Financial Health Risk Models - A Presentation to QWAFAFEW-NYC December 9, 2009

1. Financial Health Risk Models -A Presentation to QWAFAFEW-NYCDecember 9, 2009

2. Popular Financial Health Risk Models • Five Tools for Managing Financial Health Risk • Altman z-Score • NRSRO Ratings • Merton Structural Models • Credit Default Swap Spread Market • Rapid Ratings FHR™

3. Altman z-score • The Z‐score formula for predicting bankruptcy was developed in 1968 by Dr. Edward I. Altman, a professor at the Leonard N. Stern School of Business at New York University. It is a multivariate discriminant analysis utilizing a linear regression model relating five financial statement ratios to whether or not a firm filed for bankruptcy protection within two years. • Altman Z‐Score = 1.2T1 + 1.4T2 + 3.3T3 + .6T4 + .999T5 where: • T1 = (Current Assets – Current Liabilities) / Total Assets. • T2 = Retained Earnings / Total Assets. • T3 = Earnings before Interest and Taxes / Total Assets. • T4 = Market Capitalization / Total Liabilities. • T5 = Sales/ Total Assets.

4. Altman z-score • Revolutionary step forward by 1968 standards but several shortcomings have been cited: • Not applicable to financial companies and utilities • Not globally calibrated • Tri-polar, not metrically continuous because of zero-one dependent variable (pre-LOGIT)

5. Origin of NRSRO Status After unexpected 1970 default by Penn Central Railroad, general recognition that reforms were needed. In 1975, Nationally Recognized Statistical Rating Organization (NRSRO )status created and conferred upon Fitch’s, S & P and Moody’s in an effort to establish standards for capital requirements. “This entry regulation is a perfect example of good intentions gone awry in accordance with the “law” of unintended consequences.” – Dr. Lawrence White, New York University Stern School of Economics 5

6. A Shock to the System – Too Big To Fail The Continental Illinois National Bank and Trust Company experienced a fall in its asset quality during the early 1980s. The bank held significant participation in highly-speculative oil and gas loans of Oklahoma's Penn Square Bank. When Penn Square failed in July 1982, the Continental's distress became acute, culminating with press rumors of failure and an investor-and-depositor run in early May 1984. Of special concern was the wide network of correspondent banks with high percentages of their capital invested in the Continental Illinois. Essentially, the bank was deemed "too big to fail," and the "provide assistance" option was reluctantly taken. To prevent immediate failure, the Federal Reserve announced categorically that it would meet any liquidity needs the Continental might have. The bank was unwound in an orderly fashion and ceased operations in 1984.

7. The Continental Illinois Shock - Implications Comptroller of the Currency C. T. Conover defended his position by admitting the regulators will not let the largest 11 banks fail. Regulatory agencies (FDIC, Office of the Comptroller of the Currency, the Fed, etc.) feared this may cause widespread financial complications and a major bank run that may easily spread by financial contagion. This implicit guarantee of too-big-to-fail has been criticized by many since then for its preferential treatment of large banks Despite a loss of half its market value as a result of share price decline, its Standard and Poor’s entity health rating was not lowered from AAA until June 1982 – and then only to A+ (high investment grade). This bolstered the position of market observers who contended that precipitous price declines generally precede ratings downgrades by considerable time lags

8. Another Wave of Credit Ratings Breakdowns Circa 2000 “Credit rating agencies received significant criticism in the wake of the recent corporate scandals. It was frequently noted in the financial press, for example, that credit rating agencies had been well behind the curve in their ratings of many failing companies, including Enron and Worldcom. Politicians, government officials, and the financial press raised questions about the rating agencies' independence and the conflicts of interest that they faced. n January 2003, the SEC produced a report, which it submitted to Congress, in which it identified several areas of concern. These included: (i) a need for improved information flow regarding the rating process; (ii) potential conflicts of interest from two sources in particular where a purchaser pays for the rating, and where the agency has developed an ancillary fee-based business; (iii) alleged anticompetitive or unfair practices by the agencies; (iv) potential regulatory barriers to entry; and (v) the need for ongoing regulatory oversight of the agencies." Felice Friedman, World Bank Policy Research Working Paper (2004)

