IA1 Lecture 9 - Exeter

1

Investment Analysis 1

BEAM011

Lecture 9

Financial Distress Analysis

Dr Jon Tucker

Xfi Centre for Finance and Investment

University of Exeter

Lecture Objectives

1. To introduce credit rating assessment

methods

2. To examine financial ratios used in credit

analysis

3. To examine a range of univariate and

multivariate models of financial distress

prediction

4. To examine how bonds are rated

2

Why engage in Credit Rating?• Why do we want to engage in credit rating?

• Who is interested?

Equity holders, Banks, Bond holders, Suppliers, Governments, Quasi-

governmental organisations, Auditors

Creditworthinessof borrowers

Can firm meet itscommitments?

Predictcorporate failure

Credit Rating

Insolvency (Business Failure)

Meaning of Insolvency

• Applies to UK companies

• Bankruptcy in US

• Company cannot pay debts as

they fall due

• Precursor to formal restructuring

arrangements

Common Causes:

• Profitability low or declining

• Inappropriate diversification

• Import penetration

• Gearing too high

• Geographical spread

• Over-trading

• Contract cost variances

• Poor working capital management

• Inability to rationalise

• Adverse contractual changes

Source: Bank of England Quarterly Bulletin (1980)

3

Bankruptcy in the US

Chapter 11

• Chapter 11 lets companies have more time to attempt to recover

from bankruptcy or allow companies to attempt to reduce losses

through planned methods (suspends creditors chasing, loan

renegotiation, allow time for liquidation to get better sale value,

reorganisation)

• Company applies to courts for Chapter 11, court must approve plan,

then company is released – at this point, shareholders either lose all

value or have some worth

Chapter 7

• Applied less often – allows companies to raise further debt, giving

new loans higher seniority to previous loans

Credit Analysis Information

Credit

Scoring

Techniques

Size,

Location,

Markets

Bond

Yield

Credit

Rating

Agencies

Information

used in

Credit Analysis

Share

Price

Movement

Financial

Ratio

Analysis

Security available

and charges

Company records

of previous credit

history

4

Financial Ratios used in Credit AnalysisBalance Sheet Ratios used in Credit Analysis:

1. Current Ratio

The current ratio gauges the risk of excess cash payments over cash currently available by comparing claims against the company that will be payable during the operating cycle with the assets that are already in the form of cash or near cash during the current operating cycle.

Current Ratio = Current assets / Current liabilities

2. Asset Protection Ratio

The greater the amount which asset values could deteriorate, the greater the “equity cushion”, and the greater the creditor’s sense of being protected. Here, equity = total assets minus total liabilities.

Asset Protection Ratio = Total assets / Total liabilities

3. Gearing Ratio

Also, lender can gauge the amount of equity “beneath them” (junior to them if liquidation) by comparing it with the amount of debt outstanding.

Gearing Ratio = Total debt / Total equity

Income Statement Ratios used in Credit Analysis:

1. Profit Margins

High profits keep plenty of cash flowing through the system and confirm the value of

productive assets such as plant and equipment. Profit margin is thus a key focus of the

credit analyst. The narrower the margin, the greater is the danger that a modest decline in

selling prices or a modest increase in costs will produce losses, which will in turn begin to

erode such balance sheet measures as total-debt-to-total-capital by reducing equity.

The margin measures reflect different aspects of management’s effectiveness.

Gross margin measures management’s skill in buying and selling at advantageous prices.

Operating margin show how well management has run the business – buying and selling

wisely, and controlling selling and administrative expenses – before taking into account

financial policies and the tax rate.

Financial Ratios used in Credit Analysis

5

Gross Margin = (Sales – Cost of goods sold) / Sales

Operating Margin = Operating income* / Sales

Pretax margin = (Net income + Income taxes) / Sales

Net Margin = Net income / Sales

*a.k.a. EBIT

2. Fixed Charge Coverage

Measures the ability of a company’s earnings to meet the interest payments on its debt,

the lender’s most direct concern.

