Show and Tell · distancing measures in Asia. In this note, we use a) mobile app usage across 7...

Asia Pacific Equity Research19 April 2020

Show and TellHigh frequency data points and what they tell us

Asia ex Japan Equity Research

James R. Sullivan, CFA AC

(65) 6882-2374

[email protected]

Bloomberg JPMA SULLIVAN <GO>

J.P. Morgan Securities Singapore Private Limited/ J.P. Morgan Securities (Asia Pacific) Limited

Rajiv Batra AC

(65) 6882-8151

[email protected]

Bloomberg JPMA BATRA <GO>

J.P. Morgan Securities Singapore Private Limited

Anurag Rajat, CFA AC

(65) 6882 2407

[email protected]

J.P. Morgan Securities Singapore Private Limited/ J.P. Morgan Securities (Asia Pacific) Limited

See page 22 for analyst certification and important disclosures, including non-US analyst disclosures.J.P. Morgan does and seeks to do business with companies covered in its research reports. As a result, 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.

www.jpmorganmarkets.com

We leverage high frequency alternative data to track evolution & impact of social distancing measures in Asia. In this note, we use a) mobile app usage across 7 categories in 10 countries from Apptopia (page 2-9); b) Google’s Community Mobility Reports (page 10-17); c) Apple Mobility Trends Report (page 17-21). We find productivity apps (Zoom, MS Team, DingTalk), and food delivery apps(Grab, foodpanda, Meituan) benefited from social distancing. Conversely, taxi & mobility, banks and payment app usage has dropped. Digging deeper, we see diverse trends across countries, reflecting stage of Covid-19 curve, intensity of social distancing measures enforced and structural adoption of digital economy. In China, app trends are showing signs of a U-shaped recovery, a potential template for the region. E-commerce app usage appears valuable in gauging consumer sentiment in China and India and, hence, a leading indicator for consumer discretionary sector. Other key observations include an average of 48% drop in visits to retail locations, & ecommerce delivery disruptions, most acutely in India. Lessons from China: China food delivery and bank apps usage show a U-

shaped recovery from T+40 to T+70 suggesting economic recovery is underway. Productivity apps usage which grew exponentially during the first 30 days, has plateaued and is showing signs of decline from T+70 as lockdown measuresare relaxed. China streaming declined ~15% compared to last year. Most of the decline is driven by music streaming (-20%), while video streaming apps (iQiyi, Tencent videos) are seeing a rise in DAU (+10%). Ecommerce usage is valuable for gauging consumer sentiment, and has tended to lead discretionary stocks’ performance since 2018. However, recent recovery of the sector is yet to be supported by usage data (Figure 1). Hence, we prefer e-commerce and grocery retailers (Sun Art Retail), which benefit from outbreak. Tourism-relatedand entertainment sectors will likely take longer to see demand recovery.

Is India destined to follow China’s path? Following strong lockdown measures, like China, India should see severe economic impact. Ride-hailing, food delivery and other online platforms saw usage drop. Online grocers (BigBasket, Grofers) and e-retailers (Amazon, Flipkart) faced logistical issues initially. But deliveries are gradually improving as permits are being issued by authorities. Retailers (DMART IN) have quickly scaled online to offset the impact from brick & mortar business.

In HK, Korea, Taiwan and Japan there seems to be a shift from offline retail (Google mobility -17% to -37%) to online channel (E-commerce app usage jumped 10-20% right after T0). The streaming apps in these countries saw increased usage. Our preferred picks are Alibaba, Vipshop, Tencent, TAL, NCSoft and Kakao which would benefit from streaming/gaming/e-commerce trends.

