how we think about thinking about telcos. mon mar 20...

H O W W E T H I N K A B O U T T H I N K I N G A B O U T T E L C O S

Asian Telecommunications

James R. Sullivan, CFA AC

+65 6882-2374

[email protected]

Bloomberg JPMA SULLIVAN <GO>

J.P. Morgan Securities Singapore Private Limited

Asia Pacific Equity ResearchMarch 2017

See the end pages of this presentation 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.

Top picks and stocks to avoid

Source: Bloomberg, J.P. Morgan estimates. Prices as of March 16, 2017.

Mkt cap Div. yield ROE

Price (LC) Code Rating (US$MM) 17E 18E 17E 18E 17E (%) 17E (%)

Top picks

LG Uplus 14,750 032640 KS OW 5,627 10.9 8.7 1,356.9 1,702.6 3.08% 11.74%

KT Corp 32,650 030200 KS OW 7,446 9.4 7.6 3,490.0 4,306.0 3.72% 7.20%

Telekomunikasi 4,140.0 TLKM IJ OW 30,992 17.9 16.1 231.11 257.82 3.38% 27.71%

BHARTI AIRTEL 360.7 BHARTI IN UW 21,606 41.4 39.0 8.7 9.2 0.83% 5.20%

AIS 175.0 ADVANC TB UW 14,925 20.1 19.3 8.7 9.1 3.47% 59.68%

P/E (x) EPS (LC)

Stocks to avoid

Completed 17 Mar 2017 02:56 PM HKTDisseminated 17 Mar 2017 03:55 PM HKT

This document is being provided for the exclusive use of JAMES ROBERT SULLIVAN at JPMorgan Chase & Co. and clients of J.P. Morgan.{[{TKWO]*\YLO\^*]_VVS`KX*TKWO]8\8]_VVS`KXJTZWY\QKX8MYW*;A9:C9<:;A}]}

Page

Agenda

Summary: Year Ahead 2017 3

5 years of Developed Markets integrated success 6

How Telcos trade 9

Why have Emerging Markets underperformed 11

Data and the unbalanced balance sheet 24

Is Asset Restructuring becoming a reality? 40

Applying factor analysis to Telcos 48

What to Buy? What to Sell? 53

Appendix 55

2

Asset Restructuring: Only option for Emerging Market wireless 31

EM interconnect, voice and capex risks 28


Investment Summary: Year Ahead 2017

3

Earnings 2017: Drivers and Risks We believe Telco spending now mirrors Consumer Staples trends i.e. growth will be

slightly less than GDP. Wireless data has not served to reaccelerate top line trend.

OpEx is largely driven by incremental base station builds (due to incremental power/backhaul/site leasing etc.) and is

therefore driven by data volumes. Rising volumes on falling pricing will drive margin compression

Rising capital requirements paired with margin compression driven by ramping data volumes are providing for a double hit

to FCF, driving leverage levels higher than expected. This is not only driving higher interest expenses, but is beginning to

trigger DPS cuts and assets sales.

Drivers, trends and data points we are tracking:

• Network Quality: Relative levels of network quality on a city by city basis drive market share trends, pricing

capabilities and CAPEX forecasts.

• Pricing Trends: Data pricing trends are driven by network quality differentials combined competitive intensity and

are the number 1 driver of incremental ROIC trends.

We look for broad telecom sector

underperformance as Asia earnings growth outlook

improves and investors shift from defensive yield

strategies towards value beta plays. We look for

exposure to stable DM integrated players within

defendable FCF (KT/LGU+), carriers with a

significant network quality gap to peers enabling

pricing power (TLKM) or EM players where

industry asset restructuring (network

nationalization, significant network sharing) is

driving ROIC improvement (China Telecom).

Asia Telecom Top picks and stocks to avoid

Source: Bloomberg, J.P. Morgan estimates. Prices as of March 16, 2017.



Top picks

LG Uplus 14,750 032640 KS OW 5,627 10.9 8.7 1,356.9 1,702.6 3.08% 11.74%

KT Corp 32,650 030200 KS OW 7,446 9.4 7.6 3,490.0 4,306.0 3.72% 7.20%



AIS 175.0 ADVANC TB UW 14,925 20.1 19.3 8.7 9.1 3.47% 59.68%

P/E (x) EPS (LC)

Stocks to avoid


Summary: A new era for Emerging Market wireless?

EMs are finally showing positive signs of implementing the significant asset restructuring that is required by the

industry

A broader recovery of EMs should benefit highly volatility EM wireless stocks over low volatility DM integrated

stocks

High yield, low volatility stocks are currently at their most expensive since the 1980’s

However, recent developments could change the way we look at EM Telcos

For 5 years, we have been arguing for DM integrated over EM wireless as:

Incremental wireless revenues available in Emerging Markets simply couldn’t support the CAPEX and OpEx

commitments necessary to achieve them, due to:

Wireless revenues growing at a “GDP minus” rate

The growth of data has cannibalized voice revenues, resulting in depressed revenue growth

Network providers are spending incrementally more on content to maintain market share

Data growth is increasing operators’ BTS requirements and hence capex requirements

Rising BTS requirements are increasing operating expenses faster than revenue is growing

Given that solutions to increase revenues were limited, we argued that the only way for EM wireless to solve

their predicament was to implement significant asset restructuring where network assets were held in a

centralized trust and hence increasing capital efficiency across Emerging Market Telcos.

4This document is being provided for the exclusive use of JAMES ROBERT SULLIVAN at JPMorgan Chase & Co. and clients of J.P. Morgan.

{[{TKWO]*\YLO\^*]_VVS`KX*TKWO]8\8]_VVS`KXJTZWY\QKX8MYW*;A9:C9<:;A}]}

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How Telcos trade 9






Appendix 55

5




Historical view of Emerging Market wireless vs Developed Market integrated While Emerging Market wireless has outperformed over a 20-year period, Developed Markets have outperformed over the last 5 years

Source: Bloomberg. As of 15-March-17

1

21998-2011: EM outperformed DM by 82%

Increasing usage of average

minutes per user (voice)

Increasing monetization of voice

Telco revenues growing faster

than GDP

1

2

2011-2016: DM outperformed EM by 14%

Data increasing as % of revenues

Declining Telco spending as % of GDP

Data Cannibalization of voice revenues

Increasing CAPEX and subsequently

OPEX requirements to sustain data

growth

3

3 YTD 2017: EM outperformed DM by 5%

Significant news flow

on industry asset

restructuring and

consolidation

Global Telcos vs GEM Telcos stock performance 1998-Feb 2017

6

020406080100120140160180

020406080

100120140160180

MSCI AC AP Ex Japan Telecom services index MSCI World telecom service index


5 years of Developed Markets’ integrated success

For the last 5 years, we have preferred DM over EM. Analysis shows our portfolio return of 256.72% since 27 July 2010 vs benchmark return of 61.39%

JPM Asia Telco Investing Recommendations since inception Global Telcos vs GEM Telcos stock performance 2011-Feb 2017

Source: BloombergSource: Bloomberg

7

0

10

20

30

40

50

60

70

80

0

20

40

60

80

100

120

140

160

MSCI AC AP Ex Japan Telecom services index


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How Telcos trade 9






Appendix 55

8




How do Telcos TradeTelcos trade aggressively off ROIC

Source: J.P. Morgan estimates, Bloomberg.

