Private Firm Summary Report Date: May 2013 (Data as of December 2012)

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1 MAY 2013 U.S. MIDDLE MARKET RISK REPORT Author Bryce Bewley Single Obligor Research Analyst Irina Korablev Single Obligor Research Director Stafford Perkins Single Obligor Research Senior Director Douglas Dwyer Single Obligor Research Managing Director Contact Us Americas Europe Asia (Excluding Japan) clientservices.asia@moodys.co Japan clientservices.japan@moodys.com Private Firm Summary Report Date: May 2013 (Data as of December 2012) This semiannual report examines credit risk in the otherwise opaque U.S. private firm credit market. We report trends in four different areas of risk measurement: realized defaults, internal bank ratings, financial statement-based information, and model-based risk estimates. We derive the statistics in this report from Moody s Analytics Credit Research Database (CRD). This report contains EDF TM (Expected Default Frequency) values calculated using the RiskCalc U.S. 4.0 Corporate model; previously, we derived EDF values using RiskCalc U.S The RiskCalc U.S. 4.0 Corporate Model, which evolved from RiskCalc U.S. 3.1, includes a number of improvements:» The new 4.0 model includes fourteen industries, as opposed to the nine included in 3.1. New sectors include Mining, Utilities, Transportation, High Tech, Communication, Business Services, and Health Care.» Basic ratios and input structure in the 4.0 model are retained. Only two ratios were updated: Change in AR Turnover was replaced by Change in Working Capital over Sales and Cash Flow/Interest expense became EBITDA over Interest Expense.» To capture the effect of the general credit cycle and macro factors, the 4.0 model includes a credit cycle adjustment (CCA) factor, which combines the Distance-to-Default (DD) from the Moody s KMV Public Firm Model and state-level unemployment rates.. Highlights» Private firm default rates have declined steadily over the past three and a half years. At 1.5, the rolling 12-month default rate is down 7 from its September 2009 peak of 5.9% (Fig.1). This trend has been driven primarily by a decline in the charge-off rate, now at its lowest level in more than nine years. In addition, the rate of borrowers in non-accrual status has decreased 6 since September The number of borrowers rated Substandard remains relatively high, and has, in fact increased during the past year, indicating that banks continue to assess risk conservatively (Fig.2).» Internal rating downgrades have increased slightly over the past year, with 17% of borrowers downgraded compared to 1 a year ago (Fig.6). This finding suggests conservatism from banks and indicates that the credit improvement trend of the past three years is beginning to slow.

2 » Construction remains the sector with the highest percentage of balances adversely rated, with Agriculture replacing Real Estate & Leasing with the second highest percentage (Fig.5). Across all industries, the percentage of balances adversely rated declined from 16. to 12. during the past year. While this figure is the lowest since 2008, it remains well above pre-crisis levels.» In mid-2012, the median RiskCalc 4.0 CCA EDF credit measure fell to its lowest level in ten years,.5. This figure rose slightly over the next six months, ending 2012 at.59%, remaining below the.7 median of December These statistics demonstrate the declining risk in credit markets (Fig.8).» Financial statement ratios generally improved in 2011, reflecting economic conditions as well as banks decisions in extending credit. The Sales Growth and Debt Coverage ratios showed strong improvements over 2010, along with continued increase in borrower size (Fig.10).» Real Estate/Leasing and Construction continue to post the highest EDF levels, 0.8 and 0.83%, respectively (Table 4). Agriculture and Construction showed the greatest EDF level improvements in 2012, both improving by more than 3 (Fig.13). The Construction and Information sectors show the largest concentrations of very risky borrowers (Fig.14).» Risk levels improved for all states during the past ten years. The three states with the greatest improvements in one-year median CCA EDF level are Michigan, Alabama, and Ohio (Fig.16). Michigan has led the recent recovery in credit quality with an 87% decline in its median EDF level since June 2009 (Fig.18). Most states, however, have yet to return to pre-crisis risk levels. New Mexico s median EDF credit measure remains 4 above its June 2007 value (Fig.17). 2 MAY 2013

