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Date: May 17, 1997
Contacts: Molly Galvin, Media Relations Associate
Shannon Flannery, Media Relations Assistant
(202) 334-2138; Internet <email@example.com>
Overall U.S. Economy Gains From Immigration,
But It's Costly to Some States and Localities
WASHINGTON -- Immigration benefits the U.S. economy overall and has little negative effect on the income and job opportunities of most native-born Americans, says a new report* by a panel of the National Research Council. Only in areas with high concentrations of low-skilled, low-paid immigrants are state and local taxpayers paying more on average to support the publicly funded services that these immigrants use.
"Immigrants may be adding as much as $10 billion to the economy each year," said panel chair James P. Smith, senior economist at RAND Corp., Santa Monica, Calif. "It's true that some Americans are now paying more taxes because of immigration, and native-born Americans without high school educations have seen their wages fall slightly because of the competition sparked by lower-skilled, newly arrived immigrants. But the vast majority of Americans are enjoying a healthier economy as the result of the increased supply of labor and lower prices that result from immigration."
The U.S. Commission on Immigration Reform, a congressionally appointed body, asked the Research Council to examine the effects of immigration on the national economy, on government revenues and spending, and on the future size and makeup of the nation's population. It was not asked to make policy recommendations.
Each year about 800,000 people immigrate legally to the United States. In addition, between 200,000 and 300,000 new illegal immigrants stay in the country each year.
In 1990, 43 percent of immigrants came from Latin America and the Caribbean, 26 percent were from Canada and Europe, 25 percent were from Asia, and 6 percent were from other countries.
Immigrant labor allows many goods and services to be produced more cheaply, and provides the work force for some businesses that otherwise could not exist. For example, immigration has helped build and maintain America's textile and agricultural industries. Other businesses that employ many immigrants -- such as restaurants and domestic household services -- would not exist on the same scale without immigrant workers, the panel said.
Most immigrants work in specialized sectors of the economy such as the manufacturing and service industries, and compete primarily for jobs with each other and with Americans who don't hold high school diplomas. Through this competition, in fact, the wages of these native-born Americans may have fallen some 5 percent over the past 15 years. Yet even in local labor markets with high numbers of new immigrants, overall job opportunities and wages for the native-born are not significantly affected by immigration.
The effects may be minor because natives who compete directly with immigrants may be moving to other areas, and because immigration brings overall economic benefits to most Americans.
Costs to Taxpayers
The majority of America's immigrants live in six states: California, New York, New Jersey, Texas, Florida, and Illinois. Using data from California and New Jersey, the panel estimated how much immigrant households -- those headed by foreign-born people -- cost state and local taxpayers. This was calculated by subtracting the costs of services those households use -- such as public education, police and fire, welfare, and public health -- from the amount of taxes they pay on an annual basis. These estimates were made before new laws eliminated welfare benefits for legal immigrants who are not U.S. citizens.
In California, where many new immigrants live, each native household is paying about $1,178 a year in state and local taxes to cover services used by immigrant households, the panel said. In New Jersey, which has a more established immigrant population, the calculation is about $232 a year. However, annual estimates of immigrants' impact on state and local taxpayers may be inflated and should not be used to predict the long-term costs of admitting new immigrants, the panel said.
These calculations do not indicate how much immigrants and their children will pay in taxes or how they will use public services over their lifetimes.
On an annual basis, new immigrant families receive more in publicly funded services than they pay in taxes, the panel said. Most -- especially those from Latin America -- tend to have more school-aged children and require more educational services than other households. Although immigrants use about the same level of government services as native-born residents, most immigrants pay less taxes because they own less property and have lower-paying jobs.
The panel's long-term estimates indicate that on a national level,
the majority of
new immigrants and their descendants will add more to government coffers than they receive over their lifetimes. The positive fiscal effects of immigration at the federal level are shared equally by all residents across the nation. However, residents of a few states such as California -- with high numbers of new immigrants -- will bear long-term costs that are concentrated at the state and local level of government.
Immigrants and their children will bring long-term benefits for most U.S. taxpayers because -- like most Americans -- immigrants use more publicly funded services in childhood and old age, but they make positive contributions as working adults. In addition, the majority of immigrants pay taxes and add revenue for some services -- such as national defense and interest on the federal debt -- for which they do not impose costs.
The long-term fiscal contributions that immigrants make, however, will vary depending on such factors as education and age of arrival to the United States. Immigrants with higher levels of education will pay more taxes in the long term because they have higher incomes. But immigrants who don't have high school educations and those who are
age 50 or older on arrival
may receive more benefits than they pay in taxes.
Immigrant Jobs and Wages
The wage gap between new immigrants and native workers has grown rapidly in recent decades, the panel said. In 1990, for example, recently arrived male immigrants were paid 32 percent less than native workers; in 1970, new immigrants' wages were 17 percent less. New female immigrants make 22 percent less than native-born women, a gap that has grown by 10 percent since 1970. This wage gap is growing mainly because more recent immigrants -- many of whom are from poor countries in Latin America -- have much lower education and skill levels than most Americans.
New immigrants are more than twice as likely as Americans not to have a high school degree, the panel said. More than one in three new immigrants have not completed high school. As a result, a disproportionate number of immigrants hold the lowest paying jobs in restaurants for instance, or in domestic positions. In 1990, almost half of all new immigrants earned among the lowest wages in the United States.
Historically, the wages of immigrants who entered the country when they were 25 or younger eventually equaled those of native workers after immigrants had been in the work force for about 20 years. However, because new immigrants are coming to the United States with substantially lower education and skill levels and are starting with lower wages, it be may more difficult for them to close the wage gap. In particular, most Mexican male immigrants, who make among the lowest initial wages, have not seen any increase in wages relative to those of native workers even after 20 years in the U.S. work force.
Population Effects
If immigration continues at its present level, the U.S. population will grow to 387 million people by 2050 -- 124
million more than today, the panel said. Immigration would account for about two-thirds of this growth. Under current immigration policy, 26 percent of Americans will be of Hispanic ancestry, growing from 27 million to 85 million by the year 2050. About 8 percent will be of Asian heritage, increasing from 9 million to 34 million. In addition, the boundaries between distinct ethnic groups will become increasingly blurred through intermarriage.
Regardless of immigration policy, the number of older people in the U.S. population will continue to grow. The population of the United States age 65 or older is expected to double by the year 2050. Immigration will also increase the number of children in the population, the panel said. Under current levels of immigration, the number of children in kindergarten through eighth grade will increase by 17 million -- from 36.8 million to 53.7 million -- by the year 2050. If the number of immigrants entering the United States were increased by half, the school-age population would grow by 7 percent, the U.S. population over age 65 would increase by about 5 percent, and the number of people older than age 85 would be unchanged.
The study was funded by the U.S. Commission on Immigration Reform. The National Research Council is the principal operating arm of the National Academy of Sciences and the National Academy of Engineering. It is a private, non-profit institution that provides independent advice on science and technology issues under a congressional charter. A committee roster follows.
*Pre-publication copies of
The New Americans: Economic, Demographic, and Fiscal Effects of Immigration
are available from the National Academy Press at the mailing address in the letterhead; tel. (202) 334-3313 or 1-800-624-6242. The cost of the report is $55.00 (prepaid) plus shipping charges of $4.00 for the first copy and $.50 for each additional copy. Reporters may obtain pre-publication copies from the Office of News and Public Information at the letterhead address (contacts listed above).