Fixed-charge coverage = (Net income + Income taxes + Interest expense) / Interest

expense


3. Lease Expense

Off-balance-sheet operating leases have virtually the same economic impact as on-

balance-sheet debt. Thus we could include one third of rentals on the theory that one

third of a lease payment typically represents interest that would be paid if the assets had

been purchased with borrowed money, and two thirds is equivalent to principal

repayment.

Fixed-charge coverage

= (Net income + Income taxes + Interest expense + 1/3 rentals)

---------------------------------------------------------------------------------

(Interest expense + Capitalised interest +1/3 Rentals)


6

Cash Flow Statement Ratios used in Credit Analysis:

1. Cash Flow from Operations / Capital Expenditures

The higher this ratio, the greater the financial flexibility of the firm – i.e. a company that does not have to rely on external financing can take greater operating risks than one that would be forced to retrench if new capital suddenly became scarce or prohibitively expensive.

Cash flow from operations / Capital expenditures

However, this ratio will move with the cycle with capital expenditures increasing rapidly and demand growth catching up once the new capacity is in place. We should be more concerned with a longer-term deterioration in this ratio (and thus the firm’s competitiveness).

2. Depreciation / Cash Flow from Operations

We may be more interested in the future than the past. Here, depreciation is a programmed item and cash flow is assured by accounting rules. The higher the percentage of cash flow derived from depreciation, the higher is the predictability of a company’s cash flow, and the less dependent its financial flexibility on the vagaries of the marketplace.

Depreciation / Cash flow from operations


3. Capital Expenditures / Depreciation

A ratio of less than 1.0 over a period of several years raises a red flag, since it suggests

that the company is failing to replace its plant and equipment. Underspending on capital

replacement amounts to gradual liquidation of the firm. However, even if this ratio is greater

than one, it could be a result of inflation, depreciation may not represent true economic

wear and tear, the firm may still be investing at an inadequate rate compared to its

competitors.

Capital Expenditures / Depreciation


7

Further Ratios used in Credit Analysis:

1. Return on Equity = Net income / (Common equity + Preferred equity)

2. Return on Total Capital = EBIT / (Total debt + Total equity)

3. Debtors days = (Trade debtors / Credit sales) x 365

4. Creditors days = (Trade creditors / Credit purchases) x 365

5. Asset Turnover = Sales / (LT debt + Equity capital)

6. Total debt to cash flow =(ST debt + LT debt) / Cash flow from operations

This last one is of interest from a credit analyst’s viewpoint – “How long (in years) would it take for the firm to liquidate its debt?”


Beaver’s (1966) Model

• Beaver developed a simple cash flow model of the probability of corporate failure

• Firm = reservoir of liquid assets:

• Inflows supply reservoir

• Outflows drain reservoir

• Reservoir → buffer against variations in flows

• Solvency of firm can be defined in terms of the probability that the reservoir will be exhausted, at which point the firm will not be able to pay its obligations as they mature

Probability of failure = f (size of reservoir, size of inflows from operations, size of expenditures, debt burden)

Univariate Distress Prediction Models

8

Beaver’s empirics:

• Beaver tested his model on 79 firms matched for industry and size (failed and non-failed), tracking them over five years prior to failure

• He examined 7 financial ratios:

• Cashflow/total debt

• Net income/total assets

• Total debt/total assets

• Working capital/total assets

• Current ratio

• No-credit interval

• Total assets

• Distinct differences exist and clear trends are apparent in financial ratios which makes them potentially very useful for profiling potential failed and survivor firms



50.00.0130.20 to 0.25

49.40.0250.15 to 0.20

48.10.1400.10 to 0.15

41.10.4180.0250.05 to 0.10

21.40.3670.1520.00 to 0.05

10.70.0380.202-0.05 to 0.00

18.90.102-0.10 to –0.05

24.00.076-0.15 to –0.10

27.80.127-0.20 to –0.15

34.20.089-0.25 to –0.20

38.60.227Below –0.25

Errors %Non-FailedFailed

Beaver’s Model:

