10.2 practical issues in credit assessments

Copyright © 2018 CapitaLogic Limited
This presentation file is prepared in accordance with
Chapter 10 of the text book
“Managing Credit Risk Under The Basel III Framework, 3rd ed”
Website : https://sites.google.com/site/crmbasel
E-mail : crmbasel@gmail.com
Chapter 10
Practical Issues in
Credit Assessments

Copyright © 2018 CapitaLogic Limited 2
Declaration
 Copyright © 2018 CapitaLogic Limited.
 All rights reserved. No part of this presentation file may be
reproduced, in any form or by any means, without written
permission from CapitaLogic Limited.
 Authored by Dr. LAM Yat-fai (林日辉),
Director, CapitaLogic Limited,
Adjunct Professor of Finance, City University of Hong Kong,
Doctor of Business Administration,
CFA, CAIA, CAMS, FRM, PRM.

Outline
 Definition of default
 Domestic credit rating agencies
 Financial distress
 Altman's ZChina-score
 Earnings manipulation

Technical default
 A borrower fails to pay to the lender the
interest and/or principal in full on schedule
 Debt collection actions will then be initiated
by the lender to recover the whole or part of
the principal plus interest

Survival → overdue → default
Survival
Overdue
Default
Payment
Payment
Yes
Yes No
No

Cross default provision
 The default of a corporate bond will trigger
the default of the corporation and all corporate
bonds issued by the same corporation

Default under Basel III
 A borrower has an outstanding debt overdue for over ninety
days
 A borrower’s outstanding debt amount, after incorporating
the interest, has exceeded the prevailing credit limit
 A lender unilaterally reduces a borrower’s credit limit to an
amount below the current outstanding debt amount
 A lender is unlikely to recoup the principal and interest from
a borrower in full without taking recovery actions such as
selling collaterals pledged by the borrower

Outline

Domestic credit rating agencies
in America
 United States
 Egan-Jones Rating Company
 Canada
 DBRS
 Mexico
 HR Ratings
 Brazil
 Liberum Ratings

in Europe
 United Kingdom
 First Report
 Germany
 Scope Credit Rating
 Italy
 Cerved Group
 Russia
 National Rating Agency

in Japan
 Japan Credit Rating Agency Limited
 Rating and Investment Information, Inc.

in China
 Dagong Global Credit Rating
 Pengyuan Credit Rating
 China Chengxin (Asia Pacific) Credit Ratings Company
 China Lianhe Credit Rating
 Shanghai Brilliance Credit Rating & Investor Service

in India
 Brickwork Ratings India Private Limited
 Credit Analysis and Research Limited
 CRISIL
 ICRA Limited
 SMERA Ratings Limited

Basel III ECAI rating scale
Rating 3-year DR (%)
AAA and AA 0.10
A 0.25
BBB 1.00
BB 7.50
B 20.00

Outline

Default vs financial distress
 Default
 Some defaults but very few default records
 Data removed from major financial information providers
 Some countries apply confidential treatments to records of
defaulted corporations
 Horizon
 No. of defaults in 3 years > No. of defaults in 1 year
 A boarder definition of “bad” borrowers
 Records of financially distressed corporations are
accessible

Basel III ECAI Plus rating scale
Group Rating 3-year DR (%) PD (%)
Excellent AAA 0.03 0.0100
Good
AA (+,-) 0.10 0.0333
A (+,-) 0.25 0.0834
BBB (+,-) 1.00 0.3345
Moderate
BB (+,-) 7.50 2.5652
B (+,-) 20.00 7.1682
Bad
CCC 40.00 15.6567
CC 65.00 29.5270
C 95.00 63.1597

Financial distress of listed companies
 Rated “CCC” to “C” by credit rating agencies
 Listed companies
 Delisted due to reasons other than privatization
 Special treatment in China
 Negative cumulative earnings over two consecutive years
 Equity value < registered capital
 Likely to be dissolved
 Re-organization, settlement or liquidation
 Opinions from auditors
 Concerns from the CSRC

PD vs 3-year PFD
 3-year probability of financial distress
 Linear Probit regression to derive 3-year PFD
 
1
3
No. of defaults in 3 years
κ
No. of migrations to financial distress in 3 years
+ No. of defaults in 3 years
3-year DR = 3-year PFD κ
PD = 1 - 3-year PFD κ




High credit quality borrower
 Differentiation
 Good – to stay as investment grade in the
following three years
 Bad – to migrate to high yield grade or default in
the following three years

PD vs 3-year
probability of migration
1
3
No. of defaults in 3 years
κ
No. of migrations to high yield grade in 3 years
+ No. of defaults in 3 years
1 - Probabilty of migration to
PD = 1 -
high yield grade or default in 3 years κ

 
 
 

Low default borrower
 What is the PD of a group of
 Extremely high credit quality borrowers
 Very little or zero experience in default
 Justification
 Quantitative: Almost 0% historical default rate
 Qualitative: e.g. collateral = 10 times of EAD
Total no. of historical default records + 1
PD =
Total no. of historical records + 1

Outline

Altman’s ZChina-score for
listed companies in China
6
7
8
9
Total liabilities
X =
Total assets
Net profit
X =
Total assets
Currrent assets - Current liabilities
X =
Total assets
Retained earnings
X =
Total assets
 Explanatory variables

Altman’s ZChina-score for
listed companies in China

 Good ZChina > 0.9
 Moderate 0.5 < ZChina < 0.9
 Bad ZChina < 0.5
 Market value of equity/Total assets not an
explanatory financial ratio
China 6 7 8 9Z = 0.517 - 0.460X + 9.320X + 0.388X + 1.158X
Example 10.1

New lending operations
 Even financial distress records over 3 years
are not available
 Proxy by a similar group of listed companies
in terms of industry, country and asset size
 Cutoff scores and PD scaled up in accordance
with the experience of and subjective view
from a lender

Outline

SEC’s 2008 report on
financial statement frauds
38%
12% 12%
38%
Earnings manipulation Expenses manipulation
Improper disclosures Others

How good is the quality
of financial statements?
 All financial statements are subject to accounting
cosmetics
 To reject ALL loan applications assuming unreliable
quality
 Loss all lending businesses
 Good borrowers cannot obtain funding
 To accept ALL loan applications by assuming good
quality
 Potentially large default loss

Beneish’s M-score for
earnings manipulation
Net income - Cash flow from operations
TATA =
Total assets
Receivables
DSRI =
Total revenue
SGI = Total revenue
Fixed assets in current year - Property, plant and equipment
AQI =
Total assets
Total liab
LVGI =
ilities
Total assets
Selling, general and administrative expenses
SGAI =
Income
Gross profit
GMI =
Total revenue
Depreciation
DEPI =
Depreciation + Property, plant and equipment

Beneish’s M-score for
earnings manipulation

 Lower manipulation M > 2.22
 Moderate manipulation 1.78 < M < 2.22
 Higher manipulation M < 1.78
Examples 10.2 to 10.6
DSRI current year
M = 4.84 - 4.679 × TATA current year - 0.92 ×
DSRI previous year
SGI currrent year AQI current year
- 0.892 × - 0.404 ×
SGI previous year AQI previous year
LVGI current year
+ 0.327 ×
LVGI
SGAI current year
+ 0.172 ×
previous year SGAI previous year
GMI previous year DEPI previous year
- 0.528 × - 0.115 ×
GMI current year DEPI current year

Two-dimensional assessment

10.2 practical issues in credit assessments

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