credit analysis, bond ratings, distress forecast and financial information
TRANSCRIPT
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
1/59
1
Credit Analysis, Bond
Ratings, Distress Forecastand Financial Information
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
2/59
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.
2
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
3/59
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?
3
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
4/59
4
Existing Loan Decisions
Loan Approvals
Loan Monitoring
Loan Terminations
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
5/59
5
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
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
6/59
6
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.
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
7/59
Five Cs of Good Credit
Character
Capital
Capacity Conditions
Collateral
7
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
8/59
Five Cs of Bad Credit
Complacency Carelessness
Communication
Contingencies Competition
8
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
9/59
9
Credit Scoring
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
10/59
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.
10
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
11/59
11
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.
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
12/59
12
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.
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
13/59
13
Loss functions for the misclassifications
Uniform loss function.
Loss functions supplied by the bank.
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
14/59
14
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.
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
15/59
15
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.
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
16/59
Credit Analysis andFinancial Ratios
16
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
17/59
17
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
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
18/59
18
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
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
19/59
19
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
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
20/59
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
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
21/59
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
21
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
22/59
22
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
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
23/59
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.
23
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
24/59
24
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%
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
25/59
25
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
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
26/59
26
ITEMS AFFECTING THE RATINGS OFCORPORATE BONDS
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
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
27/59
27
ITEMS AFFECTING THE RATINGS OFCORPORATE BONDS
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
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
28/59
Distress Forecast andFinancial Information
28
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
29/59
29
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
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
30/59
30
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
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
31/59
31
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.
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
32/59
Univariate model ofdistress prediction:
involves the use a singlevariable in prediction model.
32
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
33/59
33
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)
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
34/59
34
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)
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
35/59
35
1. Dichotomous classification tests:
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
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
36/59
36
1. Dichotomous classification tests:
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.
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
37/59
37
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
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
38/59
38
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
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
39/59
39
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.
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
40/59
40
2.Profile Analysis(1)
debtTotal
flowCash
0.45 0.45
-0.12
0.17
-5 -4 - 3 -2 - 1
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
41/59
41
2.Profile Analysis(2)
AssetsTotal
IncomeNet
0.08 0.08
0.05
-0.20-5 -4 - 3 -2 - 1
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
42/59
42
2.Profile Analysis(3)
0.51
0.85
0.37
0.38
AssetsTotal
DebtsTotal
-5 -4 - 3 -2 - 1
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
43/59
43
2.Profile Analysis(4)
0.42 0.43
0.30
0.05
-5 -4 - 3 -2 - 1
AssetsTotal
CapitalWorking
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
44/59
44
2.Profile Analysis(5)
3.5
3.2
2.5
2.1
-5 -4 - 3 -2 - 1
Current ratio
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
45/59
45
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
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
46/59
46
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).
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
47/59
47
(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
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
48/59
48
(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
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
49/59
49
(1) Discriminant Analysis:
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
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
50/59
50
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
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
51/59
51
(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
(1) Discriminant Analysis:
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
52/59
52
(1) Discriminant Analysis:
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
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
53/59
53
(1) Discriminant Analysis:
Correct Classification Ratio =22211211
2211
AAAA
AA
(1) Discriminant Analysis:
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
54/59
54
(1) Discriminant Analysis:
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
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
55/59
55
(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).
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
56/59
56
(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
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
57/59
57
(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
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
58/59
58
(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
-
8/2/2019 Credit Analysis, Bond Ratings, Distress Forecast and Financial Information
59/59
59
Other devices that predictfinancial distress
Qualitative dependent variable regression:probit and logit regressions
Artificial Neural Network