FINANCIAL STATEMENT ANALYSIS SPRING 2006 CLASS

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FINANCIAL STATEMENT ANALYSISSPRING 2006CLASS
9CREDIT ANALYSIS PREDICTING FINANCIAL
DISTRESS
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AGENDA --- CREDIT ANALYSIS
What is Credit Risk Analysis? What are Credit
Ratings? How is Credit Analysis done?
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CREDIT RISK ANALYSIS
DEFINITIONS
CREDIT RISK refers to the ability and willingness
of a borrower (e.g., a firm) to pay in full what
it owes to its creditors. CREDIT RISK ANALYSIS
identifies and measures the quantitative
qualitative factors that affect the probability
of default the costs of defaulting.
LENDERS or CREDITORS PERSPECTIVE 4
AGENDA --- CREDIT ANALYSIS
What is Credit Risk Analysis? What are Credit
Ratings? How is Credit Analysis done?
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WHAT ARE CREDIT RATINGS?
DEFINITION per Standard Poors (2005)
CREDIT RATING is an opinion of the
creditworthiness default risk of an obligor
with respect to a specific financial obligation,
class of financial obligations, or a specific
financial program (e.g., ratings on medium term
note commercial paper programs). A credit
rating is not a recommendation to purchase, sell,
or hold a financial obligation, inasmuch as it
does not comment as to market price or
suitability for a particular investor or creditor.
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WHAT OBLIGATIONS ARE RATED (or SCORED)?

• You name it, its rated by someone!
• Country debt
• Corporate bonds, paper
• Private public companies
• Traded non-traded debt
• Aggregated disaggregated (e.g., ratings on
  payments to suppliers averaging 2 days beyond
  terms, relative to industry)
• Asset debts (e.g., loans held by banks)
• Your personal credit score! (e.g., Equifax)

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BIG TWO RATING AGENCIES MOODYS and SP
Example Standard Poors www.standardandpoors.
com
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INVESTMENT vs. SPECULATIVE GRADE RATINGS
Corporate charters of many institutions prevent
them from buying or investing in Below-Investment
Grade debt
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DISTRIBUTION OF RATINGS
Usually bell-shaped, but varies over time
Example Moodys European Corporate Ratings,
1990 2001
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SHORT-TERM DEFAULT RATES BY RATING
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LONG-TERM DEFAULTS BY RATING
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WHEN DOES DEFAULT ACTION OCCUR?
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CREDIT SCORING MODELS
Rate credit applications on basis of current
past performance data

• Mortgage lending, credit card lending, etc.
• In a typical application, credit performance
  measures and borrower characteristics are
  computed for a sample of borrowers.
• Measures used to develop statistical scoring
  models.
• Output score is forecast of credit performance
  for borrowers with similar characteristics.
• Simple decision rule accept credit
  application if probability of default critical value ?.

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AGENDA --- CREDIT ANALYSIS
What is Credit Risk Analysis? What are Credit
Ratings? How is Credit Analysis done?
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DOMAIN of TYPICAL CREDIT RISK ANALYSIS
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QUICK, SHORT-CUT CREDIT EVALUATION
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QUICK, SHORT-CUT CREDIT EVALUATION
Buy it from a professional provider

• Suppose you are a NC bank that has been
  approached for a loan by Charles Colvard, a
  small publicly traded firm that makes sells
  Moisannite, an artificial diamond.
  www.moissanite.com
• So hop online and purchase a Comprehensive Credit
  Report from Dun Bradstreet _at_ www.dnb.com
• 120 for an html file

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LONGER, DO-IT-YOURSELF APPROACH
One approach Take the 9 Cs steps in Credit
Analysis
C1. CIRCUMSTANCES leading to loan
request C2. CASH FLOWS C3. COLLATERAL C4. CAPACITY
for debt C5. CONTINGENCIES C6. CHARACTER /
EXPERIENCE of management C7. CONDITIONS
COVENANTS C8. CORPORATE STRATEGY C9. COMMON
EQUITY VALUE
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C1. CIRCUMSTANCES leading to loan request

• GOOD, NEUTRAL or BAD reason(s)?
• GOOD
• Fund growth opportunities (buy PPE, RD)
• NEUTRAL
• Manage seasonal cash flow needs
• Replace naturally expired working capital
• BAD
• No-one else will lend to the firm!
• Poor internal controls, cash management

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C2. CASH FLOWS

• Ability to repay debt f (CFOPS, CFINV, B/S)
• Scrutinize levels trends in cash flow
  statement, conditioning on firms industry
  life-cycle stage.
• Demand firms projected / pro-forma financial
  model
• Beware of persistent
• NI CFOPS and components accruals undo!
• CFOPS
• Free Cash Flow
• CAPEX ?
• Cutting dividends
• Shifting from long-term to short-term debt

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C3. COLLATERAL

• Lenders want availability value in collateral
• Pecking order of preferred assets
• Marketable securities
• A/R
• Inventories
• PPE
• Intangibles?
• Rare in general, lenders are suspicious of
  patents, customer lists, IT, IP and the like
• Lack of liquid markets (though getting better)

Improving liquidity
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C4. CAPACITY FOR DEBT
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C5. CONTINGENCIES

• Hard to uncover off-B/S liabilities
• Disclosure is typically small and buried
• SPEs, VIEs
• Joint ventures
• Guarantees of subsidiary debt
• Lawsuits
• Environmental cleanup costs
• Derivatives
• Undue dependence on key executives

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C6. BUSINESS CHARACTER of MANAGEMENT TEAM

• Is the management team
• Experienced? In this industry?
• Been through both up down business cycles?
• Criminal records (check!! e.g., www.123nc.com)
• Invested in the firm?

