tend to be

LEVONIAN / PERSISTENCE OF BANK PROFITS

sustained in future periods, therefore might be considered
an indicator of market competitiveness. A "competitive"
market is one that rapidly reachieves competitive (zero
profit) equilibrium.s
Firms also may have negative economic profits, earning
less than the normal return to capital. No firm would
intentionally create such a "negative spread" for itself;
negative spreads arise through unsuccessful product innovation, overestimation of demand, process experimentation gone awry, relative successes of competitors, or simple
misfortune. Whatever the source, negative spreads also
represent disequilibria. The return to equilibrium occurs
through exit, broadly defined: Abandonment of unsuccessful processes or products, reduction of capacity, or perhaps
even the disappearance of firms from the market. As with
positive-profit disequilibria, adjustment is unlikely to be
instantaneous, so negative spreads may persist as well. 3

m. EMPIRICAL STUDIES
OF COMPETITIVE ADJUSTMENT

I am aware of no previous papers that look directly at the
persistence of bank profits, although profit persistence has
been examined in other industries." For example, Mueller
(1977) examines changes in the profitability rankings of
firms; Mueller (1986) deals with profit persistence and
related issues in more depth. Geroski and Jacquemin
(1988) present data on rates of profit change for a sample of
industrial firms in three European countries. These studies
examine the convergence of profitability to a long-run
mean value, either for industries or for the economy as
a whole; without exception, they exclude banks. Relevant
conclusions from previous studies are discussed in Section VIII.
Several studies in the banking literature examine the
loosely related issue of price "stickiness." For example,
Neumark and Sharpe (1992) assess the speed with which
interest rates on retail bank deposits change when market interest rates change; Hannan and Berger (1991) examine the frequency of deposit rate changes. These price
2. Conceivably, the frequency with which nonzero profits arise also
might be relevant, although frequent deviations from zero profits might
simply indicate a high rate of innovation within the industry.
3. The term "equilibrium" as used here refers to long-run steady-state
equilibrium. If adjustment to demand and supply shocks occurs over
several periods, changes may follow some optimal path, with each
period's outcome therefore representing a short-run equilibrium. In the
terminology of this paper, the intermediate states all are referred to as
points of disequilibrium.
4. Unpublished work by Gup, Lau, Mattheiss, and Walter (1992) sheds
indirect light on the persistence of bank profits. The relevant results from
their Markov analysis are discussed below.

5

adjustment studies generally focus on the effect of market
structure-primarily the number and relative size of competitors-on price adjustment. The idea that firms in
concentrated markets might have some degree of market
power and use it to manipulate prices in their favor(dynamically as well as statically) is intuitively plausible. Such
firms could act to accelerate or retard the rate at which
prices adjust to supply and demand shocks, affecting the
speed of adjustment when the equilibrium point shifts.
However, Worthington (1989) points out that the relationship between market structure and the degree of price
stickiness is theoretically ambiguous; markets characterized by fewer firms might have either faster or slower
rates of price adjustment. 5
Despite the theoretical ambiguity, the deposit rate studies generally find that banks in more concentrated markets
have been slower to change interest rates on deposits when
market rates change. Neumark and Sharpe (1992) find
evidence of asymmetry: Banks in concentrated markets are
less likely to raise rates when market rates rise, but more
likely to reduce them when market rates fall; the asymmetry thus runs in the banks' favor. However, the pricing results must be interpreted with some caution. Bank
deposits are multidimensional products; if deposit rate
changes are costly for banks, then banks may find it less
expensive to adjust other aspects of the deposit product
when market conditions change. Prices might appear to be
sticky even if full, multidimensional adjustment is rapid
and continuous.
Overall, the price stickiness literature suggests the presence of factors in the banking industry that could lead to the
persistence of disequilibria. Prices that are slow to adjust to
exogenous changes might be one manifestation of the types
of impediments to competitive adjustment discussed above
in Section II. If these factors create a rate of adjustment that
is materially slower than in other industries, then bank
profits may be measurably more sticky as well.

IV. PROFIT PERSISTENCE AND MARKET VALUE
Let Rit , represent the rate of economic return for a discrete
period (taken to be one year) on beginning-of-period
shareholder equity for bank i at time t. I model this return
as the sum of a longer-term component, R it , and a transitory component, 'YJit' so thatR it = R it + 'TJit. I assume that 'YJit
5. Worthington (1989) demonstrates that price stickiness increases with
seller concentration, all else equal, but falls with the conjectural
variation parameter (a measure of the extent of collusive/cooperative
behavior in setting output levels), which probably also rises with
concentration; hence, structure and conduct may have offsetting effects
on price stickiness.

6

FRBSF EcoNOMIC REVIEW 1994, NUMBER 2

has a stationary distribution withan expected value of zero
for all t, sothattheexpected rate of returnforperiodtis RirThe normal risk-adjusted return on equity capital (that is,
the costof equityincluding an appropriate riskpremium) is
assumedconstantex anteat ki foranygivenfirm, but varies
acrossfirms. Economic profits differfrom zero if Rit =f ki ,
which can occur either because R it differs from k i, or as a
resultof transitory shocks ('l1it =f 0). Competition generally
pushes R it toward k i , and in perfectly competitive equilibrium, spreads are zero, with Rit = ki •
It is important to understand the distinctions between
the different rates of return defined here. The required
return k, is the return an investor can expect to earn on
the firm's shares if purchased in the secondary market. It
is the opportunity cost of capital. Ex ante, that rate can be
estimatedfrom an asset pricingmodel, such as the singlefactorCapital Asset Pricing Modelused. below. If a firmis
earningeconomic rents, an investor fortunate enoughto be
ableto investa dollardirectly in thefirm'sassets-through
reinvestment of earnings or throughthe purchase of newly
issued shares, as opposed to secondary sharepurchaseswill participate in those rents and receive a return that
exceeds the opportunity cost ki • The expected value of this
rate of return on equity is R it • Efficient capital markets
adjust the price of shares so that the expected return in the
secondary marketis always ki , but adjustments in markets
for the goods and services producedby the firm (or for the
inputs consumed by the firm) are necessary if R it is to be
drivento ki • A maintained assumption of this paper is that
capital markets are efficient, but markets for goods and
services may not be, so that market power or the other
impediments to complete and instantaneous adjustment
discussed above could lead to observed differences betweenR, and k, at any pointin time. A firmwith a positive
spread has Rit>ki by definition. 6
Modeling the path of R it as a partial adjustment process
with adjustment speed X. gives:
(1)

R it = R it- 1

-

X.(R it_1

-

kJ,

that polar case, oncea wedge betweenR and k develops, it
lasts forever. Intermediate values of X. implythat the rate of
return on equitygradually moves toward k in the longrun.
The actual speed of adjustment depends on aspects of
structure and conduct within the industry, as discussed
above.

Time Series versus Cross Section
Given observations or estimates of R, and k.; the adjustment parameter X. in principle could be estimated from
time-series data, either for individual firms or for the
industry as a whole. However, time-series methods generally require either that X. be constant over time or that it
change in some systematic way. The size, speed, and
unpredictability of recent changes in the financial sector,
andespecially in banking, makeit unlikely that X. wouldbe
sufficiently stable over a period long enough to permit
confident statistical estimation. Time-series estimates of
the adjustment speed therefore may be untrustworthy. 7
An alternative approach, takenhere, is to inferthe speed
of adjustment from the market capitalizations of banking
firms. If share markets are efficient, shareprices incorporate marketexpectations of the future stream of economic
earnings;thatis, thepathofthe expected RiJor t= 1... 00 is
embedded in the current market value of equity M iO'
AlthoughRit and X. cannotbe observed directly, theimplicit
values used by the market to evaluate bank shares can be
calculated. Inferring R it and X. requires an iappropriate
model of the relationship between M iO and the R it path; the
values inferred are then conditional on market beliefs and
on the model used to replicate market pricing. I assume
that the adjustment speed is the same for all firms, so that
cross-sectional estimation is possible.

A Model ofMarket Value with Persistent Profits
A simple financial model of the value of shares" specifies
thatthe currentmarketvalue of equityat time t = 0 is equal

or
(2)

(R it - ki) = (1 - X.) (Rit_ 1

-

kJ.

Thus, if X. is equal to one, adjustment is essentially
instantaneous, in that nonzero spreads vanish within one
period of their appearance. If X. is equal to zero, no
adjustment occurs, and spreads are infinitely persistent; in

6. I assumethroughoutthat producersare differentiatedto somedegree,
so that R; and k, may vary across banks. The differentiation may stem
from variations in product characteristics, or from differences in the
geographiclocationof productsfor whichvaluedependsto somedegree
on proximityto the consumer, as is likelyin the case of retail deposits.

7. The likelyinstabilityof the competitiveadjustmentspeed might seem
to question the assumption that A is independent of t in equation (1).
However, Acan be viewedas an expectation based on current information; I assume that Ais not expected to changesystematically overtime.
This assumption about expectations is necessary for cross-sectional
estimation. (Theassumptionmaybe violatedif, forexample, changesin
regulations lead to anticipation of future changes in the vigor of
competitionor the difficultyof entry intobankingmarkets.) In contrast,
time-series estimation would require that A actually be constant (or its
variationcaptured within the model) during the period from which the
sample data are drawn.
8. A similar model is developed by Wilcox(1984), althoughthat model
has an arbitrary finite horizon, and implicitly assumes that the rate of

LEVONIAN / PERSISTENCE OF BANK PROFITS

to the discounted value of expected future cash flows CFit
for firm i:
(3)

Expected cash flow is defined as expected economic earnings flowing to equity, net of any reinvestment or new
equity contributions." Letting E it represent total contributed (including previously reinvested) equity at date t (the
end of period t), and defining git as the rate of increase in E,
during period t, expected economic earnings at date t are
equal to R itEit_1, and new investment is equal to gitEit-l'
Then expected cash flow for bank i at date tis:

cr; =

(4)

(R it - kJ = (1 - A)t (RiO - ki).

(5)

Rearranging gives an expression for Rit in terms of current
values:

R it = (1 - A)t (RiO - kJ

(6)

+

k..

Note that at t= 0 the expected return and its realization are
identical and equal to RiO'
I make the simplifying assumption that the rate of equity
investment is expected to be constant for each firm, so that
git= gi for all t>O. (This corresponds, for example, to a
constant "plowback" rate for earnings.) It directly follows
that contributed equity evolves over time according to
E it= (1 + gJE it-l. In terms of the current value E iO' contributed equity at future dates can be written as:

E it_1 = (1

(7)

+

CFit = ((1- A)t(RiO- ki)

+ ki - gi)(1 + giy- 1EiQ'

Finally, substitution back into the equity market valuation
equation (3) gives:

t~-l
00

(9) M iO =

I

I.

(10)

t=l

=

((1- A)t(R i - ki) + k i - g) (1+ gy-1
(1 +k)t

(_1_)
+
1 gi

[(R.-k.)
I

~

+ (ki- gi) t~l

I

I.

t= 1

((I-A)(I+ gi
(1 + ki)

))t

(l++g )t]
1 k

(R i - k i)(I- A)

ki-gi+A(l+gJ

i

i

+1.

Thus, the market value of equity can be written as:
(11)

M. =
I

(R.- k-)(I- A)E.
I
I
I
ki-gi+A(l+g)

+ Ei .

Additional insight into the model is gained by dividing (11)
by E, to create an analog of the commonly constructed
market-to-book ratio:
(12)

Mi _
(
(I-A)
)
_
E - (R, ki) k gi+ A(l + gJ
ii

+1

.

Equation (12) shows that the divergence between market
value and contributed equity for firm i is positively related
to (R, - k), the spread between the actual economic rate of
return on equity and the required rate of return. If R, = ki ,
thenM/E, = 1; a company without a positive spread has no
surplus value to pass along to shareholders in the form of
higher equity value." The effect of any given spread

gY'-l EiQ .

. Substituting (6) and (7) into the cash flow equation (4)
yields:

(8)

Since all variables in (9) are as of t = 0, the time subscripts
on M.l' R·Z' and E.l can be suppressed to simplify notation;
from this point forward, these variables should be understood to have an implicit time subscript of O.
This expression can be simplified further by splitting the
infinite sum into two separate summations and applying
the useful fact that
x t = x / (l - x) as follows: 10
t= 1

(Rit - git)Eit-l.

However, only the realizations of variables at t = 0 or
earlier points in time can be observed currently, so (4) must
be rewritten to express cash flow at any t in terms of current
values. Recursive substitution in (2) yields an expression
for the spread (Rit - kt ) in terms of the current spread
between RiO and ki:

7

((1- AY(RiO- k i ) + ki - gi)(1 + gy-1EiQ
(1 + kiY

competitive adjustment is zero and that accounting returns accurately
represent economic returns. The model of PIE ratios in Leibowitz and
Kogelman (1990) is in the same spirit.
9. This definition of cash flow roughly corresponds to total dividends paid, although it also accounts for new equity contributed by
stockholders.

10. Of course, this relation requires Ixl < 1 for the sum to converge. For
(10), the restrictions on the summands require only that X. be nonnegative and that k exceed g. Both conditions should be satisfied in general:
>..~O is necessary for dynamic stability of the adjustment model, and
k<g could occur only if a firm were expected to invest forever at a rate
exceeding the market rate of return on equity.
11.TheMI E ratio, which is the current market value of equity divided by
the value of equity contributions, can be related to Tobin's q (Tobin and
Brainard, 1977). If the market value of bank liabilities is assumed to be
equal to the book value of those liabilities, then q> 1 if and only if
MIE> 1, and similarly for q< 1 and q = 1. The implications of q equal to
or different from 1 would apply to MIE as well, subject to the usual
qualifications related to the distinction between average q and marginal
q. The relationship between M IE and R - k implies that the cases in
which R is not equal to k also correspond conceptually to values of
Tobin's q different from one; in competitive equilibriumR=k, M=E,
andq=1.

8

FRBSF ECONOMIC REVIEW 1994, NUMBER 2

between R, and k, on the M)E i ratio depends on A in an
intuitively appealing way: Faster speeds of adjustment to
equilibrium-meaning less persistence in profits-imply
smaller differences between M, and E i . If A = 1, then
Mi=Ei. If spreads are more durable (A is closer to zero)
then any difference between R, and ki will persist longer,
and those abnormal returns raise the market value of equity
relative to E i .

V.

ACCOUNTING PROFITS
AND ECONOMIC PROFITS

Practical application of equation (11) requires knowledge
of the rate of economic return on equity R; No established
methods exist for computing such economic profit rates.
Accounting income can be observed and a return on equity
computed; however, a variety of well-known peculiarities
of accounting practice make it unlikely that accounting.
returns will be equal to the underlying nontransitory economic rate of return.
Fisher and McGowan (1983; hereafter, F-M) are widely
cited as demonstrating that accounting returns cannot
proxy for economic returns. F-M present several numerical
examples to establish their key propositions. As Mueller
(1986) notes, F-M demonstrate that use of accounting data
can lead to serious errors, but neither their examples nor
theory can prove that the problems are material in practice.
The many studies that find relationships between accounting returns and other economic variables make it implausible that in practice those returns contain no information
about economic returns, as F-M appear to argue (see
Mueller, 1986, pp. 107-108). However,it would be naive to
argue the opposite, that accounting data portray underlying
economic flows with perfect accuracy. A prudent interpretation of F-Mis that accounting returns are potentially
misleading, and may be both biased and noisy as indicators
of economic returns; the possible bias and imprecision
must be recognized within any model that uses accounting
data.
The cash flow definition in equation (4) adjusts for some
of the factors most commonly believed to make accounting
and economic returns differ. For example, if an arbitrary
schedule for the amortization of intangible assets improperly reduces reported income in a period, it also
causes the net value of assets to decline by more than is
economically appropriate during the period. The relative
reduction in assets (and equity) tends to reduce measured
growth g, offsetting the inappropriate reduction in the rate
of return on equity. In practice, this offset is not complete
because g is assumed constant over time for each firm;
some allowance for possible errors in reported earnings
still must be introduced.

I assume that accounting returns are related to nontransitory economic returns as follows:
ROEi =

(13)

<Y

+ R, +

Ei ,

where ROEi is the accounting return on equity at t= O. The
parameter <Y is an unobserved industry-wide bias in-reported earnings incorporating two types of effects: (i) distortions due to the failure of accounting practices to. reflect
economic realities, and (ii) the cross-sectional mean of
transitory shocks to rates of return at t = O. Profits also are
affected by an unobserved firm-specific deviation Ei , which
like <Y subsumes any transitory shocks that cause Ri to
differ fromR i , as well as any distortionary accounting conventions peculiar to individual firms. Firm-specific deviations may be the result of events with dissimilar impact on
different firms (for example, the effect of a given interest
rate shock depends on the structure of a bank's portfolio);
alternatively, they may reflect divergent choices in the
application of accounting principles if generally accepted
accounting permits some degree of latitude. Solving equation (13) for R, gives:
R i = ROEi -

(14)

<Y -

Ei .

This equation can be viewed either as a model ofhow the
market forms beliefs regarding R, from accounting ROEi,
or simply as a means to deduce a market estimate of R,
using the biased and noisy information in ROEi.
For estimation, the unobserved firm-specific deviation is
treated as a random variable in cross section, with the Ei
identically distributed with the same variance for each
firm, and zero mean across firms. This formulation addresses the F-M criticisms of accounting returns. It is
consistent with the possibility, stressed by F-M, that for
any two firms i and j, ROEi>ROEj but Ri<Rj . Given a
statistical distribution of E, such an occurrence in the data
has some well-defined likelihood; there is always some
probability that a bank with a higher observed ROE does
not actually have a higher economic rate of return R. In
fact, such an apparent "anomaly" may be very likely if the
variance of Ei is large. That the probability is positive, or
even large, does not imply that accounting ROE is void of
information. For a given distribution of E, the larger the
difference in ROE between two firms, the less likely it is
that the relationship between economic returns runs in the
opposite direction.

