Banking Review 

2003 VOLUME 15, NO. 3 

Using Market Information to Help Using Market Information to Help

Identify Distressed Institutions Identify Distressed Institutions

The SCOR System of Off The SCOR System of Off-Site

Monitoring Monitoring

Recent Developments ecent Developments

FEDERAL DEPOSIT INSURANCE CORPORATION 
 

Banking Review 

2003 VOLUME 15, NO. 3 

Using Market Information to Help Identify
 

Distressed Institutions: A Regulatory Perspective (page 1)
 

by Timothy J. Curry, Peter J. Elmer, and Gary S. Fissel 

This article explores the notion that publicly available stock price, return, and other market-related 

variables can provide timely information about bank and thrift financial condition; the article also 

determines whether such information can be used to improve the predictive accuracy of traditional 

off-site monitoring models for the purpose of anticipating changes in the CAMEL ratings assigned by 

regulators. 

The SCOR System of Off-Site Monitoring:
 

Its Objectives, Functioning, and Performance (page 17)
 

by Charles Collier, Sean Forbush, Daniel A. Nuxoll, and John O’Keefe 

The FDIC monitors all insured institutions and attempts to identify previously sound institutions 

that have developed significant weaknesses. In late 1998, the FDIC adopted a statistical model, 

SCOR, as its basic method of monitoring insured institutions between examinations. This article 

describes how the SCOR model was designed to maximize accuracy and to give analysts insight into 

the potential weaknesses of financial institutions. 

Recent Developments Affecting Depository 

Institutions (page 33) 

by Lynne Montgomery 

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. 

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 

FDIC’s Division of Insurance and Research with a copy of any publications containing reprinted material. 

Chairman Donald E. Powell 

Director, Division of Insurance Arthur J. Murton 

and Research
 

Deputy Director Fred Carns
 

Executive Editor George Hanc
 

Managing Editors Jack Reidhill
 

Lynn Shibut
 

Publication Manager Geri Bonebrake
 
Identifying Distressed Institutions 

Using Market Information to Help Identify 

Distressed Institutions: A Regulatory Perspective 

Timothy J. Curry, Peter J. Elmer, and Gary S. Fissel* 

In recent years the call for incorporating market 

signals into bank supervision has spread from academic circles to U.S. bank regulators, Congress, 

and international regulatory bodies.1 Donna 

Tanoue, Chairman of the Federal Deposit Insurance Corporation (FDIC) from 1998 to 2001, 

Alan Greenspan, Chairman of the Federal 

Reserve System, and other Federal Reserve Governors have commented on the importance of 

harnessing market forces to help with supervisory 

* Timothy Curry and Gary Fissel are senior financial economists in the Division of Insurance and Research of the Federal Deposit Insurance Corporation (FDIC). Peter Elmer was a senior economist in the FDIC’s Division of 

Research and Statistics when this work was being conducted. The authors 

would like to thank John O’Keefe, Daniel Nuxoll, Gerald Hanweck, and 

Richard Bogue for helpful comments, and Audrey Clement and Justin Combs 

for extensive research assistance. 

1 This note focuses on the academic literature. Flannery (1998) summarizes 

this literature through the late 1990s. More recently, Berger and Davies 

(1998) use event-study methodology to find that the equity market anticipates upgrades in regulatory ratings but follows downgrades. Berger, 

Davies, and Flannery (2000) find that regulators acquire information sooner 

than the equity markets and bond rating agencies do, but the regulatory 

assessments are generally less accurate than either stock or bond market 

indicators in predicting the future performance of bank holding companies. 

Elmer and Fissel (2000) find that equity market variables can be used to 

augment accounting-related information to predict bank failure. Krainer and 

Lopez (2001) find that equity market variables such as stock returns and 

equity-based default frequencies are useful to bank regulators for assessing 

the condition of bank holding companies. Gunther, Levonian, and Moore 

monitoring and to encourage market discipline.2 

The Basel Committee on Bank Supervision, 

which establishes capital standards for international banks, recently proposed using market 

forces as one of its “three key pillars” of comprehensive capital-adequacy regulations.3 

Interest in the use of market information arises 

from the ability of financial markets to interpret 

public information very quickly. Even though 

bank supervisors have an advantage over the 

market owing to their access to extensive private 

information from on-site bank examinations, 

these examinations occur only after relatively 

long intervals, usually every 12 to 18 months, and 

(2001) find that a measure of financial viability based on stock prices 

(expected default frequency) helps predict the financial condition of bank 

holding companies as reflected in their supervisory ratings. Curry, Elmer, 

and Fissel (2001) find that incorporating market data into traditional off-site 

monitoring models helps identify downgraded and upgraded banks and 

thrifts that were not affiliated with multibank holding companies. Curry, 

Fissel and Hanweck (2003) find that market-indicator variables add value to 

models in predicting bank holding company supervisory ratings. 

2 Tanoue (2001); Greenspan (1998); Meyer (1998). The term market discipline generally refers to the ability of the market to price or impose costs 

on institutions based on their risk. The costs, for example, might take the 

form of higher issuance costs in the bond markets and/or lower equity 

prices. 

3 The three pillars include minimum capital requirements, supervisory review 

and market discipline. 

FDIC BANKING REVIEW 1 2003, VOLUME 15, NO. 3 
 

Identifying Distressed Institutions 

may quickly become outdated.4 As for off-site 

reviews, they depend on quarterly accounting 

data that may not be audited or may not reflect 

the changing risk profile of the institution. However, these same quarterly accounting data are 

widely available to the public and therefore are 

used by financial markets as well as regulators to 

assess risk. If financial markets can process and 

interpret this public information more efficiently 

than bank regulators, market prices might either 

complement or supplement the off-site and/or onsite monitoring systems used by the regulators. 

Studies that have examined the potential use of 

market signals in bank supervision have focused 

primarily on the debt market for signs of changing 

risk patterns in insured institutions. This focus 

has been popular because the concerns of 

investors in these markets, particularly subordinated-debt investors, are closely aligned with the 

concerns of bank supervisors. Equity markets, 

however, should provide as much information as 

debt markets because equity investors are the first 

to lose if a bank experiences problems.5 Moreover, the number of banking institutions with 

publicly traded equity is much larger than the 

number of institutions with publicly traded subordinated debt, and trading volume tends to be 

much higher for equities than for subordinated 

debt. 

The purpose of this article is to assess the relationship, in timing and magnitude, between equity market valuations of commercial banks and 

thrift institutions and changes in the supervisory 

ratings for these organizations. In particular, we 

ask two questions: to what extent do market variables such as stock prices, returns, and trading 

volume (among others) provide timely market 

signals? And if they do provide timely signals, 

can they add incremental value to off-site moni4 Federal law mandates that all federally insured banking institutions be 

examined at least every 12 to 18 months, depending on the size and condition of the institution. Weaker institutions are often subject to more frequent scrutiny. For evidence that bank examinations may age quickly, see 

Cole and Gunther (1998). 

5 Levonian (2001) has shown that equity market information and debt market information should produce similar results. 

toring systems that attempt to predict changes in 

the CAMEL ratings assigned by regulators?6 We 

begin to address these questions by discussing the 

institutional setting for the downgrading of a 

bank’s CAMEL rating. We then evaluate problems associated with interpreting market data 

before examination ratings are changed. Finally, 

we perform statistical tests to test the incremental 

predictive content of market-related variables 

compared with accounting data from bank financial reports. 

The Institutional Setting 

Modern bank supervision uses information gathered from on- and off-site supervisory tools as the 

starting point for its analysis. The larger banks 

and bank holding companies are monitored by 

on- and off-site inspectors (examiners), who keep 

abreast of any information that can be found, 

including news reports, Wall Street analyses, and 

traditional quarterly financial data.7 Most smaller 

and midsized banks are initially monitored with 

automated analysis of quarterly financial statements and then, if risk is identified, are reviewed 

by analysts in addition to regular on-site examinations. 

Periodic on-site safety-and-soundness examinations begin with off-site pre-exam reviews of quarterly and other pertinent data. This information 

is then checked in on-site reviews, which also 

explore issues that might not be revealed in the 

quarterly reports. In fact, on-site examinations 

provide extensive financial information that is 

not generally available to the public, such as the 

current status of performing and nonperforming 

6 The acronym “CAMEL” stands for Capital, Assets, Management, Earnings, 

and Liquidity, five components of a bank’s financial operation that are 

examined by the regulators. In the late 1990s a sixth component was 

added to the CAMEL rating system, recognizing bank and thrift Sensitivity 

to interest-rate or market risk (CAMELS). CAMELS ratings are assigned on a 

scale of 1 to 5 with 1 being the highest and 5 the lowest. Because the 

empirical portions of our analysis relate to ratings assigned before the late 

1990s, we reference the five-component rating system in effect at that 

time. 

7 It should be noted that for the largest U.S. banks, in recent years the 

Comptroller of the Currency and other regulators (including the FDIC) have 

established supervisory programs with continuous on-site presence. 

2003, VOLUME 15, NO. 3 2 FDIC BANKING REVIEW 
 

Identifying Distressed Institutions 

loans, loan classifications and the adequacy of 

loan-loss provisions, and bank capital; on-site 

examinations also provide a close-up view of 

managerial abilities and expertise. 

At the end of on-site examinations, bank inspectors assign an overall, or composite, CAMEL rating (see note 6); these ratings range from 1 to 5. 

Ratings of 1 or 2 are assigned to institutions in 

fundamentally sound financial condition. When 

a previously 1- or 2-rated bank is downgraded to 

3, an important signal of supervisory concern is 

sent and is normally accompanied by an understanding between the bank’s primary regulator 

and senior bank management specifying the 

nature of the bank’s weakness and procedures for 

changing bank policies to rectify the perceived 

problems. These understandings are classified by 

regulators as “informal” enforcement actions 

because they are not administratively or judicially 

enforceable in a court of law in the event of noncompliance.8 Nevertheless, such actions represent a loud “shot across the bow” signaling 

significant regulatory concern and the need for 

change. Institutions downgraded to a 3 will typically retain that rating for periods ranging from 

six months to several years before being assigned 

a higher or lower rating. 

Downgrading a bank’s CAMEL rating to 4 or 5 

indicates the existence of serious problems that, if 

not resolved, present a distinct possibility of insolvency. In practice, the term “problem” bank is 

often reserved for institutions with a composite 

rating of 4 or 5, and regulatory “problem-bank 

lists” tend to specify institutions with these ratings, although practices vary. Banks downgraded 

to 4 typically require immediate remedial actions 

and intensive monitoring by regulatory officials. 

In some cases, bank supervisory officials may opt 

not to choose the more serious “formal” enforcement actions for 4-rated banks as long as bank 

management addresses regulatory concerns. 

8 Informal enforcement actions may require institutions to make changes, 

such as raising new equity capital, limiting the origination of certain types 

of loans, or increasing loan-loss reserves. Although regulators vary in their 

practices, the most common type of informal action accompanying a downgrade to 3 is a “memorandum of understanding” (MOU), which is written 

by bank supervisors and signed by bank officials and supervisors. 

However, consistent with supervisory policy, most 

banks downgraded to a 4 or 5 are subject to formal enforcement actions, and these actions have 

been made public since 1989.9 Institutions with a 

CAMEL rating of 4 can continue in business for 

as long as several years before either returning to 

an improved rating, moving to a worse rating, or 

being declared insolvent by their primary regulator. A rating of 5 indicates a high probability of 

failure, usually within the next 12 months. 

Interpreting Market Signals 

If information embedded in market prices is to be 

integrated into the off-site monitoring process, 

the message contained in the information must be 

clear and timely and must add incremental predictive value to other sources of information commonly used by off-site monitoring, such as the 

quarterly financial data. If these characteristics 

are lacking, the value of the information declines 

either because its interpretation is vague or 

because it fails to improve existing supervisory 

practices. 

The interpretability and practical usefulness of 

market information are keys to integrating it into 

off-site monitoring. (Here we discuss interpretation; in the remaining sections of the article we 

discuss usefulness.) Market prices are notorious 

for their wide fluctuations over short periods of 

time, and interpreting the information contained 

in prices that repeatedly jump upward and downward may be difficult. Although short-term fluctuations would be reduced if the focus were on 

longer-term price and return trends, the choice of 

a time period to use for these types of analyses is 

subjective, and smoothing trends over longer periods reduces the timeliness of the information. 

Interpretation issues aside, the use of market data 

would open the door to a substantial list of variables that might be helpful in bank analysis. Two 

such variables are return volatility and trading 

9 The Financial Institutions Reform, Recovery, and Enforcement Act of 1989 

(FIRREA) mandated that formal enforcement actions become part of the 

public record. 

FDIC BANKING REVIEW 3 2003, VOLUME 15, NO. 3 
Identifying Distressed Institutions 

volume. For example, Merton’s (1973) option 

model expects a rise in return volatility as an 

institution approaches insolvency. Wang (1994) 

ties trading volume to the flow of information 

about a firm’s financial health, suggesting that 

trading volume should rise as information about 

financial distress is released. Although a comprehensive analysis of market-related variables goes 

beyond the scope of this article, return volatility 

and trading volume represent two that are easily 

observed and that are expected by financial theory to contain predictive content. 

In summary, although the interpretability of price 

and other market changes remains an issue, there 

are nevertheless compelling arguments for finding 

ways to integrate market data into the off-site 

monitoring process, and there are also a variety of 

market variables that might be used to this end. 

Therefore, debate about regulatory use of marketrelated information in prudential bank supervision should focus on empirical, not conceptual, 

issues. One particular empirical issue is whether 

market-related variables add incremental predictive value to quarterly accounting data or other 

information that is easily available to regulators 

in off-site monitoring systems. Unless market signals increase predictive value, they may be 

viewed as redundant information with little 

supervisory value. 

The Sample 

Our empirical analysis begins with a sample of 

publicly traded banks and thrifts whose ratings 

were downgraded to problematic levels between 

the first quarter of 1988 and the last quarter of 

1995.10 Since a CAMEL rating of 3 signifies 

10 The sample population was drawn from a universe of all banks and 

thrifts from 1988 to 1995 that were publicly traded, as reflected in the 

availability of stock price information from the Center for Research in Security Prices (CRSP). To obtain stock price information for individual commercial banks and thrifts, we matched CRSP data against bank quarterly 

reports going back to 1986. We then matched the firms against bank 

examination ratings to obtain the historical CAMEL ratings. Within the 

group for which all this information was available, we identified all institutions that were downgraded to a 3, 4, or 5 during our period. To form the 

sample in our study, we reduced this group by the additional restrictions 

discussed in the next paragraph of the text. The sample of CAMEL 1- or 

2-rated, or “healthy,” banks against which the downgraded groups were 

matched was also taken from this universe of publicly traded institutions 

(see note 14). 

significant regulatory concern but ratings of 4 and 

5 signify more severe financial distress that is 

often followed by failure, we separate institutions 

downgraded to 3 from those downgraded to 4 or 

5. Combining the 4s and 5s into a single group 

appeared reasonable inasmuch as institutions pass 

to failure from these two ratings fairly commonly, 

but do so from a rating of 3 or better only occasionally. 

