China Banking Sector Who needs capital? - DBS Bank
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76
SECTOR BRIEFING
number
DBS Asian Insights
DBS Group Research • June 2019
China Banking Sector
Who needs capital?DBS Asian Insights SECTOR BRIEFING 76 02 China Banking Sector Who needs capital? Ken SHIH Research Director kenshih@dbs.com Cindy WANG Associate Research Director cindywangyy@dbs.com Produced by: Asian Insights Office • DBS Group Research go.dbs.com/research @dbsinsights asianinsights@dbs.com Wen Nan Tan Editor Martin Tacchi Art Director
DBS Asian Insights
SECTOR BRIEFING 76
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04 Executive Summary
07 Who needs capital?
07 What if the economy suffers a hiccup
ahead…
19 What if the residential credit bubble
bursts?
30 What if POEs continue to suffer?
38 Expect Rmb2tr capital needed to be raised
for the industry
45 What would happen if China factors in
countercyclical capital buffer?
51 How far away from meeting TLAC
regulation?
57 Expanding capital-replenishing tools to fill
the gap
65 Stimulating growth in China’s ABS market
68 Speeding up capital raisingDBS Asian Insights
SECTOR BRIEFING 76
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Executive Summary
What if the economy suffers a hiccup ahead…
Given the challenging macro economy ahead, we ran stress test on 19 China banks based on
FY20F estimates to gauge their capital sufficiency.
These 19 banks are Agricultural Bank of China (ABC), Bank of China (BOC), Bank of
Communications (BOCOM), China Construction Bank (CCB), China Everbright Bank (CEB),
China CITIC Bank (CITIC), China Merchants Bank (CMB), China Minsheng Bank (CMSB),
Chongqing Rural Commercial Bank (CQRCB), Bank of Ningbo (BON), Bank of Shanghai (BSH),
Bank of Zhengzhou (BZZ), China Development Bank (CDB), China Zheshang Bank (CZB),
GuangFa Bank (GFB), Ping An Bank (PAB), Postal Savings Bank of China (PSBC) and Shanghai
Pudong Development Bank (SPDB).
Under scenario 1: Macro and economic risks
We assume China GDP slowdown to result in lower loan demand, benchmark rate cut to
ease corporates’ financing burden, and trade disputes to adversely affect vulnerable export-
related sectors.
The result shows that CZB, CDB, SPDB and BOC’s CET1 capital will be hit by 106-122bps
under the bear case when loan growth slows down by 2% from FY19 assumption, loan/
deposit benchmark rate cut by 150bps/75bps, NPL ratio for manufacturing and wholesale and
retail up 5% from FY19, and 10% of special-mention loans migrating to NPLs.
Under scenario 2: Rising residential leveraging
What if residential China’s household debt in GDP surged to 51.5% in 3Q18, up from 18% in 2008, or ~triple
credit bubble bursts? during the past ten years. The fast growth in residential leveraging has triggered market
concerns as China has never experienced a credit downcycle in retail loans.
1. Mortgage risks, we assume that China’s housing prices would fall 31% in FY20F under
the bear case which we based on the US housing bubble where residential housing prices
were down 34% from the peak during 2007-2012, and we also assume foreclosure
discounts of 30%. ABC, CCB, PSBC and CDB‘s CET1 capital will be more vulnerable and
be hit by 98-113bps given high mortgage loan exposure at 29-36%, vs peers’ 19%
2. Credit card loan risks, we assume FY20F credit card NPL ratio to be +5ppts above FY19F
level, which will hit ~7% NPL ratio in the bear case, in line with US/TW/S. Korea’s credit
card bubble burst with its NPL ratio at 6-8%, and we also assume credit card loan growth
slowdown to 10%. GFB, CEB, CMB and PAB’s CET1 ratio would be hit by 70-133bps due
to their credit card loan exposure of 18-36%, vs peers’ 9%DBS Asian Insights
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Under scenario 3: POEs risks
What if POEs continue Privately owned enterprises (POEs) have been experiencing financing difficulties and
to suffer? liquidity crunch since 2017 when China started to combat shadow banking which used
to be the main financing channel for POEs. To support SMEs and “Sannong” economy to
recover, China regulators have released a series of policies in FY19 to alleviate companies’
tax and interest burden, and increase funding support from banks.
1. Non-standard asset (NSA) risks, we assume NSA growth to decline by 10% per annum
in FY19/20F given ongoing WMP restructuring, and 10% of NSAs deteriorating to
NPLs in FY20F under bear-case scenario. BZZ, CZB, CEB and BON’s CET1 capital will
be hit by 84-170bps given their exposure to NSAs at 16-33%, vs peers’ 9%
2. SME loan risks, we assume SME loan to grow by 15% in FY19F but come down to
10% in FY20F when asset quality starts to deteriorate, and assume FY20F SME NPL
ratio to rise 4.5% on top of FY19F basis. PSBC, SPDB and CDB’s CET1 capital will be
hit by 180-221bps given their higher SME loan exposure of 39-54% vs peers’ 27%
Expect to raise Rmb2tr When an economic downturn occurs, the above scenarios will happen sequentially, like
capital under bear-case a domino effect. Under the all-in situation, we estimate ~Rmb1.5tr capital needs to be
scenario raised for 19 banks under the bear-case scenario, implying an aggregate of Rmb2tr capital
need to be raised for the industry as the 19 banks represent ~76% of total assets.
CCB, CMB, CQRCB and BSH passed the stress test under the bear case, helped by lower
exposure to risky segments and sufficient capital level to cover credit risks. BOCOM will
have 1% equity dilution, BOC and ABC have 10% equity dilution, whereas other mid-to-
small banks have 14-35% dilution based on BASEL III requirement.
Up to Rmb3.9tr to be Based on BIS, the CCyB is 0-2.5% determined by the gap between non-financial credit to
raised if factoring in GDP ratio and its long-term trend.
CCyB
Given China regulators’ shift from deleveraging to stable liquidity in FY19, banks are
encouraged to distribute more loans to support the private sector’s credit, and the
recovering bond and equity financing, driving credit to GDP gap upwards. We estimate
that banks need to raise CET1 capital by up to Rmb3.9tr if factoring in CCyB by 2.5%
based on FY2020 RWAs assumed growth of 8% per annum. An additional of Rmb780bn
of CET1 capital will need to be raised per 0.5% CCyB.
Expect to raise Rmb4.1- Based on TLAC, BASEL conservative buffer and GSIB additional capital buffers, the
4.5tr for Big Four minimum capital requirement ratio for the Big Four banks would be 19.5-20% in January
banks to meet TLAC 2025, and 21.5-22% in January 2028, or three years’ ahead of schedule, assuming CCyB
requirement to be 0%.DBS Asian Insights
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Capital needs to raise would be Rmb4.1tr-4.5tr. BOC needs to raise Rmb1.2-1.4tr capital and/
or LTD given they are required GSIBs buffer at 1.5%, vs 1% for CCB and ABC. Meanwhile,
ABC needs to raise Rmb1.1-1.2tr, while CCB only requires Rmb550m.
Since 2019, China banks have completed or announced the issuance of more than Rmb1tr
of capital replenishing as they are facing high pressure on:
1. Loans shifting back to on-balance sheet
2. Building up WM subsidiary
3. Increasing loan distribution on POEsDBS Asian Insights
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Who needs capital?
What if the economy suffers a hiccup ahead…
A stressed economic With the challenging macro economy ahead, deleveraging campaign on shadow banking,
scenario and uncertainty between US-China trade, China’s economy has been facing downward
pressure on corporates’ lack of willingness to invest and expand capacity, increasing
unemployment rate, as well as faltering domestic demand.
PMI for mid- and small-sized firms has been trending below 50 since 3Q18 and their
deterioration accelerated in 4Q18, likely due to a lagged effect towards the liquidity crunch
in 1H18 when many SMEs faced financing and refinancing difficulties. On the other side,
the CIER index, which is published by China Institute for Employment Research to reflect
the overall trend of China’s job market, showed that demand for recruitment cooled down
in 2018, which would inevitably impact consumer spending thereafter.
