Copyright ©KMV 2001 KMV LLC 1 Modeling the Default Risk of Unlisted Firms (020)7778 7400 London...

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Copyright ©KMV 2001 KMV LLC

Modeling the Default Risk of Unlisted Firms

www.kmv.com(020)7778 7400

London

GARP

November 14, 2001

Stephen Kealhofer

Brian Dvorak

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Background

• First model for listed firms 1989

• Analyzing the Z-score models

• First model for unlisted firms 1993

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What we discovered

• Information content of the accounting variables– book leverage– volatility– value

• Statistical methodology• Quality of actual accounting data• Problem of reverse causation

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What we did

• Reverse causation:– New class of model

• Statistically determine the inputs to a structural model of default• More data / isolate key causal variables

• Quality of accounting data:– Minimize usage / focus on more robust data

• Information content:– Look to traded markets to improve information

•

5


Testing

• What to test?– Default prediction– Correlation with secondary market values

for credit instruments

• Need to test against target populations– Benchmark against feasible alternatives on

same population

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Result

• Ongoing– Data is still primitive in many markets so

we are still learning– Third major version

• Transportable• Current• Analytic• Transparent

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Market-Based Models Allow Global Application

The market-based approach doesn’t need to be re-fit or calibrated to each country. Instead it uses the information imbedded in local market data.

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Cause and Effect ModelA firm defaults because the value of its business falls below its liabilities due.

Market Asset Value

Book Liabilities

This firm defaulted after several years

of difficulty in its construction

materials sector.

The market asset value reflected the

firm’s negative prospects.

Asset Volatility

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Cause and Effect ModelA firm defaults because the value of its business falls below its liabilities due.

Market Asset Value

Book Liabilities

Book Assets

Book accounting figures often give misleading signals on the value of the

business

How can we get market

signals about asset value for private

firms?

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Two Major Modeling ApproachesAlthough there are many models to characterize credit risk, they fall into two categories:

• Statistically fitted models

• Market-based models

Fitted models use the relationships found in historical accounting data predict credit events.

Market-based models add market data on the industry and country to anticipate credit events.

In this presentation, we examine how the market based approach helps us understand credit risk, particularly for turning points in the credit cycle – like today.

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Three Measures of the UK Credit Cycle

Median UK PFM EDF

Median UK BBB Corp Spread

Median UK Z-Score (inverted so up is greater risk)

In the UK, Private firm EDFs and Corporate Bond spreads indicate that we have headed into the trough of the credit cycle. Scores based solely on fitting to accounting data do not yet show the deterioration because of the lagging nature of financial statements.

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Market vs Fitted Models

You can improve the use of the data in the fitted model from the Z-score model through more sophisticated econometrics.

Ultimately, however, linear regression, non-linear fitting techniques, neural nets or other econometrics can only extract the signal inherent in the data.

That signal can only be refreshed at the frequency that the statement data arrives – quarterly at the very best, annually in most cases. Hence the consistent lag we see relative to the market based signals above.

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• 3 Drivers– Market Value of Assets (Business value) – Asset Volatility (Business risk) – Default Point (Liabilities due)

Extracting Market Data:Cause & Effect Model

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Asset Value for the Private Firm• The value of a business, public or private, is

driven by its ability to generate future cash flows

• In most times, historical statements provide some modest insight into future cash flows

• At turning points in the sector or the economy, historical statements provide no information

After September 11th, what insight can a model gain from historical financial statements for airlines? Hotels? Retailers? Where is the signal about 2002 in trailing ROA, book equity,

or last year’s inventory-to-cost of goods sold?

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• When there is no equity market price on that specific firm to signal its future cash flows,

• KMV uses the firm’s own current financial statement, with trailing 12-month figures

plus

• The equity market’s view on future cash flow prospects in that sector

Key reason for the model’s predictive power

Asset Value for the Private Firm

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• The equity market’s view on future cash flow prospects in that sector

Getting Market Asset Values

KMV gets market asset values for public companies by using an options approach, the Vasicek/Kealhofer model. This model allows us to back out implied underlying asset values from market equity prices.

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Getting Market Asset Values

Equity derives its value from the cash flows of the firm.

