Credit Analysis, Bond
Ratings, Distress Forecast
and Financial Information
1
Credit Analysis

The process of evaluating an applicant's loan
request or a corporation's debt issue in order
to determine the likelihood that the borrower
will live up to his/her obligations.
2
Credit Analysis


Evaluate a borrower’s 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
Existing Loan Decisions
Loan
Approvals
Loan Monitoring
Loan Terminations
4
Loan Application
Customer
relation
Financial
performance
Strategic
Interest rate
Collateral Yes
factor
Management
quality
Economic
condition
Risk
Approval
Amount
Monitoring
Repayment timing
Current
Especially
Mentioned
Covenant
Market value of
collateral
Substandard
Others
Insurance
Covenant
Doubtful
Loss
5
The categories: classification of existing
loans into
A. Current: normal acceptable banking risk.
B. Especially mentioned: evidence of weakness in the
borrowers’ financial condition or an unrealistic
repayment schedule.
C. Substandard: severely adverse trends or
developments of a financial, managerial,
economic, or political nature that require prompt
corrective 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.
6
Five C’s of Good Credit





Character
Capital
Capacity
Conditions
Collateral
7
Five C’s of Bad Credit





Complacency
Carelessness
Communication
Contingencies
Competition
8
Credit Scoring
9
What is credit scoring?



A statistical means of providing a quantifiable
risk factor for a given customer or applicant.
Credit scoring is a process whereby
information provided is converted into
numbers that are added together to arrive at a
score. (“Scorecard”)
The objective is to forecast future performance
from past behaviour.
10
A Simple Linear Model to Replicate the
Judgment 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 estimated
risk of the loan.
11
The hindsight for a simple scoring method



The loan officers may consider more than three
variables.
The loan officers may consider non-linear or nonadditive functional form.
The loan officers may consider non financial
information.
12
Loss functions for the misclassifications


Uniform loss function.
Loss functions supplied by the bank.
13
The loss function for model
prediction errors



C1: (Resulted from type I error) the cost of predicting
a loan applicant will not repay when it subsequently
repay. It will be contribution margin on the loan that was
foregone, assuming that applicants predicted not to
repay are refused loans.
C2: (Resulted from type II error) the cost of predicting
that a loan applicant will repay when it subsequently
does not repay. It will be the loss associated with the
interest and principal the bank can not receive when due.
Note: Using estimates of C2 based on loan loss
recovery statistics estimated in the 1971-1975 period,
researchers have reported that a C2 error was 35
times more costly than was a C1 error.
14
Scoring methods and sample sizes


There is a trade off between having a large enough set
of observations to efficiently estimate a scoring method
and having a set of firms that are homogeneous with
respect to attributes relevant to their loan decision.
Solutions:
1.Build a separated scoring system for each
industry. But this always resulted in a small sample,
especially very few observations for problem loan
categories.
2. To control for the hypothesis source of
heterogeneity across observations, such as the use
of industry relative ratios as a means controlling for
differences across industries in their average
financial ratios.
15
Credit Analysis and
Financial Ratios
16
Credit
Analysis
Short
Long
Term
Term
Days Sales in AR
Days Sales in Inventory
Days Purchases in AP
Cash 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
Common Size
(To total assets)
Cash
AR
Inventory
Total Current Assets
PP&E
Intangibles
Current Liabilities
Total Liabilities
Equity
LT Debt/Equity
Total Liab/Equity
PPE/Total Assets
Interest Coverage
Op. Cash Flow/Tot Liab
Op. Cash Flow/PPE Exp
17
The importance of financial ratios used in
credit decision:
---Survey conducted on loan officers
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Debt/Equity
Current ratio
Cash flow/Current maturities of long-term debt
Fixed charge coverage
Net profit margin after taxes
Times interest earned
Net profit margin before taxes
Degree of Financial leverage
Inventory turnover in days
Accounts receivable turnover in days
18
The importance according to the
frequency adopted in loan agreements
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Debt/Equity
Current ratio
Dividend payout ratio
Cash flow/Current maturities of long-term debt
Fixed charge coverage
Times interest earned
Degree of Financial leverage
Equity/Asset
Cash flow/Total debt
Quick ratio
19
What are bond ratings?


Bond ratings are opinions of relative
creditworthiness, derived through
fundamental credit analysis and expressed
through a symbol system.
Creditworthiness: tendency to pay obligations
on time.



Default probability and severity of loss given
default
Not statement of default timing
Not Buy and sell recommendations
20
The role of ratings:

Improve the information flow between
borrowers and lenders.



