STATS20 University of Indianapolis Z Score and Standard Deviations Exam

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STATS20

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Please read the exam questions below.

In August 2017, Tesla Motors raised $1.8 billion in corporate bonds, priced at 5.3%, 8-year notes. The bonds were subordinated to more senior debt and received a B- rating from S&P and a B3 rating from Moody’s.

(1)Using the credit analytics discussed in our class, and traditional metrics, what does your group think should be Tesla’s bond rating before and just after the new bond issue?

(2)Would your answer change if the firm raised an additional $3 billion in bonds to meet production objectives?

(3)What is your estimate of the Bond Rating Equivalent (BRE) as of the most recent (Q1-2019) financials and latest (June 26, 2019) stock price?

(4)Given your answers #1 and #2 above, what are your expected cumulative PD (Probability of Default) and LGD (Loss Given Default) for Tesla for one-year and five(5) years?

(5)What are the bonds issued in 2017 now (June 26, 2019) selling at and what is the bond’s yield to maturity?

(6)Which of the two Z-Score models (Z or Z”) is most applicable to a firm like Tesla? Why? (7)What are the main differences between Z and Z”? List up to four differences.

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TRIUM Exam Question Professor Edward Altman June 2019 In August 2017, Tesla Motors raised $1.8 billion in corporate bonds, priced at 5.3%, 8-year notes. The bonds were subordinated to more senior debt and received a B- rating from S&P and a B3 rating from Moody’s. (1) Using the credit analytics discussed in our class, and traditional metrics, what does your group think should be Tesla’s bond rating before and just after the new bond issue? (2) Would your answer change if the firm raised an additional $3 billion in bonds to meet production objectives? (3) What is your estimate of the Bond Rating Equivalent (BRE) as of the most recent (Q1-2019) financials and latest (June 26, 2019) stock price? (4) Given your answers #1 and #2 above, what are your expected cumulative PD (Probability of Default) and LGD (Loss Given Default) for Tesla for one-year and five (5) years? (5) What are the bonds issued in 2017 now (June 26, 2019) selling at and what is the bond’s yield to maturity? (6) Which of the two Z-Score models (Z or Z”) is most applicable to a firm like Tesla? Why? (7) What are the main differences between Z and Z”? List up to four differences. Please work on this question with no less than two (2) members and no more than three (3). Do not confer with other groups with respect to any aspect of this question. Limit your answer to no more than three (3) pages, (double-spaced) plus Exhibits (no Appendices). Please make sure the typeface is clear and large enough so it is easily legible.        dZ/hD'>K>D ůĂƐƐŽĨϮϬϮϬ    ^ƚƌĂƚĞŐLJĂŶĚ&ŝŶĂŶĐĞĨŽƌ'ůŽďĂůdžĞĐƵƚŝǀĞƐ    EĞǁzŽƌŬ ^ƵŶĚĂLJϭϲʹ&ƌŝĚĂLJϮϴ:ƵŶĞϮϬϭϵ     ƌĞĚŝƚZŝƐŬŽƵƌƐĞWĂĐŬ WƌĞͲDŽĚƵůĞZĞƋƵŝƌĞĚZĞĂĚŝŶŐ         TRIUM Class of 2020: Module 4 − Pack 3: Credit Risk TRIUM Global Executive MBA THIS PRINT COURSEPACK AND ITS ELECTRONIC COUNTERPART (IF ANY) ARE INTENDED SOLELY FOR THE PERSONAL USE OF PURCHASER. ALL OTHER USE IS STRICTLY PROHIBITED. XanEdu™ publications may contain copyrighted materials of XanEdu, Inc. and/or its licensors. The original copyright holders retain sole ownership of their materials. Copyright permissions from third parties have been granted for materials for this publication only. Further reproduction and distribution of the materials contained herein is prohibited. WARNING: COPYRIGHT INFRINGEMENT IS AGAINST THE LAW AND WILL RESULT IN PROSECUTION TO THE FULLEST EXTENT OF THE LAW. THIS COURSE PACK CANNOT BE RESOLD, COPIED OR OTHERWISE REPRODUCED. XanEdu Publishing, Inc. does not exert editorial control over materials that are included in this course pack. The user hereby releases XanEdu Publishing, Inc. from any and all liability for any claims or damages, which result from any use or exposure to the materials of this course pack. Items are available in both online and in print, unless marked with icons. − Print only − Online only TRIUM Class of 2020: Module 4 − Pack 3: Credit Risk Table of Contents “Toward a Botton−Up Approach to Assessing Sovereign Default Risk: An Update” by Altman, Edward I; Rijken, Herbert 1 “The Fate of the Euro: It is Still Italia!” by Altman, Edward I 31 “Defaults and Returns in the High−Yield Bond and Distressed Debt Market: The Year 2018 in Review and Outlook” by Altman, Edward I; Kuehne, Brenda J 37 “The Investment Performance and Market Dynamics of Defaulted and Distressed Corporate Bonds and Bank Loans: 2018 Review and 2019 Outlook” by Altman, Edward I; Kuehne, Brenda J 116 “A 50−Year Retrospective on Credit Risk Models, the Altman Z−Score Family of Models and Their Applications to Financial Markets and Managerial Strategies” by Altman, Edward I 169 i Toward A Bottom-Up Approach to Assessing Sovereign Default Risk: An Update* Edward I. Altman, New York University Stern School of Business, Herbert Rijken, Vrije University ** Abstract We propose a totally new approach toward assessing sovereign risk by examining rigorously the health and aggregate default risk of a nation’s private corporate sector. Models such as our new Z-Metrics™ approach can be utilized to measure the median probability of default of the non-financial sector cumulatively for five years, both as an absolute measure of corporate risk vulnerability and a relative measure compared to other sovereigns and to the market’s assessment via the now liquid credit-default-swap market. Specifically, we measure the default probabilities of listed corporate entities in nine European countries, and the U.S.A., as of 2009 and 2010. These periods coincide with the significant rise in concern with sovereign default risk in the Euro country sphere. We conclude that our corporate health index of the private sector measured at periods prior to the explicit recognition by most credit professionals, not only gave an effective early warning indicator but provided a mostly appropriate hierarchy of relative sovereign risk. Policy officials should, we believe, nurture, not penalize, the tax revenue paying and jobs generating private sector when considering austerity measures of distressed sovereigns. ========================================================= Key Words: Sovereign Risk, Financial Crisis, Default Probability, Z-Metrics JEL classification: F34, F36 * This is an updated version of the article originally published in The Journal of Applied Corporate Finance, vol.23, No. 3, Winter, 2011. **The authors would like to thank Dan Balan and Matthew Watt of RiskMetrics Group, a subsidiary of MSCI, Inc., for computational assistance, and Brenda Kuehne of the NYU Salomon Center for her research assistance. 1 1 During the past four years, bank executives, government officials, and many others have been sharply criticized for failing to anticipate the global financial crisis. The speed and depth of the market declines shocked the public. And no one seemed more surprised than the credit rating agencies that assess the default risk of sovereign governments as well as corporate issuers operating within their borders. Although the developed world had suffered numerous recessions in the past 150 years, this most recent international crisis raised grave doubts about the ability of major banks and even sovereign governments to honor their obligations. Several large financial institutions in the U.S. and Europe required massive state assistance to remain solvent, and venerable banks like Lehman Brothers even went bankrupt. The cost to the U.S. and other sovereign governments of rescuing financial institutions believed to pose “systemic” risk was so great as to result in a dramatic increase in their own borrowings. The general public in the U.S. and Europe found these events particularly troubling because they had assumed that elected officials and regulators were well-informed about financial risks and capable of limiting serious threats to their investments, savings, and pensions. High-ranking officials, central bankers, financial regulators, ratings agencies, and senior bank executives all seemed to fail to sense the looming financial danger. This failure seemed even more puzzling because it occurred years after the widespread adoption of advanced risk management tools. Banks and portfolio managers had long been using quantitative risk management tools such as Value at Risk (“VaR”). And they should also have benefited from the additional information about credit risk made publicly available by the new market for credit default swaps (“CDS”). 2 2 But, as financial market observers have pointed out, VaR calculations are no more reliable than the assumptions underlying them. Although such assumptions tend to be informed by statistical histories, critical variables such as price volatilities and correlations are far from constant and thus difficult to capture in a model. The market prices of options—or of CDS contracts, which have options “embedded” within them—can provide useful market estimates of volatility and risk. And economists have found that CDS prices on certain kinds of debt securities increase substantially before financial crises become full-blown. But because there is so little time between the sharp increase in CDS prices and the subsequent crisis, policy makers and financial managers typically have little opportunity to change course.1 Most popular tools for assessing sovereign risk are effectively forms of “top-down” analysis. For example, in evaluating particular sovereigns, most academic and professional analysts use macroeconomic indicators such as GDP growth, national debt-to-GDP ratios, and trade and budget deficits as gauges of a country’s economic strength and well-being. But, as the recent Euro debt crisis has made clear, such “macro” approaches, while useful in some settings and circumstances, have clear limitations In this paper, we present a totally new method for assessing sovereign risk, a type of “bottom-up” approach that focuses on the financial condition and profitability of an economy’s private sector. The assumption underlying this approach is that the fundamental source of national wealth, and of the financial health of sovereigns, is the economic output and productivity of their companies. To the extent we are correct, such an approach could provide financial professionals and policy makers with a more effective means of anticipating financial 1 See, for example, Hekran Neziri’s “Can Credit Default Swaps predict Financial Crises?” in the Spring 2009 Journal of Applied Economic Sciences, Volume IV/Issue 1(7). Neziri found that CDS prices had real predictive power for equity markets, but that the lead time was generally on the order of one month. 3 3 trouble, thereby enabling them to understand the sources of problems before they become unmanageable. In the pages that follow, we introduce Z-Metrics™, as a practical and effective tool for estimating sovereign risk. Developed in collaboration with the Risk Metrics Group, now a subsidiary of MSCI, Inc., Z-Metrics is a logical extension of the Altman Z-Score technique that was introduced in 1968 and has since achieved considerable scholarly and commercial success. Of course, no method is infallible, or represents the best fit for all circumstances. But by focusing on the financial health of private enterprises in different countries, our system promises at the very least to provide a valuable complement to, or reality check on, standard “macro” approaches. But before we delve into the details of Z-Metrics, we start by briefly reviewing the record of financial crises to provide some historical perspective. Next we attempt to summarize the main findings of the extensive academic and practitioner literature on sovereign risk, particularly those studies designed to test the predictability of sovereign defaults and crises. With that as background, we then present our new Z-Metrics system for estimating the probability of default for individual (non-financial) companies and show how that system might have been used to anticipate many developments during the current EU debt crisis. In so doing, we make use of the most recent (2009 and 2010) publicly available corporate data for nine European countries, both to illustrate our model’s promise for assessing sovereign risk and to identify scope of reforms that troubled governments must consider not only to qualify for bailouts and subsidies from other countries and international bodies, but to stimulate growth in their economies. 