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EXHIBIT 12
One-year corporate default rate forecasts by industry
Industry
US
Europe
Industry
US
Europe
Consumer goods: Non-durable* Energy: Oil & Gas Beverage, Food, & Tobacco FIRE: Finance
Media: Broadcasting & Subscription Telecommunications Automotive
2.4% 2.0% 1.7% 1.5% 1.4% 1.3% 1.3%
0.8% 1.4% 0.6% 0.8% 1.0% 0.6%
Forest Products & Paper* FIRE: Real Estate Banking
Sovereign & Public Finance Utilities: Electric Utilities: Water*
0.4% 0.4% 0.4% 0.1% 0.0%
1.2% 0.9% 0.1% 0.2% 0.2%
* Default rate forecasts are not reported in these sectors in either Europe or the US due to small sample size (fewer than ten issuers).
Rating accuracy metrics
Moody’s ratings have historically proven to be effective predictors of default. This can be seen in Exhibit 13, which plots the median ratings of over 1,900 corporate issuers which defaulted from 1983 to 2014. The chart demonstrates that, historically, Moody’s-rated issuers have been downgraded to the B1 level as early as five years prior to default. The comparable rating was lower at B3 among issuers which defaulted in 2014. The median rating one year prior to default for all defaulters in 2014 was Caa2, two notches lower than that rating measured over the entire period 1983-2014.
EXHIBIT 13 Median ratings prior to default, 2014 vs. long-term average 2014Ba3B1B2B3Caa1Caa2Caa3CaC605550454035302520151050Months prior to default1983-2014Source: Moody’s Investors Service The data in Exhibit 13 above demonstrate that Moody’s corporate ratings are correlated with subsequent default experience. To further demonstrate the ability of ratings to separate issuers with low credit risk from those with high credit risk, we use the Average Position of defaults (“AP”) to evaluate the accuracy of Moody’s ordinal rating systems (see Exhibit 14).22 AP measures the average position for defaulters with position defined as the percentage of issuers with higher or equal ratings. A greater AP indicates a more discriminatory rating system as there are more issuers rated higher than the defaulters, or equivalently that defaulters are generally found in lower rating categories. Exhibit 14 reveals that between 1983 and 2014, the Average Position of defaulters has been consistently high during the entire period, with an average of
22
For a detailed discussion of average default position and the mathematical derivation of the accuracy ratio from the average default position, please refer to Moody’s Special Comment, Measuring Ratings Accuracy Using Average Default Position, Feb 2011.
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92.0% for the one-year horizon and 86.6% for the five-year horizon. Such high APs indicate that Moody’s ratings have been effective in predicting defaults over both the short- and long-term periods. The lowest one-year AP was observed in 2008 when Lehman Brothers and several other high grade financial institutions failed. Since then, the AP has quickly recovered and reached 92.5% in 2014.
Across broad sectors, the average APs are higher among non-financial corporate issuers than for financial institutions, particularly in Europe over the five year horizon. The lower AP in the European financial sector mainly results from a few defaults among high rated financial institutions, most of which were in the form of distressed exchanges on junior obligations only. This reflects the structural changes in the European banking sector. Specifically, the systemic support which was widely expected in the financial sector before the global financial crisis had its limits with the junior debt holders uncovered in some cases.23 As a growing number of countries have moved toward adopting bank resolution regimes that include provisions for burden-sharing with creditors (or “bail-in”) to resolve failing banks, Moody’s has updated its banking methodologies to appropriately assess government support assumption in bank ratings.24
EXHIBIT 14 One- and five-year accuracy default position by cohort year, 1983-2014 1-Year100????upe`UP%5-YearCohort YearSource: Moody’s Investors Service
23
See more details in Moody’s Special Comment, European Corporate Default and Recovery Rates, 1985 – 1H 2014, December 2014.
See Moody’s Special Comments - Supported Bank Debt Ratings at Risk of Downgrade Due to New Approaches to Bank Resolution (February 2011), European Banks: How Moody’s Analytic Approach Reflects Evolving Challenges (January 2012), FAQs: Moody’s Finalizes Approach for Rating Certain Bank Contingent Capital Securities and Changes Baseline Assumptions for Rating Bank Subordinated Debt (May 2013), Reassessing Systemic Support for EU Banks (May 2014) and Bank Systemic Support Global Update: Resolution Regimes Drive Shifts in Support (July 2014). For Moody’s latest banking rating methodology and request for comment for proposed changes in its bank rating methodology, please see Global Banks (July 2014) and Proposed Bank Rating Methodology (September 2014).
24
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Moody’s Related Research
Special Comments: ? ? ? ? ? ? ? ? ? ? ? ? ? ?
2015 Outlook – North American Non-Financial Corporates, December 2014 (177645) 2015 Outlook - EMEA Non-Financial Corporates, December 2014 (177584) 2015 Outlook - Global Banks, December 2014 (178070)
Annual Default Study: Corporate Default and Recovery Rates, 1920-2013, March 2014 (165331) European Corporate Default and Recovery Rates, 1985–2014H1, December 2014 (177782) Glossary of Moody’s Ratings Performance Metrics, September 2011 (135451)
Industry Credit Risk: Recent Trends for Global Non-Financial Corporations, October 2013 (159346) Lower Oil Prices in 2015 Reduce E&P Spending and Raise Risk for OFS Sector, January 2015 (1001977) Measuring Ratings Accuracy Using Average Default Position, February 2011 (129451)
Moody’s Global Macro Outlook 2015-16 - Lower oil price fails to spur global growth, February 2015 (1002683)
Moody’s SGL Monitor - Liquidity Pressure Confined to Energy, February 2015 (179258).