.VI.

ESTIMATION FRAMEWORK

Substituting R, from (14) into (11) yields:

(15)

M. = (ROE i I

<Y - Ei-ki)(I-A)Ei

ki - gi+ A(1 + gJ

+ n;

LEVONIAN / PERSISTENCE OF BANK PROFITS

Forstatistical estimation of the parameters ex and A, this
relationship is assumed to hold in cross section at any point
in time. The coefficient ex may vary in sign and magnitude
across sample dates, since the shocks that cause ROE;
to differ from R; may vary over time. The adjustment speed
A also may vary, since at any point in time it reflects
the market's expectation of the future path of profits
conditional on available information. To account for such
variation, time subscripts are added to all variables and
parameters in (15). With this notational change, solving for
the spread between ROE; and k; yields a more readily
estimable form with an additive firm-specific error term:

Wilcox 1984 as an examplej.P Denoting the GSSE from
the null model as GSSEo, a generalized goodness-of-fit
statistic is computed as:
(19)

(17)
where IN is the N-dimensional identity matrix, and I is a
TxT matrix with error variances for each period on the
diagonal and intertemporal covariances off the diagonal.
The coefficient estimates minimize a generalized sum of
squared errors:

F =

(GSSEo - GSSE)
GSSE

x

DFR,

where DFR is the ratio of the denominator's degrees of
freedom to the numerator's degrees of freedom for the first
term in (19). This is analogous to the familiar regression F
statistic that is isomorphic to the R2 statistic in linear
estimation.

VIT.

(The time subscripts in (16) refer to sample dates, in
contrast to the time subscripts used in Section IV above,
which referred to future periods viewed from a single point
in time.) The coefficients of equation (16) can be estimated
using nonlinear least squares from pooled cross sections of
N firms at each of T sample dates.
The firm-specific E;t are assumed to be independently
and identically distributed with zero mean in cross section
at each sample date t. However, the variance of the Eit
need not be the same for all t. In addition, the error terms
may not be independent across sample dates; for example,
a bank with large E at one date may be likely to have large
values at other dates as well. To account for such possibilities, the variance structure of the E;t is assumed
to be:

9

DATA

A sample of U.S. banks and bank holding companies with
exchange-traded shares was drawn from Standard and
Poors' Compustat database.P Since banking assets dominate most of the holding companies, all of the institutions
in the sample are simply called"banks" throughout the paper. Cross-sectional data sets were constructed for the ends
of the second and fourth quarters (June and December) for
each of the six years from 1986 through 1991; these dates
are denoted as 86:II through 9l:IY. Banks were included in
the sample if (i) the necessary market and financial data
existed for each sample date; (ii) no more than two monthly
stock price observations were missing over the period 81:II
through 9l:IV (the sample period plus the 60 months
preceding the first sample date); and (iii) the stock price did
not fall below one dollar at the close of any of those
months. Of the roughly 150 banks in the Compustat
database, 83 survived this screening.
Market capitalization M; for each firm was calculated as
price per share multiplied by the number of outstanding
shares, both as of the quarterly financial reporting date;
contributed equity E; was approximated by the book value
of equity at that same date. 14 By definition, g; should equal
the annual growth rate of E;; g; was estimated as the average growth in book equity over the previous five years. The
return on equity ROE; was computed as the sum of the four

(18)

where S is the estimate of I and Eis the residual vector. In
essence, this is a nonlinear version of seemingly unrelated
regressions. Approximate standard errors for each coefficient are computed as the square root of the corresponding diagonal element of the matrix (G'(S-l@IN)G)-l,
where G is the NT-by-2T Jacobian matrix of first partial
derivatives. To measure the fit of the model, the GSSE for
the model is compared to the GSSE from a null model
specified as ext = At= 0 for all sample dates t: this null
model corresponds to the textbook constant growth accounting cash flow model with no dynamic adjustment (see

12. Another interesting null model might be the case of A= 1, which
corresponds conceptually to unitary Tobin's q at all times. A model with
A = 1 also has constantly growing cash flows, but those cash flows
provide only a normal return on equity. However, such a model fits the
data so poorly that it seems an unlikely alternate hypothesis, and thus
does not present a useful standard for goodness-of-fit.
13. Foreign banking organizations issuing American Depository Receipts are included in Compustat; they were excluded from this analysis.
Subsidiaries of foreign banks were left in the sample.
14. This approximation is common in banking research; see for example
Keeley (1990). Book value may be a reasonable proxy for the replacement value of bank equity, since bank assets and liabilities are short
term, and therefore tum over relatively frequently.

10

FRBSF EcONOMIC REVIEW 1994, NUMBER 2

preceding quarters' net income divided by book equity as
of the beginning of the first of those four quarters. (For example, ROE for 86:II is the sum of quarterly earnings for
85:III, 85:IV, 86:1, and 86:II, divided by book equity as of
85:II.) Fifteen banks reporting a rate of return on equity
of less than -50 percent were dropped from the sample. 15
Drawing the sample from Compustat introduces the
possibility that the results may not typify all firms within
the banking industry, since Compustat includes only banks
with publicly traded equity and such institutions tend to be
larger than the average firm. For example, as of 91:IV the
banks in the sample ranged from $355 million to $217
billion in assets, with a mean of $20 billion and a median
of $7 billion; in contrast, the mean for the entire U.S.
banking industry at that date was $257 million in assets.
On the other hand, precisely because they are large, these
firms account for nearly two-thirds of the assets ofthe U.S.
banking sector, and therefore may provide a useful picture
of the industry.

The Equilibrium Return on Equity
The firm-specific cost of equity ki was calculated using the
Sharpe-Lintner Capital Asset Pricing Model, using methods recommended by Ibbotson Associates (1991) for computing discount rates for long-term investments. Beta
coefficients were estimated for each bank using monthly
stock returns for the preceding 60 months." Annualized
required rates of return on equity were constructed from
the betas by adding a base Treasury bond rate to the
product of the estimated beta and a market risk premium of
stocks over Treasury securities. Ibbotson Associates report
an average equity premium of about seven percentage
points, with only minor differences depending on the bond
maturity used; accordingly, 0.07 is taken as the market risk
premium.
Most references on practical calculation of risk-adjusted
rates of return (for example, Copeland, Koller, and Murrin
1990, and Ibbotson Associates 1991) recommend using a
rate on medium or long-term Treasury bonds as the riskfree rate to construct discount rates for equity cash flows.
This leaves open a fairly wide range of possible maturities.

15. Within the simple partial adjustment model of profit persistence,
sufficiently negative rates of return on equity can imply negative market
values, which are impossible under limited liability. The elimination of
banks with large negative profits is a stopgap solution; more elegant
approaches might allow for the rate of profit adjustment to be faster for
these very unprofitable firms.
16. Returns were computed as the change in the log of the monthly
closing stock price. One firm, Landmark Bancshares, had negative betas
for some dates; it was dropped from the sample.

A rough estimate of the "modified duration" of the bank
stocks in the sample was constructed by computing the
theoretical partial derivative of market value with respect
to k. On average for this sample, a 100 basis point increase
in k reduced market value by approximately 23 percent,
suggesting a duration of about 23 years. However, it is
unlikely that changes in k would occur without some
change in other variables, most notably R, the rate of return
on equity; this is especially true for banks. If R changes in
the same direction as k, then the partial derivative with
respect to k overstates the duration of equity. Under the
alternative assumption that changes in Rand k are equal
(parallel shifts in all rates of return), the average duration
falls to approximately 10 years. The true duration of these
stocks is probably somewhere between the extremes of 10
and 23 years .17 A rate on 20-year Treasury bonds might be
ideal, as the duration typically would fall in the desired
range, but consistent data are not available for that maturity.
However, since there is generally a difference of only a few
basis points between any of the maturities from 10years on
out, the rate on lO-year Treasuries is used as the risk-free
rate for the CAPM calculations. Sensitivity analysis (discussed below) shows that the main results of the paper are
robust to changes in assumptions regarding k.

Vlll.

ESTIMATION RESULTS

The model was estimated from the pooled cross sections
using equation (16). Figure 1 presents the resulting adjustment coefficients (the Ait ) graphically. The shaded band
reflects a 95 percent confidence interval based on the
standard errors of the estimates, with the point estimates
given by the solid line in the middle of the band. The
estimated adjustment speeds are not significantly different
from zero during the period 88:II-90:II, but otherwise
significantly exceed zero, reaching a high of 0.082 in
87:IY. A conservative conclusion is that the market believes competitive forces operate in the banking industry to
push economic profits toward zero, although the forces are
not strong and at times may be nonexistent. Moreover, in
all periods A clearly is significantly less than the value of
1.0 that would characterize an ideal world of frictionless
instantaneous adjustment to zero profit equilibrium.
To put the adjustment speeds in perspective, A can be
reinterpreted in terms of the time required for nonzero
spreads to decay. Corresponding to each A is an implicit

17. Such a range also is consistent with the likely range of asset and
liability durations for banks. For example, with 8 percent capital, asset
duration of 1.5 years, and liability duration of 0.5 years, the balance
sheet identity implies an equity duration of 13 years.

11

LEVONIAN / PERSISTENCE OF BANK PROFITS

FIGURE

1

FIGURE

2

ADJUSTMENT SPEED COEFFICIENT

ACCOUNTING RETURNS MINUS ECONOMIC RETURNS

0.20

0.04
0.02

0.15

-0.00
0.10

-0.02
-0.04

0.05

-0.06
-0.00

-0.08
-0.10

-0.05
-0.10

-0.12
-t-....,....---y-,-....,....---.-,---r---r-r--r-~----.

86:11

87:11

88:11

89:11

90:11

91:11

half-life of positive or negative spreads. From (5), the
number of years h required for any initial spread between
RiO and k, to fall by half can be calculated by setting (1- A)h
equal to 12, or:
(20)

h = -log(2)
log(l- A)

Excluding the five consecutive periods for which the
estimated A is not significantly different from zero, h
ranges from 8.1 years in 87:IV to 13.2 years in 91:IY.
Figure 2 presents the results for the ROE bias coefficient
a, again with a 95 percent confidence interval shaded
around the central line of point estimates. As the figure
shows, almost all of the estimates of a are negative,
implying that the stock market prices bank shares as if
accounting ROE understates expected economic returns.
In seven of the twelve sample periods, the estimates of a
are significantly negative at the 5 percent level.
For the set of estimates illustrated in Figures 1 and 2, the
goodness-of-fit measure defined in equation (19)was 9.93.
This statistic is roughly comparable to the conventional
F statistic testing the restrictions of a null model with
a = A = 0 for all periods, for which the 5 percent critical
value is 1.53 in this case. The difference suggests that
allowing for persistent profits and biased accounting may
add significantly to the fit of the cash flowvaluation model.

-0.14
86:11

87:11

88:11

89:11

90:11

91:11

Sensitivity to the Asset Pricing Model
The constructed equilibrium rates of return on stockholder
equity k reflect many relatively arbitrary assumptions. The
beta coefficients used to calculate the individual k.I are
themselves subject to estimation error, and the simple
CAPM may not even be an appropriate model of returns,
particularly for banks (for example, see Flannery and
James 1984). Thus, the robustness of the results to errors in
k must be examined.
As one test of the sensitivity of the results to potential
errors, the individual k, were replaced at each date by the
average k for all of the sample banks in that period. This
substitution eliminates all interfirm differences in the assumed equilibrium return. The model was then reestimated; the resulting adjustment coefficients were little
different from the results reported in Figure 1. The model
also was reestimated under several alternative assumptions
about the CAPM parameters: The assumed risk-free rate
was raised and lowered by 1 percentage point, and the
market risk premium was raised and lowered by 1 percentage point. In all cases, the resulting estimates of the
adjustment speed were well within one standard error of
the original estimates for each period.
As one final sensitivity test, required returns were set
uniformly equal to the average ROE for the sample at each
date. This case is of more than passing interest, since
previous studies of profit persistence (Mueller 1986, and
Geroski and Jacquemin 1988) use the average accounting

12

FRBSF EcONOMIC REVIEW 1994, NUMBER 2

return for the industry as the estimate of the normal or
equilibrium rate of return. Figure 3 shows the estimated
adjustment speed coefficients in the same format as the
earlier charts. The estimates of Agenerally are higherthan
in Figure 1, althoughthe basic pattern and overallconclusions are unchanged. Figure 4 gives the corresponding
estimatesof a., the ROE bias coefficient. Not surprisingly,
the estimates are insignificantly different from zero for
most periods; if average ROE is the expected long-run
economic rate of return, then observed ROE for each bank
is much more likely to be an accuratereflection of "true"
economic returns. Despite these differences, the key substantive conclusions regarding bank profit persistence are
unchanged.
Since in all cases the essential qualitative conclusions
are the same, it seems safe to infer that the results are
relatively insensitive to therequiredratesof return andthat
any errors introduced by the assumptions are likely to be
inconsequential. Moreover, the fundamental conclusions
probably wouldbe robustunder alternative modelsof bank
stock returns. One notable exception might be a return
modelof the type suggested byFamaand French(1993), in
which required stock returns depend on variables such as
the size of the firm and the price-to-book ratio. Since the
price-to-book ratio appears in the estimation above, and
since bank size may be related to variables such as profitability or the growth rate, the results of the model might
be substantially affected if theFama-Frenchapproachwere
used to construct estimates of k i •

FIGURE 3

Test of Coefficient Restrictions
Tests of the stabilityof the accounting bias parametera. and
the adjustment ~peed Acan be constructedfrom thepooled
cross-sectional model by restricting the coefficient estimates acrosssample dates. Besides indicatingwhetherthe
estimates differ significantly over time, the restrictedestimatesare useful as rough indicators of the typicalvalues of
the parameters across the 1986-1991 period.
The restricted estimate of the adjustmentspeed (with a.
permittedto varyovertime) is 0.048, with a standarderror
of 0.006. Therestrictedestimateof a. with Aunrestricted is
- 0.044, also with a standard error of 0.006. When both
coefficients are restricted, the resulting estimates. are A=
0.056 and a. = -0.042, bothwithstandarderrorsof 0.005.
However, in all caseslikelihood ratio testsrejecttherestrictions (at the 5 percent level) in favor of the unrestricted
model.

Comparison with Other Studies
Thespeedsof profitadjustmentin Figure1are muchslower
than the speeds of price adjustment found in studies of
bank deposit interest rate stickiness. For example, Neumark and Sharpe (1992) findrates of adjustment of 0.25 to
0.35permonth formoneymarketdepositaccounts and sixmonthcertificates of deposit(seetheirTable III andrelated
discussion). This difference suggeststhatprofitpersistence
is not'simply an extension of deposit rate persistence.

FIGURE 4

ADJUSTMENT SPEED, WITH K

= AVERAGE

ROE

ACCOUNTING BIAS, WITH K

0.20

0.06

0.15

0.04

= AVERAGE ROE

0.02

0.10

0.00
0.05
-0.02
-0.00

+---------'

-0.04

-0.05

-0.06
-0.08
86:II

87:II

88:II

89:II

90:II

91:11

86:II

87:II

88:II

89:II

90:II

91:II

LEVONIAN I PERSISTENCE OF BANK PROFITS

13

The degree of profit persistence also can be compared to
figures for nonbank firms. Results in Geroski and Jacquemin (1988, their Tables 1 and 2) for European firms
imply annual adjustment speeds for profits averaging 0.48
to 0.55. Individual industries range from 0.35 for metal
processing to 0.75 for metal manufacturing and 0.68 for
chemicals and automobiles.
Themost extensive previous study of profit persistence
is the book by Mueller (1986). Several significant differences between Mueller's study and the more limited
present paper should be noted. Mueller samples 600 U.S.
firms (none of them banks), and constructs a time series of
abnormal profits for each firm covering the period 1950
through 1972. 18 Mueller estimates two models: one in
which the economic profit rate converges hyperbolically to
a rate that may differ from the competitive equilibrium
level, and a partia.l adjustment model that is more nearly
analogous to the model of returns in equation (5) above. In
the partial adjustment model, Mueller's results imply
profit adjustment speeds ranging from 0.434 for the 100
highest profit firms to 0.546 for the 100 lowest profit
firms .19 Comparing these results to Figure 1, Mueller's
adjustment speeds are well above any reasonable confidence interval for the estimated values of A in the banking
industry, implying that bank profits are significantly slower
to adjust than are profits in other industries. A degree of
caution is appropriate, since the estimation methods differ
considerably between Mueller (1986) and the present paper. Figure 3 may provide a better comparison, since those
estimates incorporate an assumption parallel to Mueller's,
namely that returns tend toward their average. Even the
higher adjustment speeds in Figure 3 are well below
Mueller's estimates.
Mueller also finds that the firms with the highest profitability have significantly more persistent profits. Some
related evidence for the banking industry emerges from
Gup, Lau, Mattheiss, and Walter (1992). Gup, et al., compute Markov transition probabilities for banks in various
states of asset size and profitability, as measured by return
on assets (ROA). Each ROA state is defined to be 5 percentage points wide, with the median state at -.03 to
+ .02. Gup, et aI., find that banks in the ROA state just
above the median have a lower probability of moving to the
median state each period than do banks in the ROA state

just below the median, .39 versus .58. 20 These results
suggest the need to allow for possible asymmetries in
adjustment; this idea is developed further in the next
section. 21

18. Unlike the present study, Mueller takes average pre-interest return on
assets as the estimate of the normal profit rate; the constructed economic
returns therefore do not allow for differences in risk across firms.

20. The results cited here are for banks in the $500 million to $33 billion
range, the only group in Gup, et al., for which any banks diverge from
the median ROA state.

