1. Stress Testing China’s
Banking System
Summer Research Project
Thomas Fu
Intern
Moore Capital Management
August, 2014

2. Global Financial Crisis (2008-2009)
20
25
30
35
40
45
50
55
60
65
70
9/30/2003 9/30/2004 9/30/2005 9/30/2006 9/30/2007 9/30/2008 9/30/2009 9/30/2010 9/30/2011 9/30/2012 9/30/2013
Global Market Cap ($tn)
58% peak to trough drop

3. -6%
-4%
-2%
0%
2%
4%
6%
8%
10%
12%
14%
Global GPD Growth Rates
Eurozone China USA Brazil India Average global growth

4. Credit Intensity of GDP Growth
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Nominal GDP growth (lhs) 9% 13% 18% 17% 20% 29% 29% 10% 19% 23% 12% 12%
Tota Social Financing growth (lhs) 7% 13% 10% 9% 12% 16% 18% 31% 24% 19% 20% 18%
Credit Growth / Productivity Growth (rhs) 73% 101% 55% 55% 62% 57% 61% 297% 127% 79% 159% 150%
0%
50%
100%
150%
200%
250%
300%
350%
0%
5%
10%
15%
20%
25%
30%
35%
Everything comes
with a price!

5. What was happening in China
2008, August 8, Beijing Olympics
2008, May 12, Sichuan Earthquake
2010, May 1, Shanghai Expo 2009, China High speed train
Steel, Infrastructure, cement

6. Where did the credit go?
Real Estate,
18%
"Overcapacity
" sectors,
28%
Other
Corporates,
6%
Consumer,
8%
Government,
40%
Other, 48%
2008 Chinese System Credit
(bn$)
Total= $7,293
Real Estate,
24%
"Overcapacity"
sectors, 34%
Other
Corporates,
10%
Consumer, 9%
Government,
23%
Other, 32%
2013 Chinese system Credit
(bn$)
Source: CICC, Goldman Sachs, Merrill Lynch, Barclays, Credit Suisse, HSBC, MCM Analysis
Total= $23,034

7. Shadow Banking
• Banking regulation has forced credit off balance
sheet, exacerbating the credit intensity
– Wealth Management Products
– Trust Products
– Entrust Loans
2007 2008 2009 2010 2011 2012 2013
Trust 117 170 299 453 746 1,193 1,776
Entrust 223 299 409 545 769 927 1,430
NSCA WMP 46 81 100 289 427 477 541
0
200
400
600
800
1,000
1,200
1,400
1,600
1,800
2,000
Sources: Barclays Research, HSBC China Research, Credit Suisse Research, Merrill Lynch Research, MCM Analysis

8. China Shadow Banking System Credit (tn$)
2008 2009 2010 2011 2012 2013
Shadow Banking in $ $763 $1,183 $2,046 $2,833 $4,221 $6,114
% of Shadow Banking/GDP 17% 23% 34% 39% 50% 64%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
$-
$1
$2
$3
$4
$5
$6
$7
Sources: Barclays Research, Bloomberg, MCM Analysis

9. China Total Real Estate Credit (tn$)
2008 2009 2010 2011 2012 2013
Total Real Estate sector credit $1,294 $1,937 $2,688 $3,311 $4,155 $5,562
% Real Estate Credit/GDP 28% 38% 45% 45% 50% 58%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
$-
$1
$2
$3
$4
$5
$6
Sources: Barclays Research, HSBC China Research , Credit Suisse Research . Merrill Lynch Research, MCM Analysis

10. China “Overcapacity” Sector Credit (tn$)
2008 2009 2010 2011 2012 2013
Total Overcapacity sector credit $1,999 $2,767 $3,722 $4,743 $6,259 $7,883
% Overcapacity/GDP 44% 54% 63% 65% 75% 83%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
$-
$1
$2
$3
$4
$5
$6
$7
$8
$9
Sources: Barclays Research, HSBC China Research, Credit Suisse Research, Merrill Lynch Research, MCM Analysis

11. “ Debt and overcapacity form a vicious spiral….As local governments and businesses
borrow more in the name of new projects to pay interest on existing debt, they
exacerbate the overcapacity situation. China must stop the debt-capacity spiral.
Continuing it provides no way out.”
—Andy Xie (Former Morgan Stanley Chief Asia Pacific Economist)
“ Very rapid growth is often driven by the worsening imbalances, a rebalancing of
China is necessarily going to experience much lower growth for many years.”
—Michael Pettis (Peking University)
Does the rapid growth in credit make China unstable?