Commission on Behavioral and Social Sciences and Education
Committee on Population
Panel on the Demographic and Economic Impacts of Immigration
James P. Smith
Santa Monica, Calif.
Alan J. Auerbach
Department of Economics
University of California
George J. Borjas
John F. Kennedy School of Government
Harvard University
Cambridge, Mass.
Thomas J. Espenshade
Office of Population Research
Princeton University
Princeton, N.J.
Richard B. Freeman
Department of Economics
Harvard University, and
Labor Studies
National Bureau of Economic Research
Cambridge, Mass.
John F. Geweke
Department of Economics
University of Minnesota
Charles Hirschman
Department of Sociology
University of Washington
Robert P. Inman
Department of Finance
Wharton School of Business
University of Pennsylvania
Guillermina Jasso
Department of Sociology
New York University
New York City
Ronald D. Lee
Department of Demography and Economics
University of California
Mary C. Waters
Department of Sociology
Harvard University
Cambridge, Mass.
Finis R. Welch
Department of Economics
Texas A&M University
Barry Edmonston
Study Director
Member, National Academy of Sciences


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The African-American Experience in the Civil War Era
Morning Session
"The African-American Experience in the Civil War Era—A symposium at Lipscomb University"
9:30–11:30 a.m. • Shamblin Theatre
"Blueprint from Freedom: Black Men in Blue"
James McPherson, Pulitzer Prize-winning author and retired Princeton professor
"Forged in Battle: The Relationship Between White Officers and Black Soldiers"
Joseph Glatthaar, Stephenson Distinguished Professor of History, University of North Carolina
"The Washingtons of Wessyngton Plantation: Stories of My Family's Journey to Freedom"
John Baker, author and genealogy expert
After the morning session, each presenter will be available to sign copies of their books, which will be available for sale.
All events and times are subject to change.
Click below to view more information about the Battle of Nashville events:


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Home > Industry Analysis > Research & Analysis > FDIC Working Papers Series
FDIC Working Papers Series
Regulator Use of Market Data to Improve the Identification of Bank Financial Distress
December 2001
Timothy J. Curry
Peter J. Elmer/Gary S. Fissel
Financial Economist
Federal Deposit Insurance Corporation
Washington, D.C. 20429
(202) 898-3949
The views stated here are those of the authors and do not necessarily reflect those of the Federal Deposit Insurance Corporation. The authors would like to thank Audrey Clement and Justin Combs for extensive research assistance. Sarah Belanger assisted with the tables. Peter J. Elmer was a senior financial economist at the Federal Deposit Insurance Corporation at the time that the authors conducted this research.
This paper assesses the extent to which stock market information may help bank regulators identify bank financial distress. The research specifies a variety of stock return and other market-related variables that might contain elements of longer-term trends and be capable of anticipating changes in regulatory ratings of commercial banks and thrift institutions. Univariate tests confirm a remarkable tendency for market-related variables to decline, or otherwise move, far in advance of formal regulatory rating downgrades, a finding suggesting that these variables may have useful predictive content. Furthermore, multivariate tests support the notion that market-related variables add predictive value to the value contained in publicly available Call Report financial data. The evidence supports the use of market-related variables in off-site monitoring applications.
JEL Classification: G1, G23
I. Introduction
During the past two decades, research on the relation between market signals and bank financial health has repeatedly implied that bank supervision might benefit from using information embedded in stock market prices, returns, or other market-related data. This implication was first voiced by Pettway (1980) after he found that stock returns of 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. More recently, Flannery (1998) reiterates the theme, pointing out that regulators might use market data either to reduce the time required to recognize problems or to increase the accuracy of forecasts of future changes in bank condition.
In theory, it is not clear whether stock price, return, or other market-related data should lead, move contemporaneously with, or follow regulatory awareness of problems. The theory of efficient markets suggests that the market evaluates and prices public information at the fastest rate possible. Because the primary source of bank financial information, the quarterly Report of Condition and Income (Call Report), is available to regulators and the market at about the same time, market efficiency suggests that market awareness of Call Report-related problems could precede regulator awareness.1 However, managers can often hide "bad" news from public scrutiny, whereas regulators have authority to access nonpublic information through confidential on-site examinations.2 Regulator access to confidential information could easily offset any processing speed advantage held by the market and enable regulators to recognize problems either contemporaneously with or ahead of the market.
Empirical evidence does not consistently either support or reject the hypothesis that information embedded in market prices has useful predictive value to regulators. Pettway (1980) examines a small sample of large-bank failures and finds market signals that preceded regulatory awareness by long lead times, but Simons and Cross (1991) analyze a more recent sample to find that regulators appear to have been aware of problems before the market recognized them. Berger and Davies (1998) find that the market anticipates upgrades in regulatory ratings but follows downgrades. Flannery and Houston (1999) find that the market placed a high value on regulatory certification of bank accounting data in 1988, but a much lower value in 1990. Berger, Davies, and Flannery (2000) find that regulators acquire information sooner than bond rating agencies do, but the regulatory assessments are less accurate in predicting future changes in the performance of bank holding companies than are either stock or bond market indicators.
This study examines the extent to which market data can be used by regulators in evaluating bank financial condition. Market-related information offers the potential for assisting regulators at two points in their assessment, or rating, of bank capital, assets, management, earnings, and liquidity ("CAMEL" ratings).3 First, during the period preceding a CAMEL rating downgrade, market-related information might be used to anticipate the need for the downgrade. Second, during the period after a CAMEL rating downgrade, market information might help distinguish institutions that will subsequently recover (rating upgrades) from institutions that subsequently encounter more serious problems (rating downgrades or failure).
Since our interest is to identify variables that are useful for early-warning purposes, we focus on longer-term trends that are observable in practice and that precede rating changes with sufficient advance warning to provide regulators with a timely tool for policy change. Although market variables may meet these criteria on a univariate basis, their usefulness is enhanced by an ability to add marginal predictive value to other information used by regulators to monitor financial healthinformation such as Call Report financial data.
Some previous studies have incorporated market data into traditional default or bankruptcy models to assess the riskiness of institutions. For example, Altman (1968), KMV Corporation (1995), and Shumway (2001), have adopted this approach each using a different statistical methodology to analyze the issue. In contrast to these earlier studies, this paper utilizes logistic regression analysis and incorporates significantly more and a greater variety of market variables into the model to identify financially distressed institutions.
Section II of this paper describes the institutional setting in which regulators assess bank financial health, and Section III discusses conceptual issues relating to regulatory use of market data. Section IV describes a sample of publicly traded banks and thrifts that received CAMEL rating downgrades, and this sample is used in Sections V and VI to examine the performance of market-related variables around the time of the CAMEL rating changes. Section VII specifies a logistic regression model to test the marginal predictive content of market-related variables compared with accounting data from bank financial reports in anticipating changes in CAMEL ratings. Section VIII concludes.