19. Note that Mueller structures his model to estimate a persistence
parameter rather than an adjustment parameter; thus the "A" he
estimates is equal to 1- A as defined in this paper.

21. However, Gup, et al., also find that the few banks in ROA states
substantially below the median tend to remain there, which suggests a
more complicated asymmetry than is investigated here.

IX.

ASYMMETRY IN PROFIT ADJUSTMENT

There are good theoretical reasons to suspect that symmetric treatment of positive and negative spreads is inappropriate. For example, firms with positive spreads may
take steps to extend, protect, and prolong those spreads if
the marginal benefit of such actions outweighs their marginal cost. Firms with negative spreads, on the other hand,
probably attempt to eliminate or reverse the situation as
rapidly as possible. Moreover, if information is imperfect,
a determinant of the rate of adjustment may be the speed
with which any situation of nonzero economic profits is
recognized, its sources understood, and appropriate actions taken in response. For a firm with a positive spread, it
is outsiders who must notice the advantage, decide what
has created that nonzero spread, and figure out how to
replicate what the successful firm is doing. Outside observers face a filtering problem, since positive profits for
one firm may be the result of transitory shocks to rates of
return. A firm with a competitive edge might even act to
obscure relevant information from competitors. In contrast, negative spreads often may result from a firm's own
miscues, so that much of the information necessary to
make the adjustment is internal and therefore much more
readily available at lower cost. A pronounced information
asymmetry for above-normal profits compared to belownormal profits would make positive spreads more persistent than negative spreads.
One test for such differences would divide the sample
into two groups according to profitability, firms with Rj?f!;k j
in one group and firms with Rj<kj in the other. But as noted
above, nontransitory economic rates of return on equity R,
are unobservable. A sample division based on the spread
between accounting ROEj and k j would result in some firms
with particularly large positive or negative Ej (transitory
return shocks or accounting distortions) being misclassified. However, note from equation (12) that Rj<k j if and
only if Mj<Ej; thus, an appropriate division of the sample
results from grouping firms according to whether the observable M, is greater than or less than the observable E j •

14

FRBSF ECONOMIC REvIEW 1994, NUMBER 2

The sample was separated accordingly into two groups
for each date around an MJE j ratio of 1.0, and the model
was reestimated allowing A to differ between the groups.
Figure 5 presents the adjustment speed results for the
profitable firms-that is, those with Mj;;:;'E j and Rj;;:;'k j with unprofitable firms in Figure 6.

FIGURE 5
ADJUSTMENT SPEED, HIGH-PROFIT BANKS

0.40
0.30
0.20
0.10
0.00
-0.10
-0.20
86:II

87:II

88:II

89:II

90:II

91:II

The results show that restricting A to be the same across
the entire sample at a given date masks substantial differences between profitable and unprofitable firms. Comparing the results to Figure 1, profitable firms tend to have
slower adjustment speeds, unprofitable firms faster. The
estimated adjustment speed for profitable firms is significantly different from zero for only the first 4 of the 12dates.
Adjustment speeds for the unprofitable group tend to be
higher, but vary considerably over the 1986-1991 period,
and tend to have larger standard errors. The lowest values
in 88:II-89:II are insignificantly different from zero. The
high in 86:II is 0.25, and the adjustment speed for unprofitable banks rises to 0.21 at 91:IV from its trough in 88:IY.
Figure 7 displays the difference between the adjustment
speed coefficients for unprofitable and profitable firms,
with a 95 percent confidence interval for the difference.
The difference is generally positive, significantly so at 8 of
the 12 sample dates, consistent with the hypothesis that
negative spreads disappear more quickly than positive
spreads. As in the symmetric case, a likelihood ratio test
solidly rejects restricting the coefficients to be equal across
time periods.
The results imply that positive spreads in banking are
more persistent than negative spreads. The difference in
the speeds with which R approaches k from above and
below implies a prediction for studies of intraindustry
mobility. Banks that achieve superior performance should
maintain that position for a relatively long time; hence the
same names should appear consistently among the top

FIGURE 6

FIGURE 7

ADJUSTMENT SPEED, LoW-PROFIT BANKS

DIFFERENCE IN ADJUSTMENT SPEED

0.40

0.40

0.30

0.30

0.20

0.20

0.10

0.10

0.00

0.00

-0.10

-0.10

-0.20

-0.20
86:II

87:II

88:II

89:II

90:II

91:II

86:II

87:II

88:II

89:1I

90:II

91:II

LEVONIAN / PERSISTENCE OF BANK PROFITS

group of banks for several periods. The quicker reversal of
situations of low profitability means that firms falling
toward the bottom ranks should tend to climb up fairly
rapidly, to be replaced by other firms suffering setbacks
that push returns below the cost of capital. A rough but
simple test ofthis empirical prediction might use any of the
many sets of published rankings of banks to look at relative
turnover in upper and lower quantiles. Mueller (1977)finds
evidence of such an effect in his study of profit rates using a
broad sample of industrial firms.

X. A COMMENT ON CAPITAL RATIOS
AND DEPOSIT INSURANCE

Federal insurance of bank deposits is a prominent feature
of the U.S. banking system. This section considers the
implications of deposit insurance for the adjustment speed
results. The insurance pricing schedule in effect at the
sample dates probably led to imperfect pricing of the federal guarantee.s- Premiums paid by each bank depended
only on the size of the bank as measured by deposits, and
did not reflect other factors that affect the economic value
of the insurance, most notably risk. It is likely that banks
with a higher probability of failure underpaid for their
insurance; for these banks, the net value would represent
an off-balance-sheet asset. Other banks that overpaid
for insurance bore a net off-balance-sheet liability. (See
Marcus and Shaked 1984, or Ronn and Verma 1986, for
estimates of the fair market value of deposit insurance.)
It is easy to find evidence that the net value of deposit
insurance might be correlated with the profitability of a
bank. Chi-square tests using the data set from this paper
confirm that banks with M/E < 1 tend to have lower equity
capital ratios than banks with M/E~I. Conventional theory says that a bank's market capital ratio affects the
probability of failure, and therefore the value of deposit
insurance: Lower ratios raise the value of the guarantee, all
else equal. As a result, the value of deposit insurance
probably is related to capital ratios to some extent, and thus
to market-to-book ratios, although the latter correlation
may be spurious.
The impact of any such relationship on the model may
be minor. One implication is that use of the simple CAPM
is not strictly appropriate, since bank stocks have significant aspects of contingent claims under these conditions.
However, as discussed above, the estimation results are not
very sensitive to the choice of required rates of return. A
22. This comment refers to the explicit pricing structure. It is possible
that other elements of the regulatory process associated with deposit
insurance brought the true cost of the insurance more closely in line with
its value.

15

second implication is that market-to-book ratios will tend
to be higher for low capital banks than they would have
been absent imperfectly priced deposit insurance. But
measured rates of return on equity also will be higher, as
the benefits of the deposit insurance subsidy flow through
to the banks' income. The relationship between returns and
theM/E ratio, which is the foundation of the model, may
be little affected. Put differently, the model specifies a
relationship between expected future cash flows and the
market value of equity. If deposit insurance affects expected cash flows, it affects market value, and that effect is
captured within the model; if deposit insurance does not
affect expected cash flows, then it cannot affect market
value.23

XI.

CONCLUSIONS, IMPLICATIONS,
AND POSSIBLE EXTENSIONS

This paper presents a model of the market value of firms in
which profits are persistent. Zero profit equilibrium is
reestablished gradually when positive or negative spreads
generate a return on equity different from the required
return. The resulting nonlinear model can be estimated
using stock market data, in which case the parameter
estimates reflect market beliefs about the degree of profit
persistence. The model is applied to the banking industry,
with a sample of large U.S. banks. Results generally
indicate that the market views the rate of competitive
adjustment as positive. Despite the protection extended to
the banking industry by government regulatory policies,
there appear to be forces operating to eliminate nonzero
profits, and any nonzero spreads can be expected to be
temporary rather than permanent; however, the pace of
adjustment is slow. When the sample is split into two
groups-banks with economic returns below the cost of
equity and banks earning at least their cost of equity-the
estimated speed of adjustment for negative spreads generally exceeds that for positive spreads, although not always
significantly so.
The model used is a relatively simple discounted cash
flow model of the value of shares. The features that
23. The correlation between capital ratios and market-to-book ratios
creates a degree of ambiguity, in that the data cannot be used reliably to
test the hypothesis that profitable banks have slower adjustment speeds
than unprofitable banks against an alternative hypothesis that highcapital banks have slower adjustment speeds than low-capital banks.
However, it is not clear why adjustment speeds should depend on
capital. It is occasionally claimed that regulators pressure low-capital
banks to increase profits to rebuild capital; this story rests on questionable assumptions about bank behavior, since it is in banks' interest to
raise profits as rapidly as possible, regardless of any pressure from
regulators.

16

FRBSF EcONOMIC REVIEW 1994, NUMBER 2

distinguish it from other such models are (i) that accounting ROE reflects, albeit imperfectly, the economic rate of
returnR (and therefore the rate of economic profits for any
given required return k), and (ii) that competitive forces
tend to push economic profits toward zero over time.
No effort is made to identify the sources of the profit
persistence evident in the banking data. A high degree
of profit persistence might simply follow from the nature of
the banking business; perhaps information costs are exceptionally high, or innovations are difficult to imitate successfully. On the other hand, persistence could result from
impediments to competition created intentionally by the
banks or unintentionally by government policies. In principle, one could identify the markets in which sample banks
operate, and test whether profit persistence is systematically related to market structure. However, a valid test
would be difficult or impossible with available data. Banks
that are large enough to have publicly traded equity generally operate in many different banking markets, each with
different structural and behavioral features. Profit persistence almost certainly varies across geographically separate markets, but there is no realistic wayto attribute total
bank profitability to specific local markets.
Nevertheless, the results may have implications for the
way competitive performance is evaluated. In a world of
uncertainty, imperfect information, and adjustment costs,
profits may turnpositive temporarily and then adjust back
to zero over time; the existence of nonzero profits at a
single point in time, or even over several periods, is not
a practical signal of lack of competition. Ultimately, dynamic competitive performance may be more important
than static performance. Thus it may be more useful to
gauge the degree to which a market is competitive by how
rapidly any excess profits disappear. Adjustment speeds
could be calculated using the method described in this
paper, especially when the results of time-series estimation
may be untrustworthy. The method used here distinguishes
between economic returns and accounting returns, and
filters out transitory elements of the economic returns.
Several possible enhancements to the model seem desirable. One modification would allow for the possibility that
profit rates might converge to some nonzero long-run
value, that is, that R might converge to some value R* =1= k.
Mueller (1986) finds substantial differences across industries in the long-run profit rate to which individual firms
converge (although he also reports evidence that in the very
long run these differences tend to disappear). Lambson
(1992) provides theoretical justification for Mueller's empirical observation, arguing that long-run economic profits
can be nonzero. Another potential modification would
allow the adjustment speed to be a function of the absolute
spread between R and k. Larger positive spreads may

be more likely to stimulate a response for two reasons:
(i) large excess profits are more obvious, and (ii) greater
potential rewards might compensate would-be entrants for
the uncertainty they face, or for any fixed costs of entry.
Similar comments apply to negative spreads, with the
affected firm facing a large potential payoff from rapid
adjustment. Finally, it may be useful to investigate whether
other obvious groupings-related to firm size or other
characteristics-e-affect the degree of profit persistence.
The model presented above permits the growth rate to
vary· across banks, but assumes that the market expects
growth to be constant for any individual bank; this assumption is made for the sake of tractability, not realism.
An enrichment of the model would allow g to depend on
the size of any positive spread. In the model, the value of
equity increases with g, provided R>k. It is possible that
firms with a spread-creating competitive advantage face a
strategic choice: Raise profit margins to increase the
spread between Rand k, or hold prices down to grab
market share from competitors and raise g. Competitive
adjustment then might occur in both the profit dimension
and the growth dimension.
The adjustment speeds estimated here are lower than
those found in studies of nonbank firms. However, the
methods used in previous studies are different, making
direct comparisons difficult. Application of the model
developed in this paper to other industries is an obvious
avenue for future research. The degree of asymmetry in
bank profit persistence could be compared with similar
estimates for other industries; for example, do bank profits
adjust more slowly in both directions?
As a final note, this model specifies a theoretically
defensible relationship between market data and the accounting data used to construct ROE, g, and E. The
relationship seems to fit reasonably well, and may provide a
framework for using accounting figures to generate pseudomarket-value numbers. Such an imputation of market value
would be helpful in analyzing the condition of banking
firms more generally, particularly those without publicly
traded equity.

LEVONIAN / PERSISTENCE OF BANK PROFITS

REFERENCES
Copeland, Tom, Tim Koller, and Jack Murrin. 1990. Valuation: Measuring and Managing the Value of Companies. New York: John
Wiley & Sons.
Fama, Eugene F., and Kenneth R. French. 1993. "Common Risk
Factors in the Returns on Stocks and Bonds." Journal ofFinancial
Economics 33(1) pp. 3-56.
Fisher, Franklin M., and John 1. McGowan. 1983. "On the Misuse of
Accounting Rates of Return to Infer Monopoly Profits." American
Economic Review 73(1) pp. 82-97.·
Flannery, Mark 1., and Christopher M. James. 1984. "The Effect of
Interest Rate Changes on the Common Stock Returns of Financial
Institutions." Journal of Finance 39(4) pp. 1141-1153.
Geroski, Paul A., and Alexis Jacquemin. 1988. "The Persistence of
Profits: A European Comparison." Economic Journal 98 (June)
pp. 375-387.
Gup, Benton E., S.M. Lau, T.H. Mattheiss, and John R. Walter. 1992.
"An Empirical Analysis of Growth in Banking: A Markovian
Approach." Unpublished.
Hannan, Timothy H., and Allen N. Berger. 1991. "The Rigidity of
Prices: Evidence from the Banking Industry." American Economic
Review 81(4) pp. 938-945.
Ibbotson Associates. 1991. Stocks, Bonds, Bills and Inflation. Chicago.
Keeley, Michael C. 1990. "Deposit Insurance, Risk, and Market Power
in Banking." American Economic Review 80(5) pp. 1183-1200.
Lambson, Val Eugene. 1992. "Competitive Profits in the Long Run."
Review of Economic Studies 59, pp. 125-142.
Leibowitz, Martin L. , and Stanley Kogelman. 1990. "Inside the P/E Ratio: The Franchise Factor." Financial Analysts Journal (NovemberDecember) pp. 17-35.
Marcus, A., and I. Shaked. 1984. "The Valuation of FDIC Deposit
Insurance Using Option-pricing Estimates." Journal of Money,
Credit, and Banking 16(4) pp. 446-460.
Mueller, Dennis C. 1986. Profits in the Long Run. Cambridge University Press.
_ _ _ _ _. 1977. "The Persistence of Profits Above the Norm."
Economica 44, pp. 369-380.
Neumark, David, and Steven A. Sharpe. 1992. "Market Structure
and the Nature of Price Rigidity: Evidence from the Market for
Consumer Deposits." Quarterly Journal of Economics (May)
pp.657-680.
Ronn, Ehud I., and Avinash K. Verma. 1986. "Pricing Risk-Adjusted
Deposit Insurance: An Option-Based Model." Journal ofFinance
41(4), pp. 871-896.
Tobin, James, and William C. Brainard. 1977. "Asset Markets and the
Cost of Capital." In Economic Progress, Private Values, and
Public Policy, eds. Bela Balassa and Richard Nelson, (Chapter 11).
Amsterdam: North-Holland.
Wilcox, Jarrod W. 1984. "The PIB-ROE Valuation Model." Financial
Analysts Journal (January/Febrnary) pp. 58-66.
Worthington, Paula R. 1989. "On the Distinction Between Structure
and Conduct: Adjustment Costs, Concentration, and Price Behavior." Journal ofIndustrial Economics 38 (December) pp. 235-238.

17

Financial Regulation and Banking Sector
Performance: A Comparison of Bad Loan
Problems in Japan and Korea

I. INTRODUCTION

Chan Huh and Sun Bae Kim
We would like to thank Se II Ahn of the Bank of Korea and
Tetsufumi Yamakawa of the Bank of Japan for arranging
access to data, and to Ken Kasa, Mark Levonian, Ramon
Moreno, and Jonathan Neuberger for helpful comments
and suggestions. We also would like to thank the Bank of
Japan, the Bank of Korea, the Korea DevelopmentInstitute,
and the Korea Tax Institute for providing opportunities to
present this paper. Able research assistance was provided
by Andy Biehl, Robert Ingenito, Dung Anh Nhan, and
Jacob Pozharny.

We estimate the bad loan rate in Japan and Korea for

1973-1992 using data on defaults on notes issued by the
corporate sector. This method exploits institutional features common in both countries which suggest a close
linkage between default on notes and default on bank
borrowing. Our main findings are as follows. First, the
pattern of the estimated bad loan rate series generally
conforms to past business cycle patterns in both countries.
Second, the bad loan rate is substantially higher in Korea
than Japan. Lastly, a much tighter linkage is observed for
Japan between the bad loan rate and a set of plausible
economic explanatory variables. Weoffer some interpretationfor these findings.