4 4 More specifically, we examine the effectiveness of calculating the median company fiveyear probability of default of the sovereign’s non-financial corporate sector, both as an absolute measure of corporate risk vulnerability and a relative health index comparison among a number of European sovereigns, and including the U.S. as well. Our analysis shows that this health index, measured at periods prior to the explicit recognition of the crisis by market professionals, not only gave a distinct early warning of impending sovereign default in some cases, but also provided a sensible hierarchy of relative sovereign risk. We also show that, during the current European crisis, our measures not only compared favorably to standard sovereign risk measures, notably credit ratings, but performed well even when compared to the implied default rates built into market pricing indicators such as CDS spreads (while avoiding the well-known volatility of the latter). Our aim here is not to present a “beauty contest” of different methods for assessing sovereign risk in which one method emerges as the clear winner. What we are suggesting is that a novel, bottom-up approach that emphasizes the financial condition and profitability of a nation’s private sector can be effectively combined with standard analytical techniques and market pricing to better understand and predict sovereign health. And our analysis has one clear implication for policy makers: that the reforms now being contemplated should be designed, as far as possible, to preserve the efficiency and value of a nation’s private enterprises. Modern History Sovereign Crises When thinking about the most recent financial crisis, it is important to keep in mind how common sovereign debt crises have been during the last 150 years—and how frequently such debacles have afflicted developed economies as well as emerging market countries. Figure 1 5 5 shows a partial list of financial crises (identified by the first year of the crisis) that have occurred in “advanced” countries. Overall, Latin America seems to have had more recent bond and loan defaults than any other region of the world (as can be seen in Figure 2). But if we had included a number of now developed Asian countries among the “advanced” countries, the period 19971999 period would be much more prominent. FIGURE 1 Financial Crises, Advanced Countries 1870-2010 Crisis events (first year) Austria Brazil Canada Czechoslovakia China Denmark DEU GBR Greece Italy Japan Netherlands Norway Russia Spain Sweden USA 1893, 1898, 1873, 1870, 1921, 1877, 1880, 1890, 1870, 1887, 1942 1897, 1899, 1918, 1920, 1876, 1873, 1989 1902, 1906, 1910, 1939 1885, 1891, 1974, 1894, 1891, 1921, 1921, 1998 1924, 1897, 1884, 1914, 1931, 1939 1923, 1983 1931, 2008 1902, 1901, 1984, 1932, 1907, 1907, 1931, 1991, 2009 1931, 1921, 1931, 1987 2008 2007 1930, 1935, 1990 1939 1931, 1988 1931, 1978, 2008 1907, 1922, 1931, 1991 1893, 1907, 1929, 1984, 2008 Source: IMF Global Financial Stability Report (2010), Reinhart and Rogoff (2010), and various other sources, such as S&P’s economic reports. 6 6 Source: Compilation by Ingo Walter, NYU Stern School of Business The clear lesson from Figures 1 and 2 is that sovereign economic conditions appear to spiral out of control with almost predictable regularity and then require massive debt restructurings and/or bailouts accompanied by painful austerity programs. Recent examples include several Latin American countries in the 1980s, Southeast Asian nations in the late 1990s, Russia in 1998, and Argentina in 2000. In most of those cases, major problems originating in individual countries not only imposed hardships on their own people and markets, but had major financial consequences well beyond their borders. We are seeing such effects now as financial problems in Greece and other southern European countries not only affect their neighbors, but threaten the very existence of the European Union. Such financial crises have generally come as a surprise to most people, including even those specialists charged with rating the default risk of sovereigns and the enterprises operating 7 7 in these suddenly threatened nations. For example, it was not long ago that Greek debt was investment grade, and Spain was rated Aaa as recently as June 2010.2 And this pattern has been seen many times before. To cite just one more case, South Korea was viewed in 1996 as an “Asian Tiger” with a decade-long record of remarkable growth and an AA- rating. Within a year however, the country was downgraded to BB-, a “junk” rating, and the county’s government avoided default only through a $50 billion bailout by the IMF. And it was not just the rating agencies that were fooled; most of the economists at the brokerage houses also failed to see the problems looming in Korea. What Do We Know about Predicting Sovereign Defaults? There is a large and growing body of studies on the default probability of sovereigns, by practitioners as well as academics.3 A large number of studies, starting with Frank and Cline’s 1971 classic, have attempted to predict sovereign defaults or rescheduling using statistical classification and predicting methods like discriminant analysis as well as similar econometric techniques.4 And in a more recent development, some credit analysts have begun using the “contingent claim” approach5 to measure, analyze, and manage sovereign risk based on Robert Merton’s classic “structural” approach (1974). But because of its heavy reliance on market 2 On April 27, 2010, Standard & Poor’s Ratings Services lowered its long- and short-term credit ratings on the Hellenic Republic (Greece) to non-investment grade BB+; and on June 14, 2010, Moody’s downgraded Greece debt to Ba1 from A2 (4 notches), while Spain was still Aaa and Portugal was A1. Both of the latter were recently downgraded. S&P gave similar ratings. 3 One excellent primer on sovereign risk is Babbel’s (1996) study, which includes an excellent annotated bibliography by S. Bertozzi on external debt capacity that describes many of these studies. Babbel lists 69 potentially helpful explanatory factors for assessing sovereign risk, all dealing with either economic, financial, political, or social variables. Except for the political and social variables, all others are macroeconomic data and this has been the standard until the last few years. Other work worth citing include two practitioner reports—Chambers (1997) and Beers et al (2002)—and two academic studies—Smith and Walter (2003), and Frenkel, Karmann and Scholtens (2004). Full citations of all studies can be found in References section at the end of the article. 4 Including Grinols (1976), Sargen (1977), Feder and Just (1977), Feder, Just and Ross (1981), Cline (1983), Schmidt (1984), and Morgan (1986). 5 Gray, Merton and Bodie (2006, 2007) 8 8 indicators, this approach to predicting sovereign risk and credit spreads has the drawback of producing large—and potentially self-fulfilling—swings in assessed risk that are attributable solely to market volatility. A number of recent studies have sought to identify global or regional common risk factors that largely determine the level of sovereign risk in the world, or in a region such as Europe. Some studies have shown that changes in both the risk factor of individual sovereigns and in a common time-varying global factor affect the market’s repricing of sovereign risk.6 Other studies, however, suggest that sovereign credit spreads are more related to global aggregate market indexes, including U.S. stock and high-yield bond market indexes, and global capital flows than to their own local economic measures.7 Such evidence has been used to justify an approach to quantifying sovereign risk that uses the local stock market index as a proxy for the equity value of the country.8 Finally, several very recent papers focus on the importance of macro variables such as debt service relative to tax receipts and the volatility of trade deficits in explaining sovereign risk premiums and spreads.9 A number of studies have also attempted to evaluate the effectiveness of published credit ratings in predicting defaults and expected losses, with most concluding that sovereign ratings, especially in emerging markets, provide an improved understanding of country risks for 6 See Baek, Bandopadhyaya and Chan (2005). Gerlach, Schulz and Wolff (2010) observe that aggregate risk factors drive banking and sovereign market risk spreads in the Euro area; and in a related finding, Sgherri and Zoli (2009) suggest that Euro area sovereign risk premium differentials tend to move together over time and are driven mainly by a common time-varying factor. 7 See Longstaff, Pan, Pedersen and Singleton (2007). 8 See Oshino and Saruwatari (2005). 9 These include Haugh, Ollivaud and Turner’s (2009) discussion of debt service relative to tax receipts in the Euro area; Hilscher and Nobusch (2010) emphasis on the volatility of terms of trade; and Segoviano, Caceres and Guzzo’s (2010) analysis of debt sustainability and the management of a sovereign’s balance sheet. 9 9 investment analytics.10 Nevertheless, the recent EU debt crisis would appear to contradict such findings by taking place at a time when all the rating agencies and, it would seem, all available models for estimating sovereign risk indicated that Greece and Spain—and others now recognized as high-risk countries—were still classified as investment grade.11 What’s more, although most all of the studies cited above have been fairly optimistic about the ability of their concepts to provide early warnings of major financial problems, their findings have either been ignored or have proven ineffective in forecasting most economic and financial crises. In addition to these studies, a handful or researchers have taken a somewhat different “bottom-up” approach by emphasizing the health of the private sectors supporting the sovereigns. For example, a 1998 World Bank study of the 1997 East Asian crisis12 used the average Z-Score of listed (non-financial) companies to assess the “financial fragility” of eight Asian countries and, for comparison purposes, three developed countries and Latin America. Surprising many observers, the average Z-Score for South Korea at the end of 1996 suggested that it was the most financially vulnerable Asian country, followed by Thailand, Japan, and Indonesia. As noted earlier, Korea’s sovereign bond rating in 1996 was AA- (S&P). But within 10 For example, Remolona, Scatigna and Wu (2008) reach this conclusion after using sovereign credit ratings and historical default rates provided by rating agencies to construct a measure of ratings implied expected loss. 11 To be fair, S&P in a Reuter’s article dated January 14, 2009 warned Greece, Spain and Ireland that their ratings could be downgraded further as economic conditions deteriorated. At that time, Greece was rated A1 by Moody’s and A- by S&P. Interestingly, it was almost a full year later on December 22, 2009 that Greece was actually downgraded by Moody’s to A2 (still highly rated), followed by further downgrades on April 23, 2010 (to A3) and finally to “junk” status (Ba1) on June 14, 2010. As noted earlier, S&P downgraded Greece to “junk” status about three months earlier. 12 See Pomerleano (1998), which is based on a longer article by the author (1997). Taking a somewhat similar approach, many policy makers and theorists have recently focused on the so-called “shadow banking system.” For example, Gennaioli, Martin and Rossi (2010) argued that the financial strength of governments depends on private financial markets and its ability to attract foreign capital. They concluded that strong financial institutions not only attract more capital but their presence also helps encourage their governments to repay their debt. Chambers of S&P (1997) also mentions the idea of a “bottom-up” approach but not to the assessment of sovereign risk, but to a corporate issuer located in a particular country. He advocates first an evaluation of an issuer’s underlying creditworthiness to arrive at its credit rating and then considers the economic, business and social environment in which the entity operates. These latter factors, such as the size and growth and the volatility of the economy, exchange rates, inflation, regulatory environment, taxation, infrastructure and labor market conditions are factored in on top of the micro variables to arrive at a final rating of the issuer. 