Market Information, Bank Holding Company Risk, and Market Discipline 

Timothy Curry 

Gary Fissel 

Gerald Hanweck* 

Working Paper 2003-04 

October 2003 

* Timothy Curry and Gary Fissel are senior financial economists, Division of Insurance and 

Research, Federal Deposit Insurance Corporation (FDIC), Washington, DC 20429. Gerald 

Hanweck is a visiting scholar, Division of Insurance and Research, FDIC, Washington, DC, and 

Professor of Finance, School of Management, George Mason University, Fairfax, VA 22030. 

The authors thank Audrey Clement, Justin Combs, and Brian Deitch for excellent research assistance; and Andrew 

Davenport, anonymous reviewers, and participants in the FDIC Research Seminar Series and in the George Mason 

University School of Management Finance and Accounting Seminar Series for comments on an earlier draft. The 

views expressed are those of the authors and do not necessarily reflect those of the FDIC or its staff. 
Abstract 

This paper assesses the timing and magnitude of equity market valuations of bank 

holding companies (BHCs) in relation to changes in their risk assessments, as proxied by 

changes in supervisory ratings for these organizations. In particular, equity market indicators 

such as market-to-book value, abnormal returns, return volatility, market valuation, price 

covariance, and trading volume are used to determine if they can provide timely market signals 

as well as add incremental value to models predicting changes in supervisor-assigned BOPEC 

ratings of bank holding company risk. To analyze this issue, we took 3,974 bank holding 

company inspections from a sample of bank holding companies whose stock was publicly traded 

on major exchanges over the 1988–2000 period. We specify two statistical models: (1) an 

ordered logistic model, which is used to test the ability of lagged financial market variables, 

lagged financial accounting data, and past supervisory assessments to predict BOPEC rating 

changes; and (2) an OLS model, which is used to test the relationship of lagged BOPEC rating 

changes and lagged financial accounting ratios to predict financial market variables. These 

models taken jointly are used to test the hypotheses that (1) equity market information adds to 

the ability to forecast changes in banking company risk, as measured by changes in BOPEC 

ratings, and (2) supervisory risk ratings have the ability to lead stock market valuations of 

banking companies’ performance. The analysis is conducted for three distinct economic and 

banking periods: recession and banking crisis (1988–1992), economic recovery (1993–1995), 

and economic expansion (1996–2000). 

The findings for the first model show that financial markets can add forecast value to 

financial accounting data and supervisory factors in predicting future BOPEC ratings. The 

results reveal a relationship between market indicators and supervisory rating changes of BHCs, 

reflecting risk conditions that flow from market valuations to supervisory rating changes. The 

findings for the first model were statistically significant for all three of the periods studied. Outof-sample forecasts show that market information sometimes improves the ability of the models 

to forecast upgrades, downgrades and no-rating changes. This suggests that a multiple-model 

approach may be superior to one using a single model to forecast BHC risk assessments. The 

findings for the second model reveal that regressing financial market variables on lagged 

BOPEC changes and lagged financial accounting information is only moderately suggestive of a 

relationship between lagged supervisory rating changes and equity market variables. In 

summary, the findings provide empirical support for the presence of market discipline to the 

extent that the hypothesized market variables add incremental value to the model in predicting 

changes in bank holding company risk ratings. To this extent, the financial markets appear to be 

providing some degree of independent oversight to BHC management, besides the oversight 

provided by bank supervisors and holding company directors. 

2 
 

 

Introduction 

The prospects of using market information for the identification of risk in banking 

organizations have become a focus of bank supervision in recent years. This concern has arisen 

in part because of the rapid growth of large, global banking organizations whose portfolios and 

global operations have become increasingly complex in scope and increasingly opaque to 

supervisors and financial markets.1 In this regard, bank supervisors are attempting to glean any 

information that may assist them in identifying the changing risk structures of these firms. This 

interest in turning to markets for assistance has been accelerated by empirical research that 

suggests bank supervision might benefit from using information embedded in the capital market 

valuation of banking organizations’ debt and equity securities.2 This work has found that 

financial market information can be used to supplement and complement the traditional 

supervisory practices of on-site examinations and off-site monitoring. If markets are 

meaningfully efficient and investors are able to monitor and/or anticipate emerging risks of 

organizations, then investors' expectations become embedded in financial market information.3 

Market assessment and the pricing of risky management policies may restrain risky behavior 

(direct market discipline) and/or assist supervisors in the monitoring process by providing timely 

signals on the changing risk patterns of these firms (indirect market discipline). The potential 

use of the signaling features of markets for modern bank supervision and discipline offers 

1

 Another reason is that the increasing size and scope of banking organizations have contributed to the organizations' 

ability to cause a systemic event that would have catastrophic market effects. 

2

 For some recent studies on this issue, see Flannery and Sorescu (1996); Flannery (1998, 2000); Berger and Davies 

(2000); Berger, Davies, and Flannery (2000); Curry, Elmer, and Fissel (2001); Krainer and Lopez (2001, 2003); 

Gunther, Levonian, and Moore (2001); and Evanoff and Wall (2001). 

3 
 

promise, as evidenced by the calls for including market indicators into the supervisory process 

that are being voiced at the highest levels of the U.S. bank regulatory agencies and the U.S. 

Congress and, on the international front, by the Basel Committee on Bank Supervision.4 

The interest in using market information to assist in bank supervision is a fairly recent 

phenomenon in the United States. Historically, bank supervisory officials have questioned the 

ability of outsiders such as uninsured depositors, other unsecured creditors, Wall Street analysts, 

capital market investors, or other market participants to accurately evaluate the risks of bank 

portfolios without the confidential information acquired through on-site examinations. Bank 

supervisors have access to highly sensitive information, such as the payment histories of 

performing and nonperforming loans, the level of loan classifications, the adequacy of loan-loss 

reserves and bank capital, and a close-up view of managerial abilities and expertise. During 

intervals between examinations, regulators depend primarily on quarterly, unaudited financial 

statements submitted by commercial banking companies to monitor changes in financial 

condition. While supervisors expend considerable resources uncovering idiosyncratic 

information on bank financial condition, there are limitations to the process. On-site 

examinations take place only at widely spaced times, usually every 12 to 18 months, and the 

information may become outdated after only brief intervals.5

 Also, bank examinations generally 

document past occurrences, such as changes in the credit quality of loan portfolios. In addition, 

accounting data used in off-site monitoring programs are reported with several months' delay, are 

3

 The issue of the opacity of bank assets has been studied extensively in the academic literature. The weight of the 

evidence suggests that investors in debt and equity securities do price changes in bank risk, although some studies 

disagree. For a review of some of this literature, see Krainer and Lopez (2003). 

4

 See Tanoue (2001). Also, the Gramm-Leach-Bliley Act of 1999 directed the Federal Reserve Board and the 

Treasury Department to analyze the value of requiring large banking organizations to issue subordinated debt. See 

also the comments from Federal Reserve Board Governor Lawrence Meyer (1998a, 1998b, 1998c, 1998d). 

5

 See Cole and Gunther (1998). 

4 
 

released as of a single date, are usually unaudited, and often require considerable analysis and 

screening to yield a meaningful interpretation of financial condition.6 

Financial markets, on the other hand, are fluid and forward looking in their appraisal of 

financial condition and, unlike the extended cycles between examinations, continuously 

incorporate and discount new information into security prices. While markets may not have 

access to extensive amounts of confidential information about financial condition as bank 

inspectors do, markets may be more timely and accurate in evaluating public information 

released in financial statements.7 Markets also have the ability to garner information about 

financial condition from a vast array of other sources, such as an institution's board of directors, 

market analysts, and industry observers. For these reasons, it has been suggested that markets 

may have information similar to the information of bank supervisors and may update it 

continuously, and that market participants have the profit incentive to expend resources to better 

forecast banking company profitability and risk taking. 

The goal of this paper is to assess the timing and magnitude of equity market valuations 

of bank holding companies (BHCs) in relation to changes in supervisory ratings for these 

organizations. In particular, market indicator variables are used to determine if they can provide 

timely market signals as well as add incremental value in predicting changes in regulatory 

assigned BOPEC ratings for institutions whose stock was traded on major exchanges over the 

1988–2000 period.8

 To accomplish this, we conducted a univariate statistical analysis and 

6

 For example, it has been shown that there is a correlation between bank examinations and the level of loss 

recognition for commercial loans. Also, enhanced loan-loss charge-offs have been associated with the timing of 

examinations of commercial banks. This suggests that accounting information contained in the publicly available 

quarterly financial statements may not always capture the risk profile of the reporting bank. See Dahl, O’Keefe, and 

Hanweck (2000); Gunther and Moore(2003). 

7

 There is some evidence for this statement. See Berger, Davies, and Flannery (2000). 

8

 The bank holding-company-system rating is the acronym BOPEC, assigned by the Federal Reserve Board, and it 

stands for Banking subsidiaries, Other (nonbanking) subsidiaries, Parent company, consolidated Earnings, and 

5 
 

specified an ordered logistic model to test for time lags in the relationship between measures of 

financial market valuation and the assignment of supervisory ratings, using methods similar to 

those presented by Berger, Davies, and Flannery (2000) and Gunther, Levonian, and Moore 

(2001). In addition, to investigate the possibility that supervisory changes are reflected in future 

market indicators—a reverse causality—we developed OLS regression models of market 

variables on changes in BOPEC ratings and financial statement variables used in the logistic 

models. 

The main hypothesis tested is that, like rating assignments by Moody’s and Standard and 

Poor's, BOPEC rating evaluations lag by several quarters changes in the financial condition of 

bank holding companies. This occurs because of recognition, decision, and implementation lags 

associated with the examination process. For example, time is required to recognize the 

changing condition of banking companies, schedule special or extraordinary examinations or 

visitations, revise examination ratings, share the results with management, and rechart bank 

policies. Because of these lags, it has been suggested that market indicator variables such as 

price volatility, abnormal returns, return volatility, trading volume, and others may be observed 

to lead BOPEC rating changes. 

Conversely, it is possible that early monitoring by supervisors leads to changes in the 

financial condition of the organization that are reflected in the next quarterly report so that a 

change in a BOPEC rating is nearly concurrent with changes in financial performance as shown 

in the financial reports. Also, banking companies may attempt to manage their financial 

reporting so that supervisors and markets are not alerted to deleterious changes that could trigger 

Consolidated capital. A rating from 1 to 5 is assigned for each component, with 1 being the best and 5 the worst. A 

composite rating also from 1 to 5 is assigned, reflecting the overall condition of the organization. 

6 
 

a review or even a downgrade. For these reasons, changes in market indicators may not lead 

changes in BOPEC ratings. 

Most previous studies that have examined market signaling and discipline focused on the 

subordinated debt (subdebt) markets. It was generally thought that concerns of investors in these 

markets were more closely aligned with those of bank supervisors. It has also been suggested 

that equity market prices are unsuitable for monitoring purposes because the relationship 

between equity prices and bank default risk is not as readily apparent as that of the pricing of 

unsecured subdebt.9 However, the equity markets may have some advantages over the debt 

markets. These markets are generally deeper and more liquid than the debt markets. They are 

generally thought to be more efficient in pricing behavior than the debt markets, with liquidity 

premia playing a smaller role in the pricing of the securities. Also, data are more readily 

available from the equity markets with many more financial institutions being publicly traded 

than issuing traded subordinated debt securities. 

Our univariate results show that changes in bank holding company BOPEC ratings 

clearly track changes in financial condition as reflected in quarterly bank accounting and market 

data. They also show that the dominant BOPEC rating assignment for the three different periods 

analyzed was “no change” in rating. This finding indicates that in specifying models that attempt 

to predict regulatory rating changes, the no-rating-change option must be accounted for 

simultaneously with the other supervisory options of rating downgrades and upgrades. This has 

generally not been done in previous studies that have examined this issue; they have principally 

focused on the market effects of downgrades. 

9

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

also Hanweck and Spellman (2002). 

7 
 

The results from the ordered logistic regressions show that one-quarter lags of market 

data add to the identification of bank holding company BOPEC changes for all three of the 

periods examined when these data are used in conjunction with quarterly financial data and past 

rating information. On the basis of these results, we conclude that equity market data provide an 

independent source of timely information and/or a different perspective in predicting BOPEC 

rating changes beyond those provided by quarterly accounting and other supervisory data for the 

three periods examined. Considering the comprehensive sample used in this study, our findings 

support those reported in the most recent empirical literature and extend them to periods 

primarily before FDICIA and during the recent banking expansion.10 We go further to provide 

extensive out-of-sample tests and show that market information improves the ability of the 

models to forecast upgrades and downgrades. We also show that, with the use of financial data 

alone for common 2001 out-of-sample data, models based on 1993–1995 data improve forecasts 

for upgrades, and models using 1996–2000 data substantially improve forecasts for downgrades. 

This suggests that a multiple-model approach to identifying banking company risk conditions 

may be superior to an approach using a single combined model. 

To further investigate the timeliness of supervisory versus market assessments of BHC 

condition, we pose the additional hypothesis that supervisory rating changes can forecast 

changes in market variables (the reverse of the above analysis). The direction of influence, from 

markets to supervisory action or vice versa, is important because it establishes the timing, which 

may be concurrent, of the influence of supervisory rating changes. As noted in Berger, Davies, 

and Flannery (2000), supervisory and market assessments may be concurrent, implying that each 

may be emphasizing or finding different information. This direction of influence from markets 

to regulatory actions or vice versa is not a matter of exogeneity or endogeneity but is important 

10 See Gunther, Levonian, and Moore (2001) and Krainer and Lopez (2001, 2003a, 2003b). 

8 
 

to identify in order to establish any significant role of supervisory rating changes influencing 

market variables. We accomplish this by regressing market variables against lagged BOPEC 

rating changes and financial ratio variables (in other words, information that the market would 

know from the published financial reports of the BHC). The results suggest that lagged BOPEC 

ratings are statistically significant, at least at the 10 percent level, in only 6 of 15 possible tests 

(five separate market variables for three periods). Although a Granger causality test could not be 

directly performed, these results indicate that the predominant relationship is one of market 

information as predicting future banking company risk rather than changes in supervisory ratings 

predicting market variables. This finding reveals a relationship between financial market data 

and bank risk assessment—a necessary and fundamental condition for market discipline to be 

effective. 