Google Mobility indicates sharp decline in ASEAN retail: Movement restrictions and closure of non-essential retail establishments forced consumers to shift to online channels. Negatively impacted sectors are retail REITs, apparel & footwear (LPPF IJ, MAPI IJ), alcohol beverages (FB PM, THBEV SP), and restaurants (JFC PM, MINT TB). Defensive consumer staples and health-related categories could outperform. This includes food (INDF IJ, ICBP IJ, URC PM, TU TB), super markets/hyper markets (PGOLD PM, BJC TB, DFI SP, SSG SP), and medical supplies (TOPG MK, HART MK) (refer to ASEAN Consumer -Covid-19 series, Yutan et al, 3 April 2020).

https://www.jpmm.com/research/content/GPS-3324345-0



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James R. Sullivan, CFA(65) [email protected]

App usage in the times of Covid-19We leverage daily active usage (DAU) data from Apptopia for more than 250 apps (please see Table 4 for complete list) across 7 categories (e-commerce, productivity, taxi & mobility, streaming, food delivery, banking, and payments) for 10 countries to track the impact of social distancing measures enforced across Asia. China app usage is available only for iOS. While we acknowledge this shortcoming, the trends could provide valuable insights.

Consumer sentiment vs. e-commerce app usage

In China and India, e-commerce DAU appears to be correlated with performance of Chinese consumer discretionary (Figure 1 - left) and Indian retailers (Figure 2 - left).However, the relationship appears to have broken down recently. This dynamic, together with the sluggish trends in taxi & mobility (Page 5) and payment apps (Page 8), point to the unsustainability of this recovery. We believe e-commerce and grocery retailers could stay more resilient during social distancing. On the other hand, tourism, and entertainment related sectors will likely take longer to see demand recovery.

Figure 1: MSCI China consumer discretionary & E-commerce usage. Long-term trend (Left) vs. Recent trend (Right)

Source: Apptopia Data as of April 13th, Bloomberg data, J.P. Morgan.

Figure 2: India retailer index & E-commerce usage. Long-term trend (Left) vs. Recent trend (Right)

Source: Apptopia data as of April 13th, Datastream, J.P. Morgan.

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E-commerce

Various levels of social distancing and resulting growth headwinds are generally weighing on consumer demand. The data shows that e–commerce trends differ across countries. A possible explanation could be level of movement restrictions imposed, which could hinder logistics/delivery to varying extents. This dynamic led us to prefer platforms with well-developed logistic systems to better cope with a restricted environment.

Figure 3: E-commerce apps – North Asia Daily Average Users trend

Source: Apptopia, J.P. Morgan. Data as of April 13th.

Figure 4: E-commerce apps – South Asia Daily Average Users trend


Ecommerce apps show mixed trends in different countries.

China’s e-commerce apps had a decline of usage in the 1st 30 days, and settled at a low level. Hong Kong and Korea saw usage surge by 10-20% right after T0. While usage has normalized from day 20 onwards, it is still above the starting point.

In South Asia, e-commerce apps usage dropped sharply, in some cases even before T0. This could possibly be due to: 1) supply chain disruptions (including fulfilment delays from China) and 2) lower discretionary spending.

In India, online e-commerce operators have been affected by lack of adequate manpower, and supplies disruption. Delivery is gradually picking up as permits are being obtained from various authorities. Thailand is the only country where e-commerce usage bounced back recently.

In the longer term, weaker sentiment could impact e-commerce, but the broader question is if this period of physical distancing leads to meaningful behavioral shift towards e-commerce.

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Productivity applications

Productivity apps that facilitate working remotely and communications saw usage surge across Asia. Work from home (WFH) and virtual meeting arrangements are allowing corporates to acclimatize themselves to an alternative way of doing business. We believe it could also lead to gradual increase in work flexibility, that could possibly lead to: 1) a reduction in traditional office demand for prime Central locations; 2) increasing use of co-working space, which will favor decentralization especially when the importance of physical location reduces. (See Hong Kong Property: When virtual become reality, by Cusson Leung, April 3rd 2020)

Figure 5: Productivity apps – North Asia Daily Average Users trend

Source: Apptopia, J.P. Morgan, Data as of April 13th.

Figure 6: Productivity apps – South Asia Daily Average Users trend


In China, productivity app usage grew exponentially during the initial 30 days as government imposed strict lockdowns. Usage plateaued in the following month and isnow showing early signs of decline from Day 70 onwards. This coincides with China relaxing lockdowns, but elevated levels show there is more room to go.

