Asia Date China Greater China Consumer Industry Updatepg.jrj.com.cn/acc/Res/CN_RES/INVEST/2018/1/4/c174d... · paper household products, ... Key players can gain more market share

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

[email protected]

Mark Yuan

Research Analyst

(+852 ) 2203 6181

[email protected]

John Chou

Research Analyst

(+852) 2203 6196

[email protected]

Louise Li

Research Associate

(+852 ) 2203 6152

[email protected]

________________________________________________________________________________________________________________ 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


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

[email protected]

Mark Yuan

Research Analyst

(+852 ) 2203 6181

[email protected]

John Chou

Research Analyst

(+852) 2203 6196

[email protected]

Louise Li

Research Associate

(+852 ) 2203 6152

[email protected]

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


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


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


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




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




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



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



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



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


4 January 2018

Consumer



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


4 January 2018

Consumer



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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-6

-4

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-10

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-14

May

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-15

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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



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.


4 January 2018

Consumer



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



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

Apr-

07

Oct

-07

Apr-

08

Oct

-08

Apr-

09

Oct

-09

Apr-

10

Oct

-10

Apr-

11

Oct

-11

Apr-

12

Oct

-12

Apr-

13

Oct

-13

Apr-

14

Oct

-14

Apr-

15

Oct

-15

Apr-

16

Oct

-16

Apr-

17

Oct

-17

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



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


- 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


-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


-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


-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)


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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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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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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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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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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


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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



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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

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me

Mac

Mak

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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


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, 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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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


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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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Home appliances

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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Home appliances

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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3

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


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%


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)



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 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


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)



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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



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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%


North America athletic retailers' revenue growth (LHS)




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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%


Pou Sheng & Belle revenue growth (LHS)


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)


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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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Hong Kong

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 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


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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


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, Bloomberg Finance LP

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Hong Kong

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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Hong Kong

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, 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


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

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5000

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7000

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0Ju

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&…

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v-1

6Jan

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Mar-

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May-1

7Ju

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Sep

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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

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Dec

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Mar

-11

Jun-

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Sep

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Dec

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Mar

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Jun-

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Sep

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Sep

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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


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



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


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; 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; HKTB, CEIC


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Appendix F


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

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06

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08

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09

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09

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11

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11

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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; 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



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Appendix G


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%



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


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



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



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



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