Exploring the links between a country's financial system
and its real economic performance has been an increasingly
active research area in recent years. One strand of the
literature has focused particularly on Japan's bank-centered
financial system and within it, the role of the so-called
main banks in attenuating capital market imperfections
and hence supporting rapid growth (e.g., Hoshi, et al.,
1990, 1991). More recently,interest has extended to include
other rapidly growing economies in the region, such as
Korea and Taiwan (e.g., World Bank 1993).
One puzzle that emerges from this literature is that, for a
subset of East Asian countries at least, which includes
Japan, rapid growth occurred alongside a financial system
that many would describe as "repressed;" that is, interest
rates were strictly controlled and capital markets were
segmented both domestically and vis-a-vis international
transactions. In other words, these countries' experience
seems to contradict the received wisdom that financial
repression impairs efficient accumulation and allocation of
financial resources and hence retards economic growth.
Was financial repression indeed costless? This paper
tackles this question by indirectly assessing the cost
of financial repression by comparing Japan and Korea.
Although Korea clearly has followed Japan in terms of
economic development, both countries experienced rapid
investment-led growth spearheaded by heavy and chemical
industries-Japan in the early 1960s and Korea in the
1970s-and growth wasfinanced by a bank-centered financial system within an environment of segmented capital
markets and regulated interest rates. The notable difference, however, is that Korea's banks, as governmentowned institutions, were much more stringently regulated
than Japanese banks, which have been privately owned.
This affords an opportunity to assess whether this greater
degree of regulation of banks in Korea has engendered
greater costs or inefficiencies.
To the extent that industrial financing has been virtually
the exclusive preserve of banks in both countries until
recently, we propose to assess the relative efficiency of the
two systems by focusing on the bad loan rate. The rationale
is, other things equal, a more efficiently run banking

19

HUH AND KIM/BAD LoAN PROBLEMS IN JAPAN AND KOREA

industry will engender a lower bad loan rate. A major contribution of the paper is to derive an estimate of the bad
loan rate which is unavailable from published sources. To
anticipate a key result of this paper, we find that the bad loan
problem has been unambiguously more severe in Korea
than in Japan. We attribute this difference to the lack of
discretion Korean banks have had in allocating funds and
their lower incentive to control bankruptcy risk through
screening and monitoring corporate borrowers.

II.

ESTIMATING THE BAD LoAN RATE

Measuring the bad loan rate directly is difficult for at least
two reasons. First, continuous data are not available because neither Japanese nor Korean banks are required by
law to report nonperforming loans. Second, for Korea,
even in instances where patchy data exist, banks are bound
to understate severely the true amount, since banks frequently have been required to retain nonperforming loans
on their books instead of writing.them off by drawing on
loan loss provisions.'
We propose to circumvent data problems on banks' (i.e.,
the lenders') balance sheets by turning to the (aggregate)
balance sheet of the corporate sector (i.e., the borrowers).
This indirect method of estimating the extent of badloans
exploits a salient feature of corporate finance common to
both economies: the extensive use of notes and accounts
payable (henceforth, notes), which are essentially very
liquid short-term financing instruments. Why these data are
useful for the stated purpose requires some elaboration. 2
Table 1 shows that notes constitute a significant share of
the liabilities of both Korean and Japanese firms. For the
Korean manufacturing sector as a whole, notes accounted
for about 27 percent of current liabilities and 17 percent of
total liabilities in 1990, while the share of short-term bank
borrowing was 33.5 percent and 20.6 percent, respectively. The reliance on notes is even higher in Japan, at 30
percent of total liabilities, compared to 16.7 percent for
short-term bank borrowing -.The share of notes in Japanese
corporate liabilities is more than double the level observed
in the U.S.
One important reason for the relatively heavy use of
notes, especially in Korea, has been the chronic excess
demand for funds in the corporate sector. Firms unable to

1. The Bankof Koreacompensated the commercial banksby extending
various forms of concessions. One common method was payment of
interestto commercial banksforreserve deposits theyheldat thecentral
bank, although the law did not require such payment. See Kwack and
Chung (1986).
2. Descriptions of data and theirsources are provided in the Appendix.

TABLE 1
LIABILITY STRUCTURE OF
CONSOLIDATED BALANCE SHEET

1990
ITEMS

Current liabilities
Notespayable
Short-termforeign borrowing
Short-termbank borrowing
Currentmaturitiesof long-term debt
Other short-termborrowing
Other current liabilities
Long-term liabilities
Bonds payable
Foreigndebt
Long-term debt to banks
Other long-term debt
Capital
Liabilities plus Capital

JAPAN

(%)

45.1
20.5
11.5
1.5
10.5
23.4
7.1
11.1
5.2
31.5
100.0

KOREA

(%)

45.7
12.5
0.2
15.3
2.8
2.3
12.6
28.2
7.7
1.5
12.2
6.8
25.9
99.8

NOTE: Data for Japan are from the 1990 year-end consolidated balance
sheets of 96,758 firms in all industries, with aggregate assets of ¥337
trillion.Korea's datacomefromthe 1990 year-end consolidated balance
sheets of 2081 manufacturing firms,withaggregateassetsof 163trillion
won.

meet their external financing requirements through bank
borrowing have resorted to the issue of short-term notes to
raise additional liquidity. In Japan, notes have been used
relatively more intensively by the small and medium-sized
firms, while in Korea, perhaps reflecting more widespread
and severe credit rationing, use of notes payable appears
Ubiquitous across the corporate sector. Within Japanese
corporate groupings (keiretsu), major firms have been
providing de facto financing to smaller firms (typically
subcontractors) by selling longer-term notes, while paying
their own bills on a short-term basis (Aoki 1984). Another
common reason for the intensive use of notes in Japan and
Korea is the lack of a developed corporate bond or commercial paper market until recently.
Although time series data are not available for nonperforming bank loans, they are available (at a monthly
frequency) for the amount of notes defaulted for both Japan
and Korea. We propose that these note default data may be
an unbiased indicator of the financial health of the corporate sector and, by implication, the extent of the bad loan
problem in the banking sector. The reasoning becomes
evident as we examine how the notes are issued, discounted, and cleared in both countries.
In Korea, firms typically issue notes on a standardized
check drawn on an account at a bank with which it has

20

FRBSF ECONOMIC REvIEW 1994, NUMBER 2

established creditworthiness through its business relationship. The maturity ranges from three to six months and, as
a transferable security, the notes can be endorsed successively by several firms and are widely used as a means
of payment in business transactions. Firms often sell the
notes directly to banks prior to maturity at a discount, with
the amount of discount equivalent to the interest charge
that would accrue from the date of discount to the maturity
date. Essentially similar practices apply to Japan, where it
is estimated that about 25 percent of bank loan transactions
in Japan take the form of discounts of notes (BaIlon and
Tomita 1988).3 The bulk of the notes are cleared through
clearinghouses which are managed as associate institutions of the Bankers' Association.
Banks promptly report notes in default when funds in the
firm's account are insufficient to cover the amount submitted for clearance. In Japan, firms that default twice within
six months are subject to two years' prohibition from
transactions with member financial institutions of the
clearinghouse (Suzuki 1980: p.301). In Korea, although a
firm with "insufficient funds" is not legally bankrupt, for
practical purposes such a default almost always leads the
bank to suspend business and in severe cases puts the firm
into receivership for liquidation.
Given that corporate banking in Japan and Korea combines traditional lending activities with discounting and
clearing of notes, a suspension of bank transactions triggered by a note default would imply that, from the bank's
point of view, the overall creditworthiness of the firm in
question has significantly deteriorated. In other words,
movements in aggregate suspension of bank transactions
due to notes defaults should be closely tied to the business
sector's general financial conditions and hence the extent
of the bad loan problem. It is also important to note that
since no government intervention constrains this reporting
procedure, note default data would not be fraught with the
underreporting bias of bad loans."

3. Notes are welcomed by the banks for two reasons: (i) they are selfliquidating (on the due date they are settled and the money loaned is
automatically paid); (ii) when the original issuer is unable to meet the
note, all subsequent endorsers (collectively) are also liable to the bank
for the face value of the note (Kitagawa 1984). Japanese banks have an
added motive. In the process of clearing these notes, banks can collect
valuable up-to-date information on the general health of their corporate
clients.
4. Additionally, since the bank acts purely as an agent and not as a
fiduciary as in the case of loan arrangements, there is little scope or
incentive for the banks themselves to under or overreport the incidence
or the amount of note default.

Bad Loan Estimate: Japan
To ascertain more formally the link between defaults on
notes and the severity of problem loans in the corporate
sector,.we first estimated a simple regression; the dependent variable is (changes in) the aggregate liability of
bankrupt enterprises (BANKLIAB) and the explanatory
variable is (changes in) the aggregate liability of firms
whose business transactions with banks were suspended
due to note default (SUSPLIAB). 5 The results are reported
in Table 2. A high correlation is observed between these
two variables, with SUSPLIAB statistically significant at
the 1 percent level and explaining almost 90 percent of the
year-over-year changes in the aggregate liability of bankrupt firms.
We also regressed BANKLIAB on GNP growth instead
of SUSPLIAB to see the extent to which fluctuations in
aggregate growth explain changes in corporate bankruptcy.
The coefficient on GNP is negative and statistically significant; that is, higher output growth is associated with lower
corporate bankruptcy. However,GNP growth explains only
38 percent of the changes in corporate bankruptcy. Moreover, its explanatory power does not appear robust. When
GNP and SUSPLIAB are both included as explanatory
variables, the former loses statistical significance while the
latter retains it.
Having established that SUSPLIAB provides a good
gauge of the corporate sector's overall financial health, we
now turn to the task of actually measuring the extent of the
bad loan problem in Japan. For any given quarter, t, we
estimated the bad loan rate (BLR) by applying the following
formula:
(1)

BL

~t =

(BL t
BB t

)

= (SUSPLIAB t

TOTLIAB t

)

where BL is the level of bad loans, which is unobserved,
BB is the aggregate outstanding balance of short-term plus
long-term bank borrowing, TOTLIAB is the aggregate
liability of the corporate sector and, as before, SUSPLIAB
is the combined liability of firms with suspended business
transactions with banks due to defaulting on notes. The
intuition underlying this equation is straightforward:
The proportion of problem loans to total loans is the same
as the proportion of liabilities accounted for by firms with
suspended transactions with banks to the aggregate liability of all firms. The key underlying assumption, to

5. It would be reasonable to expect that movements in BANKRTLIAB
would closely track changes in the aggregate level of bad loans.
However, these data are available only on an annual basis.

HUH AND

TABLE

KIM/ BAD

21

LoAN PROBLEMS IN JAPAN AND KOREA

2

RELATIONSHIP BETWEEN CORPORATE BANKRUPTCY AND SUSPENSIONS OF BUSINESS TRANSACTIONS
WITH BANKS, JAPAN
1968.Q2~1992.Q4
DEPENDENT

EXPLANATORY

VARIABLE

VARIABLE

BANKRUAB

SUSPUAB,
SUSPUAB,_1

BANKRUAB

BANKRUAB

COEFFICIENT

P. VALUE

D.W.

ADJUSTED

R2

OFQ

1.01***
0.43

0.89

2.5

0.17

GNP,
GNP,_1

-0.98***
2.2

0.38

1.9

0.75

GNP,
GNP,_1
SUSPUAB,
SUSPUAB,_1

0.13
-0.04
1.07***
0.17

0.89

2.6

0.68

NOTE: BANKRLIAB is the total liability of the firms that wentbankruptin a given year, SUSPUAB is the aggregateliabilityof firmswhose business
transactions with banks have been suspendeddue to defaulting on notes, and GNP is the real year-over-yeargrowthrate of GNP. All seriesare logged
and first differenced; *** denotesa marginal significance level of 1 percent.

reiterate, is that firms that default on notes are also likely
to be the ones defaulting on bank loans. 6
Figure 1 presents the estimated bad loan rate (BLR) for
Japan for the sample period of 1973 to 1992.7 Three
noteworthy patterns emerge in the series. First, the bad
loan rate rose sharply during the 1970s. It first peaked in
1974 at about 1.5 percent, in the wake of monetary and
fiscal tightening in early 1973 geared to restrain inflation
and the October 1973 oil crisis. The rate rose to yet higher
levels in 1977, apparently reflecting the slump in exportdependent industries triggered by a sharp appreciation of
the yen."
Second, the series does not exhibit any discernible trend
from the late 1970s through the mid-1980s. That is, no
marked increases in bad loan problems appear to havebeen
triggered by the second oil shock in 1979, the recession
of the early 1980s, or the sharp appreciation of the yen after
the Plaza Accord of 1985.

6. Our estimate might overstate somewhat the actual magnitude, to the
extent that banks generally secure loans with some tangibleasset and
recover some of the loan after the eventual liquidation.
7. Our samplebegins in 1972and not earlierbecausethe Bankof Japan
changed the reporting criteria for note default in October 1971. See
Economic Statistics Monthly, November 1971.
8. The yen/dollar exchange rate appreciated from about 290 at the
beginning of 1977, to a peak of 170 in October 1978.

FIGUREl
BAD LoAN RATE ESTIMATE: JAPAN

Percent

o

I

73

75

77

79

81

I

I

83

I

I

85

87

I

89

i

I

91

22

FRBSF ECONOMIC REVIEW 1994, NUMBER 2

Third, the bad loan rate declined markedly during the
bull market (the so-called bubble economy) of the second
half ofthe 1980s, reaching a low of 0.25 percent at the end
of 1989. The rate then sharply reversed trend, soaring to an
all-time high of nearly 2 percent in 1991. This surge
coincides with the steep decline in asset prices since late
1989 and the onset of Japan's current recession, which
many now consider the most severe in the postwar period.
Our estimate of the bad loan problem corroborates this
view in a striking way. The severity of problem loans
appears to have subsided somewhat in 1992 but no definitive statement can be made without more up-to-date data.
It is important to note that our estimates are of new bad
loan rates for each year. To the extent that banks may carry
some or even a substantial part of bad loans from previous
periods over time, the actual bad loan rate may be better
approximated by a cumulative measure. To explore this
possibility, we cumulated the bad loan estimate from
1990.Ql to 1992.Q4, the latest period for which data are
available. The rationale for this experiment is to see how severe the current bad loan problem is in Japan, assuming that
banks have not been able to write off any portion of
nonperforming loans since 1990.9 According to this worst
possible scenario, bad loans in Japan would have totaled
some ¥43.8 trillion, or 10.4 percent of total outstanding
(short-term plus long-term) bank loans at the end of 1992.
This estimate is remarkably close to some private sector
estimates reported in the financial press in recent months. 10

Bad Loan Estimate: Korea
Due to the lack of data on liabilities of suspended firms
(SUSPLIAB), the bad loan rate for Korea was estimated
using a slightly different equation:
(2)

K_
BLRt -

(BL t
BBt

)

_
-

(

DEFNOTEt
TOTNOTEt + DEFNOTEt

)

where DEFNOTE is the aggregate value of defaulted notes
and TOTNOTE is the total amount of notes outstanding.

9. According to Japanese practices, loans are not considered delinquent
until six months without a payment, and even then a bank may accept a
token payment, so the troubled debt may ride another six months. The
implicit assumption here is that prior to 1990, Japanese banks were
capable of writing off bad loans. The situation changed after the onset of
the steep decline in asset prices; it wiped out a significant portion of
banks' hidden reserves, which otherwise could have been used to write
off bad loans. For further details on the effect of stock price movements
on Japanese bank capital and lending, see Kim and Moreno (1994).
10. Many financial analysts maintain that, by U.S. standards, total bad
loans in Japan may be as high as ¥30 trillion. See WallStreetJournal,
January 20, 1994.

The equation simply states that the bad loan rate is equal to
the rate of default on notes issued. n ,12 Again, as in Japan,
this method of estimating the bad loan rate rests on the
premise that firms that default on notes also are likely to be
the ones defaulting on bank loans.
Figure 2A shows the estimated bad loan rate for Korea.13
Several noteworthy patterns emerge. First, Korea's bad
loan rate is significantly higher than Japan's estimatetypically more than double-and is also more volatile. We
will discuss possible reasons for this in Section III.
Second, as in Japan, a local peak in bad loans occurred
after the first oil shock. Unlike Japan, however, the bad
loan problem appears to have been most severe inthe early
1980s, with the rate exceeding 7 percent at its peak in 1981
-1982. This surge in bad loans can be reconciled with several adverse shocks to the Korean economy around that
time. For one, Korea's GNP shrank by almost 5 percent as a
result of the drought-induced recession of 1980. Weak
domestic economic conditions were compounded by the
world recession after the second oil shock, pushing many
highly leveraged firms into insolvency. 14
Third, as in Japan, the bad loan rate trended downward
in the second half of the 1980s, though in Korea's case the
decline was punctuated by a minor surge in 1987. This
surge coincides with the well-known episode in 1987 when
many Korean construction companies went bankrupt as a
result of cancellations of large overseas contracts.
11. The Bank of Korea's Financial StatementAnalysis does not provide
data on notes issued for all industries. We therefore estimated TOTNOTE by summing notes issued in manufacturing, construction, wholesale, retail, and electricity. These industries collectively accounted for'
about 90 percent of total corporate bank borrowing. By contrast,
DEFNOTE data pertain to defaulted notes in all industries. Therefore,
our estimate of the default rate on notes has a slight upward bias.
12. As noted earlier, unlike other forms of liability, such as bank
borrowing, bad notes are netted out of total notes outstanding (TOTNOTE) quite promptly. We added DEFNOTE to the denominator since
dividing by TOTNOTE alone, which is a net amount, would yield an
overestimate of the extent of the bad loan rate.
13~ The sample begins in 1973 because of limited data availability for
earlier years and a sharp break in the data due to the Presidential
Emergency Decree in 1972. The Decree essentially came in response to
widespread financial distress in the corporate sector in the early 1970s.
To lighten the corporate debt burden, the government placed a moratorium on all loans in the informal credit market (curb market) and
slashed the bank loan rate from 23 percent per annum to 15.5 percent,
when the inflation rate was as high as 16 percent. The Decree also
converted approximately 30 percent of high interest rate short-term
commercial bank loans into long-term loans at concessional rates.
14. Industries that were particularly hard hit during this time included
overseas construction, shipping, textile machinery, and lumber. Concern over unemployment and financial instability prompted the government to bailout many ofthese troubled firms. See Cho and Kim (1993)
for details.