Korea, Hong Kong, and Japan did not impose strict lockdowns, and thus saw less muted growth. However, even in these countries usage jumped 2-3x.

South Asia saw a much steeper trend compared to China & other North Asia countries. India leads the pack where usage rose 10x after almost complete lockdown across the country. MY/PH/ID, which had strong movement restrictions, saw 5x-7.5x growth.

SG increased the intensity of social distancing measuresover time, leading to ~4.5x rise in usage, but at a slower pace.

TH has seen limited growth in productivity apps.



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Taxi and Mobility applications

Figure 7: Taxi & mobility apps – North Asia Daily Average Users trend


Figure 8: Taxi & mobility apps – South Asia Daily Average Users trend


Taxi and Mobility apps have seen gradual downtrend for all Asian countries. Even in countries like China, where gov’t is loosening restrictions, we are not seeing signs of increased taxi app usage.

The drop in taxi app usage in South Asia seems to have started before T0. This could be the impact of early social distancing & travel restrictions before more stringent lockdowns.

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Streaming: Music vs Video

In China, the usage of music and video streaming apps show divergent trends. Music apps saw a 10-40% drop in DAU compared to pre-T0 levels, while video streaming apps usage rose 20-30%. Lower level of commute and time spent outside could be driving this, in our view. At home, the music consumption could shift away from apps to other devices. Further, video streaming apps are replacing other forms of out-of-home entertainment avenues.

Figure 9: Streaming apps - Daily Average Users trend


Figure 10: Streaming apps - Daily Average Users trend


Streaming app activity shows a gradual uptrend in Hong Kong, Japan during Covid-19 (5-10% higher than T0).

In contrast, China streaming declined by about 15% compared to T0. Usage drop in China is largely due to music apps. In contrast, video streaming services picked up considerably.

In South Asia, we are seeing either a flat or negative trend for streaming apps. Philippines was an exception, posting gradual growth over the period.

Indonesia has the strongest decline during the lockdown period with a ~ 15% drop from the recent peaks.

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Food delivery applications

Figure 11: Food apps – North Asia Daily Average Users trend


Figure 12: Food apps – South Asia Daily Average Users trend


Generally, food delivery apps are benefitting from movement restriction and social distancing measures.

However, we note that in some countries with stricter lockdown measures (India/China), food delivery app usage appears to be dropping

In North Asia, China food app usage dropped initially but hasrecovered post T+40.

Food delivery apps saw the strongest increase in Japan & Hong Kong with 20-30% jump in usage since T0. In Korea, food delivery usage increased initially but has come down to almost the same level as T+0.

In South Asia, the general trend of food delivery app usage is rising, except for India. Strongest app growth is seen in Thailand and Philippines with growth of 20-30% since T0.

In India, food delivery operations have restarted slowly and aggregators are operating offering no-contact delivery option. The number of restaurants is increasing on every platform though stillrunning below pre-COVID levels.

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Bank applications

Figure 13: Bank apps – North Asia Daily Average Users trend


Figure 14: Bank apps – South Asia Daily Average Users trend


Bank app usage drop around the start of outbreak in all Asian countries. However, the effect is limited to 10-20% drop in activity levels.

Most notably, China has seen a U-shaped recovery from T+40 to T+80, suggesting a recoveryin economic activity in the country.

Another exception is a sharp 35% drop in Indian bank app usage since T0.

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Payment applications

Figure 15: Payment apps – North Asia Daily Average Users trend


Figure 16: Payment apps – South Asia Daily Average Users trend


Payment app usage droppedshortly before T0.

China posted ~20% drop in payment app usage. This could be due to heavy reliance on payment apps (using QR) in daily life.

Similar to North Asia, Malaysia and Singapore payment apps show a notable drop in activity starting from T0.

However, the trends in other countries are unclear. The reason for this, in our view, could be low penetration of payment apps.

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Mobility trends from Google & Apple

Governments across Asia have implemented various levels of social distancing measures to flatten the Covid-19 curves. These vary from complete lockdown in countries like India and Malaysia to less stringent measures in countries like Japan, and Singapore (prior to Circuit breaker measures on 7th April). We leverage Google's Community Mobility Reports (link) & Apple’s Mobility Trends Reports (link) to gauge the efficiency of these measures and impact on the economy. Google’s data is as of 11 April while Apple data is available as of 15 April.