3 Table of Contents Overview 4 Introduction 4 Definition of Default 4 Lending Performance Measures 4 Private Firm Default Rates 4 Adversely Rated Credits 6 Internal Risk Rating Migration Summary 8 Coupon Rates 8 EDF Credit Measures 9 RiskCalc 4.0 EDF Credit Measure Trends 9 Industry Analysis 12 Credit Migration 14 Regional Analysis 16 Conclusions 19 3 MAY 2013

4 Overview Introduction In this report, we use realized default information, bank risk ratings, financial statement data, and RiskCalc Credit Cycle Adjusted (CCA) private firm EDF credit measures to provide insights into a market where data is otherwise unavailable. Our source is Moody s Analytics Credit Research Database (CRD). The CRD collects quarterly data from 16 U.S. lending organizations, including large institutions as well as smaller regional banks. The breadth and depth of the CRD make the data strongly representative of the U.S. credit market. The CRD actively works with each institution to ensure a complete and thorough understanding of loan accounting and financial statement data. The CRD captures defaults in a consistent and accurate manner using information from each institution s loan accounting data. TABLE 1 TABLE 2 CRD Data Characteristics Distribution of Defaults by Type COUNTS PERCENTAGE OF TOTAL DEFAULTS Statements 1,606, Days Past Due (Non-Pass) 7% Loan Accounting System Defaults 196,608 Loss Provision 47% Statements with CCA EDF for Dec ,725 Non-Accrual 2 Defaults with Statements 53,080 Charge-Off 1 Loss Definition of Default We define default in accordance with our interpretation of the Basel II directive. Our methodology detects default and neardefault events over time for all banks. Borrowers are flagged as defaulters if they are 90 days past due with a non-pass rating, are in non-accrual status, have an internal bank rating corresponding to the regulatory ratings Doubtful, or Loss, or have an obligation partially or entirely charged-off. Ratings of Substandard are also flagged, but we consider these indicative of neardefaults and exclude them from our definition of default. After detecting all defaults, we aggregate the data into a single default event for each defaulted borrower. For the date of default, we use the date of the borrower s earliest default event; for the severity of default, we use the borrower s most severe default type. Table 2 shows the distribution of defaults in the CRD by default type. Lending Performance Measures Lending performance measures utilize loan accounting data provided by participating U.S. institutions. This data provides quarterly snapshots of loan level information, such as internal bank ratings, coupon rates, balances, and commitments for each institution s middle market commercial portfolio. We use this data to detect default events, to map internal bank ratings to a standard regulatory rating, and to track balance and commitment information over time. The CRD began collecting this data in Private Firm Default Rates We present the default rate in two forms. Figure 1 provides a rolling 12-month default rate for U.S. private firms by default type. As noted above, a borrower is considered to be in default if they are 90 days past due with a non-pass rating, in non-accrual status, rated Doubtful or Loss, or partially or entirely charged-off. This default rate does not include borrowers rated as Substandard. Figure 2 presents the rolling 12-month default rate including borrowers rated as Substandard. While Figure 1 shows the actual default occurrences over time, Figure 2 illustrates how banks perceive potential, future default risk. We calculate the default rate using all loan accounting records, regardless of whether or not there is a corresponding financial statement. Charts also include a projection value. We include this value because a significant reporting lag exists between when a default occurs and when the information regarding that default is actually received. For example, as of December 2012, we observe borrowers that are 90 days past due but have a pass rating. A certain proportion of these borrowers will become non-pass rated in the future. In such cases, we record each default as having occurred on the date the borrower first became 90 days past due. An analysis using eight years of data shows that, on any given quarter end date, banks report approximately 8 of defaults occurring one year prior, 87% of those occurring nine months prior, 63% of those occurring six months prior, and only 5 of those 4 MAY 2013