II. The Institutional Setting
Modern bank supervision uses information from on- and off-site supervisory tools as the starting point for its analysis. The largest banks and bank holding companies are monitored by on- and off-site analysts (examiners) who keep abreast of any information that can be found, including news reports, Wall Street analysis, and traditional Call Report financial data. Most smaller and mid-sized banks are monitored initially with automated analysis of Call Report information, and, if risk is identified, they are then reviewed by analysts.4
Periodic on-site safety-and-soundness examinations begin with off-site pre-exam reviews of Call Report and other pertinent data. On-site reviews then check the reported information and explore issues that might not be revealed in the quarterly reports.5 On-site examinations provide extensive financial information that is not generally available to the public, such as the payment histories of performing and nonperforming loans, loan classifications and the adequacy of loan-loss provisions, and bank capital.6
Bank examiners assign overall, or composite, CAMEL ratings of 1 or 2 to institutions in sound financial condition. Downgrading a banks rating to 3 sends an important signal of financial weakness and is normally accompanied by an agreement between the banks primary regulator and senior bank management specifying the nature of the banks weakness and procedures for changing bank policies to rectify the perceived problems. These agreements are classified by regulators as "informal" enforcement actions because they are not administratively or judicially enforceable in a court of law in the event of noncompliance.7 Nevertheless, the agreements represent a loud "shot across the bow" signaling significant regulatory concern and the need for change. Informal enforcement actions are kept confidential by regulators out of concern that public exposure of a banks problems may result in a decline in the deposit base or otherwise increase the difficulty or cost of recovery. As might be expected, the financial health of banks receiving a CAMEL rating of 3 varies, although regulators make no effort to distinguish composite quality ratings beyond the integers 15.8 Institutions downgraded to 3 may remain in that state for periods ranging from several months to several years before passing to a higher or lower grade.
Downgrading a banks CAMEL rating to 4 or lower indicates the existence of serious problems that could lead to insolvency. In practice, the term "problem" bank is often reserved for institutions with composite ratings of 4 or lower, and regulatory "problem-bank lists" tend to specify institutions with these ratings, although practices vary. Banks downgraded to 4 typically require immediate remedial actions and intensive monitoring by regulatory officials. In some cases, bank supervisory officials may not assign the more serious "formal" enforcement actions to 4-rated banks as long as bank management addresses regulatory concerns. If feasible, regulators prefer to work with informal enforcement actions because they are confidential and less confrontational than the more serious "formal" enforcement actions. However, most banks downgraded to ratings 4 and 5 receive formal enforcement actions, and these actions were made public beginning in 1989.9 Institutions with CAMEL ratings of 4 can continue in business for as long as several years before either returning to a higher grade, moving to a lower grade, or being declared insolvent by their primary regulator. A rating of 5 indicates an extremely high probability of failure, usually within the next 12 months.
III. Prudential Bank Supervision and the Interpretation of Market Data
Prudential bank supervision recognizes that bank examinations impose costs on banks and that higher costs should be justified by higher benefits, such as a comparable reduction in the expected cost of failure. Regulators have a responsibility to balance the need to limit the expected cost of failure against the need to minimize oversight costs, especially for well-run institutions.
With an eye toward balancing the costs against the benefits of bank supervision, we specify four criteria as a starting point for assessing whether information contained in market data is useful to regulators. First, the information should be "separable" at the bank level. This point simply recognizes that a change in a banks rating must be justified by the circumstances of that bank. Informational messages that are applicable to groups of banks, such as a change in the risk of all banks in a geographic region, are difficult (costly) to review or apply at the bank level because of their broad application. Second, the message must be "clear" in the sense that there exists a low likelihood of multiple interpretations. The hope is that the message specifies the nature of the markets concern to the point that a meaningful regulatory response can be formulated. Unclear messages may motivate inappropriate regulatory response, raising the supervisory cost borne by the regulated institutions. Third, the informational message must precede problems with sufficient advance warning to provide regulators with a timely tool for policy change. Finally, the market signals should, on the margin, contain information not available in other sources of information commonly used by off-site monitoring, such as Call Report financial data. Market information that cannot add incremental benefit to the content of commonly used financial data may be viewed as redundant and could fail to justify an expenditure of regulatory resources.
For the purposes of this study, the first two hurdles can be overcome by a focus on elements of longer-term trend in stock prices, returns, and other market-related variables. Since stock prices and returns of firms in the same industry are correlated, short-term informational messages for specific institutions are often reflected in the prices and returns of many other firms in the same industry.10 This characteristic presents a relatively high cost for distinguishing and interpreting shorter-term signals of specific firms compared with those of other firms in the industry. Over longer periods, however, conflicting signals tend to offset, and it becomes easier to assign informational signals to specific institutions. Focusing on longer-term trends thereby allows regulators to limit the use of regulatory resources by targeting only the most appropriate firms for regulatory review.
The third hurdle requires that market signals have predictive content and sufficient advance notice. The vast resources of the market, which contains tens of thousands of traders, analysts, and similar participants, greatly exceed the resources of all banking industry regulators. Apart from vast resources, the market may also access certain types of nonpublic information that is not available to regulators, such as large stock sales by insiders and private contacts between individuals who may be old friends or former business associates. These points combine to suggest that market awareness of problems might precede regulatory awareness, possibly by a large margin, despite the ability of regulators to access nonpublic information through examinations.
The final hurdleidentifying the incremental value added by market signalsis the most difficult to deal with because it involves distinguishing the predictive content of market variables from the content of other common sources of information. But market variables should not be expected to be independent of other variables or effects. Rather, they need only provide sufficient additional value so that their contribution can be clearly distinguished from the contributions of the other variables. For example, if the market has a unique ability to interpret accounting data contained in quarterly reports, then in regressions that explain changes in financial position we expect market variables to provide significant additional explanatory power to financial ratios derived from the quarterly reports.
An intriguing 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, Mertons (1973) option model anticipates a rise in return volatility as an institution approaches insolvency.11 Wang (1994) ties trading volume to the flow of information about a firms financial health, suggesting that trading volume should rise as information about financial distress is released.12 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, may contain predictive content.
In summary, it appears conceptually feasible for a number of market-related variables to provide regulators with the ability to speed their identification of risk without imposing burdensome costs on regulated institutions. Debate about regulatory use of market-related information in prudential bank supervision should therefore focus on empirical questions, not conceptual ones. In particular, do market-related variables add marginal predictive value to Call Report or other information easily available to regulators in off-site monitoring systems? In the absence of marginal predictive value, market signals may be viewed as redundant information with little supervisory value.
IV. The Sample
The empirical analysis begins with a sample of publicly traded banks and thrifts whose ratings were downgraded to problematic levels over the 19881996 period. Since a CAMEL rating of 3 signifies significant regulatory concern, but ratings of 4 and 5 signify more-severe financial distress that is often followed by failure, we separate institutions downgraded to 3 from those downgraded to 4 or 5. Recognizing that the financial industry had many problems in the early and middle 1980s, we limited the sample to institutions that were downgraded to 3 beginning in 1988 or were downgraded to either 4 or 5 beginning in 1989.13 Combining the 4s and 5s into a single group appeared reasonable, given that institutions may pass to failure from these two ratings but almost never from a rating of 3. As noted earlier, informal enforcement actions, which are not made public, are associated with a rating downgrade to 3, whereas formal enforcement actions, required by FIRREA to be made public, are associated with the assignment of 4 and 5 ratings.
To improve the integrity of the analysis, we also imposed several additional restrictions. Since our focus is on longer-term stock market behavior around the time of rating downgrades, the sample was limited to institutions that had a lengthy period of superior ratings before their downgrade. This condition is implemented by the requirement that institutions have CAMEL ratings in the 12 range at least three years before downgrade to a 3. Similarly, institutions downgraded to a 4 or 5 were required to have ratings in the 13 range for at least three years preceding downgrade to 4 or 5. The sample is also limited to banks and thrifts that either were not affiliated with bank holding companies or were members of holding companies that held only a single banking-related institution. Restricting the sample in this fashion ensures that the extensive financial data reported on bank Call Reports corresponds closely to the institution that issues the stock.14 This restriction also reduces contamination from activities of nonbank subsidiaries of bank holding companies.15 Since the empirical analysis combines Call Report financial data with stock market information reported by the Center for Research in Securities Prices (CRSP), both sources of data were required for a bank to be included in the sample.