To improve the integrity of the analysis, we 

imposed several additional restrictions. The sample was limited to institutions that had a lengthy 

period of superior ratings before being downgraded. We implemented this condition by requiring 

that institutions have CAMEL ratings in the 1–2 

range for at least two years before being downgraded to 3. Similarly, institutions downgraded to 

a 4 or 5 were required to have had ratings in the 

1, 2, or 3 range for at least two years preceding 

downgrade to 4 or 5. The sample was also limited 

to banks and thrifts that either were not affiliated 

with bank holding companies or were members of 

bank holding companies that held only a single 

institution. Restricting the sample in this fashion 

ensured that the extensive financial data reported 

on bank quarterly reports corresponded closely to 

the institution that issued the stock. This restriction also reduced contamination from activities of 

nonbank subsidiaries of bank holding companies.11 Since the empirical analysis combines 

quarterly financial data with stock market information reported by the Center for Research in 

11 Analysis of multibank holding company equity securities carries disadvantages (as well as advantages) compared with analysis of non-affiliated 

banks and thrifts and one-bank holding companies. For example, multibank 

holding companies tend to be large institutions that are widely traded and 

rated by nationally recognized rating agencies. Although one-bank holding 

companies and banks not affiliated with holding companies tend to have 

the opposite characteristics, their quarterly financial data nevertheless correspond directly to the institution that is publicly traded, and the quarterly 

financial data are far more extensive than financial data released at the 

holding-company level. Moreover, the many activities of holding company 

subsidiaries cannot be separated from the aggregated data reported at the 

holding-company level, and this lack of separability obscures the extensive 

information released by individual banks. Market signals at the holdingcompany level may or may not correspond to the performance of the bank 

subsidiary. The potential disconnect between the performance of individual 

banks and the market signals of their holding companies may widen as 

holding companies respond to passage of the Gramm-Leach-Bliley Act of 

1999 by diversifying into additional nonbank activities. 

2003, VOLUME 15, NO. 3 4 FDIC BANKING REVIEW 
 

Identifying Distressed Institutions 

Security Prices (CRSP), both sources of data were 

required for an institution to be included in the 

sample; in addition, historical CAMEL ratings 

over the period had to be available. For the logistic regressions, the downgraded banks in each of 

the two groups are paired against a sample of 

healthy banks (those rated a 1 or 2). 

Table 1 provides summary statistics for the two 

groups of downgraded institutions. The sample 

size is relatively large for both groups, with 83 

institutions downgraded to 3 and 107 downgraded 

to 4 or 5. Considerable diversity is apparent in 

the sample. For example, both groups of downgraded institutions include thrifts as well as banks, 

and both groups had a wide range of asset sizes, 

encompassing institutions with total assets under 

$100 million as well as institutions with assets 

over $5 billion. More than 75 percent of the 

institutions had assets under $1 billion, while 

slightly less than 20 percent had assets in the 

$1–5 billion range and about 5 percent were in 

the over-$5 billion range. The relatively healthier condition of institutions downgraded to 3 is 

reflected in their higher book equity-to-assets and 

return-on-assets ratios compared with the ratios 

Table 1 

reported for institutions downgraded to 4 or 5. 

Stronger financial health appears to be recognized 

by the market, as reflected in a more favorable 

book-to-market equity ratio for institutions downgraded to 3, compared with the ratio for those 

downgraded to 4 or 5. 

Univariate Trends Preceding Downgrades 

Table 2 displays univariate characteristics of stock 

prices, returns, and other market-related variables 

for banks and thrifts during the eight quarters preceding the institutions’ downgrades to CAMEL 

rating 3, 4, or 5.12 The sample varies slightly 

from quarter to quarter because the delisting rules 

of various exchanges (rules such as minimums for 

capital requirements and trading activity) reduce 

the availability of stock price information for 

individual firms. 

12 Examinations that lead to rating downgrades can last from several weeks 

to a month or more, depending on the severity of the case. They conclude 

with a notification to management that the institution’s rating will be 

downgraded. Thus, the zero quarter can be regarded as contemporaneous 

with the notification quarter or the quarter of the rating change. 

Summary Statistics for Sample of Downgraded Institutions 

A. At Time of Downgrade to 3 B. At Time of Downgrade to 4 or 5 

No. Minimum Median Maximum No. Minimum Median Maximum 

Call Report Financial Data 

Total Assets ($000s) 83 36,647 40,381 9,375,411 107 20,316 381,583 15,469,836 

Book Equity/Asset Ratio (%) 83 4.82 7.37 96.98 107 0.00 6.02 16.50 

Return on Assets (%) 83 –7.71 0.40 2.27 107 –16.75 –1.03 1.20 

CRSP Market Data 

Market Price ($ per share) 83 0.74 7.96 36.25 107 0.53 5.23 16.87 

Market Capitalization ($000s) 83 2,523 21,434 656,355 107 444 14,700 453,148 

Book/Market Equity Ratio 83 0.09 1.45 10.60 107 0.02 1.58 9.04 

Total Sample 83 107 

Number with Assets <$1 Billion 64 79 

Number with Assets $1–5 Billion 16 19 

Number with Assets >$5 Billion 3 9 

Number of Banks 77 99 

Number of Thrifts 6 8 

Note: The data are from quarterly financial data reported to regulators or are derived from CRSP during the quarter in which the 

CAMEL rating of the institution was downgraded. Market capitalization equals equity price times number of shares at the end of the 

quarter of the downgrade. 

FDIC BANKING REVIEW 5 2003, VOLUME 15, NO. 3 
Identifying Distressed Institutions 

Table 2 

Characteristics of Stock Price, Return, and Other Market Variables 

by Quarter Preceding Downgrade in CAMEL Rating 

Change CRSP CRSP Industry Std. Change in Avg. Avg. 

Qtrs Avg. in Cum. Equal Wt. Value Wt. Value Wt. Dev. Std. Dev. Daily Qtrly 

to Stock Stock Qtrly. Excess Excess Excess Daily Daily Trading Turnover 

Rating Price Price Return Return Return Return Return Return Volume Ratio 

Change Sample (dollars) (dollars) (percent) (percent) (percent) (percent) (percent) (percent) (shares) (percent) 

A. Trends Preceding Downgrade to 3 

–8 79 15.42 0.16 2.95 –4.10 –1.55 –1.55 2.63 –0.02 10,449 13.52 

0.53 1.51 –2.47** –0.96 –0.91 9.93*** 

–7 83 14.66 –0.78 –0.83 –6.08 –4.58 –4.58 2.59 –0.07 10,077 13.29 

–1.88* –0.50 –3.81*** –2.89*** –2.85*** –0.70 

–6 83 13.64 –1.04 –2.40 –7.01 –5.98 –5.49 2.55 0.00 9,595 13.65 

–2.76*** –1.42 –4.62*** –3.98*** –3.54*** –0.08 

–5 84 12.98 –0.77 –0.88 –4.79 –3.86 –2.61 2.79 0.25 10,660 13.39 

–1.37 –0.44 –3.04*** –2.30** –1.65* 2.23*** 

–4 84 12.32 –0.66 0.45 –6.88 –3.68 –4.24 2.82 0.00 10,646 12.50 

–2.99*** 0.24 –4.51*** –2.02** –2.23** 0.26 

–3 84 11.78 –0.54 –0.04 –8.74 –7.08 –6.06 2.97 0.15 11,991 13.89 

–2.92*** –1.94* –5.63*** –4.23*** –3.73*** 1.20 

–2 83 11.34 –0.50 –3.77 –7.05 –5.02 –3.57 3.54 0.55 12,372 14.87 

–2.82*** –1.47 –3.33*** –2.20** –1.68* 4.75*** 

–1 83 10.52 –0.82 –2.73 –9.08 –6.15 –6.71 4.05 0.51 12,023 15.12 

–3.55*** –1.15 –4.84*** –2.91*** –3.59*** 2.88*** 

0 83 9.91 –0.56 –1.69 –11.16 –5.43 –7.37 4.01 –0.06 12,625 15.52 

–2.36** –0.65 –5.24*** –2.19** –2.99 –0.32 

B. Trends Preceding Downgrade to 4 or 5 

–8 105 12.20 0.15 0.46 –3.60 –2.74 –1.51 2.92 –0.14 14,908 15.19 

0.30 0.25 –2.40** –1.71* –1.00 –0.81 

–7 107 11.76 –0.32 –4.87 –7.66 –7.41 –5.58 3.06 0.20 13,620 14.06 

–1.68* –2.90*** –5.05*** –4.76*** –3.48*** 1.68* 

–6 107 11.09 –0.66 –3.32 –8.50 –7.44 –6.84 3.08 –0.03 13,196 13.75 

–2.02** –1.72* –5.39*** –4.33*** –4.32*** –0.23 

–5 109 10.26 –0.69 –5.89 –11.17 –9.60 –8.79 3.45 0.23 12,130 12.94 

–5.39*** –3.59*** –7.32*** –6.57*** –5.73*** 1.72* 

–4 110 9.83 –0.34 –6.52 –11.87 –10.51 –10.42 3.53 0.36 12,400 13.35 

–1.87* –3.05*** –6.41*** –5.39*** –5.59*** 2.52*** 

–3 108 9.19 –0.75 –5.32 –9.98 –8.49 –7.02 4.08 –0.10 14,619 13.94 

–3.07*** –1.97** –4.32*** –3.40*** –3.00*** –0.15 

–2 107 8.14 –1.03 –9.89 –15.72 –12.59 –12.83 4.89 0.58 13,424 12.67 

–5.80*** –4.53*** –8.39*** –5.90*** –6.14*** 3.23*** 

–1 107 6.94 –1.20 –5.89 –12.80 –9.04 –9.48 5.79 0.48 14,739 13.45 

–6.28*** –1.59 –4.06*** –2.56** –2.90** 2.05** 

0 107 5.97 –0.97 –9.68 –15.52 –11.42 –12.42 5.87 0.61 15,506 13.61 

–5.48*** –2.63*** –4.85*** –3.28*** –3.83*** 2.43** 

Note: The data reported on each of the quarter-to-quarter rating change lines (–8 to 0) are calculated as simple averages for all trading days in each quarter. If the data 

required for any quarterly calculation are missing, they are omitted from the calculation. Excess returns are calculated as the difference between the cumulative quarterly 

return of each stock and the cumulative quarterly return of the various indices. T-statistics testing the hypothesis that the mean equals zero are shown below many of the 

quarterly average return and change-in-return statistics. A single, double, or triple “*” indicates significance at the 10 percent, 5 percent, or 1 percent level, respectively. 

2003, VOLUME 15, NO. 3 6 FDIC BANKING REVIEW 
 

Identifying Distressed Institutions 

The data show quarterly average stock prices 

falling continually throughout the eight quarters 

before the downgrades. As expected, the decline 

in stock prices is more precipitous for the more 

distressed group—the 4- and 5-rated institutions. 

To examine the consistency of changes in stock 

prices across the sample, we used a t-test to test 

the hypothesis that the mean change of each 

quarterly sample equals zero. For the 3-rated 

group, this test shows that the decline in stock 

price is consistently statistically significant beginning in the fourth quarter preceding the downgrade. For the 4- or 5-rated group, the change is 

significant seven quarters before the downgrade, 

reflecting the more distressed nature of this group. 

The t-test results suggest that a simple test can be 

used to identify declining stock prices that might 

precede a drop in an institution’s CAMEL rating. 

The steady decline in quarterly prices preceding a 

downgrade causes persistent patterns of negative 

cumulative quarterly returns as well.13 Quarterly 

returns are negative preceding downgrades for 

both groups under consideration, although the 

t-tests are not as conclusive as they are for declining prices. For institutions downgraded to 

CAMEL 3, the negative returns are not significantly distinguishable from zero preceding the 

downgrade, although institutions downgraded to 

4 or 5 have significant negative returns in most 

quarters preceding their rating change. 

Patterns of negative returns are more easily seen if 

one calculates the differences between quarterly 

stock returns and the quarterly returns for either 

of several indices of market performance. Using 

three indices of market returns (the CRSP equalweighted and value-weighted indices and an 

industry value-weighted index constructed from 

bank and thrift institutions), table 2 shows that 

market excess returns are generally negative and 

statistically significant during the eight quarters 

preceding a downgrade, regardless of the market 

13 The cumulative quarterly return is calculated by multiplying unity plus the 

daily return for each stock i on day t(1+rit) across all trading days in each 

quarter, then subtracting unity. 

index used as a benchmark. These results hold 

for the 3-rated group as well as the 4- or 5-rated 

group. The consistency of the t-test results again 

supports the notion that simple tests might be 

used to identify problematic institutions, while 

reaffirming Pettway’s (1980) finding of negative 

excess returns for lengthy periods preceding financial distress. 

Consistent with financial theory, a measure of 

return volatility—the standard deviation of daily 

returns—tends to rise as the time of downgrade 

approaches. That is, the volatility variable rises 

steadily for both groups as the downgrades 

approach, especially during the four quarters 

immediately preceding the downgrades. Volatility 

is noticeably higher for the most distressed institutions (CAMEL 4 or 5) than for the moderately 

distressed institutions (CAMEL 3). The statistical significance of the rising volatilities is confirmed with significant t-statistics for two quarters 

preceding a downgrade, but these patterns are not 

consistently found in earlier periods preceding a 

downgrade. 

Two measures of trading activity are used to 

examine the hypothesis that trading increases as 

distress approaches. These variables, however, 

generally fail to follow the rising trend preceding 

downgrade (financial theory expected otherwise). 

The most direct measure of trading activity— 

average daily trading volume for the quarter— 

increases slightly before the downgrades for the 

3-rated group but fails to follow a consistent trend 

for the 4- or 5-rated group. A second measure of 

trading activity, known as turnover, divides the 

shares traded in any quarter by total shares outstanding at the end of each quarter. The turnover 

variable also increases slightly before downgrades 

to 3 but not before downgrades to 4 or 5. Therefore, the trading activity variables appear to contain very little informational content before 

CAMEL rating downgrades. 

FDIC BANKING REVIEW 7 2003, VOLUME 15, NO. 3 
 

 

Identifying Distressed Institutions 

Incorporating Market Information into 

Supervisory Models 

Testing the incremental importance of stock 

price, return, and other market variables against 

the traditional financial variables contained in 

the quarterly reports allows us to formally distinguish the marginal predictive value of the two 

types of explanatory variables. This approach 

proceeds by initially specifying a traditional, or 

ratio-based, CAMEL rating prediction model, 

then extending the model to include marketrelated variables. Although the market variables 

need not dominate the traditional ratio-based 

model, a minimum level of competency is 

required to justify a conclusion that market-related information provides a meaningful addition to 

the traditional analysis. For example, if the market has a unique ability to interpret quarterly 

financial data, then market variables should provide statistically significant explanatory power to 

models that predict rating downgrades on the 

basis of traditional financial ratios. 