Google provides trends on visits/length of stay across 6 categories. The data is comparison with median day of the week value during 5 weeks of 3rd Jan – 6th Feb 2020.

Table 1: Summary of Google's Community Mobility Report trends

CountryRetail &

RecreationGrocery & pharmacy

ParksTransit

StationsWorkplaces Residential

Hong Kong -31% -4% -21% -41% -38% 19%Taiwan -18% 0% -20% -32% 4% 6%Japan -30% 4% 3% -48% -22% 12%Korea -16% 10% 17% -13% -7% 6%India -80% -55% -52% -69% -64% 30%Indonesia -43% -24% -46% -59% -30% 17%Malaysia -78% -49% -68% -82% -53% 35%Philippines -82% -60% -59% -86% -67% 32%Singapore -61% -21% -51% -66% -51% 32%Thailand -51% -21% -57% -63% -22% 17%Vietnam -59% -33% -43% -58% -29% 21%

Source: Google's Community Mobility Reports, J.P. Morgan. As of Apr 11, 2020.

Apple uses direction requests made in Apple Maps, split across the modes of transport (driving, walking and transit). Jan 13, 2020 is used as the baseline for comparison.

Table 2: Summary of Apple’s Mobility Trends Reports

Driving Walking AverageHong Kong -48% -55% -51%India -81% -73% -77%Indonesia -58% -67% -62%Japan -23% -29% -26%Malaysia -49% -60% -54%Philippines -81% -78% -79%Korea -41% -57% -49%Singapore -63% -74% -69%Taiwan -19% -27% -23%Thailand -63% -78% -71%Vietnam -45% -47% -46%

Source: Apple Mobility Trends Reports, J.P. Morgan. Note: relative to baseline as of 13 Jan 2020. Data as 15 April 2020

Performance of retail stock sector ytd, appears to be related to the drop in retail visitsreported by Google. The key exceptions are India, Korea and Taiwan. Indian retailers are seeing less drop relative to retail visit trends. This could be due to higher weighting on supermarkets which fall more under essentials category. Korea and Taiwan retailer stocks have performed worse than the trends indicated by retail visits. This could be due to stocks in Korea being levered more to tourism. Taiwan retailer indices appear to have some focus on auto and hardware, which are more discretionary in nature and could be getting impacted more.

https://www.apple.com/covid19/mobility

https://www.google.com/covid19/mobility/

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Figure 17: Google’s Retail & recreation visit trends vs. retail stock performance ytd

Source: Google Community Mobility Reports, Datastream retailers index, J.P. Morgan. YTD

performance data as of 16 April 2020

Figure 18: Google’s Retail & recreation visit trends vs. retail stocks relative performance ytd

Source: Google Community Mobility Reports, Datastream retailers index, J.P. Morgan. YTD


Figure 19: Apple Mobility trends vs. retail stock performance ytd

Source: Apple Mobility Trends Reports, J.P. Morgan, Datastream retailer’s index. YTD


Figure 20: Apple Mobility trends vs. retail stock relative performance ytd

Source: Apple Mobility Trends Reports, J.P. Morgan, Datastream retailer’s index. YTD


Google Community Mobility Report - Details

Transit station trends indicate a sharp ~56% average drop across the region. The most severe drops are in Philippines (-86%), Malaysia (82%), where the restrictions appear to be most severe. India (-69%), Singapore (-66%), Thailand (-63%) and Vietnam (-58%) also saw material drop in visits to transit points. Visitsto transit points in Indonesia are down ~59%, but this could move up higher as tighter lockdown of Jakarta would impact public transports’ hours of operation. North Asian countries show a relatively smaller drop in workplace activities, with KR/TW showing drops of just 13%/32%. HK/JP with 41%-48% drops appear to have more impact from social distancing measures relative to KR/TW.