12. This is Not News!
• China has a credit problem
• What have I been doing all summer?
• Creating detailed map of the credit
system by vintage!
• Using the data to create a banking
system stress test

13. Data Resources
Primary
• PBOC—Bank Loans
• CBRC—NPL, RE, LGFV, Overcapacity
• China Trust Association— Trust sector
• China Bonds Association—Bonds
Secondary
• Barclays (May Yan)—China Shadow Banking System, Property
• CICC—LGFV Loans
• Goldman Sachs (Roy Ramos, Ning Ma)
• Merrill Lynch (Michael Li)—Bank Loans
• Deutsch (Michael Zhang)—Banking system PnL
• HSBC (Todd Dunivant)—Bonds and economic outlook
• Credit Suisse (Steven Zhu)—Trust breakdown
• JP Morgan—property, NPL
• Bloomberg—NPL, Currency

14. China System Credit by Vintage 2008 2009 2010 2011 2012 2013
On Balance Sheet
Loans
LGFV 570 3,050 1,890 10 200 2,600
Real Estate 342 1,830 1,134 6 120 1,560
Others 228 1,220 756 4 80 1,040
Mining 103 184 211 236 262 160
Manufacturing 473 1,019 1,116 1,671 1,626 943
Construction 81 134 406 363 454 460
Transportation 352 689 745 349 409 356
Property 176 593 878 151 290 536
All other 498 404 -174 2,152 1,089 1,604
Total industry loans 2,253 6,073 5,072 4,932 4,330 6,659
Total consumer loans 454 2,084 982 1,438 1,030 2,440
Mortgage 185 1,532 1,070 856 641 1,637
Overseas loans 97 119 195 371 289 592
Policy bank loans 1,366 1,353 1,702 134 2,547 -786
Net new system loans 4,170 9,629 7,951 6,875 8,196 8,905
Bonds
Mining(Materials) 56 295 108 139 265 118
Manufacturing 75 146 246 288 561 0
Property + Construction 40 127 163 135 394 326
Transportation 31 159 138 69 143 111
All other 568 393 385 506 426 681
Net new private sector bonds 769 1,121 1,039 1,136 1,788 1,236
LGFV 107 338 315 355 962 1,100
Railway 76 98 189 99 150 149
Municipal 0 200 200 200 50 212
FINS (policy banks) 877 900 736 1,518 1,741 1,263
PBOC bills 1,234 -572 -354 -1,761 -784 -612
Central government 118 674 702 419 515 916
Net new government bonds 2,412 1,638 1,788 830 2,634 3,028
Net new system bonds 3,181 2,759 2,827 1,966 4,422 4,264
Off Balance Sheet
Fin guarantee 140 419 676 537 258 362
NSCA of WMP 225 107 1,257 815 235 331
Entrusted loan 426 678 875 1,296 846 2,980
LGFV -79 136 11 879 677 276
Trust 319 841 999 1,771 2,660 3,436
Infrastructure 0 148 176 21 634 952
Industrial and Commercial 0 245 292 410 914 1,039
Real Estate 0 69 82 256 0 345
Financial 0 2 149 439 123 524
Bonds 0 38 75 113 307 155
Stocks 6 15 18 24 41 89
Other 313 324 208 508 641 332
Discounted bills repo 240 480 1,486 -444 1,146 -300
Security firm's AMP 10 56 39 95 1,608 3,310
All other 441 107 332 466 1,406 997
Net new system shadow credit 1,801 2,688 5,664 4,536 8,159 11,116
Net new system credit 9,153 15,076 16,443 13,377 20,777 24,285
% of GDP 28% 43% 41% 28% 40% 41%
ChinaSystemCreditbyVintage 2008 2009 2010 2011 2012 2013
Total
o/wRE 1,825 4,081 4,831 3,305 4,661 8,146
o/wovercapacity 2,298 4,768 6,111 5,594 8,601 9,224
o/wconsumerloans 454 2,084 982 1,438 1,030 2,440
o/wothercorporate 904 1,490 1,344 2,431 2,267 3,333
o/wothergovernment 3,671 2,653 3,175 609 4,219 1,142
Totalsystem 9,153 15,076 16,443 13,377 20,777 24,285
OnBalanceSheet
o/wRE 1,562 4,124 4,315 3,139 4,176 6,431
o/wovercapacity 2,037 4,744 5,304 5,486 7,685 6,723
o/wconsumerloans 454 2,084 982 1,438 1,030 2,440
o/wothercorporate 768 1,399 1,147 2,238 1,965 2,635
o/wothergovernment 3,671 2,653 3,175 609 4,219 1,142
Totalonbalancesheet 8,492 15,004 14,923 12,911 19,074 19,370
93% 100% 91% 97% 92% 80%
OffBalanceSheet
o/wRE 263 -43 516 165 485 1,715
o/wovercapacity 260 24 807 108 916 2,501
o/wconsumerloans 0 0 0 0 0 0
o/wothercorporate 136 90 197 192 302 698
o/wothergovernment 0 0 0 0 0 0
Totaloffbalancesheet 660 72 1,520 466 1,703 4,915
Vintage Model

