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Deutsche Bank Markets Research
Asia
China
Consumer
Industry
Greater China Consumer
Date
4 January 2018
Industry Update
2018 outlook – a more widespread recovery
Since the end of 2012, China consumer sentiment has continued to improve after a prolonged period of weakness. We see a more widespread cyclical recovery as an additional sales driver, on top of secular growth from consumption trading up. For 2018 in general, we pick companies that are leaders in mass segments with nationwide networks, which have product/business model upgrade capability/potential. Our Top Buy picks are: Dali, Mengniu, Yum China, Anta, Midea, and Hengan.
2018: live and let live – embracing various forms of retailing/branding strategy
Anne Ling
Research Analyst
(+852 ) 2203 6177
anne.ling@db.com
Mark Yuan
Research Analyst
(+852 ) 2203 6181
mark.yuan@db.com
John Chou
Research Analyst
(+852) 2203 6196
john.chou@db.com
Louise Li
Research Associate
(+852 ) 2203 6152
louise.li@db.com
________________________________________________________________________________________________________________ Deutsche Bank AG/Hong Kong
Deutsche Bank does and seeks to do business with companies covered in its research reports. Thus, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of this report. Investors should consider this report as only a single factor in making their investment decision. DISCLOSURES AND ANALYST CERTIFICATIONS ARE LOCATED IN APPENDIX 1. MCI (P) 083/04/2017. THE CONTENT MAY NOT BE DISTRIBUTED IN THE PEOPLE’S REPUBLIC OF CHINA (“THE PRC”) (EXCEPT IN COMPLIANCE WITH THE APPLICABLE LAWS AND REGULATIONS OF PRC), EXCLUDING SPECIAL ADMINISTRATIVE REGIONS OF HONG KONG AND MACAU.)
Distributed on: 04/01/2018 20:34:19 GMT
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Deutsche Bank Markets Research
Asia
China
Consumer
Industry
Greater China Consumer
Date
4 January 2018
Industry Update
2018 outlook – a more widespread recovery
2018: live and let live – embracing various forms of retailing/branding strategy
________________________________________________________________________________________________________________ Deutsche Bank AG/Hong Kong
Deutsche Bank does and seeks to do business with companies covered in its research reports. Thus, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of this report. Investors should consider this report as only a single factor in making their investment decision. DISCLOSURES AND ANALYST CERTIFICATIONS ARE LOCATED IN APPENDIX 1. MCI (P) 083/04/2017.
Anne Ling
Research Analyst
(+852 ) 2203 6177
anne.ling@db.com
Mark Yuan
Research Analyst
(+852 ) 2203 6181
mark.yuan@db.com
John Chou
Research Analyst
(+852) 2203 6196
john.chou@db.com
Louise Li
Research Associate
(+852 ) 2203 6152
louise.li@db.com
Top picks
Dali Foods (3799.HK),HKD6.89 Buy
China Mengniu Dairy (2319.HK),HKD23.40 Buy
Yum China (YUMC.N),USD42.24 Buy
Anta (2020.HK),HKD35.60 Buy
Midea (000333.SZ),CNY55.80 Buy
Source: Deutsche Bank
Companies Featured
Dali Foods (3799.HK),HKD6.89 Buy
2016A 2017E 2018E
P/E (x) 16.0 23.2 19.3
EV/EBITDA (x) 8.9 13.6 11.2
Price/book (x) 3.5 5.2 4.7
China Mengniu Dairy (2319.HK),HKD23.40 Buy
2016A 2017E 2018E
P/E (x) 29.5 31.0 22.4
EV/EBITDA (x) 13.1 14.3 11.8
Price/book (x) 2.4 3.4 3.0
Yum China (YUMC.N),USD42.24 Buy
2016A 2017E 2018E
P/E (x) 20.4 29.6 25.6
EV/EBITDA (x) 9.0 13.2 12.0
Price/book (x) 4.2 6.2 5.7
Anta (2020.HK),HKD35.60 Buy
2016A 2017E 2018E
P/E (x) 17.4 25.6 21.1
EV/EBITDA (x) 10.2 14.4 11.7
Price/book (x) 5.2 5.7 5.3
Midea (000333.SZ),CNY55.80 Buy
2016A 2017E 2018E
P/E (x) 11.9 21.6 17.5
EV/EBITDA (x) 5.6 6.1 4.4
Price/book (x) 3.0 5.1 4.3
Hengan Intl. (1044.HK),HKD87.05 Buy
2016A 2017E 2018E
P/E (x) 18.9 23.0 20.3
EV/EBITDA (x) 13.0 14.6 13.1
Price/book (x) 4.1 5.5 5.0
Source: Deutsche Bank
Since the end of 2012, China consumer sentiment has continued to improve after a prolonged period of weakness. We see a more widespread cyclical recovery as an additional sales driver, on top of secular growth from consumption trading up. For 2018 in general, we pick companies that are leaders in mass segments with nationwide networks, which have product/business model upgrade capability/potential. Our Top Buy picks are: Dali, Mengniu, Yum China, Anta, Midea, and Hengan.
Cyclical and a more widespread recovery… We note consumer sentiment continues to improve. This is evident from: 1) brands/retailers reaccelerating store openings from 2H17; and 2) price hikes (either through raising ex-factory price or reducing promotions) in the FMCG space since 3Q17. The decision to raise prices (rather than absorbing costs internally) suggests firmer volume demand. We expect the recovery in consumption to spread beyond Tier 1/2 cities in 2018. DB forecasts China GDP/private consumption/retail sales to rise by 6.3/7.6/10.8%. We expect CPI to increase by 2.7% (vs. 1.7% in 2017E), which should also help consumption.
…but consumption upgrading remains the secular trend Consumption upgrading continues to be the secular trend given the growth in the middle-income class (300m population in 2017, expected to double in future). Retailers/brands in China can cater for such demand changes, thanks to the country’s technology advances and relatively short consumption history. Corporates (both on and offline) have thus made changes in their business structures in the past 12 months: 1) embracing different retail formats to cover varying demand; and 2) dedicating product assortments to different cities.
Hong Kong should continue to experience a recovery in domestic consumption Since the end of 3Q17, HK has also experienced a recovery in domestic spending, thanks to strong property and equity markets. To recap, mainland tourist spending for mass segments like cosmetics improved first (since 2H16) with tourist arrivals stabilizing. Our top picks for HK are CTF and Lifestyle.
Top picks (China): Dali, Mengniu, Yum China, Anta, Midea, and Hengan For 2018, we pick companies that meet the following criteria: 1) mass segment players with nationwide networks so as to enjoy the cyclical recovery; 2) segment leaders, as they have sourcing advantages at times of raw material price hikes; and 3) agile and able to demonstrate product/business model upgrade capability. Mengniu, Yum China, Anta, and Midea meet these criteria. With a national sales network, large raw milk sourcing scale, and a solid track record in new product launches, Dali also fits the category. As the leader in paper household products, Hengan has made significant strides with its recent launch of wet tissue and sanitary napkins and its ecommerce penetration.
Valuation and risks We use DCF, P/E-to-growth, and relative P/E as tools to value the sector. DCF captures the future cash flow of consumer companies, while PE/G and relative P/E show a company’s relative value vs. peers. Downside risks: higher-than-expected raw material prices, SSSg failing to pick up, and intense competition. Upside risks: a greater-than-expected rise in overall SSSg or sales volume, higher operating leverage, and weaker-than-expected input costs.
4 January 2018
Consumer
Greater China Consumer
Page 2 Deutsche Bank AG/Hong Kong
Table Of Contents
Executive summary ............................................................. 6 2017 – Eat (and live well), pray (for visibility/stability), love (being different) ...... 6 2018 – Live and let live – embracing various forms of retailing/branding strategy ............................................................................................................ 10 Recommendations............................................................................................ 12
Valuation table .................................................................. 15
Greater China SSSg trends ................................................ 16
Greater China SSSg trends ................................................ 17
Macro ................................................................................ 18 Continuous improvement in consumer sentiment ............................................ 18
Macro ................................................................................ 19 Recovery spread to lower tier cities – witnessed by strong consumer confidence pick-up ........................................................................................... 19
Macro ................................................................................ 20 Consumer credit to drive consumption, structurally ......................................... 20
Macro ................................................................................ 21 Higher CPI – FMCG price hikes since Sept/Oct 2017........................................ 21
Industry trends .................................................................. 22 Further cut in import tariff to force “supply-side reform” positive for consumption ..................................................................................................... 22
Industry trends .................................................................. 23 The new digital world embracing ecommerce as an additional revenue/profit driver ................................................................................................................ 23
Industry trends .................................................................. 24 Key players can gain more market share within a cost hike environment ........ 24
Industry trends .................................................................. 25 Two-tier market –cookie-cutter model no longer valid ..................................... 25
Consumer staples – dairy .................................................. 26 Entering milk shortage cycle - Buy Mengniu and Yili ....................................... 26
Consumer staples – dairy .................................................. 27 Mengniu/Yili to gain more market share in new cycle ...................................... 27
Consumer staples – dairy .................................................. 28 IMF: supply-side reform to bear fruit from 2018 .............................................. 28
Consumer staples – high-end liquor .................................. 29 Restocking is still ON, but it is entering late part of the cycle – Buy Moutai and Wuliangye ........................................................................................................ 29
Consumer staples – high-end liquor .................................. 30 High visibility on volume and ASP growth in 2018 ........................................... 30
Consumer staples – high-end liquor .................................. 31 Long-term growth remained solid .................................................................... 31
Consumer staples – brewers ............................................. 32 Competition to remain intense in 2018 ............................................................ 32
4 January 2018
Consumer
Greater China Consumer
Deutsche Bank AG/Hong Kong Page 3
Table Of Contents (Cont'd.)
Consumer staples – brewers ............................................. 33 Mix upgrade and efficiency improvements to be core driver: Hold CR Beer; Sell Tsingtao ............................................................................................................ 33
Consumer staples – noodles and soft drinks .................... 34 High-end product to be key sales driver – Buy UPC; Hold on Tingyi ................ 34
Consumer staples – snack foods ....................................... 35 Growth to be driven by new products and new channels – Buy Dali Foods; Hold on Want Want .......................................................................................... 35
China sportswear .............................................................. 36 2018 – accelerating industry consolidation ...................................................... 36
China sportswear .............................................................. 37 2018- Accelerating industry consolidation (continued) .................................... 37
China sportswear .............................................................. 38 Anta – leader’s benefits .................................................................................... 38
Cosmetics .......................................................................... 39 Summary – across-the-board recovery in 2017, but might be more selective in 2018 ................................................................................................................. 39
Cosmetics .......................................................................... 40 What drives the strong recovery? ..................................................................... 40
Cosmetics .......................................................................... 41 What drives the strong recovery? (cont.)......................................................... 41
Cosmetics .......................................................................... 42 Key trends in 2018 ............................................................................................ 42
Food retailing .................................................................... 43 Summary - New Retail - More to come, more to go; efficient leaders to stay .. 43
Food retailing .................................................................... 44 Offline traffic still declining, but stabilizing in 2017; 2018 potentially benefitting from higher CPI and virtual traffic .................................................................... 44
Food retailing .................................................................... 45 New retail format mushroomed – still testing the right business models ......... 45
Food retailing .................................................................... 46 Potential development in 2018 and beyond ..................................................... 46
Gold, jewellery and watches ............................................. 47 China market continues to recover, benefiting luxury spending ...................... 47
Gold, jewellery and watches ............................................. 48 Two tier market: A steadier recovery in lower-tier cities (reacceleration in store openings) .......................................................................................................... 48
Gold, jewellery and watches ............................................. 49 China market continues to recover ................................................................... 49
Gold, jewellery and watches ............................................. 50 Brands cater for customer behavioral changes and demand fragmentation .... 50
4 January 2018
Consumer
Greater China Consumer
Page 4 Deutsche Bank AG/Hong Kong
Table Of Contents (Cont'd.)
Gold, jewellery and watches ............................................. 51 High-end watches demand remains strong ...................................................... 51
Home appliances ............................................................... 52 Air conditioner - robust growth in 2018 ........................................................... 52
Home appliances ............................................................... 53 Air conditioner - building leading indicators ..................................................... 53
Home appliances ............................................................... 54 Air conditioner – using leading indicators to forecast demand......................... 54
Home appliances ............................................................... 55 Air conditioner tests our assumptions using channel inventory ....................... 55
Home appliances ............................................................... 56 Air conditioner: Midea’s aggressive market share gain .................................... 56
Home appliances ............................................................... 57 Refrigerator - 2018 could finally see some acceleration ................................... 57
Home appliances ............................................................... 58 Refrigerator - recovering from stagnation ........................................................ 58
Home appliances ............................................................... 59 Washing machines - healthy growth sustainable ............................................. 59
Home appliances ............................................................... 60 Washing machines - healthy growth sustainable ............................................. 60
International companies .................................................... 61 Summary – a steady unexciting growth story .................................................. 61
International companies .................................................... 62 Uncertainties in the flow of travel spending, but expected rise in domestic spending ........................................................................................................... 62
International companies .................................................... 63 Asia remains global sales growth driver ........................................................... 63
International companies .................................................... 64 The US might not be as bad, tax bill suggesting a tailwind; Europe stabilization continues on a low base, but with uncertainties .............................................. 64
Restaurants ....................................................................... 65 Dining out is taking more wallet share ............................................................. 65
Restaurants ....................................................................... 66 Trendy restaurants are catering for consumption, but they vanish quickly ...... 66
Restaurants ....................................................................... 67 Further growth driver expects to come from lower tier city recovery .............. 67
Restaurants ....................................................................... 68 Embracing O2O as the right choice .................................................................. 68
Textile and footwear ODMs .............................................. 69 US sporting goods cycle should bottom soon .................................................. 69
4 January 2018
Consumer
Greater China Consumer
Deutsche Bank AG/Hong Kong Page 5
Table Of Contents (Cont'd.)
Textile and footwear ODMs .............................................. 70 US retailer boom-bust cycle (timing) ................................................................ 70
Textile and footwear ODMs .............................................. 71 US retailer boom-bust cycle (recovery)............................................................. 71
Textile and footwear ODMs .............................................. 72 China upcycle appears intact ........................................................................... 72
Hong Kong ........................................................................ 73 Summary – Positive outlook: improving local consumption sentiments plus new infrastructure facilitating more traffic flow ............................................... 73
Hong Kong ........................................................................ 74 Improving local consumption sentiments......................................................... 74
Hong Kong ........................................................................ 75 New infrastructure facilitating more traffic flow ............................................... 75
Hong Kong ........................................................................ 76 New infrastructure facilitating more traffic flow (cont’d) ................................. 76
Hong Kong ........................................................................ 77 Sales growth momentum picking up, resulting in better OPM ......................... 77
Appendix A ........................................................................ 78 Macro in Charts - China .................................................................................... 78
Appendix B ........................................................................ 79 Macro in Charts - China .................................................................................... 79
Appendix C ........................................................................ 80 Macro in Charts - China .................................................................................... 80
Appendix D ........................................................................ 81 Macro in Charts - China .................................................................................... 81
Appendix E ........................................................................ 82 Macro in Charts - HK ........................................................................................ 82
Appendix F ........................................................................ 83 Macro in Charts - HK ........................................................................................ 83
Appendix G ........................................................................ 84 Macro in Charts - HK ........................................................................................ 84
Appendix H ........................................................................ 85 Macro in Charts - HK ........................................................................................ 85
4 January 2018
Consumer
Greater China Consumer
Page 6 Deutsche Bank AG/Hong Kong
Executive summary
2017 – Eat (and live well), pray (for visibility/stability), love (being different)
Despite the smoky global macro outlook at the beginning of the year, 2017
was a fairly good and eventful year for consumer companies in both staples
and discretionaries.
For discretionaries, brands learnt to make use of ecommerce as an additional
channel while at the same time investing in technology and big data such that
they can differentiate their product assortment/ business models in different
cities. Key retailers are benefiting from online companies’ need to offer
consumers offline shopping experiences through strategic investment and
business integration.
Staples, which have experienced volume declines and CBEC challenges in the
past, experienced stabilization in volume for mass products. The government’s
action to encourage competition (through introducing CBEC and cutting import
tariffs) has forced local FMCGs to reinvent themselves by launching new
products, focusing on quality and branding to channel restructuring. This has
resulted in improvement in OPM while sales have stabilized.
Most A-share companies outperformed Shanghai/Shenzhen Index with CE
companies and Baijiu names leading the way (up 70-97% for major CE
companies and up over 100% for leading Baijiu names). H-share consumer
names delivered a divergent trend. Key staples outperformed the MSCI staples
China index, while discretionaries underperformed the MSCI discretionaries
China index, which comprised Tencent, Alibaba and some auto names; these
performed far better than traditional consumer discretionaries. HK retail names
underperformed. See figures 1-3 for details.
4 January 2018
Consumer
Greater China Consumer
Deutsche Bank AG/Hong Kong Page 7
Figure 1: Share price performance of major HK/China consumer names – 2016 vs. 2017
-40% -20% 0% 20% 40% 60%
Oriental PearlLi & Fung
Shanghai JahwaGehua CATV
GomeEclat Textile
Haier ElectronicsYum China
Television BroadcastsFeng Tay
Shenzhen Component IndexHengan
Bright DairyTsingtao Brewery Co Ltd-H
GiantTingyi
Want WantShanghai Composite Index
MSCI CN Disc IndexUni-President China
DaliMSCI CN Staples Index
China Modern DiarySamsonite
China FoodsYonghui Superstores
HSCEI IndexL'Occitane
Qingdao HaierHSI Index
CRBLifestyle International
Yue YuenYili Dairy
AntaPrada
ShenzhouGree
Cafe de CoralTWSE
GiordanoLi Ning Co Ltd
Sun Art Retail GroupSA SA International
China Mengniu DairyChow Tai Fook
Pou ShengRobam
Wuliangye YibinMidea
BiostimeKweichow Moutai
2016
-50% -30% -10% 10% 30% 50% 70% 90% 110% 130%
Pou ShengOriental Pearl
China Modern DiaryGehua CATV
Cafe de CoralGiant
Eclat TextileL'Occitane
SA SA InternationalGiordano
GomeLifestyle International
Shanghai Composite IndexPrada
Shenzhen Component IndexYue Yuen
Television BroadcastsFeng Tay
TWSEBright Dairy
Uni-President ChinaSun Art Retail Group
HSCEI IndexLi & Fung
Li Ning Co LtdWant Want
HSI IndexShanghai Jahwa
Tsingtao Brewery Co Ltd-HChow Tai Fook
China FoodsMSCI CN Staples Index
ShenzhouHengan
Yum ChinaChina Mengniu Dairy
TingyiSamsonite
MSCI CN Disc IndexRobam
DaliHaier Electronics
GreeCRB
Yili DairyQingdao Haier
MideaYonghui Superstores
Kweichow MoutaiBiostime
Wuliangye Yibin
2017
Source: Deutsche Bank, Bloomberg Finance LP
4 January 2018
Consumer
Greater China Consumer
Page 8 Deutsche Bank AG/Hong Kong
Figure 2: Share price performance of major HK/China consumer names – 1Q17 vs. 2Q17
-30% -15% 0% 15% 30% 45%
Pou ShengEclat Textile
China Modern DiaryAnta
Li Ning Co LtdChina Foods
Oriental PearlGiant
Gehua CATVLi & Fung
SA SA InternationalBright Dairy
ShenzhouUni-President China
Feng TayGiordano
Cafe de CoralHengan
MSCI CN Staples IndexShenzhen Component Index
TingyiShanghai Composite Index
Yum ChinaLifestyle International
TWSESun Art Retail Group
Yili DairyChina Mengniu Dairy
L'OccitaneBiostime
Want WantYue Yuen
HSCEI IndexDali
HSI IndexShanghai Jahwa
Yonghui SuperstoresGome
CRBKweichow Moutai
MideaMSCI CN Disc Index
Tsingtao Brewery Co Ltd-HTelevision Broadcasts
Qingdao HaierPrada
Wuliangye YibinChow Tai Fook
SamsoniteGree
RobamHaier Electronics
1Q17
-30% -15% 0% 15% 30% 45%
BiostimeLi & Fung
Sun Art Retail GroupPou Sheng
China Modern DiaryPradaGome
Television BroadcastsTingyi
China Mengniu DairyOriental Pearl
Gehua CATVTsingtao Brewery Co Ltd-H
GiantMSCI CN Staples Index
Want WantBright Dairy
Shanghai Composite IndexCafe de Coral
HenganDali
SA SA InternationalHSCEI Index
Shenzhen Component IndexLifestyle International
China FoodsShenzhouGiordano
TWSEYue YuenHSI Index
Shanghai JahwaChow Tai Fook
Feng TayCRB
L'OccitaneRobam
Haier ElectronicsYili Dairy
SamsoniteUni-President ChinaMSCI CN Disc Index
AntaEclat Textile
Kweichow MoutaiQingdao Haier
Yonghui SuperstoresMidea
Wuliangye YibinGree
Li Ning Co LtdYum China
2Q17
Source: Deutsche Bank, Bloomberg Finance LP
4 January 2018
Consumer
Greater China Consumer
Deutsche Bank AG/Hong Kong Page 9
Figure 3: Share price performance pf major HK/China consumer names – 3Q17 vs. 4Q17
-20% -5% 10% 25% 40% 55% 70% 85% 100%
GiantTsingtao Brewery Co Ltd-H
Television BroadcastsYue Yuen
GomeGree
PradaOriental Pearl
Haier ElectronicsL'Occitane
Cafe de CoralRobam
SA SA InternationalShanghai Jahwa
TWSEQingdao Haier
Eclat TextileYum China
Gehua CATVLifestyle International
Pou ShengFeng Tay
MideaSamsonite
Wuliangye YibinWant WantBright Dairy
Shanghai Composite IndexHSCEI Index
Shenzhen Component IndexGiordanoHSI Index
CRBChina Modern Diary
Kweichow MoutaiMSCI CN Disc Index
Yonghui SuperstoresChow Tai Fook
Sun Art Retail GroupLi Ning Co Ltd
ShenzhouMSCI CN Staples Index
China FoodsUni-President China
DaliHengan
TingyiAnta
Yili DairyLi & Fung
China Mengniu DairyBiostime
3Q17
-25% -15% -5% 5% 15% 25% 35% 45%
Pou ShengEclat Textile
Oriental PearlL'Occitane
Uni-President ChinaChow Tai Fook
Gehua CATVCafe de Coral
GiordanoChina Modern Diary
Li Ning Co LtdLifestyle International
Feng TaySA SA International
Shanghai Composite IndexShenzhen Component Index
Yum ChinaMSCI CN Disc Index
TWSEYue Yuen
PradaChina Mengniu Dairy
GomeSamsonite
HSCEI IndexTelevision Broadcasts
HSI IndexLi & Fung
Haier ElectronicsRobam
Sun Art Retail GroupBright Dairy
Shanghai JahwaGiant
China FoodsGree
Yili DairyWant Want
MSCI CN Staples IndexHengan
ShenzhouQingdao Haier
MideaYonghui Superstores
DaliTingyi
CRBKweichow Moutai
Tsingtao Brewery Co Ltd-HBiostime
Wuliangye Yibin
4Q17
Source: Deutsche Bank, Bloomberg Finance LP
4 January 2018
Consumer
Greater China Consumer
Page 10 Deutsche Bank AG/Hong Kong
2018 – Live and let live – embracing various forms of retailing/branding strategy
A cyclical and wider spread recovery as an additional driver…
Following a recovery in the luxury space since 3Q16 (price harmonization and
property market boom), we note consumer sentiment continues to improve
after a prolonged weakness. This is evident from: 1) brands/retailers
reaccelerating store openings from 2H17, and 2) price hiking (either through
raising ex-factory prices or reducing promotions) in the FMCG space since
3Q17. These price hikes are due to improvements in the demand/supply
situation, rises in raw material prices, or increases in costs relating to
environmental regulations. However, the decision to raise prices (rather than
absorbing higher costs internally) suggests firmer volume demand. We expect
the recovery in consumption to spread beyond Tier 1/2 cities in 2018.
DB forecasts China GDP/private consumption/retail sales to increase by
6.3/7.6/10.8%. We expect CPI to increase by 2.7% (vs. 1.7% in 2017), which
should also help consumption.
…but consumption upgrading remains the secular trend
Consumption upgrading continues to be the secular trend given the growth in
the middle-income class (300m population in 2017 and expected to double in
future).Retailers/brands in China can cater for such demand changes (which
are normally fragmented and sometime spontaneous), thanks to the country’s
technology advancement on this front and relatively short consumption
history. Corporates (both on and offline) have thus made changes to their
business structures in the past 12 months by: 1) embracing different retail
formats to cover varying demand, and 2) dedicating product assortments to
different cities.
Hong Kong should continue to experience a recovery in domestic consumption
HK has also experienced a recovery in domestic spending since the end of
3Q17, thanks to strong property and equity markets. To recap, mainland tourist
spending for mass segments like cosmetics improved first (since 2H16) with
tourist arrivals stabilizing.
Thus, for 2018, we expect the domestic recovery to continue with additional
drivers from new infrastructure once the high-speed train (operating in 3Q18)
and HK-Zhuhai-Macao Bridge (operating in 1Q18) are in operation, which is
likely to help attract same-day/overnight visitors.
Our top picks for HK are CTF and Lifestyle. We like Chow Tai Fook as a HK
recovery play as we expect its key growth driver will come from a recovery in
its SSSg in HK. Operating leverage should be significant as HK has a fixed cost
model. HK accounts for ~37% of sales in FY18E and we expect EBIT profit to
increase by 47% and account for 38% of EBIT. We also like Lifestyle as we
believe its SSSg will continue to recover as it completed its refurbishment in
October 2017. Around 60% of its sales come from domestic customers.
Figure 4: Price transmission: PPI to
CPI?
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e
CPI vs PPI to converge in 2018
cpi % CPI DBe ppi % PPI DBe Source: Deutsche Bank, NBS, Wind
4 January 2018
Consumer
Greater China Consumer
Deutsche Bank AG/Hong Kong Page 11
Figure 5: 2017 Price hike milestone timeline
Source: Deutsche Bank, company data
Figure 6: Selective brands/retailers are accelerating store openings in China amid a market recovery environment
Company Store acceleration plan
Chow Tai Fook Management of CTF is more bullish, raising its store opening plan for China from 80-100 net store openings for FY18 to 140-150 (it might open more stores in lower tier cities in the future with recovery in Tier one/two cities potentially spreading to lower tier
cities)
Luk Fook Luk Fook management guided 50 net store opening at the beginning of the fiscal year. It added 46 in 1HFY18. During the 1HFY18 results call it thus raised its fully-year target to at least 80 new stores in China.