5 occurring three months prior. The projection provides an estimate of what the actual default rate will likely be once we receive all the default information. As of December 2012, private firm default rates have declined steadily over the past three years. At 1.57%, the rolling 12-month default rate is down 7 from its September 2009 peak of 5.93% (Fig.1). The trend has been driven primarily by a decline in the charge-off rate, now at its lowest level in more than nine years. In addition, the proportion of borrowers in non-accrual status has decreased by 6 since September The number of borrowers rated Substandard remains relatively high, indicating banks continue to assess risk conservatively (Fig.2). According to our projections, the percentage of borrowers rated Substandard has fallen approximately 3 since its peak in September Figure 1: Rolling 12-Month Private Firm Default Rate by Default Type Projection 90 Days Past Due Non-accrual Doubtful Charge-off 7% 3% - 03q4 04q4 05q4 06q4 07q4 08q4 09q4 10q4 11q4 12q4 Figure 2: Rolling 12-Month Private Firm Default Rate by Default Type, Including Near-Defaults 1 9% Projection Substandard 90 Days Past Due Non-accrual Doubtful Charge-off 7% 3% 03q4 04q4 05q4 06q4 07q4 08q4 09q4 10q4 11q4 12q4 1 We calculate the rolling 12-month default rate as the total number of defaulted and near-defaulted (i.e., Substandard) borrowers during the past four quarters, divided by the average number of borrowers across the same four quarters. 5 MAY 2013

6 Figure 3: Migration from Substandard to Other Classifications 3 2 Downgrade Upgrade Dec Jun Dec Jun Dec Jun Dec10-11 Jun Dec Adversely Rated Credits This analysis examines the actual and perceived future risk of the portfolios contained in the Credit Research Database. Figure 3 shows the rates at which borrowers classified as Substandard migrated to more or less severe classifications during the past five years. Figure 4 presents adversely rated credits as a percentage of all loan balances over time. Table 3 shows adversely rated credits as a percentage of total loan balances by year and industry. Figure 5 presents adversely rated credits as a percentage of loan balances as of December 2012 by regulatory rating and industry. Because many banks cease tracking loan information once a loss occurs, loss percentages are most likely higher than presented. Though the overall number of downgrades remains larger than upgrades (Fig.6), during the second quarter of 2012 the gap between the number of Substandard ratings upgraded compared to those downgraded is thinning (Fig.3). This improvement follows a trend that began in December 2009; since peaking at nearly 27% downgrades have settled around 1. The percentage of balances adversely rated is decreasing from the 2 high mark in December It now stands at 13%, still a fair amount above the years preceding the credit crisis (Fig.4). By this measure, the Information sector showed the largest improvement in 2012, with a decline of eleven percentage points. Construction, Admin Support, Hospitality, Wholesale, and Management each improved by six percentage points (Table 3). Despite this improvement, adversely rated balances continue to comprise a relatively large portion of banks exposure to the Construction and Agriculture sectors. In contrast, adverse ratings are much less prevalent in sectors with significant government interest or oversight, such as Public Administration and Utilities. Figure 4: Percentage of Balances with Adverse Bank-Assigned Regulatory Ratings, by Year 2 3 Loss Doubtful Substandard OLEM Watch % 7% 9% 7% 3% 3% 3% 3% 3% 3% 3% 3% 3% 3% 03q4 04q4 05q4 06q4 07q4 08q4 09q4 10q4 11q4 12q4 TABLE 3 2 Balances are calculated using loan information from the fourth quarter of each year. 6 MAY 2013

7 Percentage of Balances with Adverse Bank-Assigned Regulatory Ratings, by Industry and Year A&E 19% 23% 17% 1 1 Admin Support 1 29% Agriculture 2 43% % Construction % Education 7% 7% 7% 9% Finance Health Care % 9% Hospitality Information % 2 1 Management 19% 2 23% 2 1 Manufacturing 27% % 1 Mining 9% % 1 Other Services 17% 2 23% 19% 1 Prof. Services 13% 2 17% 1 13% Public Administration 3% 3% 3% RE & Leasing Retail Transportation % 1 9% Unknown % 2 1 Utilities Wholesale Figure 5: Percentage of Balances with Adverse Bank-Assigned Regulatory Ratings in Current Period, by Industry Construction Agriculture RE & Leasing Mining Management Other Services Unknown Manufacturing Admin Support Retail Prof. Services Hospitality Wholesale A&E Information Health Care Education Transportation Finance Utilities Public Administration Watch OLEM Substandard Doubtful Loss Internal Risk Rating Migration Summary 3 Balances are calculated using loan information from the fourth quarter of each year. 7 MAY 2013