15. Total Credit to GDP ahead of Banking Crises
66%
147% 151%
160% 160%
230%
241%
287%
355%
370%
Indonesia 1998 Thailand 1997 Malaysia 1998 Korea 1998 China 2008 Japan 1990 China 2013 Hong Kong
1998
United States
2008
UK 2008
Source: Goldman Sachs Equity Research, MCM Analysis

16. Roy Ramos, from “Parting Thoughts”, after 17 years
covering Asian Banks at Goldman:
“…we have observed empirically…that every time interest expense for
the aggregate private sector goes beyond 15% of GDP, a banking crisis
has ensued for that particular sector/country…We point to several
instances of banking crises ensuing at an even lower(10% to 11%)
interest expense to GDP ratio—Indonesia. Japan, Korea.”
Interest expense as % of GDP
=
(Total Banking loans/GDP) Ratio
x
Average borrowing cost

17. Roy Ramos—“Rule of 15%”
Interest expense as % of GDP
6.1%
10.1%
12.2%
13.1%
14.1% 14.3%
15.0%
16.4% 16.7%
18.5%
21.3%
22.1%
China, with
Shadow, 2008
Japan 1990 China, with
Shadow, 2013
Indonesia
1998
Korea 1998 China, with
Shadow, 2014
Malaysia 1998 China, with
Shadow, 2014
Hong Kong
1998
UK 2008 United States
2008
Thailand 1997
Assumes +100bps impact from interest
rate liberalization

18. Banking System Profitability and Capital
2007 2008 2009 2010 2011 2012 2013
NIM 2.9% 3.0% 2.3% 2.4% 2.6% 2.6% 2.6%
PPP ROA 1.8% 1.9% 1.5% 1.6% 1.8% 1.8% 1.9%
TCER 3.1% 5.6% 5.1% 5.8% 5.9% 6.1% 6.3%
ROTCE 30.1% 18.3% 18.6% 18.6% 20.1% 19.8% 19.4%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%

19. 0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
8.0%
9.0%
10.0%
11.0%
12.0%
13.0%
14.0%
15.0%
16.0%
17.0%
18.0%
3/1/2004
6/1/2004
9/1/2004
12/1/2004
3/1/2005
6/1/2005
9/1/2005
12/1/2005
3/1/2006
6/1/2006
9/1/2006
12/1/2006
3/1/2007
6/1/2007
9/1/2007
12/1/2007
3/1/2008
6/1/2008
9/1/2008
12/1/2008
3/1/2009
6/1/2009
9/1/2009
12/1/2009
3/1/2010
6/1/2010
9/1/2010
12/1/2010
3/1/2011
6/1/2011
9/1/2011
12/1/2011
3/1/2012
6/1/2012
9/1/2012
12/1/2012
3/1/2013
6/1/2013
9/1/2013
12/1/2013
3/1/2014
China Banking System NPL Ratio

20. Total Credit to GDP ahead of Banking Crises
66%
147% 151%
160% 160%
230%
241%
287%
355%
370%
Indonesia 1998 Thailand 1997 Malaysia 1998 Korea 1998 China 2008 Japan 1990 China 2013 Hong Kong
1998
United States
2008
UK 2008
Source: Goldman Sachs Equity Research, MCM Analysis

21. Japan Australia Hong Kong Singapore Korea Phillipines Malaysia China Thailand Indonesia
1990's NPL 6% 7% 10% 13% 14% 18% 18% 40% 48% 59%
0%
10%
20%
30%
40%
50%
60%
1990's Global Peak NPL Ratios
Australia
Hong
Kong
Indonesia Singapore Japan USA Spain Korea Phillipines Malaysia Ireland China Thailand
2000's NPL 2% 7% 8% 8% 9% 10% 11% 12% 17% 18% 19% 20% 31%
0%
5%
10%
15%
20%
25%
30%
35%
2000's Global Peak NPL Ratios