Giordano Giordano is working to ensure that all its franchisees (~30+ from 130+ three years ago) are profitable, which will allow it to accelerate its store opening plans. It continues to close self-operated stores, however. More franchise stores are expected to open
in 4Q17.
Cafe de Coral On the back of a strong SSSg, Cafe de Coral plans to accelerate store openings in 2HFY18. For reference, it closed three stores (net) in 1HFY18. In late October, it closed the last two stores in East China. It will focus on South China, moving forward.
Yum China Yum China guided gross opening per annual is 550-600. We believe most of the new stores will be located in lower tier cities. It also accelerated net openings to 185 (with 123 for KFC) for 9M17 vs. 154 (with 84 for KFC) in 9M16. We expect it net open 403
stores on a full-year basis vs. 386 in 2016.
Anta Sportswear companies, such as Anta, Li Ning, have started to extend their per store space since the beginning of 2017. The company expects average store space of Anta’s Fila to increase from 150sm per store in 2017 to 200sm per store in 2018, which
implies the total GFA is likely to see an acceleration.
Yonghui Yonghui guided for 100 stores in 2017 and its management commented 150 new large stores were achievable in 2018. It also expects to accelerate its Super Species expansion in 2018 with 100 new stores vs. 25 new stores in 2017.
Source: Deutsche Bank, company data
4 January 2018
Consumer
Greater China Consumer
Page 12 Deutsche Bank AG/Hong Kong
Recommendations
Top picks – Dali, Mengniu, Yum China, Anta, Midea, and Hengan
Corporates moving forward need to have flexible and agile operating
structures.
For 2018, we pick companies that meet the following criteria:
Mass segment players with nationwide networks so as to enjoy the
cyclical recovery;
Leaders in their segments as they have sourcing advantage at times of
raw material price hikes;
Agile and able to demonstrate product/business model upgrade
capability/potential.
We like Mengniu, Yum China, Anta and Midea, which meet the criteria. Given
its national sales network, large raw milk sourcing scales, and solid track
record with new product launches, Dali also fits the category. Furthermore, it
has demonstrated its strength in launching new products in a timely manner.
As the leader in paper household products, Hengan has made great strides
with its recent launch of wet tissue and sanitary napkins and ecommerce
penetration
Dali (3799.HK)
We expect Dali’s earnings growth to speed up from 6% in 1H17 to high teens
in 2018, driven by increasing sales from soy milk and breakfast breads and
better operating leverage. In addition, given RMB10bn net cash on its book,
we expect Dali to seek M&A that could bring synergies; should such happen,
this would increase earnings growth. The stock currently still trades at a 20-
30% discount to the average level for HK listed staples. We expect the stock to
re-rate on increasing transparency and continued solid financial results.
Mengniu (2319.HK)
We expect the industry’s raw milk supply to change from an over-supplied
cycle in 2014-16 to an under-supplied cycle in 2017-19. Helped by better
relationships with upstream dairy farms, Mengniu should be able to gain
market share from smaller players within an under-supplied cycle. Meanwhile,
we expect its liquid milk margin to improve in 2018-19, helped by new
management restructuring from 2017 (changed from a regional basis to a
product basis) and improving product mix. Thirdly, we expect its infant formula
segment to return to profitability in 2018, helped by the company’s increasing
operating efficiency and higher entry barrier in the IMF sector after 2018.
Yum China (YUMC.N) - Pizza Hut (PH) should have higher potential to
penetrate into lower-tier cities vs. its casual dining peers
We expect the company to benefit from a more widespread recovery in 2018
in lower tier cities. In our view, YUMC has the most sophisticated store
development team in China (compared to peers), allowing it to grow new
stores in lower tier cities more efficiently. We believe PH will have better
growth opportunities to expand in lower tier cities when compared to other
casual dining chains, levering on its strong store development team. On the
other hand, the new management team’s approach to launching new products
4 January 2018
Consumer
Greater China Consumer
Deutsche Bank AG/Hong Kong Page 13
with innovative branding strategies (e.g., angry burger with TF Boys in summer
17) should continue to ensure its relevance in Tier 1 cities.
Anta (2020.HK) Widening leadership vs. peers thanks to efficiency: Anta’s new
logistics center, store renovation since 2H16 and abundant
sponsorship resources could drive upside to Anta Brand retail sales in
2018, enabling it to take market share from peers.
Product upgrade: distributors indicated strong product design in 2018
that offers better value-for-money.
Fila expansion continues: Fila’s addition of new product collections
should help sustain its growth and keep the brand fresh.
Midea (000333.SZ) Aggressively taking air conditioner market share from Gree and Aux,
by leveraging flexible marketing campaigns.
Accelerating industry consolidation in China’s washing machine and
refrigerator industries to benefit leaders including Midea.
Further implementing “t+3” through channel reform: Midea has
established “operating centers” to replace sales companies, aiming for
better synergy and faster response to market changes.
Hengan (1044.HK) – developing into a fully-fledged HPC player with its
Amoeba system
We believe Hengan’s Amoeba strategy (launched in 2Q17) has been successful
so far. Alignment in interest with sales team should not only improve its cost
ratio, but also lead to a resumption of sales growth from 3Q17 onwards. The
next phase is to further implement this system from channel to upstream
manufacturing and product development from 2018 onwards.
Separately, management is also expanding the company’s product categories
so as to be a fully-fledged HPC company. Its three product categories will be
renamed from sanitary napkins, tissues and diapers to women & personal care,
household & personal care and children’s care. We believe such a move will
allow the company to resume is sales growth longer term.
Figure 7: Weighted average PE chart for consumer
Discretionary (2006- 2017)
Figure 8: Weighted average PE chart for consumer
Staples (2008- 2017)
10
13
16
19
22
Oct
-06
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Oct
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Oct
-08
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-14
Apr-
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Oct
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Weighted Discretionary PE (x) -2x
-1x Avg
+1x +2x
15
20
25
30
35
Sep-
08D
ec-0
8M
ar-0
9Ju
n-09
Sep-
09D
ec-0
9M
ar-1
0Ju
n-10
Sep-
10D
ec-1
0M
ar-1
1Ju
n-11
Sep-
11D
ec-1
1M
ar-1
2Ju
n-12
Sep-
12D
ec-1
2M
ar-1
3Ju
n-13
Sep-
13D
ec-1
3M
ar-1
4Ju
n-14
Sep-
14D
ec-1
4M
ar-1
5Ju
n-15
Sep-
15D
ec-1
5M
ar-1
6Ju
n-16
Sep-
16D
ec-1
6M
ar-1
7Ju
n-17
Sep-
17D
ec-1
7
Weighted Staple PE -2x -1x Avg +1x +2x
Source: Deutsche Bank, Bloomberg Finance LP, using 27 DB covered consumer discretionary companies, closing price as of 31 Dec 2017
Source: Deutsche Bank, Bloomberg Finance LP, using 27 DB covered consumer staples companies, closing price as of 31 Dec 2017
4 January 2018
Consumer
Greater China Consumer
Page 14 Deutsche Bank AG/Hong Kong
Figure 9: Summary of macro outlook, industry trends, and key recommendations
China
Sector Macro Outlook Industry trends Key Recommendations Overall view
Baijiu
- income growth of middle class
consumers
- wealth effect benefits
"- high-end liquor names to gain market share on consumers'
trading up.
- demand is shifting from government consumption to
business and private consumption
- Moutai’s price increase to release more room for other high-
end liquor brands to increase.
Wuliangye(000858.SZ)
Moutai (600519.SS)positive
Beer
-Disposable income growth
-Consumption upgrades
-Higher CPI expectation
- product mix upgrade will be key sales driver, while volume
growth will be limited;
- consolidation will be core driver for key player to expand
scale;
- competition will switch from channel penetration to brand
budilings; companies with better branding turn to outperform;
- main players will continue to streamline production
capacities to improve operating efficiencies
N/A neutral
Consumer Staples
-Consumption upgrades
- Higher CPI expectation
-Improved consumer sentiments
-Supply side reform
-Dominant players crowding out small players in undersupply
cycle
-Channel restocking driven by distributors’ price hike
expectations
-Mix upgrade and rising raw material costs driving up price
-Emerging new channels
Mengniu Dairy (2319.HK)
Dali Foods (3799.HK)
Hengan (1044.HK)
most positive
China Sportswear-Disposable income growth
-Improved consumer sentiments
-Better efficiency and industry consolidation
- Multi brand strategy; exploring kids segment
-More investments in marketing expenseAnta (2020.HK) positive
Cosmetics
-Disposable income growth
-Supportive government policy (on tax,
CBEC, etc)
-Premium market (mainly international brands) outperforming
mass market (mainly domestic brands)
-Niche demand (health related), product category expansion
(color cosmetics ) and new penetration gains (emerging
channels)
-Active in capital market (IPO and PE investment)
NA neutral
Food Retailing -Higher CPI expectation
-A new wave of partner-seeking (online offline integration,
but synergies are yet to be achieved)
-Two tier market (hyper markets in lower tier cities and
premium retailing formats in higher tier cities)
-New retail format yet to test out its sustainability.
Sun Art Retail Group (6808.HK)
Yonghui Superstores (601933.SS)positive
Gold, Jewellery & Watches
-Income growth and wealth effect
benefits
-Consumption upgrades
-Two tier market (diversification in higher tier cities and
penetration in lower tier cities)
-Acceleration in store openings with different store formats
-Customer behavior changes (towards daily wear, design-wide
sophistication and women self-purchases)
Chow Tai Fook (1929.HK) positive
Home Appliances
-Slowing down in property market
- Income gorwth and consumption
upgrades
-Air conditioners: restocking upcycle remains while Midea
taking market share.
-Refrigerator: may see accelerating growth thanks to better
industry discipline.
-Washing machine: sustainably healthy growth.
Midea (000333.SZ)
Gree (000651.SZ) positive
International Companies
-Geopolitical instability
-Global travel remains positive but
volatile due to geo political issue
-US tax reform
-Diversified brand portfolio & product innovation
-Asia markets driving global growth with price harmonization
-US market trending better in general
Samsonite (1910.HK)neutral
Restaurants
-Disposable income growth
-Stricter food and online delivery
regulations
-QSR chains posting higher entry barrier
-Deeper penetration in lower tier cities
-Rising online delivery share; Regulation helps easing price
competition
Yum China (YUMC.N) positive
Textile & Footwear ODMs -Retail sales recovery (US and China)
-US sporting goods cycle should bottom soon
-Lifestyle sportswear outperforming functional sportswear
-Major ODMs capacity expansion easing
Shenzhou (2313.HK) positive
Hong Kong
Sector Macro Outlook Industry trends Key recommendations
-Cosmetics
-Department stores
-Gold, Jewellery & Watches
-Media
-Domestic retail sentiment recovery
(wealth and income effect)
-Tourist arrivals and spending recovery
-New infrastructure facilitation
- SSSg improvement for more categories and declining costs
ratio
- end of store closure phase
-Advertising expenses picking up
Chow Tai Fook (1929.HK)
Lifestyle International (1212.HK)most postive
Source: Deutsche Bank Notes: In key recommendations, we rate Buy unless specified; Stock in Bold are our Top Picks
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Valuation table
Figure 10: Valuation table
Ticker Company Recom Target Share Price Mkt Cap Rpt. Ccy Y/E
(12 mth) Price Local (USD) FY1E FY2E FY1E FY2E FY1E FY2E FY1E FY2E FY1E FY2E FY1E FY2E FY1E FY2E
Staples
Food & Beverage
0506.HK China Foods Buy 5.00 4.57 1,865 HKD Dec NM 30.63 2.06 2.18 5.74 1.67 15.48 8.06 26.09 6.92 NM 5.68 -4.59 -2.91
3799.HK Dali Foods Buy 7.40 7.10 12,070 CNY Dec 23.18 19.29 5.21 4.68 3.02 3.63 13.55 11.16 23.40 25.56 4.81 4.57 -77.62 -77.03
0151.HK Want Want Hold 6.30 6.55 9,630 CNY Dec 20.09 18.12 4.72 4.00 0.00 0.00 12.19 10.70 25.68 23.89 5.62 6.50 -30.96 -46.57
0322.HK Tingyi Hold 13.28 15.20 10,620 CNY Dec 39.07 31.34 3.62 3.43 1.28 1.60 10.53 9.95 9.50 11.24 7.93 1.42 4.67 4.70
0220.HK Uni-President China Buy 8.50 6.54 2,940 CNY Dec 25.91 20.06 1.85 1.73 1.16 1.50 7.08 5.87 7.31 8.92 4.16 6.19 -5.64 -13.75
Dairy
1112.HK Biostime Hold 25.00 51.90 3,391 CNY Dec 28.69 23.84 5.88 4.86 0.00 1.70 15.52 12.14 23.20 22.64 5.59 6.59 134.87 92.62
600597.SS Bright Dairy Hold 14.00 15.16 2,827 CNY Dec 28.37 26.02 3.45 3.18 1.19 1.30 9.35 8.83 11.96 12.03 4.01 3.44 -33.69 -35.58
2319.HK China Mengniu Dairy Buy 26.80 23.25 10,297 CNY Dec 27.69 20.07 3.01 2.70 0.83 1.15 12.77 10.54 11.24 14.06 1.94 4.57 5.29 -3.35
1117.HK China Modern Diary Hold 1.70 1.51 927 CNY Dec NM 43.93 1.08 1.05 0.00 0.00 10.57 7.55 -10.15 2.55 5.44 4.82 72.06 66.99
600887.SS Yili Dairy Buy 35.00 32.20 28,808 CNY Dec 29.52 24.99 7.53 6.80 2.58 2.96 19.36 16.54 26.72 28.59 2.35 3.36 -54.71 -55.25
Alcoholic beverages
600519.SS Kweichow Moutai Buy 745.00 696.51 130,700 CNY Dec 34.20 27.97 9.89 8.30 1.56 1.90 21.85 17.40 31.58 32.28 3.85 4.03 -85.60 -89.90
000858.SZ Wuliangye Yibin Buy 98.00 79.88 47,232 CNY Dec 33.33 24.89 5.81 5.07 1.51 2.02 21.30 15.22 18.43 21.75 3.39 5.53 -76.60 -86.30
600600.SS Tsingtao Brewery Co Ltd-A Sell 24.74 39.35 6,660 CNY Dec 49.27 43.49 2.60 2.48 1.39 1.39 13.64 12.69 7.76 8.17 5.88 4.85 -57.04 -62.28
0168.HK Tsingtao Brewery Co Ltd-H Sell 28.20 40.30 5,664 CNY Dec 37.69 36.32 2.20 2.10 1.63 1.63 10.25 9.90 8.42 8.33 7.78 5.90 -58.35 -63.74
0291.HK CRB Hold 17.50 28.05 9,011 CNY Dec 29.55 24.56 2.78 2.55 0.68 0.81 15.50 13.19 9.79 10.83 4.52 4.63 9.88 -0.96
FMCG
1044.HK Hengan Buy 97.29 86.75 13,323 CNY Dec 22.55 19.86 5.40 4.93 2.93 3.33 14.32 12.77 25.03 25.98 5.57 4.91 -12.17 -19.32
Average 30.65 27.21 4.19 3.75 1.59 1.66 13.95 11.41 16.00 16.48 4.86 4.81 -16.89 -24.54
Discretionary
Apperal / Footwear
0709.HK Giordano Buy 4.91 4.16 826 HKD Dec 13.62 12.76 2.59 2.59 7.39 7.89 6.56 6.22 19.39 20.74 8.79 8.27 -38.70 -39.23
Channels (department stores, food retails, sports brands distributors)
1212.HK Lifestyle International Buy 13.90 10.38 2,319 HKD Dec 7.02 10.13 5.89 4.37 5.86 3.95 11.39 10.72 102.95 49.50 8.96 6.23 202.75 151.05
3813.HK Pou Sheng Hold 1.20 1.13 798 CNY Dec 12.04 9.15 0.79 0.74 0.00 1.01 5.18 4.48 6.07 7.70 NM 0.84 13.96 12.33
6808.HK Sun Art Retail Group Buy 8.70 8.26 10,502 CNY Dec 24.33 22.72 3.09 2.99 3.05 3.27 8.19 7.29 12.92 13.38 4.01 4.30 -43.12 -44.66
601933.SS Yonghui Superstores Buy 10.50 10.10 14,828 CNY Dec 52.85 36.50 4.80 4.51 0.95 1.37 28.38 20.92 9.30 12.73 2.68 3.25 -56.84 -54.25
Cosmetics
0973.HK L'Occitane Buy 17.40 14.32 2,812 EUR Mar 20.83 18.60 2.65 2.44 1.70 1.91 8.92 7.85 12.13 12.53 2.72 5.40 -26.68 -32.57
0178.HK SA SA International Buy 3.84 3.01 1,051 HKD Mar 20.04 13.88 4.81 4.81 4.79 6.91 12.28 8.89 23.98 34.63 4.90 6.90 -39.75 -44.16
600315.SS Shanghai Jahwa Hold 30.04 36.68 3,789 CNY Dec 60.59 47.65 4.53 4.25 0.51 0.65 32.47 25.53 7.70 9.24 NM 2.60 -6.86 -14.71
Luxury
1929.HK Chow Tai Fook Buy 10.40 8.20 11,265 HKD Mar 21.78 18.54 2.98 2.81 3.08 3.61 14.18 11.98 13.18 15.60 2.89 5.06 19.54 13.59
1913.HK Prada Hold 30.50 28.30 9,303 EUR Jan 27.32 31.72 3.20 2.59 4.04 3.27 11.63 13.02 8.96 8.08 4.81 3.48 -0.75 2.04
1910.HK Samsonite Buy 39.80 35.90 6,119 USD Dec 26.12 21.28 3.84 3.50 1.63 2.02 14.04 12.04 15.30 17.28 0.33 4.87 93.04 74.58
Restaurants
0341.HK Cafe de Coral Hold 24.50 21.45 1,489 HKD Mar 25.88 22.72 3.89 3.79 3.89 3.89 10.56 9.47 15.58 17.49 4.63 4.70 -23.93 -26.40
YUMC.N Yum China Buy 45.30 40.02 16,292 USD Dec 29.74 25.77 6.24 5.75 0.88 1.22 13.34 12.10 23.07 24.23 2.98 4.06 -41.46 -45.24
Textile / Sourcing
1476.TW Eclat Textile Buy 400 298 2,419 TWD Dec 22.14 17.97 5.11 4.57 3.06 4.17 13.45 10.86 21.12 26.96 1.61 3.46 -16.77 -16.68
9910.TW Feng Tay Buy 155 136 2,421 TWD Dec 18.98 16.55 5.85 5.23 3.88 4.53 9.83 8.61 31.69 33.36 2.91 3.36 9.86 11.56
0494.HK Li & Fung Hold 3.80 4.29 3,797 USD Dec 15.31 15.49 1.16 1.15 5.24 5.18 8.42 8.10 7.78 7.57 6.40 7.92 14.55 13.11
2313.HK Shenzhou Buy 73.00 74.40 14,554 CNY Dec 25.65 21.39 5.48 5.03 2.15 2.45 20.04 16.87 23.16 24.92 2.67 3.01 -2.27 -4.62
0551.HK Yue Yuen Buy 45.00 30.70 5,996 USD Dec 10.67 9.36 1.43 1.36 17.61 5.98 6.99 6.12 12.58 14.88 3.56 6.42 16.84 15.60
Sportswear
2020.HK Anta Buy 40.00 35.45 11,391 CNY Dec 25.63 21.13 5.74 5.30 2.69 3.31 14.40 11.75 26.04 26.08 3.64 4.36 -76.30 -76.11
2331.HK Li Ning Co Ltd Buy 7.50 6.33 1,592 CNY Dec 25.39 17.97 2.77 2.48 0.00 2.06 8.42 6.00 12.31 15.70 5.82 6.07 -54.69 -61.70
9921.TW Giant Manufacturing Sell 125.00 163.50 1,958 TWD Dec 24.01 20.83 2.78 2.68 2.83 3.29 14.64 13.05 12.46 13.80 0.91 0.06 27.05 30.37
White goods
0493.HK Gome Hold 0.82 0.94 1,976 CNY Dec 46.28 26.00 0.75 0.74 0.86 1.54 9.25 7.61 1.63 2.87 NM NM -0.47 0.53
000651.SZ Gree Buy 49.20 43.70 41,669 CNY Dec 13.17 12.16 4.44 3.87 4.56 4.93 6.27 5.17 36.16 34.02 15.20 11.72 -179.11 -182.15
000333.SZ Midea Buy 63.00 55.43 34,512 CNY Dec 20.25 16.41 4.79 4.01 1.75 2.16 5.48 3.90 26.30 27.28 11.28 8.46 -95.14 -102.69
1169.HK Haier Electronics Hold 25.00 21.40 8,045 CNY Dec 16.41 14.07 2.70 2.36 1.23 1.79 8.58 6.90 17.99 18.25 4.94 6.12 -62.42 -64.27
600690.SS Qingdao Haier Buy 23.50 18.85 17,787 CNY Dec 17.17 13.99 3.77 3.17 1.76 2.16 8.13 6.54 23.97 24.81 NM 8.89 49.56 26.89
002508.SZ Robam Buy 50.00 48.10 5,152 CNY Dec 28.53 22.07 9.14 7.08 1.04 1.35 16.89 12.58 36.07 36.14 2.85 3.81 -72.37 -75.63
Average 24.14 19.88 3.90 3.49 3.20 3.18 12.14 10.17 20.73 20.36 4.76 5.14 -14.46 -19.76
Media
600037.SS Gehua CATV Hold 17.00 12.99 3,158 CNY Dec 25.58 22.92 1.67 1.59 1.30 1.46 9.73 9.11 6.61 7.08 2.01 1.76 -56.08 -54.21
600637.SS Oriental Pearl Buy 35.00 16.66 7,111 CNY Dec 17.66 15.32 1.44 1.32 1.05 1.08 4.97 3.96 10.49 9.90 4.32 5.38 -52.84 -55.30
0511.HK Television Broadcasts Buy 30.30 28.10 1,592 HKD Dec 37.73 22.78 2.31 2.60 9.15 9.15 12.55 9.67 5.70 10.74 2.65 4.20 -38.81 -33.24
Average 26.99 20.34 1.81 1.84 3.84 3.90 9.08 7.58 7.60 9.24 3.00 3.78 -49.24 -47.58
Headline PEx PBx EV/EBITDA ROEDiv Yield Net Debt/Equity
Free cashflow
yield
Source: Deutsche Bank, closing price as of 31 Dec 2017
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Greater China SSSg trends
Figure 11: Deutsche Bank China SSSg trends by company
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
Food Retailing
6808.HK Sun Art Dec China ####
0980.HK Lianhua Dec China
601933.SS Yonghui Dec China
0814.HK Jinkelong Dec China -4%
WMT.N Walmart China Dec China
Department store
1833.HK Intime Dec China
0017.HK NWDS June China
3368.HK Parkson Dec China
0848.HK Maoye Dec China
1212.HK Lifestyle Dec China - Shanghai
1212.HK Lifestyle Dec China - Suzhou
3308.HK Golden Eagle Dec China
002419.SZ Tianhong Dec China
002419.SZ Tianhong-Shopping mall Dec China
CE retailers
0493.HK Gome Dec China
002024.SZ Suning Dec China
Cosmetics brands/retailers
0973.HK L'Occitane Mar China
0001.HK Watsons Dec China
090430.KS AmorePacific** Dec China
051900.KS LG H&H** Dec China
Apparel and footwear
0999.HK I.T. Feb China
0709.HK Giordano Dec China
0210.HK Daphne Dec China
1836.HK Stella Dec China
0738.HK Le Saunda Feb China
3306.HK JNBY Design Jun China
Luxury/International companies
3389.HK Hengdeli Dec China
0398.HK Oriental Watch** Mar China
0398.HK Oriental Watch Mar China
1929.HK Chow Tai Fook Mar China
0116.HK Chow Sang Sang Dec China +SD +DD
0590.HK Luk Fook Mar China
1913.HK Prada Jan Greater China
0887.HK Emperor Watch & Jewellery Dec China
1910.HK Samsonite** Dec China
PANDORA.CO Pandora** Dec China
COH.N Coach June Greater China
7453.JP Muji China Feb China
Sportswear
2331.HK Li Ning - Overall Dec China
2331.HK Li Ning - Retail Dec China
2331.HK Li Ning - Wholesale Dec China
2331.HK Li Ning - e-commerce Dec China
2020.HK Anta - Overall Dec China
2020.HK Anta - Adult Dec China
2020.HK Anta - kids Dec China
2020.HK Anta - e-commerce Dec China
1368.HK Xtep Dec China
1361.HK 361 Degrees Dec China
3813.HK Pou Sheng** Dec China
3813.HK Pou Sheng (DOS) Dec China
1880.HK Belle Feb China
Restaurants
YUMC.N - Overall Yum China Dec China
YUMC - KFC Yum China Dec China
YUMC - Pizza Hut Yum China Dec China
0538.HK Ajisen Dec China
0520.HK Xiabu Xiabu Dec China
3666.HK Xiao Nan Guo Dec China
1314.HK Tsui Wah Mar China
0341.HK Café de Coral Mar China
0047.HK Hop Hing Dec China
down 5-7% SD down
FY15: +3% -7% 1.2% 10.8% 15.0% +DD
-3.8% -3.8% -5.8% -7.0% -2.2% -3.2% 4.7% 9.9% 4.7% 9.7% 4%
-3.3% -4.7% 0.6% 7.2%
1.3% -0.2% Negative -1.4% -2.8% -2.00% -1.3%
