Commercial bank financial policies and their impact on market-determined measures of risk

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Faculty Working Papers

College of Commerce and Business Administration

University of Illinois at Urbana-Champaign

I

FACULTY WORKING PiiPERS
College of Comaerce and Business Administration
University of Illinois at Urbana-Champaign
April 4, 1979

CCMIiERCIAL BAivIK FIilANCIAL POLTCIES AM'^ -"HEIP Tl>fPACl
ON liARiCET-DETERilDIED MEASURES OF RISK

Ali Jahankhani, Assistant Professor, Departanent of
Finance

horgan J. Lynge, Jr., Assistant Professor, Depart-
ment of Finance

#556

Summary;

This paper investigates the relationship between certain accounting measures that
purport to reflect a firm's risk and tx;o laarket-based measures of risk. The firms
examined are commercial banks and bank holding companies. Some commonly used ratios
to indicate risk in banking are capital to total assets, loans to deposits, liquid
assets to total assets, and loan losses to total loans. These and other measures are
included in multiple regression equations using systematic risk (beta) and total risk
(standard deviation of return) as dependent variables. Results indicate that the
accounting measures do explain from 25%;: to 43% of the variation in the market-based
risk measures for banks. Signs of the estimated coefficients are usually consistent
with expectations, supporting the conventional views of the usefulness of these ratios
in measuring the riskiness of a bank.

Commercial bank nanagement, through decisions about uses and sources
of funds, determines expected return and an associated level of risk for
the owners of the bank's common stock. The results of these management
decisions influence Investors' expectations which are then reflected in
the price of the common stock. The nature of the connection between
management decisions and stock price is of Interest to the management
that is trying to maximize the wealth of the bank's shareholders. Stock
price is influenced by the Investor's consideration of both expected
return and risk. Thus the connection between management decision making
and the risk of the common stock investment is a subject of importance.

A previous study by Beighley, Boyd and Jacobs fl975] (BBJ) examined
the relationship between financial leverage and stock price for 113 bank
holding companies (BHC). The focus of the BBJ study is on one management
decision, the degree of financial leverage to employ, and attempts to
isolate the sensitivity to this measure exhibited by equity investors.
BBJ use the average level of the common stock price (3 month average)
as a dependent variable. However, this does not capture the true measure
of the benefit to the investor, which is the return on the investment
in the common stock. To get a measure of return, the change in the stock
price and the associated dividend paid must be considered. The BBJ
results say that for the given sample of bank holding companies, the
higher a bank's degree of financial leverage at a point in time, the
lower is the bank's stock price (after controlling for bank size, earnings
growth, dividends and loan losses). It says nothing about the behavior
of the bank's stock price over time, or the return to the investor from
holding the bank's stock.

-2--

In our study the effect of a bank's financial leverage, as well
as measures of other management decisions, on the riskiness of the
investment in the bank's stock is exaaiined. Rather than using stock
price as a dependent variable, we use two measures of the riskiness
assigned to a bank's stock by "the market", or by the equity investors
in that common stock. This enables us to identify, for each market measure
of risk, how management decisions effect these risk assessments.

In section I the idea of risk in a commercial bank will be examined,
and two market-determined measures of a bank's risk are introduced.
Other studies of market-determined risk and accotintlng measures are
reviewed in section II. The following sections expli^in the accounting
measures that are expected to influence a bank's risk and present em-
pirical measures of the degree of association between accounting data
and mar'icet-determined risk measures. The final section contains a
summary and scne conclusions.

^* R^sk in Comnercicil Banking
An Investor In the ccnnaon stock of a commercial bank has some expec-
tation of the return on his investoient as well as the risk of this invest-
ment. The riskiness of the investment: is the chaice that the return will
not turn out to be what is expected. The hypothesis that is to be tested
in this study is that this risk, or che investor's perception of the risk,
is strongly affected by the bank management's decisions that are reflected
in its financial statements. For example, bank A (for aggressive) may
have an asset portfolio rhat embodies a high level of credit risk — -a high
percentage of loans, few U.S. government securities. Further Bank A may

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employ a high degree of financial leverage (low level of equity capital)
and, perhaps, rely heavily on borrowed funds to finance assets. Bank
C (for conservative) may hold relatively high levels of U.S. government
securities and relatively riskless loans, have a high level of equity
capital, a stable deposit base, and not rely heavily on borrowed funds.