0.97 0.98 0.98 0.95 0.95 0.96

-

0.20

0.40

0.60

0.80

1.00

1.20

2013 2014 2015

Correlation

R2

Source: J.P. Morgan estimates, Bloomberg

BHARTI IN

RCOM IN

IDEA IN

941 HK

728 HK

762 HK

ISAT IJ

TLKM IJ

EXCL IJ

AXIATA MK

MAXIS MK

T MK

GLO PM

TEL PM

030200 KS032640 KS

017670 KS

2412 TT

4904 TT

3045 TT

ADVANC TB

TRUE TB

DTAC TB

VOD SJ

MTN SJ

MFON LIMBT US

VIP USTCELL TI

VIVT4 BZ

TIMP3 BZ

AMX US

OIBR4 BZ

-

1

2

3

4

5

6

7

8

9

-10% 0% 10% 20% 30% 40% 50%

EV/

IC

ROIC

EV/IC vs ROIC (2015)

y = 16.82x + 0.1888R2 = 0.9623

Y=16.82x +0.1888

R² = 0.9623

Telcos show a very high consistent correlation with ROIC (97-98% with an R2 of .95-.96).

Telcos trade like banks, not like tech stocks. A future view of ROIC returns is a future view of sector performance

We can leverage this relationship between EV/IC and ROIC to quantify the future level of capital returns implied by current share prices.

Correlation and R2 stats for EV/IC vs ROIC: GEM Telcos EV/IC vs ROIC GEM Telcos 2015

Background Research- Magic Multiples & Telcos: Return on capital based valuation methodologies February 2015- An Analytical Framework for Telcos September 2014

9

This slide includes excerpts from previously published research. For access to the full reports, including analyst certification and important disclosures, investment thesis, valuation methodology, and risks to rating and price targets, please contact your salesperson or the covering analyst’s team or visit www.jpmorganmarkets.com


https://jpmm.com/research/content/GPS-1637810-0.pdf


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How Telcos trade 9






Appendix 55

10




Why has EM wireless underperformedIncremental revenues can’t support increasing CAPEX and OpEx demands

Lack of

incremental

revenues

Increasing

CAPEX

requirements

As Telcos have become a consumer staple, revenue growth has been reduced to a “GDP minus” equation, limiting revenue upside

EM providers aggressively grew data revenues, cannibalizing voice revenues, resulting in a depressed revenue growth

EM providers have become incremental spenders on content as they seek to retain subscribers

A lack of incremental revenues, combined with rapidly increasing CAPEX & OpEx requirements meant that EM wireless operators saw declining EBITDA margins and therefore ROIC.

As data usage grows, incremental Base Transceiver Station (BTS) requirements increase rapidly

Incremental BTS requirements meant that OpEx grew significantly faster than revenue

A

D

B

C

E



Revenues: Telcos as a consumer staple Telco revenues as a % of GDP are declining

1Fully penetrated markets fall to “GDP minus” revenue growth

3 While five Asian markets out of 11 had revenue growth in excess of GDP over 2010-2013, only two are forecast to replicate the feat over 2014-20172

Industry revenue growth is now relatively fixed, with price now controlling ROIC through usage growth


0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

2010

2011

2012

2013

2014

2015E

2016E

2017E


0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

2010

2011

2012

2013

2014

A

Background research-- Telcos are like rice July 2015

Telco spend as a % of GDP: GEM Telco spend as a % of household expenditure

12




Revenues: Data leads to voice cannibalizationData growth has severely cannibalized voice revenues, resulting in depressed revenue growth

Above the 20-40% sweet spot, we appear to

begin seeing dilution due to more marginal users,

and we suggest a likely acceleration of

cannibalistic user behavior surrounding both voice

and messaging services

As data as a % of revenues grows, ARPU

attributable to voice show a significant decline

B

Source: Company reports and J.P. Morgan estimates.

y = -0.4716x + 0.1255R² = 0.8639

-10%

-5%

0%

5%

10%

15%

0% 10% 20% 30% 40% 50%

Y = -0.4716x + 0.1255

R² = 0.8639

As markets pass through a smartphone penetration

range of 20-40%, overall data revenue growth

peaks

Source: Company reports and J.P. Morgan estimates.Source: Company reports and J.P. Morgan estimates.

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

0.0

5.0

10.0

15.0

20.0

25.0

30.0

2008A 2009A 2010A 2011A 2012A 2013A 2014A 2015A 2016E

AR

PU

(R

p 0

00)

Average ARPU - Voice (Rp 000) Data as % of revenue

Data as a % of total (X axis) vs. Voice revenue growth (Y axis): North America

Indonesia: Data as % of revenues & Voice ARPU's: 2008-16 Indonesia: Data as a % of revenue and revenue growth: % 2006-16

Source: Company reports and J.P. Morgan estimates.

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

Data as % revenues industry Wireless revenue growth



Revenues: Providers are overpaying for content to maintain market shareProviders continue to incrementally spend on content in order to retain subscribers and therefore revenues

Source: Ericsson ConsumerLab TV & Media 2010-2015.

C

Source: J.P. Morgan estimates, Bloomberg. Note: 2013-2016 numbers are consensus forecasts according to Bloomberg.

Content producers are capturing a greater % of content created value (2016: 98% vs 2004: 92%) to the detriment of providers, the major reasons are:

Premium content goes direct to consumer

With a greater proportion of content being consumed through mobile , content providers are able to go ‘direct to customer’

Introduction of HBONow

US sports leagues and EPL have or are

looking to launch direct to fan streaming

products

1

OTT aggregators are the new drivers of content pricing

1. Pay and FTA operators risk either losing

content entirely to direct models, or

paying up significantly higher than

expected to counter new OTT bidders

LeTV paid 100% more than the prior

contract for EPL

2

61% of consumers watch TV and video on mobile devices

Emerging Markets content value creation



Revenues: Decline in Telco value capture even worse than expected

We refresh our 2013 Global Ecosystem Value Capture analysis (here) with 2004-2019 trends for 644 companies:

Loss of value capture for network owners: We compared our 2013 estimates against actual results and find that

Telcos lost more value share than originally anticipated between 2013-16; .

We expect this trend to continue in 2016-19 with increase in data usage and thus reiterate our preference for content

providers.

The trend is even more pronounced in Asia

15

40%

45%

50%

55%

60%

65%

10%

12%

14%

16%

18%

20%

22%

24%

26%

28%

30%

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Equipment -LHS Content -LHS Network Owners-RHS 2013 estimates

65%

70%

75%

80%

85%

90%

10%

15%

20%

25%

30%

35%

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Content -LHS Network Owners-RHS 2013 estimates

Global Sector Value Capture - Sales Asia Sector Value Capture - Sales

Source: Bloomberg, J.P. Morgan estimates. 2016-2019 Bloomberg consensus estimates. Source: Bloomberg, J.P. Morgan estimates. 2016-2019 Bloomberg consensus estimates.

We reiterate: Don’t own Telcos as we expect the impact of data to accelerate the decline in value capture by Telcos.

C



Revenues: Own Content, specifically internet software/services and internet retail

Strong value capture growth of content is being driven by internet software/services and internet retail.

We believe that an overall preference for content is insufficient and a more specific recommendation for internet

software/services and Internet retail is required

In Asia, we see an even larger shift of value towards Internet Retail and Internet software/services, particularly

Internet retail.