-1.0% -8.0% -12.0% -11.0% 2.0% 0.0% 0% 1%*
-6.3% -0.6% -0.5% 4.2% 9.70%
12.4% 6.5% 6.0% 6.2% 5.5% 6.0% 4.6% 4.5%
14.0% 11.0% 8.0% 3.0% 7.5% 5.9% 0.4% 8.6% 2%
1.8%
-low thirties
mid-teens +20-30%
+LSD +mid teens*
14.0% 11.0% 8.0% 3.0% 7.5% 5.9% Low Single Digit +7% 19% 15%* 13.4%
91% 62%
10.0% 4.0% 3.0% 5.0% 2.0% 10% 5% 6% 2% 6%
5.0% 7.0% -9.0% Mid-High SD HSD 10% 6.0%
-5% 0% 2% -10% -19% -24%
-2887% -15% -6% 177% 9%
26% 29% 14%
MSD-HSD
-3.0% -6.3% -8.0% -13.8% -15.4% -9.9% -7.1% -15.3%
+2%
1.4%
2017
+MSD* slower
+HSD*
3.60%
+DD*
-3.10%
-9.7% -14.30%
5%
+SD similar with 1Q *+ive
+DD* +SD* +SD*
100% 125%
1.5%
7% 4-5%*
+MSD
11%
-LSD
-MSD
-MSD
up pver 30% up close to 30%
up pver 50%
-1.8%
+ive
15%
- 18.2%
-10%-10% -11%
lsd
-LSD
+ high sixties
+20%-30%
-ive DD
11%
-4.2% -57.4% -18.7%
200%+ +DD +DD +DD 340% 120%
20.6% 38.6% 22.6%
+low teens
-23.5%
+11%
msd High-teens
+MSD +Low-teens +MSD +LSD +LSD +HSD
Negative MSD flatmsd msd down MSD
32.0%
+HSD +MSD +MSD*+HSD +HSD
hsd hsd Negative LSD flat
+130s High-sixties more than doubled High-sixties low-sixties
msd
+High teens
similar to 3Q
lsd LSD down MSD
mid-nineties 50-60%
+LDD*
low-eighties
23.0%
7.9% 7.2%
+MSD
-LSD
up over 50% 50%
Up 50-60% Up over 30% Up over 30% Up over 30% Up over 30%
Up LSD Up LSD Up LSD over 10%
up over 30%
+HSD*
low-seventies
msd
+HSD
HSD
-18.4% -21.5% -22.4% -14.1% -2.3% 11.9% 9.2%
12%
11.3%
6.0% -7.0% -25.0% -17.0% -22% 4%
-5.0% -12.4%
-1.2%
2%
-15.9% -17.7% -19.7% -20.2% -6.2% -15.9%
-7.0% 7.1% -4.7% -4.1%
8.3% -2.2% -1.8% 6.6% 2.6%
0.1%
69.6% 103.2% 80.2%
4.2%
Ticker Company YE Region 2015
2.9%
0.4%
-4.2%
5.1%
9.7%
-4.3% -4.3%
1.0%
-3.5%
-4.5%
-3.4% -5.8% -9.7%
-4.8%
-12.7% -5.3%
-5.0% -13.0% -7.0%
2016
-0.3%
+1-2% >2.0%0.4% -1.5% -2.0% 5.0% 1.0% 2.5% 2.0%
-1.50%
-6.3% -6.7% 0.7% -2% -4% -1.2%
-3.2% -3.6% -3.0% -3.0% 1.3% -2.0% 0.2% -1.0%
1.4%
-0.5%
-3.4% in July -1.80% -2.5%
-16.0% -ive
-9.0%
-1.4% -0.7% -3.0% 1.4% 1.6%
-3.50%-1.6%
1.3% -2.9% 12.3%-4.8%
-10.0%
-6.4%
+LSD
+LSD
-2% 2.4%
-0.8%
flat to very LSD
2.3%
2.6% -3.4%
+ive
-1.3% 0.6%
-9~10%
-10.0%
-6.7% -5.1% -7.0% -7.0% -6% -msd
-13.1%
+MSD
+12.8%
-0.8% -6.0% -1.7%
-11.6% -7.0% -4-5% +LSD
3.22%
-4.0% 12.0%
+ 10%
-10.4% -7.3%
4.3%
2.0%
-3.3% -15.7%
-6.8% 4.0%
+12.5%
-0.7%
2.3%
-22.2%
5.6%
1.0% 3.0% 6%
+MSD+MSD +MSD +LSD
7.0%7.5% 7.0%7.3%7.0%
-1.0% 0.0%
3.2% 1.0% -3.0% -7.8% -4.3% -4.5% -19.2%
-0.4%
0.4% 2.4% 2.3%
3.7%
- -
-0.5%
-13.0%
12.0%
-10.3% -8.5% -12.5%
15.0% 2.9%
-2.70%
5.9%
14.7%13.8%13.2%
51.0% 52.2% 61.4% 54.2% 52.8% 51.0% 47.0% 40.5%
-3.0% 5.2%1.3% 3.9%
42.6% -2.5% -2.7% -4.1% 1.5% 24%
12.0% 4.0% 17.0% -5.0% 1.0% 1.0% -6.0% 3.0% 0.4%
4.9%3.5% 18.5% 0.7%
+mid 30%
-20.6% -13.1%
10.40%
-10%
27.6% 2.9% -10.5% -0.3% -0.5% 8.1% 12.7%
-7.0%
7.2%
-23% 5%
flat
-16% for 1H better than 1Q flat
15.9% 9.7% 4.7% 5.4% 0.8% 7.6% 5.8%
-14.0% -12.0% 3.0%
-12.0% -10.0% 2.0% 2.0% 6.0% 0.0%
6.0% 12.0%
Up over 60% Up over 50%
Up MSD
Up 50-100% Up over 50%
+ve hsd +ve hsd +HSD +MSD +MSD +MSD
Up HSD
6.3% 6.3% 6.3%
+3-4%
3% 8.0%
+3-4%SD down decline narrowed +mid-high SD*
-3.0%
3.0% -1.0% 1.0% 1.0% 4.0%
-10.1% -9.2% -6.6% -6.1% -9.6%-6.0%
5.2%
-4.0%-6.0% -4.0%
-9.0%
Mid-teens Flat
-8.1%
8.6% 7.0% 10.6% 3.5% -0.7% 4.9% -1.0%
15.8%
+MSD*8.6%
*flat
+MSD*
trending up
+LSD
+MSD*
+HSD*
4.3% 0.4%
30%+
-10%
-4.5% -14.5% -8.9% 5.9%
Source: Deutsche Bank estimates, company data Note: * Dbe, Ajisen sssg gross rate (inclusive of BT or VAT, as applicable); **AmorePacifc and LG H&H, Pou Sheng, Samsonite/Coach/Pandora data, Oriental Watch are total sales growth in LCY rather than SSSg; MSD-mid single digit, DD-double digit, SD-single digit
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Greater China SSSg trends
Figure 12: Deutsche Bank China SSSg trends by company
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar April May Jun Jul Aug Sept Oct Nov Dec
Department store
Dec HK - CWB
Dec HK-TST +SD*
Apparel and footwear
0999.HK I.T. Feb HK
0709.HK Giordano Dec HK
Luxury / International companies
3389.HK Hengdeli Dec HK +DD
0398.HK Oriental Watch Mar HK
1929.HK Chow Tai Fook Mar HK
0116.HK Chow Sang Sang Dec HK +DD -ive
0590.HK Luk Fook Mar HK
1913.HK Prada Jan Greater China**
0887.HK Emperor Watch
& Jewellery Dec HK
1910.HK Samsonite Dec HK
7453.JP Muji HK Feb HK
Cosmetics
0178.HK Sa Sa Mar HK
0973.HK L'Occitane Mar HK
Restaurants
0538.HK Ajisen Dec HK
1314.HK Tsui Wah Mar HK
0341.HK Café de Coral Mar HK
2017
+ive +M-HSD*
10%+* +10%+
+MSD to HSD
+LSD
+4-5%
3% >3%
Ticker Company YE Region 2015 2016
-ive HSD* +LSD
10%+*
-0.5% -1.3% -2.0% -0.2% 0.0% -0.9% -4.6% -11% -5.0%
-9%
-LSD*
-0.7%
-17.6% -1.8%
1.6% 3.4%
9.7% -15.3% -17.1% -19.5%
2.0%
-1.6% -6.8% -10.1% -12.2%
-5%
1.30%-4.8% -2.5% -2.0%
Flat single digit decline
-ive MSD -ive MSD*
12.0% 4.0% 12.0% -1.0% 1.0% 1.0% 3.0% 1.4%
+ive HSD* +ive MSD1212.HK Lifestyle
-1.4% -9.0% -5.0% 13-14% down 4-5% down
na na 11.3%
-29.1% -21.4% -3.4% +LSD
-26.0% -24.0% -13.0% -23.0% -27.0% -20.0% -30% -2%
-6%
4.5%
-low 20% -MSD*
4.2% -8.0%
-12.0% -10% -26.0% -High DD
-39%-22% -19% -7% -26% -28%
-19.1% -12.1% -11.5%
-24% -11% 5%
-5.1%
5% 3.0%
-16.9% -11.7% -15.7% -16.7% -9.1%
6.8% 4.6% -0.1% -2.0% -0.7%
-LSD* -HSD*
-1.7% -5.1%
-9%
5%
-32.7%
18.0%
-4.6%
+LSD*
<1Q, -ive
-16% for 1H better than 1Q -ive dd* less -ive vs 3Q +ive SD
-26% (1Q -31% ; 2Q -25%) + low teens-29.6% ( 1Q and 2Q similar) -40.0%
+MSD
-3.3%
FY15+9% mid-single digit 4%
>2Q
-msd
-2.9% -4.8% -1.8%16.0% 8.9% 9.8% 0.5%
5.0% 8.6% -2.9% -6.4% 12.0% 25.9%
10%
weaker than 2Q
-LSD
+MSD +MSD
10%3.3%
4%
-1.4%
-4.1%
+HSD*
12.0%
>+20%
-0.8%
flat
3%
Source: Deutsche Bank estimates, company data Note: * DBe, **Greater China means HK, Macau and China; MSD-mid single digit, DD-double digit, SD-single digit
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Macro
Continuous improvement in consumer sentiment Disposable income growth for urban areas has recovered since 4Q16 while income growth in rural areas has stabilized. The government’s moves to
increase urbanization should help drive growth in urban areas. Per capital spending in urban area has moved from: 1) quantity to quality in the same category and behavior shift; and 2) shifting to other segments, e.g.,
from basic consumption (F&B, apparel) to lifestyle/services and then to investment-related (like education, property and healthcare). Spending in rural areas remains largely on basic necessities
Anti-corruption headwinds have gradually tapered off after five years. Government/corporate spending has somewhat relaxed (e.g., Sun Art’s pre-paid cards grew at a double-digit rate during the Mid-Autumn Festival 2017).
Figure 13: Disposable income picked up in both urban and rural areas with
the latter lagging
Figure 14: The government aims to increase the Hukou-based
urbanization rate to 45% by 2020
6.0%
7.0%
8.0%
9.0%
10.0%
11.0%
12.0%
13.0%
2014
-03
2014
-06
2014
-09
2014
-12
2015
-03
2015
-06
2015
-09
2015
-12
2016
-03
2016
-06
2016
-09
2016
-12
2017
-03
2017
-06
2017
-09
Per capita disposal income for urban citizens (yoy%)
Per capita disposal income for rural citizens (yoy%)
458 465 478 486548 570 583 596 609 622
50 51.3 52.6 53.7 56.1 57.3 58 58.7 59.3 60
34.2 34.5 35.3 35.739.9 41.2 42 43 44 45
0
200
400
600
800
1000
1200
0
10
20
30
40
50
60
70
2011 2012 2013 2014 2015 2016 2017e 2018e 2019e 2020e
Hukou-based urban population (RHS)
Residency-based
Hukou-based
2020 target Urbanization rate % mn
13% CAGR
Source: Deutsche Bank, Wind Source: Deutsche Bank ,Wind
Figure 15: Wallet share shifted from basic spending to lifestyle to self-
investment spending (urban)
Figure 16: Wallet share shifted from basic spending to lifestyle and self-
investment spending (rural)
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
16.0%
2014
-03
2014
-06
2014
-09
2014
-12
2015
-03
2015
-06
2015
-09
2015
-12
2016
-03
2016
-06
2016
-09
2016
-12
2017
-03
2017
-06
2017
-09
Per capita spending on basic vs. lifestyle vs. self-investment (Urban) Per capita
spending on F&B,apparel (Urban)yoy%
Per capitaspending on dailynececcities andservice andtransportation(Urban) yoy%
Per capitaspending onhousing, educationandentertainment,healthcare and others(Urban) yoy%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
16.0%
18.0%
20.0%
2014
-03
2014
-06
2014
-09
2014
-12
2015
-03
2015
-06
2015
-09
2015
-12
2016
-03
2016
-06
2016
-09
2016
-12
2017
-03
2017
-06
2017
-09
Per capita spending on basic vs. lifestyle vs. self-investment (Rural)
Per capita spendingon F&B, apparel(Rural) yoy%
Per capita spendingon daily nececcitiesand service andtransportation(Rural)yoy%
Per capita spendingon housing,education andentertainment,healthcare and others(Rural) yoy%
Source: Deutsche Bank, NBS, Wind Source: Deutsche Bank, NBS, Wind
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Macro
Recovery spread to lower tier cities – witnessed by strong consumer confidence pick-up Consumption momentum is driven by the wealth effect (strong property market). Property prices led CCI by ~6m and the correlation is more meaningful in
Tier 3/4 cities. The two waves of tightening policies in Apr/Oct (lower tier cities focus) might cool off the property market but our economist expect the government might loosen the property market in 2Q18 to secure healthy growth.
Tier 2 and Satellite Tier 3 cities have 64% of the urban population, thus their purchasing power matters at the macro level. As the latest household survey suggests, today's Tier 3 city residents' per capita income is almost at the same level as that of the Tier 2 cities just five years ago. The increasing share of non-food expenditure suggests that s consumption upgrade is underway.
Figure 17: Property price largely led consumer
confidence index by six months in Tier 3/4 cities
with a strong correlation
Figure 18: Retail sales in Tier 3/4 have
outperformed Tier 1/2 since 3Q16
Figure 19: Satellite Tier 3 cities have similar
urban populations to Tier 2 cities
-10.0%
-5.0%
0.0%
5.0%
10.0%
15.0%
95
97
99
101
103
105
107
109
111
113
115
3Q11 1Q12 3Q12 1Q13 3Q13 1Q14 3Q14 1Q15 3Q15 1Q16 3Q16 1Q17 3Q17
Consumer confidence for Tier3/4 cities (lagging 6M)
Property price yoy% for Tier 3/4 cities (RHS)
4%
6%
8%
10%
12%
14%
16%
18%
Tier 1 ytd retail sales yoy% growth Tier 2 ytd retail sales yoy% growth
Tier 3&4 ytd retail sales yoy% growth
Tier 3&4 retail sales growth rate picks up
71
115
151
236
387
219
-
50
100
150
200
250
300
350
400
450
Tier 1 Top tier 2 Bottom tier 2 Satellite Tier 3 Tier 1,2 andsatellite Tier 3
Other urbanresidents
No. of population (m)
266m population in Tier 2 cities in total
Source: Deutsche Bank, Nielsen, Sofun Source: Deutsche Bank, Wind, data from a city portfolio built by DB economics team Source: Deutsche Bank, Note: Satellite tier-3 cities are defined as those within 1.5-hour rail travel, or 2-hour car travel to tier1 and tier 2 cities.
Figure 20: Property price also led consumer
confidence index by six months in Tier 1/2 cities
(correlation not as strong)
Figure 21: Lower tier cities contributed over 60%
of total retail sales yoy growth %
Figure 22: Wealth creation spreads to lower tier
cities
-10.0%
-5.0%
0.0%
5.0%
10.0%
15.0%
20.0%
95
100
105
110
115
120
Consumer confidence for Tier1/2 cities (lagging 6M)
Property price yoy% for Tier 1/2 cities (RHS)
11.2%
31.6%
57.2%
8.2%
30.6%
61.2%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
Tier 1 Tier 2 Tier 3 an rural areas
Contribution to retail sales growth %
2009-2011 2015-2017
50
55
60
65
70
75
80
10000 20000 30000 40000 50000 60000
2010 2015
Share of non-food
expenditure, %
Per capita disposable
income, yuan
Source: Deutsche Bank, Nielsen, Sofun Source: Deutsche Bank, Wind Source: Deutsche Bank, wind
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Macro
Consumer credit to drive consumption, structurally Bank loan data also suggest consumer confidence is very high. New short-term consumer loans soared over 300% to RMB1058bn in 10M17, despite the
recent jump, distorted by housing purchasing using consumer loans. Based on the trajectory in developed countries, consumer loan consumption should experience gradual growth.
According to a 2017 Consumer Loan Development Report jointly published by Analysis and Rongzhijia recently, the total consumer loan market (ex-mortgages) is expected to reach RMB9.8tr, accounting for 12.32% of GDP, but still much lower than 20.26% in US.
According to a CCWE consumer credit survey, among all consumer goods (apart from auto purchasing), white goods purchases are the major purchases (35.8%) using consumer credit, followed by education, traveling and home decoration, etc. The loan amount is usually one to two months’ salary. With fin-tech development, more categories and a wider population (especially youngster) will be covered.
Figure 23: Consumer loans correlated to Top 50/100 retail sales Figure 24: Consumer credit as percentage of GDP, China vs. US
15%
20%
25%
30%
35%
40%
45%
50%
-10%
-5%
0%
5%
10%
15%
20%
25%
Top 50 retail salesyoy% Top 100 retail salesyoy%
Short Term cons. credit (yoy, RHS)
Recent jump is due to consumer credit on housing purchasing
18.57% 19.09% 18.96% 19.67% 20.26%
5.33% 5.98%6.92%
7.94%
12.32%
0%
5%
10%
15%
20%
25%
2013 2014 2015 2016 2017F
China US
Source: Deutsche Bank, Wind, CEIC Source: Deutsche Bank, Analysis
Figure 25: Retail loans excluding residential mortgages Figure 26: The trend shifted to online
10%
15%
20%
25%
30%
35%
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
1Q13
2Q13
3Q13
4Q13
1Q14
2Q14
3Q14
4Q14
1Q15
2Q15
3Q15
4Q15
1Q16
2Q16
3Q16
4Q16
1Q17
2Q17
3Q17
Retail consumer loan (excluding residential mortgage, adjusted)
Retail consumer loan (excluding residential mortgage, adjusted)
yoy% (RHS)
RMBbn
0.07
0.21
0.35
0
0.2
0.4
0.6
2015 2016 2017
Consumer Credit at Online PlatformRMBtri
Source: Deutsche Bank, Wind; Note: 2017 numbers are adjusted using DBe yoy % to reflect abnormal hike due to purchase of housing Source: Deutsche Bank, Analysis
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Macro
Higher CPI – FMCG price hikes since Sept/Oct 2017 DB expects CPI to reach 2.7% in 2018. CPI inflation could rise further if food prices bottom out. More recently, non-food inflation has been rising steadily –
it reached 2.4% in Oct, an unusually high level compared to the historical average of 1.3%. Our economists forecast CPI inflation could reach 3% in early 2018 around CNY, then edge down through the rest of the year.
Price hike for FMCG products due to higher raw material prices and demand recovery. We have noticed that, since 3Q17, there have been price hikes in tissue paper, noodles, fast food and dairies and others. While these are cost driven, we believe price hikes have not affected volume growth so far.
Figure 27: Over CPI vs. food CPI Figure 28: Price transmission: PPI to CPI?
-10.0%
-5.0%
0.0%
5.0%
10.0%
15.0%
20.0%
No
v-09
Ap
r-1
0
Sep
-10
Feb
-11
Jul-
11
Dec
-11
May
-12
Oct
-12
Mar
-13
Au
g-1
3
Jan
-14
Jun
-14
No
v-14
Ap
r-1
5
Sep
-15
Feb
-16
Jul-
16
Dec
-16
May
-17
Oct
-17
CPI overall CPI food
-8
-6
-4
-2
0
2
4
6
8
10
0
1
2
3
4
5
6
7
Jan
-10
May
-10
Sep
-10
Jan
-11
May
-11
Sep
-11
Jan
-12
May
-12
Sep
-12
Jan
-13
May
-13
Sep
-13
Jan
-14
May
-14
Sep
-14
Jan
-15
May
-15
Sep
-15
Jan
-16
May
-16
Sep
-16
Jan
-17
May
-17
Sep
-17
20
18
e
CPI vs PPI to converge in 2018
cpi % CPI DBe ppi % PPI DBe Source: Deutsche Bank, NBS, Wind Source: Deutsche Bank, NBS, Wind
Figure 29: 2017 Price hike milestone timeline
Apr 2017
July 2017 Oct 2017
Sep 2017 2018
Snack
• Dali increased its
ex-factory price of
bread and cakes
products
Dairy
• Mengniu, Yili and Bright revised up ex-
factory prices by 5-10% and retail prices by
5-12% for chilled products in Shanghai
Tissue
• 4 key tissue trends rose ex-
factory price by MSD
Catering
• Yum China rose LFL price
increase 2% in Q3
CRB
• CRB Ex-factory price
increase in 2017, which is
yet to pass on to retail end
in 2018
Noodles
• Tingyi’s noodle rose ex-
factory price by 3%
(RMB 1-1.5 per case)
Source: Deutsche Bank, company data
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Industry trends
Further cut in import tariff to force “supply-side reform” positive for consumption
Imported goods benefit from trading up trend. Imports excluding processing trade rose by 22% yoy for 11M17, compared to -14/-9% in 2015/2016. This is compared to YTD retail sales growth of x% for 11M17 (vs. 10.3%/10.4% in 2015/2016).
Import tariffs for a wide range of product categories were reduced from 1 December 2017. These product categories mainly include food, health supplements, daily necessities, apparel and footwear, home appliances, culture and entertainment, groceries, etc. (details are set out in the table below). The average import tax for the covered categories has been reduced from 17.3% to 7.7%.
This creates more competition for local brands, pushing them to implement restructuring and develop new business strategies and better-quality products.
Figure 30: 4-rounds import tariff cuts for major consumer goods since June 2015 Figure 31: Imports soared but retail sales are stable
1st Cut
June 2015
12nd Cut
Jan 2016
3rd Cut
Jan 2017
4th Cut
Jan 2018
Skin care, Apparel,
Sportswear and
Footwear50% cut
Diapers 75% cut
Luggage, bags,
Aparrel and sports
shoes50% cut
Sunglasses 70% cut
Color cosmetic,
HPC, Luggages,
Apparel, sanitary
napkins, seafood,
health supplements
50% cut
Small appliances,
Wine and Alcohols
60-80%
cut
IMF, Diapers 100% cut
Tuna and Arctic
shrimptax cut
6
8
10
12
14
16
-30
-20
-10
0
10
20
30
40
201
2/0
1
201
2/0
5
201
2/0
9
201
3/0
1
201
3/0
5
201
3/0
9
201
4/0
1
201
4/0
5
201
4/0
9
201
5/0
1
201
5/0
5
201
5/0
9
201
6/0
1
201
6/0
5
201
6/0
9
201
7/0
1
201
7/0
5
201
7/0
9
Non-processing imports Retail sales, rhs3mma
yoy%
yoy%
Source: Deutsche Bank, Minister of Finance Notes: All dates are policy implementation dates Source: Deutsche Bank, NBS, Wind
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Industry trends
The new digital world embracing ecommerce as an additional revenue/profit driver
Brands with strong brand equity win. Estee Launder, Lancôme, Nike and Adidas have been doing consistently well in all channels because: 1) they fit the secular trend of consumption upgrade, and 2) they have strong brand equity.
Retailers are still struggling but benefit from cyclical recovery. Being a destination mall becomes a crucial differentiating factors in retailers’ physical real estate selection process, especially in a world of ‘Omni-channel’. Bigger concept/lifestyle flagship stores have become a crucial component of this strategy.
Figure 32: Online sales contribution to overall sales in China (reflecting
different sub-sectors’ trends and brand strategies)
Figure 33: Top 10 sportswear brands on Singles’ Day 2012-2016
(international sportswear brands taking the lead; also, in 2017 top three
players for online are the same as for offline)
1%3% 4% 5% 6%
9% 10% 10% 10% 11% 11% 12% 13% 13%15%
17% 18%
23% 24%
28%30%
0%
5%
10%
15%
20%
25%
30%
35%
2017 Online sales contribution to overall sales in China
Sportswear 2012 2013 2014 2015 2016 2017
1 Camel Nike Nike Nike Nike Nike
2 Adidas Adidas Adidas NB Adidas Adidas
3 Toread NB NB Li Ning NB Anta
4 NB Li Ning Li Ning Adidas Li Ning NB
5 Li Ning Anta X-step X-step Anta Li Ning
6 Kappa X-step Anta Anta Skechers
7 Decathlon Jordan Jordan ERKE Puma
8 X-Step Converse Converse Jordan X-Step
9 Hbr Vans ERKE 361 Degree UA
10 Nike ERKE ASICS Skechers Camel
Source: Deutsche Bank, NBS, Wind Source: Deutsche Bank, NBS, Wind
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Industry trends
Key players can gain more market share within a cost hike environment
Major raw material prices of staple companies began to increase from late-2016, e.g., paper price increased 44% yoy, milk powder increased 25% yoy, and sugar increased 8% yoy in the first 11 months of 2017. Logistics costs also increased, by 3.5% yoy. Compared to the raw material price hike in 2010-11, the key difference in this round is that it is more driven by PPI increase due to upstream supply control, rather than by retail end demand. We expect large players in each segment to gain market share from smaller players in this scenario, because: Small players have more cost pressure when industry raw material prices are increasing, as they have weaker bargaining power with suppliers, lower raw
material inventory, and less cash flow to secure low-cost inventory in advance.
Smaller players’ core competitive edge is lower pricing (as they have weaker branding and channels). Yet, when raw material prices begin to increase, smaller players have to raise their prices, and as a result lose their core competitive edge.
A stricter regulatory environment is one of the key drivers for the price hike in this round, i.e., the Ministry of Transportation strengthened its inspections on truck overloading from September 2016 (resulting in logistical cost hikes), and the Ministry of Environment Protection strengthened regulations on anti-pollution (resulting in commodity price hikes). The large players are more compliant than small players and therefore could have been less impacted by strengthening regulation.
