Credit Analysis, Bond Ratings, Distress Forecast and Financial Information

8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information

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Credit Analysis, Bond

Ratings, Distress Forecastand Financial Information


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Credit Analysis

The process of evaluating an applicant's loanrequest or a corporation's debt issue in orderto determine the likelihood that the borrowerwill live up to his/her obligations.

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Credit Analysis

Evaluate a borrowers ability and willingness

to repay

Questions to address What risks are inherent in the operations of the

business?

What have managers done or failed to do in

mitigating those risks? How can a lender structure and control its own

risks in supplying funds?

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Existing Loan Decisions

Loan Approvals

Loan Monitoring

Loan Terminations


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Loan Application

Customerrelation

Financialperformance

Strategic

factorManagementquality

RiskEconomic

condition

YesAmount Interest rate

Collateral Covenant OthersInsurance

Repayment timingMonitoring Market value ofcollateral

Covenant

Current EspeciallyMentioned Substandard Doubtful

Loss

Approval


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The categories: classification of existingloans into

A. Current: normal acceptable banking risk.

B. Especially mentioned: evidence of weakness in theborrowers financial condition or an unrealisticrepayment schedule.

C. Substandard:severelyadverse trends ordevelopments of a financial, managerial,economic, or political nature that require promptcorrective actions.

D. Doubtful:full repayment of the loan appears to be

questionable. Some eventual loss seems likely.Interest is not accrued.

E. Loss:loan is regarded as uncollectible.


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Five Cs of Good Credit

Character

Capital

Capacity Conditions

Collateral

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Five Cs of Bad Credit

Complacency Carelessness

Communication

Contingencies Competition

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Credit Scoring


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What is credit scoring?

A statistical means of providing a quantifiablerisk factor for a given customer or applicant.

Credit scoring is a process wherebyinformation provided is converted intonumbers that are added together to arrive at ascore. (Scorecard)

The objective is to forecast future performancefrom past behaviour.

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A Simple Linear Model to Replicate theJudgment Used in Classifying the Loan Risk(Dietrich and Kaplan ,1982)

Yi = -3.90 + 6.42 DEi - 1.12 FCCi + 0.664 Sdi

where

DEi = Total debt/total assets FCCi = funds from operation/(interest + minimum rental

commitment + average debt maturing within three years)

SDi = number of consecutive years of sales decline

The higher the Yi score, the higher the estimatedrisk of the loan.


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The hindsight for a simple scoring method

The loan officers may consider more than threevariables.

The loan officers may consider non-linear or non-

additive functional form. The loan officers may consider non financial

information.


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Loss functions for the misclassifications

Uniform loss function.

Loss functions supplied by the bank.


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The loss function for modelprediction errors

C1: (Resulted from type I error) the cost of predictinga loan applicant will not repay when it subsequentlyrepay. It will be contribution margin on the loan that wasforegone, assuming that applicants predicted not torepay are refused loans.

C2: (Resulted from type II error) the cost of predictingthat a loan applicant will repay when it subsequentlydoes not repay. It will be the loss associated with theinterest and principal the bank can not receive when due.

Note: Using estimates of C2 based on loan lossrecovery statistics estimated in the 1971-1975 period,researchers have reported that a C2 error was 35times more costly than was a C1 error.


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Scoring methods and sample sizes

There is a trade off between having a large enough setof observations to efficiently estimate a scoring methodand having a set of firms that are homogeneous withrespect to attributes relevant to their loan decision.

Solutions:1.Build a separated scoring system for eachindustry. But this always resulted in a small sample,especially very few observations for problem loancategories.

2. To control for the hypothesis source ofheterogeneity across observations, such as the useof industry relative ratios as a means controlling fordifferences across industries in their averagefinancial ratios.