10 10 a year, Korea’s rating dropped to BB-; and if not for the IMF bailout of $50 billion, the sovereign would almost certainly have defaulted on its external, non-local currency debt. A traditional macroeconomic measure like GDP growth would not have predicted such trouble since, at the end of 1996, South Korea had been growing at double-digit rates for nearly a decade.13 The Z-Metrics™ Approach14 In 2009, we partnered with RiskMetrics Group with the aim, at least initially, of creating a new and better way of assessing the credit risk of companies. The result was our new ZMetrics approach. This methodology might be called a new generation of the original Z-Score model of 1968. Our objective was to develop up-to-date credit scoring and probability of default metrics for both large and small, public and private, enterprises on a global basis. In building our models, we used multivariate logistic regressions and data from a large sample of both public and private U.S. and Canadian non-financial sector companies during the 20-year period 1989-2008.15 We analyzed over 50 fundamental financial statement variables, including measures (with trends as well as point estimates) of solvency, leverage, size, profitability, interest coverage, liquidity, asset quality, investment, dividend payout, and financing results. In addition to such operating (or “fundamental”) variables, we also included equity market price and return variables and their patterns of volatility. Such market variables 13 Afterwards, the World Bank and other economists such as Paul Krugman concluded that that crony capitalism and the associated implicit public guarantees for politically influential enterprises coupled with poor banking regulation were responsible for the crisis. The excesses of corporate leverage and permissive banking were addressed successfully in the case of Korea and its economy was effectively restructured after the bailout. 14 For more details, see Altman, et al, 2010 “The Z-Metrics™ Methodology for Estimating Company Credit Ratings and Default Risk Probabilities,” RiskMetrics Group, continuously updated, available from http://riskmetrics.com/ZMetrics. 15 Our first model’s original sample consisted of over 1,000 U.S. or Canadian non-financial firms that suffered a credit event and a control sample of thousands of firms that did not suffer a credit event, roughly a ratio of 1:15. After removing those firms with insufficient data, the credit event sample was reduced to 638 firms for our public firm sample and 802 observations for our private firm sample. 11 11 have typically been used in the “structural distance-to-default measures” that are at the core of the KMV model16 now owned by Moody’s. In addition to these firm-specific, or micro, variables, we also tested a number of macroeconomic variables that are often used to estimate sovereign default probabilities, including GDP growth, unemployment, credit spreads, and inflation. Since most companies have a higher probability of default during periods of economic stress—for example, at the end of 2008—we wanted to use such macro variables to capture the heightened or lower probabilities associated with general economic conditions.17 The final model, which consists of 13 fundamental, market value, and macroeconomic variables, is used to produce a credit score for each public company. (And as discussed later, although our primary emphasis was on applying Z-Metrics to publicly traded companies, we also created a private firm model by using data from public companies and replacing market value with book value of equity.) The next step was to use a logit specification of the model (described in the Appendix) that we used to convert the credit scores into probabilities of default (PDs) over both one-year and five-year horizons. The one-year model is based on data from financial statements and market data approximately one year prior to the credit event, and the five-year model includes up to five annual financial statements prior to the event. To test the predictive power of the model and the resulting PDs, we segregated all the companies in our sample into “cohorts” according to whether they experience “credit events” 16 Developed by Crosbie in 1998 and adapted for sovereigns by Gray in 2007. In all cases, we carefully examined the complete distribution of variable values, especially in the credit-event sample. This enabled us to devise transformations on the variables to either capture the nature of their distributions or to reduce the influence of outliers. These transformations included logarithmic functions, first differences and dummy variables if the trends or levels of the absolute measures were positive/negative. 17 12 12 that include either formal default or bankruptcy (whichever comes first). All companies that experienced a credit event within either one year or five years were assigned to the “distressed” or “credit event” group (with all others assigned to the non-distressed group). Our test results show considerable success in predicting defaults across the entire credit spectrum from the lowest to the highest default risk categories. Where possible, we compared our output with that of publicly available credit ratings and existing models. The so-called “accuracy ratio” measures how well our model predicts which companies do or do not go bankrupt on the basis of data available before bankruptcy. The objective can be framed in two ways: (1) maximizing correct predictions of defaulting and non-defaulting companies (which statisticians refer to as Type I accuracy) and (2) minimizing wrong predictions (Type II accuracy). As can be seen in Figure 3, our results, which include tests on actual defaults during the period 1989-2009, show much higher Type I accuracy levels for the Z-Metrics model than for either the bond rating agencies or established models (including an older version of Z-Scores). At the same time, our tests show equivalent Type II accuracies at all cutoff levels of scores.18 18 We assessed the stability of the Z-Metrics models by observing the accuracy ratios for our tests in the in-sample and out-of-sample periods and also by observing the size, signs and significance of the coefficients for individual variables. The accuracy ratios were very similar between the two sample periods and the coefficients and significance tests were extremely close. 13 13 FIGURE 3 Type I error for Agency ratings, Z”-score, and Z-Metrics agency equivalent (AE ratings (19892008): one year prediction horizon for publicly owned firms type I error rate (defaulters classified as non-defaulters / total defaulters) 1 AE rating: Z" score type I error rate 0.8 Agency rating AE rating: Z-Metrics public one year 0.6 0.4 0.2 C CC BB B- BB BB - B+ B B- BB + /C C/ C 0 rating class (cutoff score = score at upper boundary of rating class N) Perhaps the most reliable test of credit scoring models is how well they predict critical events based on samples of companies that were not used to build the model, particularly if the events took place after the period during which the model was built (after 2008, in this case). With that in mind, we tested the model against actual bankruptcies occurring in 2009, or what we refer to as our “out-of-sample” data. As with the full test sample results shown in Figure 3, our Z-Metrics results for the “out of sample” bankruptcies of 2009 outperformed the agency ratings and the 1968 Z-score and 1995 Z”-score models using both one-year and five-year horizons. 14 14 A “Bottom-Up” Approach for Sovereign Risk Assessment Having established the predictive power of our updated Z-score methodology, our next step was to use that model (which, again, was created using large publicly traded U.S. companies) to evaluate the default risk of European companies. And after assuring ourselves that the model was transferable in that sense, we then attempted to assess the overall creditworthiness of sovereign governments by aggregating our Z-Metrics default probabilities for individual companies and then estimating both a median default probability and credit rating for different countries. In conducting this experiment, we examined nine key European countries over three time periods, end of 2008, 2009 and 2010 (Figure 4) and again at the end of 2010 (Figure 5), when the crisis was well known. People clearly recognized the crisis and concern for the viability of the European Union in June 2010, when Greece’s debt was downgraded to non-investment grade and both Spain and Portugal were also downgraded. Credit markets, particularly CDS markets, had already recognized the Greek and Irish problems before June 2010. Market prices during the first half of 2010 reflected high implied probabilities of default for Greece and Ireland, but were considerably less pessimistic in 2009. By contrast, as can be seen in Figure 4, which shows our Z-Metric median PD estimates alongside sovereign CDS spreads over both periods,19 our PD estimates were uniformly higher (more risky) in 2009 than early in 2010, even if the world was more focused on Europe’s problems in the latter year. In this sense, our Z metrics PD might be viewed as providing a leading indicator of possible distress. It should be noted that the statistics 19 The median CDS spread is based on the daily observations in the six/four-month periods. The median Z-Metrics PD is based on the median company PDs each day and then we calculated the median for the period. The results are very similar to simply averaging the median PDs as of the beginning and ending of each sample period. 15 15 in Figure 4 report only on the non-financial private sector, while those in Figure 5 include results from our banking credit risk model, as well. For the first four months of 2010, our Z-Metrics’ five-year PDs for European corporate default risk placed Greece (10.60%) and Portugal (9.36%) in the highest risk categories (ZCratings), followed by Italy (7.99%), Ireland (6.45%) and Spain (6.44%), all in the ZC category. Then came Germany and France (both about 5.5% - ZC+), with the U.K. (3.62%) and the Netherlands (3.33%) at the lowest risk levels (ZB– and ZB). The U.S.A. looked comparatively strong, at 3.93% (ZB-). For the most part, these results are consistent with how traditional analysts now rank sovereign risks. Nevertheless, there were a few surprises. The U.K. had a fairly healthy private sector, and Germany and France were perhaps not as healthy as one might have thought. The U.K.’s relatively strong showing might have resulted from the fact that our risk measure at this time did not include financial sector firms, which comprised about 35% of the market values of listed U.K. corporates and were in poor financial condition. And several very large, healthy multinational entities in the U.K. index might have skewed results a bit. The CDS/5-year market’s assessment of U.K. risk was harsher than that of our Z-Metrics index in 2010, with the median of the daily CDS spreads during the first four months implying a 6.52% probability of default, about double our Z-Metrics median level. Greece also had a much higher CDS implied PD at 24.10%, as compared to 10.60% for Z-Metrics. (And, of course, our choice of the median Z-Metrics PD is arbitrary, implying as it does that fully 50% of the listed companies have PDs higher than 10.60%.) We also observed that several countries had relatively high standard deviations of ZMetrics PDs, indicating a longer tail of very risky companies. These countries included Ireland, 16 16 Greece and, surprisingly, Germany, based on 2010 data. So, while almost everyone considers Germany to be the benchmark-low risk country in Europe (for example, its 5-year CDS spread was just 2.67% in 2010, even lower than the Netherlands (2.83%), we are more cautious based on our broad measure of private sector corporate health. 