In this section, a binomial logistic model is estimated to explain changes in financial institution 

supervisory (CAMEL) ratings. The binary 

dependent variable (CAMELCAT) in the equation takes a value of “1” if the institution is 

downgraded to the 3, 4, or 5 level over the 

1988–1995 period, and a “0” if the institution 

remains a healthy 1- or 2-rated institution.14 The 

logistic regression estimates the likelihood that a 

bank or thrift will be downgraded. Table 3 

defines the variables used in the regression model 

for the downgraded and control groups, along 

with their means and standard deviations. The 

14 As mentioned above, the control sample of healthy banks was also 

selected from the universe of CAMEL-rated banks and thrifts that were publicly traded over the 1988–1995 period. To be eligible for inclusion in the 

control sample, these institutions had to have a 1 or 2 CAMEL rating for 

two consecutive years and had to maintain that rating at their first on-site 

examination after the two consecutive years were completed. When these 

criteria were satisfied, the control sample selected contained 151 institutions. 

regressions are run four quarters (one year) before 

the quarter of the downgrade. Since bank regulators generally release financial data one to two 

months after the end of each quarter, the quarterly financial data in the regressions are measured 

five quarters before the downgrade quarter, whereas the data from the market variables are measured four quarters before the downgrade quarter. 

Several control variables are used to account for 

factors that might influence the likelihood of a 

downgrade. The first variable (BK_SIZE) controls for differences in institution size and is measured as a dummy variable, with a value of “1” for 

institutions greater than $1 billion and “0” otherwise. To the extent that firm size provides greater 

opportunities for diversification and access to capital markets, a negative relationship between the 

probability of a downgrade and institution size is 

expected. A second control variable (STATE) 

accounts for differences in economic conditions 

over the period of this study among the states and 

regions from which the sample was drawn. The 

STATE variable is defined as measuring the quarterly percentage change in requests for housing 

permits. A negative sign is expected between the 

STATE variable and the probability of being 

downgraded. 

A regulatory variable is specified in the model to 

account for differences in the amount of private 

as opposed to public information available at the 

time of the downgrades. Bank supervisory officials have access to considerable amounts of private information about the financial condition of 

their regulated institutions: confidential financial 

data, previously assigned confidential CAMEL 

ratings, information gathered during discussions 

with management, and so forth. Since much of 

this information is considered in the assignment 

of the management component of the CAMEL 

rating, this variable (MGT_RAT) makes a convenient summary measure of regulatory interpretations of private information. We include the 

variable in our test by measuring it from the bank 

examination on record as of four quarters before 

the institutions were downgraded. 

2003, VOLUME 15, NO. 3 8 FDIC BANKING REVIEW 
 

Identifying Distressed Institutions 

Table 3 

Definition of Variables, Means, and Standard Deviations 

CAMEL 3-Rated CAMEL 4/5-Rated 

Standard Standard 

Mean Deviation Mean Deviation 

Control Sample 

Standard 

Mean Deviation 

Dependent Variable 

CAMELCAT Dummy variable equal to “1” if the institution experienced 1.00 0.00 1.00 0.00 

a CAMEL rating downgrade to a 3 or a 4 or 5, and “0” 

otherwise. 

Control Variables 

BK_SIZE Dummy variable equal to “1” if the institution was over 0.21 0.41 0.24 0.43 

$1 billion, and “0” otherwise. 

STATE Percentage change in quarterly residential housing –19.52 20.86 –25.57 15.59 

permits by state 

Regulatory Variable 

MGT_RAT Component rating for quality of bank management. 2.02 0.38 2.43 0.67 

Financial Variables 

EQ_ASSET Equity capital divided by total assets (%). 10.11 10.06 8.09 2.78 

NC_ASSET Noncurrent (delinquent) assets less loan-loss reserves, 1.79 1.27 2.77 2.03 

divided by total assets (%). 

RES_ASSET Reserves for loan losses divided by total assets. 0.76 0.49 1.04 0.70 

LPROV_ASSET Loan-loss provisions divided by total assets. 0.31 0.40 0.59 0.80 

ROA Quarterly annualized earnings divided by total assets (%). 0.52 1.33 0.03 1.69 

SEC_ASSET Securities divided by total assets (%). 17.04 13.80 14.59 12.71 

VOL_ASSET Volatile liabilities divided by total assets (%). 21.47 10.73 21.46 11.00 

Market Variables 

EXPRC Log of the ratio of the stock price divided by the S&P –2.76 0.68 –3.02 0.82 

bank-stock industry index. 

EXRET Market excess or abnormal return, calculated as the –0.04 0.17 –0.11 0.20 

difference between the cumulative quarterly return of 

each stock and the cumulative quarterly return of the 

CRSP value-weighted index. 

COEFVAR Coefficient of variation is equal to the standard deviation 6.09 4.18 8.50 8.35 

of the stock price for the quarter divided by the average 

quarterly stock price (%). 

BKEQ_MEQ Book equity divided by market capitalization. 1.41 0.89 1.91 2.45 

TURNOVER Number of shares traded in a quarter divided by the 12.50 11.58 13.35 12.64 

number of shares outstanding at the end of the quarter (%). 

Number of observations 84 110 

0.00 0.00 

0.13 0.33 

–2.76 18.22 

1.73 0.52 

10.71 4.40 

0.59 0.93 

0.61 0.46 

0.10 0.21 

1.02 0.56 

26.52 14.14 

13.42 11.23 

–2.35 0.83 

–0.02 0.20 

6.08 4.36 

0.91 1.12 

11.98 16.43 

151 

FDIC BANKING REVIEW 9 2003, VOLUME 15, NO. 3 
 

 

 

Identifying Distressed Institutions 

The first accounting-related variable in the 

model, the equity-to-assets ratio (EQ_ASSET), 

measures the ability of a firm to absorb loan losses 

before bankruptcy and is expected to be negatively related to the likelihood of future distress. The 

credit quality of the loan portfolio is captured by 

three variables: the level of delinquent or noncurrent assets less loan-loss reserves relative to total 

assets (NC_ASSET), the level of loan-loss 

reserves to total assets (RES_ASSET), and the 

quarterly amount of loan provisions to total assets 

(LPROV_ASSET). The bank reserve variable is 

expected to be negatively related to the likelihood of a rating downgrade, whereas the noncurrent asset and quarterly loan provision variables 

are expected to be positively related. Profitability 

is measured by the return-on-assets variable 

(ROA), which is expected to be negatively related to future downgrades. Two measures of liquidity are the securities-to-assets ratio (SEC_ASSET) 

and the volatile-liabilities to assets ratio 

(VOL_ASSET). The SEC_ASSET variable is 

expected to be negatively related to future distress, reflecting the fact that higher levels of securities to assets provide sources of additional 

liquidity in troubled times. A positive sign is 

expected for the volatile-liabilities ratio 

(VOL_ASSET), reflecting the notion that higher 

levels of volatile liabilities reflect expensive 

and/or potentially risky funding strategies. 

Market prices and returns are our primary market 

variables. Stock price (EXPRC), measured as the 

natural logarithm of the ratio of the average quarterly price divided by the S&P bank-stock industry index, is expected to be negatively related to 

rating downgrades. Market excess returns are 

captured by EXRET, which measures the CRSP 

value-weighted excess quarterly returns for each 

observation (and is discussed above in the univariate analysis). Given the high degree of negative excess-return persistence observed above, we 

expect EXRET to have at least some predictive 

content and be negatively related to the future 

downgrades. 

Several market variables reflect risk, as in the 

market model of Fama and French (1993) or the 

option model of Merton (1974). Price volatility 

is captured by the coefficient of variation in equity prices (COEFVAR) and is expected to be positively related to downgrades. The book-equity to 

market-equity ratio (BKEQ_MEQ) provides a second measure of the market’s valuation of the firm 

and is expected to have a positive coefficient 

because the ratio moves inversely with changes in 

an institution’s stock prices. A trading activity 

variable, TURNOVER, which measures stock 

turnover on a quarterly basis, is expected to be 

positively related to rating downgrades. 

The following equation shows the basic logit estimation equation: 

2 

Camelit = α i +∑β j

(Control Variablesijt )

j=1 

3 

+∑β j (RegulatoryVariablesijt )
 j=3
 

10
 

+∑β j (Financial Variabless ) ijt
 

j=4
 

15
 

+ ∑β j (MarketVariables s )+ ε ijt
 it

j=11
 

The regression results are presented in table 4. 

Panel A shows the results for firms that were 

downgraded to 3, and panel B shows the results 

for firms that were downgraded to 4 or 5. Five 

models are specified to test the downgrade-predictive value of publicly available as opposed to confidential supervisory information. In particular, 

specifications 1–3 focus on publicly available 

information in bank quarterly reports and stock 

market data, whereas specifications 4–5 add confidential supervisory management ratings to the 

publicly available information used in models 

1–3. 

Specification 1 displays a traditional model of 

bank financial distress, based on publicly available 

financial data. The model contains two control 

variables, bank size (BK_SIZE) and geographic 

location (STATE), although the size variable is 

generally not statistically significant. Following 

the two control variables are seven financial 

ratios, most of which perform as expected. The 

2003, VOLUME 15, NO. 3 10 FDIC BANKING REVIEW 
Identifying Distressed Institutions 

Table 4 

Logit Regression Results: Four Quarters before Downgrade 

Independent Anticipated A. CAMEL 3-Rated Group Specification B. CAMEL 4/5-Rated Group Specification 

Variable Sign 1 2 3 4 5 1 2 3 4 5 

Intercept –0.86 –3.39 –2.13 –3.76 –4.46 0.90 –5.63 –1.09 –5.82 –7.58 

(1.23) (5.00)*** (2.07)** (2.71) *** 2.95 *** (0.83) (5.98)*** (0.69) (2.96)*** (3.12)*** 

Control Variables 

BK_SIZE – 0.09 0.82 0.21 0.12 0.21 –0.46 1.38 –0.52 0.34 0.12 

(0.16) (1.83) (0.36) (0.21) (0.34) (0.69) (2.86)*** (0.66) (0.43) (0.13) 

STATE – –0.03 –0.05 –0.03 –0.03 –0.03 –0.07 –0.09 –0.07 –0.11 –0.13 

(2.97)*** (5.06)*** (2.72)*** (3.05)*** (2.80)*** (4.50)*** (6.93)*** (4.17) *** (4.29)*** (3.91)*** 

Regulatory Variable 

MGT_RAT + 1.33 1.23 3.26 3.41 

(2.46)** (2.18)** (4.34)*** (3.96)*** 

Financial Variables 

EQ_ASSET – –0.02 –0.04 –0.01 –0.02 –0.42 –0.51 –0.48 –0.61 

(1.04) (1.54) (0.25) (0.68) (3.30)*** (3.26)*** (3.08)*** (–3.04)*** 

NC_ASSET + 0.88 0.83 0.82 0.77 1.22 1.17 1.13 1.11 

(3.99)*** (3.63)*** (3.70)*** (3.34)*** (4.44)*** (4.17) *** (3.87)*** (3.52)*** 

RES_ASSET – –1.19 –1.24 –0.91 –0.94 –0.71 –0.95 –1.08 –1.31 

(1.81)* (1.80)* (1.36) (1.35) (1.23) (1.56) (2.01)** (2.18)** 

LPROV_ASSET + 2.78 2.89 2.67 2.78 4.29 4.63 5.87 6.61 

(2.37)** (2.34)** (2.28)** (2.29)** (3.48)*** (3.43)*** (3.79)*** (3.63)*** 

ROA – –0.83 –0.65 –0.75 –0.61 –1.29 –0.86 –1.09 –0.68 

(2.40)** (1.96)** (2.20)** (1.84)* (2.59)*** (1.43) (1.76)* (0.92) 

SEC_ASSET – –0.04 –0.05 –0.04 –0.05 –0.05 –0.05 –0.09 –0.09 

(2.82)*** (2.89)*** (2.83)*** (2.92)*** (2.14)** (2.07)** (3.08)*** (2.82)*** 

VOL_ASSET + 0.07 0.07 0.07 0.07 0.08 0.08 0.09 0.09 

(4.08)*** (4.05)*** (4.00)*** (4.01)*** (3.38)*** (3.08)*** (2.84)*** (2.66)*** 

Market Variables 

EXPRC – –0.94 –0.52 –0.41 –1.34 –0.95 –0.78 

(3.38)*** (1.56) (1.23) (3.88)*** (2.38)** (1.66)* 

EXRET – 0.25 –0.07 –0.10 –1.17 0.60 0.06 

(0.28) (0.06) (0.09) (1.31) (0.45) (0.04) 

COEFVAR + –0.07 –0.04 –0.05 –0.01 –0.05 –0.11 

(1.50) (0.59) (0.86) (0.22) (1.27) (1.89)* 

BKEQ_MEQ + 0.12 0.25 0.26 0.03 0.39 0.45 

(0.72) (1.29) (1.28) (0.18) (0.98) (0.87) 

TURNOVER + 0.00 0.01 –0.01 0.01 0.00 0.02 

(0.11) (0.43) (0.35) (0.54) (0.18) (0.73) 

AIC 194.50 261.36 197.50 189.50 194.07 128.11 224.75 126.32 95.03 97.86 

R2 0.43 0.23 0.45 0.45 0.46 0.61 0.43 0.63 0.66 0.67 

χ2 (relative to specification 1) 7.00 7.00 *** 11.79 ** 35.08 *** 

χ2 (relative to specification 4) 5.43 7.16 

degrees of freedom 5 1 5 5 1 5 

Note: This table performs logit regression analysis on the sample of commercial banks and thrift institutions. All independent variables are defined in table 3. T-statistics are 

shown in parentheses below their corresponding regression coefficients. A single, double, or triple "*" indicates significance at the 10%, 5%, or 1% level, respectively. 

FDIC BANKING REVIEW 11 2003, VOLUME 15, NO. 3 
 

Identifying Distressed Institutions 

equity-to-asset ratio (EQ_ASSET) has a negative 

sign as expected for both groups, thereby confirming the importance of equity levels in models predicting distressed CAMEL ratings. The first 

asset-quality variable, NC_ASSET, is highly significant at the 1 percent level for all specifications for both groups, showing a direct link 

between the level of loan delinquency and the 

likelihood of obtaining a rating downgrade as 

expected. Another asset-quality variable, 

RES_ASSET, has the expected negative sign, but 

it is significant in only four of the eight specifications that use this variable. A third asset-quality 

variable, LPROV_ASSET, has its expected sign 

and is significant at the 1 percent level for all relevant specifications. The return-on-asset variable 

(ROA) also exhibits a negative sign as expected 

and is generally significant for both groups. The 

two liquidity measures (SEC_ASSET and 

VOL_ASSET) also perform as expected. Since 

almost all the coefficients in specification 1 have 

their expected signs and are significant at the 1 

percent level, this specification provides a good 

benchmark for assessing the marginal or incremental value of information in market-based variables or in confidential supervisory data. 

Specification 2 displays a model with only publicly available market variables. Five market variables are specified: the excess price (EXPRC), a 

measure of abnormal returns (EXRET), price 

volatility (COEFVAR), the book-equity to market-equity ratio (BKEQ_MEQ), and the turnover 

ratio (TURNOVER). The results show that of 

the five market variables for the two downgraded 

groups, only the EXPRC variable is statistically 

significant at the 1 percent level for both the 3­

rated group and the 4/5-rated group. None of the 

other market variables appears to be a good predictor of the downgrades. The comparison of the 

first two models shows that the model using only 

market variables, specification 2, performs poorly 

in comparison with the basic CAMEL prediction 

model using only quarterly accounting data, specification 1. 

The analysis proceeds with specification 3, where 

market variables are added to the benchmark 

regression of specification 1 to form a combined 

model to determine if the market data add significantly to the predictive ability of the model. In 

addition to identifying the significance of variable 

coefficients and t-tests, we are able to compare 

the models through the Akaike information criterion (AIC) and the likelihood-ratio-test statistic. 

