Federal Deposit Insurance Corporation

FDIC

Banking

Review

1998

Volume 11, No. 4

l Mutual-to-Stock Conversions

l Loan-Loss Recognition

l Recent Developments

l Mutual-to-Stock Conversions

l Loan-Loss Recognition

l Recent Developments
Table of Contents

FDIC

Banking

Review

1998

Volume 11, No. 4

Mutual-to-Stock Conversions: Problems with the

Pricing of Initial Public Offerings

by Joseph A. Colantuoni Page 1

This article examines the factors that affect conversion returns. The author

concludes that preconversion equity in mutual thrifts is what creates the

windfall gains that accompany mutual-to-stock conversions.

The Influence of Examiners and Auditors on Loan-Loss

Recognition

by Drew Dahl, John P. OKeefe, and Gerald A. Hanweck Page 10 

The authors find that when bankers have some discretion in their treatment

of loan losses, bank examinersbut not auditorsinfluence the timing of

loan-loss recognition. For their part, auditors appear to focus on the

provision for loan losses.

Recent Developments Affecting Depository Institutions

by Lynne Montgomery Page 26

This regular feature of the FDIC Banking Review contains information on

regulatory agency actions, state legislation and regulation, and articles and

studies pertinent to banking and deposit insurance issues.

Chairman

Donna Tanoue

Division of Research

and Statistics,

Director

Wm. Roger Watson 

Deputy Directors

Barry Kolatch

Steven A. Seelig

Editor

Steven A. Seelig

Editorial Committee

Jack T. Reidhill

Detta Voesar

Managing Editor

Detta Voesar

Editorial Secretary

Cathy Wright

Design and Production

Geri Bonebrake 

The views expressed are

those of the authors and do

not necessarily reflect official positions of the Federal

Deposit Insurance Corporation. Articles may be

reprinted or abstracted if

the FDIC Banking Review

and author(s) are credited.

Please provide the FDICs

Division of Research and

Statistics with a copy of any

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Single-copy subscriptions

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free of charge. Requests for

subscriptions, back issues

or address changes should

be mailed to: FDIC Banking

Review, Office of Corporate

Communications, Federal

Deposit Insurance Corporation, 550 17th Street, N.W.,

Washington, DC 20429.
Mutual-to-Stock Conversions

Mutual-to-Stock Conversions: 

Problems with the Pricing

of Initial Public Offerings

by Joseph A. Colantuoni*

The U. S. thrift industry, including both savings

and loans and savings banks, has long been

characterized by both stock and mutual forms

of ownership. Stock-form thrifts are owned by shareholders and may be either closely held or publicly traded institutions. Mutual thrifts are typically owned by

their depositors.1 Since the early 1980s, a large number of mutual institutions have converted to the stock

form of ownership. They have done so to raise equity

capital, to expand their operations, to compensate company officers, or for a variety of other reasons. The primary method of converting has been through an

initial public offering, or IPO. Equity interests in the

new thrift are first offered for sale to eligible depositors,

managers, employees, and then to the general public.

Initial purchasers who were fortunate enough to buy

shares of the 143 mutual thrifts that converted to a

stock form of ownership in 1995, 1996, 1997, and the

first half of 1998 saw their share prices rise by an average of approximately 24 percent on the very first day of

trading. Even more dramatic has been the price appreciation on the 13 conversions that took place in the

first four months of 1998, producing an unprecedented

average one-day return of 59 percent. Moreover, the

pops appear to be more prominent the larger the institution is.

Given the remarkable single-day returns associated

with recent conversions, it seems appropriate to review

the effectiveness of current regulatory appraisal guidelines in pricing mutual-to-stock conversions. These

guidelines were revised in 1994 to ensure that conversion stock is accurately appraised and sold at its pro

forma market value, eliminating any windfall distribution in the value of the converting association.2 Yet

conversion activity since 1994 suggests that these revised guidelines have had a limited effect at best.

Unfortunately, the current appraisal guidelines hinge

on the assumption that the converting thrift can be valued in such a way that windfall gains are eliminated

when the thrifts stock begins trading. As this article

shows, however, this assumption is unreasonable. 

The first section of the article describes the conversion process and the attempt of regulatory guidelines to

eliminate windfall gains that have, in fact, accrued in

recent conversions. The second section summarizes

recent studies of the conversion process, gives a mathematical explanation why pricing formulas cannot reduce first-day price appreciation, and empirically tests

for the most important factors that affect conversion returns. The article concludes that preconversion equity

in mutual thrifts is what creates the windfall gains that

accompany mutual-to-stock conversions. Thus, unless

a converting mutual thrift has no book equity at conversion, we should always expect significant price appreciation on the first day of trading. 

1

* Joseph A. Colantuoni is a financial economist in the FDICs Division

of Research and Statistics. The author would like to thank John P.

OKeefe, Steven Seelig, James Wilcox and Timothy Critchfield for

numerous comments and suggestions on earlier drafts of this article.

He is also grateful for the valuable research assistance of Paul Moloney.

1 Much like mutual insurance companies that are mutually owned by

their policyholders.

2 Office of Thrift Supervision Guidelines (1994).
FDIC Banking Review

2

Appraisal Guidelines and 

Windfall Gains

Concern over the Initial Public Offering

Price of Mutual Thrifts

Regulators have become increasingly concerned

over the dramatic short-term increases in the market

value of converted thrifts. Initial shareholders in these

institutionspeople who first subscribed to the IPO

have been blessed with single-day returns far above

the average for other types of companies that first issue

stock to the public. Before 1994, the concern was that

insiders benefited from these excessive returns at the

expense of depositors, who often opted not to purchase

shares of their newly converting thrift.3 These insidersa group that includes management and professional investorstypically accrued gains during a

conversion by exercising their right to purchase stock

in the thrift before it was publicly traded. After trading

began, share prices would often rise dramatically from

the initial public offering price insiders had paid. In

October 1994, the Office of Thrift Supervision issued

revised appraisal guidelines designed to eliminate

these gains.4 Table 1, figures 1 and 2, and table 2

(pages 5-6) show the single-day returns and the size of

converting institutions before and after 1994.

A puzzling question now arises. If the revised

guidelines provide a better estimate of a converting

thrifts value than previous guidelines, then why do we

still observe such remarkable, and rising, returns? The

answer, as shown below, is that mutual-to-stock conversions are a unique type of initial public offering, and

because of their unique characteristics, they cannot be

priced to eliminate any windfall distribution of value

arising from the conversion process.

Why Price Appreciation Occurs in

Mutual-to-Stock Conversions

Many stock-form firms, including banks and thrifts,

have their equity capital privately held: their equity

shares are not publicly traded but are held by a small

number of individuals or companies. If privately held

firms seek additional equity funding, or their owners

wish to liquidate a portion of their investment, they

may raise capital through an initial public offering of

stock. In these cases, owners are essentially selling all

or some portion of their ownership interest in the firm

to new shareholders (the purchasers of the IPO).

In contrast, mutual-form thrifts do not have explicit

owners. They do, however, have net worth, or equity,

in the form of retained earnings. Management has created this equity by prudently investing depositor

funds. When mutual-form thrifts are converted to

stock form, eligible depositors and managers can purchase shares of the thrift at the subscription price before public trading begins and a market price is

established. The proceeds collected during the subscription period are not transferred to the mutuals

managers or depositors. Instead, these proceeds are retained by the thrift and added to its total net worth.

Those who purchased the thrifts stock during the subscription period now own its preexisting net worth plus

the total proceeds raised in the public offering. The

equity pie has grown in size, and each of the new shareholders can enjoy a larger piece of pie for the cost of a

smaller one because the original (preconversion) equity remains in the thrift. The sudden and dramatic rise

in the market price of stock above the offering price

initially set by the underwriter is a reflection of the

original equity.

If an institution has a positive amount of preexisting

net worth5 and can invest its IPO proceeds in profitable projects, attempts to eliminate rapid price appreciation are impossible. Two simple examples can

explain this situation. Suppose a mutual-form thrift

with $10 million in net worth converts to stock form. In

one example, if the institutions initial stock offering is

sold for $1 million, initial shareholders should expect to

receive a 1,000 percent increase in the value of their

shares. As a group, they pay $1 million for $11 million

in net worthinitial retained earnings plus proceeds

collected during the stock subscription period. In a

second example, if the institution could somehow be

sold for an unrealistic $1 billion, initial shareholders

would still realize a 1 percent initial return. As absurd

as these examples may seem, they illustrate a simple

point: Regardless of the final IPO price, price appreciation

will occur as the market realizes the value of an institutions undistributed (preexisting) net worth.

Even in the IPOs of privately held stock-form companies, other than savings and loans or savings banks,

some amount of price appreciation can be expected.

Barry, Gilson, and Ritter (1998) report that between

1990 and 1996, standard IPOs averaged a 14 percent

one-day return. Among the many factors affecting

3 In 1981 and 1983, the OTS issued guidelines for the conversion of

mutual savings and loans to stock form.

4 12 CFR Part 303.15 and Part 333.4, 12 CFR Part 563b.7

5 Including positive book equity, retained earnings, and franchise value.
Mutual-to-Stock Conversions

3

these single-day returns, they argue, is the new-issue

discount: a company going public for the first time

may be offered at a discount to its perceived market

value in order to attract sufficient investor interest to

the company. For example, there may be a lack of information about the company, and potential investors

need compensation for the additional risk they assume

when purchasing shares of a new public company. 

However, the 14 percent price appreciation observed for standard IPOs is dramatically less than the

average single-day returns of 40 and 44 percent registered by mutual-to-stock conversions in the past two

years. Most of the price appreciation observed in newly converted thrifts is not created by a new-issue discount since converting thrifts make public a wealth of

financial information. The availability of this information allows analysts to determine readily the pro forma

market value of a thrift. When initial investors pay for

shares of that value, they receive an equity interest in

that value plus the money all of them together used to

purchase that equity. This sudden realization of value

is what causes the market price of a converted thrift to

rise on the first day of trading.

Old Rules Applied to a New Market

In the 1980s, undercapitalized mutual-form thrifts

were commonplace and they often sought to convert to

stock form in an effort to shore up their capital positions. With these conversions, very little price appreciation occurred as the newly issued shares merely

brought in capital to buffer thinly capitalized institutions. Weakly capitalized thrifts had little preconversion equity and therefore did not display remarkable

first-day returns when they went public. Given that

between 1980 and 1989, thrifts averaged only approximately 1.6 percent in tangible equity capital, it is no

surprise that studies like Jordan, et al. (1986) found

conversions at the time experienced a mere 5.6 percent

pop on the first day of trading. But in the mid-1990s,

as the industry recovered its financial stability, conversions continued; and stalwart mutual associations that

had survived the industrys worst crisis added IPO proceeds to their already strong capital positions. 

Presented with large sums of preexisting net worth

and opportunities for profitable growth, todays mutual-form thrifts converting to stock form can expect

nothing less than spectacular initial returns. The appraisal guidelines that regulators once applied to weak

thrifts are no longer appropriate. In the 1980s, thrifts

need for capital and access to equity markets caused

hundreds of them to convert. Today, the impetus for

conversions seems to derive more from competitive

pressures and managerial desires for better compensation.6 The industry has fundamentally changed since

the early 1980s, but the way mutual-to-stock conversions are valued has not.

Evidence That Windfall Gains Have 

Not Been Eliminated

Published Research on Initial Returns

and Underpricing

Many recent studies have argued that conversions

are not merely mispriced but impossible to price accurately under the current appraisal guidelines. An early

study, by Jordan et al. (1986), surprisingly notes positive returns from many conversions in the 1980s and

suggests the existence of a one-time transfer of wealth

from those depositors who did not exercise their right

to purchase shares to those who did. Two other studies discuss the general phenomenon of mutual-to-stock

conversions and describe the general incentives managers have to underprice their institutions initial public offering.7 The second of the two studies illustrates

the difference between conversions and standard IPOs

(as discussed above). 

Another study, Barth et al. (1994), also discusses why

conversions are unique relative to IPOs and confirms

that initial returns have become more pronounced in

recent years. The authors even suggest that a moratorium should be placed on conversions until a more equitable distribution of net worth can be found. Still

another study, Unal (1997), argues that the regulatory

appraisal methodology is invalid because assumptions

in the pricing equations are unreasonable. This author,

too, calls for a moratorium on conversions. Finally, the

most recent study, by Wilcox and Williams (1998),

shows that excess returns on conversions have been

fairly consistent and predictable. Their research shows

that mutual-to-stock conversions grant higher returns

to investors for less-than-expected risk. 

6 See Eccles and OKeefe (1995).

7 Masulis (1987) and Maksimovic and Unal (1993).
FDIC Banking Review

4

Why Pricing Formulas Dont Work

A more detailed analysis of how conversions are valued will illustrate why it is not possible to eliminate

first-day price appreciation in mutual-to-stock conversions.8 The post-conversion value of a thrift, Vr, is

equal to the net present value of the existing mutual

institution and all future earnings generated by investment of the proceeds of the banks initial public offering, V.

where the variable r is the net rate of return for new

IPO proceeds. If these proceeds are not leveraged,

then r represents the simple return on investments

funded by the new capital. If the new IPO proceeds

are leveraged, then r represents the rate of return on

new investments minus the cost of borrowed funds.

The variable W is the preconversion net worth already

present in the thrift. For the sake of simplicity, assume

that r is constant over time. The variable d should represent some appropriate estimate of the markets discount rate on the thrifts earnings. Ideally, this would

be an average return on assets for the industry. However, to illustrate the implicit assumptions made in the

current conversion appraisal guidelines, assume that

d = 1/(P/E), where (P/E) is the price to earnings ratio

for a group of comparable institutions.9 Then equation

(1) becomes

From (2) we see that a positive return on the IPO, Vr >

V, will be realized for any conversion if the proceeds

from the public offering are positive (V > 0) and the

bank is expected to invest these proceeds in positive

NPV projects (r > 0, or equivalently, W > 0 and r > 0).

These are reasonable assumptions for any prudent investor to make.

The 1994 appraisal guidelines attempt to eliminate

windfall gains on IPOs by pricing the converting institutions stock such that the expected post-conversion

price of the stock is exactly equal to the initial public

offering price (that is, Vr = V). Substituting Vr for V in

equation (2) and solving for the post-conversion value

of the bank yields

Equation (3) represents the basic regulatory pricing

model for mutual-to-stock conversions.10 For us to obtain a positive value for the initial price, Vr in equation

(3), two key assumptions discussed above must hold.

First, setting Vr = V implies that any preexisting net

worth is assumed to be zero. Second, the denominator

of equation (3) must be positive, which implies that

This equation requires the institution to reinvest the

proceeds of the offering in projects with negative net

present values. These assumptions may have been

reasonable in the 1980s, when many mutual savings associations had low net worth and less-than-desirable investment opportunities. However, such assumptions

do not apply to the industry today, and thrifts that go

public should not be valued as if such assumptions remain valid.

(1) V' NPV of Existing Bank NPV of IPO Proceeds

 W

Vr

(1+ d)

 = W

Vr

d

t

t=1

= +

= +

+

∞

∑

(2) V' W P

E = + Vr 



 





(3) V' W P

E

 V'r 

 V' P

E

 r W

 V' W

P

E

 r

= + 



 





− 



 



 



 



 =

=

− 



 



 



 





1

1

(4) 1 P

E

 r > 0

 E

P

r

 d > r

− 



 









 



 >

08 This section borrows from Cassidy (1975), Maksimovic and Unal

(1993), and Unal (1997).

09 Where (P/E) is the ratio of a publicly traded thrifts stock price to

its reported earnings.

10 Table 3 shows the three pricing equations actually used in appraisal

reports submitted to the FDIC and the OTS.
Mutual-to-Stock Conversions

Recent Data on Conversion Returns

The historical single-day returns for standard conversions support the hypothesis that the value of a converting thrift cannot be priced to eliminate all windfall

gains. Single-day returns, or pops, for post-1994 conversions have been significantly higher than those for

conversions during the 19871993 period (see figures 1

and 2). Since 1994, pops have averaged 24 percent (see

table 1). Moreover, since year-end 1996, the lowest

pop observed among 35 conversions has been 26 percent, and the largest pop was 105 percent. A likely explanation for the post-1994 returns is the increase of

preconversion equity in mutual thrifts (see figure 3).

In general, the larger this equity, the greater the firstday price pop will be. The appraisal process and specific pricing equations have not had the intended effect

of minimizing IPO single-day returns. 