2010 Results Figure 5 shows the weighted-average median PDs for 11 (including now Sweden and Belgium) European countries and the U.S. as of the end of 2010. Note that we now are able to include PDs for the banking sectors (listed firms only) for these countries, an important addition, especially for countries like Greece, Ireland and the U.K. The results show the large difference between Greece (16.45%) and all the rest, but also that the “big-five PIIGS” stand out as the clear higher risk domains. Indeed, we feel that Italy could be the ‘fulcrum” country to decide the ultimate fate of the Euro (see our “Insight” piece in the Financial Times, June 21, 2011). CDS Implied PDs Figure 6 shows the implied PDs for the “Big-Five” European high-risk countries from the start of 2009 to mid-July 2011, just after the European Union’s comprehensive rescue plan was announced (July 21, 2011) for Greece and a contingent plan for other countries. Note that while the PDs, based on CDS spreads and assuming a 40% recovery rate, all came down from their highs, all still imply a considerable default risk. 2010 vs. 2009 As noted earlier from Figure 4, our PD estimates for 2009 were uniformly higher (more risky) than those for early 2010. One important reason for the higher PDs in 2009 is the 17 17 significant impact of the stock market, which is a powerful variable in the Z-Metrics model—and in many other default probability models (notably, Moody’s KMV). Recall that the stock markets were at very low levels at the end of 2008 and into the early months of 2009, while there was a major recovery later in 2009 and in early 2010. FIGURE 4 Financial Health of the Corporate, Non-Financial Sector: Selected European Countries and U.S.A. in 2008-2010 Z-Metrics PD Estimates: Five-Year Public Model Five-Year Implied PD from CDS Spread* Listed Companies Y/E 2010 Median PD Y/E 2009 Median PD Y/E 2008 Median PD 2010 2009 2008 85 3.56% 3.33% 5.62% 2.03% 2.83% 6.06% U.S.A. 2226 3.65% 3.93% 6.97% 3.79% 3.28% 4.47% Sweden 245 3.71% 5.31% 6.74% 2.25% 4.60% 6.33% Ireland 29 3.72% 6.45% 7.46% 41.44% 12.20% 17.00% Belgium 69 3.85% 5.90% 5.89% 11.12% 4.58% 5.53% U.K. 507 4.28% 3.62% 5.75% 4.73% 6.52% 8.13% France 351 4.36% 5.51% 7.22% 4.51% 3.75% 4.05% Germany 348 4.63% 5.54% 7.34% 2.50% 2.67% 3.66% Italy 174 7.29% 7.99% 10.51% 9.16% 8.69% 11.20% Spain 91 7.39% 6.44% 7.39% 14.80% 9.39% 8.07% Portugal 33 10.67% 9.36% 12.07% 41.00% 10.90% 7.39% Greece 93 15.28% 10.60% 11.57% 70.66% 24.10% 13.22% Country Netherlands *Assuming a 40% recovery rate (R); based on the median CDS spread (s). PD computed as 1-e(-5*s/(1-R)). Sources: RiskMetrics Group (MSCI), Markit, Compustat. 18 18 FIGURE 5 Weighted Average Median Five-Year (PD) for Listed Non-Financial* and Banking Firms** (Europe and U.S.), 2010 Non-Financial Firms Banking Firms CDS Weighted Spread Average (%) PD (%)*** Rank Country PD (%) Weight PD (%) Weight Rank Netherlands 3.56 0.977 11.1 0.023 3.73 1 2.03 1 Sweden 3.71 0.984 17.3 0.016 3.93 2 2.25 2 Belgium 3.85 0.972 12.4 0.028 4.21 3 11.12 8 France 4.36 0.986 14.0 0.014 4.49 4 4.51 5 U.K. 4.28 0.977 15.5 0.023 4.54 5 4.73 6 Germany 4.63 0.983 13.1 0.017 4.77 6 2.50 3 U.S.A. 3.65 0.837 13.8 0.163 5.30 7 3.79 4 Spain 7.39 0.948 10.9 0.052 7.57 8 14.80 9 Italy 7.29 0.906 20.0 0.094 8.48 9 9.16 7 Ireland 3.72 0.906 77.6 0.094 10.65 10 41.44 11 Portugal 10.67 0.971 12.1 0.029 10.71 11 41.00 10 Greece 15.28 0.921 30.1 0.079 16.45 12 70.66 12 *Based on the Z-Metrics Probability Model. **Based on Altman-Rijken Model (Preliminary). ***PD based on the CDS Spread as of 4/26/11. FIGURE 6 Five-Year Implied Probabilities of Default (PD)* from Capital Market CDS Spreads, Jan 2009 – Jul 22, 2011** 90 Default Probability (As %) 80 Greece 74.52 70 60 Portugal 53.38 50 Ireland 51.57 40 Spain 22.70 30 20 Italy 19.01 10 Spain Italy Portugal 4-Jul-11 4-Jun-11 4-Apr-11 4-May-11 4-Jan-11 4-Feb-11 4-Mar-11 4-Dec-10 4-Oct-10 4-Nov-10 4-Sep-10 4-Jul-10 4-Aug-10 4-Jun-10 4-Apr-10 Greece 4-May-10 4-Jan-10 4-Feb-10 4-Mar-10 4-Dec-09 4-Oct-09 4-Nov-09 4-Sep-09 4-Jul-09 4-Aug-09 4-Jun-09 4-Apr-09 4-May-09 4-Jan-09 4-Feb-09 4-Mar-09 0 Ireland * Assumes 40% Recovery Rate. PD computed as 1-e(-5*s/(1-R)). ** On July 19, 2011, PDs for all countries peaked as follows: Greece 88.22, Portugal 64.74, Ireland 64.23, Spain 27.54, and Italy 23.74. Sources: Bloomberg and NYU Salomon Center. 19 19 Figure 7 shows, for each of our nine European countries and the U.S., the percentage increases in median stock market index levels and sovereign PD levels between the first six months of 2009 and the first six months of 2010. As can be seen in the figure, most countries enjoyed increases of greater than 20%. Only Greece had a relatively low increase (5.5%), consistent with its modest improvement in its Z-Metrics PD (-8.4%). Figure 6 shows the percentage improvement (lower risk) in sovereigns’ PDs in 2010, which are largely consistent with the increases in stock market index values. Note that Ireland stands out in that while its stock market index value increased by 26.2%, its corporate sector experienced only a modest improvement (-7.4%) in its Z-Metrics’ median PD. This may be attributable to the earlier austerity measures taken in Ireland, as compared to those in other distressed European nations. But likely more important were changes in the many other variables in the Z-Metrics model that are not affected by stock prices, particularly the fundamental measures of corporate health. FIGURE 7 Median Percentage Change in Various Country Stock Market Index Values and Z-Metrics’ PDs Between the First Six Months of 2010 vs. 2009 Country Index France Germany Greece Ireland Italy Netherlands Portugal Spain UK USA CAC40 DAX ASE ISEQ FTSEMIB AEX PSI-20 IBEX35 FTSE100 S&P500 Median Percent Median Z-Metrics Percent Change (2010 vs. 2009)* Change (2010 vs. 2009) 24.1% 31.8% 5.5% 26.2% 18.2% 34.4% 17.8% 20.9% 27.8% 31.9% -23.6% -24.5% - 8.4% - 7.4% -24.0% - 25.3% -22.4% -12.9% -37.6% -43.6% *Median of the various trading day stock index values and PDs, first six months of 2009 vs. First six months of 2010. Sources: Z-Metrics Model calculations from RiskMetrics (MSCI) Group, Bloomberg for stock index values. 20 20 Comparing PD Results Based on Privately Owned vs. Publicly Owned Firm Models As shown in Figures 4 and 5, the improvement (reduction) in Z-Metrics PDs for most countries in 2010—a period in which most EU sovereigns appeared to be getting riskier—looks attributable in large part to the stock market increases in almost all countries. But to the extent such increases could conceal a deterioration of a sovereign’s credit condition, some credit analysts might prefer to have PD estimates that do not make use of stock market data. With this in mind, we applied our private firm Z-Metrics model to evaluate the same nine European countries and the U.S. The private and public firm models are the same except for the substitution of equity book values (and volatility of book values) for market values. This adjustment is expected to remove the capital market influence from our credit risk measure. Figure 8 summarizes the results of our public vs. private firm Z-Metrics models comparative PD (delta) results for 2010 and 2009. For eight of the ten countries, use of the private firm model showed smaller reductions in PDs when moving from 2009 to 2010 than use of the public model. Whereas the overall average improvement in PDs for the public firm model was a drop of 1.91 percentage points, the drop was 0.79% for our private firm model. These results are largely the effect of the positive stock market performance in late 2009 and into 2010. But improvements in general macro conditions, along with their effects on traditional corporate performance measures, also helped improve (reduce) the PDs. Moreover, in two of these eight countries—the U.K. and France—not only did the public firm model show an improved (lower) PD, but the private firm model’s PD actually got worse (increased) in 2010 (as indicated by the positive delta in the last column of Figure 8). 21 21 FIGURE 8 Private Vs. Public Firm Model PDs in 2010 and 2099 Country No. Listed Companies 2010 2009 Public-Firm Z-Metrics Model Private-Firm Z-Metrics Model PDs PDs PDs PDs 2010 2009 Delta* 2010 2009 Delta* Netherlands 61 60 3.33% 5.62% -2.29% 5.25% 6.00% -0.75% U.K. 442 433 3.62% 5.75% -2.13% 6.48% 5.97% +0.49% U.S.A. 2226 2171 3.93% 6.97% -3.04% 4.28% 4.80% -0.52% France 297 294 5.51% 7.22% -1.71% 7.33% 7.19% +0.14% Germany 289 286 5.54% 7.34% -1.80% 6.29% 7.56% -1.27% Spain 82 78 6.44% 7.39% -0.95% 8.06% 9.32% -1.26% Ireland 28 26 6.45% 7.46% -1.01% 6.31% 6.36% -0.05% Italy 155 154 7.99% 10.51% -2.52% 8.14% 9.07% -0.89% Portugal 30 30 9.36% 12.07% -2.71% 8.73% 9.62% -0.89% Greece 79 77 10.60% 11.57% -0.97% 11.03% 13.93% -2.90% Average 6.28% 8.19% -1.91% 7.19% 7.98% -0.79% _________________________________________________________________________ *Negative sign means improved credit risk. Sources: Figure 4 and Riskmetrics (MSCI). Correlation of Sovereign PDs: Recent Evidence on Z-Metrics vs. Implied CDS PDs As a final test of the predictive of our approach, we compared our Z-Metrics five-year median PDs for our sample of nine European countries (both on a contemporary basis and for 2009) with the PDs implied by CDS spreads in 2010. The contemporary PD correlation during the first third of 2010 was remarkably high, with an R2 of 0.82. This was a period when it was becoming quite evident that certain European countries were in serious financial trouble and the likelihood of default was not trivial. But if we go back to the first half of 2009, the correlation drops to an R2 of 0.36 (although it would be considerably higher, at 0.62, if we excluded the case of Ireland). Ireland’s CDS implied PD was considerably higher in 2009 than 2010 (17.0% vs. 12.0%), while the Z-Metrics PD was relatively stable in the two years (7.5% and 6.5% 22 22 respectively).20 In 2010, whether we calculate the correlation with or without Ireland, the results are essentially the same (0.82 and 0.83). Given the predictive success of Z-metrics in the tests already described, we were curious to find out whether it could be used to predict capital market (i.e., CDS) prices. So, we regressed our public firm model’s 2009 Z-Metrics median, non-financial sector PDs against implied CDS PDs one year later in 2010. Admittedly, this sample was quite small (10 countries) and the analysis is for only a single time-series comparison (2009 vs. 2010). Nevertheless, these two years spanned a crucial and highly visible sovereign debt crisis, whereas the PDs implied by prior years’ Z-Metrics and CDS showed remarkably little volatility.21 As can be seen in Figure 9, the correlation between our Z-Metrics PDs and those implied by CDS one year later proved to be remarkably strong, with an r of 0.69 and R2 of 0.48. In sum, the corporate health index for our European countries (plus the U.S.) in 2009 explained roughly half of the variation in the CDS results one year later.22 A potential shortcoming of our approach is that we are limited in our private sector corporate health assessments to data from listed, publicly held firms. This is especially true for relatively small countries like Ireland (with just 28 listed companies), Portugal (with 30), Greece (79), Netherlands (61), and Spain (82). Since the private, non-listed segment is much larger in 20 No doubt the CDS market was reacting quite strongly to the severe problems in the Irish banking sector in 2009, while Z-Metrics PDs were not impacted by the banks. This implies a potential strength of the CDS measure, although the lower CDS implied PD in early 2010 was not impressive in predicting the renewed problems of Irish banks and its economy in the fall of 2010. 21 The last time an entire region and its many countries had a sovereign debt crisis was in Asia in 1997-1998. Unfortunately, CDS prices were not prominent and the CDS market was illiquid at that time. 22 Several other non-linear structures (i.e., power and exponential functions) for our 2009 Z-Metrics vs. 2010 CDS implied PDs showed similar results. In all cases, we are assuming a recovery rate of 40% on defaults in calculation of implied sovereign PDs. 23 23 all of the countries, we are not clearly assessing the health of the vast majority of its firms and our sovereign health index measure is incomplete.23 But if the size of the listed firm population is clearly a limitation in our calculations, there does not seem to be a systematic bias in our results. To be sure, the very small listings in Ireland, Portugal, and Greece appear heavily correlated with their high PDs, but the country with the lowest PD (the Netherlands) also has a very small listed population. Another potentially important factor is that the listed population in countries like the U.K. and the Netherlands is represented quite heavily by multinational corporations that derive most of their income from outside their borders.24 23 We suggest that complete firm financial statement repositories, such as those that usually are available in the sovereign’s central bank be used to monitor the performance of the entire private sector. 