8 The rate at which banks change internal risk ratings reveals how they interpret changes in the credit risk of their portfolios. Figure 6 shows the migration of internal risk ratings assigned to borrowers by institutions annually since A borrower is considered Upgraded when an institution changes its risk rating to one that is less severe; a borrower is considered Downgraded when the rating increases in severity. In 2012, internal rating downgrades increased slightly to 17% of borrowers, while upgrades reached a six-year high at 1 of borrowers. Downgrades have fallen significantly from their peak in 2009, which saw 3 of borrowers downgraded, while only 1 were upgraded. Although downgrades are still slightly more common than upgrades, the improvement suggests increased confidence among lenders (Fig.6). Figure 6: Migration Summary of Bank-Assigned Risk Ratings Downgraded Unchanged Upgraded 03q4 04q4 05q4 06q4 07q4 08q4 09q4 10q4 11q4 12q Coupon Rates Figure 7 presents the average coupon rate by bank-assigned regulatory rating over time. The figure also shows the primary credit discount rate offered by the Federal Reserve. Interest rates in the middle market have remained relatively stable during the past three years. For borrowers rated Pass, the mean coupon rate fell slightly from 4.8 to 4.69% in For adversely rated borrowers, the rate increased slightly from 5.8 to 5.9. The interest rate spread between the two widened, from 101 basis points in December 2011 to 128 basis points in December Figure 7: Mean Coupon Rates by Regulatory Rating Over Time Pass Non Pass Discount 1 9% 7% 3% 13q1 12q1 11q1 10q1 09q1 08q1 07q1 06q1 05q1 04q1 03q1 8 MAY 2013

9 EDF Credit Measures We generate the EDF credit measures presented throughout this report using the Credit Cycle Adjusted (CCA) mode of RiskCalc 4.0. Unlike the Financial Statement Only (FSO) mode, which delivers EDF credit measures based mainly upon financial and industry information, the CCA factors in an adjustment meant to account for the market s current assessment of the credit cycle. This factor is estimated using the distance-to-default estimate from Moody s Analytics Public Firm Model. The CCA EDF credit measure is a forward-looking indicator of probability of default. RiskCalc 3.1 EDF Credit Measure Trends Figures 8 and 9 present Credit Cycle Adjusted (CCA) One-Year EDF credit measures over time. Figure 9 is segmented by regulatory rating. Figure 10 presents RiskCalc ratio percentiles over time and does not incorporate any credit cycle adjustment. In mid-2012, the median RiskCalc 4.0 CCA median EDF credit measure fell to.5, its lowest level in ten years. This value increased slightly to.59% by the end of These levels demonstrate that the trend of declining risk in credit markets, as measured by Moody s Analytics Public Firm Model, is beginning to wane (Fig.8). Overall, the median CCA EDF declined for both Pass and Non-Pass borrowers in 2012 (Fig.9). Borrowers financial ratios generally improved in Debt Coverage is at its highest level in more than ten years and is up nearly 4 from its 2009 level. Leverage has fallen to below pre-crisis levels once again. Interestingly, borrower size increased substantially, indicating that banks increasingly prefer to lend to larger borrowers (Fig.10). Figure 8: Trends of CCA EDF Credit Measures th Percentile Median 75th Percentile 9% 7% 3% Figure 9: Median CCA EDF Credit Measures by Regulatory Rating 1 9% 7% 3% Pass Non Pass 13q1 12q1 11q1 10q1 09q1 08q1 07q1 06q1 05q1 04q1 03q1 4 Does not include borrowers from industries for which RiskCalc is not designed, such as vehicle dealers, finance, insurance, government services, and real estate. 9 MAY 2013