The next section discusses the empirical literature. The subsequent section presents an 

overview of BOPEC ratings migration behavior over the 1988–2000 period. This is followed by 

an overview of the general conceptual approach of this study. The data and sample are then 

discussed, followed by a presentation of the univariate results. The final sections present the 

ordered logistic regression and OLS models, the findings, and the conclusions. 

Empirical Literature 

As mentioned above, most studies that have examined the usefulness of market signaling 

and discipline have focused on the subordinated debt markets.11 The empirical evidence shows 

that subordinated debt spreads generally increase as a bank’s risk increases. More recent work 

11 See Flannery (1998), Flannery and Sorescu (1996), Evanoff and Wall (2001), and the Federal Reserve Board of 

Governors and U.S. Treasury Department (2001). 

9 
focuses on equity market valuations, which generally support the notion that equity market 

variables do add value in providing timely information. Berger and Davies (1998) use event 

study methodology and find that the equity market anticipates upgrades in regulatory ratings but 

follows downgrades. Berger, Davies, and Flannery (2000), using a sample of 184 bank holding 

companies over the period 1989:Q4 to 1992:Q2, find that regulators acquire information sooner 

than bond rating agencies and the equity markets, but the regulatory assessments are less 

accurate in predicting the future performance of bank holding companies than either stock or 

bond market indicators unless the regulatory ratings have been recently assigned. 

Several studies have incorporated market data into traditional default or bankruptcy 

models to determine if the information adds value in identifying troubled institutions. For 

example, Elmer and Fissel (2000), with a sample of 93 failed banks for the years 1989–1995, 

found that equity market variables can be used to augment accounting-related information to 

predict bank failures several years before failure. Curry, Elmer, and Fissel (2001) found that 

equity market variables when combined with bank financial data help predict downgrades and 

upgrades in bank and thrift CAMEL ratings. Krainer and Lopez (2001, 2003) find that equity 

market variables, such as stock returns and equity-based expected default frequencies, can be 

useful to bank regulators for assessing the financial condition for bank holding companies over 

the 1990–1999 period. Using event study methodology, they found that equity market measures 

provide timely information by anticipating supervisory rating changes by up to four quarters. 

However, they found no evidence that combining supervisory and market data improves the outof-sample forecasting of BOPEC ratings in a statistically significantly way. Gunther, Levonian, 

and Moore (2001) found that a measure of financial viability based on equity market data 

10 
 

(expected default frequency, or EDF, from KMV) helps predict the financial condition of bank 

holding companies as reflected in their supervisory ratings.12 

This paper adds to existing empirical work in a number of ways. First, we use equity 

market variables along with quarterly financial data and past supervisory information to predict 

bank holding company BOPEC rating changes over three very different economic and banking 

periods—recession and banking crisis (1989–1992), recovery (1993–1995), and expansion 

(1996–2000—and we do so for a substantial sample, which varies from over 3,500 to 3,900 new, 

full-scope bank holding company inspections. Previous empirical work has generally not 

focused on such a broad time horizon under such varying economic conditions or with a 

comparable sample size.13 Our approach has several benefits. First, breaking out the analysis for 

such disparate periods will allow for testing the stability of the models during these different 

periods, allowing us to compare the findings with those of the previous literature and to identify 

models able to forecast upgrades and downgrades. Second, an ordered logistic model is 

specified to account for all possible choices available to bank supervisors in assessing changes in 

the financial condition of BHCs; these choices consist of rating upgrades, downgrades, and no 

change in ratings. By simultaneously modeling the dominant rating event of “no change” along 

with downgrades and upgrades, we overcome some of the limitations of earlier work, where the 

primary focus was on predicting either the rating levels or downgrades. We expect our approach 

to yield improved predictive power over previous models. Third, unlike some of the previous 

work, we focus on predicting newly assigned BOPEC rating changes rather than past rating 

12 Some early work on the use of the equity markets as an early-warning system shows mixed results. Pettway 

(1980) found that stock returns of 6 large banks destined for failure signaled problems almost a year (38 weeks) 

before regulators began the examination process that led to the banks' being classified on the problem-bank list. 

Conversely, Simons and Cross (1991) analyzed a more recent sample of 22 bank holding companies and found that 

regulators appear to have been aware of problems before the market recognized them. 

11 
 

levels. The estimation of downgrade, upgrade, and no change models should allow for a more 

complete focus on the contribution of market data to the analysis. 

BOPEC Ratings Migration Behavior 

Table 1 displays the frequency and migration behavior of BOPEC rating assignments 

over the 1988–2000 period, broken down into the three periods under observation. The column 

on the left shows the initial BOPEC rating (1–5), and the columns to the right are the newly 

assigned ratings from an inspection. Thus, each cell shows the percentage in that category for 

newly assigned BHC inspections.14 Only certain rating categories can be upgraded or 

downgraded or both. For example, institutions in the highest BOPEC rating category of 1 can 

only remain within the same category or be downgraded as the result of new inspections. These 

data show that for the 1988–1992 period, 68.5 percent of institutions inspected remained in the 

highest rating category of 1 from the previous inspections; 27.1 percent were downgraded to a 2 

rating, 1.7 percent went to a 3, and 2.8 percent migrated to a 4 over this period. Conversely, 

those in the lowest rating category of 5 can only remain in that category or be upgraded. For 

example, 81.8 percent of the 5 rated remained in this category, while only 18.2 percent were 

upgraded to the 4 level. Those in the middle categories (BOPEC ratings 2, 3, and 4) could either 

be upgraded, downgraded, or remain the same. Reading down the diagonal shows the 

proportions of inspections that remained in the initial categories, and reading across to the left or 

right shows the number of upgrades or downgrades. Following the diagonal reveals that 71.1 

13 Krainer and Lopez (2001) have a roughly comparable period (1990–1999) and sample size but their model 

focuses primarily on predicting rating levels rather than new inspections. 

14 These migration rates consider all full-scope inspections that occurred over the period in question. As a result, a 

BHC will appear and be counted as many times as it is inspected during the period. 

12 
percent of the companies in the 2-rated group remained in the same category they began in, as 

did 57.8 percent of the 3-level companies and 62.2 percent of the 4-rated group. 

One can observe trends in migration patterns over time by comparing the matrices for the 

three different time periods (table 1). Financial conditions are seen improving as reflected in 

newly assigned BOPEC ratings, as we move out of the recession toward expansion. For 

example, 68.5 percent remained in the highest rating category over the 1988–1992 period, 

increasing to 88.5 percent in the 1993–1995 period before sliding slightly for 1996–-2000 period 

to 87.0 percent. Conversely, 81.8 percent remained in the worst category—5—in the 1988–1992 

period, declining to no 5-rated institutions by the 1996–2000 period. As another example, while 

62.2 percent of the BOPEC 4-rated BHCs remained 4s in the1988–1992 period, by the 1996– 

2000 inspections, only 33.3 percent remained in this category, with a larger share (50.0 percent) 

being upgraded than downgraded (16.7 percent).15 

The key observations from the rating migration matrices are, first, that while rating 

patterns were changing throughout the economic cycle, the likelihood or probability of 

remaining in the same rating category, or “no rating change,” was significantly higher than that 

of being downgraded or upgraded; and second, that downgrades and upgrades performed very 

differently over the cycles. Downgrades dominated rating changes in the 1988–1992 period, 

while upgrades dominated in the other two periods. These data suggest that analysis by period is 

important. (See Figure). 

Data and Sample 

13 
 

The data consist of quarterly Y-9C bank holding company financial reports, confidential 

bank holding company (BOPEC) ratings, confidential CAMELS ratings, and quarterly financial 

data on lead banks of the holding company. Equity market information was obtained from the 

Center Research Security Prices (CRSP). CRSP data contain historical information such as daily 

stock prices, daily returns, equal and value-weighted indexes of market returns, dividend 

information, daily trading volume, and other variables for all organizations publicly traded on 

national exchanges. To obtain information for only commercial banking companies, we matched 

CRSP against historical Y-9C holding company files and quarterly financial data (Call Reports) 

back to 1984. We then matched these files against bank holding company BOPEC ratings. All 

quarterly data for the market indicator variables used in the analysis were calculated from the 

daily stock price and trading information provided by CRSP. 

From this universe of bank holding company and CRSP matches, we obtained a sample 

for analysis. For the univariate analysis, which focuses on individual financial accounting and 

financial market variables, we used a sample of 3,974 full-scope bank holding company 

inspections. For each inspection, there is a BOPEC rating assignment. For the logistic and OLS 

regression models, the samples differ slightly from those in the univariate analysis because it was 

necessary for this analysis that each holding company have quarterly financial data, CRSP 

market data, and inspection information for the time it was in operation over the 1988–2000 

period. To meet these requirements, we used a slightly reduced sample of 3,534 full scope 

inspections for the logistic analysis, which resulted in 458 BOPEC downgrades, 461 BOPEC 

upgrades, and 2,615 inspections with no rating changes. For the OLS regression analysis, the 

number of BOPEC inspections analyzed declined further to 3,068. The reduction in the OLS 

15 The number of BHCs was increasing over the 1988–2000 period so that a comparison by number of companies or 

inspections is meaningless. To get an idea of the relative distribution of companies by initial BOPEC rating, see 

14 
 

sample was due to the lagging of certain variables in the regression, which led to a loss of some 

observations. Only full-scope inspections with assigned composite ratings of 1, 2, 3, 4, or 5 are 

considered because other on-site reviews, such as limited-scope inspections and visitations, 

generally do not produce as much complete information on the financial condition of the entire 

holding company organization as do full-scope examinations. For each inspection, data were 

collected on the date when each began, the date it ended, and the date when the institution was 

notified of its assigned BOPEC rating. To analyze the timeliness issue, we selected a benchmark 

or “event” quarter. The event quarter is the one in which the board of directors of the holding 

company was informed by bank supervisors of the final rating from the inspection. 

No action was taken to deal with merged institutions over the 1988–2000 period. As long 

as a bank holding company was publicly traded, remained on CRSP, and filed quarterly financial 

reports, the institution remained in the sample. Acquired institutions disappeared from the 

sample after acquisition, when they no longer filed financial reports. It is not anticipated that this 

approach for dealing with mergers would have any important effects on the analysis. One 

possible problem may arise if a merger was randomly announced in the quarter preceding a bank 

holding company inspection, such that changes in market information would reflect the 

anticipated effects of the proposed merger and not necessarily the current financial condition of 

the company. Like most other studies in this area, this one made no attempt to control for this 

possibility. 

Methodology 

tables 2–4. 

15 
 

BOPEC rating changes are the result of inspections by trained field examiners. The 

process by which these changes occur forms the foundation for estimation of the models. We 

consider the changes in ratings in terms of time series where supervisors can choose to either 

change a rating or leave it the same. Thus, in contrast to failure models, once a bank is assigned 

a BOPEC rating, it need not remain there. With the exception of an initial BOPEC rating of 1 as 

indicated above, a bank holding company can be upgraded or remain the same, and with the 

exception of an initial BOPEC rating of 5, a holding company can be downgraded or remain the 

same. On the basis of these supervisory options and the empirical evidence presented, the 

underlying conceptual model of the formation of decisions by supervisors to change a banking 

company’s BOPEC rating is that the no-change option must be explicitly considered. 

The approach of this paper is to use an ordered logitistic regression model to better 

account for the broader decision-set open to bank supervisors in assessing the condition of a 

BHC. The logit model predicts BOPEC ratings as a function of several factors, including 

institution size, past supervisory information, current financial data, and market information. 

Data for these variables are taken from one quarter preceding the inspection quarter, when 

possible.16 In this regard, the information set is drawn to closely approximate the state of 

knowledge that bank supervisors should possess about the institution immediately before an 

inspection.17 The model is then run at a one-quarter interval before the quarter in which the new 

rating is assigned. The model estimates the probability for the three possible outcomes of the 

16 Financial and market data are taken from one quarter before the inspection quarter in the model. The previous 

examination ratings are taken from the examinations on record that were given before the current ratings were 

assigned.

17 While preliminary Call Report and bank holding company data are first released about 45 days after the end of the 

quarter, bank examiners will have access to the most recent information available before starting an examination, 

regardless of when the data are released to the public by the supervisors. Furthermore, banking firms typically 

release financial statement information within two weeks after the end of the quarter, so this information is available 

to the market well before the released Call Report data. It is for these reasons that we use only a one-quarter lag for 

16 
 

inspection: a downgrade, an upgrade, or no rating change. A dependent variable of 1 is specified 

for institutions that were downgraded, a 2 for institutions whose rating remained the same, and a 

3 if the institution was upgraded, thus ordering the regression. For classification purposes, the 

critical level of probability is determined by the size of each group as a proportion of the total 

sample.18 If the market variables significantly add to the prediction of BOPEC ratings, they then 

provide an independent source of information about BHC risk apart from the other explanatory 

variables. 