Frequency distributions class intervals for net income to total assets

9


4749454238Total assets

3738433823No-credit interval

4538363220Current ratio

4145333424Working capital/total assets

2827342519Total debt/total assets

2829232013Net income/total assets

2224232113Cashflow/total debt

54321Ratio

Years before failure

Beaver’s Model: % of firms mis-classified by univariate analysis

Problems with Beaver’s model:

1. Absence of clear distinction between the groups

• Cut-offs involve some element of error

• Type 1 error→ failed firms classified as non-failed

• Type 2 error→ non-failed firms classified as failed

Confusion matrix:

9% error shown

2. Loss function to decision-maker of type 1 and 2 errors is different

• Altman, Haldeman and Narayanan (1977) - 35 times more costly for each type 1 error firm compared to type 2 error firms

• Could lead to a change in cut-off point to avoid type 1 errors

3. There exist more secure than failing companies

• Table 9.6 - Imbalance of failed and non-failed firms – makes use of the model problematic where different proportions to testing sample are experienced


487Non-failed (Predicted)

243Failed (Predicted)

Non-failed

(actual)

Failed

(actual)

10

4. Problem of identifying failed firms

• Difficult to identify such firms – instead the following may happen:

• Severe rationalisation of firm

• Government intervention

• Takeover of ailing company by a successful firm

• ALSO failing firm may ‘hang on’ longer than the end of the study → downward bias in numbers of actually failed firms

5. Financial ratio selection

• Which ratios to select? Beaver was somewhat arbitrary in selection

• Zavgren (1983) → more popular ratios used are more subject to creative accounting by a troubled firm

6. Univariate test

• Beaver’s test is univariate

• BUT financial distress could come from a number of causes

• Multivariate model might be better – joint influence of certain ratios may be important


However, advances made by Beaver:

1. All the ratios tested exhibit some degree of predictive ability

2. Some give significant advance warning of failure

3. Subsequent studies confirm predictive ability of ratios, but clearly

cut-off points would vary over time, industries and countries

4. Rates of return and gearing are the more reliable measures of

future financial distress (and not so much liquidity and efficiency

measures)


11

Why the need for multivariate models?

• Achievement of more accurate predictions by combining univariate (single)

ratios?

• Financial distress = f (number of factors)

Discriminant analysis is used in early studies of distress prediction:

• Multiple Discriminant Analysis (MDA) is a statistical technique used to classify

an observation into one of several a priori groupings dependent on the

observation’s individual characteristics

• Statistical technique in which a linear equation is estimated which produces an

individual Z-score

• Z-score = composite ratio with the most efficient weights such that:

• Dispersion of Z is minimised for each category (failed and survived)

• Distance between mean Z for each group is maximised thereby reducing

chance of overlap

• Technique maximises the model’s ability to discriminate between the groups

Multivariate Distress Prediction Models

Example

Use quick ratio and profitability to attempt to classify correctly 10 failed and 10

surviving firms

Figure 9.4

• No clear distinction between two groups

Figure 9.5

• Cut-off Z-score of 1.45 produces 4 misclassifications and 15 correct

classifications

• Could use a zone of ignorance approach


12

Z-Score Analysis:

There may exist a statistical relationship between financial ratios and financial distress:

1. Collect information on defaulting and non-defaulting firms through time

2. Work out which financial ratios are the best predictors of default historically

3. Rank companies on how closely their profiles match those of companies which have defaulted


1. Altman’s Z Score Model (1968):

Z = 1.2x1 + 1.4x2 + 3.3x3 + 0.6x4 + 1.0x5

x1 = Working capital / Total assets (%)

x2 = Retained earnings / Total assets (%)

x3 = Earnings before interest and taxes / Total assets (%)

x4 = Market value of equity / Total liabilities (%)

x5 = Sales / Total assets (no.of times)

IF Z < 1.81 ⇒⇒⇒⇒ serious credit problems

IF Z > 2.99 ⇒⇒⇒⇒ healthy company

IF 1.81<Z<2.99 ⇒⇒⇒⇒ zone of ignorance


13

Results of Altman’s testing:

• Altman carried out experiments using a paired sample of failed

businesses for five years prior to failure

• Found that model was able to predict failure for up to two years prior

to bankruptcy

• He identified the ratios used through trial and error (data-mining?) –

no underlying theory of financial distress

See confusion matrix in Table 9.9

• Overall classification power = 16.5% error

• Only one failed firm is classified as survived


BUT problems with Altman’s study:

1. Variables selected for their predictive performance, not from underlying

theory

• ALSO, variables exhibit considerable sample dependence

• Later developments used quadratic rather than linear formulations (Altman et

al., 1977)

2. Samples were not random/distribution problems

• Samples were matched for industry and size to control for these variables

• However, this contravenes one of the basic assumptions of discriminant

analysis

• Normality assumption is probably contravened

3. No cost function for type 1 and type 2 errors

• This would be useful to assess the performance of the model


14

4. Definition of what constitutes a failed firm is dubious

• Altman uses firms filing under Chapter 10 (predecessor of Ch.11)

5. Variables in equation are accounting values

• Inherent difficulties in measuring variables, particularly across industrial, national or size boundaries – potential bias in results

BUT advances made by Altman:

• Innovative approach, leading to further developments in practice of credit evaluation

• Subsequent studies have not significantly altered his conclusions:

Deakin (1976) – used Beaver’s ratios and random sampling rather than matched samples – improved predictive power but poor performance generalisation

Altman, Haldeman and Narayanan (1977) – refined accounting data for different reporting practices, financing strategies and used quadratic classifier, but little improvement in results

Diamond (1976) – refinements in technique and kept to statistical assumptions, but little improvement in power


2. Taffler’s (1983) model for the UK

Z = C0 + C1 return + C2 working capital + C3 financial risk + C4 liquidity

• Contribution of variables = 53%, 13%, 18% and 16%

• Equation classified 115 out of 825 quoted industrial companies as ‘at risk’

• Over next 4 years, 35% went bankrupt and 27% were still at risk

3. Conditional probability models

• Use of conditional probability models to replace discriminant analysis

• E.g. logit models – estimate probability of occurrence rather than simply fail or survive → probably more useful for lenders as most firms are not clear winners or losers

• BUT Ohlson (1980) showed no apparent improvement in results

• Zavgren (1983) develops Ohlson’s model but does not find significant improvement in predictive power either


15

General problems with failure prediction models:

Foster (1986):

• “The absence of an economic underpinning to modelling in this area has led

to trenchant criticism of research on financial distress”

• BUT he concludes:

“The major contribution of this research to date is documenting empirical

regularities. This documentation is important for both decision-making by

creditors and management and to researchers wishing to model economic

aspects of financial distress.”


Specific warnings when considering failure prediction models:

1. Matched samples control for size and industry

• What would happen if size and industry variables were included in the model?

• Would models still predict better than chance?

2. Value of models is difficult to assess

• Little consideration of cost function in most of the models

3. Precise specification of model is sample-specific

• And specific to sample characteristics, variables chosen, prediction horizon

• Criticism of ‘data-mining’

• ‘Portability’ of model is unknown to decision-maker


16

4. The use of capital market data

• Lack of relevance, reliability, timeliness and consistency in accounting

variables → difficulties in failure prediction models

E.g. accounting numbers do not measure economic characteristics of

firms, are prone to measurement problems, are not standardised

• THUS use capital market-based variables instead – idea that share

price contains reactions which are relevant to insolvency

Beaver (1968)

• Small share price movements can be detected up to five years prior to

bankruptcy, but bulk of movement is in year before failure


Queen and Roll (1987)

• Look at a number of potential predictors of failure

Market capitalisation, Price, Return, Variance, Beta

• Firm mortality divided into two types:

Favourable (acquired by merger)

Unfavourable (bankrupt, suspended from trading, delisted)

Table 9.12

• Used CRSP data and divided firms into deciles according to the relevant

variable in each year and assessed cumulative mortality in subsequent years

from 1963 to 1985

• Less likelihood of unfavourable mortality if:

