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

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

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
  • T4 Market Capitalization / Total Liabilities.
  • T5 Sales/ Total Assets.

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

Origin of NRSRO Status
  • After unexpected 1970 default by Penn Central
    Railroad, general recognition that reforms were
  • In 1975, Nationally Recognized Statistical Rating
    Organization (NRSRO )status created and conferred
    upon Fitchs, S P and Moodys 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

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.

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
    Poors 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

Another Wave of Credit Ratings Breakdowns Circa
  • 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
  • Felice Friedman, World Bank Policy Research
    Working Paper (2004)

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
  • The First Amendment defense
  • Revelation before House committee hearings by
    former Moodys 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

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 firms 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.

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

Default Probability From Merton Structural Model
  • Firms asset value follows
  • Default probability

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.

Key Quantity- Distance to Default
  • 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.

Distance to Default Why Agency Ratings Fail the
  • The direct approachextracting 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

Distance to Default Alternative Methods Commonly
  • 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
  • Employing historical data and interest rate
    assumptions to determine each firms relative
    Value-at-Risk (VAR).
  • The second method is only as good as its
    assumptions and has waned in popularity in recent
  • The first method exacerbates the problem of using
    the junior part of a firms capital structure
    (its equity) to estimate risks for its senior
    part (its debt).

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)
  • Distance to default extraordinarily difficult to
    determine for financial institutions. (Jorge
    Chan-Lau and Amadou Sy, Journal of Banking
    Regulation, 2007)

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.
Problems With the CDS Market As a Risk Management
  • 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.

Uses and Limitations of Market-based Tools
  • 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
  • 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. 

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
  • It is based upon robust and adaptive
    global-industry-specific models that combine
    extensive financial ratio analyses with nonlinear
    modeling techniques, without market pricing
  • 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

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
  • 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)

Interpreting the FHR
  • Using the FHR to Identify Firms at Risk
  • FHR gt 80 Top tier financial health
  • FHR gt 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 lt 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

Key Analytical DifferencesWhat 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 firms true current
    financial health
  • We do NOT attempt to see through the cycle

Key Analytical Differences Efficiency Ratio
  • Operating Performance
  • Cost Structure
  • (Examples COGS/tot. exp. taxes/revenues)
  • Profitability
  • (Examples NPAT/assets EBIT/capital employed)
  • Sales Efficiency
  • (Examples sales/inventories sales/working
  • 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)

Key Analytical DifferencesWhat 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
  • 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

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 companys 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

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
  • 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
  • 95 of the firms were below the investment grade
    threshold when they defaulted 
  • Time period January 1, 2008 June 10, 2009

Risk Management Applications Comparison with
  • 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.

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
  • 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
  • Becoming known as the alternative ratings
    system for corporate financial health to
    regulators, customers and Congress

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
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 FHRs 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.