Figure 34: Major raw material price change Figure 35: A dairy farm’s raw milk prices in different cycles
Major commodity price unit 2016 2017 YoY 2018E
PET USD/MT 851 964 13.3% ↑ 10%-20%
Aluminum USD/MT 1,611 1,973 22.5% ↑ 2%-5%
Cardboard paper USD/ton 3,284 4,746 44.5% ↓ 5%-10%
Sugar Rmb/ton 6,045 6,511 7.7% ↓ 6%-9%
Flour Rmb/kg 3.78 3.39 -10.3% ↑ 4%-5%
Palm oil MYR/MT 2,658 2,806 5.6% ↓ 2%-5%
Raw milk Rmb/kg 3.47 3.48 0.3% ↑ 3%-8%
Milk powder (WMP) USD/ton 2,463 3,070 24.6% ↓ 20%-30%
Imported barley USD/ton 233 200 -14.2% ↑ 4%-5%
Corn Rmb/ton 1,775 1,618 -8.8% ↑ 2%
Pulp Rmb/ton 3,975 4,459 12.2% ↑ 5%-8%
Crude oil USD/bushel 45.13 54.29 20.3% ↑ 9%
Logistic cost Price Index 103.4 107.0 3.5% ↑ 0%-3%
3.60 3.65 3.80
2.30
3.90
5.00
0.00
1.00
2.00
3.00
4.00
5.00
6.00
2016 (oversupply) 2017 (balanced supply) 2018 (undersupply)*
Mengniu/Yili Small brands
Source: Deutsche Bank, Wind, ndex.0256.cn Source: Deutsche Bank. Farm‘s raw milk price is based on channel check in a north dairy farming company with >10,000 herd size. 2018 number was based on estimated for illustration purpose.
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Industry trends
Two-tier market –cookie-cutter model no longer valid
Successful model now – adapted to multi-brand, multi-format and multi product assortment model vs. successful model in the past – a standardized format or a hero product, which is then rolled out /distributed nationwide.
Tier 1/2 cities – multi band, multi format focus; consumption upgrade is the secular trend that dominates the market. ASP growth is the driver.
Tier 3/4 cities – different product assortment vs. Tier 1/2 cities depending on local income level. Penetration growth is still there. There is still room for hero products. Consumption upgrade is one of the drivers.
Value for money vs. premiumization. Consumption trading up does not apply to all products for consumers as each will have its own value proposition. For example, NetEast’s Yanxuan vs. Muji; Xiaomi/ Oppo vs. iPhone. These local brands are also doing well, catering for consumers’ need for value for money. In the FMCG space, premium/mass segment will co-exist for some categories while some will just opt for premiumization.
It is only with investment in technology that a company can be flexible in its business organization and in due course cater for fragmented demand.
Figure 36: Premium segments grow faster for high brand loyalty products
-10%
-5%
0%
5%
10%
15%
20%
25%
30%
Premium category
Mass category
Source: Deutsche Bank, Bain
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Consumer staples – dairy
Entering milk shortage cycle – Buy Mengniu and Yili
Due to capacity reductions at upstream dairy farms in 2014-16. In 2016, the herd size declined by 6% nationally. Yet during our channel checks in
some northern regions, the herd size of some counties had declined by 20-30%
National raw milk price has recovered from RMB3.4 in 1H17 to RMB3.5 in 2H17
Some raw milk production regions, Heilongjiang, Hebei and Shanghai all report qoq price increases in 4Q17 vs. 3Q17
Figure 37: Number of cows and yoy change Figure 38: Raw milk price change
-15%
-10%
-5%
0%
5%
10%
15%
20%
25%
30%
35%
0
2000
4000
6000
8000
10000
12000
14000
16000
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
YoYCows
Cows YoY
2.00
2.50
3.00
3.50
4.00
4.50
5.00
5.50
Jan
-11
Jun
-11
No
v-1
1
Ap
r-1
2
Sep
-12
Feb
-13
Jul-
13
Dec
-13
May
-14
Oct
-14
Mar
-15
Au
g-1
5
Jan
-16
Jun
-16
No
v-1
6
Ap
r-1
7
Sep
-17
Feb
-18
Jul-
18
Dec
-18
May
-19
Oct
-19
RMB/kg
China domestic CMD raw milk price
Source: Deutsche Bank, Wind Source: Deutsche Bank, Wind ,company reports
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Consumer staples – dairy
Mengniu/Yili to gain more market share in new cycle
Within the undersupplied cycle, we expect a similar trend to that of the last cycle to happen in 2018-19 with leading players gaining market share, because:
First, as large dairy players, Yili’s and Mengniu's sales growth will accelerate, driven by faster market share gains. This is because small brands’ raw
milk sourcing capability is weaker during upcycles.
Secondly, while gross margins will be under pressure due to rising raw milk prices, most of this pressure can be passed on to consumers through
reducing price discounts at an early stage, and raising prices at a later stage.
Thirdly, the selling expense ratio will decline on easing competition. Yili’s and Mengniu’s financial results in 9M17 also show this trend. Thanks to an
easing off of competition and lower SG&A expense, Yili’s and Mengniu's core operating margins expanded 120bps in 9M17 and 190bps yoy in 1H17,
respectively.
Figure 39: Mengniu’s and Yili’s sales growth recover from 2017 Figure 40: Yili’s growth and margin in different cycles
-10%
-5%
0%
5%
10%
15%
20%
25%
30%
35%
1H11 2H11 1H12 2H12 1H13 2H13 1H14 2H14 1H15 2H15 1H16 2H16 1H17 2H17E
Mengniu Yili
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
30.0
40.0
50.0
60.0
70.0
80.0
90.0
2011 2012 2013 2014 2015 2016 2017E 2018E 2019E
Sales core EBIT margin
Rmb bn
Sales CAGR = 17%2011-13
Sales CAGR = 8%2014-16
Sales CAGR = 11%2017-19
Oversupply cycle
Undersupply cycle
Undersupply cycle
Source: Deutsche Bank, company reports Source: Deutsche Bank, company reports
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Consumer staples – dairy
IMF: supply-side reform to bear fruit from 2018
Due to CFDA’s registration policy, most private brands will exit the market in 2018. These private brands account for 15-20% of the total IMF market.
We expect the IMF segment to have more balanced supply and demand scenario afterwards, and registered brands to regain growth and profitability
From 2018, consolidation levels will increase due to further penetration of international leading brands and increasing brand awareness of consumers
The introduction of the second-baby policy have a positive impact on new baby numbers in 2016-17, which will drive IMF’s demand growth in 2018-
19, though new babies number growth should slow down again from 2018 due to the ageing population structure
Figure 41: IMF market share (2016) Figure 42: Newborn growth likely positive in 2017-18
Nestlé , 15% Mead
Johnson , 8%
Danone, 8%
Abbott , 8%
Yili, 6%
Friesland, 6%
Beingmate , 5%
Mengniu, 4%
Other main
brands, 22%
private brands (E),
18%
to exitmarket from 2018
16.4 16.4
16.9 16.6
17.9
19.5
20.2
19.2
1.9%0.3% 2.9%
-1.9%
7.9%
9.0%
3.0%
-5.0%-6%
-4%
-2%
0%
2%
4%
6%
8%
10%
15.0
16.0
17.0
18.0
19.0
20.0
21.0
2012 2013 2014 2015 2016 2017E 2018E 2019E
New Borns YoY Change
MillionsYoY
China New Borns
Source: Deutsche Bank, Euromonitor
Source: Deutsche Bank, chyxx.com, moh.gov.cn, E=Deutsche Bank estimates
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Consumer staples – high-end liquor
Restocking is still ON, but it is entering late part of the cycle – Buy Moutai and Wuliangye
In contrast to the market consensus that high-end liquor’s recovery from 2016 has been driven mainly by consumption upgrade, we believe channel
restocking has been a more relevant growth driver in the past two years, with channel restocking having been driven by distributors/investors’ price
hike expectations.
We estimate Moutai’s current channel inventory equals six months of Moutai's real consumption, indicating there is still room for distributors and
high-net-worth individuals to fill their warehouses. More importantly, this would leave management time to deal with the channel stocking problem.
Three events that might lead to channel destocking in 2018: 1) Moutai strictly implements a fixed price ecommerce policy; 2) Moutai raises its ex-
factory price in early 2018; and 3) the retail price rises to RMB2000 suddenly, which was the peak level in the last cycle. Before these three catalysts
happen, we expect restocking to continue driving sales growth.
Figure 43: Moutai’s retail price (2009-17) Figure 44: Moutai’s channel inventory model
500
700
900
1,100
1,300
1,500
1,700
1,900
2,100
2,300
Au
g-09
Feb-1
0
Au
g-10
Feb-1
1
Au
g-11
Feb-1
2
Au
g-12
Feb-1
3
Au
g-13
Feb-1
4
Au
g-14
Feb-1
5
Au
g-15
Feb-1
6
Au
g-16
Feb-1
7
Au
g-17
Feb-1
8
Au
g-18
Feb-1
9
Re-stocking cycle(2009-2012)
De-stocking cycle(2013-2016)
Re-stocking cycle(2017-2019)
Rmb
Moutai’s retail price history (Rmb/bottle)
(thousands tons) 2012 2013 2014 2015 2016 2017E 2018E 2019E
Channel stocking - beginning ("A") 13 14 15 14 10 6 12 18
Add: Moutai ex-factory sales ("B") 15 17 18 20 23 30 33 35
Less: retail consumption ("C") -14 -16 -20 -24 -27 -24 -27 -30
Channel stocking - ending 14 15 14 10 6 12 18 23
Channel inventory months 11 9 7 4 3 5 7 9
Actual retail price (RMB/bottle) 1,771 1,145 951 865 942 1,400 1,600 1,800
YoY 17% -35% -17% -9% 9% 49% 14% 13%
Source: Deutsche Bank, wind, jianiang.cn, baidu.com, company data; retail price after June 2017 is Deutsche Bank forecast
Source: Deutsche Bank, the parameters A and C are based on Deutsche Bank estimates
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Consumer staples – high-end liquor
High visibility on volume and ASP growth in 2018
Moutai group targets to supply over 28,000 tons of mainstream Moutai in 2018, 4% higher than its 2017 guidance of 26,800 tons. We estimate the
actual supply in 2017 will be 30,000 tons, and grow 10% to 33,000 tons in 2018.
We expect Wuliangye group to increase its premium products supply by 9% in 2018 due to its improving channel management.
We do not expect Moutai and Wuliangye to raise ex-factory prices in 2018, due to the potential risk of channel destocking. Instead, we forecast
Moutai and Wuliangye’s ASP to increase 8% and 10% yoy, respectively, helped by an increasing sales portion from super premium products.
Figure 45: Moutai’s premium products sales volume growth Figure 46: Wuliangye’s premium products sales volume growth
-15%
-10%
-5%
0%
5%
10%
15%
20%
25%
30%
35%
0.00
5,000.00
10,000.00
15,000.00
20,000.00
25,000.00
30,000.00
35,000.00
40,000.00 YoYVolume (tons)
Volume (tons) YoY
-30.0%
-20.0%
-10.0%
0.0%
10.0%
20.0%
30.0%
0.00
2,000.00
4,000.00
6,000.00
8,000.00
10,000.00
12,000.00
14,000.00
16,000.00
18,000.00
20,000.00 YoYVolume (tons)
Volume (tons) YoY
Source: Deutsche Bank, company reports
Source: Deutsche Bank, company reports
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Consumer staples – high-end liquor
Long-term growth remained solid
High-end liquor now only represents c.1% of total liquor volumes, and c.15% of total liquor industry value. We expect high-end liquor’s market share
to increase, driven by continued expansion of the high income group.
The major target consumer group is shifting from government officers’ consumption to business and private consumption
Moutai’s increasing retail price leaves more room for other high-end liquor brands to raise prices.
Figure 47: How many bottles of Moutai can an average urban resident
afford on one month’s salary Figure 48: Baijiu consumption by consumer groups
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
40%
5%
42%
50%
18%
45%
2012 2017E
Private
Business
Government
Source: Deutsche Bank. Wind
Source: Deutsche Bank, chyxx.com
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Consumer staples – brewers
Competition to remain intense in 2018
As opposed to the market’s view that competition will ease, we expect competition to remain intense in 2018, as competition usually picks up before
stabilization during periods of consolidation. If a brewer wants to gain market share organically in one region, it needs to either lower its selling prices
or increase incentives to channels or increase advertisement spending, all of which impact operating margins.
While the top four brewers’ market share increased from 44% in 1970 to 87% at end-1990, advertisement expenses per barrel of beer sold increased
from USD2.2 in 1970 to USD3.5 in 1989.
In 2018, we do not expect brewers to raise price on a like-for-like basis, and mix upgrade will be the key driver for price increases.
Figure 49: Industry margin declines though concentration level increases Figure 50: US marketing expenses continue to increase even when
consolidation levels improve
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
16.0%
18.0%
0%
10%
20%
30%
40%
50%
60%
70%
80%
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
CR4 market share vs margin
CRB Tsingtao ABI Yanjing Top 4 EBITDA margin (RHS)
40%
68%
EBITDA marginMarket share
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
30%
40%
50%
60%
70%
80%
90%
100%
19
69
19
71
19
73
19
75
19
77
19
79
19
81
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
20
01
20
03
20
05
20
07
US CR4 market share (LHS) Average advertisement expense (RHS)
68%
42%
US: marketing spending vs CR4 marekt share US$/barrel
China in 2016
Source: Deutsche Bank, Euromonitor, Company reports
Source: Deutsche Bank, Plato Logic, US Federal Trade Commission, Book” The U.S. Brewing Industry” by Victor J. Tremblay; Company reports
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Consumer staples – brewers
Mix upgrade and efficiency improvements to be core driver: Hold CR Beer; Sell Tsingtao
We forecast only 2% volume growth in 2018. China’s per capita beer consumption has been stable at about 35 litres of beer since 2011, which is
already higher than the global average and higher than other nearby markets such as Hong Kong and Taiwan.
We expect 5% ASP growth in 2018, driven by: 1) increasing portion of specialty and imported beer; 2) increasing portion of beer in aluminium cans;
and 3) major brewers continue to promote their mid/high-end products.
Major brewers will continue to improve operating efficiency through streamlining their production capacities and channel management. For example,
CRB plans to replace some of its mid-small size factories with large factories.
Figure 51: Beer production volume and growth Figure 52: Growth of average beer retail price
-30%
-20%
-10%
0%
10%
20%
30%
40%
0
100
200
300
400
500
600
700
20
10
-01
20
10
-05
20
10
-09
20
11
-01
20
11
-05
20
11
-09
20
12
-01
20
12
-05
20
12
-09
20
13
-01
20
13
-05
20
13
-09
20
14
-01
20
14
-05
20
14
-09
20
15
-01
20
15
-05
20
15
-09
20
16
-01
20
16
-05
20
16
-09
20
17
-01
20
17
-05
20
17
-09
YoY'0000 KL
Monthly beer production volume ('0000 KL) yoy
-2.0%
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
-
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
E
20
18
E
20
19
E
20
20
E
YoYRMB/ton
Retail price (RMB/ton) YoY
Source: Deutsche Bank, Wind
Source: Deutsche Bank, Euromonitor
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Consumer staples – noodles and soft drinks
High-end product to be key sales driver – Buy UPC; Hold on Tingyi
Tingyi’s and UPC’s low-end instant noodles and soft drinks recovered, growing by low-mid single in 2017, helped by increasing construction activities,
increasing market share within a cost hike environment, and hotter summers.
Premium products (i.e. >RMB5/pack noodles and >RMB4/bottle beverages) will be the main growth drivers in 2018. We forecast instant noodles and
soft drink industry sales to grow at mid-single digits in 2018, similar to the growth achieved in 2017, driven by ASP growth on product mix upgrade.
In the long term, consumers should continue to shift from low end “for fullness” demand to premium “for leisure/convenient” demand.
Figure 53: Tingyi Instant noodle sales vs. newly
started housing areas – 1Q lag (correlation =
0.94)
Figure 54: Instant noodle market share by
volume Figure 55: RTD Tea market share by volume
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
0
100
200
300
400
500
600
700
3Q
01
2Q
02
1Q
03
4Q
03
3Q
04
2Q
05
1Q
06
4Q
06
3Q
07
2Q
08
1Q
09
4Q
09
3Q
10
2Q
11
1Q
12
4Q
12
3Q
13
2Q
14
1Q
15
4Q
15
3Q
16
2Q
17
RMBmm sq m
Newly started housing area -1Q (m sq m)
Sales of Noodle (RMBm)
44.0%45.7%
47.2% 46.3%
42.5%44.2%
15.8% 16.8% 17.6% 17.4%20.0% 19.9%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
45.0%
50.0%
3Q12 3Q13 3Q14 3Q15 3Q16 3Q17
Tingyi UPC
48.3%
53.6%56.0% 56.6%
54.2%51.8%
27.8% 28.5% 27.9% 27.7% 28.5%26.3%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
45.0%
50.0%
55.0%
60.0%
3Q12 3Q13 3Q14 3Q15 3Q16 3Q17
Tingyi UPC
Source: Deutsche Bank, WInd
Source: Deutsche Bank, Tingyi presentation PPT
Source: Deutsche Bank, Tingyi presentation PPT
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Consumer staples – snack foods
Growth to be driven by new products and new channels – Buy Dali Foods; Hold on Want
According to Euromonitor, snacks foods growth will reach 15% in 2018-20, driven by consumers’ increasing leisure time. Meanwhile, given that
products cycles are getting shorter and shorter, new products are key for snacks foods companies to deliver continuous growth.
Consumer traffic is moving from traditional channels to KA channels and ecommerce channels. Companies with higher penetration in the rising
channels will gain more market share. Both Dali Foods and Want Want are increasing their penetration in modern channels/ecommerce through
channel restructuring and increasing expense supports.
Figure 56: Savoury snacks market size Figure 57: Snacks market share by channels
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
0
20,000
40,000
60,000
80,000
100,000
120,000
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
YoYRMBm
Retail Value (RMBm) YoY
68.7 69.4 69.9 70.3 70.2 66.6
30.3 28.2 25.8 24.6 23.523.2
0.3 1.7 3.5 4.3 5.6 9.5
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2011 2012 2013 2014 2015 2016
Others
Internet Retailing
Traditional channel
Modern Channels
Source: Deutsche Bank, Euromonitor
Source: Deutsche Bank, Euromonitor
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China sportswear
2018 – Accelerating industry consolidation Late stage of recovery but growth surprisingly resilient: after (a) bust cycle (2012-13), (b) low-base recovery (2014-15), sportswear retail sales entered
a late stage of recovery, characterized by greater volatility.
In 2017 growth was more resilient than our expectation due to (a) industry consolidation and (b) low base from a mini down cycle in 1H16.
2018: a year of accelerating industry consolidation
Tougher comp (a high base), but better efficiency and industry consolidation will continue to drive growth…for the leaders.
More investments: marketing expenses (Asian Games, China Olympics Committee contract…etc.) and efficiency programs (Anta’s logistics
centre…etc.).
Figure 58: Industry SSSg: more volatile exiting low-base recovery in 2014-
15, but growth rate holding surprisingly well Figure 59: Industry order book yoy vs. revenue: accelerating orderbook
growth in 1H18 hints distributors are encouraged
0%
5%
10%
15%
20%
25%
-10%
-5%
0%
5%
10%
15%
1Q10 4Q10 3Q11 2Q12 1Q13 4Q13 3Q14 2Q15 1Q16 4Q16 3Q17
Average SSSg: China Sportswear Brands (LHS)
Sales growth: Pou Sheng & Belle (RHS)
-40%
-30%
-20%
-10%
0%
10%
20%
30%
1Q10 4Q10 3Q11 2Q12 1Q13 4Q13 3Q14 2Q15 1Q16 4Q16 3Q17 2Q18
Weighted AVG orderbook YoY
Actual sales YoY
Source: Source: Deutsche Bank estimates, company data
Source: Source: Deutsche Bank estimates, company data
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China sportswear
2018 – Accelerating industry consolidation (continued)
In the short term, although low-base effects are running out, we still see slight room for retail discounts to improve in 2018.
Industry discipline appears to have improved followed by XDLong bankruptcy (2Q16), Peak lost its major distributors (1H16), Erke weakened (2H16).
But Xtep (accounts receivable provision in 2H16 and inventory buyback in 2H17) is a major downside risk to watch.
Figure 60: Retail discount summary: industry disciplined have improved due to consolidation in 2016
YoY improvement
YoY deterioration
Source 1Q16 2Q16 3Q16 4Q16 1Q17 2Q17 Jul & Aug Back to school Rest of Sep
Anta IR officers 32% 25% 30% Stable YoY 1ppt better Flat YoY
Distributor- Eastern China Jiangsu Distributor n.a. n.a. Worsened YoY Worsened YoY 4ppt worse YoY 2ppt better YoY
Distributor- Central China Anhui Distributor 2ppt worse YoY 2ppt worse YoY 1ppt worse YoY 1ppt better YoY 1.5ppt worse YoY Better YoY
Distributor- Southern China Guangdong Distributor 5ppt worse YoY 5ppt worse YoY 7-8ppt worse YoY Worsened YoY 2ppt better YoY 2ppt better YoY
Li Ning IR officers
Improved by
couple ppt QoQ,
flat YoY
low-30%-off (LSD
better YoY)40% off 3ppt better YoY Flat YoY 1.5-2ppt better YoY
Distributor- general feedbacks 1-3ppt better YoY 0-3ppt better YoY Better YoY 3-5ppt better YoY
-Retail: 40%-off (tracking behind target;
March was bad)
-Wholesale: 28%-off (improve 1.3 ppt)
-Overall discount flat YoY
n.a.
-Retail: 37%-off 3ppt better YoY
-Wholesale: 21%-off (3.2 ppt better)
-Overall discount 3 ppt better YoY
Sustainable improvement YoY
-Retail: 5ppt better YoY
-Wholesale: 2ppt better YoY
-Overall discount 3 ppt better YoY
Xtep IR officers Flat YoY Flat YoY 25%, better YoY 25%, better YoY n.a. n.a.
Distributor- general feedbacksWorsened
significantly YoYn.a. n.a. n.a.
Flat YoY (better YoY for new products,
worse YoY for old products).
Peak IR officers 30% n.a. n.a. n.a. n.a. n.a.
361 IR officers 28% 25% 25% 25% n.a. n.a.
Nike & Adidas- Pou Sheng Cross read from Really Sports 24% 20% 22% 23% 23% (1ppt better YoY) 22% (2ppt worse YoY)
Slight improvement YoY (c. 1ppt YoY)
General feedback: clothing 5 ppt
better YoY, footwear largely flat YoY
General feedback: clothing 5 ppt
better YoY, footwear slightly better
YoY
3Q17E
Slight improvement YoY (Anta brand at 29%-off)
YoY improvement sustained
Source: Deutsche Bank estimates, company data
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China sportswear
Anta – Leader’s benefits
Anta Brand: 1H18 order book growth has clearly outperformed that of the peers, allowing more room to invest in 2018.
Fila Brand: 2018 revenue growth may be more resilient than our expectation (DBe 25% yoy), increased expenses well-communicated.
Descente: a robust winter 2017 selling down jackets. May turn profitable ahead of our expectation (DBe 2019).
Efficiency programs: logistics centre soon to be completed (in 1H18), gradually (a) reducing time-to-market and (b) introducing direct supply (skipping
distributors’ warehouses).
Figure 61: Anta: Fila continues to deliver in 2018, while Descente loss may reduce faster than our expectations
2014 2015 2016 2017E 2018E 2019E 2020E
1-1. Revenue- YoY
Anta-adult 13% 1% -7% 5% 4% 3%
Anta-kids 54% 76% 35% 27% 20% 13%
E-commerce 87% 92% 81% 45% 35% 30%
Fila 50% 39% 52% 25% 20% 18%
Descente 9900% 338% 215% 60%
Consolidated 25% 20% 21% 22% 21% 18%
EBIT YoY
Anta-adult 13% -7% -4% 2% 9% 8%
Anta-kids 54% 59% 41% 23% 28% 20%
E-commerce 101% 115% 85% 46% 36% 28%
Fila 90% 65% 69% 22% 19% 16%
Descente loss loss +/- 292%
Consolidated 28% 19% 27% 20% 24% 19% Source: Deutsche Bank estimates, company data
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Cosmetics
Summary – Across-the-board recovery in 2017, but might be more selective in 2018
Sales growth for cosmetics has so far outperformed overall retail sales in 2017 due to the rocket growth in colour cosmetics, consumption
premiumization and new emerging channels. Among the brands, international brands led the way of recovery, Korean brands cooled down due to
geopolitical headwinds. Local brands continued to gain market share in the mass market segment.
Going to 2018, we still like this segment, as it caters to the consumption upgrade trend, enjoys the penetration story in lower-tier cities and has a
wider customer base (men’s). Korea cosmetics might benefit from an easier comp and ease of geopolitical pressure. Local cosmetic brands have been
and will likely be favoured by venture capital. Meanwhile, demand will likely be further fragmented and become a new driver for most brands, as
consumers generally look for products that are related to health improvement.
We have a Buy recommendation for L'Occitane and Hold on Shanghai Jahwa.
Figure 62: Cosmetics outperformed total retail sales yoy% since Mar 2017 Figure 63: China cosmetic market has experienced recovery across all
channels since 2H16 – sales growth momentum continues
2%
4%
6%
8%
10%
12%
14%
16%
18%
Jan
- Feb
13
May
-13
Aug
-13
Nov
-13
Mar
-14
Jun-
14
Sep-
14
Dec
-14
Apr
-15
Jul-
15
Oct
-15
Jan-
Feb
16
May
-16
Aug
-16
Nov
-16
Mar
-17
Jun-
17
Sep-
17
Cosmetics retail sales (ADS) vs. Overall retail sales yoy%
Cosmetics retail sales yoy% growth Total retail Sales yoy% growth
-3.5%-6.8%
-3.8%
6.3%
26%
-0.9%
-4.3%-0.6%
9.2%
40%
10.5%
0.3%5.4%
14%
37%
-10.0%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
Departmentstore
Hypermarket Supermarket CS ecommerce
Across-board recovery by channel (1H16-1H17)
1H16 2016 1H17
Source: Deutsche Bank, NBS Source: Deutsche Bank, China Market Monitor
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Cosmetics
What drives the strong recovery? 1) Category driver - colour cosmetics. Colour cosmetics grew by 21% in 1H17 vs. skin care’s growth of 7.7% (although skin care still dominated the
market with over 75% share). We believe the customer base of colour cosmetics broadened demographic wise and region wise. In addition, more
consumers in lower-tier cities started to wear make-up due to higher disposable income and the influence of Korean/Japanese pop culture.
2) Pricing driver - Prestige outperforms mass. Over the last 15 years, the prestige segment (+ 20% CAGR in sales versus + 10% in the mass segment)
drove the underlying growth in the Chinese cosmetic market. As a result, the contribution from prestige to the total mass market nearly tripled from a
mere 10% in 2002 to 27% in 2016. Prestige brands’ (mainly international brands) market share for skin care exceeded 50% for the first time in 1H17.
In addition, prestige brands’ market share for colour cosmetics was over 60% in the same period.