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Credit Analysis andFinancial Ratios

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Credit

Analysis

Short

Term

Long

Term

Common Size(To total assets)

Cash

AR

Inventory

Total Current Assets

Intangibles

Current Liabilities

Total Liabilities

Equity

Days Sales in AR

Days Sales in Inventory

Days Purchases in APCash Conversion Ratio

Current Ratio

Quick Ratio

Op. Cash Flow to

Current Liabilities

Relationships

% Chg in AR to %

Chg in Sales

%Chg in Invt to %

Chg in Sale

LT Debt/EquityTotal Liab/Equity

PPE/Total Assets

Interest Coverage

Op. Cash Flow/Tot Liab

Op. Cash Flow/PPE Exp


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The importance of financial ratios used incredit decision:---Survey conducted on loan officers

1. Debt/Equity

2. Current ratio

3. Cash flow/Current maturities of long-term debt

4. Fixed charge coverage

5. Net profit margin after taxes6. Times interest earned

7. Net profit margin before taxes

8. Degree of Financial leverage

9. Inventory turnover in days10. Accounts receivable turnover in days


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The importance according to thefrequency adopted in loan agreements

1. Debt/Equity

2. Current ratio

3. Dividend payout ratio

4. Cash flow/Current maturities of long-term debt

5. Fixed charge coverage6. Times interest earned

7. Degree of Financial leverage

8. Equity/Asset

9. Cash flow/Total debt10. Quick ratio


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What are bond ratings?

Bond ratings are opinions of relativecreditworthiness, derived throughfundamental credit analysis and expressedthrough a symbol system.

Creditworthiness: tendency to pay obligationson time.

Default probability and severity of loss given

default

Not statement of default timing

Not Buy and sell recommendations20


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The role of ratings:

Improve the information flow betweenborrowers and lenders.

Information asymmetry

Improve transparency

Minimize monitoring and principal/agent costs

Owners vs managers of firms

Fund sponsors vs fund manager

Public goods

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Bond ratings and debt covenantsCategories of Covenants Moodys Rating

Aaa Aa A Baa Ba B

Affirmative Covenants

1. Furnish annual audit financial

statements

2. Furnish quarterly interim financial

statements

3. Maintaining accounting systems

according to GAAP4. Permit banks to have access to

books/records

5. Maintaining insurance

50% 66% 100% 100% 100% 100%

-- 33 100 100 80 50

-- -- 17 9 40 50

-- -- 25 18 -- 50

-- -- 50 82 100 100

Negative Covenants

1. Minimum working capital

2. Minimum current ratio

3. Minimum tangible net worth

4. Limit on indebtedness

5. Limit on mergers and consolidations

6. Limits on dividends

7. Limit on sale of stock and/or debt of

subsidiaries

8. Limit on sale of all or substantial part ofassets

-- 67 83 91 60 75

-- -- 33 27 60 100

-- -- 17 27 40 75

-- -- 33 73 100 100

50 33 67 82 100 100

-- -- 50 91 60 100

50 -- 33 64 60 75


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Bond ratingsStandard and Poors

AAA highest gradeultimate degree of protection of principleand interest

AA high gradediffer from AAA in small degrees A upper medium grade

Have considerable investment strength but are not entirelyfree from adverse effects of changes in economic and tradeconditions. Interest and principal are regarded as safe.They to some extent reflect changes in economic conditions

BBB or mediumgrade category is borderline betweendefinitely sound obligations and those where the speculativeelement begins to dominate. These have adequate assetcoverage and normally are protected by satisfactory earnings.They are susceptible to fluctuations due to economicconditions. This is the lowest rating that qualifies for

commercial bank investment. There is a lower range of ratings ranging from BB which are

lower medium grade all the way to the D category representingbonds in default.

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ITEMS AFFECTING THE RATINGS OF

CORPORATE BONDS

Items considered:

Asset protectionmeasures the degree to which acompanys debt is covered by the value of its assets.

Tangible assets/LTD

AAA5 to 1

AA4 to 1

A3 to 3.5 to 1 BBB2.5to 1

LTD/(LTD + Equity)

AAAless than 25%

AA less than 30%

A less than 35% BBB less than 40%


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ITEMS AFFECTING THE RATINGS OFCORPORATE BONDS

Fixed-charges-coverage ratio

AAA ratingcover interest and rental chargesafter tax by 5 to 7 timesindustrial firm

AA4 times

A3 times

BBB2 times


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Cash flowcrudelynet income plusdepreciationto total funded debtnotespayable and lease obligations

65% for AAA

45-65 for AA

35-45 for A

25-30 for BBB


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Management abilities, philosophy, depth andexperienceDepth and breadth of management

Goals, planning process, strategies for R&D, product promotion,new product planning and acquisitions

Specific provisions of debt security

Conditions for issuance of future debt issues,

specific security provisions-mortgaging, sinkingfund, redemption, covenants


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Distress Forecast andFinancial Information

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Distress analysis and financial information

Definition: financial distress means that a

firm has severe liquidity problems that cannot be solved without a sizable rescaling ofthe equitys operations or structure.