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C7. COVENANTS

• What types of covenants are in place?
• Cash flow coverage ratio, leverage ratio, level
  of earnings (or EPS), book value
• Disallow redemption, repurchase or retirement of
  any outstanding capital stock (debt or equity).
• Restrict payment of dividends, issuing new debt.
• Restrict acquisitions divestments.
• How much flexibility does management have to
  avoid violating existing covenants? Is it
  sufficient?
• Violation history?

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C8. CORPORATE STRATEGY

• Does firm have a strategy for creating firm
  value?
• Does that strategy pass the smell test?
• Is it tangible-intensive or intangible-intensive?
• On what past, present future kinds of debt and
  equity funding does strategy depend?
• Would you invest in the business if you could?

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C9. COMMON EQUITY VALUE

• What is the stock market saying about the firm?
• Asymmetric default risks of under- vs.
  over-valuation.
• Agency Costs of Overvalued Equity by Michael
  Jensen, HBS The Monitor Company.

• Managers have incentives to hype their stock.
• Once overvalued, the incentives to keep it
  overvalued mean that the inevitable return to
  fair value is
• Rapid
• Destructive to fundamental value
• Accompanied by large increase in default risk
  likelihood of financial distress

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C9. Dangers of overvalued equity
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C9. Example ---
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AGENDA --- PREDICTING FINANCIAL DISTRESS
What is Financial Distress? Why predict it? What
are the causes of Corporate Financial
Distress? Financial Distress prediction
models Amazon.com in Year 2000 case
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CORPORATE FINANCIAL DISTRESS
DEFINITION
FINANCIAL DISTRESS refers to the situation in
which the liquidation value of a firms assets is
less than the total face value of creditor
claims. In such a situation, the firm will either
not be able to meet its financial obligations, or
will only be able to meet them with great
difficulty. RELEVANT TO EQUITY DEBT-HOLDERS
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DOMAIN of TYPICAL CREDIT RISK ANALYSIS
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DOMAIN of FINANCIAL DISTRESS
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FINANCIAL DISTRESS
Why care about financial distress?

• ANSWER 1 Its very costly and 2 to
  everyone !
• Damages relationships with key stakeholders.
• Suppliers tighten terms, or stop supplying.
• Customers defer or stop buying firms products.
• Employees jump ship or are demotivated.
• Creditors accelerate payment demands and cut off
  further funding.
• Manager skills are diverted from core business
• Cash becomes king to detriment of profits.

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Empirical evidence on costs of financial distress

• Sources Interrelation among events of default
  by D. Beneish E. Press (CAR, 1995) Costs of
  technical violation of accounting-based debt
  covenants by same authors (AR, 1993)

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Empirical evidence on costs of financial distress

• Source Moodys (2002)

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AGENDA --- PREDICTING FINANCIAL DISTRESS
What is Financial Distress? Why predict it? What
are the causes of Financial Distress? Financial
Distress prediction models Amazon.com in Year
2000 case
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MAIN CAUSES OF FINANCIAL DISTRESS
REASON
COMMENT
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MAIN CAUSES OF FINANCIAL DISTRESS
REASON
COMMENT
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AGENDA --- PREDICTING FINANCIAL DISTRESS
What is Financial Distress? Why predict it? What
are the causes of Financial Distress? Financial
Distress prediction models Amazon.com in Year
2000 case
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PREDICTION MODELS
Three major competitors (there are others too)
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Altman Z-Score multiple discriminant analysis
model

• Altmans MDA model derives the linear combination
  of N accounting-based ratios that best
  discriminate among firms that went bankrupt (Z
  1) vs. did not go bankrupt (Z 0) within one
  year of the financial statement date.
• Z w1X1 w2X2 wNXN
• Data used
• 66 U.S. firms (33 bankrupt, 33 non-bankrupt),
  1946-65
• Manufacturing firms matched on asset size
• N 22 ratios (X) were evaluated.
• Fell into five categories liquidity,
  profitability, leverage, solvency activity.
• Estimated model correctly classified 95 of
  bankrupt non-bankrupt firms.

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Altman Z-Score multiple discriminant analysis
model
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STRENGTHS of ALTMAN MODEL

• Simple intuitive
• Has made a ton of money for Altman!
• Model has been adapted estimated for
• Private firms (labeled the Z model by Altman)
• Specific industries
• E.g., Z model for manufacturers,
  non-manufacturer industrials, and emerging market
  credits
• Not-for profit entities
• Large vs. small firms
• Different countries
• Accounting ratios should matter if GAAP is doing
  its job

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WEAKNESSES of ALTMAN MODEL

• Not derived from any underlying economic theory,
  so it runs the risk of being ad-hoc
• Why do the coefficients change so much?
  UNSTABLE
• Does not take into account other variables that
  would seem likely to predict failure ROA,
  stability of earnings, debt service, cumulative
  profitability and size.
• Does not take into account the costs of making
  type I vs. type II errors.
• Type I error predict non-bankrupt when
  bankrupt (this is the costly error to an
  investor)
• Type II error predict bankrupt when not
• May not work well for intangible-intensive
  companies
• What is bankrupt? Does it work for defensive
  Ch. 11?

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TAKEAWAYS of FINANCIAL DISTRESS LECTURE

• For most companies, the probability of a lender
  not getting their money back is low.
• But when financial distress or default occurs, it
  is really costly for everyone involved.
• This is why such a lot of resources are put into
  analyzing firms creditworthiness and developing
  models that reliably predict the probability of
  going into bankruptcy.
• Most of the information used in credit risk
  analysis and predicting financial distress is
  accounting-based because financial statements
  help predict the future.

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Last slide AMAZON.COM IN YEAR 2000 case