24 Results showing the percentage of “home-grown” revenues for listed firms across our European country sample were inclusive, however, as to their influence on relative PDs. 24 24 25 CDS Implied PD 0.00% 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% Figure 9 2.00% 4.00% Spain Ireland 25 6.00% 8.00% Z-metrics PD France Netherlands USA Germany UK R-Square = 48% y = 1.9367x - 0.0743 12.00% Portugal Greece Italy 10.00% 2009 Z-metrics PD vs. 2010 CDS Implied PD Figure 11 14.00% Conclusion and Implications As the price for bailing out distressed sovereigns, today’s foreign creditors, especially the stronger European nations, are demanding a heavy dose of austerity. Several governments, including those of Greece, Ireland, Spain, Portugal, Italy, and the U.K., have already enacted some painful measures. Others, such as France and Hungary, have either resisted austerity measures or faced significant social unrest when austerity measures have been proposed. These measures typically involve substantial cuts in cash benefits paid to public workers, increases in retirement age, and other reduced infrastructure costs, as well as increased taxes for companies and individuals. The objective is to reduce deficits relative to GDP and enhance the sovereigns’ ability to repay their foreign debt and balance their budgets. While recognizing the necessity of requiring difficult changes for governments to qualify for bailouts and subsidies, we caution that such measures should be designed to inflict as little damage as possible on the health and productivity of the private enterprises that ultimately fund the sovereign. The goal should be to enable all private enterprises with clear going concern value to pay their bills, expand (or at least maintain) their workforces, and return value to their shareholders and creditors (while those businesses that show no promise of ever making a profit should be either reorganized or liquidated). For this reason, raising taxes and imposing other burdens on corporate entities is likely to weaken the long-run financial condition of sovereigns. To better estimate sovereigns’ risk of default, we propose that traditional measures of macroeconomic performance be combined with more modern techniques, such as the contingent claims analysis pioneered by Robert Merton and the bottom-up approach presented in these pages. Along with the intuitive appeal of such an approach and our encouraging empirical results, the probabilities of sovereign default provided by aggregating our Z-Metrics across a 26 26 national economy can be seen, at the very least, as a useful complement to existing methods and market indicators—one that is not subject to government manipulation of publicly released statistics. Using our approach, the credit and regulatory communities could track the performance of publicly held companies and the economies in which they reside—and by making some adjustments, unlisted entities as well. And if sovereigns were also willing to provide independently audited statistics on a regular basis, so much the better. Edward Altman is the Max L. Heine Professor of Finance, NYU Stern School of Business, ealtman@stern.nyu.edu. Herbert Rijken is Professor of Finance, Vrije University, Amsterdam, the Netherlands, hrijken@feweb.vu.nl. 27 27 APPENDIX: Logit Model Estimation of Default Probabilities We estimated our credit scoring model based on a standard logit-regression functional form whereby: CSi,t D  6% j &i,t  H i,t (1) CSi,t ZMetrics credit score of companyi at timet Bj variable parameters(or weights) X i ,t set of fundamenta l , marketbased and macroecono mic variables for firm i quarterobservations H i,t error terms (assumedto be identically and independently distributed ) CS i ,t is transformed int o a probability of defaultby PDi ,t x 1 1  exp (CS i ,t ) We compare Z-Metrics results with issuer ratings. To ensure a fair comparison, credit scores are converted to agency equivalent (AE) ratings by ranking credit scores and by matching exactly the actual Agency rating distribution with the AE rating distribution at any point in time. x We also compare our Z-Metrics results to the well established Altman Z”-score (1995) model.25 25 Altman’s original Z-score model (1968) is well-known to practitioners and scholars alike. It was built, however, over 40 years ago and is primarily applicable to publicly-held manufacturing firms. A more generally applicable Z”-score variation was popularized later (Altman, Hartzell and Peck, 1995) as a means to assess the default risk of non-manufacturers as well as manufacturers, and was first applied to emerging market credits. Both models are discussed in Altman and Hotchkiss (2006) and will be compared in several tests to our new Z-Metrics model. Further, the Altman Z-score models do not translate easily into a probability of default rating system, as does the ZMetrics system. Of course, entities that do not have access to the newer Z-Metrics system can still use the classic Zscore frameworks, although accuracy levels will not be as high and firm PDs not as readily available. 28 28 References Abassi, B. and R. J. Taffler, 1982, “Country Risk: A Model of Economic Performance Related to Debt Servicing Capacity,” WP #36, City University Business School, London. Altman, E. I., 1968, “Financial Ratios Discriminant Analysis and the Prediction of Corporate Bankruptcy,” Journal of Finance, v. 23, 4, September, 189. Altman, E. I. and E. Hotchkiss, 2006, Corporate Financial Distress and Bankruptcy, 3rd edition, John Wiley & Sons, NY and NJ. Altman, E. 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Chambers, W.J., 1997, “Understanding Sovereign Risk,” Credit Week, Standard & Poor’s January 1. Cline, W., 1983, “A Logit Model of Debt Restructuring, 1963-1982,” Institute for International Economics, WP, June. Feder, G. and R. E. Just, 1977, “A Study of Debt Servicing Capacity Applying Logit Analysis,” Journal of Development Economics,” 4 (1). Feder, G. R. E. Just and K. Ross, 1981, “Projecting Debt Capacity of Developing Countries,” Journal of Financial & Qualitative Analysis, 16 (5). Flynn, D., 2009, “S&P Cuts Greek Debt Rating as Global Crisis Bites,” Reuters, January 14. Frank, C. R. and W. R. Cline, 1971, “Measurement of Debt Servicing Capacity: An Application of Discriminant Analysis,” Journal of International Economics, 1. Frenkel, M., A. Karmann and B. Scholtens, eds., 2004, “Sovereign Risk and Financial Crises,” Heidelberg and New York, Springer, xii, 258. Gennaioli, N., A. Martin and S. Rossi, 2010, “Sovereign Default, Domestic Banks and Financial Institutions,” Working Paper, Imperial College, London, July. Gerlach, S., A. Schultz and G. Wolff, 2010, “Banking and Sovereign Risk in the Euro Area,” Deutsche Bundesbank, Research Centre, Discussion Paper Series 1: Economic Studies: 2010. Gray, D. F., R. Merton and Z. Bodie, 2006, “A New Framework for Analyzing and Managing Macrofinancial Risk of an Economy,” IMF Working Paper, October. Gray, D. F., R. Merton and Z. Bodie, 2007, “Contingent Claims Approach to Measuring and Managing Sovereign Credit Risk,” Journal of Investment Management, vol. 5, No. 4, p.1. Grinols, E., 1976, “International Debt Rescheduling and Discrimination Using Financial Variables,” U.S. Treasury Dept., Washington, D.C. 29 29 Haugh, D., P. Ollivaud and D. Turner, 2009, “What Drives Sovereign Risk Premiums?: An Analysis of Recent Evidence from the Euro Areas,” OECD, Economics Department, Working Paper, 718. Hilscher, J. and Y. Nosbusch, 2010, “Determinants of Sovereign Risk: Macroeconomic Fundamentals and the Pricing of Sovereign Debt,” Review of Finance, Vol. 14 (2), pp. 23562. IMF, 2010, “Global Financial Stability Report,” Washington, D.C. KMV Corporation, 1999, “Modeling Default Risk,” KMV Corporation, R. Crosbie. Krugman, P., 1989, “Financing vs. Forgiving a Debt Overhang: Some Analytical Notes,” Journal of International Business Studies, 17. Longstaff, F., J. Pan, L. Pedersen and K. Singleton, 2007, “How Sovereign is Sovereign Credit Risk?,” National Bureau of Economic Research, Inc. , NBER Working Paper: 13658. Merton, R. C., 1974, “On the Pricing of Corporate Debt: The Risk Structure of Interest Rates,” Journal of Finance, 29, May, 449. Neziri, H., 2009, “Can Credit Default Swaps predict Financial Crises?,” Journal of Applied Economic Sciences, Volume IV/Issue 1(7) Oshiro, N., Y. Saruwatari, 2005, “Quantification of Sovereign Risk: Using the Information in Equity Market Prices,” Emerging Markets Review, Vol. 6 (4), pp. 346-62. Pomerleano, M., 1998, “Corporate Finance Lessons from the East Asian Crisis,” Viewpoint, The World Bank Group, Note #155, October. Pomerleano, M., 1999, “The East-Asia Crisis and Corporate Finance – The Untold Micro Study,” Emerging Markets Quarterly. Reinhart, M. and K. Rogoff, 2010, “This Time is Different,” Princeton University Press, Princeton, NJ. Remolona, E. M. Scatigna and E. Wu, 2008, “A Ratings-Based Approach to Measuring Sovereign Risk,” International Journal of Finance and Economics, Vol. 13 (1), pp. 26-39. Saini, K. and P. Bates, 1978, “Statistical Techniques for Determining Debt Servicing Capacity for Developing Countries: Analytical Review of the Literature and Further Empirical Results,” Federal Reserve Bank of New York Research Paper, #7818. Sargen, H., 1977, “Economics Indicators and Country Risk Appraisal,” Federal Reserve Bank of San Francisco, Economic Review, Fall. Schmidt, R., 1984, “Early Warning of Debt Rescheduling,” Journal of Banking and Finance, 8. Segoviano, B., A. Miguel, C. Caceres and V. Guzzo, 2010, “Sovereign Spreads: Global Risk Aversion, Contagion or Fundamentals?,” IMF Working Paper: 10/120, p. 29. Sgherri, S. and E. Zoli, 2009, “Euro Area Sovereign Risk During the Crisis,” International Monetary Fund, IMF Working Papers: 09/222. Smith, R. and I. Walter, 2003, Global Banking, Oxford University Press, London. Trebesch, C., U. Das and M. Papaioannou, 2010, “Sovereign Default Risk and Private Sector Access to Capital in Emerging Markets,” IMP Working Paper: October. 