True, an extraordinary speculative market in financial-service stocks and regular IPOs may have contributed to these returns. However, when one factors

out an average 0.06 percent daily return on a thrift index11 in recent years and an average 14 percent initial

return for other types of IPOs, mutual-to-stock conversions still appear to have generated remarkable singleday returns. Single-day returns greater than 20 percent

seem even more conspicuous given that appraisers

adjust valuations to account for current market conditions. These results are consistent across differentsized thrifts and are even more dramatic for larger

conversions. In fact, for institutions with over $1 billion

in assets, returns have increased from an average of 17

percent before 1994 to over 30 percent since 1994 (see

table 2)even though at larger institutions capital ratios may be lower. In these cases, bandwagon effects

and general market conditions may play a more important role in a conversions first-day price appreciation. 

Table 1

Single-Day Returns for Mutual-to-Stock

Thrift Conversions, before and after 1994

Single-Day Returns

Statistic 19871993 19951998

Number of Conversions 79 143

Maximum Return 55.00% 105.63%

Minimum Return 13.07% 5.00%

Average Return 16.65% 23.94%

Standard Deviation .1490 .1936

Source: SNL Securities

5

Figure 1

Distribution of Single-Day Returns for

Conversions, 19871993

Figure 2

Distribution of Single-Day Returns for

Conversions, 19951998

-25 -15 -5 5 15 25 35 45 55 65 75 85 95 105 115

0

5

10

15

20

Returns (%)

Frequency

0

10

20

30

40

Frequency

-25 -15 -5 5 15 25 35 45 55 65 75 85 95 105 115

Returns (%)

Figure 3

Mutual Thrift Preconversion Equity Levels

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998

0.03

0.06

0.09

0.12

0.15

Year

Equity Capital Ratio

11 SNL Securities Thrift Index.
FDIC Banking Review

6

Estimating the Factors Most Affecting

Conversion First-Day Returns

To test how different market and valuation factors

are related to conversion single-day returns, two simple

models for analyzing these returns were estimated.

The purpose of this exercise is not to develop a model

for predicting returns but to identify the most important factors affecting them. 

Among the most widely cited factors affecting conversion returns are preconversion equity, market conditions, institution size, size of the offering, and interest

in the subscription. EQUITY is defined here as the ratio of preconversion equity to total assets. These data

are obtained from the Call Report filed before a thrifts

initial public offering date. The larger the EQUITY

ratio, the more we would expect the converted thrifts

market price to rise on the first day of trading, as previously undistributed equity is distributed to initial purchasers.

Favorable market conditions should have a positive

effect on conversion values. A proxy for the returns to

the thrift market, MRET, was constructed by averaging single-day returns to the SNL Thrift Index during

the month in which a thrift converted. A dummy variable, MKT, was used to record whether the general

thrift market was up or down on the day a particular

thrift converted. Thus, MKT has a value of 1 if the

market rose on a thrifts conversion day, and 0 if the

market fell on conversion day. 

The size of an institution, as represented by its asset

base, would also seem to be an important indicator of

how much a converting thrift appreciates when first

traded. A large conversion would generate more investor interest and possibly lead to a more dramatic

pop. For this reason, the model uses total assets to help

explain price appreciation. Since the relationship between the size of an institution and its pop is not likely to be strictly linear, the natural logarithm of assets,

ASSET, is used. 

The size of the offering would seem to have an effect on first-day returns. Therefore, the total gross proceeds, VAL, received during a thrifts subscription

period was taken as a ratio of the thrifts preconversion

equity. A larger VAL would imply a larger pop for the

same reason ASSET has a positive effect on first day

popsmore subscription interest and overall investor

enthusiasm about the offering. Substantial interest in

the stock during subscription increases the likelihood it

will appreciate in the aftermarket. In the past, fully

subscribed and oversubscribed offerings resulted in

heavy aftermarket trading and significant one-day price

appreciation. 

Table 2

Single-Day Returns for Thrift Conversions

by Asset Size, before and after 1994

19871993 19951998

Small Thrifts

Number 41 119

Maximum 55.00% 105.63%

Minimum 13.07% 5.00%

Mean 19.06% 18.70%

Standard Deviation .169 .250

Medium Thrifts

Number 11 15

Maximum 32.50% 83.44%

Minimum 1.47% 0.00%

Mean 16.83% 26.63%

Standard Deviation .105 .220

Large Thrifts

Number 14 9

Maximum 40.00% 72.50%

Minimum 0.00% 6.25%

Mean 17.03% 30.42%

Standard Deviation .134 .240

Note: Large thrifts: Over $1 billion in total assets.

Medium thrifts: Between $500 million and $1 billion in total assets.

Small thrifts: Under $500 million in total assets. 

Table 3

Calculation of Pro Forma Value after Conversion

Price Multiple Symbol

Price-Earnings Ratio (P/E)

Price-Book Ratio (P/B)

Price-Assets Ratio (P/A)

Valuation Parameter

Preconversion Earnings (Y)

Preconversion Book Value (B)

Preconversion Assets (A)

Reinvestment Rate (R)

Estimated Conversion Expenses (X)

Proceeds not Reinvested (Z)

Estimated Employee Stock Ownership 

Plan (ESOP) Borrowings (E)

Cost of ESOP Borrowings (S)

Amortization of ESOP Borrowings (T)

Calculation of Pro Forma Value after Conversion

1. V = (P/E) (Y  R(X+Z)  ES  (1  TAX)E/T  (1  TAX))/

(1  (P/E)R)

2. V = (P/B) (B  X  E) / (1  (P/B))

3. V = (P/A) (A  X) / (1  (P/A))

Source: Attachment IIIA of the OTS Guidelines for Appraisal

Reports for the Valuation of Savings Institutions Converting from the

Mutual to the Stock Form of Organization (Revised October 21, 1994).
Mutual-to-Stock Conversions

7

The proposed model regresses simple one-day returns for thrift i, POPi, on the factors mentioned above:

POPi = α + β1MRET + β2EQUITYi + β3VALi + β4ASSETi + β5MKT + ε i

Standard OLS techniques were used to estimate all regression coefficients with a sample of 124 conversions for

which Call Report data could be linked to market prices of converted institutions. Table 4 provides summary statistics for each of the variables in the model. Thrifts included in the regression averaged $531 million in assets and

converted to stock form between 1994 and 1998. A number of small conversions with incomplete price data and

complex multithrift holding company conversions were excluded from the sample. The regression results are presented in table 5. The estimated coefficients appear in columns (2) and (3). The time dummy variables (Y95Y98)

attempt to explain effects not captured by the other variables. 

Table 4

Descriptive Statistics for Variables Used in OLS Regression

Variable Mean Median Standard Deviation

POP 0.209 0.202 0.139

ASSET $531,387 $142,253 $1,851,084

MRET 0.117 0.107 0.166

EQUITY 0.101 0.093 0.039

VAL 1.603 1.633 0.439

Note: Asset values expressed as thousands of dollars. Log(ASSET) used in regression.

The variable POP is a percentage expressed as a decimal. 

The large and significant coefficient for EQUITY

confirms the belief that preconversion equity is an important factor in determining one-day returns. As expected, the market into which a thrift converts is also

an important factor in predicting POP, a result indicated by the significance of the MRET variable. The

large negative intercept implies that under this model,

negative single-day returns are still possible. These results are consistent with the recent findings of Wilcox

and Williams (1998), mentioned above. When the

time dummy variables were added to the pop model,

the years 1997 and 1998 were statistically significant.

This is not surprising, given the general market exuberance in both years and the fact that only conversions through mid-1998 were included in the data.

Another possible explanation for this result is the large

number of conversions and the resultant increase in investor awareness of the windfall gains accrued in conversions. 

The model shows the relative importance of several

financial and market factors in determining pops, and

it supports the hypothesis that in many thrifts preconversion equity has a significant effect on first-day price

appreciation. However, because of the low R2 value, it

Table 5

Ordinary Least Squares Regression Results

POP POP

Intercept 0.721 0.366

(4.833)* (2.815)*

MRET 0.231 0.154

(3.841)* (2.867)*

EQUITY 1.230 0.694

(4.342)* (2.852)*

ASSET 0.052 0.031

(4.626)* (3.315)*

VAL 0.117 0.071

(5.103)* (3.584)*

MKT 0.045 0.002

(2.154) (0.129)

Y95 . 0.035

(1.485)

Y96 . 0.044

(1.863)

Y97 . 0.146

(4.430)*

Y98 . 0.230

(4.741)*

Number of Observations 124 124

R2 0.370 0.602

Adjusted R 0.344 0.570

F Value 13.99 19.30

Note: t-statistics appear in parentheses. An * indicates that the coefficient is significant at the 1 percent level.
FDIC Banking Review

8

may not be appropriate to view the model as a forecasting model that one can use to predict actual increases in stock prices. 

As discussed in the second section, one can form a

measure of the expected pop by taking the ratio of preconversion equity to the proceeds raised during the

thrifts initial public offering. The amount by which

this expected pop deviates from the observed first-day

return, on average, is presented in table 6 for the years

19941998. This deviation is defined as the observed

pop minus the expected pop. Although some of the

expected price appreciation can be explained by the

factors discussed above, there appears to be a considerable discrepancy between expected and observed single-day price appreciation. On average, the actual

one-day price appreciation is less than the expected

pop. This may be caused by investors who value the

converting thrift as if there were inherent equity in the

institutionleading to a higher appraised value (or a

premium being built into the IPO price) and less price

appreciation. The expected price appreciation presented in table 6 assumes that the appraised value does

not account for this inherent equity but values the return on capital using market discount rates. The result

is a lower initial public offering price and significantly

greater first-day price appreciation. Nevertheless, it remains a mathematical certainty that no matter how the

converting thrift is valued, some price appreciation is to

be expected. 

Conclusion

A total of 815 mutual-form thrifts, holding 16 percent of industry assets, still existed as of June 1998.

Competitive pressures, need for additional capital, and

numerous other factors will force hundreds of thrifts to

convert to stock form in the future. The consistent

market reaction to mutual-to-stock conversions sends

a very important message: the current conversion process is flawed in theory and in practice. Mutual-tostock conversions are inherently different from initial

public offerings by stock-form firms, and policymakers

need to recognize that under the current conversion

process windfall gains cannot be entirely eliminated. If

the goal of public policy is to minimize potential windfall gains to individual investors, alternative means of

distributing the net worth of a thrift should be considered. Otherwise, regulators should let the market reduce the size of pops by incorporating into the initial

public offering price the knowledge that they will occur.

Table 6

Average Deviations between Expected and

Actual Pops, 19941998

Actual Expected

Year Pop Pop Deviation

1994 19.2% 46.7% 27.5%

1995 17.0 60.9 43.9

1996 16.1 46.0 29.9

1997 38.8 81.8 43.0

1998 55.2 86.6 31.4
Mutual-to-Stock Conversions

9

References

Barry, C., R. Gilson and J. R. Ritter. 1998. Initial Public Offerings and Fraud on the Market.

Unpublished Texas Christian University, Stanford University, and University of

Florida Working Paper.

Barth, James R., Dan Brumbaugh, Jr., and Allan W. Kleidon. 1994. Windfall Gains in

Mutual-to-Stock Conversions of Thrift Institutions. Challenge JulyAugust: 4349.

Cassidy, J. Henry. 1975. Conversion of S&Ls from Mutual to Stock Associations: An

Analytic Examination of the Efficiency Question. Office of Economic Research,

Federal Home Loan Bank Board, Working Paper No. 57.

Eccles, Jennifer, and John P. OKeefe. 1995. Understanding the Experience of Converted

New England Savings Banks. FDIC Banking Review 8, no. 1: 118.

Jordan, Bradford D., James A. Verbrugge, and Richard M. Burns. 1986. Returns to Initial

Shareholders in Savings and Loan Conversions: Evidence and Regulatory

Implications. Federal Home Loan Bank Board, Invited Research Working Paper

No. 55.

Maksimovic, Vojislav, and Haluk Unal. 1993. Issue Size Choice and Underpricing in

Thrift Mutual-to-Stock Conversions. Journal of Finance 48, no. 5: 165992.

Masulis, W. Ronald. 1987. Changes in Ownership Structure: Conversions of Mutual

Savings and Loans to Stock Charter. Journal of Financial Economics 18: 2959.

Office of Thrift Supervision. 1994. Guidelines for Appraisal Reports for the Valuation of

Savings Institutions Converting from the Mutual to the Stock Form of

Organization. Revised. 

Unal, Haluk. 1997. Regulatory Misconceptions in Pricing Thrift Conversions: A Closer

Look at the Appraisal Process. Journal of Financial Services Research 11, no. 3: 239-

54.

Wilcox, James A., and Zane D. Williams. 1998. Predicting Excess Returns with Public and

Insider Information: The Case of Thrift Conversions. Research Program in

Finance, Working Paper RPF-283, University of California at Berkeley.
FDIC Banking Review

The Influence of Examiners

and Auditors on

Loan-Loss Recognition

by Drew Dahl, John P. OKeefe, and Gerald A. Hanweck*

The financial statements of a bank are studied

by a number of people both inside and outside

the bank (bank management, bank creditors

and owners, state and federal regulators and industry

analysts). Yet, the usefulness of these financial statements depends entirely on their scope, accuracy and

truthfulness. And because bank managers have some

discretion in how they recognize and record certain

financial transactions, the financial statements may not

be fully accurate. Moreover, managerial discretion is

difficult to discern because valuation of a banks principal asset, the loan portfolio, is inherently subjective.

The subjectivity reflects the fact that banks generate

private information about loan customers1information not typically made available to others.

To constrain the opportunistic use of discretionary

accounting practices, which may have adverse consequences, the banking industry has made widespread

use of external monitors. These monitors include government agencies, which conduct financial safety-andsoundness examinations, and private firms, which

conduct financial audits. Walter (1991) contends that

when financial statements come out soon after a visit

from examiners, they are more likely to accurately

assess financial condition and realizable value; and the

U.S. General Accounting Office (GAO) (1991) states

that independent financial audits can improve the reliability of financial statements. Both Walter and the

GAO suggest that external monitors can help verify

whether banks are telling the truth in their published

financial reports, thereby reducing the publics uncertainty about banks financial condition.

The purpose of this article is to carry those conclusions further and determine whether examiners and

auditors influence two key aspects of banks financial

reporting: the timing of loan-loss recognition (chargeoffs) and the provisioning for loan losses. The focus on

loan losses is important partly because the Basle

Committee on Bank Supervision has recently proposed

to standardize the accounting treatment of loan losses

around the world, and stated that accounting rules that

do not require timely loan-loss recognition by bankers

may give them an unfair competitive advantage.2

The loan-valuation process in banking consists of

the interrelationships among provisions for loan losses,

allowances for loan losses, and recognition of loan losses. Banks make provisions (an expense) for expected

loan losses out of current revenue; and the provisions

fund the allowance, which is a reserve against which

losses eventually are charged. The establishment of

provisions, allowances, and losses, some argue, can be

subject to misguided optimism or deliberate misrepresentation.3 And the optimism may be related to

bank managers incentives to defer recognition of prob10

* Drew Dahl is Associate Professor of Finance, Department of

Business Administration, Utah State University; John P. OKeefe is

Chief of the Economic Analysis Section, Division of Research and

Statistics, Federal Deposit Insurance Corporation; and Gerald A.

Hanweck is Professor of Finance, School of Management, George

Mason University.

1 See Ross (1989).

2 American Banker (1998), p. 2.

3 See U.S. Department of the Treasury (1991).
Loan-Loss Recognition

11

lems, thereby forestalling regulatory sanctions4 or

enhancing perceived managerial performance.5

We contend that if there are systematic differences

between examined banks and unexamined banks in

the timing of the recognition of loan losses, these differences would constitute evidence that examiners call

risky loans to the attention of bank management.

Similarly, we contend that if there are systematic differences between audited banks and unaudited banks

in the timing of loan-loss recognition, these differences

would constitute evidence that auditors may reasonably be expected to detect material misstatements of

financial information. In other words, a finding that

incidence of examination or audit is associated with

more timely loss recognition would be consistent with

a capacity of examiners or auditors to limit managerial

incentives to delay loss recognition as long as possible.6

Because the provision for loan losses is closely related

to recognition of losses, and more generally, to a willingness to recognize problems, parallel arguments

apply to loss provisioning.

This article analyzes the effect of examinations and

audits on loan-loss recognition for two categories of

loans and on total provisions for loan losses for nearly all

commercial and savings banks between 1987 and

1997.7 The two loan categories are commercial and

industrial loans and loans secured by real estate. These

two loan categories constitute the majority of banks

total loan portfolios: between 1987 and 1997, commercial and industrial loans averaged 18 percent of total

loans and leases, and real-estate loans averaged 50 percent. More important, these two loan categories have

different characteristics; thus, if empirical tests show

that external monitors do influence loan-loss recognition, the results will be stronger. 