9. The Structured-Finance-Related Meltdown: 2007-2008 Negative attention focused on NRSROs went well beyond their failures to identify companies in failing financial health: • Lack of disclosure on differences between rating methodology employed in rating CDOs and other structure products – reliance on copula models, not analysts • Failure to review AAA ratings on mono-line insurers • The First Amendment defense • Revelation before House committee hearings by former Moody’s CEO Raymond McDaniel: ‘(Our) Analysts and MDs are continually pitched by bankers, issues, and investors and God help us, sometimes we drink the Kool-aid.” • Current SEC Chair Mary Schapiro recommended that investors not rely on issuer-paid NRSRO ratings as sufficient for due diligence • Current legislation is being considered that would make it impossible for NRSROs to invoke the First Amendment defense in the future

10. Introduction of Equity Market Volatility Into The Process 1974 – Dr. Robert C. Merton develops structural model based on tenets of Modern Portfolio Theory and market efficiency. The premise is that the equity of a firm is a call option on its underlying asset value with a strike price equal to the firm’s debt. 1989 – Stephen Kealhofer, John McQuown and Oldrich Vasicek found KMV providing software based primarily upon modified Merton structural models to help firms estimate default frequencies. These techniques eventually gained popularity for being much more responsive to events than the ratings agencies.

11. Underpinnings of Merton Structural Model Basic Idea: • All assumptions of the Capital Asset Pricing Model (CAPM) apply • A firm’s debt is a covered call option on its assets: • Equity is a call option Using the Black-Scholes formula:

12. Default Probability From Merton Structural Model • Firm’s asset value follows • Default probability 12 12 12

13. Assumptions of the Merton Structural Model All market participants have perfect information; They can trade in fractional shares; Continuous time trading; Returns are log-normally distributed; Debt financing consists of a one-year zero coupon bond; Firm value is observable, known, and invariant to capital structure changes. KMV and other structural model providers have attempted to relax some of these assumptions in the software and services they provide. 13 13 13

14. This implies the number of standard deviations the equity holders' call option is in-the-money. The probability of default is precisely the probability of the call option expiring out-of-the-money. This is approximately equal to one minus the option's normalized delta. Key Quantity:- Distance to Default 14 14 14

15. Distance to Default: Why Agency Ratings Fail the Test “The direct approach…extracting a default barrier from accounting statements is not only time-intensive, but may require expertise in handling complex liability structures. The agencies have decades of this experience as well as access to private information not available in public filings. The drawback to the indirect method is that it relies on rather strong assumptions about the rating agencies' methodologies and objective functions. It is widely acknowledged that agency ratings can be slow to respond to new information. Less widely recognized is that the agency's judgment on a firm's one-year default probability is only one factor considered in rating assignment. Rating agencies may also consider the ability of the firm to withstand the trough of a business cycle as well as the loss a senior unsecured claimant is likely to experience in the event of default.” – Gordy and Heitfield, (2001 Working Paper), Board of Governors of the Federal Reserve System 15 15 15

16. Distance to Default: Alternative Methods Commonly Used The two most common classes of indirect approaches to providing proxies for distance to default used by structural model providers. Using multifactor equity risk models (e.g., BARRA) to help create the default probability matrix. Employing historical data and interest rate assumptions to determine each firm’s relative Value-at-Risk (VAR). The second method is only as good as its assumptions and has waned in popularity in recent years. The first method exacerbates the problem of using the junior part of a firm’s capital structure (its equity) to estimate risks for its senior part (its debt). 16 16 16

17. KMV-Merton model does not produce significant statistics for probability of default. (Sreedar Bharath and Tyler Schumway, Working Paper – U. Michigan, 2004) “Dependence on price-based risk models contaminates every aspect of modern finance." (Christopher Whalen, Institutional Risk Analysis Newsletter, 2006) Distance to default extraordinarily difficult to determine for financial institutions. (Jorge Chan-Lau and Amadou Sy, Journal of Banking Regulation, 2007) Shortcomings to this Approach 17 17 17