An OLS model is specified to determine if confidential BOPEC ratings can predict 

selected market variables. In this instance, the five market variables are specified as the 

dependent variables and the BOPEC ratings as the independent variables. The purpose of a 

second set of regressions is to validate the time sequence of market indicators of financial 

condition to measured assessments of banking company financial condition as reflected in the 

BOPEC rating assignments. If BOPEC ratings, controlling for information contained in bookvalue performance ratios, can predict market indicators, this will be taken as evidence that onsite BHC inspections reveal new information concerning the institutions’ performance. If there 

is little or no relationship, the market may be interpreted as having incorporated the underlying 

information of BOPEC rating changes and valued the company accordingly. 

Univariate Analysis 

the financial data in the analysis. Regardless, we ran the regressions with two-period lags for the financial data and 

found no significant differences in the results.

18 Thus, for example, if the critical probability from this calculation is 50 percent, then if the predicted probability 

from the model is equal to or greater than .5, the institution would be classified as belonging to that particular group. 

17 
 

Sample means for supervisory, financial and market variables for bank holding 

companies broken out by period and by BOPEC rating category are presented in tables 2–to 4. 

The data show a high correlation between financial condition and examination rating. Reading 

across the first row of each of the three tables reveals that the number of days from the last 

BOPEC inspection generally declines with poorer ratings, as expected, as they are inspected and 

monitored more frequently than healthier banks. Also, weak BOPEC ratings for bank holding 

companies tend to be associated with unsatisfactory performance at the bank level as well as 

reflected in CAMELS ratings of the lead banks. Also, BOPEC ratings from the prior inspection 

are highly correlated with the current assignment of the rating. In addition, those with the 

weakest BOPEC ratings tend to have larger asset-quality problems, a greater reliance on large 

time deposits, and higher loan-loss provisions. Those with poorer ratings also have lower levels 

of capital and earnings, as expected. 

The market valuation of the firm follows a similar path tracking the financial data and 

thus shows a high correlation with financial condition. The weakest-rated firms exhibit 

relatively lower returns, increased volatility of returns, lower market valuations, and greater 

trading volume. The degree of association between all BOPEC ratings and variables is measured 

statistically by the Van der Waerden test and reflected in the p values shown on the three 

tables.19 As shown, there is a high degree of association between the BOPEC rating category 

and the financial condition of banking companies, with most variables being statistically 

significant at better than the 1 percent level. With few exceptions (abnormal or excess returns 

for the 1993–1995 period and loans past due 90 days to total assets in the 1996–2000 period), 

these results are consistent across all three periods analyzed. 

19 The Van de Waerden procedure tests whether the distribution of a variable has the same location parameter across 

different groups. 

18 
 

Multivariate Analysis (One-Quarter Lag) 

The subsections below describe the variables used in the regression models as well as the 

empirical results. The estimated coefficients for the ordered logit regression are interpreted in 

terms of the effect on the likelihood of a downgrade. 

Size of Institution 

The first independent variable is the log of total assets (LN_ASSET), which is a control 

variable for differences in institution size and is measured as the natural logarithm of total assets. 

The log form of total assets is taken to account for the potential nonlinearity in the variables 

between firm size and rating assignments. To the extent that an institution’s size provides for 

large-scale diversification, economies of scale and scope, access to the capital markets, and an 

overall source of support for the entire organization, it should reduce the likelihood of 

experiencing a BOPEC rating downgrade. Thus, a negative relationship is anticipated between 

the probability of a downgrade and institution size. 

 Past Supervisory Information 

To help predict individual BOPEC rating changes, we included three variables that reflect 

past supervisory inspections. The first is a dummy variable (BOPEC-1 to BOPEC-4) that 

reflects the BOPEC rating from the most recent holding company inspection before the opening 

of the current inspection. The probability or likelihood of being downgraded from the BOPEC-1 

to BOPEC-4 level is interpreted relative to the omitted BOPEC rating, which in this instance is 

19 
 

the BOPEC-5 rating. As mentioned the composite or overall BOPEC ratings are assigned from 1 

to 5, with 1 being the highest and 5 being the lowest rating. It is anticipated that firms with the 

most favorable ratings are the ones most likely to experience a BOPEC rating downgrade. Thus, 

a positive relationship is anticipated. 

Another dummy variable is included to capture the relationship between the last 

CAMELS rating of the lead bank of the holding company and the assignment of the current 

BOPEC rating of the parent.20 Changes in examination ratings on the bank level can lead to 

changes in ratings on the organizational or holding-company level. Like the BOPEC ratings, 

CAMELS ratings for banks carry a value of 1 to 5, with 1 being the highest and 5 the lowest. 

The rating is taken from the most recent examination record of the largest or lead\ bank in the 

holding company system before to the quarter in which the current inspection of the parent 

holding company occurs. A dummy variable (PROB_BK) is assigned a value of 1 if the 

previous rating of the lead bank was a problem-bank rating of 3, 4, or 5, and a 0 if the rating was 

a favorable 1 or 2. A positive relationship is anticipated between an existing problem-bank 

rating for the lead bank of the holding company system and the likelihood of a BOPEC rating 

downgrade for the parent holding company. 

Since earlier work has shown that bank examination ratings can become outdated in 

relatively short periods following an inspection, a variable is included that captures the age of the 

last BOPEC inspection before the most recent inspection under review (INSP_AGE). This 

variable is calculated as the number of days from the last holding company inspection to the start 

date of the current inspection. Hence, the older the prior rating, the greater the age of the 

inspection and the more likely that there will be a change in rating. Experience suggests that this 

20 The CAMELS acronym stands for each component of the examination: Capital, Assets, Management, Earnings, 

Liquidity and Sensitivity to market risk. 

20 
change is more likely to be a downgrade because more frequently inspected BHCs are most 

likely to have poorer past BOPEC ratings, and those inspected less frequently will have better 

BOPEC ratings and are more likely to be downgraded. 

Financial Accounting Variables 

Capital adequacy is measured by two ratios: equity to assets (EQ_Asset) and loan-loss 

reserves to assets (LLR_ASSET). High levels of capital and reserves provide a cushion to 

absorb loan losses and thus reduce the likelihood of insolvency, so a negative relationship is 

expected between this variable and the probability of a downgrade. The credit quality of the loan 

portfolio is captured by loans past due 90 days or more (PD90_ASSET), those in nonaccrual 

status (NA_ASSET), and the ratio of the provision for loan losses to assets (PROV_ASSET); 

provision for loan losses is income allocated to loan-loss reserves. All three variables are 

expected to be positively related to the likelihood of a downgrade because higher levels of 

delinquent loans and loan provisions to rebuild loan-loss reserves generally indicate credit 

quality deterioration and increasing levels of financial distress. 

The overall profitability of the institution is measured by the return-on-assets variable 

(ROA), which is expected to be negatively related to future downgrades. This means that a 

higher ROA implies a greater likelihood of an upgrade and a lesser likelihood of a downgrade. 

Two measures of liquidity are posited: the ratio of liquid assets to total assets (LIQ_ASSET) and 

total deposits held in accounts greater than $100,000 (TD100_ASSET). The level of liquid 

assets ratio is expected to be negatively related to future financial distress, reflecting that higher 

levels of securities to assets provide sources of additional liquidity in difficult times. However, 

21 
the amount of deposits held in accounts greater than $100,000 (TD100_ASSET) is expected to 

have a positive sign, reflecting a more aggressive, expensive, and volatile funding strategy. 

Financial Market Variables 

The market variables are specified into five categories reflecting different aspects of 

equity market valuations; these categories are: price volatility, abnormal returns, volatility of 

returns, capitalization, and share turnover. These market data are based on daily information for 

each quarter from CRSP. The first variable captures stock price volatility (COVAR_PRICE) and 

is measured by the coefficient of variation in equity prices. COVAR is defined as the standard 

deviation of the quarterly stock price divided by the average quarterly price. This variable is 

expected to be positively related to the likelihood of a rating downgrade. Market abnormal or 

excess returns are captured by the EX_RETURN variable, which measures the differences 

between the cumulative quarterly return of each stock and the cumulative quarterly return of an 

index of market performance. The simple cumulative quarterly return is calculated by 

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

each quarter, then subtracting 1. The index of market performance chosen is the CRSP valueweighted index, which covers all publicly traded institutions.21 The EX_RETURN measure is 

expected to be negatively related to the future downgrades. 

An interesting aspect of market data is that various aspects of financial theory effectively 

extend the list of market-related variables beyond prices and returns. For example, Merton’s 

(1973) option pricing model implies a direct relationship between stock return volatility and 

asset return volatility conditioned on asset values. This relationship indicates that greater stock 

22 
 

return volatility will increase the likelihood of insolvency and is directly associated with the 

likelihood of a downgrade.22 As investors become concerned over financial distress and 

potential insolvency, more variation may appear in return patterns. 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.23 Although a comprehensive 

analysis of market-related variables goes beyond the scope of this paper, return volatility and 

trading volume nevertheless are two variables that are easily observed and, according to financial 

theory, should contain predictive content. 

Return volatility is captured by the standard deviation of the quarterly returns 

(STD_RETURN) and is expected to be positively related to future downgrades. Trading volume 

within each quarter is identified by the TURNOVER variable. This is calculated as the 

percentage of shares traded during the quarter divided by the number of shares outstanding at the 

end of the quarter. This measure is expected to exhibit a positive relationship with the likelihood 

of being downgraded. The market valuation of a firm is captured by market equity divided by 

the book equity (MKT_BKEQ), with both values measured at the end of the quarter. This 

variable is expected to be negatively related to the probability of a downgrade. That is, the 

higher the value of the ratio, the lower the likelihood of financial distress and downgrade. 

Definitions of the variables used in the regression equations along with their means and 

standard deviations over the 1988–2000 period are presented in tables 5 and 6. The sample 

means show that the financial variables clearly track the different economic cycles over these 

21 The CRSP value-weighted index of returns for all publicly traded companies was chosen over one for the banking 

industry alone so as to compare banking company returns with the entire market in which all banking company 

equities compete and to avoid spurious correlation of an industry index dominated by a few large companies. 

22 French, Schwert, and Stambaugh (1987) lend empirical support to this view by documenting a positive 

relationship between the volatility of market returns and market excess returns (market return minus T-bill yield). 

23 Gallant, Rossi, and Tauchen (1992) find supportive empirical evidence that large daily price movements are 

followed by high trading volume. 

23 
 

periods, including the recession and banking crisis of the late 1980s and early 1990s, the 

recovery in the middle 1990s, and the prosperity of the late 1990s. From the start to the end of 

these periods, financial statements showed significant improvement. Earnings and capital 

increased significantly, loan-loss risk declined, and firm liquidity improved. However, strong 

loan growth was being fueled in part from increases in volatile uninsured liabilities on the 

balance sheet. Financial markets also responded favorably to the changes in firm condition over 

this period, as witnessed by significant increases in firm valuations as recognized by the marketto-book value ratios and a slightly reduced volatility of share prices and earnings. 

The ordered logistic regression equation follows the following format: 

BOPEC Change = + + β Firm Size +

7 

β (Past Supervisory Ratings ) it 1 2 1 iit −1 j jit −1 

j=2 (1) 16 21 

+ β (Financial Accounting Variables )+ β (Financial Market Variables )+ e j jit −1 j jit −1 it 

j=8 j=17 

where α1 and α2 are constants estimated for the ordered logit with three groups (downgrades, 

upgrades, and no change),24 β1 is the coefficient for firm size, β2, ...β7 are coefficients for a 

vector of variables representing past supervisory opinions unique to each BHCi , β8...β16 are 

coefficients for a vector of variables for financial condition based on quarterly accounting 

information for each BHCi, and β17 ...β21 are coefficients for a vector of variables for selected 

market measures for each BHCi. The eit is assumed to be distributed as a logit function. The 

regression results are estimated for the three different economic cycles: recession and banking 

crisis (1988–1992), recovery (1993–1995), and expansion (1996–2000). As a forecasting model, 

24 Two constant terms are estimated under the assumption that the proportional odds among the groups are 

independent of the explanatory variables such that the slope parameters are the same for each group. In general, to 

separate three groups (downgrade, no change, and upgrade) requires two planes (or hyperplanes for more than three 

dimensions). In the case of the ordered logit that we estimate, the planes are parallel. This means that the probability 

of observation it's belonging to group 1, conditional on the explanatory variables, is CL(α1+β’Xi), the conditional 

probability of its belonging to group 2 is CL(α2+β’Xi) - CL(α1+β’Xi), and the conditional probability of its 

24 
 

all independent variables are lagged relative to the BOPEC rating of the event quarter that is 

being predicted. As mentioned, we ordered the logistic regression model by relating the 

specified variables to the probability or likelihood of receiving a BOPEC downgrade.25 

The empirical results for the logistic models are shown in tables 7–9. Specification 1 for 

each table provides a traditional default or rating downgrade prediction model by specifying past 

supervisory information and quarterly accounting data. These variables are accompanied in each 

specification by information that accounts for firm size. Specification 2 in each table launches 

the analysis of equity market variables by isolating the predictive power of these variables in the 

absence of the financial data but including past supervisory information. Specification 3 

combines both financial and market data into one equation to determine if market information 

adds incremental predictive value to the financial and supervisory data in identifying firms likely 

to be downgraded. Although the equity market variables need not dominate the traditional ratiobased model, a minimum level of competence is required to justify a conclusion that marketrelated variables are a meaningful addition to traditional analysis. In the absence of incremental 

predictive value, market signals may be viewed as redundant information with little supervisory 

value. Also as mentioned, the variables in each regression are lagged for one quarter before the 

event quarter, which is the quarter when a new inspection rating is assigned—the rating that the 

model is attempting to predict. 

belonging to group 3 is [1 – CL(α2+β’Xi)], where CL() is the cumulative logit function. Throughout our analysis the 

constant terms are found to be significantly different.

25 The model can be “ordered” to estimate the probability of a rating downgrade, no change, or upgrade. However, 

the results presented in the tables refer to the likelihood of a downgrade's occurring. 