Table 1 provides summary statistics for the two groups of downgraded institutions. The sample size is relatively large for both groups, with 122 institutions downgraded to 3 and 148 downgraded to 4 or 5. The sample varies slightly from quarter to quarter for several reasons, including the lack of available data on individual firms and the de-listing rules of the various exchanges (rules such as minimum capital requirements or minimum trading activity). The number of institutions in the 4 or 5 group is larger than the number in the 3 group primarily because the three-year constraint on previous ratings limits the group of 3s more than the group of 4s and 5s.
Considerable diversity is apparent in the sample. For example, both groups of downgraded institutions had a wide range of asset sizes, including institutions with total assets under $100 million as well as institutions with assets over $5 billion. More than 75 percent of the institutions had assets under $1 billion, while almost 20 percent had assets in the $15 billion range and about 5 percent had assets in the over $5 billion range. The relatively healthier condition of institutions downgraded to 3 is reflected in the higher book equity-to-assets and return-on-assets ratios compared with the ratios reported for institutions downgraded to 4 or 5. The stronger financial health appears to be recognized by the market, as the market price summary statistics are also higher for institutions downgraded to 3 compared with those downgraded to 4 or 5. The diversity in asset size is accompanied by diversity in market capitalization (average price times number of shares at the end of the quarter), which declines with market price. The sample breakout by charter type shows that banks (65 percent) have a higher representation than thrifts (35 percent).
V. Univariate Trends before Rating Downgrades
The first point at which market information has the potential to help bank regulators is the period preceding a CAMEL rating downgrade. As explained above, downgrades to ratings of 3 are distinguished from downgrades to ratings of 4 or 5 by the distinct financial and regulatory problems of banks in the latter category. Our focus on longer-term trends is implemented by an analysis of quarterly data for several years preceding the downgrade.
Table 2 displays the univariate characteristics of stock prices, returns, and other market-related variables for banks and thrifts eight quarters (two years) preceding their downgrades to CAMEL rating 3, 4, or 5. The zero quarter contains the start date of the examination that results in the rating downgrade.16 As noted above, the sample of institutions in the 3- and 4- or 5-rated categories varies slightly from quarter to quarter because of the de-listing rules of the various exchanges (rules such as minimum capital requirements or minimum trading activity).
The stock price data show prices falling consistently throughout the two years before the downgrades, with the result that the change in stock price is negative in nearly every quarter during that period. For the 3-rated group, the average stock price starts at around $15 per share in the eighth quarter before the downgrade, then falls to $10 per share in the zero quarter, whereas for the 4- or 5-rated group, the average price declines from about $11 to $6 per share for the same period. The lower prices for the 4- or 5-rated institutions suggests that the market is able to distinguish the more serious financial problems of institutions approaching a 4 or 5 rating compared with those approaching a rating of 3.
In an effort to test the consistency of changes in stock prices across the sample, we used a t-test to test the hypothesis that the mean of each quarterly sample equals zero. For the 3-rated group, this test shows that the change in stock price becomes statistically significant in the sixth quarter preceding the downgrade. For the 4- or 5-rated group, the change is significant for all eight quarters before the downgrade, reflecting the more distressed nature of this group. The consistency of the t-test results, across many quarters before the rating downgrades, confirms that market prices have considerable univariate predictive content long before regulators formally alter an institutions rating.
Two measures of trading activity are included in Table 2 to allow an examination of the hypothesis that higher trading activity should accompany market assessment of new information. But although the data clearly reflect a long-term trend of declining prices before downgrade, neither of the trading activity variables reflects the corresponding rise in trading activity hypothesized by financial theory. The most direct measure of trading activityaverage daily trading volumedeclines slightly for the 3-rated group and follows no consistent trend for the 4- or 5-rated group throughout the eight quarters before the downgrades. A second measure of trading activity, known as "turnover," divides the shares traded in any quarter by total shares outstanding. Like the trading volume variable, the turnover variable also shows no discernible trend for either of the groups. Therefore, the trading activity variables contain no easily observed univariate predictive content before CAMEL rating downgrades.
The remainder of Table 2 displays various measures of quarterly returns commonly found in previous empirical studies. The first return-related variable is the simple cumulative return, calculated by multiplying unity plus the daily return for each stock i on day t (1+rit) across all trading days in each quarter, then subtracting unity. Consistent with the long-term decline in prices, cumulative returns are consistently negative preceding downgrade for both the 3-rated group and the 4- or 5-rated group. However, our t-tests also find that the cumulative returns are not significantly distinguishable from zero for institutions downgraded to 3, although they are significant for institutions downgraded to 4 or 5.
The cumulative return data are followed by a measure of return volatility, the standard deviation of daily returns. In contrast to the trading activity variables, the trend of volatility appears consistent with financial theory, which anticipates rising volatility as an institution encounters distress. For example, the volatility of the variable rises steadily for both groups as the downgrades approach, especially during the four quarters immediately preceding the downgrades. The level of volatility is noticeably higher for the most severely distressed institutions (downgraded to 4 or 5) compared with the moderately distressed institutions (downgraded to 3). The statistical content of the rising trend is confirmed by significant t-statistics beginning about a year before the rating downgrades.
The remaining columns examine market excess returns by calculating the differences between the cumulative quarterly return of each stock and the cumulative quarterly returns for three indexes of market performance. The first two indexes are the equal- and value-weighted indexes reported on the CRSP tapes. The third index is a value-weighted index constructed from CRSP data for the bank and thrift industries.17 Means tests are applied to all quarterly samples of excess returns to determine whether the mean excess return is statistically distinguishable from zero.
The market excess return variables consistently show patterns of negative returns that are statistically significant in most of the eight quarters before rating downgrades, although the significance varies by index. The results for the CRSP equal-weighted return index reflect the most consistent trend, as they are virtually always negative and significant at the 1 percent level. Excess returns calculated from the CRSP value-weighted index perform very similar to excess returns calculated from the CRSP equal-weighted index, with only slightly lower levels of significance for a few. The excess returns calculated from the industry value-weighted index did not precede or anticipate rating downgrades as consistently as the other excess return variables did.
From a broad perspective, the excess return data reported in Table 2 serves to reaffirm Pettways (1980) finding of negative excess returns for lengthy periods preceding financial distress, although our analysis is based on much more extensive data and statistical tests of the significance of each quarterly excess return.18 From the narrower interests of this paper, the excess return variables exhibit consistent negative patterns, effectively anticipating CAMEL rating downgrades long before they actually occur.
Table 3 performs a sensitivity analysis of the results in Table 2 by examining monthly trends of the most consistent quarterly variables. The trends are examined during the 17 months preceding the rating downgrade, in other words, for a period of almost five quarters. The monthly trends are not as consistent as the quarterly trends, although some data have reliable predictive content. The change in the stock price variable is always negative, but the change is not statistically significant for a number of months for the 3-rated group, although it is almost always significant for the more distressed firms in the 4- and 5-rated group. The performance of the volatility variable, the change in the standard deviation of daily returns does not have the anticipated negative sign in all quarters and is almost never significant at the 1 percent or 5 percent level. The CRSP equal-weighted excess return performs the best of all variables examined, showing negative returns that are significant at the 1 percent or 5 percent level in most months for both groups of firms. Consistent with our earlier findings, the remaining estimates of market excess returns, calculated with either the CRSP value-weighted index or the industry value-weighted index, are always negative, but not as consistently significant as excess returns based on the equal-weighted index. These results suggest that the equal-weighted market excess return retains, to a large degree, its predictive content over monthly as well as quarterly periods.