18. Explosive Growth of the Credit Default Spread (CDS) Market Since the BBA study, the Economist has estimated that the notional value of the CDS market topped $20 trillion during 2007. 18 18 18

19. Problems With the CDS Market As a Risk Management Tool • Share price shown to lead CDS market in most cases and CDS spreads behave unpredictably when the underlying equity liquidity dries up. (Lars Norden and Martin Weber, CEPR, 2004) • Surveys have shown that most CDS market participants rely on structural-model tools to help determine the positions they assume. • CDS spreads are market-based tools that do not correct for short term noises and distortions. The CDS market is as efficient or inefficient as the information understood and the utility functions practiced by its various participants. 19 19 19

20. Prodigious expansion of the availability of financial instruments and markets have greatly expanded the investing, hedging, and speculating tools available to market participants. As markets and the number of related access instruments expand, the number of attempted applications tends to expand as well. Empirical results confirm the usefulness of such instruments, at least in the past ten years. Share price changes tend to lead CDS spread changes which tend to lead structural-model changes which tend to lead ratings-agency downgrades. All three market measures would change simultaneously if the markets were 100% efficient. Obviously, this is not the case. Therefore, prudent risk managers do not abdicate fiduciary responsibilities to the whims and vagaries of market forces. There is no easy substitute for proper measurement of financial health risk. It requires thorough and intensive analysis whether through traditional or automated methodologies.    Uses and Limitations of Market-based Tools 20 20 20

21. The Financial Health Rating (FHR™) – a Comprehensive and Quantitative Approach • The FHR™ is a demonstrably superior metric for measuring the financial health risk embedded in a company. • It is based upon robust and adaptive global-industry-specific models that combine extensive financial ratio analyses with nonlinear modeling techniques,without market pricing inputs • Because the FHR is quantitatively derived and requires no human input, it allows for non-debt issuing peers and private companies to be compared using the same metric on an identical scale with public debt issuers

22. Inside the FHR™: Calculation • The FHR™ is the product of the automated econometric analysis of up to 62 efficiency ratios that examine how effectively a firm uses its resources • The FHR system compares each company to our proprietary data set including more than 300,000 global companies with history dating back to 1971 • Dependent variable is financial health, not default • Our proprietary model does not include any market price inputs or projections, only company financials (10-K, 10-Q for public and supplied financials for private companies)

23. Interpreting the FHR™ Using the FHR™ to Identify Firms at Risk • FHR > 80 = Top tier financial health • FHR > 64 = Investment Grade • FHR between 50 and 64 means company is currently a bit below Investment Grade but probably not at immediate risk for default • FHR 40-49 = a transition phase that signals the onset of higher risk for declining companies and the onset of less risk for rising companies • FHR < 39 or below means that the company is likely to become increasingly less competitive with its global industry peers; 80% of companies that incur default events are rated in this range at least 12 months ahead

24. Key Analytical Differences:What makes the FHR™ so different? • The FHR™ is: • 100% quantitatively derived, thus free of subjective inputs • 100% replicable, so identical inputs within the same global industry group will always result in identical outputs • Size-neutral since efficiency ratios are used rather than levels and market capitalization is not a factor • Robust because each global-industry-specific model has been calibrated with financial statement data starting in 1971 and tested for re-calibration every year • Dynamic , reflecting a firm’s true current financial health • We do NOT attempt to “see through the cycle”

25. Key Analytical Differences: Efficiency Ratio Groupings • Operating Performance • Cost Structure (Examples: COGS/tot. exp.; taxes/revenues) • Profitability (Examples: NPAT/assets; EBIT/capital employed) • Sales Efficiency (Examples: sales/inventories; sales/working capital) • Financial Positioning • Debt Service (Examples: EBIT/interest exp.; interest exp./total liabilities) • Leverage (Examples: total liabilities/sales; total liabilities/total assets) • Working Capital Efficiency (Examples: working cap./total revenue; term liabilities/cap. employed)