25 
1988–1992 Results 

Specification 1 for the 1988–1992 period shows that institution size (LN_ASSET) is 

important and that, as expected, larger institutions are less likely to be downgraded (table 7). 

Also, past supervisory information is highly associated with the likelihood of future BOPEC 

downgrades when firms with the best ratings are most likely to be downgraded (BOPEC-1 to -

4). The dummy variable representing the previous CAMELS rating of the lead bank 

(PROB_BK) of the holding company shows that when lead banks have problem CAMELS 

ratings, the result is a significantly higher likelihood of the holding companies' being 

downgraded. Contrary to what most previous literature predicts, the time lapsed from the 

previous inspection (INSP_AGE) is not a significant predictor of future downgrades. The 

financial accounting variables in the model perform largely as expected, with eight of the nine 

variable coefficients significant at mostly the 1 percent level. The Max-Rescaled R2, a measure 

of goodness of fit, for the model is 0.58.26 Since specification 1 provides a working downgrade 

prediction model, specified with the traditional capital, asset-quality, earnings, and liquidity 

ratios along with past supervisory information, it is used as a benchmark equation for comparing 

the predictive content of the market-based variables. 

Specification 2 contains only the size, supervisory factors, and stock market variables, 

excluding the financial accounting information discussed above. In this equation, institution size 

is negative and significant, indicating that larger-sized firms are less likely to be downgraded. 

As before, almost all the variables associated with previous supervisory evaluations are highly 

significant at the 1 percent level. Of the five market variables, four are significant: the volatility 

26 
 

of price (COVAR_PRICE), the value-weighted excess returns (EX_RETURN), the standard 

deviation of the returns (STD_RETURN), and the share turnover variable (TURNOVER), all of 

which are significant at the 1 percent level. The explanatory power of the regression is less than 

that of specification 1, with an Max-Rescaled R2 = 0.39. 

Specification 3 forces the stock market–based variables to compete with the financial 

accounting variables used in specification 1 to test for any added predictive power for the 

equation. As before, institution size continues to be statistically significant and negatively 

related to the prospects of future downgrades. The prior BOPEC rating and past supervisory 

assessments, with the exception of inspection age (INSP_AGE), continue to be highly 

significant, with almost all coefficients being significant at the 1 percent level. The financial 

variables continue to be strong, with seven of the eight also being highly significant. 

For the market variables, four of the five have the correct signs and are statistically 

significant. The benchmark comparisons between specifications 1 and 3 are performed through a 

likelihood ratio test, which is a chi-square statistic. If the likelihood ratio statistic is positive and 

statistically significant, we can conclude that the financial and market variables when combined 

into a single equation cause it to possess a better fit than the equation it is tested against. The 

likelihood ratio test is positive and significant at the 1 percent level, which supports the 

contention that the market variables do add some explanatory power to the model in comparison 

with specification 1. In addition, other measures of the overall fit of the regression indicate 

improved results. The Max-Rescaled R2

 statistic of 0.60 is higher than that of specification 1. 

Also, the Akaike Information Criterion (AIC), which measures the overall fit of the equation, is 

lower for specification 3 relative to specifications 1 and 2, which indicates a greater association 

26 The Max-Rescaled R2

 statistic, like the pseudo-R2, is a measure of goodness of fit. However, the pseudo-R2 for 

discrete models has a maximum of less than 1.0. The Max-Rescaled R2

 statistic adjusts the pseudo-R2 such that the 

27 
 

of the combined variable set with the BOPEC rating changes.27 For the 1988–1992 period, we 

conclude that market variables provide an independent source of information and add significant 

explanatory power to known financial and supervisory data. 

1993–1995 Results 

The regression results for the 1993–1995 economic recovery period are displayed in table 

8. The finding for the asset-size variable generally carries the expected negative sign but is 

significant only for specification 1. The coefficients for the past supervisory inspection variables 

for specifications 1–3 are similar to the previous period’s results, with the BOPEC dummies and 

the problem-bank dummy of the lead bank all exhibiting the correct signs and all exhibiting 

statistical significance at the 1 percent level. Previous BOPEC ratings continue to indicate that 

firms with the most favorable past ratings are the most likely to be downgraded in the future 

relative to a BOPEC rating of 5. The inspection age variable (INSP_AGE) continues to be 

insignificant. The results for the financial variables in specification 1 for the 1993–1995 period 

are not as consistent as for the previous period. Of the nine variables measuring financial 

condition, five are significantly related to downgrades: the level of holding company equity 

(EQ_ASSET), past due 90 days (PD90_ASSET), nonaccrual loans (NA_ASSET), the return on 

assets (ROA), and large deposits (TD100_ASSET). 

The market variables in specification 2 show that all variables have the correct signs and 

that three of the five variables are statistically significant from the 1–10 percent level: the 

coefficient of variation of price (COVAR_PRICE), the standard deviation of the returns 

statistic reaches a maximum of 1.0, in line with the standard notion of R2

 (SAS Institute, Inc. [1996], 414). 

28 
 

(STD_RET), and the market equity to book equity variable. The explanatory power of 

specification 2, with only the market variables, has a Max-Rescaled R2

 of 0.44, which is lower 

than the R2 of 0.52 for specification 1. In addition, the AIC measure for the overall fit is higher 

for specification 2 than for specification 1, which indicates a lower overall fit for specification 2. 

These findings show that the market variables in isolation do not possess as much predictive 

power as do the financial variables by themselves. 

Specification 3, which combines the financial with the market variables into one model 

for the 1993–1995 period, does improve upon specification 1. The Max-Rescaled R2 is slightly 

higher for specification 3 relative to specification 1, the AIC variable is lower, and the log 

likelihood ratio test is again significant, indicating that this specification continues to improve 

the predictive power over the first specification. In summary, the findings for the 1993–1995 

recovery period are similar to those of the 1988–1992 recession in that market data continue to 

add some value to the overall regression, but overall the results are not as strong as those for the 

earlier period. 

1996–2000 Results 

The results for 1996–2000, a period of substantial economic growth and banking 

expansion, are presented in table 9. The log of asset size has a consistently negative sign as 

expected, but only the first specification is it significantly related to the likelihood of being 

downgraded. The level of past BOPEC ratings remains the most consistent predictor of future 

downgrades in specifications 1–3, showing that institutions with the highest ratings are again the 

27 The AIC statistic is a goodness-of-fit statistic similar to the maximum likelihood ratio test, with a lower value 

indicating a better fit. 

29 
most likely to be downgraded. The prior CAMELS rating of the lead bank (PROB_BK) is also a 

reliable predictor of future downgrades and is consistent with the previous two periods analyzed. 

Contrary to expectations, the inspection age variable (INSP_AGE) is negative and significant for 

all three specifications. All of the financial variables in specification 1 have the correct signs, 

and most are significant at the 1 percent level. The Max-Rescaled R2

 of specification 1 is 0.45. 

In specification 2, four of the five market variables are significant from the 1–5 percent level. 

The Max-Rescaled R2 for the equation is 0.35 percent, or slightly below the first specification, 

and the value for the AIC variable is higher, which indicates a less favorable overall fit for 

specification 2 relative to specification 1. 

Specification 3 shows the combined model for all of the variables for the 1996–2000 

period. In this equation, six of the nine financial variables and three of the five market variables 

are significant. The overall fit of the model in specification 3 is better than for either of the first 

two specifications. The Max-Rescaled R2

 is higher and the AIC variable is much lower than for 

either of the first two specifications, indicting an improved fit for the model. In addition, the 

likelihood ratio test, which compares the explanatory power of specification 3 with benchmark 

specification 1, is positive and significant at the 1 percent level, indicating improved predictive 

power. In summary, the empirical results for the 1996–2000 expansion period are similar to the 

other two periods but overall are relatively more robust than for the 1993–1995 transition period. 

They show that the market indicator variables contribute in the hypothesized direction to the 

identification of downgraded institutions for this period. 

30 
In-Sample Classification Results 

Table 10 contains in-sample classification results for the ordered logit regressions 

summarized in tables 7–9. In the formation of these in-sample classifications, the a priori 

likelihood of being a downgrade, no change, or upgrade is the a priori frequency ratio of these 

outcomes in each classification category. We determined the critical level of probability for 

classifying each BOPEC change of a downgrade, no change, or an upgrade as correct or 

incorrect by taking the number of firms that experienced each of these BOPEC changes in the 

sample for each period analyzed and dividing by the number in the total sample. We then 

compared this critical level of probability or a priori probability with the predicted conditional 

likelihood of the model to determine the appropriate classifications. These classifications appear 

in each of the cells in table 10. 

The in-sample predictive accuracy of the model for the 1988–1992 period shows that the 

correct classifications for the BOPEC downgrades increase slightly, going from 81 percent to 83 

percent from specification 1 to specification 3, or an increase of six institutions with the addition 

of the market variables. The correct classification of those firms that had no change in ratings 

remained about the same at about 78 percent, while the identification of firms that were upgraded 

actually declined. For the 1993–1995 economic recovery period, the correct classifications for 

the downgrades improved, going from 56 percent to about 63 percent, while the no-change 

ratings and upgrade predictions were about the same. The in-sample classifications for the 

1996–2000 period shows that the correct downgrade and upgrade predictions were about the 

same, but the no-change category improved from 64 percent to about 67 percent (34 additional 

institutions). While the overall results of the model show some improvement in classification 

31 
accuracy for all periods and for most groups, the differences are not generally large in percentage 

terms. The main contribution of the inclusion of market variables is the identification of the 

BOPEC rating downgrades for both the 1988–1992 and 1993–1995 periods, when the correct 

classifications increased from about 2 percentage points to 7. 

Out-of-Sample Classification Analysis 

The out-of-sample classification analysis provides a test of the accuracy and forecast 

potential for the model when equity market variables are added to the financial accounting 

variables. In performing the out-of-sample classifications, the a priori cutoff probabilities were 

calculated in a similar fashion as for the in-sample tests. Furthermore, when classifying the outof-sample 1993–1995 period using the 1988–1992 model (panel A of table 11), we use the 

observed frequency ratio for the 1988–1992 sample to determine the cutoff probabilities. We 

chose this method because we did not want to bias the test, since in practice actual frequencies of 

future periods would not be known and an unbiased estimate of future frequencies is the 

frequency of the sample data of the prior period. The out-of-sample tests show that of the 18 

classifications in table 11 (panels A–F)—six out-of-sample classification tests, each with three 

classifications groups—the addition of market variables shows an improvement in 11 

classifications. 

The out-of-sample classifications show that the FA, SM and the combined models mostly 

show improvement in the different categories of rating change, including the no-change 

category. For example, using the 1988–1992 model on the 1993–1995 and 1996–2000 out-ofsample data shows that the FA model (panels A and B in Table 11) is more accurate than the SM 

32 
or combined models in classifying upgrades, and the SM model is much more accurate than the 

FA or combined models in classifying downgrades. In addition, the combined model is more 

accurate than either in classifying no changes. 

To further explore the differences in out-of-sample classification precision for each of the 

estimated models for the three periods examined (1988–1992, 1993–1995, 1996–2000), we apply 

each of the models to a common out-of-sample test using 2001 data as a common period (panels 

D–F of table 11). The findings indicate that the model that has the highest overall correct 

classifications varies according to the period in which the estimation occurred. For example, 

when the 1988–1992 model estimated from data for a recessionary and banking crisis period is 

used, the combined model has the most accurate overall classification (panel D), while for the 

1993–1995 and 1996–2000 periods , the FA model overall does better than either the SM model 

or the combined model (panels E and F). In another example for specific categories rather than 

overall performance, the 1996–2000 model applied to the 2001 data (panel F) indicates that the 

FA model does the best in classifying downgrades (88 percent) and the SM model does the best 

in classifying upgrades (96 percent). These results provide further evidence that using financial 

accounting and/or market variables can improve forecasting changes in bank holding company 

risk assessments. 

The implication of these results is that the models estimated from different periods in the 

economic and banking cycles can give different and sometimes improved forecasts of BOPEC 

rating changes and, by inference, risk assessments. One drawback to using different models to 

provide forecasts is that a BHC’s inspection may be classified in each of the BOPEC change 

categories. When this arises, a simple rule would be to choose to classify an inspection in the 

group that, if a misclassification, is least costly from a supervisory perspective or in the group in 

33 
 

which its estimated likelihood of membership is greatest. This multiple-models approach to 

classification can provide improved forecasting and best integrates market data into the 

supervisory process. 

BOPEC Risk Rating Changes and Financial Statement Variables as Predictors of Financial 

Market Factors 

Berger et al. (2000) compared the timeliness and accuracy of when BHC supervisors 

assessed changes in BHC financial condition with the timeliness and accuracy of when the 

financial markets assessed the changes. In their analysis, they estimate models to forecast equity 

market assessments using lagged market variables and supervisory assessments.28 They find that 

supervisory assessments add little to the prediction of market assessments. 

We conduct a similar analysis by considering the relationship between BOPEC rating 

changes and lagged market variables when, unlike what happens in the analysis above, the 

lagged BOPEC rating changes become predictors of market variables. For the testing of this 

latter relationship, the market variables used in the logit models are specified as dependent 

variables and are regressed against the lagged BOPEC rating changes and lagged financial 

statement ratios, including the natural logarithm of BHC assets, as independent variables. 