To stimulate the economy, China’s government has implemented a series of monetary and
fiscal policies, such as cutting RRR to empower banks with more loan capacity and lower
funding costs, and reducing tax rates to improve corporates’ profit margin.
But what if China’s GDP growth slows further, PBOC cuts interest rates to ease corporates’
interest burden, and asset quality deteriorates amid corporates’ solvency issues, especially
in export-related industries? How would that impact China banks’ loan growth, NIM, NPL
ratio, as well as capital level?
We conducted stress test on 19 banks under three scenarios to gauge their capital sufficiency
under base, worse and bear cases based on FY20F estimates. The three scenarios are:
1. Macro and economic risks
2. Rising residential leveraging
3. POEs risks
These 19 banks are Agricultural Bank of China (ABC), Bank of China (BOC), Bank of
Communications (BOCOM), China Construction Bank (CCB), China Everbright Bank (CEB),
China CITIC Bank (CITIC), China Merchants Bank (CMB), China Minsheng Bank (CMSB),
Chongqing Rural Commercial Bank (CQRCB), Bank of Ningbo (BON), Bank of ShanghaiDBS Asian Insights
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(BSH), Bank of Zhengzhou (BZZ), China Development Bank (CDB), China Zheshang Bank
(CZB), GuangFa Bank (GFB), Ping An Bank (PAB), Postal Savings Bank of China (PSBC) and
Shanghai Pudong Development Bank (SPDB).
Scenario 1: Macro and economic risks
In our first scenario, we stress tested China banks’ FY2020 NPL and capital level based on
base, worse and bear cases assuming that China’s GDP growth slows down to 6%/5%/4%,
benchmark rate is cut by 50bps/100bps/150bps, NPL ratio for manufacturing and wholesale
and retail sector rises 100bps/300bps/500bps, and special-mention loan migration to NPL
of 1%/5%/10% in 2020.
#1 Assuming the slowdown in GDP directly impacts loan demand
China banks’ loan growth is highly correlated with GDP growth at 0.92 (between 2010-
2018). Although China’s GDP growth is gradually slowing down, loan growth remains
strong at above 13% y-o-y (+13.7% in 1Q19) helped by PBOC’s RRR cuts which stand at
13.5%/11.5% for large banks/small banks respectively, as each 1-ppt cut could provide
liquidity of Rmb1.2-1.5tr to banks. Based on DBS’s economists, China’s GDP is expected to
grow at 6% y-o-y in 2020 and we use this number as our base case to model each bank’s
loan growth to be flattish/-1%/-2% on top of the 2019 base.
China banks’ loan growth is highly correlated with GDP
Source: PBOC, WIND, DBS HKDBS Asian Insights
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PMI for mid and small corporates dropped
Source: WIND, DBS HK
The cooling employment market in China
supply side reform
Source: CIER, DBS HK; CIER Index= number of recruitment demands/ number of market applicants
Unchanged In terms of benchmark rate, it has been unchanged with lending/deposit benchmark rate at
benchmark rate 4.35%/1.5% respectively since October 2015 after interest rates were liberalised. Although
due to interest rate interest rates are currently determined by the market, it is “window guided” by regulators
liberalization to set the rate above or below the benchmark rate. As long as the benchmark rate is fixed,
the interest spread between loans and deposits would stay at 2.85%. The spread could be
diverse depending on the capability of securing deposits and loan mix strategy.DBS Asian Insights
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High correlation for Among our coverage banks, the correlation between lending benchmark and loan yield with
banks’ loan pricing and a one-year lag is 0.84-1 during 2007-2018 which proves that banks take about 6-9 months
benchmark rate to reprice loans, while deposit benchmark rate and deposit cost with a one-year lag is 0.32
to 0.9. Big Four banks have deposit costs that are highly correlated with deposit benchmark
rates due to their strong capability of securing deposits, whereas joint-stock banks need to use
premium deposit costs to attract depositors, thus correlation is low at 0.3-0.5.
#2 Assuming lending/deposit interest rate cut at end-2019 to
impact banks’ NIM in 2020
In our base/worse/bear-case scenarios, we assume lending rate to be lowered by
50bps/100bps/150bps and deposit benchmark rate to be trimmed by 25bps/50bps/75bps
respectively at end-2019, which would be reflected in banks’ NIM in 2020. We assume
deposit rate cut to be milder than lending rate 1) as the deposit benchmark rate is already
low at 1.5%, with not much room for further cuts, 2) to ease corporates’ interest burden,
the cut in lending rate would need to be larger than that for deposit rate. The same situation
was seen during 2012-2015.
Benchmark rate vs Shibor 3M
Source: PBOC, Bloomberg Finance L.P., DBS HK
A lagging effect after benchmark rate revised
Correlation ABC BOC BOCOM CCB ICBC
Lending benchmark rate vs loan yield 0.99 0.98 0.97 1.00 1.00
(one-year lag)
Deposit benchmark rate vs deposit cost 0.87 0.78 0.32 0.90 0.90
(one-year lag)
Correlation CEB CITIC CMB CMSB CQRC
Lending benchmark rate vs loan yield 0.87 0.93 0.84 0.96 0.92
(one-year lag)
Deposit benchmark rate vs deposit cost 0.58 0.70 0.83 0.52 0.82
(one-year lag)
Source: PBOC, Company, DBS HK; data from 2007-2018DBS Asian Insights
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Historical benchmark lending and deposit rate change schedule
2008 - 1-year benchmark loan and deposit rate lowered by 1.89%
Oct. 2008 benchmark lending & deposit One-year benchmark deposit and loan interest rate were both lowered by
rate decrease 0.27% , to 3.87% and 6.93% respectively.
Oct. 2008 benchmark lending & deposit One-year benchmark deposit and loan interest rate were both lowered by
rate decrease 0.27% , to 3.6% and 6.66% respectively.
Nov. 2008 benchmark lending & deposit One-year benchmark deposit and loan interest rate were both lowered by
rate decrease 1.08% , to 2.52% and 5.58% respectively.
Dec. 2008 benchmark lending & deposit One-year benchmark deposit and loan interest rate were both lowered by
rate decrease 0.27% , to 2.25% and 5.31% respectively.
2010-2011- 1-year benchmark loan and deposit rate increased by 1.25%
Oct. 2010 benchmark lending & deposit One-year benchmark deposit and loan interest rate were both increased by
rate increase 0.25% , to 2.5% and 5.56% respectively.
Dec. 2010 benchmark lending & deposit One-year benchmark deposit and loan interest rate were both increased by
rate increase 0.25% , to 2.75% and 5.81% respectively.
Feb. 2011 benchmark lending & deposit One-year benchmark deposit and loan interest rate were both increased by
rate increase 0.25% , to 3% and 6.06% respectively.
Apr. 2011 benchmark lending & deposit One-year benchmark deposit and loan interest rate were both increased by
rate increase 0.25% , to 3.25% and 6.31% respectively.
Jul. 2011 benchmark lending & deposit One-year benchmark deposit and loan interest rate were both increased by
rate increase 0.25% , to 3.5% and 6.56% respectively.
2012-2015- 1-year benchmark loan and deposit rate decreased by 2.21% and 2% respectively
Jun. 2012 benchmark lending & deposit One-year benchmark deposit and loan interest rate were both lowered by
rate cut 0.25% , to 3.25% and 6.31% respectively.
Jul. 2012 benchmark lending & deposit One-year benchmark deposit rate was lowered by 0.25% to 3% and one-
rate cut year benchmark lending rate was lowered by 0.31% to 6%.
Nov. 2014 benchmark lending & deposit One-year benchmark deposit was lowered by 0.25% to 2.75% and one-year
rate cut loan interest rate was lowered by 0.4% to 5.6% respectively.
Feb. 2015 benchmark lending & deposit One-year benchmark deposit and loan interest rate were both lowered by
rate cut 0.25% , to 2.5% and 5.35% respectively.
May. 2015 benchmark lending & deposit One-year benchmark deposit and loan interest rate were both lowered by
rate cut 0.25% , to 2.25% and 5.1% respectively.