30 30 The Fate of the Euro: It is Still Italia! Edward I. Altman1 One year ago, I wrote about the European debt crisis and predicted that Italy would be either the hero or villain of the Euro. I felt then, as I do now, that the escalation of the crisis would come down to whether one of the key Southern European countries would be able to survive, without a bailout, the onslaught of a capital market “attack.” Based on the inherent strengths of its fundamental competitive and wealth attributes, I concluded that Italy would be the “fulcrum country,” with a 70% chance to emerge successfully, enabling the Euro itself to survive. Since the end of 2010, unfortunately, Italy’s fundamentals have deteriorated dramatically, its economy is in a double-dip recession, unemployment is over 10%, and even top European politicians are now saying that the Euro’s survival is at the critical stage. My current assessment is that Italy’s, and also the Euro’s, solvency is at best a 50-50 probability, notwithstanding the announced change in EU policy toward pumping €130 billion of fiscal stimuli into the most vulnerable European nations and steps toward a financial and political union. My increased pessimism is based on our “bottom-up” approach toward assessing the health of any sovereign. Recently, Professor Herbert Rijken of the Free University of Amsterdam and I began suggesting that financial and political analysts should not focus solely on the traditional macroeconomic metrics like Debt/GDP or Deficits/GDP, but also to monitor the health of the sovereign based on the condition of its private sector - - both its non-financial corporate sector and its privately owned banks. After all, if the corporate sector is healthy, it can pay more taxes from profits and hire more workers, as well as provide vital new investments. On 1 Max L. Heine Professor of Finance, NYU Stern School of Business, Director of Debt and Credit Markets, NYU Salomon Center 1 31 the other hand, if a significant proportion of a sovereign’s private sector is on the verge of financial distress and bankruptcy, or needs increased capital itself, it cannot hope to contribute much, particularly if companies are asked to increase tax payments. We developed an index of individual firm probability of default, based on an updated version of my somewhat ancient, but still relevant and accurate Z-Score Approach (now available in a new App called “AltmanZscoreplus.com”). We then observed the median (50th percentile) and 75th percentile firm’s probability of default (PD) for the non-financial, listedfirm, population in each of nine European countries, as well as in the U.S.A., for the years 20082010 (see Figure 1). The results were extremely revealing with the highest risk countries, Greece (16.7%) and Portugal (16.6%), followed by Italy (11.3%), and Spain (8.6%), showing the most troubling corporate PDs as of year-end 2008, even before the financial world showed much concern with these sovereigns and before the Greek PM informed the world in October 2009, that its budget deficit was 12.7% (double the previous estimate). Our updated bottom-up results through the end of 2011 are startling and highly indicative of the profound deterioration of all European nations in just one year, with Italy and France “leading the way” down. For example, the 75th percentile listed non-financial company in Italy (e.g., indicative of the 25% most risky companies), as of year-end 2010, had a PD of 14.1% over a five-year horizon (note that 25% of the private sector’s PDs was greater than 14.1%). In just one year, that figure spiked to 26.4%, (second only in Europe to Greece’s astounding 50.5%), a deterioration of 87% (see Figure 2), the largest drop in Europe. It is worth noting that these are the largest, and arguably the most solvent Italian enterprises. A weakened banking sector, with its own profitability and capital challenges, will be hard pressed to support such a problematic corporate sector. While Italy’s global competitiveness is far stronger than that of Greece, 2 32 Portugal and Spain, the problem is that unlike these smaller vulnerable countries, Italy is “too big to save.” Thus, while we focus on the private sector, the world’s markets, as well as European politicians, are traumatized by Italy’s escalating cost of new debt financing that now exceeds the thought to be unsustainable 6.0% level for 10-year bonds. Furthermore, the implied 5-year PD from CDS spreads for Italy is above 35% and close to 40% for Spain, (Figure 3). No wonder Italy’s PM, Mario Monti, has conceded that the survival of the Euro is now at stake. His country will be the last bulwark before the possible bursting of the “Euro-dyke” that EU leaders are so desperately trying to keep intact - - with good reason. A surprising recent finding in our sovereign risk assessment is that the country with the second largest percentage deterioration in the risk profile of its private, non-financial in sector in 2011 is France, whose 75th percentile-firm has a PD of 14.8%, up from 8.5% one year ago - - a 74% slide! Among the countries we analyzed in 2011, the Netherlands’s 75th percentile was the lowest (8.7%). Incidentally, the 75% percentile U.S. company’s PD is slightly higher than the Dutch at 11.7% - - a respectable figure similar to that of Germany. One can wonder if France, with its enormously leveraged and problematic banking system and with an increasingly vulnerable private sector, is indeed worthy of its AA+ (S&P) and AAA (Fitch), Aaa (Moody’s) ratings. I certainly question those lofty assessments. The same could be said for Italy’s A3/Arating. So, will Italy make it? Will the Euro survive? We should know the answer within 12 months, perhaps sooner, but the odds against this happy ending are increasing with each piece of bad news about Italy’s economy and its fundamental components. 3 33 Figure 1: Financial Health of the Corporate, Non-Financial Sector: Selected European Countries and U.S.A. in 2008 – 2011, Z-Metrics Median PD Estimates Z-Metrics PD Estimates*: Five-Year Public Model Country Listed Companies (2010) Y/E 2011 Y/E 2010 Y/E 2009 Y/E 2008 Sweden 245 2.7% 2.6% 3.1% 6.7% Netherlands 85 3.1% 2.5% 2.7% 5.0% Germany 348 4.6% 3.9% 4.5% 7.6% U.K. 507 4.6% 3.7% 4.5% 7.3% U.S.A. 2226 4.8% 3.8% 3.3% 4.5% France 351 6.6% 4.0% 4.6% 7.2% Spain 91 10.6% 7.1% 5.9% 8.6% Italy 174 11.9% 7.7% 7.7% 11.3% Portugal 33 15.1% 9.9% 8.2% 16.6% Greece 93 26.7% 18.7% 11.9% 16.7% Median PD * Since the Z-Metrics Model is not practically available for most analysts, we could substitute the Z”-Score method (available from ). Sources: RiskMetrics Group (MSCI), Markit, Compustat. 4 34 Figure 2: Financial Health of the Corporate, Non-Financial Sector: Selected European Countries and U.S.A. in 2008 – 2011, Z-Metrics 75th Percentile PD Estimates Z-Metrics PD Estimates*: Five-Year Public Model Listed Companies (2010) Y/E 2011 Y/E 2010 Y/E 2009 Y/E 2008 Netherlands 85 8.7% 5.7% 6.7% 15.7% Sweden 245 9.6% 6.8% 8.0% 13.5% U.K. 507 9.7% 5.7% 9.3% 16.6% Germany 348 11.2% 9.7% 11.9% 22.2% U.S.A. 2226 11.7% 8.0% 11.5% 19.5% France 351 14.8% 8.5% 10.3% 19.2% Spain 91 20.1% 13.2% 12.7% 18.4% Portugal 33 24.9% 20.1% 12.3% 26.6% Italy 174 26.4% 14.1% 18.1% 27.1% Greece 93 50.5% 40.1% 27.6% 31.0% Country 75th Percentile PD * Since the Z-Metrics Model is not practically available for most analysts, we could substitute the Z”-Score method (available from ). Sources: RiskMetrics Group (MSCI), Markit, Compustat. 5 35 Figure 3: Five Year Implied Probabilities of Default (PD) from Capital Market CDS Spreads*, Jan. 2009 – June 25, 2012 100 Greece (9/16/11) 94.75 90 Default Probability (As %) 80 Portugal 49.77 70 60 50 Ireland 40.27 40 Spain 38.42 30 Italy 35.65 20 10 Spain Italy Greece Portugal 4-May-12 4-Mar-12 4-Jan-12 4-Nov-11 4-Sep-11 4-Jul-11 4-May-11 4-Mar-11 4-Jan-11 4-Nov-10 4-Sep-10 4-Jul-10 4-May-10 4-Mar-10 4-Jan-10 4-Nov-09 4-Sep-09 4-Jul-09 4-May-09 4-Mar-09 4-Jan-09 0 Ireland * Assuming a 40% recovery rate (R); based on the median CDS spread (s). PD Computed as 1-e (-5*s/(1-R)). Source: Bloomberg 6 36 New York University Salomon Center Leonard N. Stern School of Business Special Report on Defaults and Returns in the High-Yield Bond and Distressed Debt Market: The Year 2018 in Review and Outlook By Edward I. Altman and Brenda J. Kuehne February 21, 2019 37 February 21, 2019 Altman-Kuehne High-Yield Bond Default and Return Report Contents Executive Summary ....................................................................................................................................... 3 Defaults, Default Rates, and Recoveries ........................................................................................................ 5 Bankruptcies in 2018 and Trends in Bankruptcy Filings ............................................................................. 10 Industry Defaults.......................................................................................................................................... 16 Age of Defaults ............................................................................................................................................ 19 Fallen Angel Defaults .................................................................................................................................. 21 Default Losses and Recoveries .................................................................................................................... 23 Recovery Forecast Versus Actual ................................................................................................................ 27 Distressed Exchanges and Recoveries in 2018 ............................................................................................ 28 Related Recovery Statistics .......................................................................................................................... 31 Mortality Rates and Losses .......................................................................................................................... 35 Returns, Yields and Spreads ........................................................................................................................ 37 A Continuing Investment Dilemma ............................................................................................................. 39 Credit Markets: The Benign Credit Cycle Continues .................................................................................. 42 Global Debt Concerns .................................................................................................................................. 44 New Issues and Other Changes in Size of the High-Yield Market .............................................................. 53 Proportion and Size of the Distressed and Defaulted Public and Private Debt Markets .............................. 53 Forecasting Default Rates and Recoveries ................................................................................................... 57 Performance of Defaulted Debt Securities ................................................................................................... 63 Appendix A: Quarterly Default Rate Comparison (1989 -- 2018) ............................................................... 64 Appendix B: Defaulted Corporate Straight Bond Issues ............................................................................. 67 Appendix C: Distressed Exchanges ............................................................................................................. 69 Appendix D: Leveraged Loan Defaults ....................................................................................................... 70 Appendix E: Chapter 11 Filings with Liabilities ≥$1 billion (2008 -- 2018) ............................................... 71 Appendix F: Chapter 11 Filings by Liability Size in 2018 .......................................................................... 75 Appendix G: Defaults by Industry ............................................................................................................... 77 Appendix H: Emergences from Bankruptcy ................................................................................................ 