HUH AND

KIM / BAD LoAN PROBLEMS IN JAPAN AND KOREA

23

Finally, as in Japan, Korea's bad loan rate increased
sharply in 1990, reaching a level comparable to that observed in the early 1980s. Part of the increase may be
attributed to the cyclical downturn in the Korean economy
in 1992, when GNP growth slowed to 4.6 percent. But the
cyclical downturn alone cannot account for the jump in
the bad loan rate. For one, the slowdown in 1992, albeit the
worst since 1980, was relatively mild compared to the
recession of 1980, or to Japan's current recession. Moreover, the Korean economy has not been plagued by a drastic
asset price deflation as in Japan. These observations suggestthat the recent surge in Korea's bad loan problem may
reflect more fundamental factors; which we explore in the
next two sections of the paper.
.
We noted earlier that our estimated series, which are net
annual rate, may significantly understate the actual extent
of the bad loan problem if banks are constrained in writing
them off in a timely manner. This discrepancy is likely to
be especially sizeable in Korea since, under government
directives, banks usually have been carrying large amounts
of nonperforming loans on their books over very long
periods.
Figure 2B presents the cumulative bad loan rate under
two alternative scenarios. First, we derived an upper bound

estimate using an average annual write-off rate of 5 percent, i.e., we cumulated 95 percent of new bad loans each
year over the entire sample period 1973.Q1-1992.Q4. This
series is represented by the solid line in Figure 2B. To
derive a lower bound estimate, we employed an arbitrary
average annual write-off rate of 10 percent. This series is
represented by the dotted line.
According to the upper bound estimate, the cumulative
bad loan rate climbed steadily from the early 1970s to a
peak of 36.7 percent in 1984.Q1. The situation eased
somewhat during the balance of the 1980s, but then deteriorated sharply after 1990.Q3. As of 1992.Q4, some 36.7
percent of total outstanding loans in Korea were nonperforming. Carrying out the same exercise using the annual
write-off rate of 10 percent yields essentially a similar
pattern, though the estimated cumulative rate is, of course,
lower, at 26.5 percent in 1983.Q4 and 27.1 percent in
1992.Q4. By either measure, however, the bad loan problem in Korea appears significant both in absolute terms and
relative to Japan.
Are these high bad loan rates indeed plausible? Chung's
(1991) study, which is based on internal Bank of Korea
data, allows a partial check for the benchmark year of
1988. For purposes of comparison, Table 3 reproduces his

FIGURE2A

FIGURE2B

BAD loAN RATE ESTIMATE: KOREA

CUMULATIVE BAD loAN RATE: KOREA

Percent

Percent

7~ 1

40

6.4

30

35

..
.... ...
"

5.6

"

"

4.8

.

........... ,

. -.

4

.

3.2

.

Low

5

2.4

0

1.6
73

75

77

79

81

83

85

87

89

91

73

75

77

79

81

83

85

87

89

91

24

FRBSF EcONOMIC REVIEW 1994, NUMBER 2

TABLE 3
NONPERFORMING LoANS OF MAJOR KOREAN COMMERCIAL BANKS

1988
BANKS

Group I
Group IT
Total

TarAL CREDIT
AND DISCOUNT

PERFORMING loANS
Type A
Amount

NONPERFORMING loANS BY TYPE
TypeB
Total
%
Amount
%
Amount

%

5,502

4,041

335

6.1

1,125

20.5

1,461

26.5

447

401

3

0.6

43

9.6

46

10.2

5,949

4,443

338

5.7

1,168

19.6

1,507

25.3

SOURCE: Chung (1991 Table 1-1, p. 16).
NOTE:. Dataotherthan percentare in billionwon. TypeA refersto loansthatare almostsurelynotrecoverable; TypeB areloans withoverthreemonth's
delay ill paymentor loans to firms with sufficientdeterioration in credit quality to warrantexplicitloan principal recovery measures.

main results. Chung's sample consists of eight major
nationwide banks divided into two groups. Group I consists of six banks that have been in business since the 1950s
or 1960s, and Group II is made up of two newer banks
established in the early 1980s. Problem loans also are
reported in two categories. Type A are loans whose probability of repayment is virtually nil, and Type B includes
loans with over three months' delay in payment and loans
extended to companies whose credit conditions have deteriorated so markedly as to warrant explicit loan principal
recovery measures.
Based on the strictest definition (i.e., Type A), some 5.7
percent of the total sample of eight banks' outstanding
loans as of year-end 1988 were bad loans. When the
broader definition of problem loans are added (Type B), the
bad loan rate swells to 25.3 percent. The bad loan problem
appears significantly more severe for Group I banks, which
are older and hence more exposed to bad loan overhang
problems. By contrast, Group II banks havehad the benefit
of a relatively clean slate. These newer banks, however,
cannot be taken as representative of Korea's banking
industry.
Our estimates of the cumulative bad loan rate appear
reasonably close to Chung's. As of 1988.Q4, our bad loan
rate was 17.9 percent using the 10 percent write-off rate
and 30.2 percent using the 5 percent write-off rate. It
would appear, therefore, that for 1988 at least, the (broadly
measured) actual bad loan rate falls between the lower and
upper bounds of our cumulative estimate. We have no
reason to believe that this should not hold for other years
as well.

m. WHY HAVE BAD LoAN RATES
BEEN HIGHER IN KOREA?15

The modem theory of financial intermediation emphasizes
the special role of banks as information producers. By
acting as delegated monitors on behalf of numerous and
scattered depositors, banks eliminate needless duplication
of monitoring which is costly (Diamond 1984).Also, given
the public goods aspect of monitoring, delegating the task
to .one intermediary potentially can help avoid the freerider problem that arises when many lenders finance a
single borrower. We say potentially because, as Diamond
pointed out, banks themselves must be provided with
proper incentives to monitor. Depositor discipline is one
incentive against banks that shirk on monitoring. 16 Another is for the bank to hold a substantial share of a
borrower's debt so that it internalizes a substantial portion
of the externality generated by its monitoring.

15. The mean bad loan rate estimate for the sample period 1973.QI1992.Q4 was 3.9 percent for Korea and 0.9 percent for Japan, with

standard deviations of 1.5 percentand 0.4 percent, respectively. To see
whetherthe difference betweenthe twoestimatesis differentfromzero
with a statisticalsignificance, the following testswerecarriedout. First,
the difference between the two series (i.e., diff, = BLR(Korea) BLR(Japan) is calculated. Second, various univariate autoregressive
regressions with a constant term are run using the diffr series. Then the
statistical significance of the constant term is examined. For AR(I)
through AR(6) specifications, the constant term remains positive and
significant at the significance level of 10 percent or less.
16. Tothe extentthata bank holdsa diversified loanportfolio,its overall
return will serveas a relatively noiseless signalof thelevelof monitoring
effort. This, in tum, enables depositors to induce banks to monitor
efficiently.

HUH AND

Both Japan and Korea have relied intensively on the
banking sector to finance growth. The banking sector's
incentive structure to screen and monitor borrowers, however, appears to have diverged significantly between the
two countries.
For Japan, a large number of studies suggest that its
banking system, in particular the so-called main banking
system, has been highly effective in mitigating informational and other imperfections in capital markets (e.g.,
Aoki, et al., 1993, Hoshi, et al., 1990, 1991, Kim 1993,
and Lichtenberg and Pushner 1992). A distinguishing
feature of the main banking system is that although the
main bank identified with a particular firm that is not its
sole lender, it is usually the only bank that undertakes the
task of monitoring. 17 Two additional features of the system
suggest that powerful incentives were present for the main
bank to be diligent in carrying out this task.
First, if a firm monitored by a given main bank faces
financial distress, that main bank is also expected to
assume the bulk of the burden in restructuring it or bailing
it out. If conditions are sufficiently bad to warrant bankruptcy, the main bank usually absorbs a larger proportion
of losses than its loan share.l" Bearing this disproportionate burden when projects go awry would act as an effective
deterrent against shirking on monitoring.l? Second, the
main bank also faces positive incentives to monitor due to
the claims structure it holds: The main bank typically not
only is the largest lender, but also is an important shareholder, usually the largest among banks. Presumably, the
large debt and equity stakes that the main bank simultaneously holds help it internalize a significant part of the
externalities associated with monitoring the firm. 20
As in Japan, banks have played a dominant role in
financing Korea's economic growth. This came about
largely as a result of conscious policy design. Following
Japan's model in the 1960s, the Korean authorities sought
to use the banking sector as a conduit of preferential credit

17. Main banking therefore has been characterized by Sheard (1989)as a
system of "delegated monitor among monitors," in contrast to Diamond's (1984) model where monitoring is delegated by depositors to an
intermediary.
18. This is extensively documented in case studies by Sheard (1985,
1989).
19. This immediately raises the question: What prevents the main bank
from reneging on this commitment? One possible explanation is that
banks enter into arrangements for reciprocal delegated monitoring as
well as for reciprocal subordination in financial distress, with loss of
reputation as a deterrent against defection (Aoki, et al., 1993).
20. Kim (1993) provides a more detailed analysis on this and related
issues.

KIM / BAD LoAN PROBLEMS

IN JAPAN AND KOREA

25

to sectors deemed strategic to Korea's economic growth. 21
The use of preferential access to credit at subsidized
interest rates (known as "policy loans") intensified in the
1970s when the government made a major push to establish
a heavy and chemical industries (HCI) sector in Korea. 22
According to one estimate, policy loans on average accounted for over 65 percent of all bank loans in 1973-1981
(Cho and Kim 1993). The actual share of governmentdirected loans would be even higher if one included loans
that were not extended through explicitly earmarked programs and hence were more difficult to measure. 23
Compared to Japan, the Korean government appears to
have wielded a much more direct control and much tighter
control over the banking sector. Most notably, unlike in
Japan, the Korean government until recently has been the
major shareholder in all major Korean banks. This has had
far-reaching ramifications on how the banking system has
operated. To quote Cho and Kim (1993, pp. 51-52): "The
banking system was used as the government's treasury unit
to finance development projects to manage risk sharing of
the economy and bankers were treated as civil servants.
Their performance was evaluated based on their compliance to the government guidance rather than their efficient management of assets and liabilities."
Tight government control of the banking sector gave rise
to two types of moral hazard problems in Korea's credit
markets. On the supply side, banks had little discretion or
incentive to control risk by screening projects and monitoring corporate performance. Declaring any sizeable industrial enterprise as bankrupt or writing off bad loans on

21. It was not until the early to mid-1980s when nonbank financial
institutions (NBFIs) emerged as an important alternative source of
financing in addition to the traditional commercial bank and curb
markets. For overviews of the postwar Korean financial system and
policies, see Kang (1990), Kwack and Chung (1986), Hong and Park
(1986), Cho and Kim (1993), and Cho and Cole (1992).
22. The government also used the banking system to guarantee foreign
financing of investments in HCI. Foreign loans accounted for a sizeable
share of external funds ofKorea's corporate business sector, averaging
37.9 percent of the total in 1965-1969, 23.3 percent in 1970-1974, and
20.4 percent in 1975-1979. As the capital-intensive HCI investment
drive waned and supplies of loans from foreign banks dwindled,
the share of foreign loans declined sharply to 6 percent in the first half
of the 1980s.
23. Another way to assess the relative importance of policy loans is to
look at the sectoral allocation of credit. According to Cho and Kim
(1993, p.39), the manufacturing sector received 46 percent of total
domestic bank loans in 1970, while its contribution to GDP was only
21.3 percent. Within manufacturing, HCI accounted for 22.6 percent of
total bank loans, while its GDP share was only 8.5 percent. By 1980,
HCI's share of total bank credit increased to 32.1 percent while its GDP
share increased to 16.5 percent. This reflects in part the longer gestation
period of HCI investment.

26

FRBSF EcONOMIC REVIEW 1994, NUMBER 2

banks' balance sheets required the explicit consent of the
government. In practice, the government averted bankruptcy at large enterprises by directing banks to provide
relief loans or rescheduling debt.
Extreme control and guidance of banking institutions
had adverse incentive effects on the demand side of the
loan market as well. The socialization of bankruptcy risk,
combined with the strict low interest rate ceilings, made
the cost of debt financing very cheap for firms in the
targeted sectors. 24 This encouraged firms to take on excessively high levels of debt. According to data in the Bank of
Korea's Financial Statement Analysis, the rate of total
liability to net worth in Korean manufacturing more than
quadrupled, from about 84 percent in the mid-1960s to
over 365 percent in the late 1970s.
High leveraging made the corporate sector as a whole
very vulnerable to external shocks and economic fluctuations. This problem grew to especially alarming proportions by the end of the 1970s, as excessive investment in
HCI bred large idle capacities, and enterprises began
encountering difficulties servicing their debt. 25 The government responded by taking greater involvement in banks'
credit allocation to bailout troubled firms and industries,
with the result that banks were saddled with ever growing
amounts of de facto nonperforming loans. 26
Mounting problems in the financial sector prompted the
Korean government to reorient its policies in the early
1980s toward giving banks greater discretion in setting
interest rates and allocating loans. To this end, the government began divesting its shareholding in commercial banks
and established the so-called principal transaction bank
system. The system sought to regulate bank credit extended
to large corporations through their principal transaction
banks. The basic aim was to reduce corporate leverage and
to improve the quality of monitoring of the financial
conditions and investment activities of corporations. 27
Pervasive government control of the banking sector
persists, however. Interest rates at all banks are still regulated. Banks that are saddled with high proportions
of nonperforming loans continue to depend on the Bank of
24. According to Cho and Cole (1992), the real cost of bank credit was
negative throughout most of the 1970s.
25. Cho and Kim (1993) estimate that almost 80 percent of all fixed
investment in the manufacturing sector during the late 1970s was
directed to HCI. Many subscribe to the view that this was an overinvestment. See for example Hong (1979), Amsden (1989), and Stem et al.,
(1992).
26. The launch into HCI itself was preceded by a major government bail
out of the corporate sector which already was highly leveraged. See
footnote 14.
27. See Nam and Kim (1993) for a detailed analysis of this system.

Korea for low-cost funds to support their outstanding loans,
the bulk of which are still policy-related. This has left
banks little choice but to heed government directives
even though they have nominally shifted to private ownership (Cho and Cole 1992). Finally, an autonomous bankcustomer relationship has yet to develop in Korea due to
continued government intervention in credit allocation. As
a result, principal transactions banks have had little incentive to monitor corporations. Nor has a principal transaction
bank's evaluation of a corporate investment and financing
plan had any significant effect on corporate behavior (Nam
and Kim 1993).
To summarize, our review of the Japanese and Korean
banking system highlights a fundamental difference. In
Japan, the cost of corporate bankruptcy ultimately fell onto
the (main) banks. The internalization of bankruptcy costs
would have induced banks to be diligent in controlling
bankruptcy risk through screening corporate borrowers as
well as investment projects. Additionally, as significant
corporate shareholders, Japanese banks also would have a
strong incentive to monitor corporate performance on an
ongoing basis. By contrast, these private incentives were
muted in Korean banks due to the government ownership
of banks until recently and due to continued heavy intervention despite nominal privatization. Other things equal,
this lower incentive faced by Korean banks to monitor
undoubtedly accounts for a significant part of the higher
bad loan rate estimated for Korea.

I\Z

DETERMINANTS OF THE BAD LoAN RATE

Our institutional explanation of the higher bad loan rate in
Korea assumes the usual ceteris paribus condition. This
section attempts a more systematic way to control for
factors other than different monitoring incentives that may
account for the observed difference in bad loan rates
between Japan and Korea. To implement this idea statistically, we estimated the following regression,
n

BLR t =

at

l

+ j~l i~l

131xL + Et

where xi is a set of economic variables (with lag structure
denoted by I, 1=4 for Japan, 1= 6 for Korea) that plausibly
will affect the bad loan rate in the economy. 28 We estimated
three models. The first model consisted only of financial
variables derived from the aggregate balance sheet. For
both Japan and Korea, these variables were the aggregate
leverage ratio of the corporate sector, defined as the ratio of
28. In addition to the variables listed in (1),the proper number of lagged
dependent variables were added to remove serial correlations. Also an
intercept dummy variable was added in the three equations for Japan to

HUH AND

total liabilities to total assets, the ratio of bank borrowing
in total liabilities, and the growth rate in bank loans. The
second model included "real" macroeconomic variables.
For Japan, the set consisted of the Nikkei stock market
index, real GDP growth rate, the nominal yen-dollar exchange rate, oil price, and the variability in industrial
production growth. The Korean equation did not include a
stock market index and used the real instead of nominal
won-dollar exchange rate. 29 The third model included both
sets of financial and real variables.
The motivation underlying this exercise is simple. If our
hypotheses on the behavior of Japanese and Korean banks
are correct, and if our estimate of the bad loan rate is
reasonably accurate, then we would expect the regression
equation to be statistically more significant in Japan compared to Korea. The rationale is that because of lower
incentives facing Korean banks to control risk through
screening and monitoring corporate borrowers, the conventional explanatory variables will explain less of the
movement in the Korean bad loan rate. Alternatively, one
can think of the adverse incentive effects on banks as
forcing the economy to operate inside the risk-return
efficiency frontier, thereby loosening the link between the
bad loan rate and the explanatory variables.
As evident in Table 4 which reports the results for Japan,
the exclusion tests are generally significant for all three
models, with roughly 75 to 80 percent of changes in the
bad loan rate explained by the right hand side variables. In
the model featuring financial variables alone, individual
exclusion tests spew that Ieverage and loan growth are
statistically significant in explaining changes in the bad
loan rate, while the rate of bank borrowing to total liability
is not. A joint exclusion test of the three balance sheet
variables, however, is significant at the 5 percent level.
Four out of the five variables in the second model-the
Nikkei index, GDP growth, oil price, and the variability in
industrial production-are all statistically significant. The
joint exclusion test of all fivereal macroeconomic variables
is also significant at the I percent level. The third model
performs the best, suggesting that both financial and real
variables are relevant, and hence both sets should be
included. 30
account for the level shift in the break:in the key data at 1971.Q4. This is
done to allow for a major accounting rule change regarding suspension
and bankruptcy in late 1971.
29. This helps account for the much larger inflation differential that
prevailed between Korea and the U.S. than Japan and the U.S. Also, for
Korea, we did not include a stock market index variable because the
market was underdeveloped until at least the mid-1980s.
30. We also ran the same set of regressions limiting the sample period up
to the end of 1989, i.e., we excluded the period of the steep asset price

KIM / BAD LoAN PROBLEMS

IN JAPAN AND KOREA

27

TABLE 4
EXCLUSION TESTS OF EXPLANATORY VARIABLES
OF THE

BAD LoAN RATE,

JAPAN

1968.Q2-1992.Q4
n

BLR t =

at

+

4

j~l i~l ~1 xL + Et
MODEL SPECIFICATIONS
Financial
Real
Financial
Variables
Variables
and Real
Only
Drily
Variables
j= 1,2,3 j=4,5,6,7,8 j= 1-8

ADJUSTEDR2

EXCLUDED VARIABLES:

1. LEV
2.BB
3.WANGR
4. NIKKEI
5. GDPGR
6. FOREX

0.74

0.80

Exclusion test: Ho: ~=O for all j's andj's
0.Ql
0.66
0.08

0.09
0.00
0.31
0.05
0.14

7. OIL
8. IPVAR
1,2,3
4,5,6,7,8
1-8

0.77

0.01
0.49
0.65
0.08
0.04
0.17
0.04
0.36

0.03

0.02
0.01

0.00
0.00

NOTE: The dependent variable BLR is the estimated bad loan rate. The
explanatory variables are: leverage ratio (LEV), bank borrowing to total
liability ratio (BB), loan growth rate (LOANGR), Nikkei stock market
index (NIKKEl), real GDP growth rate (GDPGR), nominal yen-dollar
exchange rate (FOREX), oil price (OIL), and variability in industrial
production growth (IPVAR), defined as the standard deviation of the
quarterly industrial production growth rate over the immediately preceding three years. For NIKKEI, FOREX, and OIL, we used year-overyear growth rates. Four lags of all explanatory variables were used
except for IPVAR (one lag). To correct for serial correlation, the righthand-side also included the dependent variable lagged up to four
quarters. To control for the change in the Bank of Japan's reporting
procedure in 1971 on notes default data, we also included dummy
variables (not reported), with D = 1 for t= 1968.Q1 to 1972.Q4, and
D = 0, otherwise.