Jun. 2015 benchmark lending & deposit PBOC lowered the one-year benchmark bank lending rate by 25bps to 4.85%
rate cut and the one-year benchmark deposit rate was lowered by 25bps to 2%.
Aug. 2015 benchmark lending & deposit PBOC lowered the one-year benchmark bank lending rate by 25bps to 4.6%
rate cut and the one-year benchmark deposit rate was lowered by 25bps to 1.75%.
Oct. 2015 benchmark lending & deposit PBOC lowered the one-year benchmark bank lending rate by 25bps to 4.35%
rate cut and the one-year benchmark deposit rate was lowered by 25bps to 1.50%.
Source: PBOC, DBS HKDBS Asian Insights
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Trade dispute to cause Since the US proposed a list of US$200bn worth of Chinese goods on which to impose an
shrinkage in export additional tariff of 10% in July 2018, China’s new export orders PMI index dropped sharply,
demand falling to 45.2 in February 2019 from 49.8 in July 2018, the lowest point since the global
financial crisis (GFC). The reduced business investment, delays in purchases and worsening
economic conditions in key export markets all resulted from the latest downturn. Although
the US and China agree to a temporary truce to alleviate trade tensions in December 2018,
the uncertainty remains an overhang and export demand continued to deteriorate in line
with the global trade slowdown.
In China, manufacturing and wholesale and retail industries contribute 29%/8% of total
GDP, and 50%/30% of final goods and services are export-oriented. That said, every 1%
decline in manufacturing/wholesale & retail impacted by the trade dispute would cause
GDP growth to slow by 0.15%/0.02%. Thus, China’s economic growth could be weighted
down even as trade negotiations are pending.
Breaking down China banks’ asset quality, 32%/28% of non-performing loans came from the
manufacturing/ wholesale & retail sectors respectively in 2017, while most banks’ asset quality
in relation to manufacturing and wholesale and retail further worsened in 1H18. This was
mainly due to 1) the channeling of funding away from “old economy” sectors and matured
industries struggling with overcapacity, where banks cut down on their loan quotas which led
to liquidity issues, and 2) industry’s structural changes, including manufacturing upgrades and
the booming of e-commerce, which caused legacy corporates’ solvency issues.
A leading indicator A special-mention loan, by definition, is recognised when the borrower 1) is negatively
for NPL impacted by external or internal factors which would adversely affect the borrower’s ability
to make loan payments, or 2) has potential liquidity issues due to an increase in contingent
debt, or 3) is unable to make loan payments using normal operating income, yet the bank is
able to collect principal and interest due to ample collaterals. China banks’ special-mention
loan ratio improved from 4.1% in 4Q16 to 3.1% in 4Q18, yet loan loss reserve ratio was
only 3.4% which was insufficient to cover broad-based NPL ratio at 4.96% (1.83% NPL
ratio plus 3.1% special-mention loan ratio) if all special-mention loans deteriorated and
turned into non-performing loans in the credit downcycle.
#3 Assuming asset quality further deteriorated in export-related
industries, as well as special-mention loans
In our base/worse/bear-case scenarios, we assume NPL ratios for manufacturing and
wholesale & retail sectors weakened by 100bps/300bps/500bps, and 1%/5%/10% of
special-mention loans turned into NPLs respectively, to reflect trade disputes’ adverse
effect on the industries’ solvency. Historically, manufacturing and wholesale & retail’s NPL
ratios once hit 11.9%/20.5% in 2005 (vs 4.2%/4.7% in 2017). Thus, we think the 500-bp
increase on top of FY19F’s NPL ratio to model the two sectors’ NPL ratios on the bear case
appears reasonable.DBS Asian Insights
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PMI new export orders have been trending down since trade dispute between
China/US began
Source: WIND, DBS HK
Manufacturing and wholesale & retail NPL represent 60% of total NPL in China
Source: WIND, DBS HK; data in 2017DBS Asian Insights
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Manufacturing and wholesale & retail NPL ratios were at 4-5% range
Source: Company, DBS HK
China banks’ loan loss reserve ratio is still insufficient to cover broad-based NPL ratio
Source: CBRC, DBS HK
Historical trend for manufacturing and wholesale & retail sectors
Source: CBRC, DBS HKDBS Asian Insights
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Scenario 1: Macro and economic risks
We assume China’s economic slowdown, benchmark rate cut (or market rate trending
downwards), and trade dispute to weaken export demand, leading to deterioration in
asset quality in manufacturing and wholesale & retail deteriorating, as well as migration of
special-mention loans to NPLs.
Base-case assumption: 6% GDP growth, loan growth as the same as FY19F, loan/deposit
benchmark rates cut by 50bps/25bps, NPL ratio for manufacturing and wholesale & retail
up by 1%, and 1% of special-mention loans migrating to NPLs.
Worse-case assumption: 5% GDP growth, loan growth slowing down by 1%, loan/
deposit benchmark rates cut 100bps/50bps, NPL ratio for manufacturing and wholesale &
retail up by 3%, and 5% of special-mention loans migrating to NPLs.
Bear-case assumption: 4% GDP growth, loan growth slowing down by 2%, loan/deposit
benchmark rates cut 150bps/75bps, NPL ratio for manufacturing and wholesale & retail up
5%, and 10% of special-mention loans migrating to NPLs.
CZB, CDB, SPDB and Among the 19 banks, BZZ, CZB and BOC’s FY20F NPL ratio will increase by
BOC’s capital levels 144bps/125bps/114bps respectively under the bear case as they have higher loan exposure
appear more vulnerable in export-related industries and special-mention loans at 28%/25%/22% respectively.
on Scenario 1 stress test
In terms of FY20F core-equity one ratio (CET1) of CZB, CDB, SPDB and BOC will be hit by
122bps, 111bps, 110bps, 106bps respectively. BZZ’s loans in total assets is only 35%, vs peers’
54%, and thus its interest income is less sensitive to NIM pressure although it has a higher
loan exposure to risky industries. Under our assumption, CZB, CDB, SPDB and BOC’s capital
levels are more vulnerable when China and the macro economy experience a downturn.
Loan exposure comparison in manufacturing and retail & wholesale, and special
mention loan
Source: Company, DBS HKDBS Asian Insights
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BZZ, CZB, and BOC have the major impacts on NPL ratio under Scenario 1 under
macro risks
Source: Company, DBS HK
CZB, CDB, SPDB and BOC have the highest impacts on CET1 ratio under macro risks
Source: Company, DBS HKDBS Asian Insights
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Stress test on macro and economic risks
Bank ABC BOC BOCOM CCB CEB CITIC CMB CMSB CQRCB
Ticker 1288 HK 3988 HK 3328 HK 939 HK 6818 HK 998 HK 3968 HK 1988 HK 3618 HK
Scenario 1- China economic slowdown/benchmark rate cut/trade dispute
Manufacturing loan (Rmb m) 1,317,529 1,455,177 603,462 1,250,499 255,884 343,862 313,459 355,723 68,831
Wholesale and retail loan 356,353 1,140,012 316,832 436,275 109,268 193,818 172,087 229,264 21,351
(Rmb m)
Special mention loan (Rmb m) 445,475 400,297 147,612 460,635 67,081 105,485 68,548 142,095 11,408
Manufacturing loan as % of 9.2% 10.6% 10.6% 7.6% 8.8% 8.1% 6.6% 9.3% 15.0%
total loan
Wholesale and retail loan as 2.5% 8.3% 5.6% 2.7% 3.8% 4.6% 3.6% 6.0% 4.6%
% of total loan
Special mention loan ratio 3.1% 2.9% 2.6% 2.8% 2.3% 2.5% 1.5% 3.7% 2.5%
Base case: GDP 6% (loan growth unchanged), benchmark rate cut 50bps, NPL ratio for manufacturing and retail &
wholesale up 1%, 1% of special mention loan migrate to NPL
NPL ratio impact (bps) 14.76 21.77 18.81 13.07 14.93 15.24 11.72 18.93 22.07
CET 1 ratio impact (bps) (17.82) (23.63) (21.39) (21.69) (19.98) (20.39) (19.89) (20.11) (10.54)
CAR impact (bps) (17.36) (22.82) (20.85) (21.28) (19.55) (19.90) (19.52) (19.78) (10.24)
Worse case: GDP 5% (loan growth slowdown by 1%), benchmark rate cut 100bps, NPL ratio for manufacturing and
retail & wholesale up 3%, 5% of special mention loan migrate to NPL
NPL ratio impact (bps) 47.37 67.39 58.04 42.01 45.96 47.58 35.57 61.01 67.26
CET 1 ratio impact (bps) (49.26) (64.33) (57.38) (57.01) (50.89) (52.56) (49.49) (54.15) (31.06)
CAR impact (bps) (47.85) (61.89) (55.76) (55.75) (49.61) (51.10) (48.41) (53.15) (30.16)
Bear case: GDP 4% (loan growth slowdown by 2%), benchmark rate cut 150bps, NPL ratio for manufacturing and retail
& wholesale up 5%, 10% of special mention loan migrate to NPL
NPL ratio impact (bps) 82.62 114.43 98.75 73.37 78.49 81.58 60.22 105.78 113.57
CET 1 ratio impact (bps) (82.35) (105.85) (94.14) (93.88) (82.45) (85.52) (79.27) (89.56) (51.80)
CAR impact (bps) (79.91) (101.75) (91.41) (91.72) (80.30) (83.05) (77.46) (87.85) (50.29)DBS Asian Insights
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Stress test on macro and economic risks cont.