Table 4 extends the sensitivity analysis by examining the best-performing series from Table 3 over weekly periods. We limit the reported data to weeks 36 through 10 (months 9 through 2) preceding CAMEL rating downgrades. The weekly results are noticeably less conclusive than the monthly data. Somewhat surprisingly, the best-performing variable is the stock price, which is almost always negative and statistically significant at the 1 percent or 5 percent level in more than one-half of the reported observations, especially for the most distressed 4- and 5-rated group. The two measures of market excess returns are not consistently negative, and they are only occasionally significant at the 1 percent or 5 percent level. Thus, the predictive content of individual market variables is very limited in weekly applications. This result cautions against the use of short-term return trends for identifying longer-term market movements preceding rating downgrades.
Univariate Trends after Rating Downgrades
In addition to anticipating a rating downgrade to the 3 or 4/5 level, regulators also have a special interest in the period immediately after the downgrade. In this regard, the interests of this paper prompt us to ask, "Can the market distinguish future winners (subsequent upgrades) from losers (subsequent downgrades) in the immediate aftermath of the rating downgrades?" Distressed institutions require close regulatory supervision. Most either recover (are subsequently upgraded) or encounter more serious problems (either are subsequently downgraded or fail) in reasonable periods of time after the rating changes, although some institutions languish in these rating categories for several years after their initial rating change. If market variables can anticipate the path taken, this information might be used to allocate supervisory resources, or otherwise improve regulatory oversight of problematic institutions. Although the ability of the market to anticipate the subsequent performance of a downgraded institution is interesting by itself, the supervisory value of the anticipation is enhanced if it is observed relatively soon after the downgradesuch as during the first quarter after the downgrade.
Tables 5 and 6 present univariate results similar to the results in Table 2. The previous groups of 3- and 4/5-rated firms are broken into two subsamples, depending on whether the downgraded institutions subsequently recover (Table 5) or whether they slide into deeper financial distress (Table 6). Since a subsequent rating change can occur at any time after the initial downgrade, Tables 5 and 6 show declining samples during the eight quarter period after the downgrades.19 For example, the sample of 3-rated banks that recover declines from 54 observations during the zero quarter to 48 in the eighth quarter after the downgrades, while the 4/5-rated group goes from 94 to 87. A similar pattern is observed for firms that did not recover after their downgrade (Table 6).
Table 5 suggests that immediately after the initial rating change, the market does not anticipate the recovery of institutions that have experienced CAMEL rating downgrades. The exact nature of the reaction is different, however, depending on the financial condition of the group. For example, for the 3-rated group after the downgrade, average stock prices follow a downward trend for only the first quarter, going from about $11 per share in quarter zero to $10 per share in quarter one before rebounding slightly in quarter two. In other words, for this group, the market soon determines that recovery is under way. For the 4/5-rated group, stock prices follow a downward trend for about two quarters on average after the downgrade, with prices declining from almost $7 per share in the zero quarter to about $6 per share in quarter two. Thus, the upward trend in prices associated with market anticipation of recovery for the 4/5-rated group does not occur for about six months. The downward trend in prices after the downgrades causes all estimates of market excess returns to be negative after the initial downgrade, so these variables retain no more predictive content than the stock prices. In addition, contrary to the results in Table 2, the volatility variables fail to develop the downward trend anticipated by financial theory. Consistent with the results in Table 2, no clear trend appears in the trading activity variables (trading volume and turnover). Thus, market-related variables provide little univariate predictive content for institutions that recover from a rating downgrade.
In contrast to the results in Table 5, Table 6 finds the market capable of anticipating the performance of institutions whose distress deepens after a CAMEL rating downgrade to the 3 or 4/5 level. In Table 6, stock prices for the 3-rated group follow a steady downward trend after the downgrade. A similar pattern is observed for the 4/5-rated group, with prices declining continuously for all eight quarters after the downgrade. For both groups, the downward average price trend during the quarters after downgrade causes negative cumulative and market excess returns, and the t-statistics associated with the negative returns tend to be statistically significant. The volatility variables also recover much of their earlier explanatory power, as volatility rises steadily in the period after downgrade. Consistent with our earlier results, no clear trend appears in the trading activity variables.
Comparative interpretation of results in Tables 5 and 6 is difficult to do. Table 6 in isolation suggests that market-related variables have univariate predictive content. However, similarities between the downward trends of prices and returns in Tables 5 and 6 make it more difficult to distinguish winners from losers after rating downgrades. For example, the market appears to identify future "losers" in the 4/5-rated institutions in Table 6 with declining average stock prices and returns in the first quarter after the downgrade. However, the identification of "winners" in Table 5 takes up to four quarters for the 3-rated group and up to six quarters for the 4/5-rated group. Thus, univariate market signals regarding an institutions prospects for recovery, subsequent to a rating downgrade, are difficult to interpret.
VII. The Predictive Power of Stock Returns Compared with Financial Ratios
Testing the marginal importance of stock price and return variables against Call Report financial data allows us to formally gauge the statistical strength of the two types of explanatory variables. Our approach proceeds by initially specifying a traditional CAMEL rating prediction model and then extending the model to include stock prices, returns, and other market related variables. Although the stock price and return variables need not dominate the traditional ratio-based model, a minimum level of competency is required to justify a conclusion that market-related variables are a meaningful addition to traditional analysis.
In this section, logistic regression equations are estimated to explain changes in financial institution supervisory (CAMEL) ratings with publicly available financial information.20 Table 7 defines the variables used in the regressions, along with related means and standard definitions. Two sets of regressions are estimated, one for predicting rating downgrades and the other for predicting either recovery or ultimate failure subsequent to a rating downgrade. A dichotomous variable is specified for both equations. The dependent binary variable in the first equation (CAMELCAT) tests our ability to explain CAMEL rating downgrades, whether to ratings of 3, 4, or 5. To this end, each downgraded institution is "matched" with a randomly chosen, publicly-traded bank or thrift that is healthy and is in the same asset class as the downgraded institution. If Call Report or CRSP return data are not available for an institution in any quarter, that institution is dropped from the sample during the quarter. The dependent variable in the first set of regressions takes a value of 1 if the institution is rated 1 or 2 and is downgraded to 3, and 0 if the institution is rated 1 or 2 and does not experience a downgrade. In the second set of regressions, the dichotomous dependent variable tests our ability to explain whether an institution either recovers or suffers further distress after a rating downgrade. In this case, the dependent variable takes a value of 0 if the institution recovers, and a value of 1 if its distress deepens after a downgrade.
The logit model is estimated by the use of data for market variables from quarter four preceding the downgrade, while Call Report data are taken from quarter five. The reason the market variables from a given quarter are matched with Call Report financial data from the preceding quarter is that the Call Reports are routinely released in the quarter following the quarter to which they apply.
The first independent variable is a control variable for charter type. This variable is important because in 1989 FIRREA provided funds to deal with a backlog of troubled thrifts whose financial condition had slipped below the condition of troubled banks during that era.