Although some of the market dependent variables are bounded from below by zero, we use OLS 

estimation. However, OLS should provide reasonable estimates and should not cause serious 

28 Berger et al. use three equity market variables: abnormal quarterly return on a BHC’s common stock (AR), 

quarterly change in the proportion of outstanding shares held by institutional investors (INST), and the quarterly 

change in the proportion of outstanding shares held by officers and directors (INSIDER). Our excess return variable, 

EX_RETURN, attempts to measure similar market conditions as AR, but our measure does not impose an estimated 

time series model to compute excess returns. However, our findings, which include book-value financial ratios in 

34 
 

 

problems because these variables are never observed to reach the lower bound. For the sake of 

brevity, we discuss only those results when there was a statistically significant relationship 

between the lagged BOPEC rating changes (BPchgL1) and the financial market variables. This 

occurs only five times for the three periods and for four variables: the standard deviation of 

quarterly returns (STD_RETURN), the quarterly value-weighted excess return (EX_RETURN), 

the market value of equity to book value equity ratio (MKT_BKEQ), and the covariance of price 

(COVAR_PRICE). Tables 12–16 show the regressions for each of the five market variables for 

each of the three periods. The model is identified as follows: 

MarketVariable = + β BPchgL1 + β LogFirmSize it 1 it−1 2 it−1 

(2) +

11 

β (Financial AccountingVariables )+ε j jit−1 it 

j=3 

where the financial accounting variables are those used in the logistic regressions and are defined 

above. The lagged BOPEC change, BPchgL1, takes on a value of 1 for a downgrade, 2 for no 

change, and 3 for a ratings upgrade. A negative sign for the BOPEC rating change coefficient, 

β1, means that a movement toward a downgrade will increase the dependent variable compared 

with no change or an upgrade. All variables are defined in table 5. 

The relationship between the standard deviation of return (STD_RETURN) and the 

change in the BOPEC rating (BPchgL1) has the hypothesized negative sign and is statistically 

significant for the 1988–1992 and 1996–2000 periods (table 12). The implication is that a prior 

BOPEC downgrade will be associated with a higher return volatility for a BHC, indicating a 

the regressions, show little forecasting power of equity market variables by supervisory variables and are similar to 

those of Berger et al. 

35 
 

worsening financial condition going forward. Additionally, most of the signs for the financial 

statement variables are according to expected relationships, and many are statistically significant. 

For example, a greater value of book equity to assets, EQ_ASSET, and ROA suggests that the 

STD_RETURN would be lower. Less leverage and greater earnings would be expected to 

reduce stock return volatility, ceteris paribus. The overall fit for this regression is statistically 

significant, with an Adjusted R2

 of 45 percent, 58 percent, and 17 percent in the 1988–1992, 

1993–1995, and 1996–2000 periods, respectively. 

The relationship between the quarterly value-weighted excess return (EX_RETURN) and 

the change in the BOPEC rating (BPchgL1) has the hypothesized positive sign and is statistically 

significant at the 5 percent level or better only for the 1988–1992 period (table 13). The 

implication of this relationship is that a prior BOPEC downgrade will be associated with a lower 

excess return, indicating a worsening financial condition. Additionally, some of the signs for the 

financial statement variables are according to expectations, and many are significant. However 

an interesting set of relationships is that of loans past due 90 days or more to assets 

(PD90_ASSET) and loan charge-offs (CHARG_ASSET). The estimates show different signs 

and are all statistically significant and imply that a greater value of PD90_ASSET indicates a 

decrease in EX_RETURN, whereas an increase in CHARG_ASSET will increase 

EX_RETURN. It would be expected that greater values for all of these variables would tend to 

be associated with lower excess returns because of the greater riskiness of the company that an 

increase in their values would indicate. One interpretation of the opposite signs is that greater 

reported past-due loans suggest greater future charge-offs, whereas greater actual charge-offs 

reflect recognized losses and are viewed by the market as getting bad assets off the books. It is 

also interesting to note that ROA carries the expected positive sign. The overall fit for this 

36 
regression is statistically significant but small, with an Adjusted R2

 of 9.1 percent for the 1988– 

1992 period. 

The relationship between the lagged change in the BOPEC rating and the market-to-book 

value variable (MKT_BK) is statistically significant at the 5 percent level or better and has the 

hypothesized positive sign for only the 1988-1992 period. The interpretation is that a bank 

holding company that had a previous rating downgrade will show a deteriorating market-to-book 

value in the following quarter. Furthermore, several financial statement variables are highly 

significant for each of the periods and usually have the expected signs. The variables loans on 

nonaccrual status (NA_ASSET) and ROA stand out in this regard. The overall fit for this 

regression is statistically significant, with an Adjusted R2

 of 20.9 percent in the 1988–1992 

period and 16 percent and 33 percent for the other two periods. 

The relationship between the lagged change in the BOPEC rating and the coefficient of 

variation of price (COVAR_PRICE) is statistically significant at the 5 percent level or better and 

has the hypothesized negative sign for only the 1996–2000 period. The interpretation of this 

relationship is that a bank holding company that had a previous rating downgrade will show a 

rising coefficient of variation of price in the subsequent quarter. Furthermore, several financial 

statement variables are highly significant for the period and usually have the hypothesized signs. 

The variables EQ_ASSET, LPROV_ASSET, and CHARG_ASSET stand out in this regard. The 

coefficient of variation is hypothesized to decline as EQ_ASSET increases, because of the 

decreased use of leverage. The interplay between loan-loss provisions (a prospective indicator of 

future charge-offs) and revealed charge-offs in the previous period is consistent with the 

hypothesis that taking charge-offs is current recognition of bad loans and will decrease price 

volatility, whereas higher provisions are an indicator of greater future charge-offs (a positive 

37 
sign). The overall fit for this regression is small but highly statistically significant, with an 

Adjusted R2 of 5.1 percent in the 1996–2000 period. 

The stock volume turnover variable, TURNOVER, showed no relationship for any period 

with BPchgL1. However, a number of the financial variables were statistically significant with 

anticipated signs. The overall fits were statistically significant for all periods and showed an R2 

of 27.1 percent, 25.1 percent, and 91 percent for the 1988–1992, 1993–1995, and 1996–2000 

periods, respectively. 

The conclusion that we draw from these results is that BOPEC rating changes have 

minimal predictive power for stock market variables. With statistical significance in only 6 of a 

possible 15 regressions and only 2 of these for the 1996–2000 period, this conclusion seems 

justified. However, there is ample evidence from these regressions that lagged financial 

statement variables, being readily known to the market before an inspection, have some 

consistent predictive power in every period. This in itself suggests that previous financial 

performance has some predictive power of equity market variables. 

Conclusions 

This paper explores the notion that financial market–based variables may be used to 

augment financial statements and past supervisory risk assessments for the purpose of predicting 

future supervisory risk assessments as embodied in BOPEC rating changes. This analysis is 

conducted with the use of a sample of publicly traded bank holding companies with 3,974 fullscope inspections over the 1988–2000 period. This period is characterized as representing three 

distinct economic and banking cycles: recession and banking crisis (1988–1992), economic 

recovery (1993–1995), and economic and banking expansion (1996–2000). Focusing on 

38 
different economic and banking climates allows for testing the stability of the model over these 

varying periods and permits a comparison of the findings with the previous literature. It should 

be noted that even though we are analyzing changes in BOPEC ratings arising from full-scope 

inspections, which are supervisory events, we are also implicitly analyzing changes in the 

stringency of regulatory regimes. It should be recalled that the 1988–1992 period was 

considered a “near-death” experience for commercial banking, while the 1996–2000 period 

witnessed an exuberant recovery, with market-to-book ratios often exceeding four for some of 

the larger banking companies. 

To investigate the direction of causality from market variables to changes in BOPEC 

ratings or vice versa, two empirical models are estimated. The results for the first model 

(ordered logistic) show that market indicator variables were able to add value in predicting 

BOPEC rating changes when used in conjunction with lagged supervisory and financial 

accounting data for each of the three periods examined. These findings are consistent with 

earlier studies investigating the role of equity market indicators in identifying the changing 

patterns of bank financial risk, but the results show stronger relationships when analyzed by subperiods. For example, through six different out-of-sample classification tests, we show that the 

use of stock market information generally improves out-of-sample forecasts. 

A second model (OLS) was specified to determine if lagged BOPEC rating changes, 

when used with financial accounting information, were capable of predicting the equity market 

variables used in the ordered logistic model estimation. The results were only moderately 

successful in predicting the financial market variables and were statistically significant for only 6 

of the possible 15 cases. We interpret these results as being supportive of an information flow 

from changes in equity market factors to changes in BHC supervisory risk ratings. 

39 
 

The findings in this paper provide some empirical support for the presence of market 

discipline to the extent that the hypothesized market variables add statistical and incremental 

value to the model in predicting changes in bank holding company BOPEC ratings. Thus, the 

results indicate that the market has been able to glean some independent information about BHC 

financial risk exposure in the period leading up to the inspection beyond the information that was 

known from publicly available financial reporting sources. To this extent, the financial markets 

appear to be providing some degree of independent oversight for management besides that of 

bank supervisors and holding company directors, thereby providing the fundamental conditions 

for market discipline to be effective. 

40 
 

 

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43 
Figure 1 

BOPEC Rating Changes for Bank Holding Companies: 1988-2000 

350 

300 

250 

200 

150 

100 

50 

0 

Downgrades 

Upgrades 

No Change 

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 

44 
Table 1 

BOPEC Migration Matrix for Bank Holding Companies, 1988–2000 

(Newly assigned ratings) 

1988–1992 (Recesssion and Banking Crisis) 

(percent) 

Initial BOPEC 

Rating BOPEC-1 BOPEC-2 BOPEC-3 BOPEC-4 BOPEC-5 

1 68.51 27.07 1.66 2.76 . 

2 4.82 71.07 18.66 4.19 1.26 

3 . 16.73 57.79 21.29 4.18 

4 . . 19.58 62.24 18.18 

5 . . . 18.18 81.82 

1993–1995 (Recovery) 

(percent) 

Initial BOPEC 

Rating BOPEC-1 BOPEC-2 BOPEC-3 BOPEC-4 BOPEC-5 

1 88.46 11.54 . . . 

2 15.50 82.00 2.00 0.50 . 

3 . 67.90 24.69 6.17 1.23 

4 . 10.87 41.30 41.30 6.52 

5 . . . . 100.00 

1996–2000 (Expansion) 

(percent) 

Initial BOPEC 

Rating BOPEC-1 BOPEC-2 BOPEC-3 BOPEC-4 BOPEC-5 

1 86.96 12.84 0.19 . . 

2 11.81 82.86 4.38 0.95 . 

3 . 37.14 54.29 8.57 . 

4 . . 50.00 33.33 16.67 

5 . . . . . 

Note: The cells along the diagonals represent the frequency of a BHC remaining at the same 

BOPEC after an inspection. Cells to the right of the diagonal represent the frequency 

of an downgrade from the initial BOPEC and cells to the left of the diagonal represent 

the frequency of an upgrade from the initial BOPEC. For example, for the 1996–2000 

period, 82.86 percent of the BHCs remained at a BOPEC-2 if they started as a BOPEC-2, 

11.81 percent were upgraded to a BOPEC-1, 4.38 percent were downgraded to a BOPEC-3 

and 0.95 percent were downgraded to a BOPEC-4. 

Source: Federal Reserve Board 

45 
 Table 2 

Univariate Statistics by BOPEC Rating, 1988–1992 

Means and BOPEC Ratings for New Bank Holding Company Inspections 

(Quarterly Data) 

BOPEC Rating 1 2 3 4 5 p value1 

Supervisory Variables 

Number of days from last BOPEC inspection 582 506 362 194 331 0.001 

CAMELS rating of lead bank before inspection 1.31 1.98 2.83 3.58 4.44 0.001 

Previous BOPEC inspection 1.17 1.96 2.72 3.4 4.03 0.001 

Financial Accounting Variables (percent) 

Log Total Assets 14.83 14.78 14.79 14.46 14.03 0.001 

Equity/Total Assets 8.27 7.24 6.53 5.66 1.72 0.001 

Tangible Equity/Total Assets 8.07 7.12 6.37 5.44 2.58 0.001 

Annualized Quarterly Return on Assets 1.16 0.87 0.18 –0.82 –2.43 0.001 

Loans Past Due 30 Days/Total Assets 0.84 1.12 1.53 2.06 3.04 0.001 

Loans Past Due 90 Days/Total Assets 0.21 0.29 0.37 0.48 0.92 0.001 

Nonaccrual Loans/Total Assets 0.46 0.88 2.14 3.73 5.3 0.001 

Loan-Loss Reserves/Total Assets 0.86 1.01 1.64 2.41 3.04 0.001 

Loan-Loss Provisons/Total Assets 0.19 0.29 0.66 1.25 1.44 0.001 

Charge-offs/Total Assets 0.19 0.32 0.62 1.12 1.62 0.001 

Liquid Assets/Total Assets 7.77 7.24 7.37 7.28 5.06 0.001 

Volatile Liabilities/Total Assets 9.31 8.98 9.18 7.91 4.12 0.001 

Time Deposits >$100,000/Total Assets 10.1 9.97 10.89 9.78 11.56 0.009 

Financial Market Variables 

Average Quarterly Price 30.11 23.44 16.34 9.52 2.25 0.001 

Coefficient of Variation of Price (%) 4.14 5.31 7.85 11.98 20.31 0.001 

Cumulative Quarterly Returns 0.05 0.05 0.03 0.02 –0.03 0.001 

Abnormal or Excess Quarterly Returns (equal weighted) –0.03 –0.02 –0.06 –0.08 –0.11 0.001 

Abnormal or Excess Quarterly Returns (value weighted) 0.01 0.01 –0.01 –0.02 –0.58 0.001 

Std. Deviation Quarterly Returns 0.02 0.02 0.03 0.05 0.11 0.001 

Std. Deviation ROA (8quarters) 0.12 0.19 0.54 1.05 2.22 0.001 

Market Equity to Book Equity 1.44 1.13 0.85 0.66 0.64 0.001 

Market Equity/Market Equity + Total Liabilities (%) 0.11 0.08 0.54 0.04 0.01 0.001 

Total Liabilities/Market Equity + Preferred Stock 8.79 13.05 20.55 36.94 185.73 0.001 

Share Turnover (%) 7.17 10.23 16.68 17.93 17.29 0.001 

Number of Observations 257 661 327 209 71 

1

 p values are determined by a Van der Waerden test. This procedure tests whether the distribution of the variable has the 

same location parameter across different groups. 