• Bigger size

• Higher price

• Higher return

• Lower volatility

• THUS predictive power in market-based variables


17

Aharony, Jones and Swary (1980)

• Market-based predictions confirmed that market expectations for

failing firms indicate such failure well in advance of the event

• Why? Because the market (price) is an efficient processor of publicly

available information (earnings and the possibility of bankruptcy)

5. Inflation accounting and failure prediction

• Inflation accounting should make ratios more economically relevant

and THUS financial distress prediction should improve

• BUT Keasey and Watson (1986) – little improvement for UK firms

• Mensah (1983) – little improvement for US firms


6. The use of non-financial indicators

Peel, Peel and Pope (1986)

• Examined usefulness of:

• Changes in directors shareholdings

• Resignation of directors

• Delay in submitting accounts

• Found them all to be significant predictors of failure

Schwartz and Menon (1985)

• Showed that failing firms have a greater tendency to switch auditors than successful firms

Whittred and Zimmer (1984)

• Found little relationship between reporting delays and ultimate failure in Australian firms


18

7. The Zeta® Credit Risk Model: Altman (1977)

• The Zeta Model is merely an extension of the Z-Score Model which achieved better accuracy using the following variables:

x1 = Earnings before interest and taxes (EBIT) / Total assets (i.e. return on assets)x2 = Standard error of estimate of x1 normalised for 10 years (i.e. stability of earnings)

x3 = EBIT / interest charges (i.e. debt service)

x4 = Retained earnings / Total assets (i.e. cumulative profitability)

x5 = Current assets / Current liabilities (i.e. current ratio)

x6 = 5 year average market value of equity / Total capitalisation (i.e. capitalisation)

x7 = Total tangible assets, normalised (i.e. size measure)

• Model was effective at classifying bankrupt companies up to five years prior to failure (still 70%) on a sample of manufacturers and retailers

• Developed to account for: change in financial profile of business failures; updated data; model now appropriate for non-manufacturers too; incorporates not just ratios but also footnote information; uses more up-to-date techniques.


8. The KMV Approach

• KMV approach uses market-based quantitative techniques to assess

credit risk

• Bondholders are paid first, equity holders have a residual claim

• If market value of assets falls to lower than value of liabilities then

share is worthless → becomes bankrupt

i.e. liabilities > assets

• AND declining share price ⇒ company is getting closer to bankruptcy

• THUS only need to monitor share prices


19

EDF (Expected Default Frequency)

• Market-driven measure of a company’s default risk

• Probability that a firm will default within a given time frame

• Default = failure to make scheduled principal or interest payments

• Calculated on the basis of:

• Market value of the firm

• Level of debt obligations

• Volatility of firm’s value

• EDF is calculated on a day-to-day basis

• Supposed to be forward-looking and able to outperform agency

ratings and credit scoring statistical models

• EDF of 2% = 2% probability of defaulting with 12 months


KMV example (see www.moodyskmv.com)

20

9. Recent Bank of England UK Study: Benito and Vlieghe (2000)

• Study examines micro-data (accounts) for large numbers of UK

companies over the period 1974-1998

• Examines the distribution of key financial ratios:

• Distribution of profitability has widened in recent years, despite

relatively favourable economic conditions (profitability at lower tail of

distribution is lower now than at any time over last 25 years)

• Distribution of capital gearing has widened (higher gearing than ever)

• Companies with highest gearing are not more profitable than others

• Liquidity of highly geared companies seems to have fallen since 1995

(adds to vulnerability to shocks and to financial fragility)

• Overall, perhaps companies can operate prudently with higher levels

of debt than in the past??


10. Panel Analysis: Hunter and Isachenkova (2002)

• Examine failure determinants for large quoted UK industrials using a

panel data set comprising 539 firms observed over period 1988-93

• Use a logit panel regression

• Higher risk of insolvency is associated with:

• Lower liquidity (quick assets ratio)

• Slower turnover (ratio of debtors turnover)

• Lower profitability (pre-tax profit margin)

• Economic value of the firm is a less important driver

• Conclusion: current cash-flow considerations rather than the future

prospects of the firm determined company failures over the 1990s

recession


21

How Moody’s and S&P’s calculate Ratings

They focus on determining the probability that a borrower will pay

interest and principal in full and on time

1. Identify a company’s peer group (size and industry based)

2. Compare a limited number of key ratios:

• Pre-tax interest cover

• Funds flow to total debt

• Debt to total capital

3. Also, subjective assessments of competitive position, management

quality, financial flexibility, ability to meet targets, etc.