The statistical tests treat recognition of bank loan

losses as a function of (a) concurrent changes in nonperforming loans and (b) the lagged amounts of performing loans, nonperforming loans, allowances for

loan losses, and other variables. Because loan-loss provisioning is driven primarily by the same set of factors

that drives loan-loss recognition, parallel tests for provisioning are used.

External Monitoring

The primary responsibility for a banks financial

reporting lies with the members of senior management. Their accounting practices are overseen by regulators, auditors, and members of boards of directors

(particularly those who are on audit committees). In

the United States, more than in other countries, external monitoring in the banking industry has traditionally emphasized government regulators rather than

external auditors.8 In addition, examiners and auditors

have traditionally been wary of each other. Recently,

however, attempts have been made to foster greater

cooperation, partly at the suggestion of the National

Commission on Fraudulent Financial Reporting (the

Treadway Commission), which encouraged banks to

give auditors access to examiner reports and to give

examiners access to audit information.9

Monitoring by Government Regulators

All banks are subject to periodic on-site examinations, which include evaluation of trust departments,

electronic data processing systems, compliance with

consumer protection laws, and overall financial safety

and soundness. In this study we focus only on on-site

safety-and-soundness examinations. During these

examinations, examiners assess and rate a banks capital adequacy, asset quality, management, earnings, liquidity, and sensitivity to market risk (hence the

acronym CAMELS ratings). A composite safety-andsoundness rating (CAMELS rating) is assigned to the

bank, ranging in integer value from 1 (for banks whose

performance is significantly higher than average) to 5

(for banks whose performance is severely deficient and

in need of immediate corrective actions). Depending

upon whether a bank has a state or federal charter,

bank examinations are conducted by the Federal

Deposit Insurance Corporation (FDIC), state banking

commissions, the Office of the Comptroller of the

4 See Beatty, Chamberlain, and Magliolo (1995), Collins, Shackelford,

and Wahlen (1995), GAO (1990) and Moyer (1990).

5 See U.S. Department of the Treasury (1991) and Dahl (1993). On the

other hand, bank managers may accelerate, rather than defer, recognition of problems for tax purposes. The application of this incentive to our study, however, is limited by the finding of Collins,

Shackelford, and Wahlen (1995) that bank managers are willing to

incur greater tax costs, if necessary, to report lower charge-offs.

6 See GAO (1991) and Antle and Nalebuff (1991).

7 A small number of banks were excluded from the study because of

incomplete financial data, or because they were recently chartered,

or had rapid asset growth (annual asset growth rates over 100 percent). As a result, approximately 96 percent of all commercial and

savings banks were included in the empirical tests presented in this

study.

8 See Group of Thirty (1994).

9 Black (1990), in reporting the results of a pilot program to improve

communication between examiners and auditors, stated that cooperation allowed auditors to evaluate loan quality more efficiently, while

examiners offered to include in their credit review sample loans the

auditors had identified for testing.
FDIC Banking Review

12

Currency (OCC), or the Federal Reserve Banks.10 We

include all examinations conducted by state and federal bank regulators. Under the FDIC Improvement Act

of 1991 (FDICIA), examinations occur annually for

banks with assets of $100 million or more, as well as for

smaller banks with less than superior examination ratings. For smaller banks with superior ratings the intervals between examinations can be as long as 18

months.11

Examiners focus on critical, high-risk areas, particularly those that reflect loan quality. They question

bank personnel and review accounting records, loan

documentation, financial data, and bank operating policies. Such activities are valuable if examiners obtain

new information about a bank, process data advantageously, or are more likely than bankers to reveal what

they find (because if a bank should fail, the governments losses would be greater than the losses of bank

management).12

Gilbert (1993), who found that examiners identify

problems that were not reflected in prior financial

statements, has provided support for the notion that

bank examinations do cause changes in loan valuation.

This finding suggests that examiners force the bank

to restate loans and [allowances for losses] when the

banks values deviate from the best estimates of regulators. It is consistent with the contention of the GAO

(1991) that the accuracy of accounting data in banking

is predicated on the recency of examination. In addition it is consistent with a variety of evidence, both

anecdotal and statistical, that loan-loss recognition during the so-called credit crunch of the late 1980s was

influenced by changes in examination standards.13

Monitoring by Private Auditors

Whereas examinations are required by law and regulation, banks themselves may demand external auditing. Such demands depend largely on a banks need

for an independent assessment of its financial reports.

These assessments may be required by bank owners,

creditors, and boards of directors, as well as by bank

regulators. Auditors have been described as the eyes

and ears of a banks board of directors and shareholders (Seidman (1986)). As such, they have different

monitoring incentives from examiners, who represent

the interests of bank regulators and of the deposit

insurer. Compared with examiners, auditors are likely

to know more about accounting issues (Black (1990))

but less about the banking industry and current regulatory concerns.

An audit involves examination of the financial statements, accounting records, and other supporting evidence of a bank of sufficient scope to enable the

auditor to express an opinion on the banks financial

statements as to their presentation in accordance with

generally accepted accounting principles.14 (FDIC

(1989)). The FDIC, along with the Federal Reserve

Board and the OCC, identifies procedures that are to

be undertaken during an audit, including detailed

reviews of loans with respect to their documentation,

concentrations, repayment potential, and growth. The

procedures reflect guidelines promulgated by the

American Institute of Certified Public Accountants

(1986). Auditors objectively evaluate the reasonableness of the values management presents in financial

statements, by assessing the policies, processes, and

procedures management used to identify impaired

assets. They also undertake specific testsasset by

assetto determine likely repayment and potential

loss. According to Antle and Nalebuff (1991) their

assessments are generally thought to be conservative.

Although traditionally banks were not subject to a

uniform requirement for an external audit, most large

banks (and many smaller ones) received audits, partly

as a result of specific requirements of the various regulatory agencies.15 The Financial Institutions Reform,

Recovery, and Enforcement Act of 1987 (FIRREA),

intensified the role of auditing in bank supervision, not

only by expanding the enforcement authority of bank

regulators to include institution-affiliated parties, such

as accountants, but also by requiring banks to provide

their auditors with copies of various supervisory

reports. These components of FIRREA appear to

have increased auditors access to confidential bank

information while simultaneously subjecting them to

greater regulatory oversight. 

10 To avoid excessive overlap of work, regulators normally alternate

examinations between federal and state examiners. Both groups,

however, may examine financially weak banks annually.

11 The Riegle Community Development and Regulatory Improvement Act of 1994 increased the asset size of banks qualifying for the

exclusion from annual examination from $100 million to $250 million.

12 See Berger and Davies (1994).

13 See Berger and Udell (1994).

14 FDIC (1998), p. 5302.

15 Audits have been required for troubled banks (as a result of ceaseand-desist orders), newly chartered national banks (OCC), newly

insured banks (FDIC), large bank holding companies (Federal

Reserve Banks), and state-chartered banks that are subject to the

reporting requirements of the Securities Exchange Act of 1934.
Loan-Loss Recognition

13

Four years after passage of FIRREA, FDICIA further expanded the role of private-sector auditing,

requiring that banks with total assets of $500 million or

more have annual independent audits (audits of affiliated banks can be satisfied by audits of the parent

holding companies). The audits must attest to managements responsibilities for preparing financial statements and for maintaining adequate internal control

structures for financial reporting. In an apparent

attempt to strengthen auditor independence, the Act

required banks to notify regulators (and explain) when

they changed auditors.16 The overall effect of

FDICIAs audit requirements has been described as

[deputizing] independent auditors to assist bank

examiners in identifying problem areas that examiners themselves should address.17

Further expansions of auditing are implicit in various proposals for bank reform that would reduce the

role of federal deposit insurance (see Berger and

Davies (1994)). These proposals reflect the closer relationship between regulators and auditors that exists in

other countries, as well as the greater utilization of

auditors. In Canada, Denmark, New Zealand,

Switzerland, and the United Kingdom, for instance,

external auditors are delegated extensive responsibilities for on-site bank monitoring and in some cases have

replaced regulatory supervisors.18

The ongoing expansion of bank auditing has

occurred despite underlying doubts about auditor

effectiveness. Auditors have difficulty measuring the

amounts of loan losses (Moyer (1990)), have been sued

for negligence after banks failed, have been forced to

reissue their reports because of the findings of regulators (Black (1990)), and have agreed with overstatements of asset values made by failed banks (GAO

(1990)).19 Part of the reason for such audit failures may

be that auditors do not always exert sufficient independence, whether because of privity of client information, longstanding audit relationships, economic

pressure to maintain clients, opinion shopping by

bankers, or the hiring of audit personnel by clients.20

Methodology

Our empirical tests focus on the recognition of loan losses embedded within a banks loan portfolio, which are

defined as gross losses for bank i in year t. These losses are presumed to be at least partly discretionary and therefore subject to manipulation.21 Such manipulation may involve concealing the extent of bad loans by extending

the term of loans, lending new money so that insolvent borrowers can keep up the pretense of being current on

their loans or weakening covenants so as to avoid recognizing default.22 We examined these issues separately for

commercial and industrial loan losses and for real-estate loan losses, using the following behavioral model: 

Loan losses(i,t) = c0 + c1*Performing loans(i,t – 1) + c2*Nonperforming loans(i,t – 1) + c3*Changes

in nonperforming loans (i,t – 1) + c4*Logarithm of assets(i,t – 1) + c5*CAMELS rating(i, t – 1) +

c6*Examination interval(i,t – 1) + c7*Equity(i, t – 1) + c8*Allowance(i,t – 1) + c9*Monitoring(i,t) +

Error term(i,t)

Loan losses are defined as gross loan charge-offs on commercial and industrial loans or real-estate loans. Loan

losses are measured as a percentage of gross loans and leases and should vary in value between zero and one. We,

therefore, estimate the behavioral model using a technique specifically designed for data of this type (censored

data)Tobit estimation. 

Variables

The losses, performing loans, nonperforming loans,

and changes in nonperforming loans are all defined to

match the loan category analyzedcommercial or real

estate. We hypothesize that loan losses in any given

year are determined by banks loan quality and other

relevant financial measures as of the start of the year.

We therefore relate losses at each year-end to the

lagged values of loss determinants, where lagged

values are all measured for each bank at the end of the

16 For a summary of accounting and auditing reforms under FDICIA,

see Fisher (1993).

17 Review of Banking and Financial Services (1992), p. 75.

18 See U.S. Department of the Treasury (1991) and Group of Thirty

(1994).

19 Note also that the massive problems of savings-and-loan associations in the 1980s occurred despite mandatory external audits for

virtually all institutions (U.S. Department of the Treasury (1991)).

20 See GAO (1991).

21 See Collins, Shackelford, and Wahlen (1995), Beatty, Chamberlain,

and Magliolo (1995), and Darin and Walter (1994).

22 See Rajan (1994).
FDIC Banking Review

14

year before the year over which loan losses are

incurred. To measure loan quality we include levels of

both performing and nonperforming loans. The lagged

level of performing loans is intended to account for

the effects on subsequent losses that are associated

with generic lending risk rather than the risk of nonperformance of specific loans.23 Nonperforming

loans are likely to be the main source of losses and

consist of nonaccrual loans plus loans that are 90 days

or more past due on interest and principal repayment.

The specification of this variable presumes that a

portion of loan losses is preceded by changes in nonperforming loans. The concurrent change in nonperforming loans is included to reflect recent

improvement, or deterioration, in loan quality during a

particular year. The use of this variable, which is measured as the change from the end of year t  1 to the

end of year t, presumes that nonperforming loans are

not subject to extensive manipulation. Wahlen (1994)

and Darin and Walter (1994), among others, contend

that nonperforming loans are largely exogenous.24

The model also includes other variables that may

affect the extent of losses given the quality of the

banks loans. The logarithm of lagged total assets is

included to account for the possibility that loan losses

differ systematically across bank size groups. We anticipate that losses will also be related to examiners

assessments of financial condition and therefore have

included the banks composite safety-and-soundness (CAMELS) rating as of the start of the year.

Since examiners target financially weaker banks for

more frequent examinations, the inclusion of the

examination rating is therefore intended also to control,

in part, for the influence of condition upon the incidence of examination. (This issue is discussed at

greater length in the section below on sample selectivity.) The time interval since the last examination is

included to account for the possibility that the accuracy of financial reports is lessened by longer examination intervals. The examination interval is measured

by the ratio of the number of days since a banks last

examination (before the year over which losses are

measured) to the mean interval between examinations

for all banks within that banks CAMELS rating

group. Lagged equity capital accounts for the possibility thatbecause of regulatory or other considerationscapitalization may influence the extent to

which banks are able, or willing, to recognize loan

losses. A banks lagged allowance for loan losses

accounts for the effect of accumulated prior provisions

for loan losses on subsequent loss recognition.

Following Wahlen (1994), we hypothesize that larger

lagged allowancesthat is, larger accumulated provisions in prior periodsare associated with larger loan

losses in the current period. All financial terms are

divided by the banks total gross loans and leases to

mitigate the effects of potential heteroskedasticity.

To account for external monitoring, a dummy variable, set equal to one if monitoring occurs and to zero

otherwise, is used to represent the incidence of monitoring. 

We anticipate that the ability to use discretion in loss

recognition will vary across types of loansthat banks

will have a greater ability to use discretion in loss recognition for commercial and industrial loans than for loans

secured by real estate (real-estate loans). This is

because, as reported to bank regulators in quarterly

financial statements (Call Reports), commercial and

industrial loans represent a mix of many types of loans

to business, with varying terms and collateral (if any).

Real-estate loans, however, are relatively more homogeneous and more able to reflect any industry standards for loss recognition.

The behavioral model we used to measure the influence of examinations and audits upon loan-loss provisioning is similar, but not identical. Banks provide

regulators with data on total loan-loss provisions but

not on provisions for losses by type of loan. Therefore,

in the provisioning model we use total performing and

nonperforming loans as explanatory variables, along

with the other variables shown in the behavioral model above. In addition, in the provisioning model the

financial terms are divided by total assets rather than

by gross loans and leases, since the shares of total performing and nonperforming loans would necessarily

sum to one if they had been scaled by total loans.

Sample Bias 

In the approach described above a behavioral model

of loan losses (or provisioning) included a dummy variable indicating whether a bank experienced external

monitoring of a particular type. The results of this

approach, however, may be skewed if the incidence of

23 See Walter (1991), Turman and Beaver and Engle (1996).

24 Some capacity for manipulation is suggested by the finding of

Gilbert (1993) that examinations influence the levels of nonperforming loans and by the existence of performing nonperforming

loans, as described by Darin and Walter (1994). We note that alternative versions of our model, which excluded the change in nonperforming loans, did not materially affect our reported results.
Loan-Loss Recognition

15

monitoring is influenced by factors similar to those that

influence the timing of loan-loss recognition (as well as

provisioning). If this skewing does occur, the sample of

banks for which examinations or audits take place during any given period may not be representative of the

industry. Moreover, the sample may be biased toward

banks with certain characteristics that are related to

loan losses or loss provisions. Hence, any relationships

between the incidence of examination or audit and

loan losses (provisions) could have more to do with

what drives examination or audit frequency than with

the actions of examiners or auditors themselves. 

In the case of examinations, this bias may occur if

examinations are triggered by factors similar to those

that determine loan losses (provisions), even though it

is regulators and not bankers who determine the timing of examinations. In fact, we anticipate that such a

bias is present among examined banks. It is well

known that all bank regulators target financially weaker banks for more frequent examinationsdoing so

allows regulators to use limited examination resources

where they can do the most good. If this is the case,

the sample selection bias would cause examined banks

to have high reported loan losses (provisions) relative to

unexamined banks, but not because of the effect of

examinersrather, the reason would be that examined

banks are in worse financial condition than unexamined banks. In the case of audits, the bias may occur if

a significant number of banks choose to undergo audits

when they believe they are in good financial condition

or are otherwise capable of absorbing any unforeseen

contingencies that the auditors may discover. Thus,

audited banks may have low reported losses or low loss

provisions not because of the effect of auditors but

because they are in better financial condition than the

typical unaudited bank during that period.

To address these potential biases, in part, we first

estimated separate models for commercial and industrial loan losses and for real-estate loan losses. These

biases are presumed to be general and to affect all loan

categories, so if evidence indicates that monitoring

makes a difference in one loan category but not the

other, this outcome would probably not be the result of

sample biases. Second, we include in the behavioral

model three terms to partly control for potential targeting of monitoring: the CAMELS ratings, time since

prior examination, and bank size variables. As discussed above, all three variables may be associated

with the selection bias for exams or audits.

Data and Sample

Our sample consists of annual observations of nearly all commercial and savings banks between 1987 and

1997. A small number of banks were excluded from

the study because of incomplete financial data. In

addition, we excluded recently chartered banks and

banks with annual asset growth rates over 100 percent.

As a result, approximately 96 percent of all commercial

and savings banks between 1987 and 1997 were included in the empirical tests and tables presented in this

study.