Figure 64: Color cosmetics grew much faster than skin care Figure 65: Top-8 gainers in prestige and mass make-up
14,447 15,563
3,828 4,636
-
5,000
10,000
15,000
20,000
25,000
1H16 1H17
Skin care/colour cosmetics sales 1H17 vs 1H16
Skin care Colour cosmetics
Skin care : +7.7%
colour: +21.1%
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
La
neíg
e
YS
L
Gio
rgio
Arm
an
i
La
ncô
me
Mac
Mak
e U
p F
or E
ver
Th
e H
isto
ry o
f W
hoo
Su
lwh
aso
o
Kan
S
Inn
isfr
ee
Cars
lan
Mari
e D
alg
ar
Ch
an
do
La
nsu
r
Etu
de
Th
e F
ace
Sh
op
Global Korean Expert Local Source: Deutsche Bank, GSK Source: Deutsche Bank, Euromonitor
Figure 66: High-end skin care exceeded 50% of total in 1H17 Figure 67: High-end color cosmetics accounts for over 60% in 1H17
43.8% 46.2% 48.2% 49.0% 52.9%
14.1% 15.3% 16.2% 15.8% 15.1%
38.5% 35.1% 32.7% 32.4% 29.4%
3.6% 3.4% 2.9% 2.8% 2.6%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2013 2014 2015 2016 1H17
Skin care breakdown % by price range
High end Mid-to-high end Mid end Low end
45.0% 49.8% 54.4% 56.1%63.7%
10.7%10.5%
11.1% 10.6%9.0%28.5%
26.2%23.4% 22.8%
19.3%
15.8% 13.5% 11.1% 10.5% 8.0%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2013 2014 2015 2016 1H17
Colour cosmetics breakdown % by price range
High end Mid-to-high end Mid end Low end
Source: Deutsche Bank, GSK Source: Deutsche Bank, GSK
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Cosmetics
What drives the strong recovery? (cont.)
3) Channel driver - Ecommerce and Cross Border Ecommerce (CBEC) Across the board we see recovery on all channels, including department stores, which have experienced a decline since 2015. However, ecommerce is
still leading the way in sales growth momentum.
International brands accelerated their online penetration and gained shares from online, according to the latest Single Day Beauty Brands Top Ten
Ranking (seven out of ten vs. four/five for 2015/2016, respectively). In reality, beauty products is the segment that sees the highest international
brands mix vs. others during Single Day promotion.
CBEC currently plays a key channel for consumers to buy foreign colour cosmetics due to new product availability and a relatively wider price gap.
Better credibility has been gradually established by online/CBEC channels due to more professional merchandizers and a more regulated environment.
Figure 68: International brand mix (among top ten brands in Single Day) by
category
Figure 69: Single Day Top 10 cosmetics brand in terms of sales
012345678
No. of international brands among Top 10 on Single Day Tmall
Cosmetics
ranking2013 2014 2015 2016 2017
1 AFU AFU Pechoin Pechoin Pechoin
2 Magic Magic Kans L'Oreal Chando
3 Yunifang Kans Olay SK-II Lancome
4 L'Oreal Pechoin AFU One Leaf Estee Lauder
5 Naruko L'Oreal Yunifang Chando Skii
6 Pechoin Yunifang L'Oreal Estee Lauder Olay
7 Olay Chando Estee Lauder KanS L'Oreal
8 Mofashijia Estee Lauder Lancôme Herborist One Leaf
9 Estee Lauder Olay Mofashijia Olay Innisfree
10 Inoherb Naruko Chando Lancôme Shiseido
Source: Deutsche Bank, eBrun Source: Deutsche Bank, eBrun. Foreign Brands are highlighted in blue
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Cosmetics
Key trends in 2018
Local brands will likely continue to gain share in mass skin care market. We expect market growth in Chinese mass skin care to remain robust in the
mid to high single-digits, but mainly on local. Nine of the top ten share gainers in Chinese mass skin care over the last five years have been local
Chinese players (with the other being Korean). While in premium/colour cosmetics, local brands will need to catch up. Top ten share gainers in
Chinese prestige skin care over the last five years have all been global players and been well outside the ‘mainstream’.
Korea brands might enjoy an easier comp in China. Amore Pacific China sales slowed down from 30% in 2Q16 to 12% 2Q17. We still believe Korean
brands remain solid in product development. We also believe the hiccups due to geopolitical issues have improved, especially after China and South
Korea's Foreign Ministry agreed on improving relations (end of Oct 2017). This is witnessed by the arrival of a packaged tour with 32 Beijing tourists to
Korea on 3 Dec, after 260 days of a tourist ban.
Niche demand, particularly health-related issues, created new demand. Over the recent years, health-related issues became one of the new growth
drivers for beauty or even the whole HPC industry due to more health issues arising from a fast-pace lifestyle, more pressure and increasing pollution.
According to a survey conducted by Euromonitor, today's time-poor consumers seek products that offer a quick fix to a specific skin/hair health
concerns (hair loss, wrinkle, etc.), with measured and proven benefits/improvements. One of the examples is the popularity of beauty devices, which
are one of most popular items people buy from overseas.
Active in capital markets. Cosmetics has been one of most vibrant subsectors in China capital market, while few of them applied for IPO or even
successfully listed until 2017. In Nov 2017, Proya (603605.SS) a local skin care Co., was listed on the Shanghai Exchange. Yunifang, a Tao brand that
mainly sells facial masks, applied for IPO as well. We believe as local brands gain market share and require more quality growth (in terms of R&D
capability and strong brand equity), there might be more IPOs in the upcoming year.
Figure 70: Top ten gainers in China mass skin care (market share %) Figure 71: Top ten gainers in China luxury skin care (market share %)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
Ch
an
do
Pech
oin
Inn
isfr
ee
On
e L
eaf
Lon
gri
ch
Kan
S
Pro
ya
Herb
ori
st
Yu
nif
an
g
Maru
bi
Global
Korean
Local
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
TH
O W
hoo
Kie
hl's
SK
-II
Su
lwh
aso
o
Lan
eíg
e
Bio
derm
a
La M
er
Core
an
a
Nu
Sk
in
LR
P
Global
Korean
Local
Source: Deutsche Bank, eBrun Source: Deutsche Bank, eBrun. Foreign Brands are highlighted in blue
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Food retailing
Summary - New Retail - More to come, more to go; efficient leaders to stay
2017 was the beginning of the "New Retail era", with capital investment pouring into the "new retail” arena. It was also regarded as a new direction
for online/offline giants to seek new growth drivers. Food retailers/hypermarkets, given their relatively high shopping frequency, high requirement on
product quality and supply chain capability, are the best arenas to introduce the "new retail“strategy, in our view.
Towards the end of 2017, there was a new wave of partner-seeking in the industry. In Dec, Sun Art announced that the first stage of its acquisition
had been completed. Separately, Tencent will take a 5% stake in Yonghui and 15% in its subsidiary, YH Cloud Innovation.
The quest will continue into 2018. At this stage, we believe Alibaba has more retailing expertise than Tencent, especially on O2O integration. While we
have Buy recommendations for both companies, we believe Sun Art has a better chance of catching up in terms of SSS and profitability by teaming
up with Alibaba. Yonghui has already done a good job on its own, with strong SSS and profitability. We prefer Sun Art at this stage.
Figure 72: Major new retail formats/new retail initiatives by major retailers
Project format New retail project The initiator of project Remarks
O2O community stores sfbest.com, Wochu
start-up new retail
project
Unmanned CVS/shelf Bingo Box, Xingbianli
start-up new retail
project
O2O supermarkets
Hema Fresh ( Alibaba),
JD.com Home (JD.com)
Online retailers' new
retail innovation
O2O community stores
Yonghui Life (previously
Yonghui membership stores),
Super Species, RT-Mart Fresh
Offline retailers' new
retail innovation
B2B wholesales business
RT-Mart e Lu Fa (Sun Art),
Jingkelong B2B (Jingkelong)
Offline retailers' new
retail innovation
B2B platform LST.1688.com (Alibaba)
Online retailers' new
retail innovation
While most of the projects are staying at investment stage and have yet to reach a
sizable scale. Similar with all start-ups in other industries, these new players tend to
be more innovative in business formats and business model but struggle with a
profitable and sustainable mandate when the business grows bigger.
Online giants like Alibaba and JD.com opened physical stores as trial by leveraging
new technologies and big data to direct traffic from online to offline. Yonghui/Sun Art
also invested into O2O facilities/stores embedded with "catering + supermarket"
format . While online players' strength is online delivery, while offline retailers are
good at supply chain management.
Some of them also expanded their business from B2C to B2B. We believe it is one of
the right initiatives to drive virtual traffic by leveraging large-scale food retailers'
supply chain capability.
Source: Deutsche Bank, company data, Bloomberg Finance LP
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Food retailing Offline traffic still declining, but stabilizing in 2017; 2018 potentially benefitting from higher CPI and virtual traffic Hypermarkets are seeing ASP hikes due to consumption upgrade. However, traffic continued to decline in 2017. We expect SSS in 2018 to be mainly driven by:
B2B, O2O brings virtual traffic. The rate of traffic decline has not accelerated, but narrowed, should we include online orders, as most of the operators are now leveraging on its existing stores’ resources to engage in online business.
Offering more catering - YH saw positive traffic in 3Q. It is now different from the past, where catering has largely been located in galleries. Premium supermarkets are dedicated to fresh foods, offering catering inside a cosy environment, which generates traffic.
Food Retailers will likely be the beneficiaries of a CPI rebound. We believe CPI will rebound in 2018 (2.7% in 2018 vs.1.7% in 2017 from DBe). This should benefit the hypermarket players, particularly leading players, like Sun Art.
Figure 73: Consumer-reduced regular bulky purchasing Figure 74: No. of visits to hypermarkets is declining yoy
36%
24%
10%
9%
5%
38%
22%
11%
11%
6%
0% 5% 10% 15% 20% 25% 30% 35% 40%
General restocking of daily necessities
Regular bulky purchasing
Daily purchasing (non-food)
Food sourcing
Emergency purchasing
Major purchase of visiting a hypermarket
2016 2015
24.5
23.4
22.3
21
21.5
22
22.5
23
23.5
24
24.5
25
2014 2015 2016
No. of visits to hypermarkets per family per year
Source: Deutsche Bank, Kantar & Bain, 2017 China Shopper Report by Bain and Kantar Source: Deutsche Bank, Kantar & Bain, 2017 China Shopper Report by Bain and Kantar
Figure 75: FMCG sales channel mix in urban area of China Figure 76: Food Retailers will be the beneficiaries of inflation
24% 37% 4% 10%2%
24%
24% 38% 4%9%
3%
22%
23% 40% 4%9%
4%
21%
22% 40% 4%8%
5%
21%
21% 40% 5% 7%7%
21%
0%
20%
40%
60%
80%
100%
Hypermarket Supermarket CVS Grocerystores
Ecommerce Others
Channel breakdown% for FMCG in urban market
2012 2013 2014 2015 2016
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
-6%
-4%
-2%
0%
2%
4%
6%
8%
10%
12%
14%
16%
FY11
1H12
2012
1H13
3Q13
FY13
1Q14
1H14
3Q14
FY14
1H15
3Q15
FY15
1Q16
2Q16
3Q16
4Q16
1Q17
2Q17
3Q17
4Q17
2018
e
Sun Art Yonghui CPI
Source: Deutsche Bank, Kantar & Bain, 2017 China Shopper Report by Bain and Kantar Source: Deutsche Bank, NBS, Company data
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Food retailing New retail format mushroomed – still testing the right business models
Unmanned stores at this stage are too advanced for Chinese consumers, given the current technology and credit system. Consumers still need
instructions to use automated systems to complete whole purchasing processes.
For O2O, we believe the current omni-channel is still yet to see material synergy between online and offline e.g. its online consumers seldom visit
Hema Fresh stores unless they would like have a meal in stores. People who purchase groceries in stores are generally not ecommerce savvy users.
That said, stores with O2O functions are able to extend their customer bases from offline to online.
Figure 77: Major online/offline integration
Date of announcement Buyer Seller Stake Consideration Implied valuation RemarksAlibaba
28/03/2014 Alibaba Investment Ltd.
(wholly owned sub of
Alibaba)
Intime (de-listed) 9.9% stake and
HKD3.7bn CB
HKD5.37bn 14x 2015PE for the
9.9% stake; 20x FY17
PE for privitization
Alibaba initially acquired a 9.9% stake in Intime at HKD7.53 (17%
lower than the last closing price before the announcement of the
deal) and HKD3.7bn CB in Mar 2014. Alibaba converted all of its CBs at
the conversion price of HK$7.13 into 535m conversion shares in
Intime on 30 June 2016. The conversion price was a 16% premium to
the closing price (HK$5.99) on 29 June. Post the conversion, Alibaba
is the largest shareholder of Intime, with a 27.8% stake, up from
10.1% before the conversion. Subsequently, Ali privatized the
company in Jan 17 at HK$10 share, a premium of 42% of closing price
before the announcement (20x FY17PE or 1.7x PB).
10/08/2015 Taobao China, a
subsidiary of Alibaba
Suning (002024.SZ, NR) 19.99% RMB28.3bn 127x 2014PE The acquisition price was RMB15.23/share, which was 10.44% higher
than Suning's last closing price before the announcement. Suning
also purchased 1.1% (27.8m shares )of Alibaba stake at RMB14bn. In
Nov 2017, It announced that it planned to dispose of 5.5m shares at
USD1bn.
21/11/2016 Hangzhou Alibaba ZETAI
Information Technology
(an indirect wholly-
owned subsidiary of
Alibaba)
Sanjiang (601116.SS, NR) 32% RMB2.15bn 69X 2015PE Acquisition price was RMB11.11/share, a 41% premium to the closing
price as of last trading day before the deal was announced (issued
new shares + share transfer)
23/12/2016 Shanghai Yiguo
Ecommerce owned by
Alibaba
Lianhua (0980.HK, NR) 18.00% RMB850m 3x 2015PB Yonghui acquired 21.17% of Lianhua at the price of HKD3.92/share
in April 2015. In Dec 2016, Shanghai Yiguo Ecommerce bought 21.17%
of the shares, at HKD4.01, 37% higher than the closing price of
Lianhua. Alibaba bought 18% of the shares of Lianhua from Shanghai
Yiguo Ecommerce (which is invested by Alibaba as well) in May 2017,
but they did not disclose the price.
26/09/2017 Alibaba Chengdu and
Hangzhou Hanyun
Xin hua du (002264.SZ,
NR)
10% not disclosed not disclosed Consideration was RMB555m, based on the closing price of the last
trading day before the announcement (RMB8.11/share).
20/11/2017 Taobao China, a
subsidiary of Alibaba
Sun Art (6808.HK, Buy) 36% RMB22.4bn 18.4/17.2x 2017/2018
PE(Dbe)
The acquisition price was HKD6.5/share, 24.4% lower than Sun Art's
last closing price.
JD.com
12/08/2015 JD.com Yonghui Superstores
(601933.SS, Buy) 11% RMB4.31bn 35x 2014PE
RMB9.0/share, 11% lower than closing price on 30 July. After
acquisation, JD will hold a 10% stake in Yonghui
20/06/2016 JD.com Yihaodian ( a subsidiary
of Walmart) 100% RMB10bn P/GMV: 0.82 (2013)
Walmart will receive 145m newly issued JD.com Class A ordinary
shares, amounting to 5% of total shares outstanding.
Tencent
11/12/2015 TencentYonghui Superstores
(601933.SS, Buy)5% RMB4.22bn 32x2018E
Tencent also will invest 15% into Yonghui Yunchuang. After the
investment, we believe Yonghui Yunchuang will potentially become
an associate of Yonghui listco (owning <50% stake).
18/12/2015 Tencent, JD.com VIP Shop 7%/5.5% US$604m/259m 19x2018E 55% premium vs last closing. Vipshop meanwhile will be granted
entry in WeChat Wallet, in the main page of JD.com and in the main
page of JD's level 1 access in WeChat discovery tab. The investment
is subject to a two-year lock-up restriction.Upon the expiration,
Tencent and JD will either have their shareholding increased to 12%
and 8% respectively, or maintain the director and board observer
rights by mutual agreement with Vipshop. Source: Deutsche Bank, company data, Bloomberg Finance LP
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Food retailing
Potential development in 2018 and beyond
Lower tier cities or suburban – large hypermarkets. We believe large-scale hypermarkets can move to suburban areas (where they should be, as in
other countries vs. in key areas in China) and lower-tier cities. Similar to Costco in the US, along with the decentralization of metropolitan areas and
increasing ownership of cars, consumers can do the bulky purchasing for a cheaper price. We believe this will further play out in 2018 in lower-tier
cities given that the consumption upgrade spilled over to lower-tier cities. Major hypermarkets are opening more stores in lower-tier cities.
Higher tier cities – community CVS stores in residential areas. Community CVS stores offer convenience and premium. CVS can secure robust growth
sheltered from ecommerce expansion. We believe this is mainly because of: 1) closest-to-customer location (community, office building,
transportation hub, etc.); 2) selective, but precise product offering and 3) natural touch-point for O2O model. It is common to see that CVS collaborates
with online retailers, such as an O2O pick-up hub. Referring to the CVS in Korea/Japan, CVS is one of the most important retail formats and has
evolved into an information/logistics hub. It also offers quality fast/processed food in a convenient location. In addition, food retailers can leverage its
existing hypermarket network and sourcing skills to expand its presence in a community CVS or more automotive CVS, although unmanned stores are
more at a conceptual stage. Auchan under Sun Art plans to roll out hundreds of around-the-clock unmanned CVSs in 2018. These new CVSs will be
quite small, with around 20sqm backed by its mature hypermarket
stores merchandising.
Premium supermarket/new-fresh food/catering format. We like
"dining+fresh food" stores, such as Super Species, which we believe
caters to the middle class consumption trend. Consumers are likely
to find value for their money, as they can enjoy a quality seafood
meal at a reasonable price (RMB100-120 per head) in a nice
environment. We believe the premium shopping/dining experience
and strong sourcing capability is the key to making a difference and
making profit in the long run.
No mature format shaped yet. After Alibaba introduced the word
"new retail" at the end of 2016, different new retail formats
mushroomed during the past year. Consumers' new demand not
only created entrepreneurial retail formats, but also pushed
traditional retailers to evolve. These new projects mainly include O2O
community stores. Meanwhile, traditional online/offline giants
started to break boundaries to seek further growth in an era of omni-
channel (please see examples in table below).
Figure 78: Product offering breakdown in “7-11” Japan
Daily food , 14%
Non-food , 30%
Processed food , 26%
Fast food, 30%
Product offering breakdown in "711" Japan
Source: Deutsche Bank, Ebrun. Foreign Brands are highlighted in blue
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Gold, jewellery and watches
China market continues to recover, benefiting luxury spending
The China retail market has recovered since 2017. The renewed market acceleration benefits luxury spending, such as jewellery and watch sales.
China is a two-tier market in terms of growth story. We saw potential in lower-tier cities sales from net store opening reacceleration.
Brands cater to customer behaviour changes and demand fragmentation.
Figure 79: Rising HSI driven by strong corporate profits is expected to lift retail sales, especially non-necessity product sales
-40%
-20%
0%
20%
40%
60%
80%
-40%
-20%
0%
20%
40%
60%
80%
Sep
-10
Nov
-10
Jan-
11M
ar-1
1M
ay-1
1Ju
l-11
Sep
-11
Nov
-11
Jan-
12M
ar-1
2M
ay-1
2Ju
l-12
Sep
-12
Nov
-12
Jan-
13M
ar-1
3M
ay-1
3Ju
l-13
Sep
-13
Nov
-13
Jan-
14M
ar-1
4M
ay-1
4Ju
l-14
Sep
-14
Nov
-14
Jan-
15M
ar-1
5M
ay-1
5Ju
l-15
Sep
-15
Nov
-15
Jan-
16M
ar-1
6M
ay-1
6Ju
l-16
Sep
-16
Nov
-16
Jan-
17M
ar-1
7M
ay-1
7Ju
l-17
Sep
-17
Nov
-17
Jan-
18
HS Index yoy% growth-6 month lag Top 100 retailers sales yoy% growth
Gold & Jewlery retail sales yoy% growth
~2 yrs cycle ~2 yrs ~2 yrs ~2 yrs*
Source: Deutsche Bank, CEIC Notes: Surprisingly, HSI are more correlated with Chinese retail sales/Gold & Jewellery retail sales than SZSH300 Index when observing the wealth effect spreading to retail sector. This could be because i) a large composition of HSI are companies which have significant market shares in China when compared to peer listed in SZ/SHEX; ii)HK stock market itself is in a stronger form than the Chinese stock market in terms of reflecting past information and future forecasts
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Gold, jewellery and watches Two tier market: a steadier recovery in lower-tier cities (reacceleration in store openings)
We saw major jewellery names being more bullish and raising their full-year store opening plans on the Mainland. More stores could be in lower-tier
cities since the recovery in tier 1/2 cities spread to lower-tier cities.
Growth in tier 1 cities driven by consumption upgrade while in lower-tier cities its driven by penetration
In Tier 1 and top Tier 2 cities, capex fell mainly on store refurbishment.
Penetration in bottom Tier 2 and 3 cities is underpinned by the acceleration of newly developed shopping malls.
Figure 80: Major jewellery names re-accelerate their store openings after a subdue 2015/2016
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
(20)
-
20
40
60
80
100
120
CTF Net store opening CTF SSS (RHS)
Net opening peak in 2Q14
-90%
-60%
-30%
0%
30%
60%
90%
120%
-10
0
10
20
30
40
50
60
70
80
LF Net store opening LF SSS (RHS)
Net opening low
Source: Deutsche Bank, company data
Figure 81: Lower-tier cities store net opening as percentage of total net
opening is expected to pick up given strong growth in retail sales value
Figure 82: 4Q mid-large shopping mall new opening in Tier 3 and below
cities as percentage of total opening increased, complementing
penetration opportunity
-6%
6%
-6%
-18%
-5%
10%
-20%
-15%
-10%
-5%
0%
5%
10%
15%
-20%
0%
20%
40%
60%
80%
100%
120%
1H15 2H15 1H16 2H16 1H17 2H17 1H18
CTF POS in Tier I CTF POS in Tier II
CTF POS in Tier III and others CTF Tier 3 and other cities RSV yoy% change
53.3%
36.4% 37.5%
50.0%
8.3%
25.0%
60.0%
53.9%56.3%
47.4%
30.0%
34.8%
41.7%
66.7%
8%
18%
28%
38%
48%
58%
68%
78%
East China Southern Central Southwest Northern Northwest Northeast
FY2016 FY2017
Source: Deutsche Bank, company data
Source: Deutsche Bank, Winshang, Linkshop; Notes: 1) mid-large scale are defined as mall with operating area above 30,000 sqm; 2) 4Q opening took up 50%/32% of 2017/16 full year opening, therefore can be viewed as a proxy of full year opening landscape
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Gold, jewellery and watches China market continues to recover
Tier 3 cities bridal demand and tier 1/2 cities daily wear jewellery demand are driving jewellery sales.
Bridal remains important. Bridal buys comprise 46% of sales. The demand is underpinned by a stable number of newly married couples.
However, daily wear is the trend (store format diversification). The rising power of female and millennial shoppers drives the demand of design-
oriented jewellery products. Mainland customers show a preference to pay premium for product design.
Figure 83: Bridal buys comprise 46% of sales; almost half of the brides in Tier 1-3
cities acquiring diamond Jewellery
Figure 84: Tier 3 bridal jewelry demand drive sales as demand in tier
1 & 2 cities reaches maturity
Bridal46%
Investment 32%
Collection14%
Others8%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
1990 2000 2010 2017
Brides who acquiringjewellery as % allbrides in tier 1-3cities
26%30%
43%
26%
37% 37%
0%
10%
20%
30%
40%
50%
Tier 1 Tier 2 Tier 3
Value
Pieces
Source: Deutsche Bank, De Beers Source: Deutsche Bank, De Beers
Figure 85: Profile of diamond jewellery
acquirers in China
Figure 86: Profile of self-purchasing by type of
product (% of all self-purchased pieces)
Figure 87: Mainland customers show preference to
jewellery products rather than gold products
30%
39%
43%
49%
27%
12%
0% 20% 40% 60% 80% 100%
1
2
18-29 30-44 45-54
Diamond jewellery acquirers
Female population
37
31
18
10
3
51
2422
1 2
45
14
37
2 2
0
10
20
30
40
50
60
Ring Earnings Necklace Bracelet Other
US
China
Japan
54%61% 56% 60%
27%23%
25%24%
13% 10% 13% 9%
6% 7% 6% 7%
0%
20%
40%
60%
80%
100%
120%
China HK China HK
Gold Gem-set Platinum/karat gold Watches
1Q18 2Q18
Source: Deutsche Bank, De Beers Source: Deutsche Bank, De Beers Source: Deutsche Bank, company data
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Gold, jewellery and watches
Brands cater for customer behavioral changes and demand fragmentation
Diversification in store formats and product offering will likely cater for demand fragmentation.
O2O strategy differentiates market leader. Online sales remain relevant; Omni-channel becomes the channel driver.
Quality assurance becomes the key differentiator for online market leaders. Retailers who make a larger effort to improve the quality control in
online purchases may support the market in the long term.
Figure 88: Chow Tai Fook Initiatives Summary
Metric Summary CTF Initiatives
-3 POS styles to meet (luxury/elegant/trendy) to meet different customer expectation to meet
different customer expectations
-Opened 4 no-gold-products, experienced-oriented stores in Hong Kong since July 2017
-[SONILOVE] line going niche for bridal (launched in May 2017)
-[Monologue] line targeting youngsters
-[T-mark] diamond brand fulfilling rising customer expectation for diamond authenticity and
transparency
-[Hearts on Fire] targeting sophisticated demand of craftsmanship and design (acquired in June
2014)
-Recorded 43% quarterly CAGR of online sales from 2QFY17-2QFY18
-In FY2018, 7-8% of the total delivery are fulfilled by local POS (deliver Jewellery directly from
nearly POS in shorter delivery time); 15-16% of the store participate in the program in 2017
-Launched [Smart+] empowering CRM and production planning
-198 T-mark POS allows customers to trace the life cycle of a diamond from sourcing to
production
Brands
O2O
Store format Differentiation
Diversified pruduct
offering and catering
for fragementation
Promoting efficiency
and authenticity
Source: Deutsche Bank, company data
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Gold, jewellery and watches
High-end watches demand remains strong
The Swiss watch industry is going through a deep destocking process, which explains the contraction seen since 2015, which continued in 1H17 (0%
FOR Swiss watch exports in 1H). Trends have shifted in 2H; exports are trending so far at +6%. A weaker CHF provides additional upside potential to
Swiss export high-end watches.