Minimize monitoring and principal/agent costs



Information asymmetry
Improve transparency
Owners vs managers of firms
Fund sponsors vs fund manager
Public goods
21
Bond ratings and debt covenants
Categories of Covenants
Affirmative Covenants
1.
Furnish annual audit financial
statements
2.
Furnish quarterly interim financial
statements
3.
Maintaining accounting systems
according to GAAP
4.
Permit banks to have access to
books/records
5.
Maintaining insurance
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 of
assets
Moody’s Rating
Aaa
Aa
A
Baa
Ba
B
50%
66%
100%
100%
100%
100%
--
33
100
100
80
50
--
--
17
9
40
50
--
--
25
18
--
50
--
--
50
82
100
100
--
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
50
--
33
64
60
100
22
75
Bond ratings—Standard and Poor’s





AAA highest grade—ultimate degree of protection of principle
and interest
AA high grade—differ from AAA in small degrees
A upper medium grade
 Have considerable investment strength but are not entirely
free from adverse effects of changes in economic and trade
conditions. Interest and principal are regarded as safe.
They to some extent reflect changes in economic conditions
BBB or medium –grade category is borderline between
definitely sound obligations and those where the speculative
element begins to dominate. These have adequate asset
coverage and normally are protected by satisfactory earnings.
They are susceptible to fluctuations due to economic
conditions. 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 representing
bonds in default.
23
ITEMS AFFECTING THE RATINGS OF
CORPORATE BONDS

Items considered:
Asset protection—measures the degree to which a
company’s debt is covered by the value of its assets.

Tangible assets/LTD






AAA—5 to 1
AA—4 to 1
A—3 to 3.5 to 1
BBB—2.5to 1
LTD/(LTD + Equity)




AAA—less than 25%
AA— less than 30%
A— less than 35%
BBB— less than 40%
24
ITEMS AFFECTING THE RATINGS OF
CORPORATE BONDS

Fixed-charges-coverage ratio




AAA rating –cover interest and rental charges
after tax by 5 to 7 times –industrial firm
AA—4 times
A—3 times
BBB—2 times
25
ITEMS AFFECTING THE RATINGS OF
CORPORATE BONDS

Cash flow—crudely—net income plus
depreciation—to total funded debt—notes
payable and lease obligations




65% for AAA
45-65 for AA
35-45 for A
25-30 for BBB
26
ITEMS AFFECTING THE RATINGS OF
CORPORATE BONDS

Management abilities, philosophy, depth and
experience
Depth 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, sinking
fund, redemption, covenants
27
Distress Forecast and
Financial Information
28
Distress analysis and financial information
Definition: financial distress means that a
firm has severe liquidity problems that can
not be solved without a sizable rescaling of
the equity’s 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
29
Financial Crisis, Some Warning Signals
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Heavy borrower of working capital
Gross margins narrowing
Business environment subject to rapid change
If volume drops, can production cover expenses
Outdated marketing data
Organization highly structured/decision time
Equipment age/economic downturn
Intensity of industry competition
Increasing borrowing without an increase in sales
Increasing inventory and receivables without an increase
in sales
30
Distress analysis and financial information
Indicators of financial distress:





Cash flow analysis.
Corporate strategy analysis.
Financial statements of the firm and a set of firms
in comparison.
External variables such as security returns and
bond ratings.
31
Univariate model of
distress prediction:
involves the use a single
variable in prediction model.
32
1. Dichotomous

classification tests:
Case study of U.S. Railroad Bankruptcies: Use
the ranking of certain variable(s) to predict the
bankruptcy of railroad companies. For
example, Transportation expenses to
operating revenues (TE/OR), and Times
interest earned (TIE)
33
Ranking according to (TE/OR) (cutoff = 0.4305)
Railway Companies
(TE/OR)
(TIE)
.524
-1.37
2.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
.461
-0.68
Healthy firms (1970)
1.
Ann Arbor Railroad
Distressed firms (1970)
1.Boston and Maine Corporation
34
2.Penn-Central Transportation Co.
.485
0.16
1. Dichotomous classification tests:
Railway Companies
(TE/OR)
Bankrupted
or 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
35
1. Dichotomous classification tests:

Type I error and Type II error: A type I
prediction error occurs when a nonbankrupt (NB) firm is predicted to be
bankrupt (B) firm. A type II prediction error
occurs when a bankrupt (B) firm is
predicted to be non-bankrupt firm. Be
noted that the loss function for type II error
is greatly higher than that of type I error;
research has shown that to be 35 times.
36
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
37
1.Dichotomous classification tests:

Ranking according to (TIE)
Railway firms
(TIE)
Bankrupted
or 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
Pacific
2.91
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
38
Ann Arbor Railroad
-1.37
NB
2. Profile Analysis
Comparisons of the mean ratios of distress and
non-distress firms have been common in
bankruptcy 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 nonfailed groups in each of the five years before
failure.
 It examines if there are observable differences in the
mean ratios of the two sets of firms.