Table 1 shows the frequency of examination for the

banks in the sample. The overall proportion of banks

examined increased from 71 percent in 1987 to 94 percent in 1993, before declining to 76 percent in 1997.

The changes in examination frequency appear to

reflect changes in regulatory requirements for examination frequency and in banks condition over the sample period. Regulatory requirements for examination

frequency became more stringent under FDICIA, as

discussed above. In addition, regulatory policy and

FDICIA both allow (within limits) examinations to be

less frequent, the better the banks financial condition.

Hence, the widespread economic recovery after the

199192 recession and the good performance of the

banking industry overall since 1993 have permitted

some lengthening of the examination intervals. 

Table 1

Examination Incidence

Not Examined Examined

Year (Number) (Percent) (Number) (Percent) Total

1987 3,911 29% 9,551 71% 13,462

1988 3,791 29 9,128 71 12,919

1989 3,155 25 9,454 75 12,609

1990 2,408 20 9,851 80 12,259

1991 1,897 16 10,017 84 11,914

1992 1,740 15 9,840 85 11,580

1993 695 6 10,518 94 11,213

1994 951 9 9,836 91 10,787

1995 1,579 15 8,657 85 10,236

1996 1,606 16 8,162 84 9,768

1997 2,167 24 7,049 76 9,216

Total 23,900 19% 102,063 81% 125,963

Note: The columns identify the number of banks examined and not

examined by year-end (and as a percent of banks reporting).
FDIC Banking Review

16

Since 1987, banks have been required to report

auditing activity in their quarterly Call Reports,25 using

the following categories, or levels of auditing services:

n Independent audit of the bank under generally accepted auditing standards conducted by a

certified public accounting firm;

n Independent audit of the banks parent holding company under generally accepted auditing standards conducted by a certified public

accounting firm;

n Directors examination of the bank conducted

in accordance with generally accepted auditing

standards by a certified public accounting firm; 

n Directors examination of the bank conducted

by other external auditors; 

n Review of the banks financial statements by

external auditors; 

n Compilation of the banks financial statements

by external auditors; 

n Other audit procedures (excluding tax preparation work); and 

n No external audit work.

For the purpose of this study we define an audit

as one that satisfies the conditions of the first category

abovethat is, an independent audit of the bank

under generally accepted auditing standards by a certified public accounting firm. Table 2 shows the incidence of audit, by year, for the banks in our sample.

For the entire sample period, an average of approximately 40 percent of all banks experienced audits.

The frequency of audit has been relatively stable over

time, with a slight downward trend in recent years.

Tables 1 and 2, together indicate that banks are subject to extensive and overlapping monitoring by examiners and auditors. Although the overlap of monitoring

responsibility appears consistent with the objectives of

FIRREA and FDICIA, which have increased both the

frequency of examinations and the intensity of auditing, it suggests potential redundancy.

26 As pointed out

by Berger and Davies (1994), examiners and auditors

may play much the same role in encouraging firms to

report more accurately when an examination or audited report is conducted verifying the accuracy of these

reports.

Estimation Results

We next present the results of estimations of the

behavioral model. We first discuss estimates of the

effects of audits and examinations on commercial and

real-estate loan-loss recognition. Second, we present

parallel tests for auditor and examiner influence on

loan-loss provisioning. Finally, we present our conclusions.

Commercial and Real-Estate Loan Losses

The results of Tobit estimation of models of the

determinants of commercial and industrial loan losses

are presented in table 3 (effects of examinations) and

table 4 (effects of audits). Similarly, estimates of models for real-estate loan losses are given in table 5

(effects of examinations) and table 6 (effects of audits).

Since these two models are similar in many important

ways, results for both of them are discussed jointly.

The estimation periods shown in these tables indicate

the year-ends for which loan losses occur. For example,

in the 1987 estimation, loan losses over calendar year

1987 are related to performing and nonperforming

loans, and other lagged terms, reported at year-end

1986.

In all of the estimated loan-loss equations (tables

36), the coefficients on the nonperforming loan variables were significantly and positively related to loan

losses. The same was true for the performing loan variables, although the effect was smaller in magnitude.

For example, a 100 percent increase in the proportion

of nonperforming commercial loans in 1987 (table 3)

Table 2

Auditing Incidence

Not Audited Audited

Year (Number) (Percent) (Number) (Percent) Total

1987 8,056 60% 5,406 40% 13,462

1988 7,639 59 5,280 41 12,919

1989 7,484 59 5,125 41 12,609

1990 7,246 59 5,013 41 12,259

1991 7,079 59 4,835 41 11,914

1992 6,891 60 4,689 40 11,580

1993 6,690 60 4,523 40 11,213

1994 6,562 61 4,225 39 10,787

1995 6,348 62 3,888 38 10,236

1996 6,141 63 3,627 37 9,768

1997 5,785 63 3,431 37 9,216

Total 75,921 60% 50,042 40% 125,963

Note: The columns identify the number of banks audited and not audited by year-end (and as a percent of banks reporting).

25 Banks file Call Reports, also known as Reports of Income and

Condition, with their primary federal regulators at each quarter

end.

26 See Group of Thirty (1994) and CPA Journal (1993).
would have led, on average, to an increase in loan losses (as a percentage of gross loans and leases) of 29

percent, while the same proportional increase in performing commercial loans would have led to an

increase in loan losses of 1.2 percent. 

Since all financial terms in tables 36 are computed

as a percentage of a banks gross loans and leases, the

proportion or share of a banks total loan portfolio composed of commercial and industrial loans is simply the

sum of its performing and nonperforming commercial

and industrial loans. Similarly, the share of total loans

comprised of real-estate loans is the sum of performing

and nonperforming real-estate loans. Therefore, the

effect on loan-loss recognition of an increase in the

share of total loans comprised of commercial loans,

holding constant the mix of performing and nonperforming commercial loans, is given by the sum of the

coefficients for performing and nonperforming commercial loans. For example, the effect on commercial

and industrial loan-loss recognition of an increase in the

share of commercial loans in 1987 is estimated as

0.30099 (the sum of 0.0116 and 0.2983). The effects on

real-estate loan-loss recognition of an increase in the

share of total loans comprised of real-estate loans can

be found similarly by the sum of the coefficients for

performing and nonperforming real-estate loans.

Note that the relative effect of an increase in performing versus nonperforming loans differs considerably between commercial and industrial loans and

real-estate loans. For commercial and industrial loans,

the effect of nonperforming loans in generating loan

losses is approximately 20 times greater than the effect

of performing loans. For real-estate loans, by comparison, nonperforming loans are approximately 100 times

Table 3

Tobit Estimation of Commercial and Industrial Loan Charge-off Determination

Exam Effects for All Banks, Audited and Unaudited

Estimated Coefficients (Standard Errors)

Explanatory Variable 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Intercept 1.1897* 0.937* 0.6637* 0.1045 -0.313* -0.089 0.0463 0.0603 0.4541* -0.2376 -0.0459

(0.1544) (0.1371) (0.1214) (0.1031) (0.8890) (0.0829) (0.0810) (0.1291) (0.1046) (0.1636) (0.1487)

Performing C&I loans 0.0116* 0.0134* 0.0115* 0.0093* 0.0112* 0.0085* 0.0062* 0.0059* 0.0054* 0.005* 0.0054*

(0.0008) (0.0008) (0.0007) (0.0006) (0.0006) (0.0006) (0.0005) (0.0006) (0.0005) (0.0005) (0.0005)

Nonperforming C&I loans 0.2983* 0.2581* 0.2488* 0.2483* 0.2571* 0.2224* 0.2392* 0.2556* 0.2221* 0.2991* 0.2447*

(0.0067) (0.0075) (0.0067) (0.0066) (0.0047) (0.0036) (0.0062) (0.0093) (0.0073) (0.0081) (0.0066)

Changes in nonperforming 0.1292* 0.096* 0.0995* 0.1266* 0.1074* 0.0827* 0.1771* 0.1168* 0.1134* 0.1316* 0.108*

C&I loans (0.0069) (0.0077) (0.0071) (0.0065) (0.0064) (0.0064) (0.0063) (0.0098) (0.0072) (0.0058) (0.0066)

Logarithm of assets -0.0962* -0.0542* -0.014 0.0241* 0.016* 0.007 0.0189* 0.0341* 0.0153* 0.0159* 0.0221*

(0.0093) (0.0090) (0.0073) (0.0063) (0.0061) (0.0054) (0.0046) (0.0058) (0.0041) 0.0047 (0.0043)

LCAMEL1 -0.5761* -0.9175* -1.0145* -0.7713* -0.2451* -0.245* -0.5131* -0.7524* -0.8625* -0.1154 -0.4057*

(0.1158) (0.0985) (0.0900) (0.0751) (0.0662) (0.0620) (0.0607) (0.1083) (0.0930) (0.1539) (0.1401)

LCAMEL2 -0.5332* -0.835* -0.8962* -0.6418* -0.1216 -0.1378 -0.4394* -0.6625* -0.7895* -0.0717 -0.3452

(0.1128) (0.0946) (0.0880) (0.0732) (0.0634) (0.0601) (0.0595) (0.1072) (0.0927) (0.1536) (0.1399)

LCAMEL3 -0.3481* -0.6863* -0.6497* -0.4499* 0.0161 0.01314 -0.2865* -0.5481* -0.6414* 0.0469 -0.1382

(0.1123) (0.0938) (0.0884) (0.0740) (0.0640) (0.0610) (0.0606) (0.1084) (0.0941) (0.1555) (0.1418)

LCAMEL4 -0.1998 -0.3599* -0.4* -0.2542* 0.1462 0.1702* -0.1143 -0.391* -0.4423 0.2149 0.0008

(0.1135) (0.0958) (0.0910) (0.0766) (0.0672) (0.0634) (0.0636) (0.1128) (0.0993) (0.1641) (0.1544)

Exam interval -0.06* 0.048* 0.025* 0.0071 0.003 0.0038 -0.0012 -0.0161 0.0177 0.0053 -0.005

(0.0121) (0.0107) (0.0088) (0.0075) (0.0074) (0.0068) (0.0056) (0.0087) (0.0083) (0.0098) (0.0090)

Equity -0.0054* -0.006* -0.0007* -0.0007* -0.0031* -0.0024* -0.001* -0.0014* -0.0001 -0.0015* -0.001*

(0.0008) (0.0008) (0.0002) (0.0002) (0.0005) (0.0005) (0.0003) (0.0004) (0.0001) (0.0002) (0.0003)

Loan-loss allowance 0.2587* 0.2734* 0.144* 0.1168* 0.1092* 0.0536* 0.0382* 0.0432* 0.0324* 0.0388* 0.0449*

(0.0108) (0.0094) (0.0080) (0.0073) (0.0055) (0.0032) (0.0039) (0.0062) (0.0041) (0.0051) (0.0059)

Monitor (exams) 0.0912* 0.026 0.0813* 0.0664* 0.0866* 0.0776* 0.0563 0.0394 0.0155 -0.0016 0.0242

(0.0240) (0.0233) (0.0206) (0.0195) (0.0206) (0.0186) (0.0238) (0.0256) (0.0149) (0.0170) (0.0134)

-2xlog of likelihood -19,568 -17,874 -15,524 -13,542 -12,951 -11,004 -8,754 -9,716 -6,722 -7,241 -5,751

Pseudo R squared 13.90% 13.80% 13.10% 13.40% 12.40% 12.60% 15.00% 8.00% 12.80% 10.90% 14.60%

Number of observations 13,071 12,581 12,316 11,948 11,604 11,251 10,858 10,405 9,874 9,418 8,893

Note: An asterisk indicates significance at the 1 percent confidence level.

Loan-Loss Recognition

17
more effective in generating loan losses than are performing loans. These results confirm our expectations

that the characteristics of real-estate lending are considerably different from those of commercial lending

and that analyzing each type separately allows for more

efficient estimation of the effects of examination and

audit on each.

Other results for the loan-loss model also confirm

our prior expectations. Changes in the level of nonperforming loans, measured concurrently with loan

losses, were significantly and positively related to loan

losses for both commercial and industrial loans and

real-estate loans. The effect of bank size, as measured

by the lagged logarithm of assets, was also generally

significant and positively associated with loan losses. A

relatively consistent relationship is found between loan

losses and banks composite CAMELS rating, as measured by dummy variables indicating whether the

CAMELS rating was 1, 2, 3, or 4 at the start of each

year (a dummy for CAMELS ratings of 5 was excluded

from the models in order to avoid the problem of perfect collinearity between these five dummy variables).

Tables 36 show that lower losses are associated with

better CAMELS ratingsthe dummy variable indicating a CAMELS rating of 1 (LCAMEL1) is usually significant and negative. The CAMELS rating result may

be because of a combination of higher embedded losses on loans among poorly rated banks (as recognized by

examiners) and the targeting of examinations at the

weakest banks. Further, the effect of examiner ratings

on loan losses differs by loan type, that is, it tends to be

greater for commercial and industrial loans than for

real-estate loans. This result may stem from examiners greater sensitivity to commercial and industrial

lending, given the greater complexity of these portfolios, than to real-estate portfolios, which tend to be

Table 4

Tobit Estimation of Commercial and Industrial Loan Charge-off Determination

Audit Effects for All Banks, Examined and Unexamined

Estimated Coefficients (Standard Errors)

Explanatory Variable 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Intercept 1.2869* 0.9537* 0.7485* 0.1728 -0.2405* -0.0293 0.0915 0.0896 0.4379* -0.2291 -0.0412

(0.1527) (0.1341) (0.1190) (0.1005) (0.0866) (0.0803) (0.0776) (0.1263) (0.1033) (0.1635) (0.1488)

Performing C&I loans 0.0116* 0.0133* 0.0114* 0.0092* 0.011* 0.0084* 0.0062* 0.0058* 0.0054* 0.005* 0.0054*

(0.0008) (0.0008) (0.0007) (0.0006) (0.0006) (0.0006) (0.0005) (0.0006) (0.0005) (0.0005) (0.0005)

Nonperforming C&I loans 0.2986* 0.2584* 0.2487* 0.2485* 0.2577* 0.2228* 0.2391* 0.2559* 0.2209* 0.2987* 0.245*

(0.0067) (0.0075) (0.0067) (0.0066) (0.0047) (0.0036) (0.0062) (0.0093) (0.0072) (0.0081) (0.0066)

Changes in nonperforming 0.129* 0.0958* 0.0995* 0.1268* 0.1077* 0.0828* 0.1772* 0.1172* 0.113* 0.1312* 0.1081*

C&I loans (0.0069) (0.0077) (0.0071) (0.0065) (0.0064) (0.0064) (0.0063) (0.0098) (0.0072) (0.0058) (0.0066)

Logarithm of assets -0.098* -0.0553* -0.0162 0.0221* 0.0147 0.0049 0.0185* 0.0336* 0.0155* 0.016* 0.0227*

(0.0093) (0.0090) (0.0072) (0.0063) (0.0061) (0.0054) (0.0046) (0.0058) (0.0040) (0.0047) (0.0042)

LCAMEL1 -0.5964* -0.9209* -1.0298* -0.7756* -0.2473* -0.2369* -0.5083* -0.7502* -0.8488* -0.1212 -0.4088*

(0.1158) (0.0984) (0.0899) (0.0751) (0.0662) (0.0621) (0.0608) (0.1084) (0.0926) (0.1540) (0.1401)

LCAMEL2 -0.5478* -0.8396* -0.9126* -0.6476* -0.1273 -0.1313 -0.4345* -0.6594* -0.776* -0.0763 -0.3475

(0.1128) (0.0944) (0.0879) (0.0732) (0.0634) (0.0601) (0.0595) (0.1072) (0.0922) (0.1537) (0.1399)

LCAMEL3 -0.3503* -0.6884* -0.6539* -0.45* 0.0164 0.022 -0.2819* -0.5473* -0.6297* 0.0448 0.1377

(0.1124) (0.0938) (0.0884) (0.0741) (0.0640) (0.0610) (0.0606) (0.1084) (0.0938) (0.1555) (0.1419)

LCAMEL4 -0.1971 -0.3603* -0.4024* -0.2552* 0.1429 0.1748* -0.1118 -0.3917* -0.4372* 0.2141 0.0005

(0.1136) (0.0958) (0.0911) (0.0767) (0.0673) (0.0634) (0.0636) (0.1128) (0.0992) (0.1641) (0.1544)

Exam interval 0.0607* 0.0486* 0.0258* 0.0066 0.0026 0.005 -0.0005 -0.0145 0.0201 0.0053 0.0018

(0.0121) (0.0107) (0.0088) (0.0075) (0.0074) (0.0068) (0.0056) (0.0086) (0.0079) (0.0092) (0.0081)

Equity -0.0054* -0.0061* -0.0007* -0.0007* -0.0031* -0.0025* -0.0011* -0.0014* -0.0001 -0.0015* -0.001*

(0.0008) (0.0008) (0.0002) (0.0002) (0.0005) (0.0005) (0.0003) (0.0004) (0.0001) (0.0002) (0.0003) 

Loan-loss allowance 0.2584* 0.2745* 0.1451* 0.1177* 0.1099* 0.0554* 0.0388* 0.0435* 0.0376* 0.0386* 0.0451*

(0.0109) (0.0094) (0.0080) (0.0074) (0.0055) (0.0032) (0.0039) (0.0062) (0.0014) (0.0051) (0.0059)

Monitor (audits) -0.0032 0.0417 0.0337 0.0321 0.0515* 0.0509* 0.0202 0.0238 0.01 -0.0174 0.0089

(0.0219) (0.0211) (0.0179) (0.0155) (0.0153) (0.0134) (0.0115) (0.0148) (0.0105) (0.0121) (0.0105)

-2xlog of likelihood -19,575 -17,872 -15,530 -13,545 -12,955 -11,006 -8,755 -9,716 -6,723 -7,240 -5,752

Pseudo R squared 13.90% 13.80% 13.10% 13.30% 12.40% 12.60% 15.00% 8.00% 12.80% 10.90% 14.60%

Number of observations 13,071 12,581 12,316 11,948 11,604 12,251 10,858 10,405 9,874 9,418 8,893

Note: An asterisk indicates significance at the 1 percent confidence level.