The above measures, and other similar measures, are accounting
statement values that reflect management decisions which affect the
amount of risk undertaken by a bank. A conventional view of risk would
certainly hold that bank A is riskier than bank C. Therefore any overall
measure of risk should be higher for bank A than for bank C. Some pre-
vious research has been conducted using these accounting data to deter-
mine default risk or to predict the occurrance of default or failure.
Statistical models have been used to identify those accounting measures
whose values will indicate to the regulatory authority that default
is likely and closer attention is required. The concepts of risk used
in this study include default risk, but also encompass all other risks
that come to bear on the equity investment of the shareholders. That is,
the risk referred to here is the riskiness of owning the bank's common
stock. Thus we shall use market-determined measures of risk that are
derived from portfolio theory.

Over the last decade, Sharpe [1964] and others have extended
the earlier work of Markowitz [1959] to a simplified portfolio model
and to a capital asset pricing model which determines the equili-
brium prices of all securities. Markowitz defined the riskiness of a

See, for example, Meyer and Pifer [1970], Slnkey [1975] and Sinkey
and Walker [1975].

_4~

portfolio of securities in terms of the variance of the portfolio's

2
returns [a (R )]. For a diversified portfolio composed of a large

number of securities, a security's contribution to the risk of the port-
folio is measured by its average covariance with all other securities in
the portfolio, not its variance. According to the diagonal model of
Sharpe, the return on a security (R.) can be written as:

\ = "i + h\ + ^i ^^>

where R is the return on all securities (hereafter referred to as
m

the market return), e. is the security specific factor vAiich is indepen-
dent of R , and a. and 8. are the intercept and slope associated
m* i i

with the linear relationship.

The model asserts that the return on a security is composed of two
factors, a systematic component (3.R ) which reflects common movement
of the security's return with the market return and a security specific
factor (a + e.) which reflects that portion of the security's return

which is independent of the market-wide return. The total risk of the

2
security, a (R. ), as measured by the variance can be written as

The first term is called the systematic risk of the security and measures
the security's sensitivity to market-wide events and can not be diversified
away. The second term is called the specific or diversiflable risk be-
cause that risk can be driven to zero through diversification. Thus, the
only relevant risk of a security to a risk averse investor who holds a

-J-

dlvereified portfolio is the systematic risk. The beta coefficient (g.)
bears a direct relationship to the concept of covariance. In particular
g is the risk of the security relative to the risk of the market
portfolio, or

Gov (R^,R^)

^i "^ 2

m

where Gov (R. ,R ) is the covarian-^e of securltj' i's returns with the
i' m

2
market return and a (R ) is the variance of the market return.

m

In this study we used both systematic risk, g., and total

2
risk, a (R. ), as the uiarket-detericined risk measures. Since total

risk includes both the systematic risk and the specific risk of a
bank, we would expect financial ratios to explain a larger portion
of the total risk than the systematic risk. From equation (2) it is
evident that both measures of risk are positively related to each
other. However, two banks with the same g. ^ntll not necessarily have
Identical total risk if their specific risks are different. Differ-
ences in the specific risk may be due to the differences in some of
the financial policies or events, such as liquidity position or loan
losses.

Sharpe and others hcva extended the earlier work on portfolio ana-
lysis to the capital asset pricing model. In this model the equilibrium
expected return on a security is linearly dependent upon the beta coeffi-
cient .

E(R^) - R^ -!- P^[E(R^) - R^] (3)

I

-6"

where E is the expectation operator, R^ is the risk-free interest rate

and other terms are define! previously » Note that diversif iable risk

2
[o (Ej)] does not enter into the pricing of capital assets, since that

component can be eliminated through diversification.