Workplace trends indicate ~34% average drop across the region. These numbers are lower than transit station trends, but trends appear similar. The most severe drops are in South Asian countries of Philippines (-67%), India (-64%), Malaysia (-53%), and Singapore (-51%). Drops across Thailand (-22%) and Vietnam (-29%) are less sharp. Indonesia has also seen ~30% drop in workplace visits. HK has the greatest drop (-38%) among North Asian countries, while TW is showing 4% rise.

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Figure 21: Transit Station Trends

Source: Google Community Mobility Report, J.P. Morgan. Note: Data shows visit and length of stay trends vs. baseline of median value for corresponding day of the week during Jan 3-Feb 6, 2020.

As of Apr 11, 2020.

Figure 22: Workplace Trends


As of Apr 11, 2020.

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Retail trends indicate ~50% average drop across the region. The most severe

drops are in South Asian countries of Philippines (-82%), India (-80%) and Malaysia (-78%). Drops in Vietnam (-59%) and Thailand (-51%) are also quite material and would impact retail spending. Singapore at -61% appears to have dropped materially post-April 7th circuit breaker measure. In Indonesia, retail visits were down 43%, possibly due to the sharp drop in visits to malls in Jakarta, as well as people outside Jakarta curtailing visits due to fear. HK (-31%) appears to be the most impacted among North Asian countries, despite already low tourist arrivals earlier due to political uncertainty. TW/KR/JP appear least impacted (-18% to -31%).

Grocery & pharmacy trends indicate ~23% average drop across the region. Given the essential nature of service provided by these establishments, a shallower drop compared to broader retail appears logical. The most severe dropsare in South Asian countries of India (-55%), Philippines (-60%), and Malaysia (-49%). Vietnam has registered a more muted (-33%) drop as the lockdown measure is relatively lighter. Singapore and Thailand are down ~21% in visits to groceries & pharmacies. KR/JP actually show 10%/4% higher visits to grocery stores, while TW and HK are largely flattish.

Residential trends indicate that time spent at home has increased ~21%. Malaysia, Philippines, Vietnam and India show >20% increase in time spent at home. The lowest increase is in Taiwan, and Korea, at sub-10%, while JP/HK are sub-20%.

Figure 23: Retail Trends


As of Apr 11, 2020.

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Figure 24: Grocery & Pharmacy Trends

Source: Google Community Mobility Report, J.P. Morgan. Note: Data shows visit and length of stay trends vs. baseline of median value for corresponding day of the week during Jan 3-Feb 6, 2020

As of Apr 11, 2020.

Figure 25: Residential Trends


As of Apr 11, 2020.

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Park visits are down ~36% across the region. All countries excluding SH, TW,

JP and KR, are showing drops of more than 45%. KR/JP are showing ~17%/3% increase in park visits, possibly because better weather has resulted in people going out.

Figure 26: Parks Trends


As of Apr 11, 2020.

Apple’s Mobility Trends Report – Details

Apple Mobility Trends Reports (link) are based on direction requests made in Apple Maps, split across the modes of transport (driving, walking and transit). The data is rebased to 100 as of Jan 13, 2020. Country level detailed trends are shown in Figure 27 to Figure 37.

Figure 27: Hong Kong

Source: Apple Mobility Trends Reports, J.P. Morgan. Note: Rebased to 100 as of 13 Jan 2020. Data as 15 April 2020

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https://www.apple.com/covid19/mobility

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Figure 28: India


Figure 29: Indonesia


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Appendix

J.P. Morgan COVID-19 data-driven research reports

Table 3: J.P. Morgan COVID-19 data-driven research reports

Author TitleMislav Matejka COVID-19: Tracking activity impact through high frequency data

Jesse Edgerton US Real-time Quant Econ series

Haibin Zhu COVID-19 China Monitoring

Kevin Yin Greater China Consumer: 2020 coronavirus vs 2003 SARS; Our stress test and defensive list

Cusson Leung Hong Kong Property: When virtual become reality

Sumit Kishore Know your power

Ranjan Sharma ASEAN TMT: COVID-19 impact update: Improving acceptance of digital channels

ASEAN Ecommerce: Mapping delivery disruptions: COVID-19 impact

Jeanette Yutan

Kae Pornpunarath

Benny Kurniawan

Hoy Kit Mak

ASEAN Consumer: Covid-19 series: On-the-ground color on how Covid-19 is shaping

consumer behavior

Source: J.P. Morgan.