If the AIC variable is lower and the likelihoodratio-test statistic is positive and statistically significant from a comparison of 3 to 1, we may 

conclude that a model based on public information combining quarterly and market-based data 

has higher explanatory power than the benchmark model in specification 1. The results for the 

combined model show that although only one of 

the market variables in the regression is significant, the overall model reveals a marginal 

improvement over specification 1 for the 4- and 

5-rated group but no higher explanatory power for 

the 3-rated group. For the 4- and 5-rated group 

only, the AIC variable is lower, and its log likelihood test is significant at the 5 percent level. 

This result highlights the fact the 4/5-rated group 

presents a more extreme case of financial distress 

when compared with the 1- and 2-rated control 

group than does the relatively healthier 3-rated 

group. 

Specification 4 contains financial variables similar to those of the other specifications as well as 

an additional confidential supervisory variable 

that captures the past component management 

rating (MGT_RAT) of the institution. Thus the 

model reflects a mixture of both public and private information. The supervisory variable 

(MGT_RAT) is highly significant for both 

groups, and this significance reveals that private 

information held by bank supervisors is important 

in predicting future downgrades. Furthermore, for 

specification 4, the AIC variable is lower and the 

log-likelihood-test ratio is significant at the 1 percent level for both the 3- and 4/5-rated groups, a 

result that demonstrates improvement over specification 1. 

2003, VOLUME 15, NO. 3 12 FDIC BANKING REVIEW 
Identifying Distressed Institutions 

Finally, the last model specification (5) adds market information to the model in specification 4. 

The results show no significant improvement over 

specification 4, as reflected in a higher AIC variable and an insignificant likelihood-ratio test. 

Table 5 contains in-sample tests of the model for 

both the 3- and 4/5-rated groups for all five specifications. The critical cutoff probability is 50 percent, which is used to determine how the model 

performs in identifying which banks or thrifts in 

the two groups are properly classified as likely to 

experience future CAMEL rating downgrades.15 

Within the in-sample classification for the 3-rated 

institutions, the correct downgrade prediction of 

distressed banks and thrifts is about 73 percent for 

the combined model with public data (specifica15 The “critical probability” refers to the cutoff level, which determines 

which institutions fall into the predicted downgrade group and which do 

not. The logistic regression equation calculates a probability for each observation. The institutions whose calculated probability is 50 percent or more 

are considered likely to be downgraded and are placed into the “predicted 

downgrade” category. 

Table 5 

tion 3), which is the same level as specification 1. 

Specification 2, with only stock market data, falls 

off to only 54 percent in correct downgrade predictions. 

Generally the classifications for the 4/5-rated 

group improve over those for the 3-rated group; 

and for the 4/5-rated group, specification 3 

improves over specification 1. When specification 4 is compared with specification 1, the addition of confidential supervisory information 

increases the correct downgrade prediction to 95 

percent or at the same level as specification 3. 

Adding stock market data in specification 5 yields 

the largest correct downgrade classifications, at 96 

percent.16 In terms of absolute numbers, the net 

change in forecast accuracy increases from 61 to 

16 An out-of-sample test was not conducted because of the limited number 

of observations for the sample groups. An out-of-sample test requires a 

“holdout” sample of 20 to 30 percent of the original observations. Holding 

out that many observations would have significantly reduced the size of the 

sample available for the analysis. 

CAMEL Prediction Accuracy and Error Analysis: 

Four Quarters before Downgrade 

Model 

D—Pred (D) D—Pred (ND) ND—Pred (ND) Specification (Correct D) (Type 1 Error) (Correct ND) In-Sample 

Classification Percent Number Percent Number Percent Number 

ND—Pred (D) 

(Type 2 Error) 

Percent Number 

A. CAMEL 3-Rated Group 

1 72.62 61 27.38 23 90.73 137 

2 53.57 45 46.43 39 89.40 135 

3 72.62 61 27.38 23 91.39 138 

4 71.43 60 28.57 24 90.73 137 

5 73.81 62 26.19 22 91.39 138 

B. CAMEL 4/5-Rated Group 

1 92.73 102 7.27 8 94.04 142 

2 73.64 81 26.36 29 88.74 134 

3 95.45 105 4.55 5 94.04 142 

4 95.45 105 4.55 5 94.70 143 

5 96.36 106 3.64 4 95.36 144 

9.27 14 

10.60 16 

8.61 13 

9.27 14 

8.61 13 

5.96 9 

11.26 17 

5.96 9 

5.30 8 

4.64 7 

Note: The critical value for classification of downgrades is 50 percent. 

FDIC BANKING REVIEW 13 2003, VOLUME 15, NO. 3 
 

Identifying Distressed Institutions 

only 62 institutions in the 3-rated group, an 

increase that is not significant. However, for the 

4/5-rated group the change goes from 102 to 106 

institutions as we move from specification 1 to 

specification 5. Thus, the in-sample classifications for the more distressed group show some 

incremental increase in correct downgrade predictions when stock market variables are added to 

the model. 

Conclusion 

This article explores the notion that publicly 

available stock price, return, and other marketrelated variables can provide timely information 

about bank and thrift financial condition; the 

article also determines whether such information 

can be used to improve the predictive accuracy of 

traditional off-site monitoring models for the purpose of anticipating changes in the CAMEL ratings assigned by regulators. A sample of banks 

and thrifts that were downgraded to the CAMEL 

3, 4, or 5 level between the years 1988 and 1995 

was used in the analysis and was compared with a 

sample of 1- or 2-rated healthy institutions. The 

first part of the analysis—extensive univariate 

analysis—confirms the existence of timely information: relatively simple measures of stock price 

and returns exhibit downward trends as much as 

two years before banks and thrifts experience rating downgrades, while overall return volatility 

increases. However, no simple relationship 

appears in univariate comparisons of several other 

market variables, including average trading volume and average quarterly turnover of shares. 

The second part of the analysis tests whether 

adding market information to models containing 

quarterly financial data incrementally improves 

the ability of the model to predict commercial 

bank and thrift CAMEL rating downgrades. 

Specifically, equity market variables such as stock 

price, returns, price volatility, market valuation, 

trading volume, and share turnover are combined 

in a binomial logistic model containing traditional default-prediction variables for the purpose of 

identifying distressed institutions. The results 

show that even though for the univariate analysis 

the market variables appeared to provide timely 

information before bank and thrift downgrades, in 

the regression model market information provided 

only marginal improvements when combined 

with quarterly financial data. Specifically, the 

stock market variables improved the fit of the 

regression model as well as the in-sample predictive content of traditional accounting-based models only for the most distressed institutions—the 

CAMEL 4- and 5-rated banks and thrifts. No 

similar evidence was found for the healthier 

3-rated firms. 

2003, VOLUME 15, NO. 3 14 FDIC BANKING REVIEW 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

Identifying Distressed Institutions 

REFERENCES 

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Supervisory Assessments of Bank Performance: Who Knows What When? Journal 

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Cole, Rebel A., and Jeffery W. Gunther. 1998. Predicting Bank Failures: A Comparison 

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Curry, Timothy, Peter Elmer, and Gary Fissel. 2001. Regulator Use of Market Data to 

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Curry, Timothy, Gary Fissel, and Gerald Hanweck. 2003. Market Information, Bank 

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Elmer, Peter J., and Gary S. Fissel. 2000. Forecasting Bank Failure from Momentum 

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Fama, Eugene F., and Kenneth R. French. 1993. Common Risk Factors in the Returns on 

Stocks and Bonds. Journal of Financial Economics 33:3–56. 

Flannery, Mark J. 1998. Using Market Information in Prudential Bank Supervision: A 

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Gunther, Jeffery W., Mark E. Levonian, and Robert R. Moore. 2001. Can the Stock 

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Krainer, John, and Jose A. Lopez. 2001. Incorporating Equity Market Information into 

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———. 2003. How Might Financial Market Information Be Used for Supervisory 

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2003, VOLUME 15, NO. 3 16 FDIC BANKING REVIEW 
The SCOR System of Off-Site Monitoring 

The SCOR System of Off-Site Monitoring:
 

Its Objectives, Functioning, and Performance
 

by Charles Collier, Sean Forbush, Daniel A. Nuxoll, and John O’Keefe* 

The Federal Deposit Insurance Corporation 

(FDIC) and other bank supervisors have developed 

a number of tools with which to monitor the 

health of individual banks as well as the health of 

the industry as a whole.1 One tool is on-site 

examinations: each bank is examined every 12 to 

18 months and is assigned a CAMELS rating.2 

These examinations provide the most complete 

and reliable information about banks’ financial 

health, and supervisors regard CAMELS ratings as 

the best single indicator of banks’ condition. 

However, between examinations a bank’s financial 

* The authors are all on the staff of the Federal Deposit Insurance Corporation 

(FDIC): Charles Collier and Sean Forbush are in the Division of Supervision 

and Consumer Protection (Collier as chief of the Information Management 

Section, Forbush as a senior financial analyst), and Daniel Nuxoll and John 

O'Keefe are in the Division of Insurance and Research (Nuxoll as a senior 

economist, O'Keefe as chief of the Financial Risk Measurement Section). The 

multiyear development of SCOR involved many of the authors' colleagues, a 

number of whom have contributed to this article. 

The opinions expressed here are the authors' and do not necessarily reflect 

the views of the FDIC. 

1 Throughout this article, the term banks includes all insured financial institutions—commercial banks, savings banks, and thrifts. 

2 CAMELS is an acronym for Capital, Asset quality, Management, Earnings, 

Liquidity, and market Sensitivity. Examiners have rated sensitivity only since 

1998. Strictly speaking, examination ratings before that year are CAMEL ratings, but we ignore this distinction and use “CAMELS” throughout, except in 

Appendix 1. 

condition may change so that the CAMELS rating 

is no longer accurate. Therefore, the FDIC and 

other bank supervisors have developed other tools: 

off-site systems to monitor insured institutions 

between examinations. 

The FDIC’s major off-site monitoring tool is the 

Statistical CAMELS Off-site Rating (SCOR) system. The system was designed to help the FDIC 

identify institutions that have experienced noticeable financial deterioration. This article discusses 

that objective and the data and method used to 

meet it. The article then discusses the performance of SCOR in terms of that objective, as well as 

some auxiliary features that make the system more 

useful. Two appendices address key technical 

issues that arose during the development of SCOR. 

Objectives of the Project 

The SCOR system was developed in the late 1990s 

to detect banks whose financial condition had substantially deteriorated since their last on-site 

examination. As its name indicates, the model is 

an off-site system that is meant to supplement the 

current system of on-site examinations. 

FDIC BANKING REVIEW 17 2003, VOLUME 15, NO. 3 
The SCOR System of Off-Site Monitoring 

After an examination, examiners assign the bank a 

composite CAMELS rating—a rating that reflects 

the bank’s overall financial condition. The ratings 

range from 1 to 5, with 1 the best and 5 the worst 

(the meanings of the ratings are summarized in 

table 1). Banks with a rating of 4 or 5 are considered problem banks. Examiners also rate each of 

the six CAMELS components, again on a scale of 

1 to 5. The meanings of the component ratings 

parallel those of the composite rating. 

Table 1 

Definitions of the CAMELS Ratings 

Rating Characteristic 

1 “sound in every respect” 

2 “fundamentally sound” 

3 “exhibit some degree of supervisory concern” 

4 “generally exhibit unsafe or unsound practices or condition” 

5 “exhibit extremely unsafe or unsound practices or condition” 

Source: FDIC Manual of Examination Policies. 

Off-site monitoring at the FDIC attempts to identify institutions that received a rating of 1 or 2 on 

the last examination but might well receive a rating of 3 or worse at the next examination. 

According to the definitions in table 1, institutions 

with a rating of 1 or 2 are sound, whereas those 

with a rating of 3 or worse have some significant 

problems; once an institution is rated 3 or worse, it 

has been identified as a concern, and the FDIC 

monitors it intensively. Consequently, only the 

likely passage from 1 or 2 to 3 or worse is of interest in off-site monitoring. Identifying 3- or 4-rated 

institutions that are likely to receive a worse rating 

at the next examination is not particularly useful 

from the supervisory perspective. 

The difference between a rating of 2 and a rating 

of 3 has a number of practical implications. Institutions with a rating of 3 or worse are examined 

more frequently, generally receive closer supervision, pay higher deposit insurance premiums, and 

may face some legal restrictions on their activities. 

(Supervisors often take either formal or informal 

enforcement actions against these banks, and 

enforcement actions generally restrict an institution’s activities or commit it to remedying an identified problem in its operations.)3 

Consequently, the major objective of the SCOR 

project was to identify correctly the 1- and 2-rated 

institutions that were in danger of being downgraded to 3 or worse. The accuracy of the proposed system was analyzed in terms of two types of 

error, conventionally called Type I and Type II 

errors. Type I errors consist of false negatives or, 

more colloquially, “freeing the guilty.” In our context, a false negative is failing to detect a downgrade before it occurs, so the level of Type I errors 

is the percentage of downgraded banks that the 

model did not identify as problems. Conversely, 

Type II errors consist of false positives, or “convicting the innocent.” The level of Type II errors is 

the percentage of banks that are identified by the 

model, yet are found to be sound by a subsequent 

examination.4 

There is a trade-off between Type I and Type II 

errors. Anyone can achieve 0 percent Type I error 

without a model simply by identifying all banks as 

likely to be downgraded. By identifying all banks, 

one has certainly identified all banks that will 

actually be downgraded. However, one has also 

identified as problems all of the banks not actually 

downgraded, so Type II error is 100 percent. Conversely, one can easily attain 0 percent Type II 

error by identifying no banks; however, this results 

in 100 percent Type I error. Generally, the more 

banks identified by a model, the lower the Type I 

error and the higher the Type II error. 

Ideally, the users of a model determine the acceptable trade-off of Type I and Type II errors in terms 

of the relative costs of the two types of error. At 

3 See Curry et al. (1999) for a discussion of the effectiveness of enforcement 

actions. 

4 Actually, the relevant Type I and Type II errors are not those discussed in 

the text. For the FDIC’s purposes, the critical question is whether the regional office is aware that a bank might present a supervisory concern, but 

because that awareness cannot be established retrospectively, all backtesting 

uses examination ratings. Because case managers have information besides 

examination ratings, the regional office is often aware of potential downgrades before they occur, but the backtests assume that the regional office is 

not aware of problems until an examination has begun. Thus, the backtests 

overstate the model’s ability to identify banks that present a concern. 

2003, VOLUME 15, NO. 3 18 FDIC BANKING REVIEW 
The SCOR System of Off-Site Monitoring 

the FDIC, each bank is assigned a case manager at 

the appropriate regional office. After a bank has 

been identified by SCOR as likely to be downgraded, the bank’s case manager reviews the information available about the bank and determines 

whether further action is warranted. If the review 

causes sufficient concern, the FDIC manager can 

schedule an examination and can allocate 

resources to supervise the bank more closely. In 

the context of off-site monitoring, therefore, the 

cost of Type I error is slow reaction to problems at 

a bank—that is, a delay in increasing the supervision of the bank. On the other hand, the cost of 

Type II error is the waste of staff time spent conducting unnecessary reviews. In addition, Type II 

error undermines the credibility of the system, so 

case managers have little reason to be conscientious about reviews. 