4. Rank company within a rating peer group

5. Monitor through trend analysis – particularly “cycle-to-cycle”

61.3Occidental PetroleumBBB-8

52.7Phillips PetroleumBBB7

52.4ConocoA-6

48.9Amerada HessBBB+5

38.1TexacoA+4

32.2Shell OilAAA3

31.5ChevronAA2

21.7Exxon MobilAAA1Total Debt as a % Of Capital



46.0ConocoA-6

46.3TexacoA+5


64.3Shell OilAAA3

69.8ChevronAA2

108.7Exxon MobilAAA1Funds Flow % of Total Debt




4.9ConocoA-5

5.3TexacoA+4

6.4Shell OilAAA3

9.8ChevronAA2

12.0Exxon MobilAAA1Pretax Interest Coverage

1998-00

Mean Times

CompanyS&P RatingRankRanking Variable

22

Moody’s Bond Ratings

Lowest rated, extremely poor prospects of ever attaining an

investment standing

C

Speculative in a high degree, often in default or have other marked

problems

Ca

Poor standing, may be in default or there exist elements of danger

with respect to payments of principal or interest

Caa

Lack characteristics of desirable investment, assurance of

payments may be small

B

Speculative elements, future is not well assured, moderate protection

of payments, uncertainty of position

Ba

Medium-grade, interest and principal security is adequate, but some

protective elements may be lacking, have speculative characteristics

Baa

Upper-medium grade, possess many favourable attributes, adequate

cover of principal and interest, susceptible to future risk

A

High quality, still high grade bonds, margins not as large or fluctuate

more, longer-term risk may be higher

Aa

Best quality, small degree of investment risk – “gilt edged”, interest

payments protected by large/stable margin

Aaa

Standard & Poor’s Bond Ratings

In payment defaultD

Currently highly vulnerable, some default action takenC

Currently highly vulnerable to non-paymentCC

Currently vulnerable to non-payment, dependent on favourable conditionsCCC

More speculative, but currently has capacityB

Somewhat speculative, major ongoing uncertainties affecting capacityBB

Adequate protection, susceptible to adverse effects, weakened capacityBBB

More susceptible to adverse effects, still strong capacity to meet commitmentsA

Very strong obligation to meet commitmentsAA

Highest rating, extremely strong obligation to meet financial commitmentsAAA

23

The Meaning of Debt Ratings

• The analysis of bonds (public debt) is not fundamentally different from

the analysis of bank loans and other private debt issues

• However important institutional differences:

Bankers in private debt are often very close to client to form initial

assessment of credit risk and monitor firm activities during the

loan

With public debt, investors and firms are distanced

THUS debt investors rely on professional debt analysts to:

• Assess riskiness of debt

• Monitor the firm’s ongoing activities

• THUS debt analysts attempt to address the information asymmetry

that exists between investors (lenders) and firms (borrowers)

Major debt rating agencies in the US:

Moody’s

Standard and Poor’s

Standard and Poor’s labelling system:

Ratings are AAA,AA,A,BBB,BB,B,CCC,CC,C,D (where D is default)

AAA Highest rating – firms here are large and have strong and steady

earnings and little leverage

Only 1-2% have this rating

BBB Investment grade firms are BBB or above – many funds will not

invest in lower grades

Generally moderately leveraged, with only 45% of financing

coming from debt, strong earnings, reasonable interest cover and

cash flow to debt of ratios

<BBB Firms face some significant risk, though may still be profitable

CCC Firms whose debt is 80% of capital

The Meaning of Debt Ratings

24

Examples of Debt Ratings

80.8-17.3-1.7-5.31.7

Oxford Health Plans

Trans World AirlinesCCC

78.43.10.77.418.3

Apple Computer

Greyhound LinesB

59.511.11.911.121.0

Northwest Airlines

RJR NabiscoBB

45.024.83.012.628.2

Delta Airlines

MCI CommunicationsBBB

39.034.34.816.621.8

Ford Motor Company

General MotorsA

29.459.87.225.07.0

McDonald’s Corp.