A dummy variable, INSBIF, distinguishes banks and thrifts by assigning a value of unity to institutions that come under the umbrella of the Bank Insurance Fund (BIF), and a value of zero to institutions that come under the Savings Association Insurance Fund (SAIF). As shown in Table 7, the mean of the INSBIF variable equals 0.69, signifying that commercial banks represent 69 percent of the 3-rated group, and therefore thrifts 31 percent of the same group. For the 4/5-rated group, banks represent 60 percent and thrifts 40 percent. For the overall sample, banks represent 64 percent and thrifts 36 percent. The coefficient for the "charter" dummy is expected to be positive in CAMEL rating models using financial ratios because, in the post-FIRREA period, banks tended to have a higher likelihood of downgrades than thrifts with similar asset sizes: many of the most-troubled thrifts had already been sold or liquidated by the Federal Savings and Loan Insurance Corporation or handed off to the Resolution Trust Corporation for resolution.
The traditional CAMEL prediction model is specified with variables from the Call Reports. The first variable is the equity-to-assets ratio (EQ_AS), which measures the ability of a firm to absorb loan losses before bankruptcy and is expected to be negatively related to future distress. The credit quality of the loan portfolio is captured in the NC_RES variable, which measures the amount of delinquent assets less loan-loss reserves relative to total assets.
A positive sign is expected for this variable, signifying that higher levels of delinquent loans are associated with a higher likelihood of rating downgrades and failure. The overall profitability of the institutions is measured by the return-on-assets variable (ROA), which is expected to be inversely related to future downgrades. Two measures of liquidity are posited: the securities-to-assets ratio (SC_AS) and the volatile liabilities-to-assets ratio (VL_AS). The SC_AS is expected to be negatively related to future distress, reflecting that higher levels of securities to assets provide sources of additional liquidity in troubled times. However, the volatile liabilities ratio VL_AS is expected to have a positive sign, reflecting that higher levels of volatile liabilities are normally associated with potential for problems of liquidity and funding during times of crisis.
The market variables are segmented into two groups, one specifying "core" financial variables that reflect market activity, the other accounting for market risk. In the first group, the first variable is market price, which is measured as the natural logarithm of the average quarterly price. This variable is expected to be inversely related to rating downgrades. Market excess returns is captured by EXRET, which measures the CRSP equal-weighted excess quarterly returns for each observation and is discussed extensively above in the univariate analysis connected with Table 2. Given the high degree of negative excess return persistence observed in Table 2, we expect EXRET to possess at least some failure predictive content and to be negatively related to the future downgrades. Firm dividend policy is captured by the dummy variable DIV, which is equal to unity if the bank paid a dividend in the last four quarters, or zero otherwise. The coefficient for DIV is expected to have a negative sign, reflecting financial weakness if the firm fails to pay a dividend.
In the second group of variables, those that account for market risk as suggested either by the market model of Fama and French (1993) or the option model of Merton (1974), the first variable, SDRET, is the standard deviation of annualized quarterly returns and is expected to be positively related to future downgrades. As investors become concerned over potential distress and potential bankruptcy, more variation may appear in return patterns. Another stock market model variable, TURN, which measures stock turnover in particular quarters, is also expected to be positive during periods of financial distress. The third market model variable, the book equity-to-market value ratio (BE/ME), is expected to have a positive coefficient as the ratio moves directly with changes in stock prices, holding book equity constant. This ratio, therefore, also serves as proxy for financial distress.
Equation (1) shows the basic logit estimation equation, which sequentially adds stock market data to Call Report financial data on the right-hand side of the regression:
Pre-Downgrade Results
The results for the pre-downgrade period are presented in Table 8. As noted, the regressions were run at four quarters before the event date (or, in this case, before the date the institutions experienced their downgrades to the 3, 4, or 5 levels). Panel A shows the results for firms that were downgraded to 3, and Panel B shows the results for firms that were downgraded to 4 or 5. Specification 1 shows the CAMEL prediction model primarily using Call Report data. The control variable for charter type, INSBIF, has a positive coefficient and is significant only for those banks that are in the worst financial conditionthe 4/5-rated group. This finding reveals that commercial banks generally tended to be more distressed than thrifts during the early 1990s and therefore exhibit a higher likelihood of being downgraded.
The first Call Report variable, the equity-to-asset ratio (EQ_AS), has a negative sign as expected for both groups, thereby confirming the importance of equity levels in models predicting distressed CAMEL ratings. The portfolio quality variable (NC_RES) has its anticipated positive coefficient and is significant for only the 3-rated group, confirming the relationship between downgraded supervisory ratings and credit quality.21 The return-on-asset variable (ROA) also exhibits a negative sign, as expected, and is highly significant. The two liquidity measures (SC_AS and VL_AS) also perform as expected: the former is negative and significant, confirming the fact that there is an inverse relationship between the level of security holdings and financial distress, and the latter shows a positive and significant relationship between volatile liabilities and future downgrades. For specification 1 for each group, all signs are correct and most coefficients are significant at the 1 percent level. These results suggest that this equation serves as a good starting point or benchmark for assessing the incremental value of information embedded in stock returns compared with the information contained in commonly used financial ratios.
The analysis proceeds by selectively adding market variables that offer several measures of returns or stock-return trends to the benchmark regressions containing only financial ratios. This approach facilitates the ability to examine the predictive content of each of the market variables, through their individual coefficients and t-statistics. It also tests the predictive content of market-based models compared with the content of traditional models, through the likelihood ratio test statistic and the Akaike Information Criterion (AIC).22 If the likelihood ratio test statistic is positive and significant, we may conclude that a market-based regression has significantly higher predictive content than the Call Report model reported in the first specification of the model. A similar conclusion can be reached if the AIC measure exhibits a lower value for the market-based specifications relative to the Call Report model.
The logistic regressions incorporating market variables are presented in specifications 2 and 3. Of the three core market variables added into specification 2the natural logarithm of the stock price (LN_PR), the equal-weighted market excess return variable (EXRET), and the dividend variable (DIV)all have the expected negative signs for the coefficients. The log of price variable is significant primarily for the 3-rated group. For the 4/5-rated group, both the dividend and excess return variables are significant, portraying a mixed picture for the core market variables.23
Specification 3 adds two additional market-risk variables: the standard deviation of the return variable (SDRET), which measures the variance of the returns, and the turnover ratio (TURN), which measures average trading volume. In this regression, these other market-related variables failed to show any significant increase in marginal predictive value for either of the groups. The SDRET variable was significant at the 5 percent level for the 3-rated group, and the TURN variable was significant at the 10 percent level for the 4/5-rated group. In the last specification (4), only the publicly available stock-market variables are included. Based upon the AIC, this equation does not improve upon specification 1, which incorporated only Call Report variables.