39
2.Profile Analysis(1)
Cash flow
Total debt
0.45
0.45
0.17
-0.12
-5 -4 - 3
-2 - 1
40
2.Profile Analysis(2)
Net Income
Total Assets
0.08
0.08
0.05
-5 -4 - 3
-2 - 1
-0.20
41
2.Profile Analysis(3)
Total Debts
Total Assets
0.85
0.51
0.38
0.37
-5 -4 - 3
-2 - 1
42
2.Profile Analysis(4)
Working Capital
Total Assets
0.42
0.43
0.30
0.05
-5 -4 - 3
-2 - 1
43
2.Profile Analysis(5)
Current ratio
3.5
3.2
2.5
2.1
-5 -4 - 3
-2 - 1
44
Overview of the uni-variate results
There are four categories of variables
showing the most consistent difference
between bankrupt and non-bankrupt firms
were:




Rate of return
Financial leverage
Fixed payment coverage
Stock return and volatility
45
Multivariate models of distress prediction


We can use econometric tools by applying more
than one financial variables that can effectively
discriminate healthy firms from distressed firms.
Those tools include Discriminant Analysis,
qualitative dependent variable regressions (e.g.
Linear probability models, probit regression, and
logit 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 of
non-bankrupt), or a probability estimate of group
membership (for example, the probability toward
bankruptcy).
46
(1) Discriminant Analysis:
Municipality
Assessed Property General
Moody’s
Valuation per
Obligation
Bond
Capita
Bonded Debt
Rating
per Capita
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
47
(1) Discriminant Analysis:
1.
2.
3.




Two dependent variables (Zi).
Every sample firm is featured two descriptive
variables (XI,YI).
These two descriptive variables have different
normally distributed means and same variancecovariance matrix within each group.
So there is a discriminant function Zi  aX i  bYi that can
effectively distinguish both groups:
ZI= Moody’s Rank equal to or better than A; or Moody’s Rank
equal to or lower than Baa.
XI= Assessed Property Valuation per Capita
YI= General Obligation Bonded Debt per Capita
48
(1) Discriminant Analysis:

Step 1: To estimate the coefficients for the discriminant function,
which is able to maximize the between group SSE of ZI and
minimize the within group SSE of ZI
a
b
 y 2  d x   xy  d y
 x   y   xy   xy
2
2
 x 2  d y   xy  d x
 x 2   y 2   xy   xy
=0.000329
=-0.004887
49
Municipality
Predicted
Z-score
Moody’s Bond
Rating
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
50
(1) Discriminant Analysis:

Step 2:to determine a cut off point which serves as the critical
value that separate distressed firms with healthy firms.
Cut-off point
Misclassification number
Rank >=A when ZI>1.2245
3
Rank >=A when ZI>.7650
2
Rank >=A when ZI>.4505
1
Rank >=A when ZI>.1710
2
Rank >=A when ZI>.0140
3
Rank >=A when ZI>-.7835
2
Rank >=A when ZI>-1.7735
3

Rank
51
(1) Discriminant Analysis:

Step 3: Test out-of sample forecast validity by using
another sample to test the previously set cutoff point.
Municipality
Assessed Property
Valuation per
Capita
General Obligation
Bonded Debt
per Capita
Predicted
Z-score
Moody’s Bond
Rating
1.Palo Alto, Calif.
$6,124
$110
1.474
Aa
2.Homewood, Ill.
4,134
34
1.194
A
3.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
Baa
7.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
52
(1) Discriminant Analysis:
A11  A22
Correct Classification Ratio = A  A  A  A
11
12
21
22
53
(1) Discriminant Analysis:


Altman’s Z-score models:
Altman’s Z-score for NYSE and NASDAQ firms
EBIT
Net working capital
Sales
 1.2 
 1.0 
Total assets
total assets
total assets
MVE
Accumulated retained earnings
0.6 
 1.4 
BVD
total assets
z  3.3 
Z  2.99 for normal firms
Z  1.81 for distressed firms
1.81  Z  2.99 indeterminate
Altman’s Z-score model for private firms
Net working capital
Accumulated retained earnings
z  6.56 
 3.26 
total assets
total assets
EBIT
MVE
1.05 
 6.72 
total assets
BVD
Z  2.90 for normal firms
Z  1.23 for distressed firms
1.23  Z  2.90 indeterminate
54
(2) Zeta Credit Risk:

The multivariate model was based on the following seven
variables, though the true formula was never disclosed:
EBIT
1.Overall Profitability: Total Assets ，
2.Size: Total Assets，
3.Debt service:
EBIT
Total Interest Payment ，
4.Liquidity: Current Ratio，
5.Cumulative Profitability:
R.E .
Total Assets ，
6.
Market Capitalization:
5 years average of MV of Equity
5 years average of MV of Total capital ，
7.
Earnings Stability：The 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)
55
and non-failed firms (positive).
(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
56
(2) Zeta Credit Risk:
American
Motors
Chrysler
Corp.
Zeta
Zeta
%
Ford Motors General
Motors
%
Zeta
%
Zeta
%
Mean Zeta
for
four
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
57
(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
58
Other devices that predict
financial distress


Qualitative dependent variable regression:
probit and logit regressions
Artificial Neural Network
59