FDIC Banking Review

18
Loan-Loss Recognition

19

largely composed of residential mortgages. The (occasionally) positive relationship between losses and

examination intervals is consistent with the finding of

the GAO (1990) that the length of the elapsed time

since the last examination affects the accuracy of financial reports. Lagged equity capital was usually significantly and negatively related to loan losses, while the

lagged allowance for loan losses was usually significantly and positively related to losses. 

Our key evidence on the capacity of auditors and

examiners to influence the timing of loan-loss recognition is provided by the coefficients on the variables

denoting incidence of external audit and examination,

respectively, in the various annual estimations of the

model of losses on real-estate and commercial loans.

The coefficients on the dummy variable indicating the

occurrence of an external audit (level 1 above, in the

subsection Data and Sample) were almost always

insignificant (at the 1 percent confidence level) for

commercial and industrial loan losses and usually

insignificant for real-estate loan losses. The lack of an

effect of auditors on commercial and real-estate loan

losses contrasts with the effect of examinations. With

respect to commercial and industrial loan losses, the

variable representing incidence of examination is positive and significant in 1987, 1989, 1990, 1991, and 1992.

This period includes the years with the highest industry loan losses between 1987 and 1997. This result is

consistent with the belief that examiners call loans they

believe to be risky to the attention of bank management, uncover new information about a bank, and

more generally constrain managements incentives to

delay loss recognition as long as possible.27 In contrast,

Table 5

Tobit Estimation of Real-Estate Loan Charge-off Determination

Exam Effects for All Banks, Audited and Unaudited

Estimated Coefficients (Standard Errors)

Explanatory Variable 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Intercept -0.5642* -0.9151* -1.3990033*-1.7362* -1.6* 1.3178* -1.0534* -0.998* -1.0346* -0.8346* -0.6757*

(0.0870) (0.0790) (0.0873) (0.0856) (0.0735) (0.0665) (0.0680) (0.0741) (0.0804) (0.0892) (0.0942)

Performing real-estate loans 0 0.0011* 0.0023* 0.0036* 0.0041* 0.0033* 0.0032* 0.0028* 0.0026* 0.0022* 0.0013*

(0.0004) (0.0004) (0.0004) (0.0004) (0.0003) (0.0003) (0.0003) (0.0002) (0.0002) (0.0002) (0.0002)

Nonperforming real-estate loans 0.1998* 0.2162* 0.218* 0.2162* 0.1652* 0.1286* 0.1218* 0.1332* 0.1266* 0.0615* 0.118*

(0.0044) (0.0043) (0.0046) (0.0047) (0.0044) (0.0037) (0.0036) (0.0039) (0.0028) (0.0013) (0.0025)

Changes in nonperforming 0.0868* 0.0754* 0.0874* 0.067* 0.044* 0.0296* 0.0634* 0.0589* 0.0257* -0.0064 0.0308*

real-estate loans (0.0042) (0.0042) (0.0044) (0.0045) (0.0044) (0.0035) (0.0041) (0.0047) (0.0051) (0.0037) (0.0046)

Logarithm of assets 0.0355* 0.0585* 0.0734* 0.1074* 0.0915* 0.0923* 0.0949* 0.0874* 0.0802* 0.0629* 0.0538*

(0.0052) (0.0050) (0.0052) (0.0052) (0.0047) (0.0041) (0.0038) (0.0034) (0.0034) (0.0028) (0.0029)

LCAMEL1 -0.2578* -0.3398* -0.0167 -0.1891* -0.0302 -0.1703* -0.5612* -0.4705* -0.2855* -0.2027 -0.2451*

(0.0635) (0.0543) (0.0645) (0.0604) (0.0501) (0.0466) (0.0490) (0.0600) (0.0702) (0.0839) (0.0876)

LCAMEL2 -0.1995* -0.269* 0.0446 -0.0768 0.063 -0.1214* -0.5043* -0.4171* -0.244* -0.1553 -0.2139

(0.0617) (0.0528) (0.0620) (0.0587) (0.0478) (0.0447) (0.0474) (0.0591) (0.0694) (0.0835) (0.0873)

LCAMEL3 -0.1478 -0.1937* 0.1478 0.05 0.1434* 0 -0.3995* -0.3167* -0.0744 0.0158 -0.1427

(0.0616) (0.0529) (0.0618) (0.0594) (0.0478) (0.0448) (0.0477) (0.0595) (0.0701) (0.0839) (0.0882)

LCAMEL4 -0.1135 -0.1948* 0.1185 0.0409 0.188* 0.0608 -0.1776* -0.1219 0.1652 0.1263 0.2549*

(0.0624) (0.0543) (0.0630) (0.0615) (0.0496) (0.0463) (0.0495) (0.0611) (0.0737) (0.0881) (0.0955)

Exam interval 0.0389* 0.0357 0.0239* 0.0147 0.0156* 0.0072 -0.0003 -0.0059 -0.0081 -0.0132 -0.0077

(0.0069) (0.0064) (0.0063) (0.0062) (0.0056) (0.0051) (0.0046) (0.0050) (0.0069) (0.0060) (0.0060)

Equity -0.0025* 0 -0.0031* -0.0005 -0.0032* -0.0029* -0.0015* -0.0008 -0.0037* -0.0026* -0.0009*

(0.0005) (0.0001) (0.0005) (0.0001) (0.0005) (0.0004) (0.0004) (0.0003) (0.0003) (0.0002) (0.0003)

Loan-loss allowance 0.1085* 0.0981* 0.0935* 0.1022* 0.1109* 0.0768* 0.059* 0.0469* 0.0588* 0.0553* 0.0391*

(0.0059) (0.0053) (0.0057) (0.0062) (0.0054) (0.0047) (0.0043) (0.0042) (0.0044) (0.0035) (0.0040)

Monitor (exams) -0.0163 0.0319 0.0448* 0.021 0.0154 0.0098 0.0727* 0.0153 0.0036 0.0086 0.0211

(0.0137) (0.0140) (0.0149) (0.0166) (0.0159) (0.0142) (0.0207) (0.0155) (0.0126) (0.0103) (0.0091)

-2xlog of likelihood -10,701 -9,859 -9,563 -9,372 -8,536 -7,318 -6,061 -4,775 -4,446 -3,432 -3,035

Pseudo R squared 16.40% 19.20% 18.00% 20.10% 22.60% 23.80% 26.10% 30.00% 27.10% 23.90% 25.60%

Number of observations 13,211 12,725 12,457 12,113 11,765 11,416 11,066 10,644 10,103 9,631 9,079

Note: An asterisk indicates significance at the 1 percent confidence level.

27 See Graham and Humphrey (1978), Gilbert (1993) and GAO (1991).
FDIC Banking Review

20

examinations did not appear to have an effect upon

real-estate loan-loss recognition. The variable representing incidence of examination is significant in the

real-estate model in only two years, 1989 and 1993.

The lack of an examination effect for real-estate loan

losses is generally consistent with the more systematic

industry standard for writing down home mortgages

the predominant loan type in the real-estate category.

Losses on commercial and industrial loans, by contrast,

are more discretionary insofar as most commercial loans

are interest-only until maturity, when principal is due

and when the value of the underlying collateral is considerably more difficult to determine.28 Thus, the

lesser discretion in the valuation of real-estate loans

suggests that external monitors are less likely to influence the recognition of losses within this category. Our

results on the audit and examination variablesas well

as the examiner ratings variable, the nonperforming

loan variable, and the performing loan variablehighlight the existence of differences in the valuations of

real-estate and commercial loans that are consistent

with this perspective.

Total Loan-Loss Provisions

The results of Tobit estimation of the loan-loss provisioning behavioral model were very similar to those

for loan losses for most of the explanatory variables (see

tables 7 and 8). The estimation periods shown in

tables 7 and 8 indicate the year-ends for which loanloss provisions occur. For example, in the 1987 estimaTable 6

Tobit Estimation of Real-Estate Loan Charge-off Determination

Audit Effects for All Banks, Examined and Unexamined

Estimated Coefficients (Standard Errors)

Explanatory Variable 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Intercept -0.5706* -0.8593* -1.3392* -1.7004* -1.5786* -1.3031* -0.9823* -0.9908* -1.0472* -0.8417* -0.6647*

(0.0858) (0.0770) (0.0856) (0.0832) (0.0714) (0.0646) (0.0647) (0.0723) (0.0803) (0.0889) (0.0941)

Performing real-estate loans 0.0001 0.0011* 0.0023* 0.0037* 0.0042* 0.0033* 0.0031* 0.0027* 0.0026* 0.0021* 0.0013*

(0.0004) (0.0004) (0.0004) (0.0004) (0.0003) (0.0003) (0.0003) (0.0002) (0.0002) (0.0002) (0.0002)

Nonperforming real-estate loans 0.1998* 0.2168* 0.2185* 0.217* 0.1654* 0.1288* 0.1216* 0.1327* 0.1262* 0.061* 0.1178*

(0.0043) (0.0043) (0.0046) (0.0047) (0.0044) (0.0037) (0.0036) (0.0039) (0.0028) (0.0012) (0.0025)

Changes in nonperforming 0.0871* 0.0758* 0.0879* 0.0672* 0.0442* 0.0298* 0.0635* 0.0586* 0.0254* -0.0065 0.0311*

real-estate loans (0.0042) (0.0042) (0.0045) (0.0045) (0.0044) (0.0035) (0.0041) (0.0047) (0.0051) (0.0037) (0.0046)

Logarithm of assets 0.0358* 0.0573* 0.0724* 0.1066* 0.0913* 0.0921* 0.0945* 0.0873* 0.0804* 0.0632* 0.0545*

(0.0052) (0.0050) (0.0052) (0.0051) (0.0047) (0.0041) (0.0038) (0.0032) (0.0030) (0.0028) (0.0028)

LCAMEL1 -0.2553* -0.3471* -0.0315 -0.1898* -0.0341 -0.1734* -0.5616* -0.4675* -0.2754* -0.1968 -0.2532*

(0.0634) (0.0542) 0.0644 (0.0604) (0.0501) (0.0466) (0.0490) (0.0600) (0.0703) (0.0837) (0.0876)

LCAMEL2 -0.1965* -0.2729* 0.0325 -0.0758 0.06 -0.1237* -0.5037* -0.4148* -0.2352* -0.1512 -0.221

(0.0617) (0.0527) (0.0619) (0.0587) (0.0477) (0.0448) (0.0475) 0.0591 (0.0695) (0.0832) (0.0873)

LCAMEL3 -0.1469 -0.192* 0.1431 0.0541 0.1424* -0.0007 -0.3967* -0.3168* -0.0688 0.0164 -0.1459

(0.0616) (0.0529) (0.0618) (0.0594) (0.0478) (0.0448) 0.0478 (0.0594) (0.0701) (0.0836) (0.0883)

LCAMEL4 -0.1148 -0.193* 0.1168 0.0479 0.1882* 0.0605 -0.1746* -0.1225 0.1698 0.125 0.2524*

(0.0624) (0.0543) (0.0630) (0.0615) (0.0496) (0.0463) (0.0495) (0.0611) (0.0737) (0.0878) (0.0955)

Exam interval 0.0381* 0.0362* 0.0237* 0.0138 0.0151* 0.0071 -0.0001 -0.0052 -0.0075 -0.012 -0.0019

(0.0069) (0.0064) (0.0063) (0.0062) (0.0056) (0.0051) (0.0046) (0.0050) (0.0066) (0.0056) (0.0055)

Equity -0.0024* 0 -0.0029* -0.0005 -0.0032* -0.0029* -0.0015* -0.0008* -0.0037* -0.0026* -0.0009*

(0.0005) (0.0001) (0.0005) (0.0002) (0.0005) (0.0004) (0.0004) (0.0003) (0.0003) (0.0002) (0.0003)

Loan-loss allowance 0.108* 0.0971* 0.0928* 0.1014* 0.1104* 0.0766* 0.059* 0.0472* 0.0592* 0.0556* 0.0392*

(0.0059) (0.0053) (0.0058) (0.0062) (0.0054) (0.0047) (0.0043) (0.0042) (0.0044) (0.0035) (0.0040)

Monitor (audits) -0.0346* -0.0497* -0.0223 -0.0367* -0.0108 -0.0065 0.0047 0.0195 0.0216 0.0305* -0.0008

(0.0126) (0.0126) (0.0130) (0.0131) (0.0118) (0.0104) (0.0097) (0.0089) (0.0089) (0.0073) (0.0072)

-2xlog of likelihood -10,698 -9,854 -9,566 -9,368 -8,536 -7,318 -6,067 -4,773 -4,443 -3,423 -3,037

Pseudo R squared 16.40% 19.20% 18.00% 20.10% 22.60% 23.80% 26.00% 30.00% 27.10% 24.10% 25.50%

Number of observations 13,211 12,725 12,457 12,113 11,765 11,416 11,066 10,644 10,103 9,631 9,079

Note: An asterisk indicates significance at the 1 percent confidence level.

28 Losses on consumer loans (not reported here) were also tested.

Valuation of these loans is less subjective, and losses were unaffected by audits or examinations.
tion, provisions over calendar year 1987 are related to

performing and nonperforming loans, and other lagged

terms, reported at year-end 1986. Briefly, loan-loss provisions tended to be significantly and positively related

to total performing loans and leases, nonperforming

loans and leases, changes in nonperforming loans and

leases, bank size (as measured by the logarithm of

assets), the examination interval, the loss allowance,

and equity capital. The variable of key interest to us

herethe indicator of external monitoringindicated

that audits had a significant effect on provisioning in

every year between 1992 and 1997. Examinations,

however, were generally not significantly related to

provisioning, being significant in only 3 of the 11 years

over which the model was estimated. Although the

significance of audits for provisioning coincides with

the post-FDICIA emphasis upon external auditing in

banking, we are unaware of any changes in the nature

of audits over the same period that would explain this

result.

Robustness of the Results

To assess the robustness of the results shown in

tables 38 we investigated the effects of several

changes to our methodology on those results. Those

changes, each of which was considered separately,

include altering the definition of external monitoring

by auditors, using different samples of banks for model estimation, and adding dummy variables for prior

year examinations to the behavioral model. On balance, we find that estimation of the behavioral model

after making these changes yields results that are very

similar to those in tables 38. The specific findings are

discussed next.