BON BSH BZZ CDB CZB GFB PAB PSBC SPDB CQRCB
002142 SS 601229 6196 HK non- 2016 HK non- 000001 1658 HK 600000 600000
SS listed listed SS SS SS
Scenario 1- China economic slowdown/benchmark rate cut/trade dispute
Manufacturing loan (Rmb m) 56,928 61,947 14,516 585,623 136,277 117,559 150,666 300,297 321,803
Wholesale and retail loan 32,601 59,541 32,436 0 113,448 74,796 116,394 106,985 210,055
(Rmb m)
Special mention loan (Rmb m) 2,572 17,228 5,003 845,816 16,617 56,151 83,181 39,548 135,630
Manufacturing loan as % of 11.1% 6.5% 7.8% 4.2% 13.0% 7.7% 6.2% 4.9% 7.6%
total loan
Wholesale and retail loan as 6.3% 6.2% 17.5% 0.0% 10.8% 4.9% 4.8% 1.7% 5.0%
% of total loan
Special mention loan ratio 0.5% 1.8% 2.7% 6.0% 1.6% 3.7% 3.4% 0.6% 3.2%
Base case: GDP 6% (loan growth unchanged), benchmark rate cut 50bps, NPL ratio for manufacturing and retail &
wholesale up 1%, 1% of special mention loan migrate to NPL
NPL ratio impact (bps) 17.90 14.49 28.04 10.15 25.37 16.24 14.32 7.23 15.75
CET 1 ratio impact (bps) (9.25) (14.61) (12.05) (22.36) (28.90) (17.33) (18.27) (6.44) (26.40)
CAR impact (bps) (9.05) (14.23) (11.25) (22.20) (27.66) (16.96) (17.98) (6.11) (26.13)
Worse case: GDP 5% (loan growth slowdown by 1%), benchmark rate cut 100bps, NPL ratio for manufacturing and
retail & wholesale up 3%, 5% of special mention loan migrate to NPL
NPL ratio impact (bps) 54.32 43.96 86.42 42.29 75.75 54.74 46.88 21.95 49.93
CET 1 ratio impact (bps) (27.96) (38.06) (35.61) (64.31) (75.94) (51.22) (49.50) (20.23) (67.34)
CAR impact (bps) (27.38) (36.95) (33.25) (63.64) (72.34) (50.03) (48.61) (19.29) (66.54)
Bear case: GDP 4% (loan growth slowdown by 2%), benchmark rate cut 150bps, NPL ratio for manufacturing and retail
& wholesale up 5%, 10% of special mention loan migrate to NPL
NPL ratio impact (bps) 89.19 74.11 144.47 80.71 125.21 95.81 82.16 36.74 86.51
CET 1 ratio impact (bps) (45.54) (61.52) (58.84) (110.85) (122.05) (86.73) (82.26) (33.79) (109.76)
CAR impact (bps) (44.59) (59.68) (54.94) (109.59) (116.15) (84.68) (80.71) (32.24) (108.39)
Source: Company, DBS HKDBS Asian Insights
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What if the residential credit bubble bursts?
Scenario 2: Residential leveraging risks
Concerns on rising household debt
China’s household debt as a percentage of GDP surged to 51.5% in 3Q18, up from 18% in
2008, almost tripling within the past ten years, based on BIS. Although it remains below the
global average of 59%, the fast pace of growth in residential loans has triggered market
concerns, especially when China has never experienced a credit downcycle in retail loans.
In 2008, China banks only distributed 8% of loans to residents (excluding mortgage loans),
compared to 80%/12% for corporate loans/mortgages, whereas the ratio now stands at
16%/64%/20% for consumer /corporate/mortgage loans respectively. China banks are
more willing to offer loans to retail borrowers for the purchase of houses which have a
collateral feature and lower likelihood of default, as compared to unsecured credit loans
such as credit card and consumption loans.
Residential property Historically, China’s property prices have been relatively firm and ASP per square metre only
prices are regulated dropped once, by 2% y-o-y in 2008 during the GFC, supported by the government’s relaxation
of policy restrictions on mortgage loans for second home purchases and tax reduction for sale
of homes more than two years from the purchase date, etc. Conversely, when the property
prices rise too rapidly, regulators would adjust their measures to cool down the market, such
as increasing down payment ratio and controlling residential land supply, etc.
In 2015, the relaxation policy was resumed as NPC and CPPCC stressed on the need to
stabilise residential property consumption and stimulate housing demand, to promote
shanty town transformation in lower-tier cities, and to clear existing inventory in the property
market. This boosted demand and resulted in mortgage loans increasing 21%/35% y-o-y in
2015/2016. The residents’ net savings balance (residential savings minus residential loans)
was at about the same time trending down as part of their wealth was tied down in
property. Mortgage loans reached Rmb25.8tr in 2018 from Rmb3tr in 2008, enjoying a
24% CAGR in the past ten years.
Low defaults in China’s mortgage non-performing loan (NPL) ratio is quite stable at 0.3%, which is somehow
mortgage implicitly protected by the policy that first-home/second-home buyers are required to pay at
least 30%/40-60% down payment. On the other hand, as housing prices are on an upward
trend, borrowers tend to sell their properties to repay loans rather than defaulting, which
might result in profits. Thus, mortgage is the last loan that residents will default on, given
the mortgage rate of c. 5% is lower than the interest rate of unsecured credit loans (such as
credit card at 12-15%). Banks’ loan-to-value ratio (LVR) was low at 40-50% in 2018, which
was helped by a high percentage of down payments and rising house prices.DBS Asian Insights
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China’s household debt reached 52% of GDP in 3Q18
Source: Bank of International Settlement (BIS), DBS
China residential ASP has been defensive
Source: CEIC, DBS HKDBS Asian Insights
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China residents’ net savings balance has been declining since 2016 as the wealth
locked in mortgage
Source: PBOC, DBS HK
NPL ratio for mortgage was much lower than that for other consumer loans
Source: WIND, DBS HKDBS Asian Insights
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China residential prices are well regulated
(%) Rebased Jan - 07
Announced the The The Digesting inventory NPC &CPPCC
Suggestion on implementation of implementation of in the property indicated to NPC &CPPCC
30 resolving "國四條" "國五條" market has been enhance policy stated that
difficulties of set as a priority control according residential
urban under the Central to cities, acceler- units are for
low-income (CH Prop) The Economic Working ate digestion of living rather
25 families in implementation of Conference existing inventory than invest-
Housing "新國四條" and ensure the ment or
living charateristic speculation.