16

1.00%

6.00%

11.00%

16.00%

21.00%

26.00%

31.00%

36.00%

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Internet Retail Internet Software & Services

Advertising Movies & Entertainment

2013 estimates 2013 estimates

0%

10%

20%

30%

40%

50%

60%

70%

80%

0%

5%

10%

15%

20%

25%

30%

35%

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Internet Retail Movies & Entertainment

Publishing 2013 estimates

Advertising: RHS Internet Software & Services: RHS

Global Content Owner Value Capture - Sales Asia Content owner Value Capture - Sales

Source: Bloomberg, J.P. Morgan estimates. 2016-2019 Bloomberg consensus estimates. Source: Bloomberg, J.P. Morgan estimates. 2016-2019 Bloomberg consensus estimates. Note: Internet software/services an publishing are in line with actuals

Own content, specifically Internet software/services and Internet retail

C


Revenues: We expect current value capture trends to continue

Current forecasts expect trend reversal for cable and satellite,

wireless and integrated telcos, equipment makers and content

owners. Similar forecasts were also expected in 2013 but never

materialized. We believe that the current trends are likely to

continue for the foreseeable future given the structural changes

taking place across the industry. We conclude:

We prefer cable and satellite of all network owners

We prefer equipment makers to electronic retailers

We prefer internet retail and internet software/services of all

content providers

17

-10%

-8%

-6%

-4%

-2%

0%

2%

4%

6%

8%

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Alternative Carriers Broadcasting

Cable & Satellite Integrated Telecommunication S

Wireless Telecommunication Ser 2013 estimates

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Communications Equipment Technology DistributorsComputer & Electronics Retail 2013 estiamtes

-12%

-7%

-2%

3%

8%

13%

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Advertising Internet Retail

Internet Software & Services Movies & Entertainment

Publishing 2013 estiamtes

Global Equipment Value Capture – Sales 5Y Trend Global Content Owner Value Capture – Sales 5Y Trend

Global Network Owner Value Capture – Sales 5Y Trend

Source: Bloomberg, J.P. Morgan estimates. 2016-2019 Bloomberg consensus estimates.

We expect current trends to continue for the foreseeable future


C



CAPEX: Data growth driving incremental BTS requirementsAs data grows rapidly so does the required BTS’s

186%154% 146%

96% 86% 86% 84% 74%58%

42% 38% 37%

232%198%

214%

296%

108% 117% 102%

209%

118%

50% 53% 52%

0%

50%

100%

150%

200%

250%

300%

350%

% change in BTS (2019E) % change in BTS (2024E)

45% 44%

35%

30% 29% 29%

25%24%

31%

17% 17% 16% 17%14%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Indonesia China Philippines Malaysia India SouthAfrica

Nigeria

5 year CAGR (%) 10 year CAGR (%)

Strong data usage growth will lead to significant increases in 2024 BTS requirements and hence significant CAPEX

additions

Annual data usage growth estimates range from 14-24% over a 10y period, resulting in an 52%-296% increase in

required BTS used by operators.

Source: J.P. Morgan estimates,. Source: Company data and J.P. Morgan estimates.

D

Background research- The “right price” for data? Defining V and R to solve P 1 Nov 2013- Economics of Wireless Data Part One: The Importance of Population Density and Spectrum 4 May 2011- Economics of Wireless Data Part Two: Valuing Air 7 Dec 2011- Economics of Wireless Data Part Three: What LTE means for Asia 26 March 2012- Economics of Wireless Data Part 4 Japan Country View 21 Jan 013

Estimated data usage CAGR (%) Estimated BTS build percentage change

18








CAPEX driving incremental OpEx

5%4% 4% 5%

7%6% 6%

8%7%

9%

4%5%

13%

6%7%

9%10%

9% 9%

12%

11%12%

5%

7%

0%

2%

4%

6%

8%

10%

12%

14%

10 year Revenue CAGR (%) 10 year OpEx CAGR (%)

-23%

-18%-16%

-15%-14%

-13% -12% -12%-10% -10%

-7%

-5%

-25%

-20%

-15%

-10%

-5%

0%

Source: J.P. Morgan estimates. Source: J.P. Morgan estimates.

Data growsand cannibalizes

voice

Increasing CAPEX required to

Maintain network quality

Additional BTS’s lead to High OpEx

growth

EBITDA MarginCompression

Excessive CAPEX growth leads to a significant increase in BTS requirements and therefore high OpEx growth

E

Revenue CAGR vs CAGR: 2024E EBITDA margin impact: 2024E

We estimate EM carrier OpEx will grow at an average of 9% per annum vs forecast revenue growth of 6%, leading to significant margin compression.



Quantifying Capex and Opex relationship – US$ 100 capex = US$ 7 OpexE

20

Opex revisions lag capex revisions on 9 month average across all Asian markets1

PLDT FY16 consensus capex and opex estimates AIS FY13 consensus capex and opex estimates

-2%

0%

2%

4%

6%

8%

10%

12%

-10%

0%

10%

20%

30%

40%

50%

Capex revision from base opex revision from base (RHS)

0%

5%

10%

15%

20%

25%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

Capex revision from base opex revision from base (RHS)

On average – US$ 100 of capex leads to US$ 7 of additional opex2

Source: Bloomberg

Source: J.P. Morgan estimates, company data

Source: Bloomberg

X variable Y intercept R2

Indonesia 0.079 0.0 0.86

Thailand 0.042 0.0 0.65

Singapore

(StarHub)

0.104 0.0 0.79

Philippines 0.063 0.0 0.97

Malaysia (Digi) 0.055 0.0 0.93

Hong Kong 0.111 0.0 0.94

China 0.039 0.0 0.82

Average 0.071 0.0 0.85

Median 0.063 0.0 0.86

Philippines Gross Fixed Assets vs Network costs Country level capex vs opex relationship – Y intercept 0,0


15 month lag 4 month lag

X variable Y Intercept R2 Correlation

Indonesia 0.104 -270.9 0.93 0.96

Thailand 0.042 -3.3 0.66 0.81

Singapore

(StarHub) 0.097 20.1 0.80 0.89

Philippines 0.063 -26.3 0.97 0.98

Malaysia (Digi) 0.051 6.2 0.94 0.97

Hong Kong 0.104 45.8 0.95 0.97

China 0.045 -1982.5 0.87 0.93

Average 0.072 -315.8 0.87 0.93

Median 0.063 -3.3 0.93 0.96


Quantifying Capex and Opex relationship – US$100 capex = US$ 7 OpexE

21

Strong Capex to Opex relationship holds on a operator level, country level and regional level3

TLKM – Gross Fixed Assets vs Network costs

Source: J.P. Morgan estimates, company data *including China, R2 = 0.9719. We have removed china as its disproportionate size leads to excess skew

Regional Gross Fixed Assets vs Network Costs (Excluding China)

y = 0.1256x - 532.33R² = 0.9937

-

500

1,000

1,500

2,000

2,500

4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000 22,000

Ne

two

rk A

sset

s (U

SD

)

Gross Fixed Assets (USD)


y = 0.0631x - 26.281R² = 0.9701

-

100

200

300

400

500

600

700

800

900

- 2,000 4,000 6,000 8,000 10,000 12,000 14,000

Netw

ork

opex

(USD

)