Definition of Insolvency Total liabilities of a company exceeds its assets at

a fair valuation The firms inability to pay its creditors as

obligations come due (technical insolvency) Some states prohibit the payment of cash

dividends if the company is insolvent


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Financial Crisis, Some Warning Signals

1. Heavy borrower of working capital

2. Gross margins narrowing

3. Business environment subject to rapid change

4. If volume drops, can production cover expenses

5. Outdated marketing data6. Organization highly structured/decision time

7. Equipment age/economic downturn

8. Intensity of industry competition

9. Increasing borrowing without an increase in sales

10. Increasing inventory and receivables without an increasein sales


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Distress analysis and financial information

Indicators of financial distress: Cash flow analysis.

Corporate strategy analysis.

Financial statements of the firm and a set of firmsin comparison.

External variables such as security returns andbond ratings.


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Univariate model ofdistress prediction:

involves the use a singlevariable in prediction model.

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1. Dichotomous classification tests:

Case study of U.S. Railroad Bankruptcies: Usethe ranking of certain variable(s) to predict thebankruptcy of railroad companies. For

example, Transportation expenses tooperating revenues (TE/OR), and Timesinterest earned (TIE)


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Railway Companies (TE/OR) (TIE)

Healthy firms (1970)

1. Ann Arbor Railroad .524 -1.372.Central of Georgia Railway .348 2.16

3.Cincinnati, New Orleans, and Texas

Pacific.274 2.91

4.Florida East Coast Railway .237 2.82

5. Illinois Central Railway .388 3.10

6.Norfolk and Western Railway .359 2.81

7.Southern Pacific Transportation Co. .400 3.56

8.Southern Railway Company .314 3.93

Distressed firms (1970)

1.Boston and Maine Corporation .461 -0.68

2.Penn-Central Transportation Co. .485 0.16

Ranking according to (TE/OR) (cutoff = 0.4305)


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Railway Companies (TE/OR) Bankruptedor not

Ann Arbor Railroad .524 NB

Penn-Central Transportation Co. .485 B

Boston and Maine Corporation .461 B

Southern Pacific Transportation Co. .400 NB

Illinois Central Railway .388 NB

Norfolk and Western Railway .359 NB

Central of Georgia Railway .348 NB

Southern Railway Company .314 NB

Cincinnati, New Orleans, and Texas

Pacific.274 NB

Florida East Coast Railway .237 NB


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Type I error and Type II error: A type Iprediction error occurs when a non-bankrupt (NB) firm is predicted to be

bankrupt (B) firm. A type II prediction erroroccurs when a bankrupt (B) firm ispredicted to be non-bankrupt firm. Benoted that the loss function for type II erroris greatly higher than that of type I error;research has shown that to be 35 times.


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Cutoff Type I Error Type II Error Total Error

TE/OR>0.5045 1 2 3

TE/OR>0.4730 1 1 2

TE/OR>0.4305 1 0 1

TE/OR>0.3940 2 0 2

TE/OR>0.3735 3 0 3


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1.Dichotomous classification tests:

Ranking according to (TIE)

Railway firms (TIE) Bankruptedor not

Southern Railway Company 3.93 NB

Southern Pacific Transportation Co. 3.56 NB

Illinois Central Railway 3.10 NB

Cincinnati, New Orleans, and Texas

Pacific2.91 NB

Florida East Coast Railway 2.82 NB

Norfolk and Western Railway 2.81 NB

Central of Georgia Railway 2.16 NB

Penn-Central Transportation Co. 0.16 B

Boston and Maine Corporation -0.68 B

Ann Arbor Railroad -1.37 NB


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2. Profile Analysis

Comparisons of the mean ratios of distress andnon-distress firms have been common inbankruptcy prediction.

For each failed firm, a non-fail firm of the same

industry and the same asset size was selected. The equally-weighted means of 30 financial ratios

were computed for each of the failed and non-failed groups in each of the five years beforefailure.

It examines if there are observable differences in themean ratios of the two sets of firms.