46 
Table 3 

Univariate Statistics by BOPEC Rating: 1993–1995

 Means and BOPEC Ratings for New Bank Holding Company Inspections 

(Quarterly Data) 

BOPEC Rating 1 2 3 4 5 p value1 

Supervisory Variables 

Number of days from last BOPEC inspection 535 491 404 334 384 0.001 

CAMELS rating of lead bank before inspection 1.34 2.13 3.09 3.89 4.6 0.001 

Previous BOPEC inspection 1.32 2.17 3.28 3.91 4.5 0.001 

Financial Accounting Variables (percent) 

Log Total Assets 15.2 14.86 13.75 13.41 12.7 0.001 

Equity/Total Assets 8.97 8.32 7.67 6.56 3.7 0.001 

Tangible Equity/Total Assets 8.77 8.26 7.53 6.49 4.32 0.001 

Annualized Quarterly Return on Assets 1.31 1.06 0.61 –0.51 –3.89 0.001 

Loans Past Due 30 Days/Total Assets 0.65 0.81 1.27 0.32 3.18 0.001 

Loans Past Due 90 Days/Total Assets 0.12 0.15 0.28 0.32 0.96 0.001 

Nonaccrual Loans/Total Assets 0.36 0.75 1.92 3.22 2.61 0.001 

Loan-Loss Reserves/Total Assets 1.02 1.24 1.78 2.33 3.55 0.001 

Loan-Loss Provisons/Total Assets 0.12 0.14 0.36 0.84 2.4 0.001 

Charge-offs/Total Assets 0.16 0.24 0.55 1.05 2.47 0.001 

Liquid Assets/Total Assets 25.36 22.93 15.55 12.03 9.07 0.001 

Volatile Liabilities/Total Assets 11.58 9.75 4.96 3.57 1.38 0.001 

Time Deposits > $100,000/Total Assets 6.20 6.5 6.16 5.51 10.3 0.001 

Financial Market Variables 

Average Quarterly Price 33.59 27.77 15.57 8.33 2.10 0.001 

Coefficient of Variation of Price (%) 5.37 4.52 6.71 10.28 18.5 0.001 

Cumulative Quarterly Returns 0.05 0.05 0.07 0.12 –0.06 0.087 

Excess Quarterly Returns (equal weighted) –0.04 –0.03 –0.03 0.02 –0.13 0.111 

Excess Quarterly Returns (value weighted) 0.01 0.02 0.04 0.09 –0.07 0.064 

Std. Deviation Quarterly Returns 0.02 0.02 0.04 0.06 0.1 0.001 

Std. Deviation ROA (8 quarters) 0.16 0.25 0.59 1.06 2.16 0.001 

Market Equity to Book Equity 1.65 1.37 1.10 1.02 0.93 0.001 

Market Equity/Market Equity + Total Liabilities(%) 0.14 0.11 0.08 0.07 0.04 0.001 

Total Liabilities/Market Equity + Preferred Stock 6.96 8.73 12.47 18.22 42.8 0.001 

Share Turnover (%) 9.36 0.14 0.17 21.08 0.11 0.001 

Number of Observations 239 435 97 57 10 

1

 p values are determined by a Van der Waerden test. This procedure tests whether the distribution of the variable 

has the same location parameter across different groups. 

47 
Table 4 

Univariate Statisitics by BOPEC Rating, 1996–2000 

Means and BOPEC Ratings for New Bank Holding Company Inspections 

(Quarterly Data) 

BOPEC Rating 1 2 3 4 5 p value1 

Supervisory Variables 

Number of days from last BOPEC inspection 513 522 410 405 114 0.03 

CAMELS rating of lead bank before inspection 1.13 1.91 2.72 3.50 4.00 0.001 

Previous BOPEC inspection 1.18 1.94 2.62 2.73 4.00 

Financial Accounting Variables (percent) 

Log Total Assets 14.83 14.40 13.59 13.13 12.66 0.001 

Equity/Total Assets 9.39 8.35 7.29 5.65 3.56 0.001 

Tangible Equity/Total Assets 8.86 8.10 7.44 5.82 4.96 0.001 

Annualized Quarterly Return on Assets 1.31 1.01 0.47 –0.89 –3.49 0.001 

Loans Past-Due 30 days/Total Assets 0.64 0.75 0.88 0.74 2.12 0.001 

Loans Past-Due 90 days/Total Assets 0.12 0.16 0.20 0.35 0.17 0.148 

Nonacrural Loans/Total Assets 0.28 0.45 0.85 1.24 0.17 0.001 

Loan-Loss Reserves/Total Assets 0.92 9.92 1.08 1.40 3.07 0.001 

Loan-Loss Provisons/Total Assets 0.13 0.16 0.44 0.41 5.02 0.001 

Charge-offs/Total Assets 0.14 0.16 0.42 0.46 3.93 0.001 

Liquid Assets/Total Assets 29.53 27.77 26.23 32.73 39.94 0.002 

Volatile Liabilities/Total Assets 10.41 10.14 7.77 3.69 8.18 0.001 

Time Deposits >$100,000/Total Assets 8.95 10.61 12.42 14.86 6.59 0.001 

Financial Market Variables 

Average Quarterly Price 35.96 26.32 15.36 8.25 7.06 0.001 

Coefficient of Variation of Price (%) 5.84 6.15 7.34 10.01 15.17 0.001 

Cumulative Quarterly Returns 0.03 0.03 –0.01 –0.09 0.11 0.021 

Excess Quarterly Returns (equal weighted) –0.02 –0.03 –0.08 –0.11 –0.09 0.047 

Excess Quarterly Returns (value weighted) 0.00 –0.01 –0.05 –0.08 –0.08 0.095 

Std Deviation Quarterly Returns 0.02 0.02 0.03 0.05 0.07 0.001 

Std. Deviation ROA (8quarters) 0.13 0.19 0.37 1.09 2.08 0.001 

Market Equity to Book Equity 2.24 1.91 1.24 1.09 297.27 0.001 

Market Equity/Market Equity + Total Liabilities(%) 0.18 0.14 0.09 0.06 2.97 0.001 

Total Liabilities/Market Equity + Preferred Stock 5.41 7.21 13.05 18.54 8.69 0.001 

Share Turnover (%) 10.00 13.10 15.10 22.70 9.32 0.001 

Number of Observations 736 807 55 12 1 

1

 p values are determined by a Van der Waerden test. This procedure tests whether the distribution of the variable 

has the same location parameter across different groups. 

48 
TABLE 5 

Definitions of Variables 

Dependent Variable 1=BOPEC rating downgrade 

2=BOPEC no rating change 

3=BOPEC rating upgrade 

Size of Institution 

Natural logarithm of total assets LN_ASSET 

Past Supervisory Variables 

Dummy Variable: BOPEC rating of 1 before most recent inspection BOPEC-1 

Dummy Variable: BOPEC rating of 2 before most recent inspection BOPEC-2 

Dummy Variable: BOPEC rating of 3 before most recent inspection BOPEC-3 

Dummy Variable: BOPEC rating of 4 before most recent inspection BOPEC-4 

Dummy Variable: CAMELS rating of lead bank before current PROB_BK

 inspection: 1 = prior rating of 3, 4, 5; 0 = 1 or 2 rating 

Time from prior holding company inspection to current inspection (days) INSP_AGE 

Financial Accounting Variables (percent) 

Equity/Total Assets EQ_ASSET 

Loans Past Due 90 days/Total Assets PD90_ASSET 

Nonaccrual Loans/Total Assets NA_ASSET 

Loan-Loss Reserves/Total Assets LLR_ASSET 

Loan-Loss Provisons/Total Assets LPROV_ASSET 

Charge-offs/Total Assets CHARG_ASSET 

Annual Return on Assets ROA 

Liquid Assets/Total Assets LIQ_ASSET 

Time Deposits>$100,000/Total Assets TD100_ASSET 

Financial Market Variables 

Coefficient of Variation of Price (%) COVAR_PRICE 

Abnormal or Excess Quarterly Returns (value weighted) (%) EX_RETURN 

Std. Deviation of Quarterly Returns (%) STD_RETURN 

Market Value of Firm/Book Value of Firm MKT_BK 

Quarterly Turnover of Shares (%) TURNOVER 

49 
TABLE 6 

Variable Means and Standard Deviations by Period, 1988–2000 

(Quarterly data) 

Institution Size 

LN_ASSET 

Past Supervisory Variables 

BOPEC-1 

BOPEC-2 

BOPEC-3 

BOPEC-4 

PROB_BK 

INSP_AGE 

Financial Accounting Variables 

EQ_ASSET 

PD90_ASSET 

NA_ASSET 

LLR_ASSET 

LPROV_ASSET 

CHARG_ASSET 

ROA 

LIQ_ASSET 

TD100_ASSET 

Financial Market Variables 

COVAR_PRICE 

EX_RETURN 

STD_RETURN 

MKT_BK 

TURNOVER 

Number of Observations 

Periods 

1988–1992 1993–1995 1996–2000 

Mean Std. Deviation 

14.81 1.63 

0.19 0.39 

0.46 0.49 

0.21 0.41 

0.11 0.31 

0.39 0.48 

453.11 289.12 

6.65 2.12 

0.35 0.49 

1.69 1.82 

1.42 0.89 

0.51 0.74 

0.51 0.63 

0.36 1.41 

7.23 3.78 

10.45 5.79 

7.12 6.40 

–0.01 0.22 

0.03 0.03 

1.05 1.32 

12.21 13.12 

1,295 

Mean Std. Deviation 

14.79 1.86 

0.23 0.42 

0.48 0.50 

0.16 0.37 

0.12 0.32 

0.28 0.45 

471.96 309.79 

8.12 1.77 

0.19 0.25 

1.08 1.28 

1.39 0.74 

0.27 0.49 

0.37 0.51 

0.89 0.98 

19.87 14.76 

6.23 4.46 

5.19 4.12 

0.02 0.17 

0.03 0.02 

1.37 0.48 

12.95 12.67 

787 

Mean Std. Deviation 

14.67 1.94 

0.44 0.49 

0.52 0.49 

0.03 0.18 

0.01 0.07 

0.05 0.21 

506.82 299.24 

8.66 2.21 

0.14 0.18 

0.40 0.40 

0.95 0.36 

0.17 0.25 

0.18 0.27 

1.13 0.51 

28.58 10.61 

9.76 6.06 

6.18 6.17 

–0.01 0.18 

0.02 0.01 

2.07 0.96 

11.35 13.18 

1,452 

50 
Table 7 

Ordered Logistic Regressions, 1988–1992 (1-Quarter Lag) 

This table presents ordered logistic regression results for a sample of 1,295 new bank holding 

company inspections. The ordering is BOPEC downgrades, no change in ratings, and 

BOPEC upgrades. All independent variables are defined in table 5. A single, double, or triple "*" 

indiciates significance at the 10%, 5%, or 1% level, respectively. 

Anticipated 

Independent Variable Sign 

Specification 

1 2 3 

Coefficient t-statistic Coefficient t-statistic Coefficent t-statistic 

Intercept 1 –5.3236 4.92*** –5.9482 5.86*** –5.3705 4.21*** 

Intercept 2 

Size of Institution 

0.33 0.31 –1.3123 1.31 0.50 0.39 

LN_ASSET – 

Past Supervisory Variables 

–0.2795 4.31*** –0.2067 3.69*** –0.3909 5.154*** 

BOPEC-1 + 11.84 14.46*** 7.62 13.01*** 13.17 15.28*** 

BOPEC-2 + 9.36 13.01*** 6.16 11.45*** 10.42 13.90*** 

BOPEC-3 + 5.76 6.28*** 3.60 7.46*** 6.56 10.28*** 

BOPEC-4 + 2.78 4.92*** 1.92 4.03*** 3.32 5.81*** 

PROB_BK + 2.13 8.72*** 2.28 10.55*** 2.11 8.33*** 

INSP_AGE + 

Financial Accounting Variables 

–0.3x10-3 0.95 –0.5x10-3 1.55 –0.5x10-3 1.60 

EQ_ASSET – –0.3311 6.22*** –0.3239 6.01*** 

PD90_ASSET + 0.48 2.23** 0.47 2.06** 

NA_ASSET + 0.92 9.08*** 0.87 8.41*** 

LLR_ASSET – –0.4303 2.52** –0.2332 1.33 

LPROV_ASSET 1.13 4.33*** 0.97 3.68*** 

CHARG_ASSET – –0.5653 2.25** –0.7182 2.86*** 

ROA –0.8482 5.57*** –0.7354 4.83*** 

LIQ_ASSET – –0.0336 1.58 –0.0237 1.12 

TD100_ASSET + 

Financial Market Variables 

0.03 2.08** 0.02 1.83* 

COVAR_PRICE + 0.09 5.54*** 0.07 3.43*** 

EX_RETURN – –2.9922 8.78*** –1.611 3.91*** 

STD_RETURN + 19.88 4.85*** 2.44 0.49 

MKT_BK – –0.071 1.35 –0.1999 4.20*** 

TURNOVER + 0.02 3.77*** 0.02 2.73*** 

AIC 1,347.45 1,649.88 1,304.16 

Rescaled R2 

c

2

 (relative to spec. 1) 

0.58 0.39 0.60 

53.28*** 

51 
Table 8 

Ordered Logistic Regressions, 1993–1995 (1-Quarter Lag) 

This table presents ordered logistic regression results for a sample of 787 new bank holding 

company inspections. The ordering is BOPEC downgrades, no change in ratings, and 

BOPEC upgrades. All independent variables are defined in Table 5. A single, double, or triple "*" 

indiciates significance at the 10%, 5%, or 1% level, respectively. 