App usage analysis - methodology

We use Apptopia data for daily average users count (DAU) for each mobile application in our study (See Table 4 for the list of applications)..

Data is aggregated into 70 time series, representing 7 categories for each of 10 countries/regions (Mainland China, Hong Kong SAR, Japan, South Korea, India, Indonesia, Malaysia, Philippines, Singapore, Thailand).

We use the average DAU for 2019 (2019 avg = 100) as the baseline and scale the current usage using the baseline.

We define an "starting point" (T0) for each country. T=0 is: 1) the day that country crosses 100 reported cases or 2) the day government started to impose stringent social distancing.

We extract the data trend from 30 days before T0 (T-30) to 80 days after T0 (T+80) and show on the graphs in the sections above.





https://www.jpmm.com/research/content/GPS-3332723-0.pdf






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Table 4: List of applications

Categories Apps

Banks AEON HK, Agriculture Bank of China, AmOnline, Bank Islam, Bank of China (International), Bank of East Asia (China), BCA mobile, BNI Mobile Banking, BOCHK, BRI Mobile, Bualuang mBanking, CCB (HK&MO), China Merchants Bank, CIMB Clicks Malaysia, Citibank HK, DBS digibank SG, DBS Omni, Hang Seng Personal Banking, HDFC Bank MobileBanking, Hong Leong Mobile Banking, HSBC HK Mobile Banking, IBK ONE BANKING GLOBAL, ICBC Mobile Banking, ICICI Bank Canada, K bank, K PLUS, Kakao Bank, KMA-Krungsri Mobile App, Kotak - 811 & Mobile Banking, Krungthai NEXTCitibank KR, mandiri online, Axis Bank Mobile Banking, Maybank MY, OCBC Mobile Banking, POSB digibank, Public Bank Mobile Application, RHB Mobile Banking, SC First Bank Corporate Banking, SC Mobile, Smart KDB, UOB Mighty

Ecommerce 7-Delivery, AliExpress Shopping App, Amazon - Shopping made easy, Amazon China, Amazon Prime Now, Blibli - Belanja Online, Bukalapak - Jual Beli Online, eBay, Carousell, Central Department Store, ezbuy-Global Shopping, Flipkart - Online Shopping App, Gmarket Global, GO SHOP, Hermo - Beauty Shopping, JD Finance, Jingdong-JD shopping fashion authentic, Kupang-Coupang, Lazada, Lelong.my - Shop and Save, Market Curly, Mobile Taobao-Pay for Shopping, Myntra - Fashion Shopping App, OLX: Buy & Sell near you, Pinduoduo, Pomelo Fashion, Rakuten Global Market Shopping, Sephora -Beauty Shopping, ShopBack - Cashback & Coupons, ShopClues, Shopee, G Market, Tesco Lotus Clubcard, Tesco Online Malaysia, Tmall, Tokopedia -Jual Beli Online, Tops #1 Food & Grocery, Wee Mep- Special Representative, Wish - Shopping Made Fun, ZALORA - Fashion Shopping

Food Bungkusit, Dahmakan, Daojia Food Fair-Quality Delivery, Deliveroo: Food delivery, Delivery yo yo, Domino's Pizza Malaysia, En Gerinus Card, Foodpanda-Food Delivery, GrabFood Merchant App, HappyFresh - Grocery Delivery, Hungry App, KFC, McDelivery, McDonald's® App, Meituan takeaway, MyGroser, Pizza Hut, Shuttle Delivery, Swiggy Food Order & Delivery, Uber Eats: Food Delivery, Wongnai, Zomato - Food & Restaurants