Philippines Gross Fixed Assets vs Network costs


y = 0.072xR² = 0.7291

-

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

- 5,000 10,000 15,000 20,000 25,000

Netw

ork c

osts

(USD

)



Quantifying Capex and Opex relationship – US$ 100 capex = US$ 7 OpexE

22

Who’s at risk? We have identified operators who have seen a meaningful upward revision in capex estimates without a meaningful upward revision in opex estimates. We expect street to revise up their opex revisions on an average 9 month lag

4

AIS (UW) StarHub (UW)

Source: Bloomberg

Source: Bloomberg

Source: Bloomberg

Digi (UW) M1 (UW)

-10%

-5%

0%

5%

10%

15%

Capex revision from base opex revision from base

-5%

0%

5%

10%

15%

20%


-10%

-5%

0%

5%

10%

15%


-20%

-10%

0%

10%

20%

30%

40%


Source: Bloomberg

Source: Bloomberg Source: Bloomberg


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How Telcos trade 9






Appendix 55

23




Data and the unbalanced balance sheet

24

Data is a capital intensive business which is expanding balance sheets among EM telcos

The CAPEX and opex requirements of data growth are leading to increasing assets and leverage among Asian telcos

For the markets analyzed, 74% of EBITDA revisions were negative over the last 5 years. This suggests a routine failure

to recognize increasing opex trends across the industry, especially among emerging market telcos.

78%

62%

57%

49%

41%

37%

15%

13%

11%

-13%

-20% 0% 20% 40% 60% 80% 100%

India

Indonesia

Philippines

Thailand

Taiwan

China

Malaysia

Korea

Singapore

Hong Kong

1.07

0.93

0.63

0.55

0.28

0.27

0.25

-0.06

-0.84

-1.14

-1.50 -1.00 -0.50 0.00 0.50 1.00 1.50

Taiwan

Thailand

Malaysia

Philippines

Singapore

Indonesia

India

China

Korea

Hong Kong

Change in total assets: 2010-2015 Change in net debt to EBITDA: 2010-2015

Source: Bloomberg Source: Bloomberg

Background research- Data and the unbalanced balance sheet September 2015




Stressing the balance sheet: ASEAN Case studies

25

From our case studies of ASEAN telcos, downside dividend risk begins when net debt to EBITDA reaches 1.5x and

asset sales become a realistic policy above 2.3x

Net debt to EBITDA

Dividend cuts Asset sales New equity

issued

Indosat 2.0 x 2.3 x NA

XL Axiata 1.6 x 3.0 x 3.0 x

Maxis 1.5 x NA NA

Axiata 1.0 x 1.6 x NA

SingTel 1.5 x NA NA

M1 0.7 x NA NA

PLDT 1.4 x NA NA

Median 1.5 x 2.3 x 3.0 x

Summary: Leverage triggering policy change Indonesia: XL Axiata Net debt to EBITDA and dividend payout

Indonesia: Indosat Net debt to EBITDA and dividend payout Malaysia: Maxis Net debt to EBITDA and dividend payout

0

20

40

60

80

100

120

1

1.5

2

2.5

3

Net debt to EBITDA Asset sales

Dividend cuts Dividend payout ratio (%)

Source: J.P.Morgan estimates, Bloomberg

0

10

20

30

40

50

60

00.5

11.5

22.5

33.5

Net debt to EBITDA Asset sale

Dividend cuts Dividend payout ratio (%)

0

50

100

150

200

0

0.5

1

1.5

2

2.5

Net debt to EBITDA Dividend cuts Dividend payout ratio (%)

Source: J.P.Morgan estimates, Bloomberg Source: J.P.Morgan estimates, Bloomberg

Source: J.P.Morgan estimates, Bloomberg


Stressing the balance sheet: Who’s next?

26

We analyzed leverage levels across Asian telcos and identified which

companies are most at risk from increasing balance sheet pressure.

We looked at 2 separate criteria and compared current leverage

levels against the levels at which we saw policy changes among

ASEAN telcos

Current net debt to EBITDA levels

Change in leverage levels over the past year

The company's that we identified as most at risk are Indian operators

Reliance Communications (RCOM, UW), Bharti Airtel (BHARTI,

UW) and Idea (IDEA, N), Phil operators Globe (GLO, N) and PLDT

(TEL, UW), Thai operators AIS (ADVANC, N) and Total Access

Communication (DTAC, UW) and China Unicom (762 HK, OW).

-3 -2 -1 0 1 2 3 4 5 6 7

XL Axiata

HKT

KT Corp

Indosat

SingTel

Hutchison

LG Uplus

Taiwan Mobile

Maxis

Chunghwa Telecom

SmarTone

SK Telecom

M1 Ltd

Telekomunikasi

China Mobile

DiGi

China Telecom

DTAC

Far EasTone

Telekom Malaysia

PLDT

China Unicom

Axiata

Globe

StarHub

Reliance Communications

AIS

Bharti Airtel

Idea

Y/Y change in net debtto EBITDA (x)

Current net debt toEBITDA

Current Leverage Year on year

change

5 year change

in

Net debt to

EBITDA

Total Debt to

EBITDA

net debt to

EBITDA

net debt to

EBITDA

RCOM 6.0 x 6.3 x 0.8 x 0.8 x

BHARTI 2.6 x 2.9 x 0.9 x (0.3)x

IDEA 2.9 x 3.2 x 1.3 x 0.1 x

PLDT 1.8 x 2.2 x 0.3 x 1.3 x

Globe 1.6 x 1.8 x 0.4 x 0.4 x

China Unicom 1.7 x 2.0 x 0.4 x 0.8 x

AIS 1.2 x 1.4 x 0.8 x 1.1 x

DTAC 1.1 x 1.8 x 0.2 x 1.6 x

ASIA: Balance sheet stress analysis

ASIA: Balance sheet stress analysis

Source: J.P.Morgan estimates, BloombergSource: J.P.Morgan estimates, Bloomberg


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How Telcos trade 9






Appendix 55

27




Interconnect > Unit costs > Voice Strategies

28

The Wave of interconnect rate cuts seen in Europe and Africa 3-5 years ago led to significant compression in voice

unit costs

A reduction in unit costs is reflected in rising off-net margins, which triggers a move from unit pricing to access

pricing and accelerates the decline in voice revenues.

The wave of interconnect rate cuts is now washing against the shores of LatAm and Asia, which represents a

significant change from previous trends (see Size Matters for historical EM interconnect analysis).

Overall revenue growth is negatively correlated with data as % of revenue. Therefore increasing interconnect

margins increases risk of sales misses and forecasting error.

A reduction in voice unit costs can trigger a move from unit to access pricing

Voice revenues driven by off-net margin trends Service revenue growth negatively correlated to rising data as % of revenue

We see a changing interconnect environment leading to sales forecasting risk with Indonesia (EXCL), Philippines (PLDT) and India (Bharti) most at risk , in our view



Falling voice = Rising Capex

29

The capital intensity of data is significantly higher than voice, therefore as incremental revenue drivers shift from voice to

data, overall capital intensity increases structurally.

There is a clear and measurable positive correlation (0.76) between data and capex to sales. Therefore a change in

interconnect environment can lead to increased data as % of overall revenues and therefore capex forecasting risk.