Refunding Risk and Needs 2015-19: US Speculative-Grade Corporations Record Maturities Due in 2019; New Issuance Wave Likely in 2017, February 2015 (179022)
Refunding Risk and Needs: EMEA Speculative-grade Non-Financial Companies: Record Liquidity Levels Push Maturity Wall Out to 2018, July 2014 (173568)
US Corporate Default Monitor - Fourth Quarter 2014 - Defaults Projected to Tick Up in 2015, Following Slow End to 2014, January 2015 (1002527)
To access any of these reports, click on the entry above. Note that these references are current as of the date of publication of this report and that more recent reports may be available. All research may not be available to all clients.
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Methodology and Data Sources
Moody’s Definition of Default
Moody’s definition of default is applicable only to debt or debt-like obligations (e.g., swap agreements). For details, please refer to Moody’s Rating Symbols and Definitions.
Methodology
The methodology used in this study can be found in the Glossary of Moody’s Ratings Performance Metrics. The Glossary report is a technical paper that explains how Moody’s calculates default rates, transition rates, and rating performance metrics in detail.
Changes in this Year’s Report
Moody’s occasionally discovers historical defaults, leading to minor revisions of the historical data. In 2014, Moody’s reclassified the industry codes for certain sovereign- and sub-sovereign-related issuers leading to small changes to the universe of the study. As always, the data contained in the most recently published Moody’s default study supersedes the data published in previous reports.
Data Sources
Moody’s bases the results of this study on its proprietary database of ratings and defaults for corporate bond and loan issuers. Municipal and sub-sovereign debt issuers, structured finance securities, private placements, and issuers with only short-term debt ratings are excluded unless otherwise noted. In total, Moody’s data covers the credit experiences of over 20,000 corporate issuers that sold long-term public debt at some time between 1920 and 2014. As of January 1, 2015, over 5,000 corporate issuers held a Moody’s long-term bond, loan, or corporate family rating. Moody’s database of corporate defaults covers more than 3,000 long-term bond and loan defaults by
issuers both rated and non-rated by Moody’s. Additional data sources, such as Barclay’s Fixed Income Index data, supplemented Moody’s proprietary data in the construction of the aggregate dollar volume-weighted default rates. Defaulted bond pricing data was derived from Bloomberg, Reuters, IDC, and TRACE. The majority of these market quotes represent an actual bid on the debt instrument, although no trade may have occurred at that price. Over the 1982-2014 period, the dataset includes post-default prices for
approximately 5,000 defaulted instruments issued by over 1,700 defaulting corporations. Moody’s makes the 1970-2014 credit rating, default, and recovery rate data used in this study available through its Default and Recovery Database (DRD).
Guide to Data Tables and Charts
In this section, we briefly describe the interpretation of some of the Exhibits contained in this report. Exhibit 13 was derived by mapping Moody’s ratings to a linear scale, then taking the median values of the numerically mapped ratings.
Exhibit 21 shows average senior unsecured recovery rates by letter rating and year prior to default. Each cell in the table indicates the average recovery rate on senior unsecured bonds with a specific rating within T years of default. For example, the 37.2% two-year B recovery rate reported in Exhibit 21 indicates the average recovery rate on B rated issues that default at some time within a two-year period, not recovery rate for issuers rated B exactly two years before default. Together with issuer-weighted average cumulative
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default rates, these multi-period recovery estimates are used to calculate cumulative expected credit loss rates, as presented in Exhibit 22.
Exhibits 32 through 37 show issuer-weighted historical average default rates by rating category over various investment horizons. These data were generated by averaging the multi-year default rates of cohorts formed at monthly intervals. In addition to their being statements of historical fact, these data are also useful proxies for expected default rates. For example, over a five-year period a portfolio of B-rated issuers defaulted at a 23.3% average rate between 1983 and 2014 (see Exhibit 34). For an investor with a five-year exposure to a B-rated debt obligation or counterparty, this estimate also happens to be the best estimate of the expected risk of default for a B-rated exposure based on the available historical data, particularly over long investment horizons.
Exhibit 40 shows average cumulative volume-weighted default rates by rating category. Whereas issuer-based default rates weight each issuer equally, these data weight each issuer by the total volume of
defaulted debt; larger defaults receive relatively more weight. Average default rates based on debt volume affected are less suitable estimates of expected default risk. One reason is that issuer default volumes vary considerably over time. On average, a leveraged corporate issuer defaults on approximately $300 million of bonds. However, that total has been as high as $30 billion (WorldCom). Issuer-based default rates receive particular emphasis in the rating process because the expected likelihood of default of a debt issuer holding a given rating is expected be the same regardless of differences in the nominal sizes of the exposures. Exhibit 41 shows the cumulative issuer-weighted historical default rates of cohorts formed between the years 1970 and 2014 (January 1 of each year). These data are a subset of the data used to calculate the issuer-weighted averages shown in Exhibits 32 through 34 (which, again, are based on cohorts formed at monthly time intervals). The default rates in Exhibit 41 may be useful for scenario analysis. For example, if one believed that future default rates for a given pool of issuers will behave as they did in, say, 1997, then one can use the January 1, 1997 cohort cumulative default rates as proxies for expected default rates.
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