Independent Variable Anticipated Sign 

Specification 

1 2 3 

Coefficient t-statistic Coefficient t-statistic Coefficent t-statistic 

Intercept 1 –10.4 5.80*** –11.78 7.44*** –12.29 6.05*** 

Intercept 2 

Size of Institution 

–4.12 2.40** –6.15 4.14*** –5.74 2.97*** 

LN_ASSET -

Past Supervisory Variables 

–0.2 2.63*** 0.05 0.66 –0.14 1.57 

BOPEC-1 + 14.52 9.85*** 9.69 9.63*** 15.83 10.53*** 

BOPEC-2 + 11.14 8.56*** 6.96 8.09*** 12.06 9.28*** 

BOPEC-3 + 5.86 5.10*** 2.05 2.80*** 6.42 5.75*** 

BOPEC-4 + 3.71 3.52*** 1.16 1.63 3.88 3.85*** 

PROB_BK + 2.68 5.80*** 3.19 7.34*** 2.65 5.53*** 

INSP_AGE + 

Financial Accounting Variables 

–0.4x10-3 0.99 –0.3x10-3 0.75 –0.3x10-3 0.74 

EQ_ASSET - –0.26 4.15*** –0.27 4.13*** 

PD90_ASSET + 1.12 2.75*** 0.98 2.37*** 

NA_ASSET + 0.62 4.92*** 0.51 3.87*** 

LLR_ASSET 0.01 0.04 0.12 0.6 

LPROV_ASSET + –0.03 0.08 0.20 0.52 

CHARG_ASSET - 0.16 0.47 0.23 0.62 

ROA - –0.83 5.06*** –0.60 3.65*** 

LIQ_ASSET - –0.01 0.96 –0.01 0.8 

TD100_ASSET + 

Financial Market Variables 

0.07 3.00*** 0.07 3.25***

COVAR_PRICE + 0.04 1.65* 0.08 0.27 

EX_RETURN - –0.49 0.93 0.5 0.87 

STD_RETURN + 34.87 5.36*** 19.93 2.72*** 

MKT_BK - –0.66 2.94 –0.83 3.29*** 

TURNOVER + 0.07 0.97 0.01 1.45 

AIC 836.07 914.58 820.64 

Rescaled R2 0.52 0.44 0.55 

χ2

 (relative to spec. 1) 25.43*** 

52 
Table 9 

Ordered Logistic Regression Results, 1996–2000 (1-Quarter Lag) 

This table presents ordered logistic regression results for a sample of 1,452 new bank holding 

company inspections. The ordering is BOPEC downgrades, no change in ratings, and 

BOPEC upgrades. All independent variables are defined in table 5. A single, double, or triple "*" 

indiciates significance at the 10%, 5%, or 1% level, respectively. 

Anticipated 

Independent Variable Sign 

Specification 

1 2 3 

Coefficient t-statistic Coefficient t-statistic Coefficent t-statistic 

Intercept 1 –11.92 7.69*** –13.71 10.59*** –15.27 8.53*** 

Intercept 2 

Size of Institution 

–4.53 3.06*** –7.18 5.94*** –7.54 4.45*** 

LN_ASSET – 

Past Supervisory Variables 

–0.11 2.05** –0.01 0.16 –0.08 1.39 

BOPEC-1 + 14.97 11.26*** 11.49 10.98*** 16.40 11.23*** 

BOPEC-2 + 11.03 9.19*** 8.65 9.05*** 12.20 9.26*** 

BOPEC-3 + 4.60 4.22*** 3.32 3.63*** 5.39 4.56*** 

PROB_BK + 4.67 9.23*** 4.67 10.04*** 4.70 9.18*** 

INSP_AGE + 

Financial Accounting Variables 

EQ_ASSET – 

PD90_ASSET + 

NA_ASSET + 

LLR_ASSET – 

LPROV_ASSET + 

CHARG_ASSET – 

ROA 

LIQ_ASSET 

TD100_ASSET + 

Financial Market Variables 

–0.8x10-3 2.70*** 

–0.14 3.21*** 

3.09 6.24*** 

0.81 3.4*** 

–0.34 1.34 

1.91 2.58*** 

–0.64 1.06 

–1.76 8.09*** 

–0.01 1.78 

0.03 2.2** 

–0.8x10-3 3.07*** –0.7x10-3 2.51** 

–0.15 3.13*** 

3.10 6.10*** 

0.89 3.64*** 

–0.26 0.00 

2.11 2.71*** 

–0.75 1.13 

–1.59 6.61*** 

–0.01 1.12 

0.23 1.91* 

COVAR_PRICE + –0.02 1.49 -0.01 0.97 

EX_RETURN – –1.82 4.07*** –1.89 3.82*** 

STD_RETURN + 53.43 6.57*** 46.58 5.13*** 

MKT_BK – –0.18 2.02** –0.01 0.06 

TURNOVER + 0.01 1.87* 0.01 1.92* 

AIC 1,206.73 1,336.68 1,171 

Rescaled R2 

χ2

 (relative to spec. 1) 

0.45 0.35 0.48 

45.20*** 

53 
Table 10 

In-Sample Classification Table for Each Period 

(Critical values are the in-sample ratios of downgrades, no changes 

or upgrades divided by the total in sample.) 

A. Classification Table for Ordered Logit Model, 1988–1992 

BOPEC Change 

Spec. 1: Financial Acct. 

Variables (FA) 

Spec. 2: Stock Market 

Variables (SM) 

Specification 3: FA & SM 

Variables 

Correct No. Incorrect No. 

(%) (%) 

Correct No. Incorrect No. 

(%) (%) 

Correct No. Incorrect No. 

(%) (%) 

Downgrade 

No Change 

Upgrade 

81.3 (243) 18.7 (56) 

77.5 (682) 22.5 (198) 

55.2 (64) 44.8 (52) 

70.2 (210) 29.8 (89) 

74.2 (653) 25.8 (277) 

37.9 (44) 62.1 (72) 

83.3 (249) 16.7 (50) 

78.3 (689) 21.7 (191) 

51.7 (60) 48.3 (56) 

B. Classification Table for Ordered Logit Model, 1993–1995 

BOPEC Change 

Spec. 1: Financial Acct. 

Variables (FA) 

Spec. 2: Stock Market 

Variables (SM) 

Specification 3: FA & SM 

Variables 

Correct No. Incorrect No. 

(%) (%) 

Correct No. Incorrect No. 

(%) (%) 

Correct No. Incorrect No. 

(%) (%) 

Downgrade 

No Change 

Upgrade 

56.3 (27) 43.8 (21) 

77.3 (419) 22.7 (123) 

71.6 (141) 28.4 (56) 

45.8 (22) 54.2 (26) 

74.7 (405) 25.3 (137) 

66.0 (130) 34.0 (67) 

62.5 (30) 37.5 (18) 

77.1 (418) 22.9 (124) 

72.1 (142) 27.9 (55) 

C. Classification Table for Ordered Logit, 1996–2000 

BOPEC Change 

Spec. 1: Financial Acct. 

Variables (FA) 

Spec. 2: Stock Market 

Variables (SM) 

Specification 3: FA & SM 

Variables 

Correct No. Incorrect No. 

(%) (%) 

Correct No. Incorrect No. 

(%) (%) 

Correct No. Incorrect No. 

(%) (%) 

Downgrade 

No Change 

Upgrade 

66.7 (74) 33.3 (37) 

64.3 (767) 35.7 (426) 

80.4 (119) 19.6 (129) 

58.6 (65) 41.4 (46) 

58.2 (694) 41.8 (499) 

79.1 (117) 20.9 (31) 

66.7 (74) 33.3 (37) 

67.1 (801) 32.9 (392) 

81.1 (120) 18.9 (28) 

54 
Table 11 

Out-of-Sample Classification Table for Each Period 

(Critical values are the in-sample ratios of downgrades, no changes,

 or upgrades divided by the total in sample.) 

A. Out-of-Sample, 1993–1995 

Classification Table for Ordered Logit Model, 1988–1992 

BOPEC Change 

Spec. 1: Financial Acct. 

Variables (FA) 

Spec. 2: Stock Market 

Variables (SM) 

Specification 3: FA & SM 

Variables 

Correct No. Incorrect No. 

(%) (%) 

Correct No. Incorrect No. 

(%) (%) 

Correct No. Incorrect No. 

(%) (%) 

Downgrade 

No Change 

Upgrade 

45.8 (22) 54.2 (26) 

61.8 (335) 38.2 (207) 

79.2 (156) 20.8 (41) 

81.3 (39) 18.8 (9) 

41.1 (223) 58.9 (319) 

62.9 (124) 37.1 (73) 

43.8 (21) 56.3 (27) 

66.2 (359) 33.8 (183) 

72.6 (143) 27.4 (54) 

B. Out-of-Sample, 1996–2000 

Classification Table for Ordered Logit Model, 1988–1992 

BOPEC Change 

Spec. 1: Financial Acct. 

Variables (FA) 

Spec. 2: Stock Market 

Variables (SM) 

Specification 3: FA & SM 

Variables 

Correct No. Incorrect No. 

(%) (%) 

Correct No. Incorrect No. 

(%) (%) 

Correct No. Incorrect No. 

(%) (%) 

Downgrade 

No Change 

Upgrade 

22.5 (25) 77.5 (86) 

68.0 (811) 32.0 (382) 

83.1 (123) 16.9 (25) 

85.6 (95) 14.4 (16) 

39.3 (469) 60.7 (724) 

40.5 (60) 59.5 (88) 

40.5 (45) 59.5 (66) 

74.2 (885) 25.8 (308) 

68.2 (101) 31.8 (47) 

C. Out-of-Sample, 1996–2000 

Classification Table for Ordered Logit, 1993–1995 

BOPEC Change 

Spec. 1: Financial Acct. 

Variables (FA) 

Spec. 2: Stock Market 

Variables (SM) 

Specification 3: FA & SM 

Variables 

Correct No. Incorrect No. 

(%) (%) 

Correct No. Incorrect No. 

(%) (%) 

Correct No. Incorrect No. 

(%) (%) 

Downgrade 

No Change 

Upgrade 

54.1 (60) 45.9 (51) 

79.3 (946) 20.7 (247) 

43.2 (64) 56.8 (84) 

83.8 (93) 16.2 (16) 

38.4 (458) 61.6 (735) 

64.2 (95) 35.8 (53) 

48.6 (54) 51.4 (57) 

75.9 (905) 24.1 (288) 

64.9 (96) 35.1 (52) 

55 
Table 11 

Out-of-Sample Classification Table for Each Period 

(Critical values are the in-sample ratios of downgrades, no changes,

 or upgrades divided by the total in sample.) 

D. Out-of-Sample, 2001 

Classification Table for Ordered Logit Model, 1988–1992 

BOPEC Change 

Spec. 1: Financial Acct. 

Variables (FA) 

Spec. 2: Stock Market 

Variables (SM) 

Specification 3: FA & SM 

Variables 

Correct No. Incorrect No. 

(%) (%) 

Correct No. Incorrect No. 

(%) (%) 

Correct No. Incorrect No. 

(%) (%) 

Downgrade 

No Change 

Upgrade 

17.6 (6) 82.4 (28) 

70.0 (180) 30.0 (77) 

68.2 (15) 31.8 (7) 

70.6 (24) 29.4 (10) 

45.1 (116) 54.9 (141) 

68.2 (15) 31.8 (7) 

26.5 (9) 73.5 (25) 

74.3 (191) 25.7 (66) 

68.2 (15) 31.8 (7) 

E. Out-of-Sample, 2001 

Classification Table for Ordered Logit Model, 1993–1995 

BOPEC Change 

Spec. 1: Financial Acct. 

Variables (FA) 

Spec. 2: Stock Market 

Variables (SM) 

Specification 3: FA & SM 

Variables 

Correct No. Incorrect No. 

(%) (%) 

Correct No. Incorrect No. 

(%) (%) 

Correct No. Incorrect No. 

(%) (%) 

Downgrade 

No Change 

Upgrade 

61.8 (21) 38.2 (13) 

77.8 (200) 22.2 (57) 

45.5 (10) 54.5 (12) 

97.1 (33) 2.9 (1) 

50.6 (130) 49.4 (127) 

45.5 (10) 54.5 (12) 

64.7 (22) 35.3 (12) 

76.3 (196) 23.7 (61) 

45.5 (10) 54.5 (12) 

F. Out-of-Sample, 2001 

Classification Table for Ordered Logit Model, 1996–2000 

BOPEC Change 

Spec. 1: Financial Acct. 

Variables (FA) 

Spec. 2: Stock Market 

Variables (SM) 

Specification 3: FA & SM 

Variables 

Correct No. Incorrect No. 

(%) (%) 

Correct No. Incorrect No. 

(%) (%) 

Correct No. Incorrect No. 