A likelihood ratio test is performed, measuring the equation specifications that contain stock-market variables (specifications 2 and 3) compared with the equation specification that does not contain stock-market information (specification 1). As shown in Table 8, equations 2 and 3 show greater explanatory power compared with specification 1, suggesting value added by the introduction of the market variables. Specification 4, with only the stock-market data, has significantly less explanatory power than specifications 1, 2, and 3. These results are shown by the AIC. The AIC value is lower for both regressions 2 and 3 relative to equation 1, suggesting that the stock-market variables are adding to the overall predictive content of the model. The AIC for the last equation is much higher than it is for the previous three regressions, suggesting that the model using only market-related variables performs at a lower level than the other three specifications.24
Table 9 contains in-sample and out-of-sample tests of the model for both the 3-rated and 4/5-rated groups.25 The critical probability that is used is 50 percent. This critical probability is used to determine how the model performs in identifying which banks or thrifts in the two groupings are properly classified as likely to experience future CAMEL rating downgrades. Within the in-sample classification group for the 3-rated institutions, the correct prediction of distressed or healthy banks and thrifts generally increases, moving from specifications 1 to 3; then the correct prediction level declines in specification 4. The group prediction columns generally reveal a similar pattern for the 4/5-rated group. Thus, for the in-sample forecasts, these findings show that adding stock variables increases the predictive accuracy of the model in identifying CAMEL rating downgrades in the first three specifications. This is reflected in the Type I and Type II errors, which are also displayed in Table 9.
A similar pattern is observed for the out-of-sample forecasts, although the classifications are not as accurate as for the in-sample forecasts. For example, for the specifications for the 3-rated group, the model correctly classified an average of 66 percent of the downgrades for the out-of-sample, compared with 76 percent for the in-sample. For the 4/5-rated group the classifications were relatively more accurate, identifying about 72 percent of the downgrades for the out-of-sample data relative to about 74 percent for the in-sample data. The overall accuracy of the model appears to improve as the conditions of the institutions deteriorate.
Post-Downgrade Results
Table 10 shows the logistic regression results for the first quarter after the CAMEL downgrades for the 3- and 4/5-rated groups. As mentioned previously, the second model is run to determine if it is sensitive enough to predict which institutions will likely recover or experience future downgrades or failures. Banking regulators would benefit if the model were able to distinguish these differences within a relatively short period after the initial downgrades. The results show that all the coefficients for the Call Report variables have the anticipated signs and perform as expected, but the significance of the tests varies between the 3- and 4/5-rated groups. The log of market price and the excess return variables generally add to the CAMEL predictions in most specifications, although there is some variation in levels of significance, which range from 1 percent to 10 percent depending on the specification. As before, when the market-risk variables are combined with the Call Report data and core market variables, they generally do not add significantly to the equation. Therefore, the logistic regression tests tend to suggest that relatively simple measures of market price and market excess returns appear to offer the best hope of improving the predictive content of Call Report data. Although the market excess return variable is not a dominating variable for the 3-rated group, for the most distressed organizationsthe 4/5-rated groupit adds significantly to standard models based on the accounting data in quarterly reports. Other market variables, such as return volatility, dividend payment performance, turnover in shares, and the ratio of book to market value equity, appear to have little marginal predictive value.
Table 11 contains information on the accuracy of the model in the post-downgrade period. The critical probabilities for these in-sample and out-of-sample classifications are 0.56 for the 3-rated group and 0.35 for the 4/5-rated group.26
In general, these findings show that the addition of stock-market information to the Call Report data not only increases the correct predictions of banks that will either have more financial distress or recover but also minimize the incorrect predictionsalthough the results are not as strong as in the pre-downgrade period. As we move from specification 1 with only Call Report data to specifications 2 and 3, the model generally improves the correct prediction of financial distress especially for the most-distressedbanks in the 4/5-rated group. For specification 4 in relation to specification 1, the stock-market variables have only a mixed correct prediction rate when compared with Call Report variables alone.
This paper explores the notion that stock price, return, and other market-related variables can be used to improve the predictive content of Call Report financial ratios for the purpose of anticipating CAMEL rating changes. A sample of 122 banks and thrifts that were downgraded to the CAMEL 3 level and 148 banks and thrifts downgraded to the 4 or 5 level was analyzed over the period 19881996. Extensive univariate analysis confirms that relatively simple measures of stock prices and returns exhibit downward trends as much as two years before banks and thrifts experience CAMEL rating downgrades to 3, 4, or 5. The longer-term nature of these trends suggests that the univariate trends are not commonly found in stock returns of healthy institutions. Previous research has not confirmed the pre-downgrade returns of patterns found in this papercertainly not for the length of time or for the variety of market variables used in this research. However, no simple relation appears in univariate comparisons of several other market variables, including average trading volume and average quarterly turnover of shares.
The second section of the paper adds stock return variables to regression equations that include financial ratios commonly used to predict CAMEL rating changes (both upgrades and downgrades) in off-site monitoring models. The results provide several additional points of interest. Most important, adding relatively simple measures of excess returns, stock prices, and an institutions dividend record improve the CAMEL ratings predictive content of Call Report data and otherwise appears to have a limited independent role in anticipating financial distress. The predictive content of the models is most robust for institutions experiencing the greatest financial distressthose being downgraded to the 4 or 5 level. Other market-related variables, such as return volatility, trading volume, and the book-to-market equity ratio, appear to have limited predictive value.
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Hi there! My name is Aditya and I am a second-year Software Engineering student at the University of Waterloo. While I'm not busy studying and completing assignments, I enjoy programming and developing applications.
I was first introduced to the idea of programming in Grade 7, when I had to do an animation project in Macromedia Flash. Eager to differentiate my work from the rest of the class, I looked for ways to enhance my animation. I noticed that one of my friends had a menu before his animation, increasing its interactivity. I approached him and asked him how I could do the same. He then showed me how to create a button and then typed in two lines of code to make the button serve its purpose. I was amazed that a few lines of text could make my animation much more interactive. That day, after I went home, I searched 'programming in flash' in Google. From that day on, I was enamored with programming.
I still program to this day, however I have now expanded my horizons. I am now experienced with Swift, Objective-C, Ruby, Python, and JavaScript. Furthermore, I have created numerous mobile apps, both for BlackBerry and Android.
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Rummelhart, Berkowitz lift Loras past Wheaton (Ill.), Duhawks go to semis
DUBUQUE, Iowa -- Loras held on to defeat Wheaton (Ill.) 2-1 Saturday and is the first team to advance to the semifinals of the NCAA tournament.
Loras got off to a fast start, with sophomore Johnny Rummelhart capitalizing on an early corner kick. Rummelhart won a header and knocked it into the back of the net for a 1-0 advantage.
Following a throw in from Loras' Sam Koenig, senior Forrest Olson and freshman JoJo Schmidt applied pressure in the corner, forced the ball to Schmidt who sent it to the far post where junior Erik Berkowitz headed the cross past Wheaton goalkeeper Ryan Seager.
Wheaton cut the lead in half in the 88th minute, but could not muster any more offense. A late free kick in the closing seconds was sent over the crossbar.
Loras advanced to the semifinals for the first time since 2008.


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Commanderia is a strong sweet dessert wine made in Cyprus. It has probably been produced for over 3,000 years, but the name commanderia dates from more recent times, and comes from the Commandery, a district of Cyprus ruled by the Knights Templars from 1191. Ancient production methods—such as burying amphorae in the ground to age the wine in—still obtain locally, but commercially exported commanderia is made with more up-to-date techniques.
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The Template Ceremonies
The Reconnection of the Human Bio-circuitry
through Coded Ceremony
Embodied in these ceremonies is an initiation designed to transform the frequencies of the human circuitry, to enable us cellularly and energetically to sustain the embodiment of a new paradigm. Our challenge is to embrace and integrate the new frequency realizing not only are we mental, physical and emotional beings, we are units of circuitry . . . electric beings.
The Template is a series of alchemical ceremonies that use sacred geometry and sonic codes to activate dormant Human potential. They initiate a journey of healing, forgiveness, remembrance, resurrection and, ultimately, transcendence of the dualistic mortal paradigm. There are 16 ceremonies in the full Template that are divided into 3 stages – the Foundation Phase, the Activation & Integration Phase and the Actualization Phase.