Equity is a call option on the firm’s assets: the right, but not the obligation, to “buy” the firm’s assets from the lender by re-paying the debt.

Call Option Value = Market Value of Equity

Strike Price = Book Liabilities

Implied Underlying Asset Value Market Value of Assets

Implies

Standard Options Terms KMV Approach (Vasicek/ Kealhofer Model)

For a complete description of the approach, see Modeling Default Risk, available on www.kmv.com

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Market Data Signals When Changing Cash Flow Prospects in a Sector

Each month we observe the market value of assets for a universe of publicly traded firms in 61 distinct industries

Mar

ket

Ass

et V

alu

e

EBITDA

Each square is a public firm in the same sector – the comparables.

The Private Firm Model uses the relationship between trailing EBITDA & market asset value

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Market Data Signals When Changing Cash Flow Prospects in a SectorM

arke

t A

sset

Val

ue

EBITDA

Q2 1998 -- Collapse in oil prices leads the market to “haircut” the asset values of comparables

Each month we observe the market value of assets for a universe of publicly traded firms in 61 distinct industries

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The market signaled this credit event: the 1998

collapse of oil prices triggered defaults in 1999,

to a level not seen since the mid-eighties oil price

collapse.

Oil & Gas E&P Defaults & Bankruptcies 1980-1999


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Asset values on comparables rise in anticipation of improving cash flows – an early “opportunity signal”

Mar

ket

Ass

et V

alu

e

EBITDA

Q1 1999 -- Market strongly signals the recovery in global oil prices

Market Data Also Signals Improvement

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Oil & Gas E&P Defaults & Bankruptcies 1980-2000


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Dynamic Market-Driven Measures are Critical for Industries Experiencing

Turning Points

After September 11th, are trailing 12-month accounting figures useful in characterizing risk for the next year in impacted industries?

What does historical data tell you about telecoms today when the sector has moved from stable monopolies to dynamic competition?

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# o

f Fir

ms

An Industry in “Transition”: Huge Growth in Telecom Firms Globally

Globally, the number of publicly traded firms in the sector grew by 60% over 5 years. What fueled that?

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To

tal L

iab

ilitie

s

Lending! Total liabilities in the sector tripled.

An Industry in “Transition”: Huge Growth in Telecom Liabilities

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# o

f Fir

ms

Telecom: Significantly Changing PopulationMost of the growth came from smaller firms.

Smaller & Medium Telecoms (Under $1B US in Book Assets)

All Firms

Large Telecoms($1B US or over in Book Assets)

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# o

f Fir

ms

An Industry in “Transition”: Telecom Defaults

Globally, the number of defaults in KMV’s default database in the sector has exploded.

And the type of firms defaulting is different

People’s Telephone Company, 1995

Covad, Atlantic Telecom Group PLC, …

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Telecom: Measuring Business Risk

Business risk is critical for understanding credit risk. How predictable are the firm’s earnings?

KMV measures this as Asset Volatility

Marconi has more difficult earnings to predict than British Energy, so the market re-values Marconi frequently.

Asset volatility is highly reflective of country, size & industry and can be inferred from comparables for private firms.

Marconi Asset Value

British Energy Asset Value

28% Ann. Volatility

14% Ann. Volatility

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Ass

et V

ola

tility

(A

nn

% S

td D

ev)

Telecom: Significantly Changing Business Risk

Telecom business risk is up at every size range.

$1B or over US in Book Assets

Under $1B US in Book Assets

Asset volatility is highly related to

company size (and industry!)

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Modeling Implications of a Changing Sector

Telecom business risk is up at every size range – earnings in the sector are less predictable

The composition of the population has changed towards smaller more volatile companies

Market data can be refreshed monthly to pick up such changing conditions

Historically fitted relationships for firms in the sector will biased, and must be used with caution or refit.