10 Figure 10: Financial Statement Ratios used in RiskCalc 4.0 (25 th, 50 th, and 75 th Percentiles) 5 Profitability (Δ in ROA) 1 Profitability (ROA) Debt Coverage (Cash Flow / Interest Expense) Leverage (RE / Current Liabilities) Leverage (LTD / (LTD + Net Worth)) Activity (Current Liabilties / Sales) These analyses extend only to 2011 as the majority of 2012 financial statements have yet to be transmitted to the CRD. Figures for 2012 will be included in the Fall 2013 Middle Market Risk Report. 10 MAY 2013

11 Activity (Δ in Working Capital/Sales) 1 9% 7% 3% Activity (Inventory / Sales) Growth (Sales Growth) Liquidity (Cash / Assets) 20,000 18,000 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2,000 - Size (CPI Adjusted Assets) 11 MAY 2013

12 Industry Analysis Table 4 presents median CCA EDF credit measures by industry as of December for each of the past 10 years. Figures 12 through 14 show CCA EDF credit measures as of December 2012: Figure 12 presents median EDF values, Figure 13 shows percentage changes in median EDF over the past year, and Figure 14 presents the percentage of borrowers in each industry with an EDF above the 90 th percentile for the entire population. The 90 th percentile CCA EDF in December 2012 was 3.49%, much higher than the median in any given industry. The industries with the greatest risk in December 2012, as measured by RiskCalc CCA EDF credit measures, were Real Estate & Leasing and Construction (Fig.12). Although CCA EDF values decreased across all sectors during the past year, the largest reductions occurred in the Agriculture and Construction industries (Fig.13). The Construction sector showed the largest concentration of highly risky borrowers; 16.3% of firms in this sector had CCA EDF values higher than 9 of the overall population (Fig.14). Information and Unknown were the second and third riskiest sectors by this measure, with 13. and 12.3%, respectively. The Utilities sector, at 0., had the lowest proportion of highly risky borrowers. TABLE 4 Median CCA EDF Values by NAICS Sector A&E % % 2.47% Admin Support 0.99% 0.83% % 0.57% Agriculture % % % 0.89% 0.49% Construction 1.59% % % 1.33% 0.83% Health Care 0.53% 0.43% % 0.69% 0.47% 0.39% Hospitality 1.13% % 2.67% % 0.69% 0.53% Information % 1.09% % Manufacturing % 0.93% 0.93% 1.07% Mining % % % Other Services 0.69% % % 0.93% Prof. Services 0.97% % 0.67% % 1.87% 1.07% % RE & Leasing % % 4.09% Retail % Transportation % 0.5 Unknown % Utilities % 0.29% 0.29% % 0.87% 0.49% 0.29% 0.2 Wholesale % 0.69% % % % 6 This chart presents median EDF credit measures as of December of each year. 12 MAY 2013

13 Figure 12: One-Year CCA EDF Measures by NAICS sector RE & Leasing Construction Unknown Information Manufacturing Retail A&E Admin Support Prof. Services Wholesale Hospitality Transportation Agriculture Other Services Mining Health Care Utilities % 0.57% 0.53% 0.53% % % 0.39% % 0.83% % % % 1. Figure 13: Percentage Change in CCA EDF Credit Measures Over Past Year Agriculture Construction % -23. Transportation Unknown RE & Leasing % Hospitality Retail A&E Mining Manufacturing Utilities Admin Support Wholesale Information Health Care Prof. Services Other Services MAY 2013

14 Figure 14: Percentage of Firms with CCA EDF Values Greater than 90 th Percentile Construction Information Unknown RE & Leasing Manufacturing Admin Support Prof. Services Retail Hospitality A&E Transportation Wholesale Other Services Mining Agriculture Health Care Utilities % % % 9.3% 9.9% 12.3% % Credit Migration In this section, we report CCA EDF credit measure migrations over three different periods. To construct each migration matrix, we group firms into rating categories using their CCA EDF credit measures. The migrations below show annual transition rates averaged over the periods since 2002, 2007, and 2011, respectively. TABLE 5 CCA EDF Implied Rating Migration: Average One-Year Rating Migration Rates (%) ( ) 2012) TO FROM A1 A2 A3 Baa1 Baa2 Baa3 Ba1 Ba2 Ba3 B1 B2 B3 MIGRATION SUMMARY Caa-C %UP %DOWN A A A Baa Baa Baa Ba Ba Ba B B B Caa-C MAY 2013