The regressions for Korea are reported in Table 5. One
immediately notes the significantly lower adjusted R2 in
Korea, ranging from 0.62 to 0.68. The exclusion test
corroborates the poor fit. In the first model, which is
restricted to financial variables, only leverage is significant
(at,the 5 percent level); the joint exclusion test statistic is
deflation and the current recession. Interestingly, the exclusion test for
the Nikkei was not statistically significant for this shorter sample
period, while that for the variability in industrial production was. The
main thrust of the results did not change, however.

28

FRBSF ECONOMIC REvIEW 1994, NUMBER 2

only marginally significant at 0.10. None of the variables in
the second model is statistically significant, either individually, or jointly. As was the case for Japan, combining the
two sets of variables does improve the result somewhat,
with the joint exclusion test for the financial variables significant at 5 percent and that for all seven variables signif- -;
icant at 10 percent. Overall, however, it is safe to say that
all models fare considerably less well for Korea.

TABLE

5

EXCLUSION TESTS OF EXPLANATORY VARIABLES
OF THE BAD LoAN RATE, KOREA

1973.Q1-1992.Q4
n

BLR, =

(x,

+

6

j~l i~l 131xL + E,
MODEL SPECIFICATlONS
Financial
Real
Financial
Variables
Variables
and Real
Only
Only
Variables
j= 1,2,3 j=4,5,6,7
j= 1-7

measure appears to be a reasonable approximation based
on several grounds. First, there is a general conformity
between the overall pattern of our measure to the past
business cycle patterns of the two economies. Although
the empirical relationship is weaker for Korea than for
Japan, the bad loan measure for Korea still seems to behave
in a reasonable manner following identifiable shocks.
Second, our estimate matches quite closely an independent
study that measures the bad loan rate directly for Korea in
1988 and 1989. Third, consistent with our expectation, the
bad loan rate estimate is substantially higher in Korea than
in Japan. Finally, a much tighter linkage is observed between the bad loan rate estimates and a plausible set of
economic variables for Japan. These results, in tum, suggest that while banks can make a substantial contribution
to economic growth, heavy government intervention also
can substantially impair banks' incentive to monitor and
control risk. The higher bad loan rate in Korea is but one
manifestation ofthe associated costs of "unduly" repressing the banking system. Our estimate reveals, especially in
the case of Korea, that such costs can be substantial.
ApPENDIX

AoJUSTEDR2

EXCLUDED VARIABLES:
1. LEV
2.BB
3. LOANGR
4. GDPGR
5.RFX
6. OIL
7.IPVAR
1,2,3

4,5,6,7
1-7

0.63

0.62

0.68

Exclusion test: Ho: 131 = 0 for all j's andj's
0.06
0.97

0.02
0.49
0.13

om
0.31
0.55
0.96
0.76

0.10
0.85

0.22
0.04
0.38
0.49
0.03
0.20
0.09

NOTE: The dependentvariable BLR is the estimatedbad loan rate. The
explanatory variables are: leverage ratio (LEV), bank borrowing to total
liability ratio (BB), loan growthrate (LOANGR), real GDP growthrate
(GDPGR), real won-dollar exchange rate (RFX), oil price (OIL), and
variability in industrial production growth (IPVAR), defined as the
standard deviation of quarterly industrial production growth rate over
the immediately preceding three years. Six lags of all explanatory
variables were usedexceptfor the IPVAR (one lag).To correctfor serial
correlation, the right-hand-side also included the dependent variable
lagged up to six quarters.

N.

CONCLUSION

We attempted to measure the bad loan rate based on
indirect data for Japan and Korea to shed some light on the
implications of different institutional and risk-sharing arrangements observed in the two economies. The estimated

Data Sources
Data for Japan were collected from the Quarterly Report of
Incorporated Enterprise Statistics, published by the Ministry of Finance (MOF). The Report provides aggregated
quarterly balance sheet data for manufacturing and nonmanufacturing firms, excluding financial institutions and
insurance companies. The sample consists of 1,850 firms
with capital in excess of ¥10 million, which would include
most of Japan's publicly listed firms, and 15,000 firms
drawn from various size groups below the ¥10 million
capital threshold. The Report therefore provides a fairly
comprehensive coverage of the entire spectrum of Japan's
corporate sector.
Data for Korea were collected from Financial Statement
Analysis, published annually by the Bank of Korea (BOK).
This data source is ideally suited for purposes of comparison with Japan since it is modeled closely after the
MOF Report both in its method of collection and the variables covered. The BOK's sample consists of some 1,400
firms with the number split roughly evenly between small
and large enterprises (listed or unlisted, with capital in
excess of WlO billion). One notable difference is that the
Korean data are only available on an annual basis. We
therefore estimated quarterly data series by interpolation
between two annual data points. Table 1 presents an
example of typical balance sheet data that are used.

HUH AND KiM/BAD LoAN PROBLEMS IN JAPAN AND KOREA

Data on the default rates on the business notes and outstanding loans were compiled from monthly issues of
Economic Statistics Monthly (BOJ), and Monthly Statistical Bulletin (BOK). Monthly series were aggregated to
derive quarterly series (business note default) and end of
quarter (outstanding loan and discount) data.
Japanese data on notes payable are not reported separately
from accounts payable in MOF's Quarterly Report of
Incorporated Enterprise Statistics. We estimated notes
payable by using aggregate corporate sector balance sheet
data which report these items separately. We computed the
ratio between the two and multiplied it to the MOP series to
arrive at an estimate of notes payable.

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Wealth Effects of Bank Holding Company
Securities Issuance and Loan Growth
under the Risk-Based Capital Requirements

Elizabeth s. Ladennan
The author is grateful to Fred Furlong and the members of
the editorial committee, Ken Kasa and Mark Levonian, for
their comments. The author also would like to thank
Deanna Brock for research assistance and Nyra Krstovich
for extensive library assistance.

This paper tests a two-part hypothesis: first, that during
the period between publication of the risk-based capital
requirements in early 1989 and the end of 1992, bank
holdingcompanies (BHCs) faced a statistically significant
decrease in stockreturns if theyissuednewcommon stock;
second, that this discouraged newcommon stockissuance
and therefore, in effect, forced BHCs with Tier 1 and/or
leverage capital-to-assets ratios belowtheregulatory minima to decrease loans outstanding more than did BHCs
deficient only in their total capital ratios. Empirical evidencesupporting bothparts ofthehypothesis ispresented.

In December 1992, pursuant to the Basle accord, capital requirements for banks and bank holding companies (BHCs)
changed. For the first time, the minimum amount of capital
that a banking organization was required to hold depended
on the riskiness of its asset portfolio as well as its size.
Various types of assets were assigned weights, according
to their perceived riskiness. with commercial loans receiving the highest weight and U.S. government securities
the lowest. Banks and BHCs were required to hold at least
4 percent of their risk-weighted assets in so-called Tier 1
capital and 8 percent of their risk-weighted assets in Tier
1 plus supplementary (Tier 2) capital, which includes, for
example, mandatory convertible debt and subordinated
debt. For BHCs, the bulk of Tier I capital was required to
be common shareholders' equity plus retained earnings. In
addition to the new risk-based requirements, a new minimum Tier 1 capital-to-unweighted asset ratio of 4 percent
was established.
When the new capital requirements were first made
public in early 1989, some BHCs found themselves ~n a
potentially deficient position. In order to meet the van~us
new capital requirements by the December 1992 deadhne,
they would have to increase capital and/or decrease riskweighted, or perhaps unweighted, assets. Some of the
BHCs deficient in Tier 1 capital found that they would have
to increase common shareholders' equity in particular or
decrease assets. However, it has been well established that
for a variety of firm types, the announcement of the
intention to issue common stock tends to decrease a firm's
stock value. This paper finds that this type of effect also
existed for BHCs in the period following publication of the
new capital requirements. The paper then argues that,
given the presence of such an effect, BHCs deficient in
common equity had a significant incentive to meet the
capital requirements by decreasing asset growth rather than
issuing new common stock.
The argument implies that BHCs deficient only in
supplementary capital did not decrease asset growth as
much. This is because, in contrast to the "constrained"
BHCs that had to issue common stock, these "unconstrained" but still deficient BHCs could redress their
capital insufficiency by issuing those types of securities that

LADERMAN / BANK HOLDING COMPANY SECURITIES ISSUANCE

do not lead to negative wealth effects. Deficient but
unconstrained BHCs could therefore afford greater asset
growth. Of particular interest in this context is loan growth,
given that much has been written about the effects of the
risk-based capital requirements on bank lending and that
commercial loans receive such a high risk weighting.
Therefore, I test the hypothesis that "constrained" BHCs
exhibited lower loan growth than unconstrained but deficient BHCs during the two years prior to December 1992.
Consistent with this hypothesis, I find that constrained
BHC loan growth was statistically significantly lower,
even after controlling for the size of each BHC's capital
deficiency.
The remainder of the paper falls into five sections.
Section I reviews literature related to the wealth effects of
security issuance. Section II discusses the data and the
empirical methodology used for estimating the effects of
BHC announcements of common stock and supplementary
capital securities issuances on common stock returns. This
section also presents the empirical results of this estimation. Section III discusses the implications of a negative
common stock wealth effect for common stock deficient
BHCs and presents comparative summary statistics for
capital sufficient and constrained and unconstrained capital deficient BHCs. Section IV presents the data, methodology,and results for a regression testing the effect of the
Tier 1 capital requirements on constrained BHC loan
growth. Section V concludes.

I.

THE SHAREHOLDER WEALTH EFFECTS OF
SECURITY ISSUANCE: LITERATURE REVIEW

In this section, I review the literature related to the wealth
effects of security issuance. Included will be a discussion
of various theories explaining why common stock issuance
may lower common stock returns. This section will serve
as conceptual background for the empirical estimation of
the wealth effects of BHC security issuances and for the
ensuing discussion of the interaction between negative
wealth effects, capital requirements, and loan growth.
Modigliani and Miller (1958) show that, in the absence
of tax effects, information asymmetries, or other distortions, the value of a firm should be independent of its
capital structure and therefore unaffected by the issuance
of new debt or equity. However, in the real world there are
tax effects and information asymmetries. Accordingly,
several researchers, including Asquith and Mullins (1986),
Smith (1986), and Mikkelson and Partch (1986), have
found empirical evidence that a firm's stock price typically
falls upon the announcement of upcoming issuances of new

31

common stock. In addition, economists have found that
some, but not all, non-common stock security types also
show statistically significant effects-some negative and
some positive.
Miller and Rock (1985) attribute these results to information asymmetries. Specifically, they hypothesize that
the market concludes that a firm that is seeking external
financing must be expecting lower earnings. The reason is
that, in the presence of information asymmetries, inside
financing (e.g. , increased retained earnings) usually would
be less expensive. However, Miller and Rock's theory does
not explain why announcements of issuances of different
types of securities would have different effects.
Myers and Majluf (1984) offer a possible explanation.
They argue that managers have an incentive to issue equity
when the firm's stock is overvalued and debt when its stock
is undervalued. This is because when a firm issues equity, it
sells a portion of its existing assets but acquires, for its
existing stockholders, a share in the net present value of the
new project to be undertaken. If the firm's existing assets
are significantly undervalued by the market, the dilution
suffered by existing stockholders can be greater than any
gains they receive from undertaking the new project, in
which case no new equity will be issued. However, the
project may be financed through debt, because the tradeoff for existing stockholders between losing share in existing assets but gaining a share of the new project will be
more favorable if debt is issued. On the other hand, as stock
becomes overvalued, financing a new project through
stock issuance rather than debt issuance begins to look
more favorable to existing stockholders. Therefore, the
choice between raising funds through equity or debt will be
more likely to favor equity when the stock is overvalued
and more likely to favor debt when the stock is undervalued. If there is information asymmetry such that managers have inside information regarding the value of the
firm that market participants do not have, then the issuance
of equity will impart new information to the market. In
particular, investors, knowing managers' incentives, will
interpret the issuance of new equity as a signal that the
stock is overvalued, and the price will fall.
Therefore, a synthesis of the Miller and Rock and Myers
and Majluf theories would say that equity issuance announcements should have negative effects, while debt issuance announcements should have less negative or maybe
even positive effects on common stock returns.
The first part of this paper's thesis is that, during the
period after publication of the risk-based capital standards
in early 1989, BHC's common stock issuance announcements created negative wealth effects. (Again, the second

32

FRBSF EcONOMIC REVIEW 1994, NUMBER 2

part is that the wealth effect combined with the risk-based
capital requirements to discourage common stock issuance
and encourage loan growth cutbacks among certain undercapitalized BHCs.) Wansley and Dhillon (1989), Keeley
(1989),Polonchek, Slovin, and Sushka (1989), Wall and
Peterson (1991), and Cornett and Tehranian (1994) all have
investigated the existence of negative wealth effects for
BHCs. At least for some subset ofBHCs, all found statistically significant negative abnormal returns associated with
common stock issuance. Although the time period for these
studies differed from the time period used in this paper, it is
important to review these studies' results.
Wansley and Dhillon examine the stock market response
to public security offerings by BHCs between 1978 and
1985. Using an event study methodology, they find a statistically significant decrease in common stock prices at the
time of the announcement of an upcoming common stock
issue.
Keeley investigates the period from 1975 to 1986 in
addition to two subperiods-January 1, 1975 through November 30, 1981, and December 1, 1981 through December
31,1986. The two periods are distinguished by the imposition of specific objective capital requirements in 1981.
(Prior to 1981, capital requirements were more subjective
and nebulous.)
For the whole period, Keeley finds a statistically significant negative announcement effect for common stock and a
statistically significant positive effect for perpetual preferred stock. He also finds statistically significant negative
effects for debt and common stock together in the earlier
period and for mandatory convertible debt in the later period and a significant positive effect for perpetual preferred
stock in the later period. 1
In addition, Keeley finds a statistically significant negative announcement effect for common stock in the earlier
period, but not in the later period. However, when he confines his sample to BHCs he classifies as capital deficient,
he finds statistically significant negative common stock
effects for both periods. In contrast, he finds a statistically
significant negative effect for his capital sufficient subset
in the earlier period only. Therefore, it appears that the
difference in the results for the two periods for the group as
a whole largely is driven by a difference in the results for
the capital sufficient BHCs.
In explaining his results, Keeley entertains three hypotheses. First, he rejects the hypothesis that the difference

1. Actually, Keeley has no observations for perpetual preferred stock or
mandatory convertible debt for the earlier period and no observations
for simultaneous debt and common stock announcements for the later
period.