Wal-Mart StoresAA

9.7%100.1%11.6

times

35.3%1.9%

General Electric

Johnson & JohnsonAAA

Long-

term debt

to total

capital

Cash flow

from

operations

to total debt

Pretax

interest

coverage

Pretax

return on

long-term

capital

% of public

industrials

given same

S&P rating

Example firms in 1998S&P

debt

ratin

g

Standard and Poor’s Compustat, 1998

• Some of the variation in debt ratings can be explained as a function of

selected financial ratios in a quantitative model

• Many finance companies rely heavily on such models, particularly

where a public rating is not available:

• Insurance companies

• Banks

See Palepu, Healy and Bernard Table:

• Shows the factors used by three different firms, including an insurance

company, a bank and an investment research firm

• Note that profitability and leverage play an important role in the

rating

• One firm uses firm size as a key factor, with better ratings for larger

firms

Factors that DriveDebt Ratings

25

Factors used in Quantitative Models of Debt Ratings (Palepu, Healy and Bernard Table 14.2)

Standard deviation of

return

Subordination status

Other

Total assetsSalesFirm size

Fixed charge

coverage

Coverage of short-

term debt and fixed

charges

Interest coverage

Cash flow to long-

term debt

Interest coverage

Cash flow to long-

term debt

Profitability and

leverage

Long-term debt to

capitalisation

Long-term debt to

capitalisation

Total debt to total

capital

Long-term debt to

capitalisationLeverage

measures

Return on long-term

capital

Return on long-term

capital

Return on long-term

capitalProfitability

measures

Firm 3Firm 2Firm 1

• Kaplan and Urwitz (1979) developed two models to highlight the relative importance of factors driving debt ratings

• Model 1 has greater explanatory power to explain variation in bond ratings and includes financial market variables as well as financial statement data

• Model 2 is based solely on financial statement data

• Interesting results:

• Most important factor is firm size – large firms get better ratings

• Next most important factor – whether debt is subordinated or not

• Next most important factor – Leverage (gearing)

• Less important is profitability, though this is possibly because of the fact that ROA and interest coverage are proxying similar things (profitability)

• Individual variables explanatory power ranking is given in the table

Kaplan-Urwitz Models of Debt Ratings

26


0.0060.007Pretax funds flow before

interest to interest

expense

Interest coverage

-0.53NACoefficient of variation in

net income over 5 years

Riskiness of profit

stream

NA-2.90Standard deviation of

residual from market

model (average=0.10)

Unsystematic risk

6.405.13Net income to total

assets

Profitability

NA-0.87Market model beta

(1=average)

Systematic risk

-2.72-2.85Long-term debt to total

assets

Leverage

-2.56-2.361=subordinated;

0=unsubordinated

Subordination status of

debt

0.00120.0010Total assetsFirm size

4.415.67Model intercept

Model 2

Coefficients

Model 1

Coefficients

Variable reflecting

characteristic

Firm or debt

characteristics

Performance of the models:

• When applied to an out-of-sample set of bonds, Model 1 predicted the rating correctly in 44 of 64 cases (63%)

• When the model was incorrect, it was only ever one category out and in half of those cases its prediction was more consistent with the market yield than the actual debt rating

• The implication of this testing is that rating agencies incorporateother factors in their analysis

• Such factors might include:

• Strategic analyses

• Accounting analyses

• Prospective analyses

• As all of the variables are publicly available, do public ratings contain any news?

• Holthausen and Leftwich (1986, 1992) found that downgrades are greeted with drops in bond and stock prices – so yes, they do contain news


IA1 Lecture 9 - Exeter

Documents

Transcript of IA1 Lecture 9 - Exeter