in residential units
20 Premier Li
unveiled the
concept of
"New-Type
15 urbanisation" and
aim to move
100m rural
residents to urban
10 areas by 2020
5
0
State Council demanded NPC &CPPCC reiterated the
that T2/3 cities with strong policy focus to digest
-5 The
price growth should be put existing inventory through
implementation of
in place with strictive selective policy measures
"國三條"
purchase policies according to cities
-10 MOHURD and China
Shanghai Development Bank jointly
National MOHURD indicate that NPC & CPPCC meeting started to issued "Notice on further
each city may adjust housing suggested to stabilise reduce promoting of shanty town
-15 policies according. Areas with residential property downpayment through monetisation method"
large inventories should impose consumption and ratio, followed to promote the use of
policies to acclerate inventory stimulate actual living by Shenzhen monetary method for
digestion and upgrading demand and other cities settlement to affected residents
-20
Jan-07
Jan-08
Jan-09
Jan-10
Jan-11
Jan-12
Jan-13
Jan-14
Jan-15
Jan-16
Jan-17
Jan-18
May-07
May-08
May-09
May-10
May-11
May-12
May-13
May-14
May-15
May-16
May-17
May-18
Sep-07
Sep-08
Sep-09
Sep-10
Sep-11
Sep-12
Sep-13
Sep-14
Sep-15
Sep-16
Sep-17
Sep-18
ASP growth: Residential YoY, %
Source: CEIC, DBS HK
Scenario 2(1): Stress test on mortgage
As the China government promotes the healthy development of the real estate market,
we expect property prices to be up 2% y-o-y in 2019, and we assume FY20 China housing
price to be flattish y-o-y, down 15%, and down 31% under base-, worse- and bear-case
scenarios respectively, in our mortgage stress test. We assume that housing prices would
drop by 31% based on the US housing bubble in 2007-2012 when residential property
prices fell 34% from the peak. We also assume foreclosure discounts of 30% if banks need
to clear out foreclosed homes, although the current foreclosure discounts in Shanghai are
only 10% on average.DBS Asian Insights
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ABC, CCB, PSBC and There would basically be no impact on banks’ asset quality if housing prices are flattish and
CDB’s capital levels drop 15% y-o-y under the base and worse cases. This is because borrowers would only be
more vulnerable to likely to start defaulting on their loans if housing prices fall by more than 30% which more
mortgage risks than the value of their down payments.
Under the bear case, ABC, CCB, PSBC and CDB’s FY20F NPL ratio will increase by
128bps/110bps/109bps/106bps respectively, due to higher loan exposure to mortgage
at 34%/36%/29%/29%, vs peers’ 19%. We use urban renewal loans (which China
Development Bank [CDB] provides to local governments for “shanty town redevelopment”)
as a proxy for mortgage loans.
In terms of FY20F core-equity one ratio (CET1), ABC, CCB, PSBC and CDB will be hit by
113bps, 107bps, 112bps, 98bps respectively, when housing prices drop by 31% with
foreclosure discount at 30%.
US housing prices dropped 34% during GFC
Source: US Census Bureau, DBS HKDBS Asian Insights
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ABC, CCB, PSBC, and CDB have the major impacts on NPL ratio under mortgage risks
Source: Company, DBS HK
Mortgage loan exposure comparison
Source: Company, DBS HK
ABC, CCB, PSBC, and CDB have the highest impacts on CET1 ratio under mortgage risks
Source: Company, DBS HKDBS Asian Insights
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Credit cards - The credit card business has become the key growth driver for banks’ retail banking
Honey or poison? segment, following the restructuring of wealth management products since 2017. With
the increasing demand for instalment services through online shopping and large-ticket
sized purchasing, as well as growing outbound travel, the demand for credit cards is
increasing.
Thus, credit card issuance has been growing rapidly, revolving and outstanding credit card
loans have enjoyed outstanding growth, and contributions from both interest income and
fee income have been increasing.
The number of total active credit cards in China reached 686m in 2018, up 17% y-o-y,
while credit card per capita was still low at 0.49, compared to bank cards at 5.46. The
outstanding amount of credit card revolving loans and year-end loan balance reached
Rmb15.4tr and Rmb6.9tr, up 23.4% and 23.2% y-o-y respectively, showing continued
strong growth momentum.
While credit card loan book is growing rapidly, asset quality has started to become a
concern due to increasing multiple lending risks as more small- and mid-sized banks jump
into the market.
Credit card risks are In China, credit card asset quality is somehow protected by the government as only people
under control with a credit record in CCRC could be served by banks, and currently only 500m people
have a credit record which somewhat lowers the default risks. As more-than-six-month
overdue loans accounted for 1.15% of year-end loan balance in 2018, slightly down from
1.19% a year ago, we think the credit card risks are still manageable.
No consumer credit As banks used to be only serving the top echelons, China has never experienced any
downcycle has ever consumer credit crisis. China’s credit card non-performing loan ratio has been quite stable
taken place at the 2% level and the 6-month overdue loan ratio has been at 1.1-1.5%. Besides that,
China banks’ competition in retail banking is not as intense as seen in other countries that
had experienced a personal credit bubble, such as South Korea in 2002-2003, Taiwan in
2003-2005, and the US in 2008-2009.
Take South Korea for an example, during 1999-2002, its credit card market grew rapidly
and the number of credit cards tripled while the volume of total credit card transactions
expanded more than six fold. Credit card balance as a percentage of household loans/
disposable income reached 45%/26% in 2002, resulting in the credit card crisis in 2002-
2003. Then, the NPL ratio was at 8.3-8.6%.
Although it would be hard to predict the critical NPL level in China that would result in a
consumer credit bubble, we think South Korea, Taiwan and the US could serve as useful
benchmarks as their credit card NPL ratio had once hit a peak at 8.6%/7.5%/6.3% during
their credit downcycle.DBS Asian Insights
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Credit cards have become the main fee income contributor
Source: PBOC, DBS HK
China credit card loan balance growing rapidly
Source: PBOC, DBS HK
China’s rising number of active credit cards
Source: PBOC, DBS HKDBS Asian Insights
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China credit card NPL
Source: PBOC, DBS HK
Credit card bubble in US/Taiwan/S. Korea
Source: ECOS, CBC, Federal Reserve, DBS HK
China credit card market scale by cards issued, loan balance, and asset quality
Year 2012 2013 2014 2015 2016 2017 2018
Credit cards and Quasi credit cards issued and 331 391 455 432 465 588 686
in use (m)
YoY (%) 16.1% 18.1% 16.4% -5.1% 7.6% 26.5% 16.7%
Credit card per capita 0.25 0.29 0.34 0.29 0.31 0.39 0.49
Outstanding revolving loan during the year 3,490 4,570 5,600 7,080 9,140 12,480 15,400
(Rmb bn)
YoY (%) 30.9% 22.5% 26.4% 29.1% 36.5% 23.4%
Credit card year end loan balance (Rmb bn) 1,140 1,840 2,340 3,090 4,060 5,560 6,850
YoY (%) 61.4% 27.2% 32.1% 31.4% 36.9% 23.2%
> 6 months overdue loan (Rmb bn) 15 25 36 38 53 66 79
% of year end loan balance 1.29% 1.37% 1.53% 1.23% 1.31% 1.19% 1.15%
% of year end loan balance n.a. 1.3% 1.4% 1.5% 1.2% 0.9%
Source: PBOC, DBS HKDBS Asian Insights
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Scenario 2(2): Stress test on credit card
In our base/worse/bear-case scenarios, we assume FY20F credit card non-performing loan
ratio to be 1ppt/3ppts/5ppts on top of FY19F NPL ratio if a credit card crisis happens.
Currently, China’s credit card NPL ratio is at the 2% level, and we use 5ppts on top of c.
2% to derive a 7% NPL ratio in the bear-case scenario. We think the assumption should
be reasonable based on the experience of regional players which saw a credit card bubble
when their credit card NPL shot up to 6-8%. We also forecast credit card loans to grow
mildly at 10% y-o-y in FY20, down from c. 20-25% y-o-y, as banks would slow down
credit card issuance and lower the loan quota if the above situation happens.