26. Key Analytical Differences:What do we see that others do not? • Key analytics utilized by most credit professionals today are debt-centric: Total Debt/EBITDA, Funds From Operations/Total Debt, Free Cash Flow/Total Debt, and EBITDA/Interest Expense • In contrast, Rapid Ratings focuses on efficiency through as many as 62 ratios for each industry; many conjoin elements from one financial statement with another, enabling a unique, granular and rich perspective • While the ability to generate cash flow, and free cash flow, is important, an accurate and comprehensive financial health profile demands much greater complexity, ultimately growing out of the levels, movements and interrelationships of all key indicators. In fact, the elements of operating performance and balance sheet efficiency are the building blocks of cash flow • Providing an accurate view from a different and exhaustive perspective makes Rapid Ratings the ideal benchmarking tool for an internal rating system while also providing protection against unpleasant portfolio surprises

27. Risk Management Applications: Using FHRs to Estimate Probabilities of Default • There is a strong correlation between FHRs and historical defaults: • Between 1990-2007, 50% of defaults occurred when a company’s FHR was below 25, while 80% took place when FHRs were below 40 • No default occurred above 75 • The strong linkage indicates that levels and trends of FHRs can be used proactively to help reduce risk and identify opportunities

28. Risk Management Applications: Anatomy of the Credit Crunch • Of the firms that defaulted or filed for bankruptcy* 125 had been included in coverage • Summary of the defaulters’ risk profiles: • The average FHR™ at default was 31. Twelve months prior to default: 33. Twenty‐four months prior: 35 • 50% of firms defaulted with an FHR below 27, and 80% defaulted with an FHR below 44 • 57% of firms were consistently rated High Risk or Very High Risk for at least 18 months prior to default • 95% of the firms were below the investment grade threshold when they defaulted * Time period: January 1, 2008 – June 10, 2009

29. Risk Management Applications: Comparison with Z-Scores • Rapid Ratings tested the effectiveness of FHRs™ versus Altman-type z‐scores for providing advance indications of default events between the end of 1998 through the end of 2008.

30. Summary: Advantages of Rapid Ratings • Advantages: • Demonstrated to be accurate and predictive in advance of z-score deterioration, CDS-spread widening, and traditional credit ratings agency downgrades • Metric shown to be accurate within industry group and across industries • Ability to rate public and private companies, debt-issuers and non-issuers on the same scale • Objective, replicable, and scalable process • Data and reports are easy to access and easy to understand • Becoming known as “the” alternative ratings system for corporate financial health to regulators, customers and Congress 30

31. Contact Details • Contact Details Herbert Blank Senior Vice President, Quantitative Products Rapid Ratings International Inc. 86 Chambers Street, Suite 701 New York, NY 10007 Tel: 646.233.4598 website: www.rapidratings.com Disclaimer: A Financial Health Rating (FHR™) or equity recommendation from Rapid Ratings™ is not a recommendation or opinion that is intended to substitute for a financial adviser's or investor's independent assessment of whether to buy, sell or hold any financial products. The FHR™ is a statement of opinion derived objectively through our software from public information about the relevant entity. This information and the related FHR’s™ and related analysis provided in the reports by Rapid Ratings™ do not represent an offer to trade in securities. The research information contained therein is an objective and independent reference source, which should be used in conjunction with other information in forming the basis for an investment decision. Rapid Ratings™ believes that all of its reports are based on reliable data and information, but Rapid Ratings™ has not verified this or obtained an independent verification to this effect. Rapid Ratings™ provides no guarantee with respect to the accuracy or completeness of the data relied upon, nor the conclusions derived from the data. Each FHR™ is a relative, probabilistic assessment of the credit risk of the relevant entity and its potential to meet financial obligations. It is not a statement that default will or will not occur given that circumstances change and management can adopt new strategies. Reports have been prepared at the request of, and for the purpose of, the subscribers to our service only, and neither Rapid Ratings™ nor any of our employees accept any responsibility on any ground whatsoever, including liability in negligence, to any other person. Finally, Rapid Ratings™ and its employees accept no liability whatsoever for any direct, indirect or consequential loss of any kind arising from the use of its ratings and rating research in any way whatsoever, unless Rapid Ratings™ is negligent in misinterpreting or manipulating the data, in which case, our maximum liability to our client is the amount of our fee for the report.