The Foundation phase includes the first 5 ceremonies that lay the foundation within the body – our bio-computer. Each Template ceremony utilizes specific 3D sacred geometry and sonic codes to connect different circuitry systems within your physical body. This collapses the mutant matrix within you. As you gradually integrate its luminous message, you embody a new paradigm and restore our original Divine Blueprint. The reintegration of Solar consciousness in your physical being expands the portal through which divine consciousness flows. Each set of circuitry connection builds on the previous ceremony so ceremonies must be done in order to activate.
The second phase of Activation & Integration includes ceremonies 6-10 and the third phase of Actualization begins with ceremony 11. We offer the first 6 ceremonies regularly here in the US and the later ceremonies are held by Template Architects Juliet & Jiva Carter who hold ceremonies around the world. All public events are posted on the Events page.
The reconnection of circuitry affects:
relationships – redistributing the balance of power and redefining childhood issues
the immune system
fear levels
guilt & more
Explore the connection between the circuitry and the endocrine system,
DNA activation and physical ascension.
Clues leading us to the knowledge that we are not connected are the scientific facts that we use less than 50% of our brain and that 98% of our DNA is inactive. We are the culmination of a variety of life force energy systems. The new frontier of healing lies in the knowledge that these systems are not fully connected for lasting healing to take place. These ceremonies are impeccable systems, holonomically resonant with each individual's point of soul evolution, organically regulated by their capacity for change.
After many years of working with our emotional, physical and mental bodies the time has come to work with our electro-magnetic construct. It is this subtle energy supply that activates the chakra system and animates the Human being on every level. Just as circuitry provides the pathway for electric energy to activate a computer to function properly, circuitry provides the pathways of life force in the Human Bio-computer. As a result of the genetic modification of the Human DNA several of our major circuits have been disconnected, creating a distorted sense of self that has led to fear, shame, guilt, and disease.
Each of the circuits reconnected in the alchemical process of these Ceremonies will initiate the return to wholeness and re-establish our true identities, liberating not only our relationship to each other but also to the fear-based morphogenetic grid that rules our deepest behavioral patterns, and connecting us to a new consciousness grid. Through the testing procedure of applied kinesiology("muscle testing"), both before and after the ceremony, you will see that where there was weakness, now there is strength.
What is Circuitry?
CEREMONY OF ORIGINAL INNOCENCE: Ceremony 1
The Ceremony of Original Innocence reconnects 12 major electro-magnetic circuits in the human body . . . PERMANENTLY. Circuits provide the pathways for the subtle energy supply that activates the chakra system and animates the Human being on every level. During the ceremony the 12 circuits are reconnected via the 5 elements and the resonant harmonic code.
The Water circuits are blocked by guilt, regret and anger as a result of trauma during birth and childhood. Reconnecting these circuits clears these emotions, liberating relationships from co-dependency. Reconnecting the Thymus circuit activates the T cells that regulate the immune system.
Experience this workshop and learn the function of the other 9 circuits, including the Heart, Solar, Earth, Third Eye and Crown!
Watch this ceremony online!
CEREMONY OF SYNTHESIS, THE 13TH CIRCUIT: Ceremony 2
The 13th Circuit is reconnected by the design and progression of 23 geometric forms of the 3rd dimensional creation code and their sonic counterparts which are resonant with the various aspects of the holonomic agreement that lie at the heart of the Human masterpiece. The Divine Feminine & Divine Masculine are woven together in geometric form to create a balance within the Human biocomputer.
In the ceremony pathways of electromagnetic life force are reinstated at both ends of the pranic tube running concurrent with the spinal cord and preparing it for the Ceremony of Sacred Breath. The occiput, at the top of the pranic tube, holds the resonant field for the Galactic life directive the seed impulse. The coccyx, at the base of the pranic tube, holds the resonant field in alignment with the Earths gaia spheric egg like receptors. Together, manifest creation occurs as the life force is Activated, Integrated, Synthesized and Crystallized into form. This Ceremony escorts us into this Geometric Tantric Courtship between the Mother of Form and the Father of Consciousness . . . reprogramming our breath with the essence of this unison we imbue our consciousness with the higher physics of creation.
The 13th Circuit Ceremony initiates the cleansing of the Archetypal arena not by reprogramming it but by attuning the heart/body/mind system to the resonance of the Geometric Soul Covenant embedded in the Human matrix that is the original Human blueprint.
Watch this ceremony online!
CEREMONY OF SACRED BREATH: Ceremony 3
Breath… the unspoken vow between spirit and matter. The Ceremony of Sacred Breath reconnects 7 breath circuits. 13 pieces of stellated geometry, expressing the 6th element of Stellar Radiance, provide the alchemic component within the code. Each circuit is reconnected using a particular breath focus that in turn harmonizes a specific grouping of chakras. These circuits further open the pranic tube and enable the system to absorb a wider spectrum of prana that in turn creates greater resonance with hitherto dormant receptors in the brain.
The Sacred Breath Ceremony not only activates and integrates 7 breath circuits, but also all 20 reconnected circuits as it provides the binding factor within the holonomic mechanism of the Human bio-function.
Watch this ceremony online!
SOURCE CEREMONY: Ceremony 4
Ceremony 4 concerns the redefinition of God in the Human psyche. Various aspects of a 3D Creation Mandala, or 'Flower of Life' are used in this ceremony to reconnect the 5 Source Circuits within the core fractal of the universal holography, bypassing the archetypal arena and any form of spiritual mediation. The meaning of Source opens to you, within you.
TEMPLE OF TIME CEREMONY: Ceremony 5
Ceremony 5 reinstates the symbiosis with our solar system. The planets are represented in sacred geometric form with resonant music to reconnect 8 circuits electromagnetically connecting us to the planets.
WORLDBRIDGER 1 CEREMONY: Ceremony 6
Ceremony 6 is the first ceremony in second phase, Activation & Integration, spiraling back to activate & integrate ceremony 1. It takes us back to the person in the mirror to delve deeper into the programming at a personal level.
To attend a live ceremony event see our Events Page


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Heavenly Spa by Westin
Awaken your senses. Revitalise and energise with a spa experience in the privacy of your guestroom. From therapeutic massages to cleansing facials and invigorating body treatments, each element of your in-room spa visit is designed to stimulate the senses and nourish both body and mind.
A relaxing atmosphere is created before your treatment starts with a spa basket delivered to your room, holding gifts of renewal including a healthful treat, bottle of water, and a letter with recommendations on how to make the most of your In-Room Spa experience.
As you recline on our custom spa bed, a professional therapist will provide your choice of In-Room Spa treatments and intuitively restore balance to your body. Signature services include Swedish, remedial and prenatal massage, deluxe facial, manicure and pedicure.
A massage of your choice can also be enjoyed with your loved one at your side. All treatments offer the option of 60, 90 or 120 minutes.
Available daily 7:00am to 10:00pm
By appointment only


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For the last 4 years the "Fahavalo" team has been wholeheartedly committed to bring Madagascar 1947 film to completion. We have finished the film and are currently in post-production.
The post-production process is costly. This is why we are reaching out to YOU. We have launched a KissKissBankBank campaign to help fund the last of our expenses. We would truly appreciate your help for the last push.
If you are not able to make a contribution, please consider sharing this project and spreading the word…