Table 7

Tobit Estimation of Loan-Loss Provision Determination

Exam Effects for All Banks, Audited and Unaudited

Estimated Coefficients (Standard Errors)

Explanatory Variables 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Intercept -0.2203 -0.2978* -0.7555* -1.3636* -1.7851* -1.4697* -1.1116* -0.8138* -0.9280* -1.9955* -1.3374*

(0.1299) (0.1062) (0.1017) (0.1077) (0.1036) (0.0845) (0.0828) (0.1129) (0.1129) (0.1868) (0.2115)

Performing loans 0.0127* 0.0109* 0.0090* 0.0119* 0.0137* 0.0117* 0.0097* 0.0090* 0.0083* 0.0079* 0.0069*

(0.0007) (0.0006) (0.0005) (0.0006) (0.0006) (0.0004) (0.0004) (0.0004) (0.0004) (0.0005) (0.0005)

Nonperforming loans 0.3458* 0.3193* 0.2988* 0.3184* 0.2525* 0.1753* 0.1480* 0.1641* 0.1433 0.1742* 0.1670*

(0.0070) (0.0050) (0.0066) (0.0074) (0.0077) (0.0061) (0.0055) (0.0068) (0.0073)* (0.0092) (0.0095)

Changes in nonperforming 0.2945* 0.2831* 0.2555* 0.3001* 0.2138* 0.1350* 0.1610* 0.1319* 0.1495* 0.2462* 0.1697*

loans (0.0066) (0.0050) (0.0064) (0.0066) (0.0077) (0.0062) (0.0065) (0.0081) (0.0083) (0.0099) (0.0106)

Logarithm of assets -0.0264* -0.0183 0.0402* 0.0711* 0.0705* 0.0664* 0.0558* 0.0379* 0.0506* 0.0651* 0.0624*

(0.0075) (0.0073) (0.0062) (0.0066) (0.0066) (0.0049) (0.0044) (0.0045) (0.0044) (0.0055) (0.0054)

LCAMEL1 -0.1709 -0.2277* -0.3438* -0.1743 0.0056 -0.0125 -0.1481 -0.2752* -0.2665* 0.5823* -0.0601

(0.0975) (0.0729) (0.0820) (0.0825) (0.0761) (0.0651) (0.0639) (0.0981) (0.1015) (0.1769) (0.2026)

LCAMEL2 -0.1716 -0.2102* -0.3106* -0.0960 0.1091 0.0628 -0.0921 -0.2468 -0.2255 0.6268* 0.0227

(0.0950) (0.0698) (0.0794) (0.0799) (0.0734) (0.0634) (0.0625) (0.0971) (0.1004) (0.1759) (0.2018)

LCAMEL3 -0.1431 -0.1786* -0.1680 0.0758 0.2341* 0.1188 -0.0490 -0.2607* -0.1419 0.6686* 0.1310

(0.0938) (0.0702) (0.0784) (0.0797) (0.0732) (0.0632) (0.0626) (0.0972) (0.1002) (0.1758) (0.2024)

LCAMEL4 -0.1179 -0.0310 -0.1294 0.0731 0.2186* 0.1289 0.1785* -0.1427 -0.0997 0.5752* -0.0523

(0.0936) (0.0709) (0.0792) (0.0813) (0.0757) (0.0644) (0.0646) (0.0989) (0.1039) (0.1853) (0.2180)

Exam interval 0.0292* 0.0353* 0.0197* 0.0077 -0.0002 0.0040 -0.0069 -0.0061 0.0113 0.0266 -0.0019

(0.0097) (0.0087) (0.0073) (0.0076) (0.0077) (0.0059) (0.0052) (0.0065) (0.0084) (0.0110) (0.0110)

Equity 0.0080* 0.0070* 0.0123* 0.0131* 0.0216* 0.0121* 0.0113* 0.0103* 0.0071* 0.0054* 0.0053*

(0.0028) (0.0027) (0.0013) (0.0021) (0.0020) (0.0015) (0.0013) (0.0013) (0.0013) (0.0017) (0.0015)

Loan-loss allowance 0.0804* 0.1458* 0.0675* -0.0265 0.0968* 0.0614* 0.0690* 0.0698* 0.0833* 0.2131* 0.3212*

(0.0181) (0.0158) (0.0145) (0.0161) (0.0156) (0.0114) (0.0113) (0.0113) (0.0124) (0.0154) (0.0142)

Monitor (exams) 0.0547* 0.0067 0.0577* 0.0087 0.0388 0.0847* 0.0094 0.0223 0.0238 -0.0150 0.0061

(0.0191) (0.0189) (0.0170) (0.0196) (0.0215) (0.0165) (0.0219) (0.0197) (0.0156) (0.0192) (0.0167)

-2xlog of likelihood -17,872 -16,279 -13,998 -13,848 -13,497 -9,903 -7,929 -7,490 -7,083 -8,499 -7,669

Pseudo R squared 14.60% 14.00% 15.30% 17.50% 14.00% 17.30% 17.60% 13.10% 12.60% 12.10% 13.80%

Number of observations 13,256 12,598 12,292 11,926 11,583 11,160 10,480 10,020 9,722 9,427 8,927

Note: An asterisk indicates significance at the 1 percent confidence level.

Loan-Loss Recognition

21
FDIC Banking Review

22

We first investigated the effects of auditors upon

loan-loss recognition and provisioning using a broader

measure of external monitoring by auditors. Specifically, we redefined audits to include both independent audits of a bank by a certified public accounting

firm (audit level 1) and independent audits of a banks

parent holding company by a certified public accounting firm (audit level 2). Using this broader measure of

audits, and the original behavioral model and samples

of banks, we obtain results that are very similar to those

shown in tables 4, 6, and 8. Audits are not, in general,

significantly related to commercial and industrial loanloss recognition and to real-estate loan-loss recognition.

Audits, however, are significantly related to provisioning for loan losses.

We next considered the possibility that the influence of one group of external monitors upon loan-loss

recognition and provisioning overshadows that of

another group of external monitors. First, we investigated the influence of examiners upon provisioning

using annual samples of banks that had not been audited (audit level 1) during the year. Estimates of the

original behavioral model, using samples of unaudited

banks, agree with those shown in table 7, indicating

that examiners have no significant influence on provisioning. Second, we investigated the influence of auditors (audit level 1) upon loan-loss recognition for

annual samples of banks that had not been examined

during the year. Estimates of the original behavioral

model, using these samples of unexamined banks,

agree with those shown in tables 4 and 6, indicating

that auditors have no significant influence upon loanloss recognition for both commercial and real-estate

loans.

We also considered the effects of adding a dummy

variable for prior year examinations to the behavioral

Table 8

Tobit Estimation of Loan-Loss Provision Determination

Audit Effects for All Banks, Examined and Unexamined

Estimated Coefficients (Standard Errors)

Explanatory Variable 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Intercept -0.1524 -0.2871* -0.6858* -1.348* -1.7487* -1.3928* -1.1239* -0.8180* -0.9449* -2.0189* -1.3626*

(0.1285) (0.1036) (0.0992) (0.1047) (0.1007) (0.0824) (0.0797) (0.1107) (0.1124) (0.1866) (0.2112)

Performing loans 0.0129* 0.0109* 0.0089* 0.012* 0.0137* 0.0116* 0.0096* 0.0091* 0.0083* 0.0079* 0.007*

(0.0007) (0.0006) (0.0005) (0.0006) (0.0006) (0.0004) (0.0004) (0.0004) (0.0004) (0.0005) (0.0005)

Nonperforming loans 0.3456* 0.3195* 0.2986* 0.3192* 0.2518* 0.1738* 0.1466* 0.1614* 0.1408* 0.1729* 0.1656*

(0.0070) (0.0050) (0.0066) (0.0074) (0.0077) (0.0062) (0.0055) (0.0068) (0.0074) (0.0093) (0.0095)

Changes in nonperforming 0.2935* 0.2834* 0.2557* 0.3004* 0.2135* 0.1346* 0.1604* 0.1305* 0.1484* 0.2457* 0.1694*

loans (0.0066) (0.0050) (0.0064) (0.0066) (0.0077) (0.0062) (0.0065) (0.0081) (0.0083) (0.0099) (0.0105)

Logarithm of assets -0.0268* -0.0184 0.039* 0.071* 0.0697* 0.0642* 0.055* 0.0362* 0.0498* 0.064* 0.061*

(0.0075) (0.0073) (0.0062) (0.0065) (0.0066) (0.0049) (0.0043) (0.0045) (0.0044) (0.0054) (0.0053)

LCAMEL1 -0.1884 -0.2299* -0.3568* -0.176 0.0064 -0.0051 -0.1338 -0.2599* -0.2467 0.5936* -0.0412

(0.0974) (0.0729) (0.0820) (0.0825) (0.0761) (0.0652) (0.0639) (0.0979) (0.1014) (0.1769) (0.2024)

LCAMEL2 -0.1832 -0.2115* -0.3228* -0.0964 0.1079 0.0686 -0.0815 -0.2357 -0.2093 0.6342* 0.0378

(0.0949) (0.0698) (0.0740) (0.0799) (0.0734) (0.0634) (0.0625) (0.0969) (0.1003) (0.1759) (0.2016)

LCAMEL3 -0.1467 -0.1786 -0.1714 0.0773 0.2355* 0.1278 -0.0441 -0.2596* -0.1324 0.6696* 0.1416

(0.0938) (0.0703) (0.0784) (0.0796) (0.0732) (0.0632) (0.0625) (0.0969) (0.1001) (0.1758) (0.2022)

LCAMEL4 -0.1194 -0.0308 -0.1307 0.0769 0.2177* 0.1337 0.1778* -0.146 -0.0912 0.5741* -0.0484

(0.0936) (0.0709) (0.0792) (0.0813) (0.0757) (0.0644) (0.0645) (0.0987) (0.1037) (0.1853) (0.2177)

Exam interval 0.0287* 0.0354* 0.0201* 0.0072 -0.0002 0.0052 -0.0058 -0.0042 0.0147 0.0233 -0.0012

(0.0097) (0.0087) (0.0073) (0.0076) (0.0077) (0.0059) (0.0052) (0.0064) (0.0081) (0.0103) (0.0100)

Equity 0.0086* 0.0071* 0.0123* 0.0133* 0.0212* 0.0115* 0.011* 0.01* 0.0068* 0.0053* 0.0051*

(0.0028) (0.0027) (0.0013) (0.0021) (0.0020) (0.0015) (0.0013) (0.0013) (0.0013) (0.0017) (0.0015)

Loan-loss allowance 0.0771* 0.1451* 0.0684* -0.0287 0.0986* 0.0663* 0.0731* 0.0736* 0.0863* 0.2142* 0.3227*

(0.0182) (0.0158) (0.0146) (0.0161) (0.0157) (0.0115) (0.0113) (0.0113) (0.0124) (0.0154) (0.0142)

Monitor (audits) -0.065* -0.0135 0.007 -0.0235 0.0289 0.0537* 0.0521* 0.0722* 0.0588* 0.0407* 0.0644*

(0.0175) (0.0171) (0.0148) (0.0157) (0.0161) (0.0120) (0.0108) (0.0112) (0.0111) (0.0138) (0.0132)

-2xlog of likelihood -17,869 -16,279 -14,005 -13,847 -13,497 -9,906 -7,917 -7,470 -7,070 -8,495 -7,657

Pseudo R squared 14.60% 14.00% 15.20% 17.50% 14.00% 17.20% 17.70% 13.40% 12.80% 12.10% 13.90%

Number of observations 13,256 12,598 12,292 11,926 11,583 11,160 10,480 10,020 9,722 9,427 8,927

Note: An asterisk indicates significance at the 1 percent confidence level.
Loan-Loss Recognition

23

model. Specifically, we added a dummy variable indicating whether an examination occurred during the prior year to the behavioral model used to estimate the

influence of examinations on loan-loss recognition.

Estimates of the revised model, using the original samples of banks, agree with those shown in tables 3 and 5.

Estimates of the revised model indicate that examinations that occur during the year over which loan losses

are recognized have a significant influence upon commercial loan-loss recognition but no significant influence on real-estate loan-loss recognition. In addition,

the dummy variable for prior year examinations was

generally not significantly related to commercial and

real-estate loan-loss recognition. Unfortunately, we

could not investigate the influence on loan-loss recognition (or provisioning) of both current and prior year

audits in the same manner as we did for examinations.

This is because audits are highly collinear over time.

The Pearson correlation coefficient for the dummy

variable measuring audits this year and the dummy

variable measuring audits last year was nearly 80 percent between 1988 and 1997. When explanatory variables are highly collinear one cannot include both in

the behavioral model being estimated. 

Conclusions 

We find that when bankers have some discretion in

their treatment of loan losses, bank examinersbut

not auditorsinfluence the timing of loan-loss recognition. This conclusion is based on our finding that the

incidence of examinations had a significant and positive effect upon commercial and industrial loan-loss

recognition, but no effect on real-estate loan-loss recognition. The lack of a similar finding for audits does not,

however, indicate that auditors have no effect on discretionary accounting practices by bankers. Rather,

auditors appear to focus on other aspects of bank

accountingthe provision for loan losses. This statement rests on our finding that auditors tended to have

a significantly positive effect on provisions for loan losses between 1992 and 1997, whereas examinations generally had no significant effect on provisioning.

We should mention that these results agree with the

expectations of several researchers familiar with auditing practices to whom we showed earlier drafts of this

article. They suggested that examiners, with their concern for banks safety and soundness and insured

depositors, would tend to focus on areas with the greatest effect on the realizable value of loans and bank

equityloan charge-offs (loan losses). Auditors, however, focus upon evaluating the net book value of the

banks loan portfolio, assets and income. Since the

loan-loss allowance and loan-loss provisions that fund

the allowance directly affect those book values, auditors would focus upon provisions and the allowance

more than loan charge-offs. 

Finally, our results support the findings of three

recent studies on the informational content of bank

examinations (Berger and Davies (1994); Flannery and

Houston (1995); and Berger, Davies, and Flannery

(1997)). These studies all focus on stock market reaction to bank examinations. Our results are also consistent with the findings of Gilberts (1993) earlier

research that bank examiners influence the preparation

of financial reports.
FDIC Banking Review

24

References

American Banker. 1998. Basle Committee Urges International Standards for Loan-Loss

Reserves. October 15.

American Institute of Certified Public Accountants. 1986. Auditing the Allowance for

Credit Losses of Banks.

Antle, R., and B. Nalebuff. 1991. Conservatism and AuditorClient Negotiations.

Journal of Accounting Research 29: 3154.

Beatty, A., S. Chamberlain, and J. Magliolo. 1995. Managing Financial Reports of

Commercial Banks: The Influence of Taxes, Regulatory Capital and Earnings.

Journal of Accounting Research 33: 23162.

Beaver, W., and E. Engel. 1996. Discretionary Behavior with Respect to Allowances for

Loan Losses and the Behavior of Security Prices. Journal of Accounting and

Economics 22: 177206.

Berger, A., and S. Davies. 1994. The Information Content of Bank Examinations.

Working Paper, Federal Reserve Board, Washington, DC.

Berger, A., S. Davies, and M. Flannery. 1997. Comparing Market and Regulatory

Assessments of Bank Performance: Who Knows What When. Working Paper,

Federal Reserve Board, Washington, DC.

Berger, A., and G. Udell. 1994. Did Risk-Based Capital Allocate Bank Credit and Cause

a Credit Crunch in the U.S.? Journal of Money, Credit and Banking 26: 585634.

Black, R. 1990. Auditors and Bank Examiners: A New Era of Cooperation. Journal of

Accountancy (September), 7782.

Chow, C. 1982. The Demand for External Auditing: Size, Debt and Ownership

Influences. Accounting Review 57: 27291.

Collins, J., D. Shackelford, and J. Wahlen. 1995. Bank Differences in the Coordination

of Regulatory Capital, Earnings and Taxes. Journal of Accounting Research 33:

26392.

CPA Journal. 1993. Bank Regulators Discuss Auditor Performance. (March), 4043.

Dahl, D. 1993. Managerial Turnover and Discretionary Accounting Decisions in Banks

with Concentrated Ownership. Proceedings of a Conference on Bank Structure and

Competition, 26984. Federal Reserve Bank of Chicago.

Darin, R., and J. Walter. 1994. Were Bank Examiners Too Strict with New England and

California Banks? Federal Reserve Bank of Richmond Economic Quarterly (Fall),

2547.

Federal Deposit Insurance Corporation (FDIC). 1998. Federal Deposit Insurance

Corporation: Laws, Regulations, Related Acts. Volume 2. August 31.

Fein, Melanie L. 1992. The FDIC Improvement Act of 1991. The Review of Banking

and Financial Services 8: 75.

Fisher, D. 1993. The Impact of the Accounting and Auditing Requirements. ABA Bank

Compliance (Autumn), 317.
Loan-Loss Recognition

25

Flannery, M., and J. Houston. 1995. The Value of a Government Monitor for Firms with

Hard-to-Value Assets. Working Paper, University of Florida.

Froot, K., and J. Stein. 1997. Risk Management, Capital Budgeting and Capital

Structure Policy for Financial Institutions: An Integrated Approach. Working

Paper, Harvard Business School.

Gilbert, R. Alton. 1993. Implications of Annual Examinations for the Bank Insurance

Fund. Federal Reserve Bank of St. Louis Economic Review (January/February),

3552.

Graham, D., and D. Humphrey. 1978. Bank Examination Data as Predictors of Bank

Net Loan Losses. Journal of Money, Credit and Banking 10: 491504.

Greene, William H. 1993. Econometric Analysis. 2d ed. New York, NY. 

Group of Thirty. 1994. Defining the Roles of Accountants, Bankers and Regulators in

the United States. Washington, DC.