in the results across periods for the entire sample is due to a
Myers and Majluf signaling effect. It is logical to suppose
that the institution of objective capital standards made
equity offerings more predictable and therefore diminished
their information content. However, Keeley argues, this
also would imply that capital deficient BHCs would exhibit less negative common stock issuance wealth effects
than capital sufficient BHCs, whose issuances should be
more voluntary. But, as he shows, this is not the case. In
both the earlier and later periods capital deficient BHCs
showed more negative wealth effects, and the difference
between the effects for the two groups of BHCs was statistically significant.
Keeley then suggests that the results for the two types of
BHCs differ because common stock issuance diminishes
the value of banks' deposit insurance guarantee. This is
especially true for banks with relatively low capital-toasset ratios.? However, this explanation is somewhat unsatisfactory in that it does not adequately explain the
difference in results across time periods for the sample as a
whole and for the capital sufficient BHCs.
Moreover, the deposit insurance hypothesis implies that
there should be a negative relationship between the increase in the capital-to-assets ratio and the announcement
effect; a larger common stock issuance (relative to assets)
should be associated with a more negative announcement
effect. Keeley's results only weakly support this inference:
He finds the implied negative relationship for the capital
deficient BHCs only in the later period, and even then it is
not statistically significant.
Keeley's third explanation is the most satisfactory. Here,
he suggests that the issuance of common stock reveals
private information held by regulators. As Keeley explains,
market participants can tell when a BHC may be under regulatory pressure to increase its capital ratio by looking at its
balance sheet. However, the market does not necessarily
know the future prospects of the BHC or the method the
.BHC will use to augment capital.
Therefore, investors may view common stock issuance
by capital deficient BHCs as a sign that the BHCs are under
regulatory pressure not to issue securities that require
increased payouts from earnings, such as debt or preferred
stock; thus, Keeley suggests, it also may be a signal of
management and regulator skepticism about theBHC's
ability to generate sufficient future earnings to meet the
cash flow requirements of additional debt or preferred
stock or to generate cash flow sufficient to permit the
accumulation of retained earnings to meet the new capital requirements. On the other hand, if regulators and

2. See Furlong and Keeley (1987 and 1989).

LADERMAN / BANK HOLDING COMPANY SECURITIES ISSUANCE

bank management believe that the BHC's future earnings
prospects are very good, retained earnings rather than a
security issuance can be used to meet higher capital
requirements. Moreover, he says, this explains why common stock issuance by a capital sufficient BHC might not
provide a negative signal.
The inside information hypothesis provides a plausible
explanation for all of Keeley's major findings concerning
common stock issuance. First, it can explain the difference
between common stock announcement effects for his capital deficient and capital sufficient subsets. Second, it
can explain the difference between announcement effects
for his capital sufficient subset in the earlier and later
periods. As Keeley says, prior to the institution of specific minimum capital requirements, market participants
might have been unsure whether a BHC's common stock
issuance were due to regulatory pressure. Since there
was some chance that it was, there was a small mean
negative announcement effect even for capital sufficient
organizations. However, he explains, after specific capital
requirements were introduced, market participants could
be confident that a common stock issue by a capital sufficient BHC was nota signal that regulators viewed the
firm's earning prospects unfavorably. Therefore, common
stock issuance announcements no longer lowered stock
prices for this group. Third, the insider information hypothesis also provides a plausible explanation for the
difference between the earlier and later period results for
his capital sufficient subset as well as for the full sample.
Polonchek, Slovin, and Sushka's results basically are
consistent with Keeley's results. These authors also examine apre-1981 period (January 1975 to November 1981) and
a post-1981 period (December 1981 to December 1984), as
well as an aggregated 1975to 1984 period. They findstatistically significant negative common stock announcement
effects only for the earlier period by itself.
Wall and Peterson examine the announcement effects of
BHC's securities issuances between 1982 and 1986. These
authors improve on prior studies by using information from
the Dow Jones News Wire (DJNW) rather than the Wall
Street Journal (WSJ) to identify announcement dates. The
news wire is a more accurate source of when the market
first gets the news of an impending securities issue, which
maybe a day or more.before the news appears in the WSJ.
Wall and Peterson also find that common stock announcements have statistically significant negative effects on
common stock returns.
Finally, Cornett and Tehranian study the wealth effects
of BHC announcements of issuances of various types of
securities during the period June 1983 through December
1989. The imposition of specific capital requirements for
multinational BHCs, which had previously been exempted

33

from objective capital standards, marks the beginning of
the period. Also, the "acceptable" total capital-to-assetratio (greater than the "minimum" total capital-to-asset
ratio) was increased from 6.5 percent to 7 percent in June
1983.
Cornett and Tehranian separate their sample into "voluntary" and "involuntary" issues of securities. They classify an issue as voluntary if the BHC's total capital ratio is
above 7 percent at the end of the year prior to the security
issue, involuntary if not. These authors find statistically
significant negative wealth effects for common stock for
the voluntary issues. For the involuntary issues, one type of
statistical test indicates a statistically significant negative
effect, while a second type indicates a lack of statistical
significance. In addition, the negative announcement effect
for the voluntary issuers is larger in absolute value than is
the estimated effect for involuntary issuers, and the difference between the effects for the two groups is statistically significant. These results contrast with Keeley's
results regarding his capital deficient and capital sufficient
subsets; Keeley found significant negative effects for his
capital deficient BHCs, but not for the capital sufficient
BHCs. Cornett and Tehranian also found a statistically
significant positive announcement effect for involuntary
issues of straight (not convertible into common stock)
debt.
Cornett and Tehranian attribute their results to the
capital structure signaling model found in Ross (1977).
Similar in spirit to Myers and Majluf's later paper, Ross's
paper has managers possessing inside information about
the prospects for the firm issuing equity when prospects are
poor and debt when prospects are good. As Cornett and
Tehranian explain, this is because a firm with poor prospects will want to share its downside with new claimants
and thus prefers financing via stock issuance, whereas a
firm with good prospects will not want to share its upside
with new claimants and thus prefers debt financing.
Investors recognize these incentives, and therefore the
stock price falls upon announcement of an impending voluntary equity issuance. However, Cornett and Tehranian
reason, equity issuances perceived by market participants
as involuntary need not necessarily imply poor prospects
and therefore need not depress stock returns.
Several methodological differences between Keeley's
and Cornett and Tehranian's approaches may help to
explain the differences in results. First, it is possible that
Cornett and Tehranian's sample of security issuances gives
a positive bias to their involuntary issuance results. In
contrast to Keeley, Cornett and Tehranian do not exclude
issuances that are not publicly announced. Instead, these
authors use the Securities and Exchange Commission
filing (registration) date as the announcement date for

34

FRBSF ECONOMIC

REVIEW

1994, NUMBER 2

security issuances not located in the Wall Street Journal
Index. It is likely that nonpublicly announced security
issuances have a weaker effect on the market than those that
are publicly announced. Because Cornett and Tehranian's
involuntary issuers are on average smaller than their voluntary issuers, the involuntary issuers are less likely to
announce publicly.3 Therefore, if common stock announcement effects for all BHCs tend to be negative, Cornett and
Tehranian's methodology might have biased the effects for
involuntary issues upward.
Another distinction between the two studies concerns
the definition of undercapitalized BHCs. Keeley's distinction between capital deficient and capital sufficient BHCs
depends on their capitalization as of a fixed date, December
1981, and its status does not change over time. This means
that Keeley's classification of a security issuance announcement depends only on the identity of the announcing BHC. In contrast, Cornett and Tehranian's designation
of involuntary versus voluntary issues depends on the
issuing BHC's capitalization at the end of the year before
the security offering. Therefore, their classification of
a security issuance announcement depends partially on
the identity of the issuer and partially on the timing
of the issue.
Because BHCs can change their capital-to-assets ratios
over time, Cornett and Tehranian's procedure seems more
intuitively appealing than Keeley's. Cornett and Tehranian's method more likely correctly identifies security issuance announcements by BHCs that had relatively low levels
of capital at the time of the announcement. It is somewhat
puzzling, however, that Cornett and Tehranian look at
BHC capitalization at the end of the year before the
security issuance rather than at the end of the year before
the security issuance announcement.
Keeley and Cornett and Tehranian also use different capital ratios for their classifications. Keeleyuses a 5.5 percent
primary capital ratio cutoff, and Cornett and Tehranian use
a 7 percent total capital ratio cutoff." This may be an
important distinction, but it would not, a priori, tend to
yield the particular differences in results that we see.

3. The mean value of assets for BHCs issuing voluntarily was (in
millions) $38,289.6, and the median was $16,488.5, while the corresponding figures for those issuing involuntarily were $29,809.6 and
$12,236.2.
4. In 1981, specific minimum primary capital-to-total assets ratios were
set for BHCs based on their size. The minima were 6 percent for BHCs
with assets of $1 billion or less and 5 percent for BHCs over $1 billion.
The 171argest banking organizations, the multinationals, were treated
on an individual basis. Also in 1981, the Federal Reserve set up "zones"
of adequacy for regional banking organizations, based on total capitalto-assets ratios. An "acceptable" total capital-to-assets ratio was

Finally, as Cornett and Tehranian point out, their sample
size is considerably larger than Keeley's. By itself, this
lends credence to Cornett and Tehranian's results. In
particular, it may explain why Keeley did not find a
statistically significant negative common stock announcement effect for his capital sufficient BHCs in the post-1981
period, whereas Cornett and Tehranian did. 5 Also, neither
study mentions excluding security issuance announcements contaminated by the concurrent announcement of
other important news, such as ratings changes or merger
agreements. Although not removing contaminated announcements would not, a priori, bias results in one
direction or the other, it might lead to spurious conclusions. This would more likely be a problem with small
samples such as Keeley's.

II.

ANNOUNCEMENT EFFECTS:
METHODOLOGY, DATA, AND RESULTS

Methodology
This section reexamines the effect of the announcement of
an upcoming issuance of securities on BHC stock returns.
Studies cited above did not estimate announcement effects
for the period of time relevant to this paper-after publication of the risk-based capital guidelines. Given the regulatory regime shift and the dependence of this paper's thesis
on the continued existence of a negative common stock
wealth effect, it is important to examine the post-1989
period in particular.
The announcement effect of a security issuance is the
change in the announcing firm's common stock return
resulting from the announcement, or the "abnormal return." To calculate abnormal returns, some estimate of
"normal" returns must be made. In this paper, I use the
market model to estimate normal, or expected, returns.

deemed to be 6.5 percent, and banking organizations in this zone were
subject to minimum regulatory supervision. The minimum was set at
5.5 percent.
In June 1983, the 6.5 percent cutoff for acceptable total capital was
increased to 7 percent, and the 5 percent primary capital requirement
was extended to the multinationals. Cornett and Tehranian use the 7 percent total capital requirement as their cutoff for involuntary issues.
In 1985, regulators introduced a minimum primary capital-to-assets
ratio of 5.5 percent and a minimum total capital-to-assets ratio of6 percent for all BHCs. Keeley argues that these 1985 rules were the ultimate
goal as early as 1981, so he designates anyBHC with a primary capital
ratio in December 1981 of less than 5.5 percent as capital deficient.
5. Keeley had only five observations in his post-1981 sample of common
stock issuance announcements by capital sufficient BHCs, whereas
Cornett and Tehranian had 61 observations in their sample of voluntary
common stock issuances.

LADERMAN / BANK HOLDING COMPANY SECURITIES ISSUANCE

Under the market model,
(1)

R jt

=

(Xj

+ J3j

R mt

+

Ej t ,

where R j t is bankj's common stock return on day t and R mt
is the market return on day t. I estimate the market model
for each bank and for each announcement event for a 120day period. The first part of the estimation period begins
79 trading days before the security issuance announcement
and ends 20 days before it; the second part begins 20 days
after the announcement and ends 79 days after it. 6 The
"announcement day" is defined as the day that news of the
planned issuance appears on the DJNW. The abnormal
return, or prediction error, PEj i for bankj on announcement
day tj i , is then the difference between the actual return and
the predicted return all given by the market model

PEj i =

(2)

R ji -

(Xji

1
APEk = ("j() ke~} PEj i

.

The average prediction error indicates the size of the abnormal return. A test of the statistical significance of the
abnormal return requires a transformation of the prediction
error into the "standardized prediction error," defined as

PE..
SPEj i = S.~I ,

(4)

JI

where

(5)

Sji

=

(R m, ii 120

Rm ,j J 2

ket model estimation period, and Rm,ji is the mean market
return in the estimation period associated with bankj and
event i.
The "average standardized prediction error" for security type k, ASPEk , is defined as
(6)

1
ASPEk = K ke{K}
S SPEJ'i

•

Under reasonable assumptions, it can then be shown
that the statistic

(7)
has a standard normal distribution (Mikkelson and Partch
1986).

Data

+ J3ji Rm,jJ

where R j i is bankj's common stock return on day tj i , (Xji and
J3j i are the coefficients estimated from equation (1)for bank
j and announcement event i, and Rm,ji is the market return
on day tji'
I calculate an average prediction error for various security types. The average prediction error simply adds together the prediction errors for events associated with. a
particular security type, and averages this sum across all
events (for all BHCs) of that type. Let {K} be the set of all
events associated with security type k, and let K be the
number of events of type k. Then the "average prediction
error" for security type k, APEk is defined as:
(3)

35

).

1~1 (Rmt - Rm ,j i ) 2
In (5), lji is the residual variance from the market model
regression for bankj and event i, Rm,ji is the market return
on event day tj i , Rmt is the market return on day t of the mar6. I use post-event data in addition to pre-event data to estimate the
market model because the event itself may alter stock price volatility.

Although the risk-based capital requirements were not
fully implemented until the end of 1992, final guidelines
were issued in March 1989. Therefore, this data set covers
1989 through 1992.7A list ofBHC securities issuances was
obtained from Securities Data Company (SDC). Most of
the issuances on the SDC data set include the SEC filing
date (registration date) for the offering. Relatively few have
missing filing dates. The SDCdata also include the security type, the date the security was offered to the market,
and the dollar amount raised by the offering.
The filing date given on the SDC data set was used to
locate the announcement date on the DJNW. Usually, the
first announcement was on the day of or day after the filing
or, rarely, soon before the filing." Given the widespread
coverage offered by the DJNW, issuances for which no
DJNW announcement could be located were assumed to be
not publicly announced and were omitted from the sample.
Security issuances that are filed as shelf registrations
also were omitted from the sample. A shelf registration
permits a firm to issue at any time in the future and is
therefore a weaker signal than a non-shelf registration that
the firm intends to issue in the near future. (Common stock
issuances in the SDC data set were never filed as shelf
registrations, so the omission of shelf registrations does not
affect prediction error estimates for common stock.)

7. To be included in the data set, the announcement had to be between
1989 and 1992, inclusive, and the actual issuance had to take place by the
end of 1992.
8. The close on the New York Stock Exchange.. the American Stock
Exchange, and NASDAQ (National Association of Securities Dealers
Automated Quotations System for stock traded over-the-counter) is
at 4:00 p.m., Eastern time. Therefore, if the news came over the wire
after 4:00 p.m., the announcement date was taken to be the next trading day.

36

FRBSF

EcONOMIC REVIEW

1994, NUMBER 2

In addition, if on the announcement day significant news
other than the security issuance announcement appeared
(for example securities ratings changes, unexpected
changes in earnings or loan loss provisions, and merger
announcements), that observation was dropped from the
sample. Finally, initial public offerings and secondary
offerings of securities were omitted.
Common stock returns for estimation of the prediction
errors were obtained from two sources. Returns for BHCs
whose stock trades on the New YorkStock Exchange or the
American Stock Exchange were obtained from the Center
for Research in Securities Prices. Those for BHCs whose
stock trades over the counter were calculated using stock
prices obtained from Data Resources, Incorporated. The
market return used in estimation of the market model was
the return on a broad-based index, the Wilshire 5000
Index.

TABLE

1

SECURITIES ISSUEs a

YEARS

SECURITY TYPE

1989

1990

1991

1992

Common Stock

7

1

16

20

44

Subordinated Debt

1

1

2

2

6

Preferred Stock

3

2

7

7

19

Auction-Rate

3

0

1

1

5

Nonauction-Rate

0

2

6

6

14

Au,

SOURCE: SecuritiesData Company.
aPublicly announced, non-shelfregistered issues only.