Data is more capital intensive than voice which is a sunk cost to may operators

Voice revenues driven by off-net margin trends

We see a changing interconnect environment leading to capex forecasting risk with Indonesia (EXCL/ISAT), Philippines (GLO) and Malaysia (TM, Axiata, Maxis) most at risk

JPM vs Street CAPEX forecasts 2017-19E

Source for both charts: Company, Bloomberg, J.P. Morgan estimates


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How Telcos trade 9






Appendix 55

30




Asset Restructuring: The only option for EM wireless

Economic Argument

Core thesis: Given our previous estimations of EBITDA margin compression, EM telcos have no choice but to fundamentally change the structure of industry assets through the unification of networks via nationalization, centralization under a regulated return utility, or more aggressive commercial network sharing.

ROIC trending towards unsustainable levels

Asset restructuring is not only necessary to revive the fortunes of EM wireless Telcos but is also needed to promote economicgrowth in EM

Public Policy Argument

Core thesis: Communications infrastructure is critical to GDP growth, with the World Bank estimating 10ppts of

broadband penetration drives 1.4% GDP growth. The Digital Divide is far worse than many development

organizations capture, as they focus on subscriptions rather than download speeds. Governments will face a

choice between below potential GDP growth, or intervention in telecom markets.

A well developed communications infrastructure is necessary for optimal GDP growth

Past developments in communications infrastructure has led to significant productivity growth

Historic underinvestment in EMs have seen them left behind and revenue opportunities are insufficient to

bridge the gap under the current industry model

Background research- On nationalization of Networks -April 2016

31




Economic Argument

Current forecast data growth results in CAPEX and OpEx inconsistent with available revenue growth, driving 12ppts EBITDA margin decline by 2024. Significant restructuring of the asset base is the only option

1

Operators need to change their strategies to specifically match incremental asset bases with the realistic incremental revenue opportunity, or face ongoing structural compression in returns on capital, as well as valuation multiples

Network owners continue to capture a falling

share of overall ecosystem value

This declining value capture is paired with a

significant, and ongoing, increase in capital

intensity

2

6.00

7.00

8.00

9.00

10.00

11.00

12.00

13.00

14.00

15.00

16.00

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

DM EM


3EMs’ ROIC will continue to trend lower to unsustainable levels without significant asset restructuring

Industry ROIC will trend lower to unsustainable levels without industry wide asset restructuring

Background research- Mommas, don’t let your babies grow up and buy telcos... June 2013

ROIC trends: DM vs. EM telcos, 2004-14


Revenue vs. capex growth trends: GEM telcos

32




Governments will face a very clear decision between

inaction, and structurally lower levels of economic

growth; or significantly more regulatory intervention in

telecom markets, possibly through the introduction of

nationalized networks

Communication infrastructure is a key driver of GDP

growth: World Bank estimates that every 10% in

broadband penetration drives over 1.38% of GDP

growth

Source: IMF, McKinsey Global Institute, J.P. Morgan

Public Policy Argument: Asset Restructuring a requirement for optimal

GDP growth Government inaction could result in less than optimal GPD growth

Source: World Bank, J.P. Morgan.

1.1

1.15

1.2

1.25

1.3

1.35

1.4

EM DM

MGI connections Index vs Per Capita GDP Impact on GDP growth of a 10% increase in Broadband penetration



Public Policy Argument: Connectivity driving productivity Communication services have driven significant productivity changes in the past

Source: Syverson 2013, Crafts 2015, J. P. Morgan

Labor productivity changes during the phase of

electrification in the US from 1890-1940 match

those seen from the rise in communication and

computing technologies during 1970-Present

Direct connection between particular

communication technologies, inclusive of

websites and ecommerce infrastructure, and

productivity gains

Source: Jenson, Oxford University, J.P. Morgan

When mobile phones were introduced in

Kerala, it resulted in a significant increase in

productivity, through better price discovery,

seen through price dispersion pre and post

mobile phone adoption

US labor productivity during electrification era vs. ICT era

Mobile Phone proliferation vs. price dispersion in local markets



Public Policy Argument: EMs being left behind EMs’ digital adoption levels are significantly behind DM and available incremental revenues in EM’s are insufficient to support the required network investment

Source: World Bank Development Report / Oxford Internet Institute

1 Adoption rates of digital technologies are clearly

related to income levels

2

While EM wireless services look inexpensive on an

absolute basis relative to DM charge levels, they are

clearly still very dear relative to income levels

3The north/south divide in fixed line broadband pricing: Lack of competition, low levels of investment and the inertia of state owned incumbents in many markets leaves EM fiber services extremely expensive on an absolute basis

1 2

3

Digitization vs. Income levels

Price of fixed broadband services

DM vs. EM wireless pricing: absolute and relative to income

Source: World Bank, J.P. Morgan. Source: ITU Database



Asset Restructuring: Quantifying historical underinvestment in EM

Source: J.P. Morgan estimates, Company data. Source: OpenSignal, J.P. Morgan

Rank

1

2

3

We can quantify the historic under investment across EM Asia first by looking at differential asset bases, and then at the impact

these differential asset bases have on network quality. There is a clear correlation between asset base and network quality

GFA/Sub averages over USD1,000 in markets like Korea, while markets like India struggle to break the USD100 barrier

Gross Fixed Assets per subscriber Average download speeds by country and network

EMs have a significantly lower asset base per subscriber than DM’s and it shows through network quality.

36

2.19

2.25

2.50

2.72

3.03

3.21

3.27

3.40

4.54

4.84

6.23

6.35

6.60

8.66

13.23

15.87

16.42

25.90

27.73

43.34

45.83

0.00 10.00 20.00 30.00 40.00 50.00

Philippines 3G

Thailand 3G

Laos 3G

Indonesia 3G

Malaysia 3G

Pakistan 3G

Sri Lanka 3G

Bangladesh 3G

Myanmar 3G

Vietnam 3G

Taiwan 3G

Singapore 3G

Indonesia 4G

Philippines 4G

Thailand 4G

Malaysia 4G

Hong Kong 4G

Japan 4G

Taiwan 4G

South Korea 4G

Singapore 4G

Nov-Jan17 oct-dec 16 sep-nov 16 aug-oct 16

Jul-sep 16 jun-aug 16 may-jun 16


M&A Primer: Asset vs. Cash Flow valuations?

Source: J.P. Morgan estimates, Company data.

Buy vs. Build Decisions explained (Malaysia / Singapore): The markets with the largest spread between JPMe replacement

value and current enterprise value are in Malaysia and Singapore, which supports a build rather than buy strategy for new

entrants, this is supported by the recent build strategies of Telekom Malaysia and Umobile in Malaysia and TPG in Singapore.

Potential M&A destination markets (Indonesia / Thailand / India): Large upside to replacement value from current

enterprise value may provide justification for an acquirer to pay more than current share price.

Indosat / XL (150%/101% upside to replacement value): M&A or network nationalization appears to be viable options to

break PT Telkom’s significant pricing power in the market.

DTAC (86% upside to replacement value): Our OW thesis is based on the argument that spectrum acquisition or network

sharing creates a terminal value for the shares. In our view, this is a clear example where purely focusing on analysis of

going concern structures misses the potential future state and asset value

Idea (70% upside to replacement value): Our colleague covering Vodafone calculated 40% upside to combined Vodafone

/ Idea merge. Our analysis is also supportive of this upside, showing 70% upside to replacement value.

