Transcript Slide 1

Financial Health Risk Models A Presentation to QWAFAFEW-NYC
December 9, 2009
Popular Financial Health Risk Models
• Five Tools for Managing Financial Health Risk
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Altman z-Score
NRSRO Ratings
Merton Structural Models
Credit Default Swap Spread Market
Rapid Ratings FHR™
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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.
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Altman z-score
• Revolutionary step forward by 1968 standards but several
shortcomings have been cited:
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Not applicable to financial companies and utilities
Not globally calibrated
Tri-polar, not metrically continuous because of zero-one
dependent variable (pre-LOGIT)
7/17/2015
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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
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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.
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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
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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)
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The Structured-Finance-Related Meltdown: 2007-2008
Negative attention focused on NRSROs went well beyond their failures to
identify companies in failing financial health:
1. Lack of disclosure on differences between rating methodology
employed in rating CDOs and other structure products – reliance on
copula models, not analysts
2. Failure to review AAA ratings on mono-line insurers
3. The First Amendment defense
4. 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.”
5. Current SEC Chair Mary Schapiro recommended that investors not
rely on issuer-paid NRSRO ratings as sufficient for due diligence
6. Current legislation is being considered that would make it
impossible for NRSROs to invoke the First Amendment defense in
the future
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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.
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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:
min(VA,T , X )  VA,T  max(0,VA,T  X )
– Equity is a call option
max(0,VA,T  X )
Using the Black-Scholes formula:
VE  VA N (d1)  XerT N (d2 )
ln(VA / X )  (r  .5 A2 )T
d1 
, d2  d1   A T
A T
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Default Probability From Merton Structural Model
Firm’s asset value follows
dVA,t / VA,t   Adt   Adz
ln(VA,T )  ln(VA )  (  A  .5 A2 )T   A zT
Default probability
Pdef  P (VA,T  X )  P (ln(VA,T )  ln( X ))

ln(VA / X )  (  A  .5 A2 )T 
 P  zT 


A


 ln(VA / X )  (  A  .5 A2 )T 
 N 

A T


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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.
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Key Quantity:- Distance to Default
ln(VA / X )  (  A  .5 A2 )T
Distance 
A T
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.
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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
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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.
1) Using multifactor equity risk models (e.g., BARRA) to help create the
default probability matrix.
2) 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).
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Shortcomings to this Approach
• 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)
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Distance to default extraordinarily difficult to
determine for financial institutions. (Jorge Chan-Lau and
Amadou Sy, Journal of Banking Regulation, 2007)
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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.
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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.
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Uses and Limitations of Market-based Tools
1. 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.
2. 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 ratingsagency downgrades.
3. All three market measures would change simultaneously if the markets
were 100% efficient. Obviously, this is not the case.
4. Therefore, prudent risk managers do not abdicate fiduciary responsibilities
to the whims and vagaries of market forces.
5. There is no easy substitute for proper measurement of financial health
risk. It requires thorough and intensive analysis whether through
traditional or automated methodologies.
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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
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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)
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Interpreting the FHR™
Using the FHR™ to Identify Firms at Risk
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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
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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”
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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)
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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
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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
5.0%
120%
3.0%
100%
2.5%
80%
2.0%
60%
1.5%
40%
1.0%
40%
20%
0.5%
20%
0%
0.0%
120%
4.5%
50% of defaults occur with an
FHR below 25.
4.0%
3.5%
80% of defaults occur while
companies are rated High Risk, or
at an FHR below 40.
3.0%
2.5%
100%
50% of the FHRs in the RR
universe are 60 or above.
80%
More than 70% of the
distribution of ratings fall at
or above an FHR of 40.
60%
2.0%
1.5%
1.0%
0.5%
0.0%
0
5
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Ratings at Time of Default (530 Defaults)
Ratings 12-24 Months Prior To Default
0%
0
5
10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Universe: Approximately 3,500 Companies Issuing Bonds
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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:
1. The average FHR™ at default was 31. Twelve months prior to default:
33. Twenty‐four months prior: 35
2. 50% of firms defaulted with an FHR below 27, and 80% defaulted with
an FHR below 44
3. 57% of firms were consistently rated High Risk or Very High Risk for at
least 18 months prior to default
4. 95% of the firms were below the investment grade threshold when
they defaulted
* Time period: January 1, 2008 – June 10, 2009
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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.
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Summary:
Advantages of Rapid Ratings
Advantages:
• Demonstrated to be accurate and predictive in advance of zscore 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
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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.
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