Empirical estimates of a. and g. can be cbtained from a time

series, least square regression of the iTollowing term:

R.^ = ?-. + b.R,,. + e. , (4)

It i i kiii; XL

where P^ . ^ and R are realized returns for security i and the market
it mt

in month t, respectively and e is the: di^^turbance term. The b. 's are.
estimates of the 3 for each firm. _'he value of ^ (or ics estimate, b)
will vary among firms. This reflects differing investor oxpectatio**'
about th^ relationship betwean each firm's recurn and ilin market return.
Each l then is e market measure whicn incorporates ail information about
the firm as digested by market participants. It must be pointed out
that there is no "good'' or "b^id" £ value. A high {i merely irdicates a firm
whose returns are more volatile with respect to return on the market
portfolio.

The question being esamia«i in thic paper is to what extent are the

■-'-.j't''. • ' ■ ''

commerical ban'; dscisions as reflected by their acccuncing statement data
impounded or reflected in the ^ and a(R) measures? We are interested in
examining the degree of lr.iiur.i;c9 thit different accounting measures have
ca a bank's rich measures. For example, is it the case that the degree
of financial leveraga employod strongly influences a bank's risk measures,
or is liquidity or the credit risk of its assets a more important deter-
minant of the risk measures? i-'o.' lowing a review of previous research

-7-

the methodology employed to address these questions is explained and
empirical results are presented. ~"

II. Previous Research

Besides the BBJ study cited earlier where the focus is on stock
price, there exist a number of studies investigating the effects of firm
financial policies on the risk of the firm. A pioneering study by
Beaver, Kettler and Scholes [1970] examines the relationship (using simple
correlation) between a firm's market-determined H and single indicators
of financial policy. They discover significant correlations between
g and dividend payout, financial leverage and an "accounting ^" which
measures the covariability of a firm's earnings with the earnings of
all firms. In addition, this study specifies the market ti as a function
of several accounting measures for the purpose of forecasting the market
3. Hamada [1972] investigates the relationship betw^een fcj and financial
leverage while Lev [1974] devises an operating leverage variable which
has some explanatory power.

There exists a group of studies that use a multivariate approach
to the explanation of 3. A variety of explanatory variables are used
to measure the riskiness of the firm's common stock that comes from the
firm's financial decisions. Balance sheet and income statement data are
utilized as explanatory variables in a multiple regression equation with
B as the dependent variable. In a study by Logue and Merville [1972]
return on assets, asset size, and financial leverage variables appear with
significant coefficients. Melicher [1974], using a sample of electric

-8-

utilities finds asset size, payout ratio, return on conmon equity, market
activity, the ratio of net plant to total capital, and financial leverage
to explain from 33% to 41% of the variation iu 3.

No comparable research has been conducted for commercial banks.
Besides the Beighley, Boyd and Jacobs study of EEC's cited earlier there
is a separate study by Beighley [1977] that uses the same sample as BBJ
but relates, instead of stock price, an estimate of the risk premium on
the BHC's outstanding debt issues to various financial measures. Several
financial leverage measures, asset size, and, for some equations, loan
losses, are found to have significant coefficients.

III. Methodology
The sample utilized in this study consists of all firms in the
COMPUSTAT Quarterly Bank data tape which had continuous data over the
period 1972 through 1976. A total of 95 commercial banks and bank
holding companies were qualified and included in the sample. For each
bank the beta was estimated by using equation (4) where R. and R
are the monthly percent changes in the price of security i (common stock
of bank i) and the market portfolio, respectively. The beta, g,, was
estimated using the ordinary least square regression method. The market
portfolio was approximated by the value weighted portfolio of all stocks
listed on the NYSE. For each common stock the standard deviation of
monthly price changes v;as used as a measure of total risk of the security,
o(R ) . For each bank, the following financial ratios were computed using
quarterly data for the period 1972 through 1976 (20 quarters).

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L. Dividend payout ratio (FOR), measured by avejrage cash dividends
during 1972-76 divided by average earnings available for common
stockholders. The rationalization for using payout ratio as an
explanatory variable rests on the well-known phenomenon of dividend
stabilization; firms are reluctant to change drastically, and, in
particular, to cut dividends once a certain level has been established.
Consequently firms with a high degree of earnings variability will
probably distribute a lower percentage of earnings than more stable
firms. Therefore, we expect an inverse relationship between dividend
payout ratio and both the beta (systematic risk) and the standard
deviation of monthly price changes (total risk).