For ease of presentation, this article discusses Type 

I and Type II accuracy instead of error. Type I 

accuracy is the percentage of actual downgrades 

that were identified in advance by the model. 

Type II accuracy is defined analogously as the percentage of identified banks that are in fact subsequently downgraded. 

For the designers of SCOR, accuracy was the 

major objective, and the benchmark for accuracy 

was CAEL, the off-site monitoring system developed at the FDIC during the mid-1980s. CAEL 

was an expert system that used basic ratios from 

the Call Reports (the quarterly financial reports 

filed by banks) to rate Capital, Asset quality, Earnings, and Liquidity (hence the name CAEL);5 

CAEL did not produce management ratings 

because the quality of management cannot readily 

be identified with any financial ratio. The ratings 

of the four components were combined by means 

of a complicated system of weights to produce a 

5 This expert system was designed by a group of experienced examiners, who 

decided which ratios were the best precursors of future problems. Updating 

the system would involve convoking another group of experienced examiners 

to deliberate about the model. For more information on CAEL, see FDIC 

(1997), 507ff. 

composite rating, which was used to identify institutions for off-site review. 

CAEL rated institutions on a scale of 0.5 to 5.5. 

Conceptually, CAEL ratings are easily mapped to 

CAMELS ratings: a CAEL rating between 0.5 and 

1.5 corresponds to a CAMELS ratings of 1, a 

CAEL rating of 1.5 to 2.5 corresponds to a 

CAMELS rating of 2, and so forth.6 

SCOR was intended to produce ratings comparable to CAEL’s while also being easier to analyze. 

CAEL’s use of a complicated system of weights to 

derive a final composite rating made it difficult for 

examiners to understand which financial ratios 

were responsible for the poor ratings an institution 

received. Thus, although CAEL informed examiners which institutions had problems, it was not 

always informative about the nature of the problems. Consequently, a secondary objective for the 

designers of SCOR was to develop a method of 

analyzing ratings in terms of the underlying ratios. 

Development and Functioning of SCOR 

In contrast to the expert-system approach of 

CAEL, SCOR uses a statistical model. It compares examination ratings with the financial ratios 

of a year earlier. SCOR identifies which financial 

ratios were most closely related to examination ratings and uses that relationship to forecast future 

ratings.7 For example, to predict ratings on the 

basis of the June 2003 Call Report, SCOR compares data from the Call Report of June 2002 with 

actual examination ratings from the period July 

6 Actually, the mapping is not quite that simple because CAEL was built with 

a bias toward downgrading institutions. Without any bias, an institution 

receiving a CAEL rating of 2.5 would be as likely to receive a 2 at the next 

examination as a 3. The bias, however, means that an institution with a rating of 2.5 will in fact be more likely to receive a 2 than a 3. Because of 

the bias, CAEL identifies more banks as possible problems, thus increasing 

the Type II errors while decreasing the Type I errors. 

7 The SCOR model is very similar to the SEER rating model, originally called 

FIMS, developed by the Federal Reserve System. Both SEER and SCOR draw 

on a long history of models of bank failure and distress. Demirgü�-Kunt 

(1989) reviews pre-FIMS developments, and Gilbert, Meyer, and Vaughn 

(1999) explain the rationale behind such models. For a discussion of the 

SEER system, see Cole, Cornyn, and Gunther (1995). SEER and SCOR differ 

in one important respect: SCOR does not use past CAMELS ratings to forecast 

future ratings. For a discussion of the issue of using past ratings to forecast 

future ratings, see Appendix 1. 

FDIC BANKING REVIEW 19 2003, VOLUME 15, NO. 3 
1

The SCOR System of Off-Site Monitoring 

2002 to June 2003. This procedure identifies the 

Call Report data that were the best indicators of 

ratings over the past year and uses that relationship to forecast ratings based on June 2003 data. 

The assumption is that the data that were the best 

indicators of ratings over the year just past will also 

be the best indicator over the year to come. The 

SCOR method, by identifying which ratios are 

consistently related to examination ratings, 

attempts to identify which ratios examiners consider the most significant and therefore could be 

interpreted as an attempt to read examiners’ 

minds. 

If the relationship between examination ratings 

and financial ratios changes, that change will be 

reflected in the model, generally through a change 

in coefficients, but only after a delay. For example, 

if examiners find that intangible factors (such as 

underwriting) have on average deteriorated and if 

they therefore assign poorer ratings, then the average SCOR rating will also worsen, even if the 

deterioration will not yet have affected the basic 

financial ratios. But because the model is estimated with examination ratings from the past year, the 

changes in the relationship between ratings and 

ratios will not be incorporated into the model until 

the next year.8 

It is also important to note that SCOR is estimated every quarter and that therefore the ratings for 

June 2003 (for example) do not depend on any 

data before June 2002. The estimated relationship 

between ratings and ratios depends only on very 

recent data and changes slightly from quarter to 

quarter. Consequently, even if the Call Report 

ratios were identical, the ratings for June 2003 

could be very different from those for June 1993— 

in principle. In practice, however, banks similar to 

those that had poor ratings in 1993 would also 

have poor ratings in 2003. 

8 With regard to underwriting, at the conclusion of each exam FDIC examiners 

evaluate separately the quality of the institution’s underwriting practices. The 

FDIC is currently researching whether these ratings can be used to forecast 

future examination ratings. 

SCOR uses a stepwise estimation procedure that 

eliminates ratios whose relationship with examination ratings is not consistent (that is, ratios that 

are not statistically significant). In general, the 

stepwise procedure drops relatively few variables. 

SCOR uses only two peer groups: banks and 

thrifts. Experimentation has indicated that additional peer groups do not improve the model’s forecasting power.9 

The model was developed with a somewhat conservative bias to avoid the problem of excessive 

data mining. This problem occurs because one can 

always find a complete coincidence that is statistically significant if one looks at enough data. For 

example, one might find that banks with a disproportionate number of left-handed tellers had poor 

CAMELS ratings. Clearly, one would be foolish to 

use this information to forecast ratings because 

there is no plausible connection between these two 

phenomena.10 

One can avoid this pitfall by choosing variables 

that actually do cause problems in banks. Choosing such variables necessarily involves using 

informed judgment. The original specification for 

SCOR was chosen after both a review of the literature on bank failures and discussions with bank 

examiners.11 Discussions with examiners were particularly germane because examiners actually 

assign the ratings that the model is attempting to 

forecast. Alternative specifications were tested, 

and if testing demonstrated that a specification 

clearly improved the model’s ability to detect 

downgrades of 1- and 2-rated institutions to 3 or 

worse, changes were made. 

9 In two different experiments, credit card banks and large banks were eliminated from the model. In both cases, the model’s forecasting power was 

worse. Homogeneity is the enemy of statistical models. 

10 The FDIC does not collect data on left-handed tellers, but it does collect a 

vast amount of data on banks. It would be truly remarkable if some of these 

data were not correlated with CAMELS ratings. Statisticians are well aware 

that statistics can demonstrate correlation but not causation. 

11 The variables discussed in Cole, Cornyn, and Gunther (1995) are typical of 

those used in failure models. See also Hooks (1995) and Demirgü�-Kunt 

(1989). 

2003, VOLUME 15, NO. 3 20 FDIC BANKING REVIEW 
 

 

 

 

 

 

 

 

*

The SCOR System of Off-Site Monitoring 

The final SCOR model uses 12 variables; all are 

financial data from the Call Report, expressed as a 

percentage of assets. Table 2 lists the variables and 

some ratios for a completely hypothetical bank.12 

Table 2 

SCOR Variables and Ratios for a 

Hypothetical Bank 

Percentage 

SCOR Variable of Total Assets 

Equity 13.59 

Loan-Loss Reserves 1.31 

Loans Past Due 30–89 Days 2.23 

Loans Past Due 90+ Days 0.89 

Nonaccrual Loans 1.51 

Other Real Estate 0.45 

Charge-offs* 1.18 

Provisions for Loan Losses and Transfer Risk* 1.28 

Income before Taxes and Extraordinary Charges* 0.10 

Volatile Liabilities 25.31 

Liquid Assets 28.16 

Loans and Long-Term Securities 68.79 

*Flow variables. These variables are lagging 12-month totals and have been adjusted 

for mergers. 

Tests of statistical significance show that all the 

variables are closely related to CAMELS ratings. 

In addition to the variables in table 2, we experimented with other variables, such as loan growth 

and average employee salaries. We also experimented with the definitions of some of the variables. For example, we experimented with using 

Tier-1 capital instead of simple equity, and with 

using average total assets instead of total assets in 

the denominators of the ratios. We did not find 

any other specification that produced consistently 

12 An earlier specification of SCOR used 13 variables. Dividends were included, and net income was used instead of income before taxes. Pretax income 

is now used because of the increasing number of banks that are sub-chapter 

S corporations and do not pay corporate income tax, and dividends were 

better forecasts than the model currently embodied 

in SCOR. 

In table 2, the variables marked with asterisks are 

items from the income statement (flows), in contrast to the unmarked variables, which are from 

the balance sheet (stocks). Stocks are measured at 

a point in time; SCOR uses the end-of-quarter figures from the Call Report. Flows are measured 

over a period of time; SCOR uses trailing fourquarter totals, instead of the year-to-date numbers 

found on Call Reports. 

Four-quarter totals can be significantly affected by 

mergers. To eliminate these effects, SCOR uses 

merger-adjusted data. If banks merge, SCOR does 

a pro forma merger of the data from pre-merger 

quarters. Although certainly not ideal, this 

method eliminates a major distortion due to mergers.13 

The model forecasts the probability that a bank 

will receive a specific rating. An example of ratings for a completely hypothetical bank can be 

found in table 3. According to SCOR, this completely hypothetical bank has approximately a 3 

percent chance of receiving a rating of 1, a 55 percent chance of receiving a rating of 2, and so forth. 

Table 3 

Sample SCOR Output for a 

Hypothetical Bank 

Rating Probability 

1 3.2 

2 55.0 

3 36.5 

4 4.9 

5 0.4 

Probability of 

Downgrade 41.8 

SCOR Rating 2.44 

dropped because supervisors commonly restrict dividends at troubled institutions. Thus, dividends are necessarily low at an institution after supervisors 

have identified it as troubled (it is important to remember, however, that low 

dividends do not necessarily signal that a sound institution is having trouble). 

Both changes—dropping dividends, and replacing pretax income with net 

income—demonstrate the importance of using informed judgment when selecting variables. The current version of SCOR is at least as accurate as the 

older version. 

13 However, this method might introduce another distortion. Suppose major 

portions of the disappearing bank (for example, branches or a credit card 

portfolio) were sold within 12 months of the merger. SCOR’s method of 

adjusting for mergers would include income from operations that were not 

part of the merged entity. Although examples of this sort of distortion can 

be found, they are relatively uncommon. 

FDIC BANKING REVIEW 21 2003, VOLUME 15, NO. 3 
 

The SCOR System of Off-Site Monitoring 

The SCOR model also estimates the probability of 

receiving a downgrade. If our hypothetical bank is 

currently rated 2 or better, that probability is 

defined as its chance of receiving a rating of 3 or 

worse (36.5% + 4.9% + 0.4% = 41.8%).14 Associated with these probabilities is a SCOR rating that 

equals the expected rating [(1 x 3.2%) + 

(2 x 55.0%) + (3 x 36.5%) + (4 x 4.9%) + 

(5 x 0.4%)].15 

The FDIC flags any bank with a downgrade probability of 35 percent or greater. Flagging means a 

bank must be reviewed by its case manager, and 35 

percent was chosen because case managers have 

only a limited amount of time for reviewing banks. 

SCOR flags approximately as many banks as 

CAEL, but during the 1991–1992 recession the 

SCOR system would have flagged many more 

banks than CAEL. If SCOR flags so many banks 

that the review process overwhelms regional analysts—which could happen, for example, during a 

ect, and the type of testing that demonstrated 

SCOR’s superiority to its predecessor, CAEL. 

Although the forecasts were evaluated at a variety 

of time horizons, testing focused on downgrades 

that occurred four to six months after a given Call 

Report date. The rationale for this emphasis is 

that the Call Report data are finalized 60 days after 

the Call Report date. Consequently, forecasts are 

not available to bank supervisors until 60 days 

after the Call Report date.17 

Figure 1 shows the accuracy of the model at various time horizons. These results include only the 

first examination after the Call Report was filed. 

Clearly, accuracy decreases as the forecast horizon 

lengthens. However, SCOR has some success 

even at horizons of 16–18 months. Even at this 

time horizon, SCOR is at least seven times better 

than a random guess.18 

recession—the flag can be easily changed.16 

Results 

The previous section refers to various experiments 

that were done while SCOR was being developed. 

The success of these experiments was evaluated in 

terms of the objective of the model: whether the 

modifications produced a model able to correctly 

identify banks that were subsequently downgraded. 

This section reports on the results of the final 

model and demonstrates the type of testing that 

was repeatedly done during the course of this projFigure 1 

Type I Accuracy 

Type II Accuracy 

1-3 4-6 7-9 10-12 13-15 16-18 

Horizon in Months 

0 

10 

20 

30 

Percent 

SCOR Performance by Horizon, 

1993–2002 

14 If the bank had a rating of 3, the probability of a downgrade would equal 

5.3 percent (4.9% + 0.4%). If it had a rating of 4, the probability of a 

downgrade would equal 0.4 percent. By definition, the downgrade probability 

for 5-rated banks is zero. 

15 In practice, most banks have a high probability of receiving one or two 

specific ratings and almost no probability of receiving the other three ratings. 

The example shown in table 3 is typical. In these cases, we can closely 

approximate the downgrade probability by dropping the integer part of the 

SCOR rating. For the hypothetical bank in table 3, the approximate probability of being downgraded to a 3 is 42 percent. This approximation is exact if 

three of the five probabilities are zero. 

16 When SCOR was adopted in 1999, a 30 percent downgrade probability was 

used to flag banks for review. By 2001, when the weakening economy 

undoubtedly affected the financials at banks and caused more poor SCOR ratings and more reviews, that flag was resulting in too many reviews. Accordingly, the higher probability was adopted. 

17 Another reason for focusing on the four- to six-month horizon is that after 

an examination, the Call Reports filed immediately before the examination 

almost always (but not absolutely always) are revised. Revisions to the Call 

Report that immediately precede the exam will bias the backtest because the 

SCOR model will have access to the corrected data instead of the data that 

were actually available to supervisors before the examination. This bias is 

minimized by the use of forecasts based on the Call Report filed four to six 

months before the examination—a Call Report less likely to be revised. 

18 Banks that have problems are more likely to be examined, and the reported 

results include only the first examination after the Call Report that provided 

the data for the SCOR rating. Consequently, the results for a 16- to 18-month 

horizon include only the strongest banks, and only 2.6 percent of these were 

downgraded. In contrast, 18.8 percent of the banks identified by SCOR are 

downgraded 16 to 18 months later. 

2003, VOLUME 15, NO. 3 22 FDIC BANKING REVIEW 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The SCOR System of Off-Site Monitoring 

Figure 2 shows, by Call Report year, the Type I and 

Type II accuracy achieved under the SCOR system. (The data for figure 2 are found in table 4.) 