Payment AirPay, Alipay, AXS Payment, BHIM – Making India Cashless, BigPay - Challenge Banking, BoC Pay, Boost, DanaRupiah, DBS PayLah!, Dolfin, Fave -Deals & Cashback, Google Pay, GrabPay Merchant, HyundaiCard, JD Finance-188, LINE Pay, LinkAja, Maybank QRPayBiz, NETSPay, OCBC Pay Anyone™, OVO, PAYCO, PayMe from HSBC, PayPal: Mobile Cash, Paytm- Payments & Bank Account, PayZapp - Recharge, Pay Bill, PhonePe - India's Payments App, Razer Pay, Samsung Pay, Shinhan Pei Pan, Toss, Touch ‘n Go eWallet, YONO SBI:Banking and Lifestyle

Productivity BlueJeans, Cisco Jabber, Cisco Webex Meetings, DingTalk, LINE, Microsoft Teams, Skype for Business, Slack, WeChat Work, ZOOM Cloud Meetings

Streaming Amazon Prime Video, Apple Music, Astro GO, ERA, Genie Music-Genie, Hotstar, iflix: Movies, TV Series, News, Iqiyi, JioSaavn – Music & PodcastsJOOX Music, JOOX Music, KuGou Music, LINE TV, Melon music, National party-online karaoke, singing karaoke, Netflix, OZEE - Entertainment Now., QQ Music, RCTI+, Singtel Cast, Smule, SonyLIVE, Spotify - Music and Podcasts, Tencent Video, Tik Tok - video social network, Toggle.sg, Twitch: Live Game Streaming, Vidio - Nonton TV & Video, Viu -Stream TV Shows & Serials, Voot, Xiami Music, YouTube Music, YouTube: Watch, Listen, Stream

Taxi and mobility

ComfortDelGro Taxi Booking App, DiDi - Greater China, DiDi-Rider, EzCab – Ride Hailing App, EZ-Link, FlyTaxi - call HKTaxi App, GOJEK, Google Maps -Transit & Food, Grab App, Grab Driver, HD Map, HKTaxi - Taxi Hailing App, inDriver: Offer your fare, Lime - Your Ride Anytime, LINE MAN: Taxi, Food, Postal, Mobike - Smart Bike Sharing, MyCar Driver, MyTransport.SG, NAVER Map, Navigation, Ola Cabs, SOCAR MY, TADA, Uber, Waze

Source: J.P. Morgan

21



Lockdown and infection growth

Table 5: Summary country & lock down

CountryCurrent cases

Cases per mln.people

Inflectiondate (T0)*

Lockdown status

China 81,865 57.1 1/22/2020 China imposes total lockdown on Wuhan and fifteen other cities. In late March, the government severely restricted international travel, limiting the number of flights to the country and denying entry to foreigners with previously issued visas and residence permits.

Japan 4,257 33.6 2/22/2020 On 7 April Japan proclaimed a state of emergency in Tokyo and nearby prefectures. No lockdown was imposed in Japan. In Tokyo, residents were asked to work from home and refrain from going out.

Hong Kong 960 129.1 1/25/2020 Since late March, Hong Kong has banned public gatherings of more than four people, while cinemas, bars, pubs and other leisure venues have been ordered to close for two weeks. Restaurants also have to keep tables 1.5 meters apart.

Korea 10,423 203.5 2/20/2020 Korea did not enforce a lockdown. The country imposed intensive social distancing measures: isolation of incoming passengers, closing schools, limiting people gatherings.

India 5,916 4.3 3/15/2020 India’s PM announced a ‘Janata Curfew’ and later imposed a 21-day lockdown starting from 24 March to curb virus spread. India’s federal government has asked states to quarantine migrant workers for 14 days and prevent the movement of people across borders. All private offices, shops and establishments that provide non-essential services are shutdown in key regions.

Indonesia 2,956 10.9 3/15/2020 On Apr 8, 2020, Jakarta Governor officially announced implementation of "large-scale social distancing" that will last for 2 weeks, limiting religious events, socio-cultural activities, close schools and workplaces, shut down public facilities, ban gatherings of more than 5pax and limit public transport passenger capacity.

Malaysia 4,119 128.9 3/10/2020 From 18 March, Malaysia officially implemented the movement control order (MCO). Government and private premises will be closed, except those providing essential services. Malaysia has decided to further extend the MCO by another until Apr 28.