15 TABLE 6 CCA EDF Implied Rating Migration: Average One-Year Rating Migration Rates (%) ( ) 2012) TO FROM A1 A2 A3 Baa1 Baa2 Baa3 Ba1 Ba2 Ba3 B1 B2 B3 MIGRATION SUMMARY Caa-C %UP %DOWN A A A Baa Baa Baa Ba Ba Ba B B B Caa-C TABLE 7 CCA EDF Implied Rating Migration: Average One-Year Rating Migration Rates (%) 2012) TO MIGRATION SUMMARY ( ) 2012) TO FROM A1 A2 A3 Baa1 Baa2 Baa3 Ba1 Ba2 Ba3 B1 B2 B3 Caa-C %UP %DOWN A A A Baa Baa Baa Ba Ba Ba B B B Caa-C MAY 2013

16 Regional Analysis 7 In the map below, we report EDF credit measures by state. To estimate each state s median credit risk score, we use the median One-Year CCA EDF score assigned by the RiskCalc U.S. 4.0 model as of December The spectrum legend below the U.S. heat map contains the range of observed median EDF values in December The state with the lowest median EDF was Nebraska, 0.4, while New Mexico had the highest median EDF, 0.8. Figures 16 through 18 present the ten largest changes in median EDF credit measures by state since December 2002, June 2007, and June 2009, respectively. Over the past decade, median CCA EDF values have declined for all states. Michigan showed the largest improvement with a 74.7% decline (Fig.16). Since June 2007, just before CCA EDF credit measures began to rise dramatically, most states CCA EDF values have fallen back to pre-crisis levels. Michigan and Ohio showed the largest improvements, 51.7% and 46., respectively (Fig.17). All states median CCA EDFs have fallen since June Michigan led all states during this period with an improvement of 86. (Fig.18). Figure 15: One-Year CCA Median EDF Values by State as of December The data used in constructing this map contains a relatively small number of observations from Wyoming and Vermont. For each state, median EDF values may reflect different data population characteristics including lenders (CRD Participants) and distributions of borrowers across industries, asset sizes, and other characteristics that may materially impact EDF values for that state. EDF calculations using RiskCalc 4.0 do incorporate some state-level macroeconomic factors, such as unemployment. 16 MAY 2013

17 Figure 16: Top Ten Largest Changes in One-Year CCA Median EDF During Past Decade -74.7% Michigan -73.3% -72. Alabama Ohio -70.7% South Carolina -69. Illinois -68.3% -68. Indiana Nebraska Mississippi Wisconsin Missouri Figure 17: Top Ten Largest Changes in One-Year CCA Median EDF Since June % Michigan Ohio South Carolina North Carolina New Mexico % Arkansas Kentucky Florida Indiana Alabama MAY 2013

18 Figure 18: Top Ten Largest Changes in One-Year CCA Median EDF Since June Michigan -84.9% Ohio North Carolina South Carolina -82. Florida -81.7% Kentucky Tennessee Indiana Missouri Illinois -8-87% % % -7-77% Conclusions The credit risk of private-firm borrowers in the U.S. middle market has improved since peaking in This improving credit worthiness trend is beginning to slow, evidenced by the slight increase in median CCA EDF from June 2012 to December Changes in borrowers financial positions over the past year have generally been credit positive, shown in the RiskCalc ratios (Fig.10). The actual default rate has declined steadily during the past two years (Fig.1). The RiskCalc 4.0 One-Year CCA EDF credit measure has returned to levels comparable with those seen in the pre-crisis period, indicating that the default rate is expected to maintain its current levels (Fig.8). Meanwhile, lenders have remained conservative, although signs of cautious optimism appear. The percentage of balances classified as Substandard remains high by historical standards, although it is falling (Fig.4). Similarly, the percentage of borrowers whose bank-assigned risk ratings have been downgraded in the past year still exceeds the percentage of borrowers upgraded, although this gap has narrowed (Fig.6). 18 MAY 2013