Heckman, J. 1979. Sample Selection Bias as a Specification Error. Econometrica 44:

15361.

Liu, C., and S. Ryan. 1995. The Effect of Bank Loan Portfolio Composition on the

Market Reaction to and Anticipation of Loan-Loss Provisions. Journal of

Accounting Research 33: 7794. 

Moyer, S. E. 1990. Capital Adequacy Ratio Regulation and Accounting Choices in

Commercial Banks. Journal of Accounting and Economics 13: 123154.

Office of the Comptroller of the Currency (OCC). 1990. Advisory letter (February 9).

Rajan, R. 1994. Why Bank Credit Policies Fluctuate: A Theory and Some Evidence.

Quarterly Journal of Economics (May), 399441.

Ross, S. 1989. Institutional Markets, Financial Marketing and Financial Innovation.

Journal of Finance 44: 54166.

Seidman, L. William. 1986. The Role of Outside Auditors in Todays Banking Industry.

Issues in Bank Regulation (Autumn), 2024.

Turman, K. 1991. Evaluating a Banks Loan-Loss Reserve Adequacy. Internal Auditor

(February), 5258.

U.S. Department of the Treasury. 1991. Modernizing the Financial System. Washington,

DC.

U.S. General Accounting Office (GAO). 1990. Bank Insurance Fund: Additional

Reserves and Reforms Needed to Strengthen the Fund. GAO/AFMD-90-100.

. 1991. Failed Banks: Accounting and Auditing Reforms Urgently Needed.

GAO/AFMD-91-43.

Wahlen, J. 1994. The Nature of Information in Commercial Bank Loan-Loss Decisions.

Accounting Review 69: 45578.

Walter, J. 1991. Loan-Loss Reserves. Federal Reserve Bank of Richmond Economic

Review (July/August), 2030.
FDIC Banking Review

Recent Developments

Affecting Depository

Institutions

by Lynne Montgomery*

26

Interagency Actions

Guidance on Loan-Loss Reserves

Federal bank and securities regulators announced

on November 24, 1998, a joint agreement to work

with the public accounting and banking industries to

develop new guidance to ensure the adequacy of

banks loan-loss reserves and prevent inappropriate

earnings manipulation. The agreement states that

institutions should link loan-loss reserves to specific

credits that are likely to be delinquent and should

include a margin for imprecision that reflects the

uncertainty associated with estimating credit losses

in their loan portfolios. The joint release stresses

that, although managements process for determining

allowance adequacy is judgmental, it must not be

used to manipulate earnings or mislead investors,

funds providers, regulators, or other parties. The

Securities and Exchange Commission (SEC), the

Federal Reserve Board, the Federal Deposit Insurance Corporation (FDIC), the Office of the Comptroller of the Currency (OCC), and the Office of

Thrift Supervision (OTS) issued the joint statement.

BBR, 11/30/98, p. 817.

Year 2000 Guidelines

Federal bank and thrift regulators published a

notice in the Federal Register on October 15, 1998,

which states that the regulators are establishing interagency safety-and-soundness standards for depository institutions preparing their computer systems for

the century date change. The joint Year 2000 safetyand-soundness guidelines are based on eight existing

interagency Y2K bulletins issued by the Federal

Financial Institutions Examination Council since

1996; however, the interagency Y2K guidelines will

now be legally enforced. The new standards were

issued jointly by the FDIC, the Federal Reserve

Board, the OCC, and the OTS and are effective

immediately. BBR, 10/19/98, p. 584.

Common Form for Merger Applications

On November 4, 1998, the OCC, the OTS, and

the FDIC submitted a uniform bank merger application form to the Office of Management and Budget

for review. If approved, the new Interagency Bank

Merger Act application form would be used by institutions seeking approvals to merge, consolidate, or

combine. The action is part of the regulatory agencies continuing effort to promote consistency and

reduce regulatory burden. NR 98-113, OCC, 11/4/98.

Federal Deposit Insurance Corporation

Director Neely Resigns

FDIC Board member Joseph H. Neely resigned

his position as a director, effective October 1, 1998.

Mr. Neely returned to Mississippi to pursue private-

*Lynne Montgomery is a financial analyst in the FDICs Division of

Research and Statistics.

Reference sources: American Banker (AB); The Wall Street Journal (WSJ);

BNAs Banking Report (BBR); and Federal Register (FR).

REGULATORY AGENCY ACTIONS
Recent Developments

27

sector consulting work. He served on the FDIC

Board since January 1996, and he led the FDICs regulatory review initiative, a comprehensive effort

mandated by Congress to streamline and update federal agencies regulations and policy statements. His

six-year term was due to expire in January 2002. BBR,

9/14/98.

Assessment Rates

The FDIC Board of Directors voted on October

27, 1998, to leave the current assessment rate schedules for the Bank Insurance Fund (BIF) and the

Savings Association Insurance Fund (SAIF) unchanged for the first half of 1999. Every six months

the FDIC decides whether to adjust the federal

deposit insurance premium scale for banks and

thrifts on the basis of projected changes in expected

insurance losses, growth in insured deposits, and

changes in the funds net income. The FDIC is

required to maintain a minimum reserve ratio of 1.25

percent, or $1.25 for every $100 of insured deposits,

in the BIF and the SAIF to cover the costs of resolving failed banks and thrifts. In addition, the FDIC

must administer the insurance funds under a riskbased assessment system, charging the weakest institutions a higher premium for deposit insurance. The

FDIC forecasts the BIF reserve ratio to range

between 1.38 and 1.44 percent by June 30, 1999. BBR,

11/2/98, p. 671.

Third-Quarter 1998 Financial Results

The BIF earned $808 million for the first nine

months of 1998, and the SAIF earned $363 million

for the same period. Both funds closed the third

quarter with record balances, the BIF with $29.1 billion and the SAIF with $9.7 billion. The continuing

low numbers of bank and thrift failures contributed

to the strong results. Only three BIF-insured banks

failed during the first nine months of 1998, and there

were no failures of SAIF-insured thrifts. Revenue for

the BIF totaled $1.3 billion, including $1.25 billion in

interest on investments in U.S. Treasury securities

and $16 million in deposit insurance assessments.

The SAIF earned $431 million in revenue during the

first three quarters of 1998, consisting of $421 million

in interest on investments in U.S. Treasury securities

and $10 million in deposit insurance assessments. 

The FSLIC Resolution Fund (FRF) assets in liquidation were reduced by 32 percent over the ninemonth period, to a balance of $1.6 billion on

September 30, 1998. Federal Financing Bank borrowings for the FRF, including accrued interest, were

fully repaid in August 1998. PR-75-98, FDIC, 11/5/98.

Real-Estate SurveyOctober 1998

The October 1998 issue of the Survey of Real

Estate Trends reported that the nations residential

and commercial real-estate markets remained largely

unchanged from the previous three-month period.

The survey, which was conducted in late October,

polled 295 senior examiners and asset managers from

federal bank and thrift regulatory agencies about

developments in their local markets in the preceding

three months. Results of the survey hinted that the

gains in commercial real-estate markets that had

been observed almost continuously since 1996

appeared to have leveled off. Respondents were less

positive about the strength of housing markets in

October than in recent surveys, with 36 percent noting better conditions than three months earlier, compared to 61 percent in July. However, most of this

difference represented a switch to reports of no

change (53 percent, compared to 35 percent in July),

and there was only a slight uptick in reports of worsening conditions.

The national composite index used by the FDIC

to summarize results for both residential and commercial real-estate markets was 62 in October, down

from 77 in July and from 71 in October 1997. Index

readings for residential and commercial markets were

down in every region. Index scores above 50 indicate

improving conditions, while index scores below 50

indicate declining conditions. Survey of Real Estate Trends,

FDIC, October 1998.

Report on Underwriting Practices

The October 1998 issue of the Report on Underwriting Practices reported a slight increase in the risk

associated with loan underwriting practices at FDICsupervised banks. The FDIC examiners reported

greater concerns about some current underwriting

practices, such as written lending policies differing

from actual practices, as well as concerns about the

risk in loan administration. The survey of loan

underwriting practices is aimed at providing early

warnings of potential problems in underwriting practices at FDIC-supervised, state-chartered nonmember banks. The focus of the survey is threefold:

material changes in underwriting standards for new

loans, degree of risk in current practices, and specific
FDIC Banking Review

28

aspects of the underwriting standards for new loans.

The October report includes surveys from 1,104

FDIC-supervised banks that were examined during

the six months ending September 30, 1998. Report on

Underwriting Practices, FDIC, October 1998.

Updated Trust Exam Manual

The FDIC revised its Trust Examination Manual,

simplifying and clarifying the materials that FDIC

examiners use to evaluate the safety and soundness

of depository institutions trust departments. The

new trust guidance includes revised principles of

trust department management, which are intended

to reflect current banking and fiduciary practices.

The FDIC advises that the institutions boards of

directors should ensure that trust departments conduct their activities away from all other areas of the

bank. In addition, trust assets should be separate

from other bank assets, and the assets of each trust

account should be separate from other trust assets.

The institution should maintain separate financial

books and records for the trust departments fiduciary activities. An institutions board of directors

should also designate a qualified officer to administer

the trust departments daily operations, and name a

trust committee consisting of at least three officers to

oversee the trust department. The guidance also sets

forth numerous minimum responsibilities for the

trust committee. FDIC FIL-100-98, 9/10/98; BBR, 9/21/98, p. 405.

Year 2000 Disclosure

On October 8, 1998, the FDIC sent an advisory

letter to depository institutions recommending that

the institutions follow guidance issued by the

Securities and Exchange Commission to provide

appropriate disclosure of Year 2000 issues in public

documents that report on the institutions financial

results. The SEC guidance, titled Statement of the

Commission Regarding Disclosure of Year 2000 Issues and

Consequences by Public Companies, and Municipal

Securities Issuers, went into effect on August 4, 1998.

BBR, 10/26/98, p. 636.

New Application Procedures

On November 5, 1998, the FDIC adopted a

revised regulation that expedites the processing of

applications filed by well-managed, well-capitalized

banks and savings associations that want to sell real

estate, underwrite securities or engage in a variety of

other activities. Institutions that meet specific eligibility requirements will merely have to notify the

FDIC of their intent to establish a majority-owned

securities or real-estate subsidiary. If the FDIC does

not object within 30 days, the applicant can begin

operations. PR-76-98, FDIC, 11/5/98.

Semiannual Agenda of Regulations

The FDIC published its semiannual agenda of

regulations in the Federal Register on November 13,

1998, to inform the public of the Corporations regulatory actions and encourage participation in the rulemaking process. Many of the actions are the result of

the FDIC Boards ongoing efforts to reduce the regulatory burden on banks, simplify rules, improve efficiency and comply with the Riegle Community

Development and Regulatory Improvement Act of

1994. The agenda contains 26 regulatory actions.

Five actions have been completed and the remainder

are in various stages of the rulemaking process. PR-77-

98, FDIC, 11/13/98.

Federal Reserve Board

Interest Rates

On September 29, 1998, the Federal Open Market

Committee voted to trim the targeted federal funds

rate by 25 basis points, which reduced the rate from

5.5 percent to 5.25 percent. This was the first time

the Federal Reserve Board eased the stance of monetary policy since January 31, 1996. Rates were further reduced on October 15, 1998, when the Federal

Reserve Board approved a 25-basis-point reduction

in the discount rate from 5 percent to 4 3/4 percent,

which dropped the federal funds rate down to 5 percent. The Board of Governors approved an additional 25-basis-point reduction in the discount rate on

November 17, 1998, which resulted in a discount rate

of 4 1/2 percent. The federal funds rate declined to

4 3/4 percent. FR-PR 9/29/98, 10/15/98 and 11/17/98.

Reduced Service Fees

On November 4, 1998, the Federal Reserve Board

voted to cut the fees for 1999 that regional Federal

Reserve Banks charge member institutions for electronic funds transfer and payment clearing services.

According to its 1999 fee schedules, the Federal

Reserve Banks will reduce the price index for

Fedwire funds transfers by almost 30 percent, and

reduce the fee for electronic securities transfers by

approximately 25 percent. In addition, the price
Recent Developments

29

index for automated clearinghouse services was

reduced slightly in an attempt to increase banks use

of the electronic payment system. The Federal

Reserve Board increased the fees for processing

paper checks by 3.7 percent over 1998 levels, in order

to give the banks incentive to use electronic services.

BBR, 11/9/98, p. 710.

New Threshold for Reserves

On November 24, 1998, the Federal Reserve

Board adjusted its policies for reserve requirements.

The new policies state that banks do not have to hold

reserves against the first $4.9 million of deposits and

other liabilities subject to reserve requirements,

which is an increase from the $4.7 million threshold.

Additionally, the new policies require banks to hold 3

percent in reserves on deposits between $4.9 million

and $46.5 million, and all remaining deposits are subject to a 10 percent reserve requirement. AB, 11/25/98.

Office of the Comptroller of the Currency

Hawke Named Comptroller

On December 7, 1998, President Clinton named

John D. Hawke, Jr. to serve as Comptroller of the

Currency. The President assigned Mr. Hawke to a

recess appointment, which allows Mr. Hawke to

serve one year without Senate confirmation. Mr.

Hawke has served as Treasury Undersecretary for

Domestic Finance since 1995. The Washington Post, 12/8/98.

Expanded Reinsurance Activity

According to an OCC decision made available on

October 8, 1998, a national bank may expand the

activities of its mortgage reinsurance operating subsidiary to include reinsuring mortgage insurance on

loans serviced by the bank or its affiliates. Mortgage

insurance protects a lender against default by the

mortgagor. Reinsurance is a process by which an

original insurer reduces its underwriting risk by passing all or part of this risk to another insurance company. The decision was made in response to a

request from Mellon Bank. BBR, 10/19/98, p. 601.

Community Bank Supervision Handbook

The OCC issued new guidance to examiners on

September 3, 1998, to make examinations of national banks with assets of less than $1 million more riskfocused and less burdensome to banks. The

Community Bank Supervision handbook combines

two existing handbooks, Community Bank Procedures

for Noncomplex Banks and Community Bank Risk

Assessment System. The new handbook will help alleviate unnecessary regulatory burden and make

exams more efficient by focusing examiner attention

on key areas of risk. BBR, 9/14/98, p. 356.

Fees on International Loans

On October 26, 1998, the OCC released a final

regulation requiring national banks to use generally

accepted accounting principles for fees on international loans. The rule, which is effective on January

1, 1999, applies to fees that banks charge when they

restructure international loans. The rule prohibits

banks from charging fees that exceed the administrative costs of restructuring the loan, unless that fee is

amortized over the life of the loan. BBR, 11/2/98, p. 677.

Office of Thrift Supervision

New Receivership

On October 8, 1998, the Office of Thrift

Supervision placed New West Federal Savings and

Loan Association in receivership and appointed the

FDIC as receiver. New West was created in 1988 to

liquidate the nonperforming assets of the failed

American Savings, Stockton, California. New Wests

assets total $18.4 million. The institution has no

deposits and had fulfilled its task of self-liquidation.

OTS 98-77, 10/8/98.

No Changes to 1999 Thrift Report Form

The OTS announced on November 24, 1998, that

it will make no changes to the 1999 quarterly Thrift

Financial Report form (TFR) in order to allow the

thrift institutions to concentrate on Year 2000 compliance issues. The OTS had proposed to reorganize

the TFRs equity section and to collect additional

information on the TFR, such as high loan-to-value

lending data. However, the OTS decided to postpone any changes. OTS 98-85, 11/24/98.

New Assessment Rates

The OTS adopted a revised formula to bring the

assessments on savings institutions more in line with

the actual cost of regulating them. The new assessment formula will be based on three components: a

thrifts size, its condition, and the complexity of its

operations. The new size component retains the cur-
FDIC Banking Review

30

rent economies of scale approach, but also builds

some of OTS fixed supervisory costs into the base

assessment level. Under the new formula, institutions with a CAMELS rating of 4 or 5 will continue

to pay a 50 percent premium over the asset-based

assessment because they require more supervision.

However, for the first time, institutions with a

CAMELS rating of 3 will be required to pay a 25 percent premium. In addition, the OTS will charge a

higher assessment to large thrifts that are involved in

complex activities. The OTS estimates that over 90

percent of the thrifts it supervises will pay the same

or a lower assessment under the new schedule. The

new formula is effective for the first semiannual

assessment of 1999. OTS 98-88, 11/30/98.