GFB, CEB, CMB and Among 19 banks, GFB, CEB, CMB and PAB’s FY20F NPL ratio will increase by
PAB’s capital levels are 167bps/99bps/87bps/ 91bps respectively under the bear-case scenario, as they have higher
more vulnerable to exposure to credit card loans at 36%/21%/18%/20% respectively, vs peers of 9%.
credit card risks
In terms of FY20F core-equity one (CET1) ratio, GFB, CEB, CMB and PAB will be hit by
133bps/71bps/79bps/70bps respectively, if credit card NPL ratio rises by 5%.
GFB, CEB, CMB and PAB face the biggest impact on NPL ratio from credit card risks
Source: Company, DBS HK
Credit card loan exposure comparison
Source: Company, DBS HKDBS Asian Insights
SECTOR BRIEFING 76
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GFB, CEB, CMB and PAB face the biggest impact on CET1 ratio from credit card risks
Source: Company, DBS HK
Stress test on residential risks – Mortgage and credit card loans
Bank ABC BOC BOCOM CCB CEB CITIC CMB CMSB CQRCB
Ticker 1288 HK 3988 HK 3328 HK 939 HK 6818 HK 998 HK 3968 HK 1988 HK 3618 HK
Scenario 2- China residential leveraging risks
Mortgage 4,889,573 3,968,088 1,260,189 5,969,073 489,362 722,308 1,144,793 498,730 76,274
Mortgage/total loan 34.1% 28.8% 22.2% 36.3% 16.9% 17.1% 24.2% 13.0% 16.6%
Credit card loan 456,474 645,662 715,813 972,232 610,739 507,545 847,386 390,531 4,528
Credit card/total loan 3.2% 4.7% 12.6% 5.9% 21.1% 12.0% 17.9% 10.2% 1.0%
Mortgage sensitivity test - assume foreclosure discount by 30%
Base case: property price flat
NPL ratio impact (bps) - - - - - - - - -
CET 1 ratio impact (bps) - - - - - - - - -
CAR impact (bps) - - - - - - - - -
Worse case: property price drop 15%
NPL ratio impact (bps) - - - - - - - - -
CET 1 ratio impact (bps) - - - - - - - - -
CAR impact (bps) - - - - - - - - -
Bear case: property price drop 31%
NPL ratio impact (bps) 127.72 92.66 67.02 109.69 43.30 55.50 67.63 53.40 59.83
CET 1 ratio impact (bps) (113.04) (82.17) (52.44) (107.10) (32.42) (42.54) (65.47) (36.12) (33.40)
CAR impact (bps) (111.05) (80.45) (51.48) (105.40) (31.80) (41.66) (64.39) (35.67) (32.99)
Credit card sensitivity test - assume credit card loan grow 10% y-o-y in FY20
Base case: credit card NPL ratio increase 1%
NPL ratio impact (bps) 2.49 5.49 14.08 6.12 21.25 12.81 18.88 11.56 1.23
CET 1 ratio impact (bps) (2.05) (4.55) (10.35) (5.49) (15.14) (9.38) (16.94) (7.42) (0.65)
CAR impact (bps) (1.97) (4.35) (9.94) (5.30) (14.53) (8.97) (16.34) (7.23) (0.63)
Worse case: credit card NPL ratio increase 3%
NPL ratio impact (bps) 8.57 14.46 38.20 17.44 59.92 35.82 53.17 31.01 3.20
CET 1 ratio impact (bps) (7.07) (12.01) (28.13) (15.67) (42.83) (26.27) (47.89) (19.95) (1.68)
CAR impact (bps) (6.80) (11.47) (27.03) (15.13) (41.09) (25.13) (46.19) (19.42) (1.63)
Bear case: credit card NPL ratio increase 5%
NPL ratio impact (bps) 14.65 23.44 62.32 28.76 98.59 58.84 87.46 50.46 5.16
CET 1 ratio impact (bps) (12.09) (19.48) (45.99) (25.87) (70.69) (43.23) (79.06) (32.51) (2.71)
CAR impact (bps) (11.64) (18.61) (44.19) (24.98) (67.81) (41.34) (76.25) (31.65) (2.64)
Source: Company, DBS HKDBS Asian Insights
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Stress test on residential risks – Mortgage and credit card loans cont.
Bank BON BSH BZZ CDB CZB GFB PAB PSBC SPDB
Ticker 002142 SS 601229 SS 6196 HK non-listed 2016 HK non-listed 000001 SS 1658 HK 600000 SS
Scenario 2- China residential leveraging risks
Mortgage 1,757 93,085 18,372 4,147,415 73,332 189,626 238,535 1,802,574 788,229
Mortgage/total loan 0.3% 9.7% 9.9% 29.4% 7.0% 12.4% 9.8% 29.2% 18.6%
Credit card loan 47,093 35,712 1,620 0 16,634 544,525 488,841 111,687 505,154
Credit card/total loan 9.2% 3.7% 0.9% 0.0% 1.6% 35.6% 20.0% 1.8% 11.9%
Mortgage sensitivity test - assume foreclosure discount by 30%
Base case: property price flat
NPL ratio impact (bps) - - - - - - - - -
CET 1 ratio impact (bps) - - - - - - - - -
CAR impact (bps) - - - - - - - - -
Worse case: property price drop 15%
NPL ratio impact (bps) - - - - - - - - -
CET 1 ratio impact (bps) - - - - - - - - -
CAR impact (bps) - - - - - - - - -
Bear case: property price drop 31%
NPL ratio impact (bps) 1.28 44.55 106.22 37.66 46.49 36.57 109.41 69.75
CET 1 ratio impact (bps) (0.68) (17.25) (20.75) (97.79) (32.40) (38.39) (29.23) (111.78) (57.34)
CAR impact (bps) (0.67) (16.84) (20.12) (90.57) (31.49) (37.87) (28.87) (109.31) (56.76)
Credit card sensitivity test - assume credit card loan grow 10% y-o-y in FY20
Base case: credit card NPL ratio increase 1%
NPL ratio impact (bps) 8.33 3.06 0.80 - 1.22 30.73 15.01 1.46 11.40
CET 1 ratio impact (bps) (4.23) (1.59) (0.36) - (1.01) (24.26) (11.41) (1.41) (8.79)
CAR impact (bps) (4.14) (1.52) (0.33) - (0.95) (23.58) (11.12) (1.35) (8.60)
Worse case: credit card NPL ratio increase 3%
NPL ratio impact (bps) 25.84 10.19 2.48 - 4.25 98.82 53.24 4.92 34.20
CET 1 ratio impact (bps) (13.15) (5.32) (1.10) - (3.50) (78.48) (40.61) (4.76) (26.43)
CAR impact (bps) (12.87) (5.05) (1.03) - (3.29) (76.26) (39.55) (4.54) (25.86)
Bear case: credit card NPL ratio increase 5%
NPL ratio impact (bps) 43.35 17.33 4.15 - 7.29 166.90 91.46 8.38 57.00
CET 1 ratio impact (bps) (22.08) (9.04) (1.85) - (5.99) (133.34) (69.99) (8.11) (44.13)
CAR impact (bps) (21.61) (8.59) (1.73) - (5.64) (129.56) (68.16) (7.73) (43.18)
Source: Company, DBS HK
What if POEs continue to suffer?
Scenario 3: POE risks
Deleveraging has Private companies, also known as POEs, have been experiencing financing difficulties since
caused POE’s liquidity 2017 when the China government started to clamp down on shadow banking which used
crunch to be the main financing channel for POEs. In China, banks’ loan mix is roughly 70/30 for
corporate/retail banking, and out of that, 70-80% of corporate loans are distributed to
SOEs, implying only 15-20% of total loans are allocated to POEs.