We compare replacement values against current enterprise value and cash flow valuations to indicate which markets are ripe for M&A.

37

339%

213% 206%

150% 150%105% 101% 91% 86% 81% 70% 53% 40% 34% 32% 19% 17% 11%

-13% -17% -17% -21% -28% -32%-55% -75% -81%-150%

-100%-50%

0%50%

100%150%200%250%300%350%400%

Replacement value upside from current Enterprise value

“Build markets”“Buy markets”


38

1%

2%

2%

4%

6%

7%

8%

9%

10%

10%

10%

12%

16%

17%

17%

19%

21%

24%

30%

32%

45%

47%

54%

98%

0% 20% 40% 60% 80% 100% 120%

Maxis

Digi

PT Telkom

StarHub

HKT

Chunghwa Telecom

M1

SingTel

LGU+

Taiwan Mobile

SK Telecom

KT Corp

Globe

Indosat

Fareast tone

XL

PLDT

DTAC

AIS

Hutch

Bharti

Smartone

TRUE

Idea

Mark to market spectrum value as % of Enterprise value

Vodafone / Idea – a merger could offer >40% upside

M&A Primer: Asset vs. Cash Flow valuations?We compare replacement values against current enterprise value and cash flow valuations to indicate which markets are ripe for M&A.

1 2

1

2

Spectrum can account for high proportion of replacement

values: In India, Hong Kong and Thailand we find that

spectrum can make up >40% of an operators overall enterprise

value. In the case of Idea, spectrum accounts for ~100% of the

operators enterprise value.

Vodafone / idea merger could offer 40% upside: Akhil Dattani

argument for a Vodafone / Idea merger is supported by our

analysis as the majority of Idea’s value is held through

spectrum. Consolidation could repair fixed asset valuations.

Background researchM&A primer: Asset vs. Cash flow valuations- Jan 2017Vodafone – Jan 17




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Appendix 55

39




Asset Restructuring becoming a reality?

Recent developments within certain Emerging Markets has shown significant action / interest regarding Asset Restructuring.

Tower Co. has consolidated country wide

tower assets, saving on industry wide CAPEX.

A significant step towards broader restructuring

The Thai government has already indicated

that network assets will likely sit under a

National Telco Infrastructure Fund (NTIF),

exact format is yet to be disclosed

The Indonesian government has made

redrafting legislation on network sharing a key

priority, which could lead to a Multi-Operator

Core Network (MOCN)

Asset restructuring in EM wireless is gathering pace

Current developments Past developments

Introduction of the National Broadband Network

(NBN) and rollout of the HetNet will integrate

wireless, fixed and Wi-Fi networks

Background research- NBN Enhances Competitive Positioning June 2013- Smart Nation, HetNet, and potential implications for wireless June 2015

Background research- Build-A-Tower Co. April 2015

Tower Co

National Telco Infrastructure Fund

Multi-Operator Core Network

HetNet and National Broadband Network

Australian NBN

Also a NBN and designed to replace the previous

copper system. The NBN will help share the

capex burden among providers, a similar model

would be ideal in the wireless space

Background research- Coalition would accelerate NBN - and TLS cash flow April 2013

Background research- Travels with Sully June 2016

China

Thailand

Indonesia

Singapore

Australia

40


Multi-Operator Core Network

Mexico

Mexico could enact legislation to promote fixed

line unbundling (a.k.a: network sharing) in

2017. While 700MHz has been deployed for a

wholesale only 4G network to be built by Altan.

Background research- Unbundling is net chapter in Regulatory Headwinds: June 2016








Tower Co. is consolidating network assets and helping to save on CAPEX for China Telcos.

Tower assets injected into Tower Co.

Tower Co. acquired Rmb231.4bn of tower assets from three Telcos on Oct 31, 2015, including RMB214bn from listco’s

Tower Co helping second tier players (China Unicom / China Telecom) catch up on coverage

Tower Co. built 220K new towers last year

Saved the need for 265K new towers

74% of new towers are shared

China Unicom potentially the largest beneficiary due to highest stake value to market cap ratio, followed by China

Telecom

Asset Restructuring becoming a reality? China TelcosTower Co. helping second tier players catch up on coverage

Source: Company data and J.P. Morgan estimates.



Summary of our conversation with Dr. Uttama Savanayana, Thai Minister of Communications.

National Wireless Network?

It seems likely that a significant percentage of network assets will eventually sit in some form of National Telco

Infrastructure Fund (NTIF)

The Government will likely hold a stake (best assumption 25- 30%) in this fund, with private investors and

telecom operators owning the residual

Operators, inclusive of the TOT and CAT as well as private carriers, could sell their network assets into the fund

(towers for example) in return for cash that they could then reinvest in services

Spectrum policy

Spectrum sharing regulations have passed their second reading in parliament and are expected by year end, it

remains to be seen whether spectrum assets could be sold to the NTIF

Fiber NBN already underway

BT15bn of fiber asset rollouts and Bt5bn of submarine cable expansion will all sit with the Ministry rather than

within either TOT or CAT

Asset Restructuring becoming a reality? Thai TelcosNational Telco Infrastructure Fund (NTIF) could hold network assets



Recent news reports have suggested that the Indonesian government is moving toward formalizing rules for

network sharing

Communication and Informatics Minister Rudiantara has made redrafting 2 government regulations on

telecommunications his main job in order to drive efficiencies in the industry, network sharing is a key part of the

revisions

Network sharing expected to reduce CAPEX

Reported that an Indonesian telecommunication operator has claimed that if network sharing was allowed by the

government it could reduce investment costs by 40%

Communications and Information Ministry preparing Multi-Operator Core Network (MOCN) Scheme to facilitate

network sharing

Source: Jakarta Post

Asset Restructuring becoming a reality? Indonesian TelcosMOCN Scheme could lead to network sharing



700MHz deployed for wholesale only LTE network

Altan wins deal to build and operate a nationwide wholesale mobile network. MVNO’s or current MNO’s can use

the network to expand their services and/or coverage.

Altan to build out a nationwide LTE network covering >90% of the population at an estimated cost of US$7bn. The

network will likely use 10,000 towers and use CFE fiber connections.

The wholesale network should support competition and reduce capex requirements for wireless service providers.

Source: The Wall Street Journal

Unbundling of fixed line would allow TV/SKY to use AMX/Telmex network for a fee

Mexican government likely to mandate fixed line network sharing in 2017.

SKY would be able to sell triple-play packages by adding voice and broadband to its pay TV products. While Other

players, such as Axtel or TotalPlay could expand to areas where they have no coverage.