Philippines 3,870 35.8 3/15/2020 On March 17, the government imposed stringent quarantine on Luzon – Philippines’ largest and most populous island, including the capital Manila. This has been extended to April 30. Mass public transport are suspended and residents can only leave home for essential needs.

Singapore 1,623 279.8 2/7/2020 Since 24 March, Singapore has been denying entry for short-term visitor arrivals, all incoming Singaporeans and Long Term Pass holders must be subjected to 14-day quarantine. As of Apr 3, 2020, the government announced a month-long circuit breaker period to last from 8 Apr - 4 May.

Thailand 2,369 34.3 3/16/2020 Thailand declared State of Emergency (March 24) for at least one month. The governors of "greater Bangkok" have ordered the shutdown of malls, markets, restaurants, and various other establishments for three weeks from Mar 21 to Apr 12. In addition, Thailand also impose a daily curfew from April 3.

Source: National sources.

*Note: We define an "inflection day" (T0) for each country. Inflection day is: 1) The day that country cross 100 reported cases or 2) The day that government started to impose stringent social

distancing or lockdown measures.

Figure 38: Number of confirmed cases from T0

Source: National sources, Data as of April 16.

Mainland China, 82,295

India, 11,439 Korea, 10,591

Japan, 8,100 Philippines, 5,223

Indonesia, 4,839

Malaysia, 4,987

Singapore, 3,252 Thailand, 2,643

HK SAR, 1,016

Taiwan, 395

100

200

400

800

1,600

3,200

6,400

12,800

25,600

51,200

102,400

T0 T+2

T+4

T+6

T+8

T+10

T+12

T+14

T+16

T+18

T+20

T+22

T+24

T+26

T+28

T+30

T+32

T+34

T+36

T+38

T+40

T+42

T+44

T+46

T+48

T+50

T+52

T+54

T+56

T+58

T+60

T+62

T+64

T+66

T+68

T+70

T+72

T+74

T+76

T+78

T+80

T+82

22



Companies Discussed in This Report (all prices in this report as of market close on 17 April 2020, unless otherwise indicated)Alibaba Group Holding Limited(BABA/$212.66[16 April 2020]/OW), Berli Jucker PCL(BJC.BK/Bt39.75/N), Hartalega Holdings Bhd(HTHB.KL/M$7.68/OW), Indofood(INDF.JK/Rp6250/OW), Indofood CBP(ICBP.JK/Rp10200/OW), Jollibee Foods Corp.(JFC.PS/Php145.00/UW), Kakao(035720.KS/W172000/OW), Matahari Department Store(LPPF.JK/Rp1480/UW), Minor International PCL(MINT.BK/Bt20.60/NR), Mitra Adiperkasa(MAPI.JK/Rp650/OW), NCSOFT(036570.KS/W649000/OW), Puregold Price Club(PGOLD.PS/Php45.75/OW), San Miguel Food and Beverage(FB.PS/Php58.00/UW), Sun Art (6808)(6808.HK/HK$12.00/OW), TAL Education(TAL/$50.24[16 April 2020]/OW), Tencent (0700)(0700.HK/HK$409.60/OW), Thai Beverage Public Company Ltd.(TBEV.SI/S$0.70/OW), Thai Union Group PCL(TU.BK/Bt12.90/OW), Top Glove Corporation(TPGC.KL/M$6.80/OW), Universal Robina Corp(URC.PS/Php126.80/N), Vipshop(VIPS/$18.80[16 April 2020]/OW)

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Coverage Universe: Sullivan, James: Bharti Airtel Limited (BRTI.BO), Bharti Infratel Ltd. (BHRI.NS), Cheetah Mobile Inc (CMCM), Kingsoft Corporation Limited (3888) (3888.HK), Vodafone Idea Limited (IDEA.BO)

J.P. Morgan Equity Research Ratings Distribution, as of April 04, 2020

Overweight(buy)

Neutral(hold)

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J.P. Morgan Global Equity Research Coverage 46% 40% 14%IB clients* 52% 49% 37%

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