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2.Profile Analysis(1)

debtTotal

flowCash

0.45 0.45

-0.12

0.17

-5 -4 - 3 -2 - 1


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AssetsTotal

IncomeNet

0.08 0.08

0.05

-0.20-5 -4 - 3 -2 - 1


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0.51

0.85

0.37

0.38

AssetsTotal

DebtsTotal

-5 -4 - 3 -2 - 1


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0.42 0.43

0.30

0.05

-5 -4 - 3 -2 - 1

AssetsTotal

CapitalWorking


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3.5

3.2

2.5

2.1

-5 -4 - 3 -2 - 1

Current ratio


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Overview of the uni-variate results

There are four categories of variablesshowing the most consistent differencebetween bankrupt and non-bankrupt firms

were:

Rate of return

Financial leverage

Fixed payment coverage

Stock return and volatility


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Multivariate models of distress prediction

We can use econometric tools by applying more

than one financial variables that can effectivelydiscriminate healthy firms from distressed firms.Those tools include Discriminant Analysis,qualitative dependent variable regressions (e.g.

Linear probability models, probit regression, andlogit regression), and non-linear forecasting tools,such as Neural Network techniques.

The dependent variable of these models is either a

prediction as to group membership (bankrupt ofnon-bankrupt), or a probability estimate of groupmembership (for example, the probability towardbankruptcy).


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(1) Discriminant Analysis:Municipality Assessed Property

Valuation perCapita

General

ObligationBonded Debtper Capita

Moodys

BondRating

1.Arlington, Mass. $6,685 $116 Aa

2.Highland Park, Ill. $6,360 $87 Aa

3.Springdale, Ohio $11,806 $272 Aa

4.El Cerrito, Calif. $2,957 $53 A

5.La Grange, Ga. $3,183 $47 A

6.Pampa, Tex. $2,408 $188 A

7.Coon Rapids, Minn. $2,703 $613 Baa

8.Hot Springs, Ark. $1,212 $43 Baa

9.Mauldin, S.C. $1,051 $366 Baa

10.Pascagoula, Miss. $2,684 $149 Baa


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(1) Discriminant Analysis:

1. Two dependent variables (Zi).

2. Every sample firm is featured two descriptivevariables (XI,YI).

3. These two descriptive variables have different

normally distributed means and same variance-covariance matrix within each group.

So there is a discriminant function that caneffectively distinguish both groups:

ZI= Moodys Rank equal to or better than A; or Moodys Rank

equal to or lower than Baa.

XI= Assessed Property Valuation per Capita

YI= General Obligation Bonded Debt per Capita

iii bYaXZ


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Step 1: To estimate the coefficients for the discriminant function,which is able to maximize the between group SSE of ZI andminimize the within group SSE of ZI

=0.000329

=-0.004887

xyxyyx

yxyxy dd

a

22

2

xyxyyx

xxyyx ddb

22

2


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Municipality PredictedZ-score

Moodys BondRating

1.Springdale, Ohio 2.555 Aa

2.Highland Park, Ill. 1.667 Aa

3.Arlington, Mass. 1.632 Aa

4.La Grange, Ga. .817 A

5.El Cerrito, Calif. .713 A

6.Hot Springs, Ark. .188 Baa*

7.Pascagoula, Miss. .154 Baa*

8.Pampa, Tex. -.126 A*

9.Mauldin, S.C. -1.441 Baa

10.Coon Rapids, Minn. -2.106 Baa


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(1) Discriminant Analysis: Step 2:to determine a cut off point which serves as the critical

value that separate distressed firms with healthy firms.

RankRankRankRankRankRankRankCut-off point Misclassification number

Rank >=A when ZI>1.2245 3

Rank >=A when ZI>.7650 2

Rank >=A when ZI>.4505 1

Rank >=Awhen ZI>.1710 2

Rank >=Awhen ZI>.0140 3

Rank >=A when ZI>-.7835 2

Rank >=A when ZI>-1.7735 3



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Step 3: Test out-of sample forecast validity by usinganother sample to test the previously set cutoff point.