High-end performing better than mass market. Given the favourable Swiss watch industry trends, RMB appreciation, and rigid demand, despite anti-
corruption actions, price harmonization and wider price-point choices, customers from tier 1/2 cities showed a preference for high-end imported
watches.
Mass market brands unlock lower-tier cities opportunities by partnering with real estate developers to lock down new shops in newly developed mid-
high end commercial properties (shopping malls in particular).
Figure 89: Swiss watch exports picked up only from 2H17 after a silent
2015 to 1H17
Figure 90: High-end watch retailers enjoyed higher SSS than mass market
retailers
-50%
-30%
-10%
10%
30%
50%
70%
Jan-12 Jan-13 Jan-14 Jan-15 Jan-16 Jan-17
Swiss Watch Export Value (yoy %)
HK China Total
Destocking
-30%
-20%
-10%
0%
10%
20%
30%
1H2012 1H2013 1H2014 1H2015 1H2016 1H2017
Emperor SSS Hengdeli SSS Oriental Watch SSS
Source: Deutsche Bank, FHS Source: Deutsche Bank, company data
Refer to ‘Luxury goods outlook - 2018_the year of cash’ - Francesca DiPasquantonio et al (on 4 Dec 2017)’ for more details
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Home appliances
Air conditioners – Robust growth in 2018
We forecast China air conditioner sell-in volume to post 18% yoy growth for the 2018 air conditioning year (Aug. 2017 to Jul. 2018), followed by a
deceleration to c. 5% yoy in the 2019 air conditioning year.
Although volume growth may decelerate after 2018, we forecast sustainable ASP and margin expansion due to better mix
Figure 91: China air conditioner domestic sell-in volume growth: clear cycles in the past, 2018 still in upcycle
60%
39%
7%
-3% -5%
-10%
24%
19%
29%
-9%
-16%
11%
69%
29%
42%
34%
-6%-9%
2% 2%
20% 20%
1% 0%
-27% -26%
56%
63%
20%16%
5% 5%
-40%
-20%
0%
20%
40%
60%
80%
-
10,000
20,000
30,000
40,000
50,000
60,000
AC shipment (LHS) YoY (RHS)('000 units)
Source: Deutsche Bank estimates, China IOL, Note: “Pre CNY”= Aug to Feb the calendar next year , “Pre Summer” = Mar to Jan the next calendar year
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Home appliances
Air conditioner – Building leading indicators
Understanding distributors’ decision-making process helps us build leading indicators.
After distributor interviews, we learned that:
August and March cash deposits are critical: due to the production cycle of air conditioners; once an order is placed, it is difficult to alter it.
Distributors likely base their procurement decisions on: a) property sales and b) channel inventory levels in August.
Figure 92: Air conditioner timeline: key leading indicators: 1) August channel inventory conditions; 2) initial summer sell-through; and 3) GFA yoy
New air con. year
Key Events:
Drivers behind distributors' decisions:
1. Property sales
Sellthrough (one year lag vs. property sales)
2. End-of-summer inventory (sell-in less sellthrough)
Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep
Data points Industry NBS floor size sold YoY IOL August sell-in volume IOL March sell-in volume CMM summer sellthrough
& summer-end channel inventory
Results 3Q results: 1Q results:
-AR days (channel inventory) -Rebate payable (Gree)
-Rebate payable (Gree)
2nd cash deposit /
order placement
1st cash deposit /
order placement
Peak sellthrough
Sell -in (high season)Sell -in (low season)
Source: Deutsche Bank estimates
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Air conditioners– Using leading indicators to forecast demand
Channel inventory: channel inventory was likely at a low point when distributors placed orders in August 2017. According to the LHS chart below, the
lower the channel inventory at the time when distributors place orders, the stronger order growth will be in the upcoming year.
Property sales: we recognize that China’s GFA yoy has been slowing down recently; however, when distributors placed orders (during August 2017),
China’s GFA was still growing at teen-level ppt yoy (on a YTD basis).
Figure 93: Lower channel inventory at the time when distributors place
order tends to lead to better growth for the following year…
Figure 94: …the same applies to property sales
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
60%
70%
4,000
4,500
5,000
5,500
6,000
6,500
7,000
7,500
For 14
summer
For 15
summer
For 16
summer
For 17
summer
For 18
summer (E)
For 19
summer (E)
Channel inventory ahead of placing orders (LHS)
Order YoY (RHS)
-35%
-15%
5%
25%
45%
65%
-20%
-10%
0%
10%
20%
30%
40%
For 08
summer
For 10
summer
For 12
summer
For 14
summer
For 16
summer
For 18
summer (E)
YTD GFA YoY at the point when distributors place orders (LHS)
Order YoY (RHS)
Source: Deutsche Bank estimates, CEIC, China IOL. Note: China IOL inventory is the inventory held by factories, not distributors, but we assume the figure is in-line with channel inventory Source: Deutsche Bank estimates, CEIC, China IOL
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Air conditioners test our assumptions using channel inventory
Deriving channel inventory: we noticed a relationship between
channel inventory yoy vs. the gap of sell-in and sell-through
(lower RHS chart). We also identified that sell-through is
correlated with property sales (one-year lead).
Property market: to note, we adopt DB Property team’s forecast,
which suggests 2018 GFA to decline slightly yoy.
Conclusion: using the above methodology, our assumptions imply
a slight increase in channel inventory after 2018 and 2019, but
within a healthy range. This suggests that our growth assumption
is reasonable.
Figure 96: We can use GFA to forecast sell-through… Figure 97: …and using the gap between sell-in and sell-through to forecast
channel inventory
-15%
-5%
5%
15%
25%
35%
45%
-10%
-5%
0%
5%
10%
15%
20%
25%
30%
13 summer 15 summer 17 summer 19 summer (E)
YTD GFA YoY (LHS, 1 year lead) Sellthrough YoY (RHS)
-80%
-60%
-40%
-20%
0%
20%
40%
60%
80%
For 14
summer
For 15
summer
For 16
summer
For 17
summer
For 18
summer (E)
For 19
summer (E)
Period-end inventory YoY
Sell-in over sell through
Source: Deutsche Bank estimates, CEIC, company data Source: Deutsche Bank estimates, CEIC, company data
Figure 95: Our forecast model in detail For 13
summer
For 14
summer
For 15
summer
For 16
summer
For 17
summer
For 18
summer (E)
For 19
summer (E)
Conclusion:
Order (sell-in) YoY 2% 20% 0% -26% 59% 20% 5%
Factors determining distributors' decisions
1. Inventory ahead of ordering 6,350 7,322 7,087 5,453 5,000 5,750
2. Property market -7% 26% -8% 6% 26% 14% -5%
Inventory assumptions
Order YoY 2% 20% 0% -26% 59% 20% 5%
Less: sell through YoY (CMM) -4% 10% 25% 10% -5%
Sell-in over sell through (diff) 4% -36% 34% 10% 10%
Period-end inventory 6,350 7,322 7,087 5,453 5,000 5,750 6,095
Period-end inventory YoY 15% -3% -23% -8% 15% 6%
Source: Deutsche Bank estimates, CEIC, company data
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Home appliances
Air conditioner: Midea’s aggressive market share gain
Occupying distributors’ cash = taking market share: it is common
in China for local air conditioning distributors (sub-provincial level)
to operate multiple air conditioning brands at the same time.
Restocking is a major opportunity to take market share.
Midea’s strategies:
1. Aggressive cash deposit schedule by Midea 2. Using price hikes to incentivize cash deposits
Figure 99: Midea has launched front-end loaded campaigns (toward the first half of 2018 air conditioning year) to take market share
New air con. year
Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep
Deposit ratio:
Ordinary 20% 0-5% 30-40%
2018 air con. year 30-40% >20% ???
Special cash deposit /
order placement
1st cash deposit / 2nd cash deposit /
order placement order placement
Sell -in (high season)Sell -in (low season)
Source: Deutsche Bank estimates, China IOL, company data
Figure 98: Midea’s domestic sell-in volume market share
0%
10%
20%
30%
40%
50%
60%
70%
1Q08 3Q09 1Q11 3Q12 1Q14 3Q15 1Q17
Midea Gree
Source: Deutsche Bank estimates, China IOL, company data
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Home appliances
Refrigerator – 2018 could finally see some acceleration
We forecast China refrigerator domestic sales volume to accelerate slightly in 2018 and 2019.
Accelerating industry consolidation may help the industry exit a long adjustment period since 2011.
In our view, accelerating consolidation would also mean stronger profit growth for leaders.
Figure 100: China refrigerator domestic sell-in volume growth: stagnated for 6 years, but we see signs of acceleration
49%
6%
30%
50%
6%
-5%
52%
78%
20%24%
26%
-6%-9%
5%1%
-3% -3%
-10% -9%
-1%
-8%
2%
-9%
0%3% 4% 5% 5%
-20%
-10%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
-
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
2005peak
season
2005slow
season
2006peak
season
2006slow
season
2007peak
season
2007slow
season
2008peak
season
2008slow
season
2009peak
season
2009slow
season
2010peak
season
2010slow
season
2011peak
season
2011slow
season
2012peak
season
2012slow
season
2013peak
season
2013slow
season
2014peak
season
2014slow
season
2015peak
season
2015slow
season
2016peak
season
2016slow
season
2017peak
season
2017slow
season
2018peak
season
2018slow
season
2019peak
season
2019slow
season
Refrigerator shipment (LHS) YoY (RHS)('000 units)
Source: Deutsche Bank estimates, China IOL, Note: “peak season”= Mar to Jul, “slow season” = Aug to Feb the next calendar year
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Home appliances
Refrigerator – Recovering from stagnation
Growth stagnation: upper RHS chart: China’s domestic
refrigerator sell-in growth has been stagnated, even during
property upcycles in 2013 and 2016. We attribute such to:
Depleted penetration demand by government subsidy programs
in 2008 to 2012.
Unhealthy market structure: excessive supply of brands and the
unhealthily high inventory held by smaller brands.
Looking into the future: We forecast accelerating market
consolidation to revive China’s refrigerator market. Leading
brands like Haier and Midea started to accelerate their market
share gain via advanced production and design.
Figure 102: Refrigerator: relatively poor market concentration, but
improving as leaders’ share gain accelerates
Figure 103: Small refrigerator brands still hold excessive inventory (top
three brands command >50% of sales, but only 20% of industry inventory)
30%
40%
50%
60%
70%
80%
90%
1Q08 4Q08 3Q09 2Q10 1Q11 4Q11 3Q12 2Q13 1Q14 4Q14 3Q15 2Q16 1Q17
Refrigerator Air Con. Washing Mahcine
Top 3 brands' volume market share
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
1Q14 4Q14 3Q15 2Q16 1Q17
Top 3: combined volume market share
Top 3: combined inventory market share
Source: Deutsche Bank estimates, China IOL Source: Deutsche Bank estimates, China IOL
Figure 101: Refrigerator sell-in volume growth failed to echo property
upcycles in the past five years
-35%
-15%
5%
25%
45%
65%
85%
-20%
-10%
0%
10%
20%
30%
40%
50%
2006
peak
2007
peak
2008
peak
2009
peak
2010
peak
2011
peak
2012
peak
2013
peak
2014
peak
2015
peak
2016
peak
2017
peak
YTD GFA YoY (LHS) Sell-in YoY (RHS)
Source: Deutsche Bank estimates, China IOL
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Home appliances
Washing machines – Healthy growth looks sustainable
We forecast China washing machine domestic sales volume to maintain healthy growth in 2018 and 2019.
We believe leaders’ market share gain and product upgrades will continue to be prominent.
Figure 104: China washing machine domestic sell-in volume growth: sustainable growth since recovery in 2015
35%
23%
20%
2%
10%
16%
19%
38%
13%
-3%-5%
-2%
8% 8%
1% 0%
2%
5% 5% 6%7% 7%
6%
6% 5% 5%
-10%
0%
10%
20%
30%
40%
50%
-
5,000
10,000
15,000
20,000
25,000
30,000
2006slow
season
2006peak
season
2007slow
season
2007peak
season
2008slow
season
2008peak
season
2009slow
season
2009peak
season
2010slow
season
2010peak
season
2011slow
season
2011peak
season
2012slow
season
2012peak
season
2013slow
season
2013peak
season
2014slow
season
2014peak
season
2015slow
season
2015peak
season
2016slow
season
2016peak
season
2017slow
season
2017peak
season
2018slow
season
2018peak
season
2019slow
season
2019peak
season
Washing machine shipment (LHS)
YoY (RHS)
('000 units)
Source: Deutsche Bank estimates, China IOL, Note: “slow season”= Feb to Jul, “peak season”= Aug to Jan in the next calendar year
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Home appliances
Washing machines – Healthy growth looks sustainable
Less sensitive to property cycles: similar to that of refrigerators,
washing machine domestic volume growth had a weakened
correlation with property cycles. This could also be driven by
diminished penetration growth after government subsidy
programs.
Washing machine industry structure looks healthy: but unlike
refrigerators, China’s washing machine industry is characterized
by 1) higher sales concentration within the leaders (lower LHS
chart) and 2) less inventory held by smaller brands (lower RHS
chart).
Figure 106: Significant market consolidation since 2012 (the end of
government subsidy programs)
Figure 107: Reduced inventory at peripheral washing machine brands
30%
40%
50%
60%
70%
80%
90%
1Q08 4Q08 3Q09 2Q10 1Q11 4Q11 3Q12 2Q13 1Q14 4Q14 3Q15 2Q16 1Q17
Refrigerator Air Con. Washing Machine
Top 3 brands' volume market share
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
1Q14 4Q14 3Q15 2Q16 1Q17
Top 3: combined volume market share
Top 3: combined inventory market share
Source: Deutsche Bank estimates, China IOL Source: Deutsche Bank estimates, China IOL
Figure 105: Washing machine sell-in volume growth responded mildly to
the 2013 property upcycle and has been growing steadily since
-20%
-10%
0%
10%
20%
30%
40%
50%
2007
slow
2008
slow
2009
slow
2010
slow
2011
slow
2012
slow
2013
slow
2014
slow
2015
slow
2016
slow
2017
slow
Sell-in YoY YTD GFA YoY
Source: Deutsche Bank estimates, China IOL
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International companies Summary – A steady unexciting growth story
Macro trends
Geopolitical instability escalated globally; performance of travel retail is expected to be volatile Consumer behaviour change (more experience-oriented and less shopping)
Industry enablers
Asia markets drove global sales growth Consumption upgrades opportunity (premium quality, casualization trend, etc.) Increasing leveraging on digital communication with younger generation
Company spikes
Diversified brand portfolio and product innovation to cater for consumption and fragmentation Effective marketing efforts to boost sales leveraging on brand equity New distribution gains from underpenetrated brands
Figure 108: Golden week outbound travel
destination flight allocation
Figure 109: China outbound travel to Korea
experienced a 50% decrease in volume since
THAAD launched in Mar 2017
Figure 110: Deutsche Bank major currency FX
forecasts
30% 22%
18%24%
15% 16%
17% 16%
11% 12%
9% 10%
0%
20%
40%
60%
80%
100%
2016 2017
Japan&Korea* SEA Europe HK & Macau North America Others
USDvs. spot
rateEUR
vs. spot
rateUSD
vs. spot
rateEUR
vs. spot
rate
GBP 0.78 5.4% 0.91 2.5% 0.78 5.4% 0.91 2.5%
JPY 117 3.8% 137 1.3% 118 4.7% 138 2.0%
CAD 1.30 3.4% 1.52 0.7% 1.30 3.4% 1.52 0.7%
AUD 1.33 3.8% 1.56 1.5% 1.35 5.4% 1.58 2.8%
CNY 6.66 2.3% 7.79 -0.2% 6.68 2.6% 7.82 0.2%
HKD 7.80 -0.2% 9.13 -2.7% 7.80 -0.2% 9.13 -2.7%
KRW 1,080 1.2% 1,264 -1.4% 1,060 -0.7% 1,240 -3.2%
TWD 30.00 1.1% 35.10 -1.4% 29.80 0.4% 34.87 -2.1%
SGD 1.36 1.9% 1.59 -0.9% 1.34 0.4% 1.57 -2.2%
INR 65 1.8% 75 -2.1% 65 1.8% 76 -0.8%
BRL 3.23 -2.4% 3.78 -5.0% 3.25 -1.8% 3.80 -4.5%
2018 Mar 2018 June
Source: Deutsche Bank,TravelSky Notes: North Korea is included in Japan & Korea data Source: Deutsche Bank,TravelSky Source: Deutsche Bank Macro team estimates
Notes: spot price as of 29 Dec 2017; Direct quote of currency in column
Geo political instability escalated in APAC (THAAD, Thai King passed away) and Europe (terrorists attack, region independence). Luckily, Chinese
outbound travel to Europe and other APAC regions (Japan, Thailand and Vietnam, etc.) did not seemed affected (except for Korea).
China outbound travel to Korea experienced a 50% decrease in volume since THAAD launched in Mar 2017. We believe the hiccups due to geopolitical
issues may improve after China and South Korea's Foreign Ministry agreed on improving relations (end of Oct 17). This is witnessed by the arrival of a
packaged tour with 32 Beijing tourists to Korea on 3 Dec after 260 days of a tourist ban.
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International companies Uncertainties in the flow of travel spending, but expected rise in domestic spending
Consumer behaviour change (more experience-oriented and less shopping)
It is now no longer travellers’ first outbound visit. Outbound travellers reallocated more of their total travel spending to experiential activities. Price harmonization by international brands enables domestic luxury spending to grow faster than overseas spending.
We forecast that the consistent narrowing in the price gap will further boost domestic luxury consumption, reflecting a higher sales mix from China for
international companies.
Figure 111: First time outbound traveller ranked popular shopping
destinations (such as HK, Korea and Japan) as preferred first destination
Figure 112: Eight category luxury products price (index) differences
between China and Europe has substantially narrowed in the past six years
0%
10%
20%
30%
40%
HK/TW/Macau Japan/Korea SEA Europe North America Oceania
2014
2015
2016
46
84 84
45 4836 39
27 21 19 16 15 15 10 6
-40
10
60
110
160
Perfume Colorcosmetics
Skin care Apparel Footwear Bags &Luggage
Jewellery Watches
2011
2017
Source: Deutsche Bank, TravelSky Source: Deutsche Bank, Fcvip
Figure 113: Percentage of daily local spend by category proves travellers
reallocate spending to experience-oriented activities as they travel more
frequently
Figure 114: Domestics luxury consumption picked up since 2016 and is
believed to accelerate 2017 onwards
55% 53%
41%
33%
27% 25%
14%
10%
20%
30%
40%
50%
60%
Dining Sightseeing Leisure Shopping Eco/greentours
Theme park Exhibitions
2017
2016
+11%-35%
+12%
-12%
-9%
-6%
-3%
0%
3%
6%
9%
12%
15%
2013 2014 2015 2016 2017e 2018e
China domestic luxury consumption yoy% growth
China overseas luxury consumption yoy% growth
Source: Deutsche Bank, Hotels.com Source: Deutsche Bank estimates, Fcvip
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International companies
Asia remains global sales growth driver
China (ex.HK)
Consumer loyalty towards international brands for consumption upgrading needs. This is validated by a handful of international brands, as they
have reported strong growth in China in recent quarters.
Supply side reform initiatives bode well for the continued price normalization of imported goods (tax reduction policy on Nov 2017) and offline
business transformation (guidelines released on Nov 2016). We expect strong sales growth trends to continue in 2018 and onwards, as imported
goods become more competitive in price. China is expected to deliver double-digit growth for both L’Occitane and Samsonite in FY18/19, a ~2%
increase in percentage of total sales for L'Occitane and ~0.4% increase for Samsonite in FY18/19.
In the e-commerce channel, amid market’s definite optimism, we may stay vigilant against the erosion of sales and margins growth by emerging
domestic premium substitutes, which leverages on third-party manufacturing.
Japan is on solid ground in terms of percentage of global sales mix (~18% of L’Occitane’s and ~5% of Samsonite’s in FY18/19) and steady growth
from 2018 onwards.
Others countries: we expect the retail performance of India and South Korea to pick up in 2H2018 at least (the first package tour to Korea departs on 2
Dec 2017, after an eight-month travel ban).
Figure 115: China is expected to deliver DD growth while Japan takes a
more steady step
Figure 116: China eyes a longer-term shift towards a consumption-driven
economy though another round of import tariff cuts
-2%
1%
4%
7%
10%
13%
16%
19%
22%
25%
L'Occitane Samsonite Prada
China
L'Occitane Samsonite Prada
2017
2018e
2019e
Japan
Import tariff cuts for major consumer goods (part)
Major product categories Before 1 Dec 17 After 1 Dec 17
Bags and luggage 20% 10%
Apparel and footwear made by
certain materials 8-25% 5-12%
Colour cosmetics 10% 5%
Other HPC products 10% 5%
Notes: For Samsonite, strong growth in Japan in 2017 was due to acquisition of Tumi Source: Deutsche Bank, company data Source: Deutsche Bank , company data, Minister of Finance
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International companies
The US might not be as bad, tax bill suggesting a tailwind; Europe stabilization continues on a low base, but with uncertainties
US retail sales underwent a structural channel shift (more online shopping, less offline). Actual 2017 US retail sales and department store retail sales
are not as bad as the market expected. Nevertheless, we may remain cautiously optimistic regarding the elevated retail competition amid channel
shifts (which now seem outweighed short-term by favourable market dynamics).
With the tax bill on its way to make an impact, the company may start seeing benefits from easing margin pressure depending on when the tax bill
kicks in 2018 and 2019 (more visibility as we near the end of Jan 2018).
Europe may benefit from a low-base effect, but geopolitical uncertainty and a strong euro may curb sales growth and put pressures on margin
expansion, especially for a largely euro-dominated cost base.
Figure 117: US retail sales growth vs. department store/electronic
shopping sales growth
Figure 118: Sales growth (Europe operations) trends of international
companies under our coverage
-10%
-5%
0%
5%
10%
15%
20%
320,000
330,000
340,000
350,000
360,000
370,000
380,000
390,000
Jan-
13
Mar
-13
May
-13
Jul-
13
Sep
-13
Nov
-13
Jan-
14
Mar
-14
May
-14
Jul-
14
Sep
-14
Nov
-14
Jan-
15
Mar
-15
May
-15
Jul-
15
Sep
-15
Nov
-15
Jan-
16
Mar
-16
May
-16
Jul-
16
Sep
-16
Nov
-16
Jan-
17
Mar
-17
May
-17
Jul-
17
Sep
-17
US Retail sales (LHS) US Retail sales yoy% growth
Department store sales yoy% growth Electronic Shopping yoy% growth
US$ mn
-10%
-7%
-4%
-1%
2%
5%
8%
11%
14%
17%
20%
L'Occitane Samsonite Prada
2016
2017
2018e
2019e
Europe
Source: Deutsche Bank, United Sates Census Bureau Source: Deutsche Bank estimates, company data
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Restaurants
Dining out is taking more wallet share
The Chinese catering industry has recovered since 2014 and is armed with a new business model, e.g. O2O and middle class' demand of dining out.
The total catering sales were RMB3.2tr in 10M17, with 10.9% yoy growth. Overall, the catering segment is expected to grow by ~11% in 2017,
according to the government.
The contribution to total retail sales is increasing from 10.6% in 2014 to 10.9% in 10M17.
In addition, there is a correlation between Consumer Confidence Index (CCI) and catering sales, with the former leading for around two to three
months. The continuously improving CCI (starting in Tier 1 cities and then lower tier cities) suggested that sustainable growth for catering sales can be
expected in 2018.
Figure 119: Catering spending has gained wallet share since 2015 Figure 120: Consumer index leads major catering sales for around 2-3
months
11.1%
10.8%
10.6%10.7%
10.8%10.9%
13.6%
9.0%9.7%
11.7%10.8% 10.9%
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
16.0%
10.0%
10.2%
10.4%
10.6%
10.8%
11.0%
11.2%
11.4%
2012 2013 2014 2015 2016 10M17
Catering taking wallet share since 2015
% as of total retail sales yoy% (RHS)
85.0
90.0
95.0
100.0
105.0
110.0
115.0
120.0
125.0
130.0
-6.00%
-4.00%
-2.00%
0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
14.00%
16.00%
Feb-
12
May
-12
Aug
-12
Nov
-12
Feb-
13
May
-13
Aug
-13
Nov
-13
Feb-
14
May
-14
Aug
-14
Nov
-14
Feb-
15
May
-15
Aug
-15
Nov
-15
Feb-
16
May
-16
Aug
-16
Nov
-16
Feb-
17
May
-17
Aug
-17
China catering sales (ADS) (2m lagging) CCI
Source: Deutsche Bank, China Market Monitor
Source: Deutsche Bank, NBS, Nielsen
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Restaurants
Trendy restaurants are catering for consumption, but they vanish quickly
The restaurant market is fragmented with low-entry barriers. Competition exists at a
nationwide chain store level, as well as a single store level. Over the past years, the
number of restaurants grow faster to cater to ever-changing consumer appetite;
thus, the whole market became more competitive with bigger players losing market
share.
In recent years, KOL cultural and social media also created a wave of trendy
restaurants. While starting from the closure of the first trendy restaurant, which was
opened in 2013, namely "Miss Zhao does not queue", more trendy restaurants
started to claim bankruptcy or lose traffic significantly throughout the year, including
"Nice Meeting you", "Uncle Tstsu", etc.
Small players come and go, while large players with solid operating expertise and
that evolve well will remain. QSR caters to urbanization trends and will remain
because scale matters, such as Yum China.
Figure 122: China normal GDP growth percentage vs. number of fast food
outlets percentage of growth in China
Figure 123: HK normal GDP growth percentage vs. number of fast food
outlets percentage of growth in HK
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
8.0%
9.0%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
China GDP nominal growth % (RHS) Fast Food
-4.0%
-2.0%
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Fast Food Chained Fast Food HK GDP nominal growth % (RHS)
Source: Deutsche Bank, Euromonitor
Source: Deutsche Bank, Euromonitor
Figure 121: China catering market becomes more
fragmented
34%
32%
30%
27%26%
25%
15%
17%
19%
21%
23%
25%
27%
29%
31%
33%
35%
2012 2013 2014 2015 2016 10M17
China ADS catering sales as of % total catering sales
Source: Deutsche Bank, NBS
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Restaurants
Further growth driver expects to come from lower tier city recovery
According to Nielson, consumer confidence started to pick up in 3Q17. We believe this will finally reflect consumer spending. According to 2016
Consumption Trend of Residents' Small/Mid-City Report, 50% of residents who live in small/mid cities dine out at least once a month, while the
percentage is 56% in bigger cities. In addition, the average per capita spending is much lower for dining out in lower-tier cities (<RMB50) than Tier 1
cities (~RMB100). This offers potential with further urbanization.