However, by breaking down China’s GDP, over 60% is contributed by POEs. Hence, there
is a mismatch between funding support and profit contribution.DBS Asian Insights
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POEs’ margin pressure In 2018, according to the National Bureau of Statistics (NBS), POEs with revenue size above
is also a concern Rmb20m faced net profit margin deterioration of 34bps to 5.59%, vs SOE’s increase of
35bps to 6.79%, impacted by
1. Supply-side reforms driving up commodity prices which were negative to downstream
manufacturers (mainly POEs), but positive to upstream suppliers (mainly SOEs)
2. Tight liquidity leading to higher interest burden.
Although there is no official data for small-scale POEs, which by definition are small and
micro enterprises (SMEs) with revenue lower than Rmb20m, the profit squeeze was likely
more severe in 2018.
A series of policies to To support SMEs and help the “Sannong” economy to recover, the government has released
solve SMEs’ liquidity a series of policies in 2019, including fiscal tools to reduce tax burden, and monetary tools
and solvency issues to relax interest burden and increase funding support from banks.
The State Council has announced further tax cut measures to include SMEs and broadened
the definition of SMEs to allow more enterprises to benefit from the tax benefit which
is estimated to reach Rmb200bn. On the other hand, the PBOC has relaxed its targeted
RRR cuts to incentivise banks to provide financing to SMEs, as well as to provide cheaper
funding to reduce SMEs’ interest burden.
We believe the China government’s intention of supporting the private sector, especially
small and micro enterprises, is clear. Although the market was previously concerned about
these private enterprises’ default risk, with the continued introduction of several supporting
measures, these concerns may be overdone. The slowdown in China’s GDP growth may
moderately impact China banks’ asset quality, but we expect the risk to be manageable.
POEs’ net profit margin was under pressure in 2018...
Source: WIND, DBS HKDBS Asian Insights
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Yet, margin pressure should ease amid normalising of PPI of raw materials
Source: WIND, DBS HK
A series of favourable policies to support SMEs and “Sannong” economy
Date Regulator Subject Main content
Jan. 2018 PBOC Targeted RRR cut Cut banks' RRR by 0.5ppt/1.5ppts when their Inclusive Finance reach 1.5%/10% of total loans. This inject
~RMB800bn of liquidity in the market
Sep. 2018 MoF Exemption of 1. The interest income received from loan to SME at theDBS Asian Insights
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A series of favourable policies to support SMEs and “Sannong” economy cont.
Date Regulator Subject Main content
Jan. 2019 MoF General tax cut on 1. Exempt Value-Added tax of SME whose monthly revenue is lower than 100k RMB
SME 2. For SMEs which have total taxable income < RMB 1m will be cut to 25% of the original amount and
taxed at the rate of 20%. For SMEs which has total taxable income between RMB 1m and RMB 3m , total
taxable will be half-counted and taxed at 20%.
* SMEs: Taxable Income should be lower than RMB3m with less than 300 employees and RMB50m total asset in average
3. Municipal / Province government can cut the tax rate of eight taxes* to the maximum of 50%.
4. SME who enjoyed the tax cut policy of the above 8 taxes can also be beneficial from THIRD Policy.
5. Regarding the Angel Investment / Start up firms, the definition of Start-UP Firms adjusted from RMB 5m per
firm.
9. Lower the average fee rate to below 1% on SMEs/Sannong. Charging no more than 1% for the
guarantee amount of RMB 5m
10. State-backed funds and Banking Institutions have to bear the risk liability of >20% while the risk liability
province level / Reguranteed Institution are required to be higher.
11. The municipal / Province government can subsidize any guarantee business withDBS Asian Insights
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1. Most loan quota are allocated to SOEs, whose risks tend to be lower than POEs as they
are government-backed
2. Off-balance sheet financing requires less capital and bears lower risks, as well as
contribute fee income to banks
Regulators had discouraged NSAs mainly by clamping down on WMPs which were the
main channel for banks to attract retail customers’/corporates’ idle funds and direct the
funds to corporates which require financing but are not supported by banks.
After CBIRC issued new WMP rules that no principal guarantee for investors investing
WMPs, no multi-layer investments to improve investment transparency, and non-standard
assets cannot exceed 35% of WMP’s net capital, the outstanding NSA amount was cut
down by ~10-20% y-o-y in 2018. Therefore, as most of NSAs can be recognised as a proxy
loan to private companies, we run stress test on NSAs for 19 banks to assess POEs’ risks.
We assume NSA growth to decline by 10% per annum in FY19/20F and 2%/5%/10%
of NSAs deteriorating to NPL in FY20F under base/worse/bear-case scenarios respectively.
Although it might not be necessary for factor in asset quality deterioration, we think our
assumption is reasonable if the economy is under downward pressure, as NSA assets might
go bust given POEs’ risks are relatively higher.
BZZ, CZB, CEB and Among 19 banks, BZZ, CZB, CEB and BON’s FY20F NPL ratio will increase by 318bps,
BON’s capital levels are 182bps, 156bps, 152bps, respectively, under the bear-case scenario, as they have higher
more vulnerable on exposure to NSAs (our calculation is NSA amount divided by the sum of loan and NSAs) at
NSA risks 33%/19%/16%/16% respectively, vs peers of 9%.
In terms of FY20F core-equity one (CET1) ratio, BZZ, CZB, CEB and BON will be hit by
170bps, 150bps, 109bps, 84bps, respectively, if 10% of NSAs deteriorate to NPLs.
BZZ, CZB, BON and CEB face the biggest impact on NPL ratio from NSA risks
Source: Company, DBS HKDBS Asian Insights
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NSA exposure under broad-based loan definition
Source: Company, DBS HK; calculation is based on “NSA/(NSA+loan)”
BZZ, CZB, CEB and BON face the biggest impact on CET1 ratio from NSA risks
Source: Company, DBS HK
Scenario 3(2): Stress test on small- and micro-enterprise
(SME) loan risks
On average, SME non-performing loan ratio is likely to be 2-3ppts higher than other
loans, at around the 4-5% range. However, it would depend on the individual bank’s risk-
management capabilities, where rural banks tend to have weaker risk management and
lower bargaining power to select quality borrowers as compared to big banks.
We assume SME loan to grow by 15% y-o-y in FY19F, higher than industry loan growth,
but this is likely to fall to 10% y-o-y growth in FY20F if SME loans start to show signs of
asset quality deterioration. Under our base/worse/bear-case scenarios, we assume SME
NPL ratio to go up by 1.5%/3%/4.5% on top of FY19F figures. Our assumption is justified
as SME NPL ratio would likely reach 6-8% under bear-case scenario which is fairly in lineDBS Asian Insights
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with manufacturing and wholesale & retail’s NPL level (many POEs are in these sectors)
under an asset quality downcycle.
PSBC, SPDB and CDB’s Among 19 banks, BZZ, SPDB and PSBC, CDB’s FY20F NPL ratio will increase by 258bps,
capital levels are more 233bps, 223bps, 180bps, respectively, under the bear-case scenario, as they have higher
vulnerable to SME exposure to SME loans at 54%, 49%, 51%, 39%, respectively, vs peers of 27%.
loan risks
In terms of FY20F core-equity one (CET1) ratio, PSBC, SPDB, and CDB will be hit by
221bps/183bps/180bps respectively, if SME non-performing loan ratio increases by 4.5%
compared to 2019 level. BZZ’s CET 1 ratio is less impacted, only by 116bps, likely because
its loan exposure is only 35% of total assets vs peers’ 54%.