Accuracy is assessed at a four- to six-month horizon, which corresponds closely to the period when 

the forecasts would be available to supervisors. 

Figure 2 

Performance of SCOR over Time,
 

Four- to Six-Month Horizon
 

Percent 

80 

60 

40 

20 

0 

Type I Accuracy 

Type II Accuracy 

1986 1988 1990 1992 1994 1996 1998 2000 2002 

Year 

Table 4 

Performance of SCOR at Four- to 

Six-Month Horizon 

Examined Downgraded Flagged Correct 

1986 6,465 1,038 1,800 760 

1987 6,990 691 1,027 394 

1988 6,655 669 767 318 

1989 7,236 691 776 335 

1990 7,098 894 988 490 

1991 7,740 714 1,170 397 

1992 9,403 348 727 164 

1993 9,911 187 253 40 

1994 9,444 191 129 25 

1995 8,961 153 108 19 

1996 8,279 167 142 40 

1997 7,321 156 107 30 

1998 6,805 232 82 26 

1999 7,020 302 118 39 

2000 6,676 274 139 42 

2001 6,623 312 196 56 

2002 3,878 143 93 21 

Total 126,505 7,162 8,622 3,196 

Note: This table reports on the results of examinations conducted four to six months 

after the Call Report that is the basis for a SCOR rating. Downgrades are from 

ratings of 1 or 2 to 3 or worse. 

Clearly the accuracy of the model has declined 

substantially, and performance has been especially 

weak since 1993. Since 1993, SCOR has identified approximately 16 percent of the banks that 

were subsequently downgraded (Type I accuracy), 

and approximately 27 percent of the banks identified by SCOR were downgraded (Type II accuracy). It must be noted, however, that although the 

SCOR model is not extremely accurate, it is 

informative. While Type II accuracy of 27 percent 

is low, it is approximately nine times better than a 

random guess. The model does produce valuable 

information, distinguishing banks that are likely to 

be downgraded from those that are not.19 SCOR 

was adopted to replace CAEL because it had higher levels of Type I and Type II accuracy for almost 

all time periods. 

The low level of accuracy might be expected inasmuch as SCOR relies completely on financial 

ratios. Any such model will probably be more 

accurate when the reasons for downgrades are 

financial, and less accurate when the reasons have 

to do with some aspect of bank operations that 

does not affect the bank’s financial ratios. For 

example, examiners may downgrade a bank 

because they discover that it has significantly 

weakened its underwriting standards or has weak 

internal controls—but as long as the more risky 

loans have not become past due, problems might 

not have made their way to the financial statements. Consequently, one might reasonably 

expect that SCOR would be less accurate over the 

last decade.20 

The reliance on financial data has several other 

effects on SCOR’s performance. For one thing, it 

means that SCOR is completely dependent on the 

accurate reporting of financial information. But in 

two of the more spectacular bank failures of the 

last few years—BestBank and the First National 

Bank of Keystone—the bank’s condition had been 

substantially misstated; consequently, SCOR gave 

extremely good ratings to both banks. 

19 The reviews done by case managers almost inevitably indicate that banks 

flagged by SCOR have noticeable weaknesses, even though the weaknesses 

might not warrant an examination or closer supervision. 

20 The recession of 2001 affected mostly the larger banks and had minimal 

effects on the rest of the industry. 

FDIC BANKING REVIEW 23 2003, VOLUME 15, NO. 3 
The SCOR System of Off-Site Monitoring 

These problems with SCOR demonstrate that it 

can never be a substitute for full-scope examinations. Examinations can detect unsafe practices 

before they affect the bank’s financial condition; 

examinations can also detect misstated financial 

reports.21 As we have said before and will say 

again, SCOR is a complement to bank examinations, not a substitute for them. 

Additional Features 

A secondary objective of the SCOR project was to 

produce ratings that were easier to understand and 

analyze than CAEL ratings. Several features were 

added to the model to help users of the ratings 

understand the reasons SCOR identifies a particular institution. First, the SCOR system produces 

component ratings that help identify specific areas 

of weakness in a bank. The most controversial of 

the component ratings has been the management 

rating because the conventional wisdom is that a 

model that uses financial ratios cannot identify 

weaknesses in management. Nonetheless, the 

SCOR management rating does indicate which 

banks are at risk of being downgraded. 

The second auxiliary tool is a system of weights 

that indicate which variables are causing poor ratings. The operation of these weights is discussed 

in this section, while the more technical explanation is relegated to an appendix. 

In addition to producing ratings that are more easily analyzed than CAEL ratings, SCOR has also 

proved useful for tracking trends in the industry. 

This ability is an extension of the more traditional 

off-site monitoring. 

The Component Ratings 

The SCOR model produces a forecasted rating not 

only for the CAMELS composite but also for each 

21 The FDIC’s Division of Supervision and Consumer Protection rightly insists 

that bank examiners are not a substitute for adequate internal and external 

auditing. However, it was examinations that uncovered the fraud at both 

BestBank and First National Bank of Keystone. See Berger and Davies (1998) 

for a discussion of the auditing function of examinations. 

of the six CAMELS components. Case managers 

and examiners find these ratings useful for identifying the weaknesses in banks.22 

The component ratings are produced by exactly 

the same method that is used to produce the composite rating. Most notably, the same variables are 

used for all the component ratings. But although 

all the variables in table 2 are relevant to the composite rating, some are more relevant to one or 

another of the six components. For example, the 

equity-asset ratio is obviously relevant to the capital component of CAMELS but is less important 

to the earnings component. SCOR, however, uses 

all the variables to forecast all the components 

and, by means of the stepwise procedure mentioned above, selects the variables that are more 

relevant to explaining the observed component. 

The results indicate that examiners do not rate the 

components in isolation. Consider the capital 

component. Although the equity-asset ratio is 

critical for the rating of this component, other 

variables, too, are used to forecast it. For example, 

high levels of loans past due 30–89 days are consistently related to poor capital ratings. The reason 

SCOR uses this variable for this component may 

not be obvious, but the capital rating is determined 

by the adequacy of the bank’s capital in relationship to its need for capital, and banks with high 

levels of past-due loans are likely to experience 

more losses in the future and are therefore likely to 

need more capital to absorb those losses. Consequently, if two banks have the same equity-asset 

ratio but one of them has a very high level of pastdue loans, that one would receive a worse capital 

rating.23 

Although the component ratings are widely used, 

several financial analysts have raised questions 

about using SCOR to forecast the management 

rating. In contrast to the other components, this 

one is not obviously directly related to any finan22 As mentioned above, CAEL produced ratings for capital, asset quality, earnings, and liquidity, which were then combined into a composite rating. 

23 CAEL captured this type of relationship by treating some ratios as primary 

causes of ratings and others as secondary causes. 

2003, VOLUME 15, NO. 3 24 FDIC BANKING REVIEW 
The SCOR System of Off-Site Monitoring 

cial ratios;24 internal controls and underwriting 

standards, for example, cannot be readily reduced 

to such ratios. In other words, many of the factors 

behind management ratings are intangible, and a 

statistical model cannot consider factors that cannot be reduced to accounting. 

However, all the data in the Call Report can be 

viewed as indicators of the quality of a bank’s management. Obviously factors such as economic conditions affect a bank’s financial health, but the 

quality of management is always a critical factor as 

well. In the case of loans past due 30–89 days, for 

example, a high level of such loans implies that 

the bank has a problem with the quality of its 

assets and is more likely to have a poor asset rating 

at the next examination. However, that same 

level of loans past due 30–89 days might also mean 

that the bank’s management has done a poor job 

underwriting the loan portfolio and that the bank 

is more likely to have a poor management rating. 

Other factors besides underwriting standards affect 

past-due ratios, so management ratings and pastdue ratios do not move in lockstep. 

Moreover, the management rating is not alone in 

involving factors that do not appear on the Call 

Report. All the other components also involve 

such factors. For example, the asset rating depends 

on the level of classified loans, but no data on loan 

classifications are available until after the examination is actually complete. Thus, asset ratings 

cannot be assigned only on the basis of information from the Call Report. Similarly, capital ratings depend on the level of classifications as well 

as on qualitative assessments of the risk because 

the fundamental question is whether the available 

capital is adequate for the level of risk. 

In short, the management rating is much like the 

other ratings. SCOR forecasts management ratings by using the same technique it uses for the 

other ratings: it examines the characteristics of 

banks to which examiners have recently assigned 

poor management ratings. SCOR has found that 

examiners give poor management ratings to banks 

24 Recall that this is the reason CAEL excluded the management component. 

with low earnings, low reserves for loan losses, and 

high levels of past-due and problem loans. 

Most importantly, SCOR can produce reasonably 

accurate forecasts of management ratings. Figure 3 

shows the accuracy of the component (and composite) forecasts. Although management forecasts 

are less accurate than some others, SCOR can still 

use relevant Call Report data to identify institutions likely to have management problems.25 

Figure 3 

SCOR Accuracy by Component, 

1993–2002 

Percent 

40 

30 

20 

10 

0 

Type I Accuracy Type II Accuracy 

Composite Capital Assets Management Earnings Liquidity Sensivity
 

SCOR Accuracy for Composite
 

Weights of the Call Report Data 

Besides producing forecasted composite (and component) ratings, SCOR produces a system of 

weights that highlights which aspects of a bank’s 

data are responsible for poor ratings.26 Each ratio 

is assigned a weight that indicates the contribution 

that that ratio made to the poor SCOR rating. By 

indicating which aspects of a bank’s operations 

account for the subpar rating, these weights give 

case managers and others a starting point for analyzing ratings. 

25 Some other aspects of the forecasted management rating are worth noticing. First, the accuracy of the forecasted management rating deteriorates less 

over time, so at long horizons it is among the more accurate of the component forecasts. Second, the forecasted management rating does help signal 

downgrades in the composite rating. 

26 The mathematics behind the “weighting” system can be found in Appendix 

2. 

FDIC BANKING REVIEW 25 2003, VOLUME 15, NO. 3 
 

 

 

 

 

 

The SCOR System of Off-Site Monitoring 

In order to define a poor rating, SCOR needs some Table 5 

standard for a good rating. SCOR uses the typical 

2-rated bank as the benchmark because, by definition, 2-rated banks are sound institutions with 

some minor weaknesses.27 In contrast, 1-rated 

banks are very strong institutions, and banks with 

3, 4, and 5 ratings have weaknesses severe enough 

that the institutions warrant close supervision. 

SCOR was designed to identify those banks that 

are in danger of receiving examination ratings 

worse than 2, so the 2-rated banks are the obvious 

standard of comparison. 

SCOR considers the “median-2” bank to be the 

typical 2-rated bank. The median-2 bank is constructed from the median financial data for all the 

banks that were rated 2 in on-site examinations 

over the previous year. Thus, the capital-asset 

ratio is the median ratio for all the banks that 

received a 2-rating in the previous year. The 

median-2 bank does not actually exist; it is a 

statistical construct.28 

The median-2 bank does not necessarily have a 

SCOR rating of exactly 2. If the typical 2-rated 

bank is a strong 2 (more like a 1-rated bank than a 

3-rated bank), then the median 2 would probably 

have a SCOR rating of better than 2. In fact, at 

present the industry is very healthy. As a result, 

the median-2 bank has a SCOR rating of approximately 1.6. 

Table 5 reports a hypothetical example of the 

SCOR weighting system. The weights indicate 

that the problems in the hypothetical bank are due 

primarily to poor-quality assets and low earnings. 

Income has a weight of approximately 29 percent, 

and nonaccrual loans have a weight of 28 percent. 

This means that the difference in income ratios 

accounts for approximately 29 percent of the difference between the SCOR rating of the median-2 

bank and the rating of the hypothetical bank. 

27 See table 1. Peer groups could easily be used for this analysis but currently are not. 

28 The median-2 bank is used as the basis of comparison instead of the 

“mean 2” because outliers tend to increase mean financial ratios. A simple 

example illustrates the point. If 99 banks have capital-asset ratios of 9 percent and 1 has a capital-asset ratio of 90 percent, the mean capital-asset 

ratio is 9.81 percent. The median is 9 percent, which is more representative. 

SCOR Weights for a Hypothetical Bank 

Median-2 Bank Hypothetical Bank 

Variable Ratio Ratio Weight 

Equity 9.31 13.59 –7.16 

Loan-Loss Reserves 0.80 1.31 0.00 

Loans Past Due 30–89 Days 0.75 2.23 18.56 

Loans Past Due 90+ Days 0.10 0.89 10.27 

Nonaccrual Loans 0.23 1.51 28.05 

Other Real Estate 0.00 0.45 9.31 

Charge-offs 0.13 1.18 4.66 

Provisions for Loan Losses and 

Transfer Risk 0.18 1.28 0.00 

Income before Taxes and 

Extraordinary Charges 1.38 0.10 29.13 

Volatile Liabilities 14.13 25.31 5.24 

Liquid Assets 32.34 28.16 1.95 

Loans and Long-Term Securities 71.29 68.79 0.00 

SCOR Rating 1.60 2.44 

Loans past due 30–89 days and loans past due 90+ 

days also have high weights. 

Weights can be negative or zero. Weights are used 

to explain poor ratings, and those variables that 

would actually contribute to a better rating receive 

negative weights. For example, in table 5 the bank 

actually has more capital than the median-2 bank, 

so equity has a negative weight. This ratio is better, 

not worse, than that of the median 2, so it would 

tend to be a reason for a better, not a worse, rating. 

Zero weights occur when there is no consistent 

relationship between a ratio and the examination 

ratings. For example, in table 5 loan-loss reserves 

have a zero weight. This could occur if some 

banks with high loan-loss reserves were being conservative and providing for any possible losses 

whereas other banks with high loan-loss reserves 

had asset-quality problems. In such a case, some 

banks with high reserves would have good ratings 

and some would have poor ratings, and SCOR 

would not find a consistent relationship.29 The 

29 There are other possible reasons that a stepwise procedure might eliminate 

a variable. For example, if two of the explanatory variables were highly correlated, the stepwise procedure would choose the one most closely related to 

CAMELS ratings and would ignore the other. 

In practice, the coefficients used by SCOR are very stable from one period 

to the next, and the stepwise procedure adds or drops only marginally important variables. In historical tests, SCOR uses almost all the variables each 

quarter to forecast either a composite or a component rating. 

2003, VOLUME 15, NO. 3 26 FDIC BANKING REVIEW 
The SCOR System of Off-Site Monitoring 

stepwise procedure assigned that variable a zero 

coefficient.30 

The weights in table 5 are typical of the banks that 

are identified as potential concerns. In general, 

these banks have either asset problems (high levels 

of loans past due, of nonaccrual loans, or of other 

real estate) or poor earnings. High levels of noncore funding or lack of liquid assets are also occasional contributing factors. 

The weights are a starting point for analysis. They 

do not diagnose the problem, but they do indicate 

which factors are of special concern and which are 

not particularly important. 

Trends in the Industry 

Although SCOR was developed to identify specific 

institutions, trends in SCOR ratings can also be 

used to identify changes in the overall health of 

the banking industry. Figure 4 shows the trends in 

the median SCOR composite rating and in the 

90th percentile. By definition, 50 percent of the 

banks have ratings better than the median, while 

90 percent have ratings better than the 90th percentile and 10 percent have worse ratings. The 

median can be interpreted as the rating of the typical bank, whereas the 90th percentile indicates 

trends among the 10 percent of the banks that 

have the worst ratings. These banks, of course, are 

the ones of particular concern to supervisors. 