1. Leverage (LEV), measured by stockholders' equity divided by total
assets. This ratio is important for the banking industry because
of the high degree of financial leverage used by commercial banks.
Because a higher degree of leverage increases financial risk, we
expect an inverse relationship between the equity to total asset
ratio and both systematic and total risk.

Coefficient of variation of deposits (CVDEP), measured by the standard
deviation of total deposits divided by the mean of total deposits
over the 1972-76 period. Deposits are by far the most Important
source of funds for commercial banks. The more volatile the deposits,
the more likely will nondeposit borrowings need to be utilized to
finance the asset portfolio and thus the more volatile may be the

';>;'"ba.(i.l3 3i,' , v d

Ci-'

--.'O'v,.^ u;!^j .',11.212 1 m,' -tctLM.! .;':' ,e>i.ji»!) Ii^-i.

oj bi-....I;. Mr Od o;;? ;*/*•>;' n;

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earnings of the firm. Therefore, a positive relationship between
this ratio and aysteniatic and total risk will be expected,

4. Coefficient of variation of earnings per share (CVEPS), measured by
the standard deviation of the earnings per share divided by the mean
earnings per share. The standard deviation of EPS is a widely used
accounting risk measure and we expect to see a positive relationship
between this risk and the narket detenniced risk measures (both
systematic and total risk),

5. Loan to deposit ratio (L/D) . A bank's loan portfolio contains the
most risky assets held by a bank. In addition, the higher the loan
to deposit ratio, the less are the holdings of liquid and cash assets
and thus the more expoced thr. bank is to possible liquidity problems.
Thus for both credit rick and illiquidity risk reasons the loans to
deposit ratio should ba positively related to total and systenatic
risk. ''-- "

6. Loan loss experience (LOSS'), measured by the provision for loan
loss divided by iotal loans. This is a more direct measure of

the riskineos of the loan portfolio as esti^nated by bank management.
Other things enual, a higher loss provision reflects a higher degree
of expected loss in the loan portfolio. Therefore, this ratio is
expected to be positively related to both risk measures.

7. Liquidity (LIQ) aa measured by the ratio of cash and due from plus
U.S. Treasury sec-jritles to total assets. This is a somewhat inade-
quate but a quite stardard mtasure of liquidity, or the ability to
absorb net cash outflows that occur for any reason. The greater this

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ratio, the greater the bank's ability to absorb cash drains In the
short run and thus the less is the risk of illiquidity. For this
reason a negative relationship between this ratio and both risk
measures is expected.

These ratios are taken as accounting measures that reflect manage-
ment decisions* To iflinimize the "vindow dressing" problem of financial
statements, each ratio is the average of the 20 quarters from the years
1972-1976. In this way the "average" tnanageirent decisions over this
period are reflected, rather than the specific ratio value for just one
point in time. The use of average ratios does, however, result in a
loss of Information. Substantial variation in individual accounting
values is lost when averages are used^ It is felt: that this loss of
Information is acceptable in order to circumvent the problems in-
herent in using data as of a cingle point in time, Tne five years
chosen are the most recent 3'ear?; for which complete financial data
are available on the COMTUSTAT Q-aarterly Bank data tape.

These average ratios, which are proxies for the taanageraent de-
cisions are used as variables to explain the riskiness of the bank
as measured by the narket over the 1972-76 period. Table 1 presents
the average value of each of chece ratios for the 95 bank sample and
indicates the expected ralationsh5.p between each ratio and the risk
measures. These expected relationshi'.ps are a priori expectations based
on the bivariate relationships only. Since itultiple regression will
be used to eetimate the coefficients of these ratios the expected signs
may not be realized »

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TABLE T

Average Values and Expected Signs of
Variables to be Used in Multiple Regressions

Variable

Average
Value

Expected Relationship
With Risk Measures

POR

LEV

CVDEP

CVEPS

L/D

LOSS

LIQ

.432

.058

.172

.204

.694

.0013

.229

+
+
+
+

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Miiltiple regression is used to estiaate the relationship between
these accounting measures and the market detemined risk measures.
Specifically, the following regression equations were estimated using
the ordinary least squares method:

Beta. = a^ + a^X^^ + a^y.^.^ + a^X^^ >- a^X^^ + a3X3 . -h a^X^^ + e^

(5)

and a(R.) ^- y^ ". y^X^^ + y/^,^ ^ Y3X3J + y^X^. ,- y^X.^ + y^X^. -!- c^ (6)

where X, .'g denote different accounting measures for the jth firmj

beta, is the systematic risk measure and o'(R.) is a measure of total risk
J J

for the ith firm.

IV. Eesu3 ts
In the spirit of Eeaver, Kettler and Scholes [1970] let us first
examine the direction and strength of the relationship between the market
measure of risk and individual measures of financial policy. Table II
presents correlations among all the ratios dafinec previously and the two
measures of risk, g pnd a(K)„ For example the top row of Table II indicates
that the payout ratio in negatively correlated with beta; that is, the
larger the percentage of earnings paid out as dividends, the lower the
beta risk measure. Liksvrlse, for the leverage variable, the higher the
bank's equity as a percentage of total assets, the lower the risk measure.
The remaining ratios, except for liqidlty (LIQ), exhibit the expected sign
but are not etatistically slgnificanr. at. the 5% level (absolute values
below ,200). For the total risk maasure, r(R) (see row 2 in Table II)
all correlations have the expected sign and are significant.

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The lower portion of the correlation matrix in Table II indicates
the degree of association among the financial ratios. In general
these ratios are not highly correlated with one another, indicating
that different facets of risk are being proxied. However, four of
these correlations are significantly different from zero. This pre-
sents the problem of multlcollinearlty in the models to be estimated
via multiple regression. Multicollinearity increases the standard
errors of the estimated coefficients (lowering the t-values) and
may cause some coefficient values to appear to be not significantly
different from zero. This makes difficult the indentification of in-
dividual financial policies which impact on the risk measures.

The correlations in the lower portion of Table II tend to support
some of the relationships between the ratios and various types of
risk proposed in section III. For example, the loan to deposits
ratio (L/D) is negatively correlated with the liquidity ratio (LIQ)
and positively correlated with the loan loss ratio (LOSS). This in-
dicates the ability of L/D to proxy both liquidity and credit risk.
In a similar vein the variability of earning per share (CVEPS) is
positively related to LOSS, since a larger provision for loan losses
taken in anticipation of higher loan losses reduces reported income.

These correlations indicate only bivariate relationships. They do
not control for the effects of t\7o or more ratios on risk at the same
time. A multivariate analysis is accomplished using multiple regression.
The coefficient estimates from these regressions are presented in Table
III, Here we are able to observe the effect of any one financial ratio

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TABLE II

Correlation Matrix of Dependent and Independent Variables
e o(R) POR LEV CVDEP CVEPS L/D LOSS LIQ

3 1

.756

-.313

-.204

.372

.015

.159

.036

.003

o(R)

1

-.309

-.222

.391

.331

.218

.320

-.209

POR

1

-.015

-.250*

-.058

.132

.120

-.005

LEV

1

-.184

-.072

-.077

.044

-.253*

CVDEP

1

.069

-.088

.147

.160

CVEPS

1

.029

.444*

-.103

L/D

1

.193

-.588*

LOSS

1

-.159

LIQ

1

*
Significantly different from zero at the 0.05 level.

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while simultaneously accounting for the effects of the other ratios.
When beta is used as the risk measure, the set of financial ratios explain
about one qxiarter of the variability in beta among the 95 banks. The
ratios that have significant coefficients as well as signs that are
expected are the payout ratio (POR) , the variability of deposit sources
of funds (CVDEP), and the loan to deposit ratio (L/D). The other ratios,
with the exception of LIQ, have the expected signs but are not statis-
tically significant at the 5% level.