78 Acknowledgments Dr. Altman is the Max L. Heine Professor of Finance, Emeritus, and Director of the Credit and Debt Markets Research Program at the NYU Salomon Center, Leonard N. Stern School of Business. Brenda Kuehne is a Credit and Debt Markets Research Specialist at the NYU Salomon Center. We appreciate the assistance of the several market makers who provided us with price quotations. We offer a special thanks to the various rating agencies, S&P, Ben Schlafman and Kerry Mastroianni of New Generation Research, and the members of the Credit Strategy Investment team of Paulson & Co. 2 38 February 21, 2019 Altman-Kuehne High-Yield Bond Default and Return Report Despite a continuation of extremely low default and relatively high recovery rates, the year 2018 proved to be a challenging one for investors and issuers of high-yield bonds from both a new issuance and performance perspective; the annual amount of new issuance was the lowest recorded since 2009 and annual returns, on both an absolute and relative basis, were negative. Additionally, 9.9% of outstanding high-yield bonds were classified as distressed by year-end, compared to only 6.1% one year earlier. The default rate declined to 1.74% in 2018, 6bp lower than last year, and was the lowest annual rate since 2013. Our forecast for 2018 was for a default rate of 2.53% (2.88% if we equally weight our two main forecasting models). The fourth-quarter 2018 default rate was 0.38%, slightly lower than one year (0.45%), but higher than one quarter (0.08%), prior. This marks the third consecutive quarter in which quarterly default rates were lower than 0.50%. The S&P/LSTA 12-month, dollar-denominated default rate on leveraged loans decreased from 2.05% at the end of 2017 to 1.63% by year-end 2018, while the issuer default rate fell from 1.72% to 1.56%. In the majority of months since March 2013, the dollar-denominated loan default rate exceeded the issuerdenominated rate due to relatively large defaults by only a few issuers. Typically, during benign credit cycles, however, the issuer denominated default rate on loans and bonds exceeds the dollar denominated rate. Default losses on high-yield bonds were 0.91%, based on a weighted average recovery rate of 52.1% just after default, a level notably higher than the historical arithmetic average of 46.0%, but slightly lower than last year’s rate of 56.7%, the highest rate since 2014. The weighted average recovery on bankruptcy and payment defaults in 2018 was lower at 48.6%, with the recovery on distressed exchange defaults considerably higher at 66.4%. Returns on high-yield bonds were negative in 2018, ending the year at -2.13% (FTSE Index).1 The excess return versus 10-yr US Treasuries was -2.11%, inferior compared to the 2.72% historic average, and was well below the 4.92% experienced in 2017. Yield-to-maturity (YTM) spreads versus 10-yr US Treasuries increased to 5.47% by year-end 2018, a level 153bp higher than year-end 2017, and exceeded the historical average of 5.19% for the first time since 2015. Defaulted bonds and bank loans experienced similar losses in 2018, with a combined annual return of -3.25%. US high-yield bond issuance in 2018 decreased considerably from last year ($240.7 billion), reaching a total of $143.6 billion (BoAML statistics). The proportion of high-yield bonds issued at B- or lower was slightly above the 22.7% average of the last five years, with 23.3% issued in these rating categories in 2018. Similarly, new issuance of leveraged loans (secured, noninvestment grade loans) decreased in 2018, with S&P estimating that $620 billion of new, U.S. dollar-denominated leveraged loans (institutional and “pro rata” syndicated by banks)) were issued during the year. This amount was down approximately 5% from the $651 billion issued in 2017. 1 In 2017 FTSE took over the calculation of the high-yield bond index from our theretofore usual Citi source. 39 3 February 21, 2019 Altman-Kuehne High-Yield Bond Default and Return Report The distress ratio of bonds yielding more than 1,000bp over comparable duration treasuries, measured by number of issues, increased significantly to 9.9% as of the end of 2018, from 3.9% three months earlier, but a little less so from 6.1% at year-end 2018. The distress ratio at year-end was below the historic year-end average of 16.8%, but was at the historical median of 9.9%. We estimate that the face value of the distressed and defaulted debt markets rose to $847.0 billion as of December 31, 2018, up 13% from $746.8 billion one year earlier, primarily due to the increase in the distress ratio. The market value estimate increased, as well, to approximately $477 billion from $414 billion one year earlier. Based on three different methodologies, our 2019 default rate forecasts range from 2.28% (distress ratio method) to 4.20% (mortality rate method), with an average rate of 3.46%, a level above the historic average of 3.3%. If we equally weight our mortality rate and market-level measures, the forecasted default rate rises slightly to 3.65%. 4 40 February 21, 2019 Altman-Kuehne High-Yield Bond Default and Return Report Defaults, Default Rates, and Recoveries High-yield bond default rates on U.S., Canadian and Mexican high-yield bonds decreased slightly in 2018, remaining below historic averages. The rate decreased from 1.81% at year-end 2017 to 1.74% for all of 2018. Defaults include straight corporate bonds whose firms went bankrupt, missed an interest payment and did not cure it within the grace or forbearance period, or completed a distressed exchange. The 2018 rate is based upon a mid-year market size of $1.66 trillion, up by $42 billion from a year earlier. In all, $29.0 billion of defaults were recorded in 2018. Note in Figure 1 that the historical weighted-average annual default rate is 3.27% over the 48-year period (1971-2018). This weighted-average rate is down compared to 3.38% at the end of 2017. The last year that the historical weighted average default rate was above 4.0% was 2010. Our weights are based on the par value of high-yield bonds outstanding in each year. The historical arithmetic unweighted annual average default rate fell to 3.08% from 3.10% one year earlier. Of interest, the 2018 default rate was about the same level as the median annual rate (1.77%) over the last 48 years. The fourth-quarter 2018 default rate was 0.38%, slightly lower than one year (0.45%), but higher than one quarter (0.08%), prior. This marks the third consecutive quarter in which quarterly default rates were lower than 0.50%. Between 1Q 2010 and 4Q 2014, quarterly default rates were below 0.50% in seventeen out of twenty quarters. In fact, during this period, defaults remained below 0.50% for seven consecutive quarters – from 1Q 2010-3Q 2011. Since 1989, there has been one longer, consecutive quarterly period of default rates also below 0.5% -- nine from 1Q 2006 to 1Q 2008 (Figure 2 and Appendix A). Twelve issuers defaulted in the fourth-quarter 2018 on 28 issues, representing 29% of all defaulting issuers and 26% of all issues defaulting in 2018. In all, 106 issues from 41 issuers defaulted in 2018 (Appendix B), compared to 108 issues and 60 issuers in 2017. The average dollar amount of defaulting bonds per defaulting issuer in 2018 was $707 million, compared to $448 million in 2017, and $773 million in 2016. The most sizeable total bond defaults during the year were those of iHeart Communications, Inc., with defaults of $9.1 billion, Claire’s Stores, Inc., with defaults of $1.8 billion, Sears Holding Corp., with defaults of $1.3 billion, Windstream Services, with defaults of $1.0 billion, Southeastern Grocers, LLC., with defaults of $946 million, Cenveo Corp., with defaults of $935 million and Ultra Resources Inc. with defaults of $780 million. The remaining defaulted issuers in 2018 each had total bond defaults of less than $750 million. Energy sector defaults had the largest number (14 of the 41) of defaulting issuers of any sector. In our default statistics, we include those bonds from distressed exchanges actually tendered. For example, in the Ultra Resources exchange, $780 million of bonds were exchanged of the $1.2 billion outstanding and subject to the exchange offer. In 2018, there were 20 distressed exchanges, involving 14 companies, comprising $5.62 billion of defaults (19.4% of the total). See Appendix C for the list of 2018 distressed exchanges and later our discussion of these restructurings. In 2018, S&P and Moody’s issuer-denominated default rates were 2.4% (preliminary) and 2.82%, respectively. Moody’s 2.16% dollar-denominated default 41 5 February 21, 2019 Altman-Kuehne High-Yield Bond Default and Return Report rate was lower than its issuer-denominated rate. As we have shown in past research2 and indicated in past reports, when the credit markets are in a strong and benign state, dollar-denominated bond default rates typically fall to lower levels than do issuer rates. The reverse happens in highly stressed conditions, when the dollar rate usually exceeds issuer rates. Hence, dollar default rates are more volatile, less easily modeled, but still quite relevant, especially to investors who concentrate on investments of specific sizes that are not average for the market. Due to several, very large, defaulting entities in 2014 and 2015, namely Energy Future Holdings (EFH) and Caesars Entertainment Operating (CZR), neither high-yield bond nor leveraged loan default rates in those years behaved in the manner typical of a benign period, as dollar default rates were higher than those calculated on an issuer basis. This reversed in 2016, however, when issuer default rates exceeded those of dollardenominated rates. This more typical comparison continued through the end of 2018. Fitch’s dollar-denominated default rate for 2017 was 2.39%. The rating agencies’ default rate calculations relate only to those bonds rated by their respective agencies, which is most, but not all, issues. The S&P/LSTA 12-month, dollar-denominated default rate on leveraged loans decreased from 2.05% at the end of 2017 to 1.63% by year-end 2018, while the issuer default rate fell from 1.72% to 1.56% (Figures 3a and 3b). Typically, during benign credit cycles, the issuer denominated default rate on loans and bonds exceeds the dollar denominated rate. In the majority of months since March 2013, however, the dollar-denominated loan default rate has exceeded the issuer-denominated rate due to relatively large defaults by only a few issuers. However, the reverse was true for all of 2016. Seventeen leveraged loans issuers defaulted in 2018 compared to 25 one year earlier. Leveraged loans, according to S&P, are institutional and “pro rata” (syndicated to banks and finance companies), secured loans issued by non-investment grade companies. As such, their index does not include the rare case of a fallen-angel secured loan unless that loan became secured in a distressed exchange, but was unsecured when originally issued. According to our comparison between high-yield bond defaults (Appendix B) and leveraged loan defaults (Appendix D), eight firms, American Tire Distributors, Inc., Claire’s Stores, Inc., David’s Bridal, Inc., iHeart Media, Inc., Nine West Holdings, Inc., Remington Outdoor Co., Sears Holdings Corp. and Westmoreland Coal Co. defaulted on both bonds and institutional leveraged loans in 2018. See Figure 4 for the association between dollar-denominated bond default rates and economic recessions in the U.S. since the early 1970’s, including the most recent recession that ended in mid-2009. As usual, we see the default rate peaking at or near the end of the recession, although we observed the peak before it was confirmed that the recession had indeed ended in June 2009. Our forecast for 2019 is for a high-yield bond default rate of 3.46%, higher than our forecasts for the prior two years, and above the historic average of 3.3%. If we equally weight our mortality rate and market-level measures, the forecasted 2 High Yield Bonds: Default and Loss Rate Comparison – Mid-Cap Versus Large-Cap Issuers, M. Verde, P. Mancuso and E. Altman November 11, 2005, Fitch. 6 42 February 21, 2019 Altman-Kuehne High-Yield Bond Default and Return Report default rate rises slightly to 3.65%. This forecast assumes the U.S. economy will not be in a recession in 2019. However, more than 50% of economists currently believe that the U.S. will be in a recession by the end of 2020; thus, our forecast a year from now may include an additional measure for forecasting 2020’s default rate. There are several important risks on the horizon associated with this forecast (see discussion later on the benign credit cycle) which have not been reflected in higher-than-average yield spreads by high-yield bond investors in some time, excluding a two-week period from December 20, 2018 through January 04, 2019, but we have many concerns about the intermediate term (greater than one year) horizon. We will discuss this later in our report. 43 7 February 21, 2019 Altman-Kuehne High-Yield Bond Default and Return Report Figure 1. Historical Default Rates — Straight Bonds Only, Not Including Defaulted Issues In Par Value Outstanding, 1971–2018 (Dollars in Millions) Year 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1989 1988 1987 1986 1985 1984 1983 1982 1981 1980 1979 1978 1977 1976 1975 1974 1973 1972 1971 Par Value Outstandinga ($) Par Value Defaults ($) 1,664,166 1,622,365 1,656,176 1,595,839 1,496,814 1,392,212 1,212,362 1,354,649 1,221,569 1,152,952 1,091,000 1,075,400 993,600 1,073,000 933,100 825,000 757,000 649,000 597,200 567,400 465,500 335,400 271,000 240,000 235,000 206,907 163,000 183,600 181,000 189,258 148,187 129,557 90,243 58,088 40,939 27,492 18,109 17,115 14,935 10,356 8,946 8,157 7,735 7,471 10,894 7,824 6,928 6,602 28,994 29,301 68,066 45,122 31,589 14,539 19,647 17,963 13,809 123,878 50,763 5,473 7,559 36,209 11,657 38,451 96,858 63,609 30,295 23,532 7,464 4,200 3,336 4,551 3,418 2,287 5,545 18,862 18,354 8,110 3,944 7,486 3,156 992 344 301 577 27 224 20 119 381 30 204 123 49 193 82 Default Rates(%) a As of midyear. b Weighted by par value of amount outstanding for each year. Source: NYU Salomon Center. 