As a result, the sector has less debt capacity relative to observed earnings

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0102030405060708090

100

10 20 30 40 50 60 70 80 90 100

Per

cent

of

defa

ults

exc

lude

d

Percentage of population excluded

Median EDF corresponding to percentile

76%

UK private firmsDefaults: Graydon UK / Population:Bureau van Dijk (Jordans)

PFM EDF credit measure

Z-Score Benchmark Model

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EDF Level%Population Excluded

%Defaults Excluded

Population excluded

Defaults excluded

15,45 10% 44% 166861 392 11,02 20% 61% 333722 551

5,32 30% 76% 500583 682 3,14 40% 86% 667444 774 2,05 50% 92% 834305 827 1,38 60% 95% 1001166 860 0,90 70% 98% 1168027 882 0,52 80% 99% 1334888 891 0,23 90% 100% 1501749 898 0,02 100% 100% 1668610 901

TOTAL 1668610 901

S&P Rating

CCC

BB

AA

BBB

AAA

A

B

Period covered: 1994-2001Number of companies, sample data: 31441 Number of companies, 1998: 23186Number of statements, sample data: 182777 Number of observations, sample data: 1668610

When we use PFM to discriminate credit quality:

- This means, 682 defaults will be excluded of the 901 defaults- 76% of the defaults will be excluded if we exclude 30% of the population that has the highest EDFs

- The median EDF for the 30th percentile is 5,32 (approximately a B rated company)

UK private firmsGraydon UK/BvD Data

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Two Modeling ApproachesThe market-based approach has proven very powerful. It gives distinct signals that lead fitted models, because it powers the same company financial statement information with market insight on the firm’s industry and country.

Market data becomes even more critical as banks move risk via market mechanisms. The market’s pricing will reflect dynamic credit quality – matching that up with historically oriented risk data can create distortions.

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Appendix I: Using the Structural Approach to Analyze Changes in Risk –

A Private Telecom Example

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Using the Structural Approach to Analyze Changes in Risk

For any firm – public or private, the use of a structural model allows the analyst to

• View which of the 3 drivers created the change in risk

• Sensitivity-test each driver

• Directly pro-forma the impact of new transactions

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Since June 1998, a 5-fold increase in default risk.

What drove that?

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Using the Structural Approach to Analyze Changes in Risk: EDF Drivers

Market Asset Value

Liabilities & Default Point

Market Leverage declined, then rose for this private firm.

Let’s examine the asset value drivers: EBITDA & sector market signal

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Using the Structural Approach to Analyze Changes in Risk: Asset Value Drivers

Comparables Market Asset Value

Liabilities & Default Point

Firm-specific EBITDA

Growing EBITDA & positive market signal on telecoms

indicate greater asset value

EBITDA holding flat but prospects in sector down significantly

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Why did EDF rise in 98 & 99 when asset value was rising?

Market Asset Value

Liabilities

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Why did EDF rise in 98 & 99?

Business risk or asset volatility was up significantly.

Comparables Asset Value

Comparables Volatility

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The improvement in the fitted score is driven by the firm’s positive EBITDA performance. The market signals on the sector anticipated some of the positive impact

EBITDA

Z-Score (inverted, up is increasing risk

PFM EDF

Today - -the rising EDF indicates market signal on future earnings, not yet seen in the lagged statement.

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We could continue the analysis by examining this firm relative to public and private telecom companies, other UK firms or other BB-type risks.

The analyst might drill down and examine which firms were used as public comparables, and apply his own criteria to selecting an appropriate universe for comparison.

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Banks are constantly considering new liabilities –the ability to pro-forma the post-transaction risk (under a number of structuring alternatives) is critical.

Because the user can re-run the structural model and examine the input, seeing the direct impact, the model becomes a transparent starting point for objective analysis.

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Appendix II: Other Country Testing

Notes on testing: the Private Firm Model is not fitted on private firms, or public, for that matter.

The insight the model has on German private firms, UK, etc… comes from market data on the industry and country, coupled with the firm’s own statement information on leverage and cash flow.

Because of this, the testing is completely out of sample. While results will vary in the power curves from sample to sample, we can confidently expect similar power even as new firms come into the population, as the European countries become more integrated, as accounting methods change or as dramatic events reverse the prospects of whole countries or sectors.

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German Private FirmsDeutsche Bundesbank co-project

0102030405060708090

100

10 20 30 40 50 60 70 80 90 100

Per

cent

of

defa

ults

exc

lude

d

Percentage of population excluded

Median EDF corresponding to percentile

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