All in all, we still like the QSR chain among all other restaurants due to its relatively higher entry barrier. In general, QSR chains require a good
operation efficiency, stronger bargaining power with suppliers and can achieve margin expansion through economy scale. Among all, leading QSR
players, i.e. Yum China and MCD, will be the major beneficiaries. In reality, YUMC and MCD’s SSS is highly correlated to disposable income growth in
lower-tier cities. In tier 1/2 cities, it is increasingly similar to other markets, where fast food (western or Chinese) is the most low cost meal, and
convenience is most important. Figure 13and Figure 14demonstrate that when consumers in tier 1/2 cities started to trade up over recent years, KFC
and MCD’s trends turned counter-cyclical when income continued to grow.
Figure 124: YUMC/MCD SSSg is highly correlated to disposable income in
lower-tier cities…
Figure 125: …the correlation has reversed in tier 1/2 cities since 4Q13
-30.0%
-20.0%
-10.0%
0.0%
10.0%
20.0%
30.0%
-5.0%
0.0%
5.0%
10.0%
15.0%
20.0%
1Q07 4Q07 3Q08 2Q09 1Q10 4Q10 3Q11 2Q12 1Q13 4Q13 3Q14 2Q15 1Q16 4Q16 3Q17
Disposable income yoy% in Tier 3/4 cities
YUM China SSSg (RHS)
MCD SSSg (RHS)
-30.0%
-20.0%
-10.0%
0.0%
10.0%
20.0%
30.0%
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
16.0%
18.0%
1Q07 4Q07 3Q08 2Q09 1Q10 4Q10 3Q11 2Q12 1Q13 4Q13 3Q14 2Q15 1Q16 4Q16 3Q17
Disposable income yoy% in Tier 1/2 cities YUM China SSSg (RHS) MCD SSSg (RHS)
Source: Deutsche Bank, Wind, Company data
Source: Deutsche Bank, Wind, Company data
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Restaurants
Embracing O2O as the right choice
O2O, as evidence of a consumption upgrade or lazy economy, has been proved as an inevitable trend, partially benefiting from relatively cheap
delivery costs for consumers in China. It also took away shares from instant noodles.
Whereby, online delivery is a way to extend a consumer base via diversifying dining time and location, which is eventually an important growth driver
for SSSg.
On the other hand, different from western countries, the China O2O delivery market is dominated by third-party platforms rather than self-owned
websites. This has created more low-priced competitors and introduced several food safety issues due to large proportions of small/mini restaurants.
CFDA issued a new Regulation of Online Catering Service Food Safety on 10 Nov to regulate the online O2O market, which will be effective from 1 Jan
2018. All of the operators that provide food on platforms or run a self-owned catering website need to have a brick-to-mortar presence. We believe
the regulation will likely benefit leading catering players like Yum China, as it will potentially lift opex for the third-party platforms, as well as small
restaurants, which might result in less price competition. Small operators who do not have brick-to-mortar presence or fail to comply with higher food
safety requirements might be pushed out.
Figure 126: Major new retail formats/new retail initiatives by major retailers Figure 127: Major new retail formats/new retail initiatives by
major retailers
China US
Delivery as % of total foodservice ~10% ~7%
Growth of delivery (CAGR,12-16) ~40% ~11%
Online ordering as % of delivery >50% ~40%
Third-party aggregator market share in online ordering >90% ~35%
6080
110
160
210
260
300
350
27%
34%
51%
29%
23%18%
15%
0%
10%
20%
30%
40%
50%
60%
0
50
100
150
200
250
300
350
400
2011 2012 2013 2014 2015 2016 2017e 2018e
User base of online delivery vs yoy%
User base of online delivery (million population) yoy%
Source: Deutsche Bank
Source: Deutsche Bank estimates, iResearch
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Textile and footwear ODMs
US sporting goods cycle should bottom soon Revenue yoy for Eclat (1476 TT; Buy), Feng Tay (9910 TT; Buy) and US apparel imports yoy all peaked in 3Q15.
An industry deep dive suggests 3Q15 was the peak of three cycles: 1) US retailer boom-bust, 2) capacity, and 3) functional sports.
In 2018, we expect US retailer cyclicality to improve while functional sports demand may remain under pressure.
Figure 128: Key cycles in summary: 3Q15 was the peak of three important cycles
2010 2011 2012 2013 2014 1Q15 2Q15 3Q15 4Q15 1Q16 2Q16 3Q16 4Q16 1Q17 2Q17 3Q17 4Q17
US Retailer boom-bust cycles Trough??
Capacity addition cycles Easing??
Functional sports cycles Accel.1st
PeakDecel. Decel.
1st
TroughAccel. Accel.
2nd
PeakDecel. Decel. Decel. Decel. Accel. Decel. Accel. Decel.
Need
innovation
Vietnam Cycle: capacity digestion
Store closure (stage 2)Peak (stage 1) Discount war (stage 3)Upcycle
Vietnam Cycle: capacity addition Peak
Source: Deutsche Bank estimates, CEIC, company data
Figure 129: Eclat’s revenue growth peaked out along with the cycles in
3Q15…
Figure 130: …so did Feng Tay
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
60%
-20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
1Q05 2Q07 3Q09 4Q11 1Q14 2Q16
US apparel import value YoY (LHS)
Eclat's sales YoY (RHS)
-30%
-20%
-10%
0%
10%
20%
30%
40%
-20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
1Q05 2Q07 3Q09 4Q11 1Q14 2Q16
US apparel import value YoY (LHS)
Feng Tay's sales YoY (RHS)
Source: Deutsche Bank estimates, CEIC, company data
Source: Deutsche Bank estimates, CEIC, company data
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Textile and footwear ODMs
US retailer boom-bust cycle (timing) Sports Authority’s bankruptcy in 1Q16 pushed US sporting goods retailers into an extraordinary bust cycle (previous one being in 2008).
However, retailers should be recovering, judging from retail discount conditions.
Figure 131: Typical timeline of a retailer boom-bust cycle: China (2012-14) vs. US (2016-18)
Source: Deutsche Bank estimates, SSI, company data
Figure 132: Retail discount could be a good indicator to call cycle trough;
US discount has been improving…
Figure 133: …this is similar to what happened in China in 2014
-6.0%
-4.0%
-2.0%
0.0%
2.0%
4.0%
6.0%
8.0%
Feb-16 May-16 Aug-16 Nov-16 Feb-17 May-17 Aug-17 Nov-17
YTD change in ASP & discount (2016)
YTD change in ASP & discount (2017)
MORE markdown
LESS markdown
10%
12%
14%
16%
18%
20%
22%
24%
26%
28%
2010 2011 2012 2013 2014 2015
Pou Sheng: retail discount(% -off)
MORE markdown
LESS markdown
Source: Deutsche Bank estimates, SSI, company data
Source: Deutsche Bank estimates, SSI, company data
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Textile and footwear ODMs
US retailer boom-bust cycle (recovery) Once the cycle bottoms, recovery in the first years tend to be strong. This should alter Eclat and Feng Tay’s underperformance.
However, the biggest risk remains on fashion: we continue to see “lifestyle” sportswear outperform “functional” sportswear.
Figure 134: Typical timeline of a retailer boom-bust cycle: China (2012-14) vs. US (2016-18)
Exposure to: 2017 YTD
Name Ticker Nike Adidas LULU Puma Uniqlo US EU China Others Functional Lifestyle US & functional total return
Eclat 1476 TT 12-15% <1% 12-15% <1% 0% 61% 29% 0% 10% 80% 20% Highest -10%
Shenzhou 2313 HK 32% 23% 0% 9% 29% 10% 22% 24% 43% 30% 70% Lowest 58%
Feng Tay 9910 TT 83% 0% 0% 0% 0% 53% 15% 10% 22% 70% 30% 2nd high 16%
Yue Yuen (ODM) 551 HK 28% 27% 0% <5% 0% 35% 28% 11% 27% 40% 60% 3rd high 22%
Client Exspoure (2016) Geographic Exposure (2016) Style Exposure
Source: Deutsche Bank estimates, Bloomberg Finance LP, company data
Figure 135: Retail sales growth re-accelerated significantly in China after
the down cycle in 2014…
Figure 136: …could US deliver a similar recovery?
-5%
0%
5%
10%
15%
20%
25%
30%
35%
5%
7%
9%
11%
13%
15%
17%
19%
21%
C1Q10 C4Q10 C3Q11 C2Q12 C1Q13 C4Q13 C3Q14 C2Q15 C1Q16 C4Q16 C3Q17
Pou Sheng & Belle revenue growth (LHS)
NKE & Adidias' average revenue growth (12 months lag) (RHS)
-10%
-5%
0%
5%
10%
15%
20%
25%
0%
2%
4%
6%
8%
10%
12%
14%
C1Q10 C4Q10 C3Q11 C2Q12 C1Q13 C4Q13 C3Q14 C2Q15 C1Q16 C4Q16 C3Q17
North America athletic retailers' revenue growth (LHS)
NKE & Adidias' average revenue growth (12 months lag) (RHS)
Source: Deutsche Bank estimates, Bloomberg Finance LP, company data
Source: Deutsche Bank estimates, Bloomberg Finance LP, company data
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Textile and footwear ODMs
China upcycle appears intact
Nike & Adidas’ channel inventory and retail sales yoy in China remains
healthy and bodes well for Shenzhou (2313 HK, Buy) and Yue Yuen
(551 HK, Buy).
We see one of the main Chinese retailers (Pou Sheng, HK$1.16; 3813
HK, Hold) closing down stores, but thus far, it appears to be company
specific (vs. an industry-wide down cycle in 2012-14).
Figure 138: China sportswear retailers’ channel inventory remains
healthy…
Figure 139: …so is the retail sales growth rate
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
-20%
0%
20%
40%
60%
80%
100%
C2Q11 C1Q12 C4Q12 C3Q13 C2Q14 C1Q15 C4Q15 C3Q16 C2Q17
NKE CN inventory YoY (LHS) Pou Sheng & Belle inventory-to-sales YoY (RHS)
-5%
0%
5%
10%
15%
20%
25%
30%
35%
5%
7%
9%
11%
13%
15%
17%
19%
21%
C1Q10 C4Q10 C3Q11 C2Q12 C1Q13 C4Q13 C3Q14 C2Q15 C1Q16 C4Q16 C3Q17
Pou Sheng & Belle revenue growth (LHS)
NKE & Adidias' average revenue growth (12 months lag) (RHS)
Source: Deutsche Bank estimates, Bloomberg Finance LP, company data Source: Deutsche Bank estimates, Bloomberg Finance LP, company data
Figure 137: Pou Sheng’s cleanup looks company-specific
13.8%
14.6%
11.6% 13.3% 13.1%
15.6%
14.3%
9.0%
6.0%
8.0%
10.0%
12.0%
14.0%
16.0%
18.0%
-3%
-2%
-1%
0%
1%
2%
3%
4%
1H14 2H14 1H15 2H15 1H16 2H16 1H17 3Q17E
Operating margin YoY (LHS)
Number of underperforming store to total stores (RHS)
Source: Deutsche Bank estimates, Bloomberg Finance LP, company data
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Hong Kong
Summary – Positive outlook: improving local consumption sentiments plus new infrastructure facilitating more traffic flow
HKRMA expects 2017/2018 retail sales to achieve yoy growth of >+2% /+3-4%
Domestic recovery
Domestic recovery since 3Q17. i) wealth effect – strong property prices and HSI; ii) Income effect – low unemployment rate at 3.2% in 2018 (DBe)
and 4% salary increase in 2018 (ECA Asia forecast)
Favorable cost ratio. Benefit from further rental cost reduction, but pressure on labor cost for selected sub-segments
Tourist spending
Recovery in Mainland tourist arrivals since 3Q16. SSSG of mass segments like cosmetics recovered
Next catalyst in 2018-2019. Infrastructure buildup (high-speed trains and HK-Zhuhai-Macao Bridge) and relaxation of IVS (individual travel
scheme)
Buy on major HK retailors Chow Tai Fook (SSS recovery offers operating leverage), Lifestyle (benefiting from domestic and tourist spending recovery),
Sa Sa (helped by SSS recovery/post warehouse relocation and leverage)
Figure 140: HK retail sales growth and key trends Figure 141: HK CCI picked up since
3Q16
Figure 142: Deutsche Bank HK macro forecasts
-15%
-10%
-5%
0%
5%
10%
90,000
100,000
110,000
120,000
130,000
140,000
HK retail sales value ($HK m) HK retails sales yoy% growth (RHS)
Touristarrivals recovery since 3Q16
Domestic recoverysince 3Q17
68
70
72
74
76
78
80
82
84
2016 2017e 2018e 2019e
GDP 2.1% 3.8% 2.0% 2.5%-3%
Private consumption 1.8% 5.5% 1.6% 1.1%
Gov't consumption 3.4% 3.5% 2.5% 2.0%
Gross fixed investment -0.3% 3.0% 4.6% 5.7%
Export 0.9% 5.7% 3.0% 1.9%
Import 1.2% 6.4% 3.1% 2.0%
CPI (ann avg) 2.4% 1.6% 3.9% 2.4%
Source: Deutsche Bank, HK Census and Statistics Department, CEIC,2017/18e are HKMRA forecasts
Source: Deutsche Bank, CEIC
Source: Deutsche Bank, Asia Economics Monthly by Kaushik Das et al (8 Dec 2017)
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Improving local consumption sentiments
Strong consumer sentiment due to:
Wealth effect (strong HSI and residential property prices) and Income effect – low unemployment rate at 3.1% in 2018 (DBe) and 4% salary increase in 2018 (ECA Asia)
Figure 143: HK Residential Property Price Index Figure 144: HSI soaring since 1Q17 Figure 145: HK unemployment rate
110
120
130
140
150
160
Jan-
14
Apr
-14
Jul-
14
Oct
-14
Jan-
15
Apr
-15
Jul-
15
Oct
-15
Jan-
16
Apr
-16
Jul-
16
Oct
-16
Jan-
17
Apr
-17
Jul-
17
Oct
-17
18,000
20,000
22,000
24,000
26,000
28,000
30,000
Jan-
14
Apr
-14
Jul-
14
Oct
-14
Jan-
15
Apr
-15
Jul-
15
Oct
-15
Jan-
16
Apr
-16
Jul-
16
Oct
-16
Jan-
17
Apr
-17
Jul-
17
Oct
-17
2.0%
2.4%
2.8%
3.2%
3.6%
4.0%
2014 2015 2016 2017e 2018e 2019e
Source: Deutsche Bank, CEIC
Source: Deutsche Bank, CEIC
Source: Deutsche Bank estimates, CEIC
Figure 146: HK Property price increase boosted discretionary retail sales growth Figure 147: Steadily increased nominal wage
-50%
-30%
-10%
10%
30%
50%
70%
Oct
-11
Jan-
12
Apr
-12
Jul-
12
Oct
-12
Jan-
13
Apr
-13
Jul-
13
Oct
-13
Jan-
14
Apr
-14
Jul-
14
Oct
-14
Jan-
15
Apr
-15
Jul-
15
Oct
-15
Jan-
16
Apr
-16
Jul-
16
Oct
-16
Jan-
17
Apr
-17
Jul-
17
HK Residential Property price Index yoy% growth (lag 3 month)
HK Gold and jewellery sales yoy%
190
195
200
205
210
215
220
225
230
1-M
ar
1-Ju
n
1-S
ep
1-D
ec
1-M
ar
1-Ju
n
1-S
ep
1-D
ec
1-M
ar
1-Ju
n
1-S
ep
1-D
ec
1-M
ar
1-Ju
n
HK Nominal Wage Index
Source: Deutsche Bank, Centaline Property, HK Census and Statistics Department
Source: Deutsche Bank, CEIC
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Hong Kong New infrastructure facilitating more traffic flow
Volume first
Tourist arrivals from the mainland recovered since 3Q16. In Feb 17, HKTB expected overall tourist growth to decline by 2.2% to 55.4m arrivals (of
which mainland arrivals will decline by 3.7% to 41m) in 2017. However, the trend has exceeded its expectations. Industry players in Oct 17 expect
overall tourist arrivals to be 3%+.
Daily necessities like cosmetics are key beneficiaries. Cosmetics sales taking 15-19% of total Mainland visitor spending in 2010/18. Stronger RMB
helps.
Figure 148: 2017 Mainland tourist arrivals should exceed HKTB
expectations
Figure 149: Mainland visitor spending picked up after a three-year consecutive
decline in growth rate
0
2,000,000
4,000,000
6,000,000
8,000,000
10,000,000
12,000,000
14,000,000
16,000,000
18,000,000
1H14 2H14 1H15 2H15 1H16 2H16 1H17 2H17e
Mainland visitor(overnight) arrivals Mainland visitor(sameday) arrivals
2015/16 avg Nov/Dec tourist arrivals 2H ytd tourist arrivlas
-40%
-20%
0%
20%
40%
60%
80%
40,000
80,000
120,000
160,000
200,000
240,000
2010 2011 2012 2013 2014 2015 2016 2017e
Mainland visitor shopping spending on cosmeticsTotal mainland visitor shopping spendingTotal Mainland visitor shopping spending yoy% growth
Source: Deutsche Bank estimates, HKTB
Source: Deutsche Bank estimates, HKTB
Figure 150: Total Mainland visitor (overnight) spending Figure 151: RMB appreciated against HKD since 1Q17
8,773 8,642 8,441
7,454 7,100
7,423
6,894
6256
5,000
6,000
7,000
8,000
9,000
1H14 2H14 1H15 2H15 1H16 2H16 1H17 2017e
1.1
1.12
1.14
1.16
1.18
1.2
1.22
2017e
2018e
Source: Deutsche Bank estimates, HKTB
Source: Deutsche Bank estimates, Bloomberg Finance LP
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New infrastructure facilitating more traffic flow (cont’d) 2018/2019 Catalysts:
I. New infrastructure – High-speed trains (operating in 3Q18) and HK-Zhuhai-Macao Bridge (operating in 1Q18) should help attract same-
day/overnight visitors. Officials expect total Mainland visitor arrivals via high-speed train to grow 49% from 3Q18 to 2021.
II. Relaxation of IVS –There are 49 cities under the IVS scheme. Macau has approved individual direct entry under group travel visa (category L). We
are hoping to see a similar favorable improvement in visa applications open to HK.
Figure 152: High-speed trains connected 5 short haul
destinations and10 long haul direct destinations
Figure 153: High-speed trains are expected to
significantly boost long-distance travel traffic,
serving the same purpose with HKTB’s initiative
of boosting overnight visitor arrivals
Figure 154: Guangdong-HK-Macau Greater Bay
Area vs. global comparisons - China's Greater
Bay Area initiative may help improve visitor
flows to Hong Kong
Sameday visit
Overnight visit
8h45
7h45
5hr15
4h30
24%
13%
43%
0%
10%
20%
30%
40%
50%
-
40,000
80,000
120,000
160,000
2016 2018e* 2021e 2031eShort-distance Long-distanceLong-distance traffic % grwoth
Traffic flow (two-way, per day) 2016 2018e 2021e 2031e
Short-distance 84,000 90,600 141,400 119,800
Shenzhen 65,400 67,500 74,000 93,400
Humen 5,900 4,800 48,000 5,800
Guangzhou 12,700 18,300 19,400 20,600
Long-distance 15,000 18,600 21,000 30,000
Total 99,000 109,200 162,400 149,800
Tokyo
Delta
New York
Delta
San Francisco
Delta
Guangdong-HK-
Macau Greater
Bay Area
Area (Mn km) 0.04 0.02 0.02 0.06
Population (Mn) 44 23 7 67
GDP (trillion US$) 1.8 1.4 0.76 1.36
Per-Capita GDP (US$ Mn) 0.04 0.07 10 0.02
Tertiarry industry as % of GDP 82.3 89.4 82.8 62.2
GDP share to country (%) 41 7.7 4.4 10.8
Freight Turnover / Mn teu 8 5 2 65
Flight Passenger turnover 1.12 1.3 0.71 1.75
No. of Forbes 500 companies 60 28 22 16
Source: Deutsche Bank , HKTB, Legislative Council of Hong Kong Source: Deutsche Bank, HKTB, Legislative Council of Hong Kong Notes: Official estimation to begin operating in 3Q18
Source: DB Gaming & Lodging team report: Lift 2018 GGR forecasts; u/g Galaxy to Buy; Wynn remains our top pick – Karen Tang
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Sales growth momentum picking up, resulting in better OPM
Evidence:
SSSg accelerated for Hengdeli, Emperor Watches and CTF in 3Q17 vs. 2Q17. Overall, gold & jewelry and watch sales have outperformed retail
sales, while SSSg for CDC (fast food chains are resilient in a down market) slowed in Apr-Sept 17 due to weaker dinner section.
End of store closure for gold and jewelry retailers, and cosmetic chains that we monitor in 2018F.
Share of local spending is trending higher compared to tourist spending in recent quarters for Sa Sa and CTF.
Advertisement expenditure has picked up.
Cost ratio to decline – 1) Rental costs for street-level stores should see their last year of rental reduction; 2) Staff costs (variable part) are likely to
increase with stronger sales performance, but the fixed cost side will likely remain. Cost ratio should decline, except for the restaurant segment.