Municipality Assessed PropertyValuation per

Capita

General Obligation

Bonded Debt

per Capita

Predicted

Z-scoreMoodys Bond

Rating

1.Palo Alto, Calif. $6,124 $110 1.474 Aa

2.Homewood, Ill. 4,134 34 1.194 A3.Portland, Maine 11,271 562 .962 Aa

4.East Lansing, MI. 2,835 64 .620 A

5.Dodge City, Kan. 2,781 98 .436 A

6.Flagstaff, Ariz. 1,616 50 .287 Baa7.Cambridge, Mass. 3,270 278 -0.282 Aa

8.Bogalusa, La. 1,796 333 -1.036 Baa

9.Aspen, Colo. 11,274 1,159 -1.954 Baa

10.Cape Coral, Fla. 25,763 2,304 -2.783 Baa


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Correct Classification Ratio =22211211

2211

AAAA

AA



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Altmans Z-score models:

Altmans Z-score for NYSE and NASDAQ firms

Z 2.99 for normal firmsZ 1.81 for distressed firms

1.81 Z 2.99 indeterminate

Altmans Z-score model for private firms

EBIT Net working capital Salesz 3.3 1.2 1.0

Total assets total assets total assets

MVE Accumulated retained earnings0.6 1.4

BVD total assets

Net working capital Accumulated retained earningsz 6.56 3.26

total assets total assetsEBIT MVE

1.05 6.72total assets BVD

Z 2.90 for normal firms

Z 1.23 for distressed firms

1.23 Z 2.90 indeterminate

(2) Z t C dit Ri k


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(2) Zeta Credit Risk: The multivariate model was based on the following seven

variables, though the true formula was never disclosed:

1.Overall Profitability: AssetsTotalEBIT

2.Size: Total Assets

3.Debt service: PaymentInterestTotal

EBIT

4.Liquidity: Current Ratio

5.Cumulative Profitability: AssetsTotal

.R.E

6. Market Capitalization: capitalTotalofMVofaverageyears5 EquityofMVofaverageyears5 7. Earnings StabilityThe estimated standard error of around a

10-year profitability trend.

The model was estimated by the discriminant analysis, and

zero is the dividing line between the failed firms (negative)

and non-failed firms (positive).


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(2) Zeta Credit Risk :

Zeta scores between normal and failed firms five years before distress

4.0

2.0

-5 -4 -3 -2 -1

-2.0

-4.0

(2) Z t C dit Ri k


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(2) Zeta Credit Risk:

American

MotorsChrysler

Corp.Ford Motors General

MotorsMean Zeta

for

fourZeta % Zeta % Zeta % Zeta %

1974 2.23 41 1.82 37 4.72 64 6.63 79 3.85

1975 .05 24 1.37 36 4.27 63 6.52 81 3.05

1976 -.60 19 1.61 38 4.68 65 6.80 82 3.12

1977 -.22 21 1.05 31 4.52 62 6.71 80 3.01

1978 .48 27 .42 27 4.29 59 6.31 77 2.87

1979 1.10 33 -1.12 16 4.07 58 6.24 77 2.57

1980 -2.07 10 -3.55 5 2.26 41 4.51 61 .29

1981 -3.64 5 -3.68 5 1.77 35 3.91 55 -.41

1982 -4.54 4 -3.29 6 1.55 33 3.59 52 -.67

1983 -5.29 4 -2.38 9 2.03 38 3.99 55 -.41


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(2) Zeta Credit Risk:

Year Percentile of Distribution of Zeta credit risk scores

5% 15% 25% 35% 45% 55% 65% 75% 85% 95%

1974 -3.61 -1.37 .18 1.27 2.22 3.27 4.45 5.71 7.08 9.93

1975 -3.99 -1.39 .14 1.30 2.41 3.51 4.58 5.81 7.28 9.97

1976 -4.27 -1.28 .23 1.46 2.57 3.76 4.88 5.97 7.50 10.23

1977 -4.58 -1.35 .09 1.31 2.63 3.85 4.87 6.01 7.62 10.33

1978 -4.41 -1.46 .03 1.27 2.57 3.67 4.81 6.04 7.68 10.22

1979 -3.78 -1.18 .29 1.38 2.58 3.69 4.88 6.11 7.74 10.21

1980 -3.87 -1.18 .33 1.66 2.80 3.90 4.94 6.22 7.83 10.35

1981 -4.12 -1.00 .44 1.71 2.93 3.89 4.83 6.30 8.01 10.73

1982 -4.92 -1.29 .25 1.60 2.66 3.89 4.87 6.12 7.92 10.58

1983 -4.88 -1.55 .20 1.67 2.81 3.97 5.11 6.33 8.07 10.63

Other devices that predict


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Other devices that predictfinancial distress

Qualitative dependent variable regression:probit and logit regressions

Artificial Neural Network

Documents

Credit Analysis, Bond Ratings, Distress Forecast and Financial Information