19 Copyright 2013, Moody s Analytics, Inc., and/or its licensors and affiliates (together, "MOODY'S). All rights reserved. ALL INFORMATION CONTAINED HEREIN IS PROTECTED BY COPYRIGHT LAW AND NONE OF SUCH INFORMATION MAY BE COPIED OR OTHERWISE REPRODUCED, REPACKAGED, FURTHER TRANSMITTED, TRANSFERRED, DISSEMINATED, REDISTRIBUTED OR RESOLD, OR STORED FOR SUBSEQUENT USE FOR ANY SUCH PURPOSE, IN WHOLE OR IN PART, IN ANY FORM OR MANNER OR BY ANY MEANS WHATSOEVER, BY ANY PERSON WITHOUT MOODY S PRIOR WRITTEN CONSENT. All information contained herein is obtained by MOODY S from sources believed by it to be accurate and reliable. Because of the possibility of human or mechanical error as well as other factors, however, such information is provided as is without warranty of any kind and MOODY S, in particular, makes no representation or warranty, express or implied, as to the accuracy, timeliness, completeness, merchantability or fitness for any particular purpose of any such information. Under no circumstances shall MOODY S have any liability to any person or entity for (a) any loss or damage in whole or in part caused by, resulting from, or relating to, any error (negligent or otherwise) or other circumstance or contingency within or outside the control of MOODY S or any of its directors, officers, employees or agents in connection with the procurement, collection, compilation, analysis, interpretation, communication, publication or delivery of any such information, or (b) any direct, indirect, special, consequential, compensatory or incidental damages whatsoever (including without limitation, lost profits), even if MOODY S is advised in advance of the possibility of such damages, resulting from the use of or inability to use, any such information. The credit ratings and financial reporting analysis observations, if any, constituting part of the information contained herein are, and must be construed solely as, statements of opinion and not statements of fact or recommendations to purchase, sell or hold any securities. NO WARRANTY, EXPRESS OR IMPLIED, AS TO THE ACCURACY, TIMELINESS, COMPLETENESS, MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OF ANY SUCH RATING OR OTHER OPINION OR INFORMATION IS GIVEN OR MADE BY MOODY S IN ANY FORM OR MANNER WHATSOEVER. Each rating or other opinion must be weighed solely as one factor in any investment decision made by or on behalf of any user of the information contained herein, and each such user must accordingly make its own study and evaluation of each security and of each issuer and guarantor of, and each provider of credit support for, each security that it may consider purchasing, holding or selling. MOODY S hereby discloses that most issuers of debt securities (including corporate and municipal bonds, debentures, notes and commercial paper) and preferred stock rated by MOODY S have, prior to assignment of any rating, agreed to pay to MOODY S for appraisal and rating services rendered by it fees ranging from $1,500 to approximately $2,400,000. Moody s Corporation (MCO) and its wholly-owned credit rating agency subsidiary, Moody s Investors Service (MIS), also maintain policies and procedures to address the independence of MIS s ratings and rating processes. Information regarding certain affiliations that may exist between directors of MCO and rated entities, and between entities who hold ratings from MIS and have also publicly reported to the SEC an ownership interest in MCO of more than, is posted annually on Moody s website at under the heading Shareholder Relations Corporate Governance Director and Shareholder Affiliation Policy. Credit Monitor, CreditEdge, CreditEdge Plus, CreditMark, DealAnalyzer, EDFCalc, Private Firm Model, Portfolio Preprocessor, GCorr, the Moody s logo, the Moody s KMV logo, Moody s Financial Analyst, Moody s KMV LossCalc, Moody s KMV Portfolio Manager, Moody s Risk Advisor, Moody s KMV RiskCalc, RiskAnalyst, RiskFrontier, Expected Default Frequency, and EDF are trademarks or registered trademarks owned by MIS Quality Management Corp. and used under license by Moody s Analytics, Inc.. 19 MAY 2013