The banking problems of the late 1980s and early 

1990s are apparent in the data presented by figure 

4. The figure also indicates that the banking 

industry’s health peaked in 1998, when the median 

SCOR rating was 1.52. By the end of 2001, the 

median rating was 1.71. During 2002, ratings 

improved.31 

30 Because these weights are calculated with a Taylor first-order approximation, they necessarily sum to 100 percent. 

31 As discussed above, SCOR is reestimated each quarter, so the coefficients 

change slightly over time. As also discussed above, changes in coefficients 

would occur if, for example, examiners found that underwriting standards had 

changed. However, by using the 1998 coefficients to rate banks in 2001 and 

using 2001 coefficients to rate banks in 1998, one can determine whether 

Figure 4 

Trends in SCOR Ratings,1985–2002 

Rating 

3 

2 

1 

0 

90th Percentile 

Median 

1986 1988 1990 1992 1994 1996 1998 2000 2002 

Call Report Date 

Concluding Comments 

SCOR permits the FDIC to track industry trends 

and helps identify the institutions that are especially weak. The SCOR output also helps the 

FDIC identify which financial ratios contribute to 

poor ratings. However, in periods of economic 

prosperity, SCOR forecasts are wrong more often 

than they are right, and since 1993 the model has 

missed approximately 80 percent of the downgrades, and its forecasts of a downgrade have been 

incorrect about 75 percent of the time. In contrast, when data are used from the early 1990s—a 

period when recession was causing financial problems for many banks—SCOR produces more accurate forecasts. Although this single piece of 

evidence is not conclusive, it does suggest that 

SCOR will become even more useful if economic 

troubles again begin affecting the banking industry. 

SCOR could then help the FDIC focus its limited 

resources on the institutions that need closer 

supervision. 

the change in SCOR ratings is driven by the underlying financial ratios or by 

the change in coefficients. This exercise indicates that the change in coefficients accounts for approximately half the trend between 1998 and 2001, 

while changes in the ratios account for the other half. The change in the 

model could be interpreted as reflecting examiners’ growing concern about 

aspects of bank operation (for example, underwriting) that are not measured 

by the ratios. 

FDIC BANKING REVIEW 27 2003, VOLUME 15, NO. 3 
The SCOR System of Off-Site Monitoring 

The model identifies systematic financial strength 

or weakness but does not consider intangible factors. However, intangibles are too important to 

ignore because during periods of economic prosperity, poor ratings are more likely to be the result of 

poor policies and procedures—that is, intangible 

factors—than of financial weakness. Consequently, the accuracy of SCOR will be lower during 

periods of prosperity, as it is during the current 

period. Thus off-site monitoring, with its dependence on financial ratios, cannot replace on-site 

monitoring. The SCOR model and other systems 

of off-site monitoring are an aid to examiners but 

should never be allowed to replace regular examinations. 

2003, VOLUME 15, NO. 3 28 FDIC BANKING REVIEW 
 

 

 

 

 

 

 

 

The SCOR System of Off-Site Monitoring 

REFERENCES 

Berger, Allen, and Sally M. Davies. 1998. The Information Content of Bank Examinations. Journal of Financial Services Research 14:117–44. 

Cole, Rebel A., and Jeffery W. Gunther. 1998. Predicting Bank Failures: A Comparison 

of On- and Off-Site Monitoring Systems. Journal of Financial Services Research 

13:103–17. 

Cole, Rebel A., Barbara G. Cornyn, and Jeffery W. Gunther. 1995. FIMS: A New Monitoring System for Banking Institutions. Federal Reserve Bulletin (January): 1–15. 

Curry, Timothy J., John P. O’Keefe, Jane Coburn, and Lynne Montgomery. 1999. Financially Distressed Banks: How Effective Are Enforcement Actions in the Supervision Process? FDIC Banking Review 12, no. 2:1–18. 

Demirgü9-Kunt, Asli. 1989. Deposit-Institution Failures: A Review of the Empirical Literature. Federal Reserve Bank of Cleveland Economic Review (Quarter 4): 2–18. 

Federal Deposit Insurance Corporation (FDIC). 1997. History of the Eighties—Lessons for 

the Future. Vol. 1, An Examination of the Banking Crises of the 1980s and Early 

1990s. FDIC. 

Gilbert, R. Alton, Andrew P. Meyer, and Mark D. Vaughan. 1999. The Role of Supervisory Screens and Econometric Models in Off-Site Surveillance. Federal Reserve 

Bank of St. Louis Review (November–December): 31–56. 

Hooks, Linda M. 1995. Bank Asset Risk—Evidence from Early Warning Models. Contemporary Economic Policy 13, no. 4:36–50. 

FDIC BANKING REVIEW 29 2003, VOLUME 15, NO. 3 
The SCOR System of Off-Site Monitoring 

APPENDIX 1
 

Exclusion of Current CAMELS Ratings
 

The SCOR model does not use current CAMELS 

ratings as an explanatory variable, for several reasons. First, the models that use current ratings 

produce forecasts that tend to cluster around the 

integers of 1, 2, 3, 4, or 5. For example, ratings 

near 2 (say, 2.05) are common, but ratings further 

from 2 (say, 2.40) are rare. This clustering suggests 

that most banks are really one of five identifiable 

types. If 2-rated banks are in fact substantially different from other banks, most 2-rated banks will 

have actual ratings close to 2 (say, 2.05), and only 

the odd institution that does not really fit one of 

the established types will have an intermediate rating (say, 2.40). 

However, CAMELS ratings are undoubtedly 

approximate measures of financial strength, and 

2-rated banks are not an identifiable type as much 

as they are a group of banks whose “true” financial 

strength might be rated somewhere between 1.5 

and 2.5. The category of 2-rated banks includes 

both “strong 2s” (with “true” ratings of 1.6) and 

“weak 2s” (with “true” ratings of 2.4). 

SCOR ratings tend to be more uniformly distributed than ratings produced by models that incorporate prior examination ratings; the distribution 

of SCOR ratings probably reflects the actual distribution of financial strength among banks. Figure 

A.1 illustrates the difference in the distributions of 

the two types of rating systems. The screened bars 

show the distribution of SCOR ratings based on 

December 1996 data. The solid bars show the distribution of ratings from an otherwise identical 

model that includes the CAMEL rating as of 

December 1996. The forecasts that use the 

CAMEL ratings are clearly clustered, whereas the 

distribution of SCOR ratings is smoother.32 

32 The distribution of SCOR ratings also resembles the distribution of the average of the six component ratings. It should be noted that this average is not 

meaningful because examiners would almost certainly assign higher weights 

to some components than to others. Moreover, SCOR ratings are distributed 

much like CAEL ratings, though CAEL ratings tend to be lower. (As explained 

above, CAEL was intended to be “biased” toward downgrading banks, and 

SCOR is not biased.) 

Figure A-1 

Effect of Including Previous Rating 

Percent of All Ratings 

30 

20 

10 

0 

Previous Rating 

Included 

Previous Rating 

Not Included 

1.0	 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.1 

or worse Rating 

The second reason the SCOR model does not use 

current CAMELS ratings is that examiners wanted 

a system that used only financial data: they were 

suspicious of any model that forecasted future ratings in terms of current ratings, especially when 

the model said that ratings tend not to change. A 

model that exhibits inertia might miss changes in a 

bank’s condition. There is some evidence that 

information in CAMELS ratings does become 

dated, so older CAMELS ratings might well be 

misleading.33 

Finally and most importantly, the historical data 

did produce some evidence confirming examiners’ 

concerns. Models that use CAMELS ratings are 

marginally worse than SCOR at forecasting the 

ratings of those banks of most interest to the 

FDIC—formerly sound banks that are currently 

experiencing difficulties. Over the past couple of 

years, the SCOR model has produced better 

(albeit only slightly better) forecasts of downgrades 

than models that use prior examination ratings.34 

Consequently, the SCOR model uses only financial data. 

33 See Cole and Gunther (1998). 

34 The differences are not statistically significant. 

2003, VOLUME 15, NO. 3 30	 FDIC BANKING REVIEW 
 

The SCOR System of Off-Site Monitoring 

On the other hand, including current ratings 

would have some advantages, and some prototypes 

of SCOR did use this approach.35 First, CAMELS 

ratings include information not available on the 

balance sheet. When examiners rate banks, they 

consider many intangible factors, such as the quality of internal controls, and these intangible factors 

tend to persist over time. A model that uses only 

financial data ignores this extra information. 

Second, models that include current ratings are 

more accurate in distinguishing between 1- and 2­

rated banks.36 SCOR cannot differentiate 

35 The Federal Reserve Board’s contemporaneous SEER model includes management ratings.
 

36 As discussed in the main body of the text, however, bank supervisors are
 

relatively unconcerned about distinguishing between 1- and 2-rated banks.
 

between these banks, apparently because 1-rated 

banks are financially very similar to 2-rated banks. 

Conventional wisdom holds that most of the difference between 1-rated and 2-rated banks lies in 

intangible factors. 

Third, models that use current CAMELS ratings 

tend to produce forecasted ratings that differ only 

slightly from the current examination ratings, and 

in fact the best single predictor of future ratings is 

the current rating. Almost all banks that have a 

2 rating before an examination receive a 2 rating 

after it. 

FDIC BANKING REVIEW 31 2003, VOLUME 15, NO. 3 
The SCOR System of Off-Site Monitoring 

APPENDIX 2
 

Calculation of the SCOR Weights
 

The method used to calculate the SCOR weights 

takes advantage of the linear portion of the logit 

model. Ignoring the intercept terms, the linear 

portion is a weighted sum of the bank’s financial 

data, which can be denoted βx which equals 

β1x1 + β2x2 + ... + β12x12. 

If the weights are computed for the composite 

CAMELS rating, this sum can be considered a 

measure of the bank’s general financial strength. If 

the weights are computed for the capital rating, βx 

can be considered the measure of the bank’s capital 

adequacy. 

The ratings of two banks can be readily compared. 

Consider two institutions: Bank A (with financial 

data xA = x1

A, x2A, ..., x12A) and Bank B (with 

financial data xB = x1

B, x2B, ..., x12B). The difference in the measure of financial strength of the 

two banks is βxA – βxB = β (xA – xB). The first 

variable accounts for β1 (x1

A – x1B) of this difference, or, in percentage terms: 

A B β1 (x1 − x ) 100 * A 

1 

β (x − x B )

This percentage would indicate the importance of 

the capital-asset ratio, for example, in explaining 

the difference in financial strength of the two 

banks. These percentages (for variables x1, x2, 

and so forth) necessarily sum to 100. The percentages can be negative; a negative percentage could 

occur if Bank A were stronger, on the whole, than 

Bank B but had a lower (weaker) capital-asset 

ratio. 

It might be noted that this method is closely related to a Taylor expansion of the logit model. The 

first derivative of the logistic function equals K βi 

where K is a number that depends on the point at 

which the derivative is evaluated. However, K is 

the same for all variables. Thus, the first term 

in a Taylor expansion about the point xB is 

K β1 (x1

A – x1B), and the total is K β (xA – xB). 

Of course, the intercept terms will not enter the 

Taylor expansion because they are constants. If 

the individual terms are expressed as percentages 

of the total, then K cancels from both numerator 

and denominator, and the result is identical to the 

formula above. 

2003, VOLUME 15, NO. 3 32 FDIC BANKING REVIEW 
Recent Developments 

Recent Developments Affecting 

Depository Institutions 

by Lynne Montgomery* 

CONDITION OF THE BANKING INDUSTRY
 

First-Quarter 2003 Financial Results for 

Commercial Banks and Savings Institutions 

In its Quarterly Banking Profile for the first quarter 

of 2003, the Federal Deposit Insurance 

Corporation (FDIC) reported that FDIC-insured 

commercial banks and savings institutions earned 

a record $29.4 billion, an increase of $4.1 billion 

from earnings in the first quarter of 2002. Key 

factors in the higher earnings were gains on sales 

of securities and lower expenses for delinquent 

loans. The average return on assets was 1.38 percent, up from 1.29 percent one year earlier. The 

number of commercial banks and savings institutions on the FDIC’s “Problem List” declined from 

136 in the fourth quarter of 2002 to 129 in the 

first quarter of 2003, and assets of “problem” 

banks fell from $39 billion to $35 billion. The 

Quarterly Banking Profile can be accessed at 

http://www2.fdic.gov/qbp. FDIC Quarterly Banking 

Profile, First Quarter 2003. 

*Lynne Montgomery is a senior financial analyst in the FDIC’s Division of
 

Insurance and Research.
 

Reference sources: American Banker (AB), BNA’s Banking Report (BBR), and
 

Federal Register (FR).
 

First-Quarter 2003 Financial Results for the 

Insurance Funds 

The FDIC reported that for the first quarter of 

2003 the Bank Insurance Fund (BIF) had comprehensive income (net income plus/minus currentperiod unrealized gains/losses on available-for-sale 

securities) of $332 million, compared with 

income of $258 million for the same period in 

2002. Although net income was actually $7 million lower than in the first quarter of 2002, unrealized gains on available-for-sale securities 

increased by $81 million. As of March 31, 2003, 

the BIF balance was approximately $32.4 billion, 

up from $32.1 billion at year-end 2002. The BIF 

reserve ratio rose from 1.27 percent at December 

31, 2002, to 1.28 percent at March 31, 2003. 

The Savings Association Insurance Fund (SAIF) 

reported comprehensive income of $159 million 

for the first quarter of 2003, compared with $114 

million for the same period in 2002. The increase 

in comprehensive income was due primarily to 

lower estimated losses for future failures and higher unrealized gains on available-for-sale securities. 

The SAIF balance as of March 31, 2003, was 

$11.9 billion, up from $11.7 billion at year-end 

FDIC BANKING REVIEW 33 2003, VOLUME 15, NO. 3 
Recent Developments 

2002. The SAIF reserve ratio held steady at 1.37 

percent between December 31, 2002, and March 

31, 2003. PR-45-2003, FDIC, 5/7/03; FDIC Quarterly 

Banking Profile, First Quarter 2003. 

Bank Failure 

On May 9, 2003, the First National Bank of 

Blanchardville, Blanchardville, Wisconsin, was 

closed by the Office of the Comptroller of the 

Currency (OCC), and the FDIC was named 

receiver. First National had total assets of 

approximately $35 million and total deposits of 

approximately $29 million. The Park Bank, 

Madison, Wisconsin, acquired First National’s 

insured deposits and purchased the failed bank’s 

cash and cash-equivalent assets. The remaining 

assets have been retained by the FDIC for later 

disposition. First National was the second failure 

of a BIF-insured institution in 2003. PR-47-2003, 

FDIC, 5/9/03; PR-48-2003, FDIC, 5/10/03. 