8 Historical Averages (%) Std. Dev. (%) 1.742 Arithmetic Average Default Rate 1.806 1971 to 2018 3.076 4.110 1978 to 2018 3.270 2.827 1985 to 2018 3.699 2.110 Weighted Average Default Rat273 1.044 1971 to 2018 3.273 1.621 1978 to 2018 3.276 1.326 1985 to 2018 3.287 1.130 Median Annual Default Rate 10.744 1978 to 2018 1.774 4.653 0.509 0.761 3.375 1.249 4.661 12.795 9.801 5.073 4.147 1.603 1.252 1.231 1.896 1.454 1.105 3.402 10.273 10.140 4.285 2.662 5.778 3.497 1.708 0.840 1.095 3.186 0.158 1.500 0.193 1.330 4.671 0.388 2.731 1.129 0.626 2.786 1.242 44 2.981 3.131 3.249 Altman-Kuehne High-Yield Bond Default and Return Report Figure 2. 1989–2018 Quarterly and the Four-Quarter Moving Average Default Rate 6.0% 16.0% 14.0% 5.0% 12.0% 4 - Quarter Moving Average 4.0% Quarterly Default Rate 10.0% 3.0% 8.0% 6.0% 2.0% 4.0% 1.0% 2.0% 0.0% 0.0% Quarterly Moving Source: NYU Salomon Center. Figure 3a. S&P Leveraged Loan Index 12-Month Moving Average Default Rate 1998–2018 (Number of Issuers) 9% 8% 7% 6% 5% 4% 3% 2% 1% Dec-18 Jun-18 Jun-17 Dec-17 Jun-16 Dec-16 Jun-15 Dec-15 Jun-14 Dec-14 Jun-13 Dec-13 Jun-12 Dec-12 Jun-11 Dec-11 Jun-10 Dec-10 Jun-09 Dec-09 Jun-08 Dec-08 Jun-07 Dec-07 Jun-06 Dec-06 Jun-05 Dec-05 Jun-04 Dec-04 Jun-03 Dec-03 Jun-02 Dec-02 Jun-01 Dec-01 Jun-00 Dec-00 Jun-99 Dec-99 0% Dec-98 February 21, 2019 Source: S&P Global Market Intelligence. 45 9 February 21, 2019 Altman-Kuehne High-Yield Bond Default and Return Report Figure 3b. S&P Leveraged Loan Index 12-Month Moving Average Default Rate 1998–2018 (Dollar Amount) 12% 10% 8% 6% 4% 2% Jun-18 Dec-18 Jun-17 Dec-17 Jun-16 Dec-16 Jun-15 Dec-15 Jun-14 Dec-14 Jun-13 Dec-13 Jun-12 Dec-12 Jun-11 Dec-11 Jun-10 Dec-10 Jun-09 Dec-09 Jun-08 Dec-08 Jun-07 Dec-07 Jun-06 Dec-06 Jun-05 Dec-05 Jun-04 Dec-04 Jun-03 Dec-03 Jun-02 Dec-02 Jun-01 Dec-01 Jun-00 Dec-00 Jun-99 Dec-99 Dec-98 0% Source: S&P Global Market Intelligence. Figure 4. Historical Default Rates and Recession Periods in the US High-Yield Bond Market, 1972–2018 14.0% 12.0% 10.0% 8.0% 6.0% 4.0% 2.0% 18 16 14 12 10 08 06 04 02 00 98 96 94 92 90 88 86 84 82 80 78 76 74 72 0.0% Periods of Recession: 11/73–3/75, 1/80–7/80, 7/81–11/82, 7/90–3/91, 4/01–12/01, 12/07– 6/09. Sources: Figure 1 of this report and National Bureau of Economic Research. Bankruptcies in 2018 and Trends in Bankruptcy As can be seen in Figure 5, the amount of total liabilities for Chapter 11 bankruptcies in 2018 was $99.1 billion, based on 92 filings. This data, from the NYU Salomon Center database, does not include filings with less than $100 million in liabilities, but does include private firms. The total number of filings, for bankruptcies with liabilities greater than or equal to $100 million, increased marginally from 91 at yearend 2017, and was higher than both the historical median annual number (76) from 1989-2018, and the median number since 1998 of 91 (Figure 6). These figures are 10 46 Altman-Kuehne High-Yield Bond Default and Return Report not adjusted for inflation. iHeart Communications, Inc. was the largest bankruptcy filing in 2018, with $20.3 billion in liabilities, followed by Sears Holding Corp. ($11.3 billion). Appendix F lists 2018’s Chapter 11 bankruptcies with liabilities greater than $100 million. Figure 5. Total Filings and Liabilitiesa of Public Companies Filing for Chapter 11 Bankruptcy, 1989–2018 Pre- Petition Liabilities, in $ billions (left axis) Median Liabilities Number of Filings (right axis) Median No. of Filings. $800 280 $700 240 2017 $600 $ Billion 200 $500 160 $400 120 $300 80 $200 91 filings and liabilities of $121.1 billion 2018 92 filings and liabilities of $99.1 billion 40 $100 0 $0 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 February 21, 2019 a Minimum $100 million in liabilities. Sources: Appendix F and the NYU Salomon Center Bankruptcy Filings Database. The number of billion-dollar bankruptcies in 2018 decreased from 24 in the prior year to 21, but was still 1.6 times the average over the 39-year period (1980-2018) of 13 (Figure 6). In 2018, billion-dollar bankruptcies represented 23% of all bankruptcy filings with liabilities greater than $100 million, also higher than the median yearly percentage of 21% from 1989-2018 (Figure 7). Appendix E lists all billion-dollar bankruptcies from 2008-2018. Note that excluding Lehman Brothers, the average amount of total liabilities at filing over this nine-year period for our billion-dollar “babies” was $5.2 billion (median = $1.9 billion). According to New Generation Research, the number of public companies filing for bankruptcy in 2018 was 56. The average of total liabilities for our public and private large (≥$100 million) company bankruptcy filings (92) in 2018 was $1.07 billion, down slightly from $1.33 billion one year earlier, and below the historical average since 1980 of $1.53 billion (Figure 6). 47 11 February 21, 2019 Altman-Kuehne High-Yield Bond Default and Return Report Figure 6. Year 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1989 1988 1987 1986 1985 1984 1983 1982 1981 1980 Total Historical Bankruptcy Filings 1980–2018 Total Average Total Filingsb Liabilitiesb Liabilitiesb c ($ MN) (>$100 Total Filings ($ MN) Million) (≥$1 Billion) (>100 Million) (>100 Million) Total Filingsa 56 71 99 79 54 71 87 86 106 211 138 78 66 86 93 176 229 265 187 145 122 83 86 85 70 86 91 123 115 135 122 112 149 149 121 89 84 74 62 4341 92 91 98 70 59 66 69 84 114 232 146 38 34 36 45 102 136 170 137 107 55 36 33 32 24 37 38 53 35 23 14 12 13 14 12 14 12 6 4 2393 21 24 37 20 14 11 14 7 14 49 24 8 4 11 11 26 41 39 23 19 6 5 1 7 1 5 14 12 10 10 2 1 3 2 0 3 3 1 0 503 99115.8 121079.4 125304.6 80291.3 91992.4 39480.2 71612.6 109118.9 56834.8 604269.9 724222.4 72338.4 22774.8 142950.3 40099.7 115171.8 338175.6 229860.9 99091.0 70516.0 31913.4 18865.9 11949.0 27153.0 8396.0 17701.1 64676.5 82423.6 41115.1 34516.1 6905.0 25421.0 9830.3 8605.2 3440.0 13674.0 7113.0 3960.0 746.0 3,672,704.9 1077.3 1330.5 1278.6 1147.0 1559.2 598.2 1037.9 1299.0 498.6 2604.6 4960.4 1903.6 669.8 3970.8 891.1 1129.1 2486.6 1352.1 723.3 659.0 580.2 524.1 362.1 848.5 349.8 478.4 1702.0 1555.2 1174.7 1500.7 493.2 2118.4 756.2 614.7 286.7 976.7 592.8 660.0 186.5 1,534.8 a Represents both Chapter 7 (8) and 11 (48) public company filings in 2018 (Source: New Generation Research). b Filings with Total Liabilities greater than $100 million. Includes some private company filings (Source: NYU Salomon Center Bankruptcy Filings Database). C Filings with Total Liabilities greater than $1 billion. Can include private company filings (rare). (Source: NYU Salomon Center Bankruptcy Filings Database and New Generation Research). 12 48 February 21, 2019 Altman-Kuehne High-Yield Bond Default and Return Report Figure 7. Chapter 11 Filing Statisticsa Year 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Mean No. of Filings, 1989-2018 Median No. of Filings, 1989-2018 Median No. of Filings, 1998-2018 Mean Liabilities, 1989-2018 Median Liabilities, 1989-2018 Number of Filings 23 35 53 38 37 24 32 33 36 55 107 137 170 136 102 45 36 34 38 146 232 114 84 69 66 59 70 98 91 92 Pre-Petition Liabilities ($ millions) 34,516 41,115 82,424 64,677 17,701 8,396 27,153 11,949 18,866 31,913 70,516 99,091 229,861 338,176 115,172 40,100 142,950 22,775 72,338 724,222 604,270 56,835 109,119 71,613 39,480 91,992 80,291 125,305 121,079 99,116 Number of Filings ≥ $1B 10 10 12 14 5 1 7 1 5 6 19 23 39 41 26 11 11 4 8 24 49 14 7 14 11 14 20 37 24 21 ≥$1B/Total Filings (%) 43 29 23 37 14 4 22 3 14 11 18 17 23 30 25 24 31 12 21 16 21 12 8 20 17 24 29 38 26 23 76 16 21 63 13 21 91 19 $119,767 $71,976 a Minimum $100 million in liabilities. Source: NYU Salomon Center Bankruptcy Filings Database. The issue of bankruptcy trends and their impact on the entire corporate bankruptcy system is, of course, more complicated than simply the number and dollar value of filings. For example, the time spent in reorganization from filing to emergence, the number and impact of prepackaged Chapter 11 filings, the success, or not, of the reorganization, as well as the role of senior creditors and D.I.P. lenders, are all factors that need further analysis and commentary. Indeed, conclusions and suggestions for revisions to the Bankruptcy Code can be observed in the American Bankruptcy Institute report issued four years ago on December 8, 2014.3 One aspect of bankruptcy trends that disturbs us is the frequency of multiple-filings by the same entity, i.e. the recidivism phenomenon. Figure 8 shows that from 1984- 3 After about two years of evaluation of the U.S. Bankruptcy Code, the ABI released its report on the Reform of Chapter 11 on December 8, 2014. Please see for the complete document. 49 13 February 21, 2019 Altman-Kuehne High-Yield Bond Default and Return Report 2018, 299 firms have filed twice (Chapter 22), 21 firms have filed three times (Chapter 33), three firms filed four times (Chapter 44) and one filed Chapter 55! Through 2018 (Figure 9), there were 324 multiple filings, 8% of total bankruptcy filings for the same time period. Importantly, an estimated 21% of all firms emerging from the bankruptcy process as a “going concern,” have subsequently refiled. We believe this percentage is too high, and we proposed a partial remedy in a paper published in early 2015.4 In Figure 10, we compare the date of default with the Chapter 11 filing date for firms that defaulted on bonds and went bankrupt, going back to 1981. Based on 1,009 observations from the NYU Salomon Center Master Default and Bankruptcy Databases, both events occurred on the same date in 495 instances (49%). In the remaining 51% of the cases, the lag between the default date and bankruptcy date varied considerably, with decreasing levels as the two dates became further separated from each other. Of course, some defaulting issuers never formally file for bankruptcy as their problems are settled out of court or the default is a result of a distressed exchange (DE), and they do not file for bankruptcy in subsequent years, (more than half). DEs do, however, subsequently file– in many cases – see our discussion at a later point. For more comprehensive statistics on bankruptcy trends in the U.S., please see our 2013 annual report5 as well as the publication of the “Hedge Funds in Bankruptcy”6 conference proceedings in the ABI Law Review (April 2014). 4 E. Altman and B. Branch, “The Bankruptcy System’s Chapter 22 Recidivism Problem: How Serious is it?”, The Financial Review, Vol. 50, No. 1, 2015. Also see Professor Altman’s website. 5 E. Altman and B. Kuehne “Defaults and Returns in the High-Yield Bond and Distressed Debt Market: The Year 2013 in Review and Outlook”, Paulson & Co., February 6, 2014, and the NYU Salomon Center Special Report, February, 7, 2014. 6 14 Held at St. John’s University Law School, October 04, 2013 (K. Sharfman, coordinator). 50 February 21, 2019 Altman-Kuehne High-Yield Bond Default and Return Report Figure 8. Year Chapter 22’s, 33’s, 44’s and 55’s in the U.S. (1984–2018) Chapter 22’s 1984-89 18 1990 10 1991 9 1992 6 1993 8 1994 5 1995 9 1996 12 1997 5 1998 2 1999 10 2000 12 2001 17 2002 11 2003 17 2004 ...
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Running Head: ANALYZING BONDS PRICES AND PROBABILITY

ANALYZING BOND PRICES AND PROBAILITY
PROBABILITY OF DEFAULT (PD), CURRENT PRICES AND IMPLICATIONS

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Surname 2
Running Head: ANALYZING BONDS PRICES AND PROBABILITY

ANALYZING BOND PRICES AND PROBAILITY
1) Tesla’s rating before and after issuance of $1.8 billion corporate bonds priced at 5.3% 8year notes in 2017 using the credit analytics and traditional approach
Using the traditional approach, the Tesla bonds before could be rated as B2 because they
portended high credit risk, associated with low coupon rates, and were highly speculative
to the market. It is known that the company took the action of putting corporate bonds to
leverage its performance which had fallen drastically. After the input of the $1.8billion
corporate bonds the Tesla rating upgraded to “Baa1” referring to upper medium grade
bonds that are somehow speculative and have moderate credit risk around 15% of default
(PD)
2) On addition of $3 billion Bonds to meet product...


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