BZZ, SPDB and PSBC face the biggest impact on NPL ratio from SME loan risks
Source: Company, DBS HK
SME loans exposure comparison
Source: Company, DBS HK; calculation is based on “NSA/(NSA+loan)”DBS Asian Insights
SECTOR BRIEFING 76
37
PSBC, SPDB and CDB face the biggest impact on CET1 ratio from SME loan risks
Source: Company, DBS HK
Fig 47: Stress test on POE risks – SME loan and non-standard assets
Bank ABC BOC BOCOM CCB CEB CITIC CMB CMSB CQRCB
Ticker 1288 HK 3988 HK 3328 HK 939 HK 6818 HK 998 HK 3968 HK 1988 HK 3618 HK
Scenario 3- SME loan and non-standard assets (NSA) risks
NSA 564,008 353,706 217,194 343,300 564,008 365,945 307,178 580,566 36,166
NSA/(total loan+NSA) 3.78% 2.51% 3.68% 2.05% 16.30% 7.98% 6.10% 13.13% 7.29%
SME loan 4,965,377 2,028,529 1,072,455 2,241,125 582,304 731,180 651,143 499,753 153,540
SME loan/total loan 34.6% 14.7% 18.9% 13.6% 20.1% 17.3% 13.8% 13.0% 33.4%
NSA risks sensitivity test - assume NSA growth decline 10% y-o-y in FY20
Base case: 2% of NSA deteriorating to NPL
NPL ratio impact (bps) 7.18 4.76 6.99 3.88 31.16 15.19 11.60 25.05 13.86
CET 1 ratio impact (bps) (4.78) (3.09) (4.52) (2.76) (21.50) (9.72) (7.96) (14.81) (5.59)
CAR impact (bps) (4.55) (2.91) (4.31) (2.64) (20.43) (9.20) (7.57) (14.33) (5.39)
Worse case: 5% of NSA deteriorating to NPL
NPL ratio impact (bps) 17.94 11.89 17.49 9.70 77.89 37.96 28.99 62.64 34.64
CET 1 ratio impact (bps) (11.97) (7.73) (11.31) (6.90) (53.98) (24.36) (19.94) (37.15) (14.00)
CAR impact (bps) (11.39) (7.28) (10.79) (6.60) (51.29) (23.05) (18.96) (35.93) (13.49)
Bear case: 10% of NSA deteriorating to NPL
NPL ratio impact (bps) 35.88 23.78 34.97 19.41 155.79 75.93 57.99 125.27 69.29
CET 1 ratio impact (bps) (23.98) (15.48) (22.66) (13.82) (108.74) (48.88) (40.01) (74.68) (28.07)
CAR impact (bps) (22.83) (14.57) (21.61) (13.21) (103.33) (46.24) (38.04) (72.22) (27.04)
SME loan sensitivity test - assume SME loan growth +10% y-o-y in FY20
Base case: SME NPL ratio increase 1.5%
NPL ratio impact (bps) 58.19 24.80 31.49 23.50 33.82 30.23 22.54 22.01 60.66
CET 1 ratio impact (bps) (48.21) (20.61) (23.18) (21.13) (24.13) (22.16) (20.24) (14.15) (31.93)
CAR impact (bps) (46.40) (19.69) (22.28) (20.40) (23.15) (21.19) (19.52) (13.77) (31.09)
Worse case: SME NPL ratio increase 3%
NPL ratio impact (bps) 110.06 46.91 59.83 43.96 63.99 56.22 43.20 41.53 110.70
CET 1 ratio impact (bps) (91.63) (39.07) (44.14) (39.61) (45.75) (41.30) (38.87) (26.73) (58.45)
CAR impact (bps) (88.19) (37.33) (42.42) (38.24) (43.89) (39.50) (37.49) (26.03) (56.91)
Bear case: SME NPL ratio increase 4.5%
NPL ratio impact (bps) 161.93 69.03 88.16 64.42 94.16 82.22 63.86 61.04 160.74
CET 1 ratio impact (bps) (135.48) (57.61) (65.20) (58.17) (67.48) (60.52) (57.59) (39.35) (85.12)
CAR impact (bps) (130.39) (55.04) (62.65) (56.16) (64.74) (57.88) (55.54) (38.31) (82.88)
Source: Company, DBS HKDBS Asian Insights
SECTOR BRIEFING 76
38
Fig 47: Stress test on POE risks – SME loan and non-standard assets cont.
Bank BON BSH BZZ CDB CZB GFB PAB PSBC SPDB
Ticker 002142 SS 601229 SS 6196 HK non-listed 2016 HK non-listed 000001 SS 1658 HK 600000 SS
Scenario 3- SME loan and non-standard assets (NSA) risks
NSA 97,610 105,192 91,372 822,041 246,941 89,145 211,580 197,848 551,914
NSA/(total loan+NSA) 15.95% 9.90% 33.03% 5.51% 19.04% 5.51% 7.96% 3.10% 11.52%
SME loan 216,948 162,540 99,693 5,425,417 298,233 327,054 208,214 3,169,354 2,095,725
SME loan/total loan 42.2% 17.0% 53.8% 38.5% 28.4% 21.4% 8.5% 51.3% 49.4%
NSA risks sensitivity test - assume NSA growth decline 10% y-o-y in FY20
Base case: 2% of NSA deteriorating to NPL
NPL ratio impact (bps) 30.47 18.85 63.69 10.47 36.45 10.46 15.15 5.89 21.96
CET 1 ratio impact (bps) (16.65) (10.19) (33.37) (6.90) (29.48) (7.01) (9.32) (5.44) (16.07)
CAR impact (bps) (16.25) (9.66) (30.64) (6.72) (27.31) (6.78) (8.94) (5.17) (15.62)
Worse case: 5% of NSA deteriorating to NPL
NPL ratio impact (bps) 76.18 47.13 159.21 26.18 91.12 26.16 37.86 14.72 54.91
CET 1 ratio impact (bps) (41.74) (25.53) (84.00) (17.27) (74.15) (17.56) (23.35) (13.61) (40.31)
CAR impact (bps) (40.74) (24.18) (77.12) (16.82) (68.68) (16.97) (22.39) (12.93) (39.17)
Bear case: 10% of NSA deteriorating to NPL
NPL ratio impact (bps) 152.35 94.27 318.43 52.35 182.23 52.31 75.73 29.44 109.82
CET 1 ratio impact (bps) (83.91) (51.22) (169.95) (34.62) (149.81) (35.20) (46.85) (27.26) (81.07)
CAR impact (bps) (81.89) (48.51) (156.03) (33.73) (138.75) (34.01) (44.94) (25.89) (78.77)
SME loan sensitivity test - assume SME loan growth +10% y-o-y in FY20
Base case: SME NPL ratio increase 1.5%
NPL ratio impact (bps) 67.77 25.18 96.36 64.73 45.27 35.97 16.00 82.40 84.24
CET 1 ratio impact (bps) (34.59) (13.14) (43.19) (52.13) (37.35) (28.38) (12.22) (80.40) (65.53)
CAR impact (bps) (33.83) (12.49) (40.37) (51.09) (35.14) (27.61) (11.85) (76.68) (63.96)
Worse case: SME NPL ratio increase 3%
NPL ratio impact (bps) 131.02 48.52 177.07 122.46 87.88 68.03 28.77 152.93 158.41
CET 1 ratio impact (bps) (67.10) (25.35) (79.67) (99.14) (72.79) (53.83) (21.99) (150.35) (124.03)
CAR impact (bps) (65.63) (24.09) (74.47) (97.15) (68.48) (52.36) (21.32) (143.38) (121.07)
Bear case: SME NPL ratio increase 4.5%
NPL ratio impact (bps) 194.28 71.86 257.78 180.19 130.49 100.10 41.53 223.45 232.57
CET 1 ratio impact (bps) (99.85) (37.60) (116.44) (146.63) (108.50) (79.42) (31.79) (221.38) (183.30)
CAR impact (bps) (97.67) (35.73) (108.84) (143.69) (102.08) (77.25) (30.82) (211.11) (178.92)
Source: Company, DBS HK
Expect Rmb2tr capital needed to be raised for
the industry
A domino effect when The 19 banks that we covered in this report represent close to 76% of total assets among
the bear market comes China banks, which we think would be a good proxy for the industry. When an economic
downturn occurs, GDP growth slows down, corporates’ financing demand declines due to
the step-back in capacity and fixed asset investment, and their profitability may decline to
cause solvency issues, leading to banks’ asset quality deterioration. The situation would spill
over to the residential segment as unemployment rate may rise causing the income level
of retail borrowers to decline, resulting in difficulties of repayment. Thus, when recession
hits, there would be a domino effect for both corporate and retail borrowers, who will face
difficulties to repay, rather than a single event.You can also read