(%) (%) 

Downgrade 

No Change 

Upgrade 

88.2 (30) 11.8 (4) 

65.4 (168) 34.6 (89) 

68.2 (15) 31.8 (7) 

73.5 (25) 26.5 (9) 

28.4 (73) 71.6 (184) 

95.5 (21) 4.5 (1) 

47.1 (16) 52.9 (18) 

66.1 (170) 33.9 (87) 

72.7 (16) 27.3 (6) 

56 
Table 12 

Regression of Market Variables on Lagged BOPEC Changes and 

Financial Statement Ratios: 

Standard Deviation of Stock Returns (STD_RETURN) 

This table presents regression results for a sample of 3,068 new bank holding company 

inspections with the dependent variable being the standard deviation of stock returns, 

STD_RETURN. The ordering of BPchgL1 (the change in the BOPEC) is 1 for a downgrade, 

2 for no change in ratings, and 3 for BOPEC upgrades. A negative sign for the BPchgL1 

coefficient means that a downgrade will increase the dependent variable compared to no 

change. All variables are defined in Table 5 and explanatory variables are lagged one 

quarter from the event quarter (where the BOPEC ratings change occurred). A single, 

double, or triple "*" indicates significance at the 10%, 5%, or 1% level, respectively. 

Variable 

Periods 

1988–1992 1993–1995 1996–2000 

Estimate t-statistic Estimate t-statistic Estimate t-statistic 

Intercept 0.14514 19.21*** 0.10199 15.45*** 0.05491 14.64*** 

BPchgL1 –0.00264 –2.19** 0.000073 0.07 –0.00187 –2.83*** 

LN_ASSET –0.00629 –14.7*** –0.00438 –14.18*** –0.00145 –8.66*** 

EQ_ASSET –0.00304 –7.91*** –0.000825 –2.38*** –0.000554 –3.53*** 

PD90_ASSET –0.00339 –2.07** 0.00642 2.61*** –0.000587 –0.41 

NA_ASSET 0.00633 9.75*** 0.00214 3.19*** 0.000351 0.44 

ROA –0.00212 –2.38** –0.00957 –10.61*** –0.0008 –1.16 

LIQ_ASSET –0.00027 –1.75* –0.000039 –1.09 –0.000083 –2.96*** 

LLR_ASSET –0.0021 –1.68* 0.000675 –0.65 –0.00103 –1.11 

LPROV_ASSET –0.00172 –1.01 –0.00115 –0.6 0.00839 4.05*** 

CHARG_ASSET 0.0079 4.75*** 0.00237 1.51 0.000452 0.2 

TD100_ASSET –0.000294 –2.81*** 0.000086 0.74 0.000122 2.41** 

F-Statistic 86.40*** 94.03*** 22.49*** 

Adj-R2 0.447 0.5826 0.1677 

N 1,162 733 1,173 

57 
Table 13 

Regression of Market Variables on Lagged BOPEC Changes and 

Financial Statement Ratios: 

Quarterly Value-Weighted Excess Return, (EX_RETURN) 

This table presents regression results for a sample of 3,068 new bank holding company 

inspections with the dependent variable being the value-weighted excess return, 

EX_RETURN. The ordering of BPchgL1 (the change in the BOPEC) is 1 for a downgrade, 

2 for no change in ratings, and 3 for BOPEC upgrades. A negative sign for the BPchgL1 

coefficient means that a downgrade will increase the dependent variable compared to no 

change. All variables are defined in Table 5 and explanatory variables are lagged one 

quarter from the event quarter (where the BOPEC ratings change occurred). A single, 

double, or triple "*" indicates significance at the 10%, 5%, or 1% level, respectively. 

Variable 

Periods 

1988–1992 1993–1995 1996–2000 

Estimate t-statistic Estimate t-statistic Estimate t-statistic 

Intercept 0.1568 1.64* 0.10339 1.31 -0.11336 –1.85* 

BPchgL1 0.03286 2.15** 0.02212 1.80* 0.00509 0.47 

LN_ASSET –0.01118 –2.07** –0.00687 –1.86* 0.00416 1.53 

EQ_ASSET –0.00926 –1.91* –0.00963 –2.32** -0.01057 –4.13*** 

PD90_ASSET –0.07069 –3.42*** –0.03233 –1.1 -0.03038 –1.31 

NA_ASSET –0.01563 –1.91* 0.00662 0.82 0.00365 0.28 

ROA 0.06718 5.95*** 0.06159 5.71*** 0.0436 3.88*** 

LIQ_ASSET –0.0028 –1.44 –0.000695 –1.63 0.00108 2.36** 

LLR_ASSET 0.02657 1.68* –0.01845 –1.49 0.03627 2.41** 

LPROV_ASSET 0.00975 0.46 –0.02035 –0.88 -0.00638 –0.19 

CHARG_ASSET 0.1188 5.66*** 0.10983 5.84*** -0.03928 –1.04 

TD100_ASSET –0.00402 –3.04*** 0.000243 0.17 0.00119 1.44 

F-Statistic 11.55*** 7.71*** 5.23*** 

Adj-R2 0.0908 0.0915 0.0381 

N 1,162 733 1,173 

58 
Table 14 

Regression of Market Variables on Lagged BOPEC Changes and 

Financial Statement Ratios: 

Market-to-Book Value (MKT_BKEQ) 

This table presents regression results for a sample of 3,068 new bank holding company 

inspections with the dependent variable being the market-to-book value, 

MKT_BK. The ordering of BPchgL1 (the change in the BOPEC) is 1 for a downgrade, 

2 for no change in ratings, and 3 for BOPEC upgrades. A negative sign for the BPchgL1 

coefficient means that a downgrade will increase the dependent variable compared to no 

change. All variables are defined in Table 5 and explanatory variables are lagged one 

quarter from the event quarter (where the BOPEC ratings change occurred). A single, 

double, or triple "*" indicates significance at the 10%, 5%, or 1% level, respectively. 

Variable 

Periods 

1988–1992 1993–1995 1996–2000 

Estimate t-statistic Estimate t-statistic Estimate t-statistic 

Intercept 0.38759 2.24** 0.85776 3.78*** –0.31539 –0.96 

BPchgL1 0.0768 2.78*** –0.00625 –0.18 0.05583 0.97 

LN_ASSET 0.03511 3.59*** 0.02616 2.46*** 0.12322 8.45*** 

EQ_ASSET 0.00591 0.67 –0.00837 –0.7 –0.09937 –7.26*** 

PD90_ASSET 0.02552 0.68 –0.03236 –0.38 –0.05468 –0.44 

NA_ASSET –0.07562 –5.09*** –0.10701 -4.64*** –0.21162 –3.07*** 

ROA 0.10967 5.37*** 0.16143 5.21*** 0.9841 16.37*** 

LIQ_ASSET 0.0026 0.74 –0.000675 –0.55 0.00976 4.00*** 

LLR_ASSET –0.00677 –0.24 0.09568 2.70*** –0.12796 –1.59 

LPROV_ASSET –0.02428 –0.63 0.07458 1.13 0.96554 5.34*** 

CHARG_ASSET 0.04366 1.15 –0.01466 –0.27 –0.23604 –1.17 

TD100_ASSET –0.00208 –0.87 0.01254 3.13*** 0.00205 0.46 

F-Statistic 28.87*** 13.66*** 53.65*** 

Adj-R2 0.2088 0.1596 0.3305 

N 1,162 733 1,173 

59 
Table 15 

Regression of Market Variables on Lagged BOPEC Changes and 

Financial Statement Ratios: 

Coefficient of Variation of Price, (COVAR_PRICE) 

This table presents regression results for a sample of 3,068 new bank holding company 

inspections with the dependent variable being the coefficient of variation of price, 

COVAR_PRICE. The ordering of BPchgL1 (the change in the BOPEC) is 1 for a downgrade, 

2 for no change in ratings, and 3 for BOPEC upgrades. A negative sign for the BPchgL1 

coefficient means that a downgrade will increase the dependent variable compared to no 

change. All variables are defined in Table 5 and explanatory variables are lagged one 

quarter from the event quarter (where the BOPEC ratings change occurred). A single, 

double, or triple "*" indicates significance at the 10%, 5%, or 1% level, respectively. 

Variable 

Periods 

1988–1992 1993–1995 1996–2000 

Estimate t-statistic Estimate t-statistic Estimate t-statistic 

Intercept 16.484 8.90*** 9.55632 2.62*** 4.62425 2.5** 

BPchgL1 –0.27142 –0.92 0.00764 0.01 –0.63662 –1.95** 

LN_ASSET –0.45871 –4.38*** 0.02685 0.16 0.14995 1.82* 

EQ_ASSET –0.6148 –6.53*** –0.00344 –0.02 –0.25496 –3.3*** 

PD90_ASSET –1.04164 –2.60*** –0.72987 –0.54 –0.68278 -0.97 

NA_ASSET 1.01713 6.40*** 0.75989 2.05** 0.38168 0.98 

ROA 0.00327 0.01 –4.62155 –9.29*** 0.9876 2.91*** 

LIQ_ASSET –0.07832 –2.07** –0.04319 –2.19** 0.01167 0.84 

LLR_ASSET 0.1319 0.43 0.27426 0.48 0.46569 1.02 

LPROV_ASSET 1.23628 2.98*** –6.24534 5.88*** 4.67117 4.57*** 

CHARG_ASSET 1.05346 2.58*** 1.24567 1.44 –2.34185 –2.05** 

TD100_ASSET –0.00929 –0.36 0.13442 2.09** 0.03349 1.33 

F-Statistic 47.54*** 16.88*** 6.67*** 

Adj-R2 0.3058 0.1924 0.0505 

N 1162 733 1173 

60 
Table 16 

Regression of Market Variables on Lagged BOPEC Changes and 

Financial Statement Ratios: 

Average Quarterly Stock Volume Turnover, (TURNOVER) 

This table presents regression results for a sample of 3,068 new bank holding company 

inspections with the dependent variable being the average quarterly stock volume turnover, 

TURNOVER. The ordering of BPchgL1 (the change in the BOPEC) is 1 for a downgrade, 

2 for no change in ratings, and 3 for BOPEC upgrades. A negative sign for the BPchgL1 

coefficient means that a downgrade will increase the dependent variable compared to no 

change. All variables are defined in Table 5 and explanatory variables are lagged one 

quarter from the event quarter (where the BOPEC ratings change occurred). A single, 

double, or triple "*" indicates significance at the 10%, 5%, or 1% level, respectively. 

Variable 

Periods 

1988–1992 1993 – 1995 1996–2000 

Estimate t-statistic Estimate t-statistic Estimate t-statistic 

Intercept 

BPchgL1 

LN_ASSET 

EQ_ASSET 

PD90_ASSET 

NA_ASSET 

ROA 

LIQ_ASSET 

LLR_ASSET 

LPROV_ASSET 

CHARG_ASSET 

TD100_ASSET 

F-Statistic 

Adj-R2 

N 

–0.40781 -8.95*** 

0.01036 1.42 

0.03225 12.51*** 

–0.00207 –0.89 

0.01663 1.69* 

0.006 1.53 

–0.00724 –1.34 

0.00185 1.99** 

0.01726 2.29** 

0.02695 2.64*** 

–0.0026 –0.26 

–0.00116 –1.84* 

40.33*** 

0.2713 

1162 

–0.17473 –3.33 

0.01437 1.76 

0.02231 9.1 

–0.01089 –3.96 

–0.02803 –1.44 

0.01421 2.67 

–0.00596 –0.83 

0.000384 1.35 

0.01826 2.23 

–0.03386 –2.22 

0.03483 2.79 

–0.00213 –2.31 

23.31*** 

0.2508 

733 

-0.16179 –2.81*** 

0.00371 0.37 

0.01968 7.69*** 

–0.00699 –2.91*** 

-0.03053 –1.4 

0.03512 2.9*** 

–0.00312 –0.3 

0.000333 0.78 

–0.00118 –0.08 

–0.04233 –1.33 

0.04364 1.23 

0.00249 3.2*** 

11.63*** 

0.0907 

1173 

61 
Verbal Description (508) of Equation 1 p.24. 

The dependent variable of the regression equation, BOPEC Change for Bank Holding 

Company (BHC), i, during time, t, equals the summation of the following items: 

• Alpha-one; 

• Alpha-two; 

• Beta-one times Firm Size for BHC, i, during time, t minus one; 

• Beta-two through Beta-seven, each times a different Past Supervisory Rating variable 

for BHC, i, during time, t minus one – a summation of each of these terms; 

• Beta-eight through Beta-sixteen, each times a different Financial Accounting variable 

for BHC, i, during time, t minus one – a summation of each of these terms; 

• Beta-seventeen through Beta-twenty-one, each times a different Financial Market 

variable for BHC, i, during time, t minus one – a summation of each of these terms. 

• Error term for BHC, i, during time, t. 

62 
Verbal Description (508) of Equation 2 p.35. 

The dependent variable of the regression equation, the Market variable for Bank Holding 

Company (BHC), i, during time, t, equals the summation of the following items: 

• Alpha; 

• Beta-one times BOPEC Change for BHC, i, during time, t minus one; 

• Beta-two times Firm Size for BHC, i, during time, t minus one; 

• Beta-three through Beta-eleven, each times a different Financial Accounting variable 

for BHC, i, during time, t minus one – a summation of each of these terms; 

• Error term for BHC, i, during time, t. 

63 
Verbal Description (508) of Figure 1. 

“Figure 1 shows BOPEC ratings for Bank Holding Companies from 1988 to 2000. Three 

categories of bank supervisor assigned ratings are shown including Downgrades, Upgrades and 

No-change in ratings. The data show that the dominant rating event over this period was the “nochange” in rating category. This category, while relatively level for most of the 1990s, increased 

dramatically from 1997 to 2000 during the economic expansion because so many organizations 

had already achieved the highest ratings by that time. The data also show that BOPEC 

downgrades increased significantly during the banking crises years from 1988 to 1991, and then 

this rating category declined until 1997 when downgrades began to increase. Rating upgrades 

were relatively level from 1988 to 1991, the number of upgrades increased from 1991 to the 

1993, and they declined in every year over the 1993–2000 period.” 

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