Emerging Risks to the Banking Industry 

The summer 2003 edition of the FDIC Outlook 

reports that even as the banking and thrift industries’ earnings set new records, U.S. banks and 

savings institutions continue to face risk-management challenges in several key areas. The report 

discusses how banks have been addressing their 

credit, market, and operational risks. The report 

also discusses potential bank problem areas that 

FDIC analysts and examiners continue to monitor, including commercial real estate portfolios; 

subprime consumer lending; net interest margin 

compressions; interest rate and funding risks related to the unusually low interest-rate environment; exposure to market-sensitive, non-interest 

income sources; and the adequacy of internal 

audit and other fraud controls. The FDIC 

Outlook also addresses developments in each of 

the FDIC’s six regions (Atlanta, Chicago, Dallas, 

Kansas City, New York, and San Francisco). The 

summer 2003 FDIC Outlook can be accessed at 

http://www.fdic.gov/bank/analytical/regional/

ro20032q/na/index.html. (Past editions of the 

FDIC Outlook can be accessed at 

http://www.fdic.gov/bank/analytical/regional/

index.html.) PR-66-2003, FDIC, 6/18/03. 

REGULATORY AGENCY ACTIONS
 

Interagency Actions 

Guidance on Sound Practices for Resilience of 

the Financial System 

On April 8, 2003, the Board of Governors of the 

Federal Reserve System (FRB), the OCC, and the 

Securities and Exchange Commission (SEC) 

issued a paper entitled Interagency Paper on Sound 

Practices to Strengthen the Resilience of the U.S. 

Financial System, which identifies sound practices 

to strengthen the resilience of U.S. financial markets and minimize the immediate systemic effects 

of wide-scale disruptions. The paper identifies 

four sound practices for clearing and settlement 

organizations and firms that play significant roles 

in critical financial markets. Specifically, the 

sound practices consist of (1) identifying clearing 

and settlement activities in support of critical 

financial markets, (2) determining appropriate 

recovery and resumption objectives for clearing 

and settlement activities in support of critical 

markets, (3) maintaining sufficient geographically 

dispersed resources to meet recovery and resumption objectives, and (4) routinely using or testing 

recovery and resumption arrangements. These 

sound practices are intended to supplement the 

agencies’ own policies and other guidance on 

business continuity planning by financial institutions. The agencies expect organizations that fall 

within the scope of the paper to adopt the sound 

practices within the specified implementation 

time frames. PR-FRB, 4/8/03; BBR, 4/14/03, p. 602. 

2003, VOLUME 15, NO. 3 34 FDIC BANKING REVIEW 
Recent Developments 

Guidance on Use of the Discount Window 

On July 23, 2003, the FDIC, the FRB, the OCC, 

the Office of Thrift Supervision (OTS), and the 

National Credit Union Administration issued 

guidance on the appropriate use of the Federal 

Reserve’s new discount window program, which 

was introduced in January 2003. The guidance 

provides information on the new primary and 

secondary credit programs. It also reiterates wellestablished supervisory policies on sound liquidity 

contingency planning, and discusses sound 

practices in using primary-credit-program borrowings in liquidity contingency plans. 

PR-73-2003, FDIC, 7/23/03. 

New Electronic Filing System for Beneficial 

Ownership Reports 

On July 30, 2003, the FDIC, the FRB, and the 

OCC unveiled a new interagency electronic filing 

system that allows faster and easier submission 

and public retrieval of beneficial ownership 

reports filed by directors, officers, and principal 

shareholders (“insiders”) of institutions whose 

equity securities are registered with the FDIC, the 

FRB, or the OCC. The new electronic system is 

part of the agencies’ ongoing efforts to streamline 

the submission and retrieval of reports filed with 

the agencies under the Securities Exchange Act 

of 1934. The new system will also reduce the 

burden on insiders, who are required to file these 

reports within two business days of completing a 

transaction in equity securities of the institutions. 

Initially, filing under the new system will be voluntary, although the agencies encourage insiders 

to use the system as soon as practicable. PR-74­

2003, FDIC, 7/28/03. 

Federal Deposit Insurance Corporation 

New Approach to Compliance Exams 

The FDIC adopted a new examination process for 

measuring an institution’s compliance with consumer protection laws and regulations. Under the 

new approach, compliance examinations will 

combine the risk-based examination process with 

an in-depth evaluation of an institution’s compliance-management system. The new approach 

was developed to keep pace with the banking 

industry’s compliance responsibilities, which have 

become more numerous and complex. Past examinations placed too much emphasis on checklists, 

and on-site examinations often involved reviewing every bank regulation and determining 

whether a bank was in compliance with each one. 

The new approach emerges from regulators’ belief 

that bank officials have a common-sense understanding of their compliance responsibilities and 

therefore do not require the checklist approach 

during examinations. The new process will be 

used for on-site examinations occurring after 

June 30, 2003. BBR, 6/23/03, p. 1006. 

Exam Guidance for Payday Lending 

On July 2, 2003, the FDIC issued examination 

guidance for FDIC-supervised institutions that 

engage in payday lending, which typically 

involves issuing small-dollar, unsecured, shortterm advances at high annual percentage rates. 

Payday lenders will now be subject to special 

examination procedures to verify and monitor 

their performance. In addition, the FDIC will 

hold an institution’s board of directors and management responsible for ensuring that all facets of 

the payday lending operation—including those 

handled by a third party—are conducted in a safe 

and sound manner and in compliance with all 

applicable consumer protection laws, regulations, 

and policies. Failure to meet the standards will 

result in enforcement actions, which could 

require an institution to exit the payday lending 

business. PR-70-2003, FDIC, 7/2/03. 

Federal Reserve Board 

Home Mortgage Disclosure Act (HMDA) 

Transition Rules 

On May 23, 2003, the FRB released transitional 

rules that provide lenders with guidance on how 

to comply with revisions to the Home Mortgage 

Disclosure Act that become effective January 1, 

FDIC BANKING REVIEW 35 2003, VOLUME 15, NO. 3 
 

Recent Developments 

2004, in cases in which a mortgage application is 

submitted before the effective date but final 

action is not taken until after it. The transitional 

rules provide that (1) lenders will not have to 

indicate whether an application or loan involved 

a request for preapproval or was related to a manufactured home; (2) lenders may, at their option, 

continue to apply the current (instead of the 

revised) definitions for home improvement loans 

and for refinancings; and (3) lenders need not 

report the rate spread for loans in which the rate 

lock occurs before January 1, 2004. Lenders must 

report certain information available at the time of 

final action, such as the purchaser type and 

whether a loan is subject to the Home Ownership 

and Equity Protection Act. PR-FRB, 5/23/03. 

Regulation Y 

On June 30, 2003, the FRB adopted a final rule 

amending Regulation Y, which outlines permissible derivative activities of bank holding companies. The amendment permits bank holding 

companies to (1) take and make delivery of title 

to commodities underlying commodity derivative 

contracts on an instantaneous, pass-through basis; 

and (2) enter into certain commodity derivative 

contracts that do not require cash settlement or 

do not specifically provide for assignment, termination, or offset before delivery. The final rule 

became effective August 4, 2003. PR-FRB, 6/30/03. 

Survey on Bank Lending Practices 

In its April 2003 issue of the quarterly Senior 

Loan Officer Opinion Survey on Bank Lending 

Practices, the FRB reported that since the January 

2003 survey, both domestic and foreign banks had 

continued to tighten business lending practices. 

However, the percentage of domestic banks that 

reported tightened lending standards for commercial and industrial (C&I) loans to large and middle-market firms during the period dropped 

significantly—from 20 percent in the January 

2003 survey to 9 percent. The percentage of 

domestic banks that tightened their standards for 

business loans to small firms during the period 

dropped from 20 percent in the January survey to 

13 percent. Both foreign and domestic institutions indicated that the most important reason for 

tightening standards and terms on C&I loans was 

a less-favorable economic outlook: the institutions reported that the demand for C&I and commercial real estate loans weakened between the 

January and April surveys. Domestic banks 

attributed the reduced demand to a decline in 

customers’ needs for bank loans to finance capital 

expenditures, reduced needs to finance inventories and account receivables, and reduced merger 

and acquisition business. Foreign institutions 

attributed the reduced demand to a decline in 

merger and acquisition activity and reduced customer investment in plant and equipment. For 

the report, the Federal Reserve surveyed loan officers from 56 large domestic banks and 18 foreign 

banking institutions. The survey focused on 

changes during the preceding three months in the 

supply of and demand for bank loans to households and businesses. A copy of the survey can be 

obtained at http://www.federalreserve.gov/

boarddocs/SnLoanSurvey/. Senior Loan Officer Opinion 

Survey on Bank Lending Practices, FRB, April 2003. 

STATE LEGISLATION AND REGULATION 

New Jersey 

On July 23, 2003, the OTS announced that feder- subsidiaries. Federal preemption of the New 

al law preempts provisions of New Jersey’s recent- Jersey law is based on the Home Owners’ Loan 

ly enacted anti-predatory-lending law, the New Act and the OTS regulations that comprehenJersey Home Ownership Security Act of 2002, sively and exclusively regulate lending by federal 

preventing these state provisions from applying to savings associations. The OTS says that federal 

federal savings associations and their operating law authorizes the OTS to provide federal savings 

2003, VOLUME 15, NO. 3 36 FDIC BANKING REVIEW 
 

 

Recent Developments 

associations with a uniform national regulatory 

environment for their lending operations, and 

requiring federal savings associations to treat customers in New Jersey differently would impose 

increased costs and an undue regulatory burden. 

OTS 03-22, 7/23/03. 

Texas 

The Texas Finance Commission adopted a rule, 7 

Texas Administrative Code Section 12.33, that 

authorizes state-chartered banks to offer and sell 

debt cancellation contracts (DCC) and debt suspension agreements (DSA) to consumer-loan 

borrowers. DCCs and DSAs are offered to 

borrowers to cancel or suspend payments in the 

event of death, medical disability, or unemployment. The new rule establishes standards for 

state banks when they issue DCCs and DSAs, and 

it addresses consumer protections, fees, disclosures, and affirmative elections the customer must 

make to purchase the products. Because the sale 

of DCCs and DSAs transfers to the bank a risk 

that formerly was assumed by third parties, Texas 

Department of Banking managers advise banks to 

give priority to establishing a methodology to calculate reserve adequacy for potential losses. The 

new rule became effective on May 1, 2003. 

BBR, 5/5/03, pp. 723–24. 

RECENT ARTICLES AND STUDIES
 

In a paper released on April 21, 2003, the FDIC 

reported that new capital rules being considered 

by the Basel Committee on Banking Supervision 

are expected to reduce the risk-based capital 

requirements for syndicated loans held by the 

largest U.S. banks. The risk characteristics of 

rated syndicated bank loans suggest a reduction in 

risk-based capital requirements for such loans on 

the order of 10 percent to 40 percent, with the 

magnitude of the decrease largely dependent on 

the approach used to estimate one of the key risk 

parameters: loss given default (LGD). A number 

of factors would limit the extent to which overall 

capital might decline under the new capital rules 

(known as “Basel II”), including the continued 

existence of Prompt Corrective Action capital 

tripwires, pressures from market participants, and 

Basel II’s new capital charge for operational risk. 

However, meaningful changes in risk-based capital requirements for selected portfolios at the 

largest U.S. banks still remain possible. The 

report, entitled “Risk-Based Capital Requirements 

for Commercial Lending: The Impact of Basel II,” 

was released through the FDIC’s FYI series, which 

addresses emerging issues in banking. A copy of 

the paper can be obtained at http://www.fdic.gov/

bank/analytical/fyi/2003/042103fyi.html. 

A Federal Reserve study released in May 2003 

suggests that mortgage lenders are more risk 

averse in states where tough foreclosure laws 

require lengthy court proceedings to evict a delinquent homeowner. The study, conducted by 

Federal Reserve economist Karen Pence, looked 

at Home Mortgage Disclosure Act data from 55 

communities that cross state lines, such as the 

New York City, Washington, D.C., and Kansas 

City metropolitan areas. The study reports that 

consumers have a more difficult time getting 

credit in states with a more time-consuming foreclosure process. Consumers may appreciate the 

extra protection against foreclosure, but they 

should be aware that they are paying for the protection. Laws that benefit consumers in foreclosure also lead to higher interest rates, which make 

mortgages more expensive for consumers who do 

repay their loans. Dow Jones Newswires, 5/14/03. 

On June 23, 2003, the FDIC released a report 

entitled “How Long Can Bank Portfolios 

Withstand Problems in Commercial Real Estate?” 

which states that bank loans secured by commercial real estate and construction projects continue 

to perform well, despite declining fundamentals in 

most commercial property types. The report discusses factors that have helped buffer banks’ comFDIC BANKING REVIEW 37 2003, VOLUME 15, NO. 3 
 

Recent Developments 

mercial real estate portfolios against the effects of 

declining market fundamentals, including historically low interest rates, more-conservative underwriting practices, and greater financial market 

involvement in the industry. Although these factors offer significant reassurance that the present 

downturn will not lead to credit problems on the 

same scale as those experienced in the real estate 

cycle of the late 1980s and early 1990s, in coming 

quarters bank commercial real estate loan losses 

seem likely to rise from their current low levels, as 

more borrowers experience problems servicing 

their debt. The report was released as part of the 

FDIC’s FYI series. A copy of the full report may 

be viewed at http://www.fdic.gov/bank/analytical/

fyi/2003/062303fyi.html. 

INTERNATIONAL DEVELOPMENTS
 

Argentina 

On May 8, 2003, Argentina’s Congress passed a 

law forcing foreign banks to inform the public 

whether their headquarters would use their homeland assets to honor their commitments in 

Argentina in the event of a new financial crisis. 

The legislation is in response to the behavior of 

foreign banks during the December 2001 banking 

disaster, when some foreign banks lacked the 

funds to pay off depositors. The new legislation 

seeks to ensure that customers are not misled into 

thinking that money deposited at a major foreign 

institution will be protected during a crisis. 

Argentina’s Superintendent of Financial and 

Exchange Institutions will monitor enforcement 

of the new law. BBR, 5/19/03, pp. 834–35. 

Basel Committee 

On July 17, 2003, the Basel Committee on 

Banking Supervision issued two papers setting 

international guidelines on risk-management 

principles, one for electronic banking and the 

other for the management and supervision of 

cross-border electronic banking activities. The 

first paper, “Risk Management Principles for 

Electronic Banking,” lays out 14 principles aimed 

at helping banking institutions expand their 

existing risk oversight policies and processes to 

cover their electronic banking activities. The 

principles focus on the oversight responsibilities 

of the board of directors and management, the 

need for appropriate security controls, and the 

management of legal and reputational risk associated with electronic banking activities. The second paper, “Management and Supervision of 

Cross-Border Electronic Banking Activities,” 

identifies additional risk-management principles 

specific to cross-border electronic banking activities. BBR, 7/21/03, p. 120. 

Japan 

The Industrial Revitalization Corporation (IRC), 

the Japanese governmental company responsible 

for the reconstruction of troubled borrowers, 

announced on May 12, 2003, that it had reached 

an agreement with the National Tax 

Administration to extend tax privileges to banks 

that assist corporate reconstruction. The agreement provides that a bank that writes off part of 

its loans to borrowers in cooperation with the 

IRC can treat the write-off as a loss and deduct it 

from taxable income. Previously such losses and 

deductions were allowed only with National Tax 

Administration approval. The tax measure is 

expected to encourage distressed companies and 

banks to take advantage of this opportunity for 

prompt turnaround and a return to healthy operations. BBR, 5/19/03, p. 835. 

2003, VOLUME 15, NO. 3 38 FDIC BANKING REVIEW