Asset Restructuring becoming a reality? Mexico TelcosAltan to build wholesale LTE network and fixed line services to be unbundled



Asset Restructuring: Winners and Losers 2nd-tier providers to benefit most as network speed and coverage gaps are eliminated

Winners

Losers

2nd Tier providers:

Elimination of network speed and coverage gaps reduces market leaders

competitive advantage

Providers now compete on service and not asset quality

Easier customer acquisition

Incremental CAPEX requirements significantly reduced

Less duplication of assets creates cost efficiencies among providers

As assets fall, significant ROIC increases

Market leaders:

Elimination of competitive advantage reduces ability to maintain/ grow

subscriber base

Uncertainty over asset pricing might means current assets under priced during

restructuring

Fails to see benefit of previous asset base build out

Comparative cost efficiencies reduced

Tower Companies

Reduced capex within industry leads to a smaller overall asset base



What does Asset Restructuring look like?Asset restructuring can take several forms, we believe a fully integrated model is optimal

TowersShelter and

generators Spectrum

Base

Transceiver

Station

1

Tower only

Model

China Model

(sup-optimal)

Local wireless providers Centralized entity

Service

TowersShelter and

generators Spectrum

Base

Transceiver

Station

2

PPE integrated

Model

US model

(sufficient)


Service

TowersShelter and

generators Spectrum

Base

Transceiver

Station

3

Fully integrated

Model

(Optimal)


Service



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Appendix 55

47




JPM Factor Analysis Model

48

JPMC Asia / EM QMI state cycle JPMC Asia / EM QMI state cycle

JPMC Asia / EM QMI state cycle – current position

The EM/Asia QMI is currently in the EXPANSION state. It recently also tested the Slowdown state – we have now been in Expansion for 6 months and it appears to now have 'late cycle' characteristics. This means we prefer blended yield and earnings based Value and Momentum

again.

Overall market conclusion


Applying Factors to Telcos

Since December 2016, Value and Beta stocks have

outperformed the rest of the market.

Beta value spread now stands at 0.9x P/E as investors

continue to show preference for high beta stocks in last 4

months

Value spreads narrowed to 23x but is still above the

historical average of 19x

Solidly in Expansion state - Prefer blended yield and

earnings based Value, Momentum and high vol (beta)

High vs. Low Forward P/E Spreads between high and low beta stocks

Key factor performances for MSCI Asia ex-japan: 20167YTD

49

Summary

Cheapest vs. Expensive Forward P/E – MSCI Asia ex-japan

Background research- Quant manager monthly- ASIA: March 2017




50

We have applied our quant team’s current factor expectations to Telcos by applying the following weights to different factors

and screening Asian Telco’s for exposure to those factors

Telco Screen Rules


We screen Asian Telco’s for exposure to our quant teams favored factors

Factor Metric Weight Comment

Value

Earnings yield Inverted P/E 1 yr fwd 15% Solidly in expansion state, prefer equal weight of dividend yield and earnings yield as protection against slowdown. Value spread continuing to narrow, providing additional support to value

Value P/BV – current 0%

Dividend yield Dividend Yield 15%

Momentum Historical momentum 12 Mth return 20% Overweight momentum due to expansion state.

Forward momentum EPS FY2 3mth % Chg 20%

Growth Growth EPS FY1 to FY2 10%

Quality

Quality Returns ROE – current 0% Too soon for quality but will rotate into this factor (from growth) if signs of slow down materialize

Stable Returns EPS stability 0%

Volatility

Size Current market value -10% High beta stocks have been preferred over past 4 months. We continue to prefer high beta and small capsBeta Beta (BBG adjusted) 10%


51

Telco Screen Output


We screen Asian Telco’s for exposure to our quant teams favored factors

Name JPMe RatingPE 1-Yr

Fwd(Inverted)

P/BV FY0 (Inverted)

Div YldFY0

12 Mth Ret USD

EPS FY2 3mth %

CHG

EPS FY1 to FY2

ROE FY0EPS

Stability

Current Market Val

(US$M)

Beta (Bloomberg Adjusted)

FINAL RANK

LG Uplus Corp OW 9.1% 75.7% 1.9% 37.9% 5.0% 9.0% 8.1% 36.5% 5,079.3 0.04 96%SK Telecom Co Ltd N 9.7% 92.7% 4.3% 15.8% 13.3% 3.5% 10.2% 13.2% 14,229.9 0.29 93%KT Corp OW 10.5% 150.4% 2.6% 16.4% 1.4% 6.6% 7.2% 273.9% 6,579.1 0.21 89%HKT Trust OW 6.4% 49.7% 6.0% 1.9% 1.0% 5.2% 12.7% 10.4% 10,123.6 0.16 85%

Telekomunikasi Indonesia (Persor) Tbk PT OW 6.0% 19.9% 2.5% 16.7% -0.7% 14.8% 21.7% 9.3% 28,499.1 0.94 81%Advanced Info Service PCL UW 6.0% 8.4% 5.9% 1.9% -1.5% 6.5% 67.4% 12.0% 14,362.9 1.17 78%Globe Telecom, Inc. N 6.0% 24.4% 4.5% 1.6% -2.4% 4.1% 29.0% 12.9% 4,834.5 1.06 74%Singapore Telecommunications Limited N 6.6% 40.2% 4.5% 5.0% -1.9% 5.7% 15.6% 2.6% 44,979.0 0.89 70%Far EasTone Telecommunications Co., Ltd. N 4.8% 29.4% 5.1% 18.8% -4.0% 5.0% 15.9% 2.8% 7,772.8 0.25 67%China Mobile Ltd. OW 7.7% 59.2% 2.9% 2.8% -0.7% 7.5% 12.0% 6.8% 224,441.6 0.67 63%China Telecom Corporation Limited OW 8.4% 117.3% 3.0% -4.3% -3.0% 13.4% 6.8% 11.9% 37,528.8 0.81 59%MAXIS BERHAD UW 3.9% 9.9% 3.2% -2.2% 2.5% -0.8% 45.2% 4.5% 10,672.5 0.58 56%PLDT Inc UW 6.6% 36.2% 8.4% -20.6% -6.3% -14.0% 17.8% 13.4% 6,204.3 1.12 52%

PCCW Ltd N 6.8% 32.6% 5.9% 3.6% -12.9% -7.3% 17.8% 41.1% 4,753.0 0.33 48%XL Axiata Tbk PT N 2.1% 56.3% -27.4% -8.2% 144.2% -0.2% 149.8% 2,308.1 1.19 44%DiGi.Com Bhd UW 4.2% 1.3% 4.2% -3.5% -3.2% 0.5% 314.4% 12.5% 8,709.7 0.95 41%Taiwan Mobile Co Ltd N 5.2% 20.5% 15.9% -4.6% 5.7% 25.5% 4.2% 9,520.5 0.43 37%Chunghwa Telecom Co., Ltd N 5.1% 46.4% 5.4% 7.6% -6.6% 3.2% 11.7% 6.4% 25,631.4 0.24 33%Tower Bersama Infrastructure Tbk PT OW 6.2% 6.4% 1.1% -16.6% -9.0% 12.9% 79.3% 24.2% 1,792.1 0.67 30%China Unicom (Hong Kong) Limited OW 3.5% 116.6% 2.0% 5.6% -16.3% 274.6% 4.6% 135.6% 28,747.9 0.83 26%Bharti Infratel Ltd OW 5.3% 31.2% 1.0% -14.5% 0.7% 7.0% 12.7% 13.6% 8,534.8 0.35 22%Telekom Malaysia Berhad OW 3.7% 32.9% 3.5% -9.2% -6.8% 6.5% 10.0% 15.5% 5,255.9 0.55 19%Axiata Group Bhd N 4.2% 56.9% -25.0% -14.8% 9.8% 2.1% 23.2% 9,306.3 1.35 15%

True Corporation PCL N -0.6% 59.5% 0.0% -14.3% -33.3% 133.3% -2.7% 125.1% 6,277.0 1.77 11%StarHub Ltd. UW 5.6% 3.9% -12.3% -14.3% -3.1% 178.5% 7.7% 3,518.8 0.41 7%Idea Cellular Ltd N -3.4% 64.7% 0.5% 2.9% -777.5% -160.9% 12.6% 108.8% 5,965.2 0.76 4%Bharti Airtel Limited UW 2.6% 47.0% 0.4% 10.1% -29.3% -14.8% 8.9% 59.4% 21,275.6 0.63 0%

Overall market conclusion

When combining our bottom up analysis with factor analysis we conclude (1) Reiterate OW calls on LGU, KT, HKT, TLKM. (2) Reiterate UW calls on Bharti, STH

Source: J.P. Morgan estimates, Bloomberg – Purple Buy, Orange sell


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Appendix 55

52




What to buy? what to sell?