Figure 155: End of store closure for gold and
jewelry retailers and cosmetic chains in 2018e
(19FY)
Figure 156: CTF share of local customer
spending reached four-year-high, once again
higher than tourist spending
Figure 157: Advertisement expenditure picks up
along with HK retail sales value
-4
-3
-2
-1
0
1
2
3
4
Chow Tai Fook Sa Sa
44.6% 41.3%55.1%
39.7%
56.6% 52.4% 57.4%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1HFY15 2HFY15 1HFY16 2HFY16 1HFY17 2HFY17 1HFY18
CTF-Domestic customer CTF-PRC customer
50%
-25%
-20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
Adex yoy% growth HK retails sales
Source: Deutsche Bank forecasts, company data; Notes: FY18/19 are DBe
Source: Deutsche Bank, company data
Source: Deutsche Bank, company data, Admango, HK Census and Statistics Department
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Appendix A
Macro in Charts – China
Figure 158: Deutsche Bank macro forecast Figure 159: China CPI forecast Figure 160: China PMI
24.1%
17.1%
8.3%
2.8%
-0.8%-1.4%
0.4%0.7%-0.8%
1.2%
3.9%
1.8%1.5%
4.8%5.9%
-0.7%
3.3%
5.4%
2.6%2.6%
2.0%1.4%
2.0%1.7%1.7%2.7%
-5.0%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35
40
45
50
55
60
65
May-0
5
Nov-0
5
May-0
6
Nov-0
6
May-0
7
Nov-0
7
May-0
8
Nov-0
8
May-0
9
Nov-0
9
May-1
0
Nov-1
0
May-1
1
Nov-1
1
May-1
2
Nov-1
2
May-1
3
Nov-1
3
May-1
4
Nov-1
4
May-1
5
Nov-1
5
May-1
6
Nov-1
6
May-1
7
Nov-1
7
PMI Manufacturing PMI Manufacturing - Employment sub-index Base line
Source: Deutsche Bank; 10 Nov. 2017 Asia Economics monthly by Taimur Baig, Ph.D
Source: Deutsche Bank estimates; NBS, CEIC
Source: Deutsche Bank; CEIC (monthly up to Oct. 2017)
Figure 161: China Consumer Confidence Index
vs. retail sales
Figure 162: Per capita spending vs. Per capita
disposable income yoy% for urban residents
Figure 163: Minimum wage adjustment in 2017
-5
0
5
10
15
20
25
30
95
100
105
110
115
120
Dec-07 Dec-08 Dec-09 Dec-10 Dec-11 Dec-12 Dec-13 Dec-14 Dec-15 Dec-16 Dec-17
CCI (LHS) Retail sales growth (RHS)
5%
6%
6%
7%
7%
8%
8%
9%
9%
10%
Per capita disposable income for urban residents (% yoy)
Per capita expenditure for urban residents (% yoy)
Region1st level Minimum wage
(RMB)yoy increase (%) Effective date Previous level
Shanghai 2,300 5.0% Apr-17 2,190
Shenzhen 2,130 12.4% Jun-17 1,895
Zhejiang 2,010 8.1% Dec-17 1,860
Tianjin 2,050 5.1% Jul-17 1,950
Beijing 2,000 16.3% Sep-17 1,720
Jiangsu 1,890 6.8% Jul-17 1,770
Shandong 1,810 5.8% Jun-17 1,710
Jilin 1,780 20.3% Oct-17 1,480
Inner Mongolia 1,760 7.3% Aug-17 1,640
Hubei 1,750 12.9% Nov-17 1,550
Henan 1,720 7.5% Oct-17 1,600
Fujian 1,700 13.3% Jul-17 1,500
Shanxi 1,700 4.9% Oct-17 1,620
Shaanxi 1,680 13.5% May-17 1,480
Guizhou 1,680 5.0% Jul-17 1,600
Heilongjiang 1,680 13.5% Oct-17 1,480
Jiangxi 1,680 9.8% Jan-18 1,530
Ningxia 1,660 12.2% Oct-17 1,480
Gansu 1,620 10.2% Jun-17 1,470
Liaoning 1,620 5.9% Jan-18 1,530
Hunan 1,580 13.7% Jul-17 1,390
Qinghai 1,500 18.1% May-17 1,270
Average 1,786 10.0% 1,623 Source: Deutsche Bank; CEIC
Source: Deutsche Bank; NBS, CEIC
Source: Deutsche Bank; Ministry of Human Resources and Social Security, Xinhua news
China (yoy%) 2015 2016 2017F 2018F
Real GDP 6.9 6.9 6.8 6.3
FAI (Nominal) 10.0 8.1 7.5 6.8
Retail sales (Nominal) 10.7 10.4 10.5 10.5
Exports (USD nominal) -1.0 -7.9 10.2 6.4
IP (real) 6.1 6.0 6.7 6.0
Bank Credit 16.5 10.9 10.6 11.2
CPI (average) 1.4 2.0 1.7 2.7
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Appendix B
Macro in Charts – China
Figure 164: China residential property price
Figure 165: A-share index
Figure 166: Overall retail sales, 100 large
retailers’ sales and top 50 retailers’ sales growth
-15%
-10%
-5%
0%
5%
10%
15%
20%
25%
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
Mar-
10
May-1
0Ju
l-10
Sep
-10
No
v-1
0Jan
&…
Ap
r-1
1Ju
n-1
1A
ug
-11
Oct-
11
Dec-1
1M
ar-
12
May-1
2Ju
l-12
Sep
-12
No
v-1
2Jan
&…
Ap
r-1
3Ju
n-1
3A
ug
-13
Oct-
13
Dec-1
3M
ar-
14
May-1
4Ju
l-14
Sep
-14
No
v-1
4Jan
-15
Mar-
15
May-1
5Ju
l-15
Sep
-15
No
v-1
5Jan
-16
Mar-
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May-1
6Ju
l-16
Sep
-16
No
v-1
6Jan
-17
Mar-
17
May-1
7Ju
l-17
Sep
-17
No
v-1
7
Commodity Bldg Selling Price: YTD (LHS)
Commodity Bldg Selling Price YoY change (RHS)
-
500
1,000
1,500
2,000
2,500
3,000
3,500
-
1,000
2,000
3,000
4,000
5,000
6,000
Shanghai A share Index (LHS) Shenzhen A Share Index (RHS)
-15.0%
-10.0%
-5.0%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
Jun-
10
Sep
-10
Dec
-10
Mar
-11
Jun-
11
Sep
-11
Dec
-11
Mar
-12
Jun-
12
Sep
-12
Dec
-12
Mar
-13
Jun-
13
Sep
-13
Dec
-13
Mar
-14
Jun-
14
Sep
-14
Dec
-14
Mar
-15
Jun-
15
Sep
-15
Dec
-15
Apr
-16
Jul-
16
Oct
-16
Jan-
Feb
17
May
-17
Aug
-17
Nov
-17
Top 100 retailers sales yoy Top 50 retailers sales yoy
Total retail sales yoy
Source: Deutsche Bank; NBS, Sofun (monthly data up to Oct 2017
Source: Deutsche Bank; Bloomberg Finance LP
Source: Deutsche Bank; CNCIC, CEIC
Figure 167: Concentration of major retailers in
terms of retail sales
Figure 168: China retail sales yoy %: department
stores
Figure 169: China CPI vs. China PPI
30%
35%
40%
45%
50%
55%
Jan-
10
May
-10
Sep-
10
Jan-
11
May
-11
Sep-
11
Jan-
Feb
2012
Jun-
12
Oct
-12
Mar
-13
Jul-1
3
Nov
-13
Apr-1
4
Aug-
14
Dec
-14
May
-15
Sep-
15
Jan-
Feb
2016
Jun-
16
Oct
-16
Mar
-17
Jul-1
7
Nov
-17
-20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
25%Top 50 retailers Top 100 retailers Intime
NWDS Golden Eagle
-5.0%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
-10.0%
-5.0%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
China PPI (LHS) China CPI (RHS)
Source: Deutsche Bank; NBS
Source: Deutsche Bank; NBS, company data
Source: Deutsche Bank; CNCIC, NBS
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Appendix C
Macro in Charts – China
Figure 170: Retail share by segment: 11M2011 Figure 171: Retail share by segment: 11M2017
Grains, Cooking Oil, Beverages, tobaccos
14%
Apparel11%
Cosmetics retail sales yoy% growth
2%
Jewelry, Gold and watches 3%
Goods for Daily Use4%
Home Appliances7%
Medicine5%
Stationeries and Office Accessories
2%Furniture
2%
Telecommunications Equipment
1%
Petroleum and its Derived Products
20%
Automobiles28%
Construction and Remodelling Material
2%
11M2011
Grains, Cooking Oil, Beverages, tobaccos
9% Grains, Cooking Oil7%
Beverages1%
Tobaccos2%
Apparel6%
Cosmetics retail sales yoy% growth
1%
Jewelry, Gold and watches 1%
Goods for Daily Use
2%Home Appliances
4%
Medicine4%
Stationeries and Office Accessories
2%
Furniture1%
Telecommunications Equipment
2%
Petroleum and its Derived Products
8%
Automobiles18%
Construction and Remodelling Material
1%
Others 30%
11M2017
Source: Deutsche Bank; CNCIC, CEIC
Source: Deutsche Bank; CNCIC, CEIC
Figure 172: China monthly temperature change in absolute value
Name Oct-16 Nov-16 Dec-16 Jan-17 Feb-17 Mar-17 Apr-17 May-17 Jun-17 Jul-17 Aug-17 Sep-17 Oct-17 Nov-17 Dec-17
the Northeast -1.6 -1.8 0.0 3.4 0.8 -1.3 1.9 1.7 0.0 0.6 -0.5 -0.9 1.2 3.8 11.0
Northern China -0.5 1.2 0.7 2.2 1.0 -1.1 -0.5 2.0 -0.4 1.1 -0.5 0.6 -1.1 0.7 0.2
Eastern China 0.5 -0.2 1.3 2.5 0.0 -1.0 -0.2 0.2 -0.2 1.6 -1.9 -0.7 -1.5 0.5 -1.0
Central China -0.4 0.4 0.7 2.5 0.1 -1.2 -0.4 1.8 -1.1 1.0 -0.6 -0.3 -1.3 1.1 -3.4
the Northwest -0.3 -1.3 1.6 2.3 2.0 -2.1 -0.8 2.3 -0.4 1.3 -1.4 -1.1 -0.6 1.0 5.8
the Southwest 0.8 -1.4 1.8 2.5 2.0 -1.4 -0.5 1.6 -0.8 -0.3 1.4 0.8 -1.4 0.3 -7.8
Southern China 1.2 -2.2 1.1 2.6 2.2 1.6 -2.0 -3.6 -0.6 -1.1 -0.9 1.3 -1.1 -0.3 -11.5
Average 0.0 -0.8 1.0 2.6 1.2 -0.9 -0.4 0.8 -0.5 0.6 -0.6 0.0 -0.8 1.0 -0.9 Source: Deutsche Bank; Wind; as of 31 December 2017
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Appendix D
Macro in Charts – China
Figure 173: China monthly temperature by region
Name Oct -16 Nov-16 Dec -16 Jan-17 Feb-17 Mar -17 Apr -17 May -17 Jun-17 Jul-17 Aug-17 Sep-17 Oct -17 Nov-17 Dec -17
the Nor theas t
Harbin 4.61 -9.03 -14.24 -16.44 -11.14 -1.40 9.07 16.48 20.64 24.95 21.95 15.40 6.44 -3.78 1.55
Shenyang 9.18 -1.12 -6.34 -9.00 -4.80 2.90 12.23 18.63 22.71 26.47 23.37 18.58 9.61 1.18 3.61
Changchun 6.15 -5.35 -9.32 -12.94 -8.27 0.60 11.45 19.77 20.53 24.69 23.44 16.45 7.50 -1.38 -1.98
Average 6.65 -5.17 -9.97 -12.79 -8.07 0.70 10.92 18.30 21.29 25.37 22.92 16.81 7.85 -1.33 1.06
Yoy change -20% 56% 0% -21% -9% -66% 22% 10% 0% 2% -2% -5% 18% -74% -111%
Northern China
Beijing 13.74 4.05 0.71 -1.08 2.29 8.42 17.20 23.11 25.28 27.60 25.98 22.45 12.85 4.88 -0.35
Tianjin 15.55 6.47 2.19 -0.39 3.79 9.27 17.95 23.92 26.19 29.06 27.15 24.38 14.48 6.88 -0.08
Shijiazhuang 15.26 5.97 2.24 -0.79 3.64 9.85 18.15 24.42 25.81 28.92 27.00 23.58 14.18 7.57 6.18
Jinan 16.82 8.60 3.74 1.26 4.54 9.44 17.88 24.73 26.36 28.89 26.66 24.15 15.13 9.10 -1.44
Taiyuan 12.26 4.43 -0.35 -2.81 0.43 5.42 13.57 19.45 22.64 26.24 22.29 19.60 11.42 4.50 5.23
Average 14.73 5.90 1.71 -0.76 2.94 8.48 16.95 23.13 25.26 28.14 25.82 22.83 13.61 6.59 1.91
Yoy change -3% 25% 63% -75% 52% -12% -3% 10% -2% 4% -2% 3% -8% 12% 12%
Eas tern China
Hefei 18.40 11.25 7.29 5.55 7.39 10.98 18.20 22.94 25.95 30.95 28.81 23.52 17.00 12.08 12.39
Shanghai 21.37 14.18 9.90 7.56 7.82 11.18 18.23 22.94 24.69 32.19 30.29 25.10 19.84 14.38 2.71
Hangzhou 21.18 13.53 9.35 7.44 8.07 11.47 18.68 23.19 24.48 31.92 30.82 25.28 19.31 13.87 7.05
Nanjing 18.73 11.70 7.52 5.77 6.71 10.47 15.97 16.87 24.97 30.92 22.23 23.70 17.35 12.43 7.77
Average 19.92 12.67 8.52 6.58 7.50 11.02 17.77 21.48 25.02 31.50 28.04 24.40 18.38 13.19 7.48
Yoy change 3% -2% 18% 62% 0% -8% -1% 1% -1% 5% -6% -3% -8% 4% -12%
Cent ra l China
Wuhan 18.77 11.60 7.53 6.50 7.61 11.50 18.27 22.84 25.41 30.55 28.77 23.90 17.24 12.73 -7.00
Changsha 19.58 12.62 9.52 8.34 9.16 11.98 19.05 24.50 25.38 31.23 27.52 26.35 18.52 14.05 7.11
Zhengzhou 16.24 8.97 4.89 2.71 5.57 10.24 17.95 24.66 26.33 29.97 30.37 22.88 14.92 10.20 5.55
Nanchang 21.56 14.18 7.84 9.39 10.16 12.94 19.60 23.21 25.17 31.18 28.97 27.20 20.34 14.70 10.63
Average 19.04 11.84 7.44 6.73 8.13 11.67 18.72 23.80 25.57 30.73 28.91 25.08 17.75 12.92 4.07
Yoy change -2% 4% 10% 60% 2% -9% -2% 8% -4% 4% -2% -1% -7% 9% -45%
the Nor thwes t
Urumchi 5.87 -3.53 -6.21 -11.23 -7.82 -1.81 10.10 13.08 23.43 26.53 17.79 16.85 7.50 1.65 8.05
Yinchuan 12.16 4.03 -0.52 -4.29 0.36 5.35 15.77 26.69 22.71 26.05 29.16 19.27 10.42 4.27 7.85
Lanzhou 11.47 4.92 0.50 -1.34 2.70 6.23 13.72 18.35 21.47 26.18 21.45 18.66 11.37 5.85 4.61
Xian 15.42 9.08 4.76 2.76 5.55 9.71 15.78 19.29 25.64 30.31 24.37 20.78 13.50 8.75 7.29
Hohhot 8.66 -0.47 -4.97 -8.39 -4.59 1.56 9.88 12.10 20.95 24.18 16.95 16.23 8.23 -0.92 -3.81
Xining 8.94 1.22 -3.02 -5.18 -1.20 1.69 11.07 22.84 15.24 19.60 25.87 12.63 8.16 1.80 1.45
Average 10.42 2.54 -1.58 -4.61 -0.83 3.79 12.72 18.73 21.57 25.47 22.60 17.40 9.86 3.57 4.24
Yoy change -3% -33% -50% -33% -71% -36% -6% 14% -2% 5% -6% -6% -5% 40% -369%
the Southwes t
Chengdu 18.87 12.73 9.47 8.00 9.11 12.34 18.60 22.55 24.19 27.34 26.58 22.60 16.76 13.33 -8.60
Chongqing 20.89 14.58 11.60 10.58 10.71 13.98 20.43 23.45 25.71 31.39 32.08 25.10 19.26 15.95 6.21
Guiyang 17.60 11.58 8.39 6.48 7.63 9.42 18.82 27.89 20.74 23.87 29.55 22.05 16.68 11.95 8.60
Nanning 26.52 19.48 17.26 16.34 16.64 17.68 23.00 26.37 28.76 29.10 30.95 29.65 24.77 19.45 12.21
Kunming 18.29 13.75 10.74 11.05 11.86 13.82 16.90 19.16 21.40 20.37 20.98 20.55 17.53 12.87 0.21
Average 20.43 14.43 11.49 10.49 11.19 13.45 19.55 23.88 24.16 26.41 28.03 23.99 19.00 14.71 3.73
Yoy change 4% -9% 19% 32% 21% -9% -2% 7% -3% -1% 5% 3% -7% 2% -68%
Southern China
Shenzhen 26.68 21.28 18.69 18.10 17.38 19.92 21.11 18.68 28.90 28.66 22.18 29.17 25.65 20.92 10.05
Guangzhou 26.56 19.95 17.45 16.94 16.59 18.94 22.88 26.15 29.12 29.11 30.16 29.58 25.31 19.68 -0.89
Fuzhou 25.24 19.23 15.11 13.87 12.75 14.85 20.40 24.27 26.62 30.84 30.90 29.70 24.18 19.20 5.58
Haikou 26.82 23.23 20.94 20.19 19.34 23.77 24.53 23.92 30.22 28.79 30.79 29.20 25.79 22.73 11.53
Average 26.33 20.93 18.05 17.28 16.51 19.37 22.23 23.25 28.72 29.35 28.51 29.41 25.23 20.63 6.57
Yoy change 5% -10% 7% 18% 16% 9% -8% -13% -2% -4% -3% 4% -4% -1% -64%
Others
Hong Kong 26.73 21.98 19.11 18.45 17.64 19.85 22.33 23.32 28.93 28.65 29.58 29.03 25.87 21.48 5.73
Macau 26.89 21.88 19.08 18.55 17.64 20.11 23.52 26.48 29.07 28.81 29.37 29.14 25.66 21.35 2.23
Taibei 26.90 22.83 19.32 17.94 16.66 18.82 23.13 25.16 28.50 30.74 29.53 29.97 26.50 22.65 0.60
Average 26.84 22.23 19.17 18.31 17.32 19.60 22.99 24.99 28.83 29.40 29.49 29.38 26.01 21.83 2.85
Yoy change 4% -6% 6% 16% 15% 8% -3% -6% 0% -2% 1% 4% -3% -2% -85% Source: Deutsche Bank; Wind; as of 31 December 2017
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Appendix E
Macro in Charts – HK
Figure 174: Deutsche Bank macro forecast
Figure 175: Hong Kong CPI forecast
Figure 176: Consumer confidence vs. HK retail
sales
Hong Kong (yoy %) 2015 2016F 2017F 2018F
Real GDP 2.4 2.0 3.8 3.0
Private consumption 4.8 1.8 4.5 2.2
Gov't consumption 3.4 3.4 3.3 2.5
Gross fixed investment -3.2 -0.3 4.2 5.6
Export -1.4 0.9 6.4 4.3
Import -1.8 1.2 6.9 4.2
CPI (average) 3.0 2.4 1.7 4.4
2.3
5.3
4.1
4.3
4.4
3.0
2.4
1.7
4.4
2.4
7
5.8
4.4
4.1
4
3.4
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
10A 11A 12A 13A 14A 15A 16A 17E 18E
CPI CPI forecast CPI food
-15%
-10%
-5%
0%
5%
10%
15%
20%
25%
30%
35%
-60%
-40%
-20%
0%
20%
40%
60%
80%
100%
Index of Consumer Sentiment(LHS) HK retail sales (RHS)
Source: Deutsche Bank; 30 Nov. 2017 Asia Economics monthly by Taimur Baig, Ph.D
Source: Deutsche Bank; NBS
Source: Deutsche Bank; CEIC
Figure 177: Consumer confidence vs. private
consumption
Figure 178: HK total number of inbound tourists
and mainland tourists
Figure 179: HK tourist arrivals – Same-day vs.
Overnight
-10%
-5%
0%
5%
10%
15%
20%
-60%
-40%
-20%
0%
20%
40%
60%
80%
100%
Index of Consumer Confidence (LHS) Private consumption expenditure yoy%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
60%
Oct-
09
Ap
r-10
Oct-
10
Ap
r-11
Oct-
11
Ap
r-12
Oct-
12
Ap
r-13
Oct-
13
Ap
r-14
Oct-
14
Ap
r-15
Oct-
15
Ap
r-16
Oct-
16
Ap
r-17
Oct-
17
Visitor Arrivals: Total China Visitor Arrivals: Total
-30.0%
-20.0%
-10.0%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
Visitor Arrivals: Sameday (SD) Visitor Arrivals: Total
Source: Deutsche Bank; CEIC
Source: Deutsche Bank; HKTB, CEIC
Source: Deutsche Bank; HKTB, CEIC
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Appendix F
Macro in Charts – HK
Figure 180: HK Disposable income growth vs.
retail sales
Figure 181: HK unemployment rate
Figure 182: Residential property prices vs. HK
retail sales yoy %
-15%
-10%
-5%
0%
5%
10%
15%
20%
25%
30%
35%
2Q
01
4Q
01
2Q
02
4Q
02
2Q
03
4Q
03
2Q
04
4Q
04
2Q
05
4Q
05
2Q
06
4Q
06
2Q
07
4Q
07
2Q
08
4Q
08
2Q
09
4Q
09
2Q
10
4Q
10
2Q
11
4Q
11
2Q
12
4Q
12
2Q
13
4Q
13
2Q
14
4Q
14
2Q
15
4Q
15
2Q
16
4Q
16
2Q
17
HK Disposable Income % YoY Retail sales % YoY
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
Unemployment Rate % Unemployment Rate forcast %
-30%
-20%
-10%
0%
10%
20%
30%
40%
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
180.0
Centa City Leading Index (LHS) HK retail sales yoy (RHS)
Source: Deutsche Bank; CEIC , HK Census and Statistics Department
Source: Deutsche Bank; NBS
Source: Deutsche Bank; Centreline, HK government data
Figure 183: HSI Index vs. HK retail sales yoy %
Figure 184: Mainland tourist arrivals in HK:
Same day vs. Overnight – Overnight is
performing better
Figure 185: HK CPI vs. PPI
-30%
-20%
-10%
0%
10%
20%
30%
40%
0
5000
10000
15000
20000
25000
30000
35000
Oct
-05
Feb
-06
Jun-
06O
ct-0
6F
eb-0
7Ju
n-07
Oct
-07
Feb
-08
Jun-
08O
ct-0
8F
eb-0
9Ju
n-09
Oct
-09
Feb
-10
Jun-
10O
ct-1
0F
eb-1
1Ju
n-11
Oct
-11
Feb
-12
Jun-
12O
ct-1
2F
eb-1
3Ju
n-13
Oct
-13
Feb
-14
Jun-
14O
ct-1
4F
eb-1
5Ju
n-15
Oct
-15
Feb
-16
Jun-
16O
ct-1
6F
eb-1
7Ju
n-17
Oct
-17
HSI Index (LHS) HK retail sales yoy (RHS)
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
60%
Oct
-12
Jan
-13
Ap
r-1
3
Jul-
13
Oct
-13
Jan
-14
Ap
r-1
4
Jul-
14
Oct
-14
Jan
-15
Ap
r-1
5
Jul-
15
Oct
-15
Jan
-16
Ap
r-1
6
Jul-
16
Oct
-16
Jan
-17
Ap
r-1
7
Jul-
17
Oct
-17
Visitor Arrivals: ON: China Visitor Arrivals: SD: China Visitor Arrivals: Total China
-6.0%
-4.0%
-2.0%
0.0%
2.0%
4.0%
6.0%
8.0%
-100%
-50%
0%
50%
100%
150%
200%
250%
300%
HK PPI (LHS) HK CPI (RHS)
Source: Deutsche Bank; Bloomberg Finance LP, HK government data
Source: Deutsche Bank; HKTB, CEIC
Source: Deutsche Bank; CEIC
Gre
ate
r Ch
ina C
on
sum
er
Co
nsu
mer
4 J
an
uary
20
18
Pag
e 8
4
Deu
tsch
e B
an
k A
G/H
on
g K
on
g
Appendix G
Macro in Charts – HK
Figure 186: Mainland tourist spending in HK: Same day vs. Overnight Figure 187: HK retail sales vs. tourist arrivals yoy%
-80%
-60%
-40%
-20%
0%
20%
40%
60%
0
20000
40000
60000
80000
100000
120000
140000
2011 2012 2013 2014 2015 2016 1H17*
Overnight mainland customer shopping spending
Sameday mainland customer shopping spending
yoy% growth (overnight)
yoy% growth (sameday)
HKD
-7.9%
-22.8%
-0.8%
2.7%
-6.3%
1.4%
5.3%
-9.3%
-2.6%-2.8%0.5%
7.7%
14.1%
-7.0%
12.6%
2.8%
10.3%
0.6%
7.1%
2.5%
6.3%
10.00%
-6.6%
-20.6%
-9.8%-7.5%-8.4%-8.9%-7.7%
-10.5%
-4.0%-2.9%-5.5%
-1.0%
-5.7%-3.2%
3.0%0.1% -1.0% 0.1%
4.0% 2.7%5.7%
3.6%
-30.0%
-20.0%
-10.0%
0.0%
10.0%
20.0%
30.0%
40.0%
Jan-
16
Feb
-16
Mar
-16
Apr
-16
May
-16
Jun-
16
Jul-
16
Aug
-16
Sep
-16
Oct
-16
Nov
-16
Dec
-16
Jan-
17
Feb
-17
Mar
-17
Apr
-17
May
-17
Jun-
17
Jul-
17
Aug
-17
Sep
-17
Oct
-17
Mainland tourist arrivals yoy% HK retail sales yoy%
Source: Deutsche Bank; CEIC
Source: Deutsche Bank; CEIC
Figure 188: HK retail sales vs. online retail sales yoy% growth
Figure 189: Domestic spending vs. visitor spending as a percentage of
total HK retail sales 65%
22%
27%
35%
28%
17%
5%
26% 26%
32%
18%
12%
7% 8%10%
12%
15%
19%
24%
-2%
8%
5%
9%10%
8%
2%
14%
21%
10%11%
0%
-3% -4%
1% 1% 2% 2% 3%
-10%
0%
10%
20%
30%
40%
50%
60%
70%Online retail sales yoy% Total retail sales yoy%
70% 66% 67% 65% 62% 58% 62% 65% 65% 65% 65%
30% 34% 33% 35% 38% 42% 38% 35% 35% 35% 35%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2009 2010 2011 2012 2013 2014 2015 2016 2017e 2018e 2019e
Domestic spending as % of total HK retail sales Visitor shopping spending as % of total HK retail sales
12'-19'domestic spending % avg
64.5%
Source: Deutsche Bank; HK Census and Statistics Department
Source: Deutsche Bank; CEIC; Notes: 2017/18/19e are DB property team forecasts
Gre
ate
r Ch
ina C
on
sum
er
Co
nsu
mer
4 J
an
uary
20
18
Deu
tsch
e B
an
k A
G/H
on
g K
on
g
Pag
e 8
5
Appendix H
Macro in Charts – HK
Figure 190: Retail share by segment: 10M2004 Figure 191: Retail share by segment: 10M2017
Food, Alcoholic Drinks & Tobacco
11%
Supermarkets14%
Fuels3%
Clothing, Footwear & Allied Products
13%Consumer Durable
Goods17%
Department Stores (DS)10%
Jewellery, Watches, Clocks &
Valuable Gift13%
Medicines, Cosmetics
7%
Others12%
10M2004
Food, Alcoholic Drinks & Tobacco
10%
Supermarkets12%
Fuels2%
Clothing, Footwear & Allied
Products13%
Consumer Durable Goods15%
Department Stores (DS)10%
Jewellery, Watches, Clocks & Valuable Gift
17%
Medicines, Cosmetics
10%
Others11%
10M2017
Source: Deutsche Bank; HK Census and Statistics Department
Source: Deutsche Bank; HK Census and Statistics Department
Figure 192: HK monthly temperature yoy change in absolute value
Aug-16 Sep-16 Oct -16 Nov-16 Dec-16 Jan-17 Feb-17 Mar -17 Apr -17 May -17 Jun-17 Jul-17 Aug-17 Sep-17 Oct -17 Nov-17 Dec-17
Temperature 29.0 28.5 26.7 22.0 19.1 18.5 17.6 19.9 23.7 26.0 28.8 28.7 29.3 29.0 26.3 22.2 17.8
YoY change in absolute
value-1.0% 0.4% 2.7% -8.3% 2.7% 15.6% 13.5% 13.7% 0.4% -2.6% -2.0% -5.3% 1.0% 1.8% -1.5% 0.9% -6.8%
Source: Deutsche Bank; Hong Kong Observatory
4 January 2018
Consumer
Greater China Consumer
Page 86 Deutsche Bank AG/Hong Kong
Appendix 1
Important Disclosures
*Other information available upon request Prices are current as of the end of the previous trading session unless otherwise indicated and are sourced from local exchanges via Reuters, Bloomberg and other vendors . Other information is sourced from Deutsche Bank, subject companies, and other sources. For disclosures pertaining to recommendations or estimates made on securities other than the primary subject of this research, please see the most recently published company report or visit our global disclosure look-up page on our website at http://gm.db.com/ger/disclosure/DisclosureDirectory.eqsr. Aside from within this report, important conflict disclosures can also be found at https://gm.db.com/equities under the "Disclosures Lookup" and "Legal" tabs. Investors are strongly encouraged to review this information before investing.
Analyst Certification
The views expressed in this report accurately reflect the personal views of the undersigned lead analyst about the subject issuers and the securities of those issuers. In addition, the undersigned lead analyst has not and will not receive any compensation for providing a specific recommendation or view in this report. Anne Ling
Equity rating key Equity rating dispersion and banking relationships
Buy: Based on a current 12- month view of total share-holder return (TSR = percentage change in share price from current price to projected target price plus pro-jected dividend yield ) , we recommend that investors buy the stock.
Sell: Based on a current 12-month view of total share-holder return, we recommend that investors sell the stock
Hold: We take a neutral view on the stock 12-months out and, based on this time horizon, do not recommend either a Buy or Sell.
Newly issued research recommendations and target prices supersede previously published research.
57 %
33 %
10 %17 % 18 % 14 %0
100
200
300
400
500
600
Buy Hold Sell
Asia-Pacific Universe
Companies Covered Cos. w/ Banking Relationship
4 January 2018
Consumer
Greater China Consumer
Deutsche Bank AG/Hong Kong Page 87
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