Derivatives Rule and Risk Management

Guidance

On December 1, 1998, the OTS adopted new

comprehensive guidance to assist savings associations in managing their risk. The guidance, which

covers interest-rate risk, investment securities, and

use of financial derivatives, is published in the Thrift

Bulletin 13a. This bulletin replaces seven existing

OTS thrift bulletins. The OTS also adopted a new

rule that replaces three outdated regulations concerning forward commitments, futures transactions,

and financial options transactions. The new rule

establishes requirements applicable to all derivative

instruments and is designed to work in conjunction

with the new bulletin. The goal of the new rule is to

ensure the safe-and-sound management of the risks

associated with financial derivatives and reduce regulatory burden. The rule is effective on January 1,

1999, and the bulletin is effective upon publication.

OTS 98-90, 12/1/98.

National Credit Union Administration

New Conversion Rule

On November 19, 1998, the National Credit

Union Administration approved an interim final rule

that revised the agencys regulations on the conversion of federal credit unions to mutual savings banks.

The revisions are intended to simplify the charter

conversion process and reduce regulatory burden for

insured credit unions that choose to convert. Under

the new rule, conversion to a mutual savings bank

requires the approval of the credit unions members,

but credit unions no longer need prior NCUA

approval. However, a credit union must provide the

NCUA with notice of its plans to convert at least 90

days before the membership vote. The new rule was

mandated by the Credit Union Membership Access

Act. BBR, 11/30/98, p. 833.

Federal Housing Finance Board

Board Appointee

On October 21, 1998, President Clinton

announced the nomination of Douglas M. Miller to

the Federal Housing Finance Board. Mr. Miller previously served as Chief of Staff and Administrative

Assistant to former Senator Larry Pressler (R-SD)

from 1991 to 1997, and also served as Legislative

Director and Legislative Assistant from 1979 to 1991.

If approved, Mr. Miller would replace Art Agnos, who

is the Department of Housing and Urban Developments designee on the Board. BBR, 10/26/98, p. 647.

Mortgage Pilot Program Expanded

On September 23, 1998, the Federal Housing

Finance Board authorized the expansion of a

Mortgage Partnership Finance Program in a manner

that would allow any Federal Home Loan Bank to

offer a Mortgage Partnership Finance Program to its

members. The program, which was piloted in

Chicago in late 1996, allows FHLBank members to

sell loans to the FHLBank rather than to Fannie Mae

or Freddie Mac, and thus avoid paying guarantee

fees to Fannie Mae or Freddie Mac. The FHLBank

can sell participation interests in the program to other

FHLBanks. The program is capped at $9 billion for

all 12 FHLBanks. BBR, 9/28/98, p. 473.

Community Lending Rules Approved

On October 28, 1998, the Federal Housing

Finance Board approved two final rules that extend

community lending options for the Federal Home

Loan Bank System. The rules establish a framework

for Community Investment Cash Advance (CICA)

programs and provide guidance for issuing standby

letters of credit. In 1989, Congress gave the

FHLBanks the authority to make advances to support community investment with CICAs; however,

there were no standards for what types of loans the

FHLBanks may support under this authority. Under

the new regulation, FHLBanks will develop and

adopt an annual Community Lending Plan, which

requires each FHLBank to identify and address
Recent Developments

31

credit needs and market opportunities. The new

rule will help spread out advances to support more

economic development and housing credit needs.

The Finance Board adopted a second rule that

provides guidance on existing policies to permit

expanded use of standby letters of credit by the 12

FHLBanks. Standby letters of credit are issued by

FHLBanks to a member to serve as a form of guarantee that the member will fulfill its obligations

under a contract with a third party. The new rule

expands the types of collateral the FHLBanks can

accept for letters of credit to include items such as

secured small-business loans and investment-grade

bonds. However, the expanded letters of credit are

restricted to those that support the FHLBanks mission of economic development and housing. BBR,

11/2/98, p. 685.

STATE LEGISLATION AND REGULATION

Louisiana

On October 20, 1998, the OCC granted national

banks in Louisiana the power to sell insurance from

offices in big cities. The OCC ruling expands the

powers awarded to national banks in 1997, which let

banks sell insurance statewide as long as the insurance subsidiary was based in a place with 5,000 or

fewer residents. The new ruling marks the first time

the OCC has explicitly ruled that banks may open

insurance agency offices outside of small towns. The

OCC based its ruling on a Louisiana law that specifically granted bank insurance affiliates the same

rights as other insurance agencies. AB, 11/12/98.

Pennsylvania

A proposed bill would require the FDIC to insure

all Pennsylvania building-and-loan institutions.

Pennsylvanias building-and-loan institutions are

currently insured by the Pennsylvania Savings

Association Insurance Corporation, a private entity

created in 1979. However, regulators argue that private insurance is less secure than federal deposit

insurance. The bill would give building-and-loan

institutions 30 months to either get FDIC insurance,

merge with an institution that already has FDIC

insurance, or liquidate. AB, 12/4/98. 

BANK AND THRIFT PERFORMANCE

Second-Quarter 1998 Results for

Commercial Banks and Savings

Institutions

Insured commercial banks posted record profits in

the second quarter of 1998, with net income for the

three-month period at an all-time high of $16.1 billion. Strong growth in noninterest revenues, especially income from trust activities and other fees,

attributed to much of the increase in banks secondquarter earnings. Banks annualized return on assets

(ROA) was 1.25 percent in the second quarter, compared to 1.26 percent in the first quarter and 1.24

percent in the second quarter of 1997. The number

of problem banks declined from 67 to 64 during the

quarter, while assets of problem banks remained at

$5 billion. There was one failure of an insured commercial bank during the second quarter.

FDIC BIF-insured mutual savings institutions

posted earnings of $2.8 billion in the second quarter

of 1998, up from the previous record of $2.6 billion

set in the second quarter of 1996. The annualized

ROA rose to 1.09 percent in the second quarter, compared to 1.01 percent in the first quarter and 0.95

percent in the second quarter of 1997. The number

of problem thrifts increased by 2 to 18 at the end of

the second quarter 1998, and assets of problem

thrifts rose from $2.3 billion to $2.9 billion. FDIC

Quarterly Banking Profile, Second Quarter 1998.

Second-Quarter 1998 Results 

for Thrifts

The nations thrift industry earned $2.1 billion in

the second quarter of 1998, which was up from $1.87

billion in the first quarter of 1998 and $1.69 billion in

the second quarter of 1997. Profitability and capital

levels also increased in the second quarter, while

troubled assets and delinquent loans decreased. The

equity capital to assets ratio rose from 8.40 percent in

the first quarter to 8.56 percent in the second quarter

of 1998.

The thrift industrys ROA was 1.07 percent in the

second quarter, up from 0.97 percent in the first

quarter and 0.89 percent in the second quarter of

1997. The number of problem thrifts increased to

18, compared to 14 institutions in the first quarter of

1998 and 25 in the second quarter of 1997. The OTS

attributed the strong industry earnings to higher fee

income, improved asset quality, gains on sales of
FDIC Banking Review

32

mortgage loans to the secondary market, and a stable

net interest margin. OTS 98-68, 9/2/98. 

Third-Quarter 1998 Results for

Commercial Banks and Savings

Institutions 

The commercial banking industry earned $15 billion in the third quarter of 1998, following the record

$16.1 billion in profits earned in the previous quarter.

The earnings decline was caused by weaknesses in

the overseas operations and the trading activities of a

few of the largest banks. The ROA fell to 1.15 percent in the third quarter, down from 1.25 in the second quarter of 1998 and from 1.22 percent in the

third quarter of 1997. The number of problem banks

increased from 64 to 70 during the quarter, and assets

of problem banks rose from $5.0 billion to $5.4 billion. Two commercial banks failed during the quarter.

FDIC BIF-insured mutual savings institutions

reported $3.0 billion in net income for the third quarter of 1998, surpassing the previous industry record of

$2.8 billion set in the second quarter. The industrys

average annualized return on assets was 1.14 percent,

the second consecutive quarterly record for this profitability measure. For the eighth consecutive quarter, there were no savings institution failures. The

number of problem thrifts remained unchanged at 18

and assets for this group were stable at $2.9 billion.

FDIC Quarterly Banking Profile, Third Quarter 1998.

Third-Quarter 1998 Results for Thrifts

The thrift industry earned a record $2.23 billion in

the third quarter of 1998, passing the previous record

of $2.10 billion in the second quarter. The industrys

equity capital also reached a new high, while its ratio

of troubled assets to total assets declined to a new

low. The equity capital to total assets ratio reached a

record high of 8.58 percent in the third quarter, up

from the previous record of 8.56 percent in the second quarter. Ninety-eight percent of OTS-supervised thrifts met or exceeded the requirements for

the highest capital category, well-capitalized.

The thrift industrys ROA was 1.13 percent in the

third quarter, which exceeded the previous record of

1.07 percent in the second quarter of 1998 and was

up from 0.71 percent in the third quarter of 1997.

The number of problem thrifts remained at 18 in the

third quarter of 1998, which is the same as in the second quarter. In the third quarter of 1997, the OTS

reported that there were 23 problem thrifts. PR 98-91,

OTS, 12/2/98.

RECENT ARTICLES AND STUDIES

On June 11, 1998, the Federal Reserve Board

released a report entitled Annual Report to the

Congress on Retail Fees and Services of Depository

Institutions. The report concludes that banks operating in multiple states generally charged significantly

higher fees than those operating in a single state.

The Independent Bankers Association of America

(IBAA) analyzed the data and suggested that the size

of a bank is a greater factor in fee assessment than

geography. According to a September 1998 IBAA

analysis of the Federal Reserve Boards report to

Congress, fees and minimum balance requirements

at large banks were 20 to 79 percent higher than at

small banks in 1997. The studies define small banks

as having less than $100 million in assets, and large

banks as having more than $1 billion in assets. BBR,

9/21/98, p. 406.

A study published by the Federal Reserve Bank of

Dallas reports that the recent trend of mergers

among financial-services institutions shows that the

industry is creating new opportunities from breakthroughs in technology and deregulation, and as a

result, new forms of competition are emerging in the

industry. The report, entitled Bank Mergers: Creating

Value or Destroying Competition, states that technology is creating incentives to merge because advancements in communications have created a wider array

of service products. Although mergers have reduced

the number of institutions in the marketplace, an

increased variety of financial-services offerings and

reduction in geographic restrictions have kept competition strong within the industry, the study said.

BBR, 11/30/98, p. 822.

A paper entitled Bank Diversification: Laws and

Fallacies of Large Numbers states that bigger banks

are not necessarily safer than smaller institutions.

The paper states that banks do not become less risky

as they grow larger because any single failure

becomes more harmful to the economy. Instead,

banks become less likely to fail. The paper was written by Joseph G. Haubrich, an economist at the

Federal Reserve Bank of Cleveland. AB, 12/11/98.

A paper written by Kevin T. Jacques of the OCC

and Raj Aggarwal of John Carroll University reports
Recent Developments

33

that prompt corrective action rules have caused

banks to increase capital and operate more safely.

The authors report that equity capital held by U.S.

banks rose 28 percent in the two years after Congress

adopted the Federal Deposit Insurance Corporation

Improvement Act of 1991, which authorized regulators to seize banks when capital falls below two percent. The paper is entitled Assessing the Impact of

Prompt Corrective Action on Bank Capital and Risk.

AB, 11/13/98.

INTERNATIONAL DEVELOPMENTS

Japan

On October 12, 1998, Japans Parliament approved

legislation that is intended to revive the banking system. The legislation allows the Government to deal

with large, failing banks by nationalizing them, liquidating them or transforming them into publicly

owned bridge banks, which take over the good

loans and try to collect on the bad loans. Under the

new law, the Government would establish a public

institution to resell the good assets and deal with the

bad ones in a process similar to that of the Resolution

Trust Corporation, which was established to help

resolve the savings-and-loan crisis in the early 1990s.

Japanese Prime Minister Keizo Obuchi appointed

a new minister to oversee the cleanup of the financial

sector. Hakuo Yanagisawa was appointed to the new

ministerial post and will oversee the use of up to 60

trillion yen of public money to rebuild the banking

system.

On October 23, 1998, Prime Minister Obuchi

announced that Japan would nationalize its tenthlargest bank, Long-Term Credit Bank of Japan Ltd.

(LTCB), in order to protect the stability of the financial system. Under the nationalization plan, the

Government will buy out all of LTCBs shares at a

price near zero and provide funds to keep the bank

operating. All of LTCBs deposits, debentures and

financial market obligations will be guaranteed with

government money, and a new board of directors will

be appointed.

The Government announced another bank

nationalization on December 13, 1998, when it took

over Nippon Credit Bank Ltd. Regulators are working out a plan for disposing of Nippon Credits assets.

Nippon Credit had approximately $27 billion in

problem loans and liabilities that exceeded assets by

$787 million. The New York Times, 10/13/98; WSJ, 12/14/98.

Russia

Russias three major commercial banks, Oneximbank, Bank Menatep, and Most Bank announced

that they would merge into a new bank by January 1,

1999. Russias Central Bank welcomed the merger

by the former archrivals, and stated that the merger

is a factor of stability for the entire Russian banking

system. The recent financial turmoil in Russia is

expected to cause additional bank mergers. There

are currently 1,600 commercial banks in Russia,

compared to 2,500 only a few years ago. BBR, 8/31/98, p.

342.

A statement issued on November 21, 1998,

announced that Russias Cabinet and Central Bank

have set up a new federal agency with wide-ranging

powers for restructuring the countrys ailing banking

system. The Agency for Restructuring Credit

Organizations (ARKO) will oversee bankruptcy procedures to be launched against failing banks, serve as

the governments vehicle for strengthening the

banking sector, and shape a market for debts of credit organizations and their debtors. ARKO will have

the right to acquire controlling stakes in failed banks,

manage and sell their equity stakes, fire and hire

bank managers, and attract financial resources by

issuing securities both for the operation of banks

under its control and for its own needs. The agency

will be managed jointly by the Cabinet and the

Central Bank. BBR, 11/30/98, p. 845.

Basle Committee

The Basle Committee on Banking Supervision

issued a paper on September 22, 1998, entitled

Framework for Internal Control Systems of Banking

Organizations. In the paper, the Basle Committee

presents the first internationally accepted framework

for supervisors to use in evaluating the effectiveness

of the internal controls over all on- and off-balancesheet activities of banking organizations. The paper

describes elements that are essential to a sound

internal control system, recommends principles that

supervisors can apply in evaluating such systems,

and discusses the role of bank supervisors and external auditors in this assessment process. The internal

control framework described in the paper is designed

for international banking organizations. The Com-
FDIC Banking Review

34

mittee issued two additional guidance papers on

September 22, 1998, which dealt with transparency

and operational risk management. FR-PR 10/22/98.

The Basle Committee issued a fourth paper on

October 14, 1998, which provides guidelines to

encourage banks to disclose more about their credit

risks and to adopt more universal accounting techniques for assessing loans. While most of the sound

practices recommended by the Committee follow

generally accepted U.S. standards, the public disclosure guidance is new for many countries. The report

stipulates that not all banks need make all the disclosures. An institution may not necessarily provide

all the disclosures recommended if a particular disclosure item is not relevant to the assessment of the

bank. On the other hand, banks relying on capital

markets and larger institutions with complex operations would be expected to make more extensive disclosures. BBR, 10/19/98, p. 607.

On October 28, 1998, the Basle Committee issued

an interpretation on instruments that are eligible for

Tier 1 regulatory capital treatment under the Basle

capital framework of the Basle Accord. The Basle

Accord was endorsed by the G-10 Governors in 1988

and applies to internationally active banks. The

Committees interpretation stresses the importance

of ensuring that the predominant form of Tier 1 capital in the Basle Accord framework is in the form of

common shareholders funds. The interpretation

also states that minority interests in equity accounts

of consolidated subsidiaries that take the form of special-purpose vehicles may be included in Tier 1 capital provided they meet certain criteria, including

permanence and the ability to absorb losses within

the bank on a going-concern basis. The interpretation also stresses the need for banks to disclose periodically each component of Tier 1 capital. The

FDIC and the OCC will work with the other U.S.

banking agencies to determine how the Basle

Committee interpretation will be applied to U.S.

banks. PR-72-98, FDIC, 10/28/98.

Mexico

On September 14, 1998, the Mexican political parties agreed on basic principles for resolving the deadlock over FOBAPROA, a 1995 bank bailout fund that

rescued the Mexican banking system by buying nonperforming loans from struggling Mexican banks.

Mexican President Ernesto Zedillo Ponce de Leon is

seeking to convert the liabilities of FOBAPROA into

public-sector debt, which ultimately would be paid

by Mexican taxpayers. But opposition parties in the

Mexican congress have blocked movement on the

issue, saying that banks managed by corrupt or

incompetent bankers should pay for the bailout. The

inability to resolve the bank bailout issue has

weighed heavily on Mexican markets and contributed to a depreciation of the peso. The new

agreement is too vague to be considered a solution to

FOBAPROA, but it shows willingness on the part of

Mexicos government to punish corrupt bankers. BBR,

9/28/98, p. 484.