When total risk, a(R) is used as the dependent variable all estimated
coefficients have the expected sign and all but one are statistically
significant at least at the 10% level. This set of ratios explains 43%
of the variability in total risk among the 95 banks. The fact that
financial ratios explain a larger portion of the total risk than the
systematic risk is not surprising. Total risk includes both the
systematic risk and the specific risk of a bank. Some of the finan-
cial ratios, e.g. liquidity ratios, are expected to affect mostly the
specific risk rather than the systematic risk.

The results of this study compare favorably with those of other
studies. The Logue and Merville (1972) study, hereafter IMi, ex-
amines nonflnancial industries and obtains results that are comparable
to those reported here. When the dependent variable is 6 the coef-
ficient signs for POR and LEV are the same in the L&M study as re-
ported here. For banks, however, the payout ratio coefficient is

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TABLE III

Estimated Coefficients***

Independent Variable
(financial ratios)

Dependent Variable (risk measure)
J g(R)

FOR

LEV

CVDEP

CVEPS

L/D

LOSS

LIQ

CONSTANT

r2

-1.004**

-0.058**

(-2.81)

(-3.08)

-2.45

-0.258**

(-1.17)

(-2.36)

1.36**

0.079**

( 2.92)

( 3,24)

-0.149

0.047*

(- .31)

( 1.85)

0.912**

0.022

( 2.07)

( 0.93)

3.425

4.694**

( 0.08)

( 1.99)

0.385

-0.066*

( 0.56)

(-1.83)

0.609

0.095**

( 1.17)

( 3.51)

.26

.43

*Significant at the 10% level
**Significant at the 5% level
***NuKiber8 in parentheses are t-statistics.

-18-

2
significant while for nonflnanclal firms it is not. This indicates

the importance of dividend clienteles among holders of bank stocks.
Similarly a measure of liquidity was not significant and had the
wrong sign in both the L&M study and the present study. However, whe
included in the total risk model the LIQ coefficient has the expected
negative sign and is significantly different from zero at the 0.10
level.

The Beighley, Boyd and Jacobs (1975) study, hereafter BBJ,
focused on banks but developed models to explain share price rather
than risk. Still some similarities exist between the BBJ and the
present study. BBJ found that the level of dividends exerted a posi-
tive effect on share price, consistent with the finding here that a
higher dividend payout ratio is associated with lower risk measures.
Increased leverage and higher loan losses Impact negatively on share
price in BBJ while these two measures lead to higher measures of both

systematic and total risk in this study. However the coefficients

• i* i\ - ■

of LEV and LOSS are only significant in the total risk model indi-

■ t^- ^ ff'.

eating that these are firm specific risk factors and do not signi-

ficantly affect the bank's systematic risk.

• lie: J

V. Summary and Conclusions
This study has investigated the relationship between financial
policies of commercial bansk and two market-determined measures of

2
L&M also estimated a model for A separate industries. For one

industry, the electronics-electrical supplies industry (22 firms),
the coefficient of the divldent payout ratio was negative and sig-
nificantly different from zero. In general most of the coefficients
were not significant when industries were estimated separately.

-19-

risk, Financial policies are proxied by average balance sheet and
income statement data over the period 1972-1976 for 95 commercial
banks and bank holding companies. Accounting data measures of finan-
cial leverage, liquidity, dividend payout ratio, loan loss experience
and variability in earnings and deposits are used. These are related
to a measure of systematic risk (g) and total risk (a(R)), also calculated
for the same 5-year period. Bivariate and ntultivariate relationships
are examined*

As independent variables used to explain $, the coefficients of
the dividend payout ratio, variability of deposits and the loan to
deposit ratio are significant. In explaining total risk the coef-
ficients of the dividend payout ratio, a financial leverage measure,
variability of deposits and earnings, a loan loss measure and a liqui-
dity measure are all significant.

These results reveal the nature and the degree of impact that
certain financial decisions have on bank's market-determined risk
measures. This knowledge is an Important input for managers vAiose
objective is maximization of shareholder wealth. Achievement of this
objective is vitally affected by the level of risk undertaken by the
bank and its impact on share price.

'-. t.i , - •- ■

-■V

;v.j. J'a^ '•- -1 i: .. -, if;;:ji

-20-

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M/E/136,