TABLE

2

AVERAGE PREDICTION ERRORS

(APE)

Results

1989-1992a

Average prediction errors and their associated Z statistics
were calculated for common stock, subordinated debt, and
preferred stock. I also calculated prediction errors for two
subcategories of preferred stock: auction-rate preferred
stock and non-auction-rate preferred stock. The risk-based
capital requirements state that common stock and nonauction-rate perpetual preferred stock count as Tier 1
capital for BHCs, while subordinated debt and auctionrate perpetual preferred stock count as supplementary
capital.
Mandatory convertible debt and term preferred stock
also count as secondary capital. However, neither the SDC
data set nor the DJNW specified whether debt issuances
were mandatory convertible or not, nor whether preferred
stock was perpetual or term. Therefore, no prediction
errors are provided for mandatory convertible debt. Also,
all preferred stock was assumed to be perpetual (and,
unless otherwise noted by SDC ortheDJNW, was assumed
to be non-auction-rate). Table 1 shows the number of
securities issuance announcements in the sample, by year
of announcement and type of security.
Table 2 contains the average prediction errors and their
associated Z statistics for the various security types listed
in Table 1. The results in Table 2 indicate that, on average,
there are significant negative abnormal returns associated
with the issuance of common stock. On average, the announcement of an impending issuance of new common
stock decreases common stock returns relative to their predicted values by approximately 1.6 percentage points.
Abnormal returns due to the announcement of the issuance
of other types of securities are not statistically significant.
The magnitude of the announcement effect found for

SECURITY TYPE

APE

Z

PERCENT NEGATIVEb
(SAMPLE SIZE)

CommonStock

-.0155*

-4.17

77.3c (44)

Subordinated Debt

.0012

-.11

PreferredStock

.00009

.009

63.2 (19)

.005

.35

60.0 (5)

Auction-Rate
Nonauction-Rate

-.0016

-.2

66.7 (6)

78.6 (14)

-Prediction errors are actual residual returns, not percentage point
residualreturns.
bThe null hypothesis is that the proportionof negativepredictionerrors
equals 0.5. I use the Wilcoxon signed-ranks test described by Daniel
(1978).
-Signed-ranks test is significant at the I percentlevel.
*Significantly differentfrom 0 at the I percentlevel.

common stock is remarkably similar to those found by previous researchers. Wansley and Dhillon found a two-day
announcement effect for common stock of -1.5 percentage
points; Keeley found the same for his entire sample;
Polonchek, et al., found a three-day announcement effect
of -1.4 percentage points; and Wall and Peterson found a
one-day announcement effect of -1.5 percentage points.
However, as discussed in the literature review in Section
I, Cornett and Tehranian's results cast some doubt on the
existence of a negative common stock wealth effect for
relatively low-capital banking organizations. Therefore,
given the focus of this paper, it is important to test for the
existence of a negative common stock wealth effect for
low-capital banks. I looked at BHCs' capital positions in

LADERMAN / BANK HOLDING COMPANY SECURITIES ISSUANCE

December 1990 (the first date for which risk-based capital
figures were available) and chose those that had to issue
common stock to meet the well-capitalized risk-based
capital guidelines." There were ten common stock issuance announcements by such BHCs in 1991 and 1992. The
average prediction error for this group was estimated to be
-2.74 percentage points, which was statistically significant at the 1 percent level. In addition, nine out of the ten
prediction errors were negative.

m. THE EFFECT ON CAPITAL DEFICIENT BHCs
BHCs that were capital deficient when the risk-based
capital rules were published had to redress the situation or
face tight regulatory supervision and perhaps closure. It is
reasonable to suppose that capital deficient BHCs would
not have chosen to meet the guidelines exclusively by issuing common stock given its negative wealth effect. For
some BHCs, the alternatives to issuing common stock
included decreasing assets and issuing other types of
securities that, as shown in the last section, appear not to
have negative wealth effects. I will refer to deficient BHCs
with such options simply as "unconstrained" BHCs. For
other deficient BHCs, the only alternative to issuing common stock was to decrease assets. I will refer to these
BHCs as "constrained" BHCs. BHCs thatmeetthe guidelines will be called "unaffected" BHCs.
Given their lack of attractive options, it is likely that,
following the publication of the risk-based capital guidelines, constrained BHCs decreased assets more than did
unconstrained BHCs. Whether this is in fact the case must
be ascertained empirically. The first step in this exercise is
to identify properly constrained and unconstrained BHCs,
which depends on an understanding of the risk-based
capital rules.
The calculation of Tier 1 capital for BHCs sums common shareholders' equity (including retained earnings),
non-auction-rate perpetual preferred stock, up to a certain limit, and minority interests in equity accounts of consolidated subsidiaries. The rules then deduct "goodwill"
and 50 percent of investments in unconsolidated banking and finance subsidiaries from this sum to obtain Tier 1
capital.'? Non-auction-rate perpetual preferred stock is

9. To be considered well-capitalized under the risk-based capital rules,
a BHC has to hold Tier 1 capital equal to at least 6 percent of riskweighted assets. Total capital is required to be at least 10 percent of
risk-weighted assets, and, under the leverage ratio requirement, Tier 1
capital must be at least 5 percent of unweighted assets.
10. Goodwill is an intangible asset that is entered on the books of a
banking organization when it pays more than book value to acquire
assets.

37

limited to 25 percent of Tier 1 capital exclusive of the
deductions.
The calculation of supplementary (Tier 2) capital for
BHCs sums allowance for loan and lease losses, perpetual
preferred stock not eligible for inclusion in Tier 1 capital (including auction-rate perpetual preferred), hybrid
capital instruments (e.g., mandatory convertible debt and
perpetual debt), term subordinated debt, and intermediateterm preferred stock. Then, the other 50 percent of investments in unconsolidated subsidiaries is deducted. Finally,
the rules set Tier 2 capital equal to this net amount or Tier 1
capital, whichever is greater.
Total capital is the sum of Tier 1 capital plus Tier 2 capital minus reciprocal holdings of other depositories' capital
securities. The risk-based capital rules specify minima for
three capital ratios. Stated differently, the rules require that
different types of capital be equal to at least a certain
percentage of risk-weighted or unweighted assets. Tier 1
capital is required to be equal to at least 4 percent of riskweighted assets. Total capital is required to be at least 8
percent of risk-weighted assets. The "leverage ratio"
requirement is that Tier 1 capital plus 50 percent of
investments in unconsolidated subsidiaries be at least 4
percent of total tangible assets, not risk-weighted.
Although the risk-based capital requirements were first
made public in early 1989, Tier 1 capital, Tier 2 capital,
and risk-weighted assets figures were not all available until
December 1990. Therefore, categorization of BHCs into
capitalization groups is based on year-end 1990 data rather
than early 1989 data. Capital ratio elements were obtained
from the Consolidated Financial Statements for BHCs for
all 1,119 BHCs reporting risk-weighted assets figures in
December 1990.
Constrained BHCs were identified as those BHCs that
did not meet the Tier 1 requirement, the leverage ratio
requirement, or both, in December 1990, and would not be
expected to meet them by the end of 1992, taking into
account projected growth in retained earnings. 11 The riskbased rules required full compliance by the end of 1992. In
devising a strategy to meet the guidelines by that time,
capital deficient BHCs likely took into account probable
growth in retained earnings. I assume that, at the end of
1990, BHCs projected that retained earnings growth during 1991 and 1992 would be the same as during 1989.
Therefore, the group of constrained BHCs excludes those
that would have been predicted to meet the Tier 1 and
11. This group was filtered to remove those BHCs that might have met
the Tier 1 and leverage ratio minima simply by issuing nonauction-rate
perpetual preferred stock, taking into account the limit on the use of
this type of security for Tier 1 purposes. However,no BHCs fell into this
category.

38

FRBSF EcONOMIC REVIEW 1994, NUMBER 2

leverage capital ratio minima by the end of 1992 simply
through sustained retained earnings growth. Of 82 BHCs
that failed to meet the Tier 1 ratio, the leverage ratio, or
both, in December1990,15 were excludedby this means,
leaving 67 constrained BHCs.
UnconstrainedBHCs were definedto be those that met
the Tier 1 and leverageratio minima (or were projectedto
by year-end 1992), but not the total ratio minimum, in December 1990. Unaffected BHCs were defined to be those
that met all three capital ratio minima in December 1990.
Table3 gives variousdescriptive statisticsfor the subset
of each of the three groups of BHCs that reported loans in
both December 1990 and December 1992. Of particular
interest is total loan growth between year-end 1990 and
year-end 1992, the deadline for full compliance with the
risk-based capital requirements. Becauseof the relatively
high weighting given to loans in the calculation of riskweightedassets, BHCs withinadequateTier 1or totalcapital ratios whochose to remedy the situationwith a decrease
in assets would have had a particularly strong incentive to
decrease loans. Commercialbusiness, commercial real estate, and consumerloansreceive a 100percentweightin the
calculation of risk-based capital. Residential mortgages

TABLE 3
997 BHCs REPORTING
RISK-WEIGHTED ASSETS IN DECEMBER 1990 AND
LoANS IN DECEMBER 1990 AND DECEMBER 1992
DESCRIPTIVE STATISTICS FOR

ASSETS a
(MILLIONS)

Unaffected BHCs (n
Mean
Minimum
Maximum

RISKWEIGHTED
ASSETSb

TOTAL loAN
GROWTHc

$2,603
$18.2
$104,116.3

10.28%
-82.29%
293.54%

$2,815.1
$79.8
$48,771.7

11.54%
-41.99%
316.77%

$7,604.3
$54.4
$245,556.6

-12.64%
-84.16%
173.72%

= 906)
$2,616.7
$140.9
$110,728

Unconstrained BHCs (n = 52)
Mean
Minimum
Maximum
ConstrainedBHCs (n
Mean
Minimum
Maximum

$3,107.7
$155.2
$45,389.9

receivea 50 percentweight. U.S. government securitiesreceive a zero weight. BHCs with inadequateleverage ratios
may also have chosen to decrease loans.
As can be seen from Table 3, the mean asset size of
constrained BHCs was larger than the mean asset sizes
of unconstrained and unaffected BHCs. More important,
average loan growth for the constrained BHC group was
considerably lower than for the unconstrained group. In
addition, average growth for the unconstrained group was
comparableto thatforunaffectedBHCs. This suggests that
decreasing loans, although an option for unconstrained
BHCs, was avoided as much as possible and was pursued
only by the constrained BHCs. However, this result is not
conclusive because it does not control for the extent of
capital deficiency in the unconstrained and constrained
groups, norfor changes in loandemand, bothof whichmay
influence loan growth. I will controlfor these factors when
I compare loan growth for these two groups in the next
section.
Table 4 compares the incidence of common stock issuance and the amounts raised through common stock issuance for the three groups of BHCs.
Giventhe negativewealtheffectsof commonstockissuance, the incidence of common stock issuance seen for all
three groups in Table 4 seems surprisingly high. 12 Apparently, despite its negative wealtheffects, someBHCs have
good reasons to want to issue common stock. An example
might be issuing common stock for acquisition purposes.
However, by itself, "having" to issue common stock to
avoid a decrease in assets apparently was not a very good
reason. All other things equal, one would have expected
that the issuance rate for constrainedBHCs, which had to
issue common stock or decrease assets, would have been
higher than for unconstrained or unaffected BHCs. However, the negative wealth effect seems to have been strong
enoughthat constrainedBHCs werenot especially likelyto
issue common stock. As shown in Table 4, constrained
BHCs were no more likely to have been common stock
issuers than unconstrained BHCs and only slightly more
likely than unaffected BHCs, although they did seem to
raise somewhat larger amounts when they did issue.

= 39)
$7,289.3
$152.7
$216,986

SOURCE: Consolidated Financial Statements for BHCs.
aBook valueof unweighted assets in December 1990.
bBook valueof risk-weighted assets in December 1990.
<December 1990 to December 1992.

12. TheConsolidated FinancialStatements data set coversa muchwider
universeof commonstockissuersthandoesthe SDC data set, but it has
no information on filing dates or announcement dates. Only the larger
BHCs with publiclytraded securities are reportedon the SDC data set.
For 1991 and 1992, SDC reported 53 BHC common stock issuers,
whereas the Consolidated Financial Statements reported 424 issuers.

LADERMAN / BANK HOLDING COMPANY SECURITIES ISSUANCE

TABLE 4
COMMON STOCK ISSUANCES BY SAMPLE BHCs
DECEMBER 1990-DECEMBER 1992

NUMBER OF BHCs

AMOUNT ISSUED"

Unaffected BHCs
(382 out of 906, or 42.16%)

Mean
Minimum
Maximum

.67%
.0006%
11.66%

Unconstrained BHCs
(24 out of 52, or 46.15%)

Mean
Minimum
Maximum

.99%
.001%
8.75%

Constrained BHCs
(18 out of 39, or 46.15%)

Mean
Minimum
Maximum

1.49%
.0007%
10.43%

SOURCE: Consolidated Financial Statements for BHCs.
"Amount raised as a percentof risk-weighted assets in December
1990. Statisticsare based on issuing BHCs only.

IV:

THE EFFECT OF THE TIER 1
CAPITAL REQUIREMENTS ON
CONSTRAINED BHC loAN GROWTH

In this section, I investigate whether the difference shown
in Table 3 between constrained and unconstrained BHC
loan growth is statistically significant, controlling for other
factors likely to affect loan growth. I will test the hypothesis that constrained BHC loan growth between year-end
1990 and year-end 1992 was statistically significantly more
negative than unconstrained BHC loan growth over the
same period.
Given the results in Section Il, I will assume that
negative common stock wealth effects apply to constrained
BHCs.13 Thesimple regression that I will estimate has loan
growth as a function of the BHC's maximum capital ratio
13. The idealapproachwouldbe to estimatecommonstockannouncementeffects for constrained BHCs for 1991 and 1992.(Announcements
during1991 and 1992are relevant becausethe dependent variable in the
regression will be loan growthbetweenDecember 1990 and December
1992.) Unfortunately, the sample size was insufficient to permit such
estimation. There were four common stock issuance announcements
betweenDecember 1990and December 1992byconstrained BHCs.SecuritiesDatareportedno filing datesforthreeof these, andtherefore no
announcement dates were located. The remaining announcement, by
Riggs National Corporation, resultedin a 1.11 percentage pointdropin
the return on commonstock. Therefore, results for constrained BHCs
were proxied by the announcement effects reported in Section IT for
BHCsthat had to issue commonstock (or decreaseassets)to meet the
well-capitalized guidelines.

39

shortfall. This is the maximum of the three differences
between the three required minimum levels and the three
corresponding actual ratios in December 1990.
For unconstrained BHCs, the maximum capital ratio
shortfall is the percentage point difference between the
total capital ratio minimum and the actual total capital
ratio. For constrained BHCs, it is the difference between
the total capital ratio minimum and the actual total capital
ratio, or the difference between the leverage ratio minimum and the actual leverage ratio, whichever is greater. In
making loan growth a function of the capital shortfall
below the minimum, I assume that the minimum is the
target for most BHCs.14 In addition, I control for the
possibility that lower loan growth by the constrained BHCs
is simply the result of a greater capital deficiency, however
capital is defined, and is not the result of a deficiency in
common equity in particular.P
To control for changes in loan demand, the regression
also includes economic growth in the BHCs' subsidiaries'
states. This is the weighted average personal income
growth between December 1990 and December 1992, in
percent, in the BHC's subsidiary banks' states, weighted
by the share of total BHC assets held by the BHC's
subsidiaries in that state. I expect that there is a positive
relationship between economic growth and loan growth.
A constant is included to help capture the effects of other
influences on loan growth not stemming from the need to
achieve regulatory capital minima. BHCs that reported
loans in December 1990 but not in December 1992 are
omitted from the regression. A dummy variable indicates
whether or not the BHC is constrained or unconstrained.
The model was estimated using ordinary least squares regression on a sample of 75 unconstrained and constrained
14. Furlong (1993) argues that, for many capital deficient BHCs,
becoming well-capitalized, not just adequately capitalized, was the
goal. Furlong examines changes in capital and risk-based assets between December 1990 and December 1992. Official requirements for
being considered well-capitalized were not published until June 1992.
However, it is reasonable to assume that these rules just codified
unwritten rules already well-understood by BHCs. Therefore, it is
reasonable to arguethat manyBHCs that did not meetwell-capitalized
guidelines in December 1990 intended to do so by December 1992.
However, using the capital shortfall belowthe minimum rather than the
well-capitalized level is consistent withthe BHCgroupdefinitions and,
in the regression, merely changes the relative sizesof thecoefficients on
the constant and the shortfall variable.
15. Strictly speaking, given a target ratio, the increasein capital and
decreasein assetschosento achieve thegoalwilldependon theshortfall
and the initial levels of capitaland assets. However, wheninitial levels
for capital and assets wereincludedin the regression, theircoefficients
were not statistically significant.

40

1994, NUMBER 2

FRBSF EcONOMIC REVIEW

BHCs .16 The dependentvariable is percentgrowthin total
loans outstandingbetween the fourth quarter of 1990and
the fourth quarter of1992,as indicatedon the Consolidated
FinancialStatements forBHCs. Table 5 reports the regression results.
The results in Table 5 support the hypothesis that constrained BHCs had statistically significantly lower loan
growth over the 1991-1992 period than unconstrained
BHCs, evencontrolling fordifferences in loandemandand
general capital deficiency. The results indicate that, on
average and with other factors held constant, loan growth
at BHCs that were constrained either to issue common
stock or to decreaseassets was about28 percentagepoints
lower than loan growth at BHCs that could reach the minimum by issuing othertypes of capital instruments. This
difference is comparable to, but somewhat larger than,
the difference in meanloangrowthbetweenthe two groups
seen in Table 3. Thecapital shortfall and economic growth
variables also have the expectedsigns and are statistically
significant.

TABLE 5
REGRESSION RESULTS
LoAN GROWTH BETWEEN DECEMBER

1992 FOR 75

1990 AND

DECEMBER

CONSTRAINED AND UNCONSTRAINED

V.

CONCLUSION

This paper tests a two-part hypothesis. FIrst, during the
period between publication of the risk-based capital requirements in early 1989 and the end of 1992,BHCsfaced
a statistically significant decrease in stock returns-a
negative shareholder wealth effect-if they issued new
common stock. Second, this negative wealth effect discouraged new common stock issuance and therefore in
effect forced BHCs deficientin common stockto decrease
loans outstanding more than did BHCs deficient in other
types of capital. Empirical evidence supporting both parts
of the hypothesis was presented.
One interpretation of the results presented in this paper
is that, had the risk-based capital rules not included a
requirement for a certain level of common shareholders'
equity, loan growthforthe groupof BHCs identified in this
paper as constrainedwouldhave been considerably higher.
This doesnot necessarily meanthat it wouldtherefore have
been wiseto reduceor eliminaterequirements forcommon
shareholders' equity. This typeof capitalarguably provides
the best protectionto the depositinsurance fund in case of
bank failure. However, it does mean that if we are concerned about the flow of bank credit to the economy, we
shouldtakeinto accountthe type of effectdescribed in this
paperin weighing thelikelycostsandbenefits of thedesign
and enforcement of capital regulations.

BANK HOLDING COMPANIES

EXPLANATORY VARIABLE
Constant
Capital Ratio Shortfall
Economic Growth in
BHC Subsidiary States
Constrained

COEFFICIENT

t RATIO

-48.878**

-2.046

-2.83*

-1.865

7.157***

2.867

-28.103***

-2.708

AdjustedR2 = .164
*Significantly different from zero at the 10 percent level.
**Significantly different from zero at the 5 percent level.
***Significantly different from zero at the 1 percent level.

16. The number is less than the sum of unconstrained and constrained
BHCs indicated in Table 3 (91).The BHCs excluded are ones for which

the required information on bank subsidiary .location could not be
located (including foreign BHCs), BHCs with no commercial bank
subsidiaries, and individuals or pseudo BHCs.

LADERMAN / BANK HOLDING COMPANY SECURITIES ISSUANCE

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41