22. Scenario 1: Doomsday
Scenario 1
Loss Assumptions
2008
Vintage
2009
Vintage
2010
Vintage
2011
Vintage
2012
Vintage
2013
Vintage
Frequency
RE 40% 50% 50% 50% 40% 40%
Overcapacity 40% 50% 50% 50% 40% 40%
Consumer 10% 10% 10% 10% 10% 10%
Other Corporate 5% 5% 5% 5% 5% 5%
Government 0% 0% 0% 0% 0% 0%
Severity
RE 60% 70% 70% 70% 60% 60%
Overcapacity 60% 70% 70% 70% 60% 60%
Consumer 60% 70% 70% 70% 60% 60%
Other Corporate 60% 70% 70% 70% 60% 60%
Government 0% 0% 0% 0% 0% 0%
Loss rate
Total 11% 22% 24% 25% 16% 18%
Other Assumptions
Duration of loss recognition: 2
Loss on pre-2008 vintage 7%
Dividends cut? Y
Draw existing reserves? N
Bank Loss Model 2014 2015 2016 2017 2018 2019 2020
System NIM 2.6% 2.6% NA NA NA NA NA
System PPP 2,884 2,929 NA NA NA NA NA
Losses on '08-'13 vintage 8,521 8,521 NA NA NA NA NA
Losses on pre-'08 vintage 1,166 1,166 NA NA NA NA NA
Net income -5,238 -5,203 NA NA NA NA NA
System tangible common equity 4,369 -834 NA NA NA NA NA
TCER 3.1% -0.6% NA NA NA NA NA
ROTCE -75.0% -294.4% NA NA NA NA NA
Cumulative loss 25%
Current P / Burndown -12.8x
Capital hole $bn -1,576
Dilution to recap to 6.3% TCER -91%
• 25% cum. Loss
• 2 years of loss
recognition and PPP
• Dividends cut
• No draw on existing
reserves
• 0% avg asset growth
• No NIM declines, no
interest rate
liberalization
• Equity wiped out

23. Scenario 2: Doomsday lite
• 20% cum. Loss
• 3 years of loss
recognition and PPP
• Dividends cut
• No draw on existing
reserves
• 0% avg asset growth
• No NIM declines, no
interest rate
liberalization
• Low single digit
ROTCE’s
• 2.2x 2016 burndown
TBV
Scenario 2
Loss Assumptions
2008
Vintage
2009
Vintage
2010
Vintage
2011
Vintage
2012
Vintage
2013
Vintage
Frequency
RE 30% 50% 50% 30% 30% 30%
Overcapacity 30% 50% 50% 30% 30% 30%
Consumer 10% 10% 10% 10% 10% 10%
Other Corporate 5% 5% 5% 5% 5% 5%
Government 0% 0% 0% 0% 0% 0%
Severity
RE 65% 65% 65% 65% 65% 65%
Overcapacity 65% 65% 65% 65% 65% 65%
Consumer 65% 65% 65% 65% 65% 65%
Other Corporate 65% 65% 65% 65% 65% 65%
Government 0% 0% 0% 0% 0% 0%
Loss rate
Total 9% 20% 22% 14% 13% 15%
Other Assumptions
Duration of loss recognition: 3
Loss on pre-2008 vintage 6%
Dividends cut? Y
Draw existing reserves? N
Bank Loss Model 2014 2015 2016 2017 2018 2019 2020
System NIM 2.4% 2.3% 2.1% NA NA NA NA
System PPP 3,032 3,236 3,450 NA NA NA NA
Losses on '08-'13 vintage 4,665 4,665 4,665 NA NA NA NA
Losses on pre-'08 vintage 666 666 666 NA NA NA NA
Net income -1,771 -1,614 -1,449 NA NA NA NA
System tangible common equity 7,836 6,222 4,774 NA NA NA NA
TCER 4.9% 3.5% 2.4% NA NA NA NA
ROTCE -20.3% -23.0% -26.3% NA NA NA NA
Cumulative loss 20%
Current P / Burndown 2.2x
Capital hole $bn -1,226
Dilution to recap to 6.3% TCER -71%

24. Scenario 3: More realistic
Scenario 3
Loss Assumptions
2008
Vintage
2009
Vintage
2010
Vintage
2011
Vintage
2012
Vintage
2013
Vintage
Frequency
RE 20% 25% 25% 25% 25% 20%
Overcapacity 20% 25% 25% 25% 25% 20%
Consumer 8% 10% 10% 10% 10% 8%
Other Corporate 5% 5% 5% 5% 5% 5%
Government 0% 0% 0% 0% 0% 0%
Severity
RE 60% 60% 60% 60% 60% 60%
Overcapacity 60% 60% 60% 60% 60% 60%
Consumer 60% 60% 60% 60% 60% 60%
Other Corporate 60% 60% 60% 60% 60% 60%
Government 0% 0% 0% 0% 0% 0%
Loss rate
Total 6% 10% 11% 11% 10% 9%
Other Assumptions
Duration of loss recognition: 4
Loss on pre-2008 vintage 6%
Dividends cut? N
Draw existing reserves? N
Bank Loss Model 2014 2015 2016 2017 2018 2019 2020
System NIM 2.6% 2.6% 2.6% 2.6% NA NA NA
System PPP 3,086 3,354 3,644 3,959 NA NA NA
Losses on '08-'13 vintage 2,133 2,133 2,133 2,133 NA NA NA
Losses on pre-'08 vintage 500 500 500 500 NA NA NA
Net income 349 555 779 1,021 NA NA NA
System tangible common equity 9,358 9,316 9,497 9,920 NA NA NA
TCER 5.9% 5.5% 5.2% 5.1% NA NA NA
ROTCE 3.7% 5.9% 8.3% 10.5% NA NA NA
Cumulative loss 13%
Current P / Burndown 1.1x
Capital hole $bn -392
Dilution to recap to 6.3% TCER -23%
• 13% cum. Loss
• 4 years of loss
recognition and PPP
• Dividends not cut
• No draw on existing
reserves
• 7% avg asset growth
to support economy
• No NIM declines, no
interest rate
liberalization
• Mid single digit
ROTCE’s
• 1.1x 2017 burndown
TBV

25. Scenario 4: Base case
Scenario 4
Loss Assumptions
2008
Vintage
2009
Vintage
2010
Vintage
2011
Vintage
2012
Vintage
2013
Vintage
Frequency
RE 15% 15% 15% 15% 15% 15%
Overcapacity 15% 15% 15% 15% 15% 15%
Consumer 8% 10% 10% 10% 10% 8%
Other Corporate 5% 5% 5% 5% 5% 5%
Government 0% 0% 0% 0% 0% 0%
Severity
RE 60% 60% 60% 60% 60% 60%
Overcapacity 60% 60% 60% 60% 60% 60%
Consumer 60% 60% 60% 60% 60% 60%
Other Corporate 60% 60% 60% 60% 60% 60%
Government 0% 0% 0% 0% 0% 0%
Loss rate
Total 5% 6% 7% 7% 6% 7%
Other Assumptions
Duration of loss recognition: 5
Loss on pre-2008 vintage 5%
Dividends cut? N
Draw existing reserves? N
Bank Loss Model 2014 2015 2016 2017 2018 2019 2020
System NIM 2.4% 2.3% 2.2% 2.1% 2.0% NA NA
System PPP 3,032 3,236 3,507 3,800 4,117 NA NA
Losses on '08-'13 vintage 1,146 1,146 1,146 1,146 1,146 NA NA
Losses on pre-'08 vintage 333 333 333 333 333 NA NA
Net income 1,196 1,353 1,562 1,787 2,031 NA NA
System tangible common equity 10,205 10,960 11,924 13,114 14,548 NA NA
TCER 6.2% 6.0% 6.0% 5.9% 6.0% NA NA
ROTCE 12.1% 12.8% 13.6% 14.3% 14.7% NA NA
Cumulative loss 9%
Current P / Burndown 0.7x
Capital hole $bn -119
Dilution to recap to 6.3% TCER -7%
• 9% cum. Loss
• 5 years of loss
recognition and PPP
• Dividends not cut
• No draw on existing
reserves
• 10% avg asset
growth
• 80bps gradual NIM
decline on interest
rate liberalization
• Low to mid teens
ROTCE’s
• 0.7x 2018 burndown
TBV

26. Takeaways
• In our base case China takes 5 years to clean
up the NPL’s – we think the gov’t can manage
this with their liquidity resources
• ROTCE’s sustain in 14-15% range
• If COE = ~12% in this setup, stocks would be
fairly valued at ~1.3x TBV

27. Takeaways cont’d
• In the absence of more clarity on
– Real estate prices
– The impact of fiscal, monetary, SOE reforms
– Corruption crack down
– FAI decrease
– LOSS RECOGNITION AND NPL FORMATION
• …we think trading the banks in a range of
0.85x – 1.3x TBV makes sense

28. ICBC P/TBV range
Current
range =
0.9x –
1.25x TBV

29. “ China’s future problem is not about questioning its growth
potential. It is how to reform its social and economic
structure. “
— Andy Xie