Source: Bloomberg; J.P. Morgan estimates, prices as of March 16, 2017

53

• We look for broad telecom sector underperformance as Asia earnings growth outlook improves and investors

shift from defensive yield strategies towards value beta plays. We look for exposure to stable DM integrated

players within defendable FCF (KT/LGU+), carriers with a significant network quality gap to peers enabling

pricing power (TLKM) or EM players where industry asset restructuring (network nationalization, significant

network sharing) is driving ROIC improvement (China Telecom).

Asia telecom top picks and stocks to avoid



Top picks

LG Uplus 14,750 032640 KS OW 5,627 10.9 8.7 1,356.9 1,702.6 3.08% 11.74%

KT Corp 32,650 030200 KS OW 7,446 9.4 7.6 3,490.0 4,306.0 3.72% 7.20%



AIS 175.0 ADVANC TB UW 14,925 20.1 19.3 8.7 9.1 3.47% 59.68%

P/E (x) EPS (LC)

Stocks to avoid


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Appendix 55

54




AppendixNetwork quality statistics – Bangladesh, Hong Kong, Indonesia, Japan, Laos

Bangladesh 3G Hong Kong 4G

Indonesia 3G

All charts: Source: OpenSignal, J.P. Morgan

Indonesia 4G

Japan 4G Laos 3G

55

8.35

4.68

16.45

10.02

3.48

4.80

10.12

7.98

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

Indosat Smartfren Telkomsel XLMay-Jun 16 Jun-Aug 16 Jul-Sep 16 Aug-Oct 16 Sep-Nov 16 Oct-Dec 16 Nov'16-Jan'17

2.452.94

0.63

5.44

3.16

2.171.84

0.60

5.69

3.28

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

3 Indosat Smartfren Telkomsel XL

May-Jun 16 Jun-Aug16 Jul-Sep 16 Aug-Oct 16 Sep-Nov 16 Oct-Dec 16 Nov'16-Jan'17

23.06

9.28

26.64

16.9118.43

8.96

25.74

12.56

0.00

5.00

10.00

15.00

20.00

25.00

30.00

3 CMHK csl. SmarTone

May-Jun 16 Jun-Aug16 Jul-Sep 16 Aug-Oct 16 Sep-Nov 16 Oct-Dec 16 Nov'16-Jan'17

24.42

20.63

27.2627.57

21.96

28.16

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

KDDI NTT DoCoMo SoftBank

May-Jun 16 Jun-Aug 16 Jul-Sep 16 Aug-Oct 16 Sep-Nov 16 Oct-Dec 16 Nov'16-Jan'17

1.89

2.96

2.14

2.85

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

Lao Telecom Unitel


3.18 3.18

3.99

2.843.23

2.76

4.41

3.18

0.000.501.001.502.002.503.003.504.004.505.00

Airtel Banglalink Grameenphone Robi



AppendixNetwork quality statistics – Malaysia, Myanmar, Pakistan, Philippines

Malaysia 3G Malaysia 4G

Myanmar 3G


Pakistan 3G

Philippines 3G Philippines 4G

56

7.00

9.23

7.43

9.89

0.00

2.00

4.00

6.00

8.00

10.00

12.00

Globe Smart


2.161.99

2.252.13

0.00

0.50

1.00

1.50

2.00

2.50

Globe Smart


3.61

2.55

3.07 3.143.30

2.91 3.052.86

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

Celcom DiGi Maxis U Mobile


4.173.64

4.824.48

3.31

5.82

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

MPT Ooredoo Telenor


3.22

1.86

3.463.67

2.49

3.47

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

Mobilink Telenor Zong


13.7915.31

19.38

14.3015.71 15.01

21.20

11.56

0.00

5.00

10.00

15.00

20.00

25.00


May-Jun 16 June-Aug 16 Jul-Sep 16 Aug-Oct 16 Sep-Nov 16 Oct-Dec 16 Nov'16-Jan'17


Network quality statistics – Singapore, South Korea, Sri Lanka, Taiwan

Singapore 3G Singapore 4G

South Kora 4G


Sri Lanka 3G

Taiwan3G Taiwan 4G

Appendix

57

39.1444.46

51.6047.51

37.42

52.55

0.00

10.00

20.00

30.00

40.00

50.00

60.00

M1 SingTel StarHub


5.20

6.175.48

6.64 6.89

5.51

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

M1 SingTel StarHub

May-July 2016 Sep-Nov 16 Oct-Dec 16 Nov'16-Jan'17

42.1343.09

47.46

44.86 44.96

40.19

36.00

38.00

40.00

42.00

44.00

46.00

48.00

50.00

LG U+ olleh SKTelecom


28.9425.32

23.24

28.3127.06

32.23

21.73

29.88

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

Chunghwa FarEasTone T Star Taiwan Mobile


6.16

7.12

3.73

7.41

6.49

7.43

4.12

6.88

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00



2.73 2.94

5.06

2.03

3.292.97 3.05

5.18

1.87

3.25

0.00

1.00

2.00

3.00

4.00

5.00

6.00

Airtel Dialog Etisalat Hutch Mobitel



Network quality statistics – Thailand, Vietnam

Thailand 3G Thailand4G

Vietnam 4G


Appendix

58

20.18

14.40 13.6813.02 13.26 13.40

0.00

5.00

10.00

15.00

20.00

25.00

AIS DTAC TrueMove


2.582.88

1.912.03

2.472.25

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

AIS DTAC TrueMove


3.273.68 3.53

4.865.47

4.20

0.00

1.00

2.00

3.00

4.00

5.00

6.00

MobiFone Viettel Mobile Vinaphone



Network quality statistics – Regional- India

India: Karnataka 3G

India: Maharashtra 3G


India: Maharashtra 4G

India: Delhi 3G India: Delhi 4G

Appendix

59

2.462.75

1.94

4.32

2.76

3.68

2.18

4.74

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

5.00

Aircel BSNL Mobile TATA DoCoMo VodafoneMay-Jun 16 Jun-Aug 16 Jul-Sep 16 Aug-Oct 16 Sep-Nov 16 Oct-Dec 16

3.32 3.15

1.74

3.13

3.91

3.38

1.84

3.52

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

Airtel Idea Reliance Vodafone

May-Jun 16 Jun-Aug 16 Jul-Sep 16 Aug-Oct 16 Sep-Nov 16 Oct-Dec 16

17.55

8.549.57

16.10

8.57 8.00

0.002.004.006.008.00

10.0012.0014.0016.0018.0020.00

Airtel Idea Vodafone


4.09

2.36 2.502.16 2.01

4.26

4.90

2.813.12 2.91

2.43

4.34

0.00

1.00

2.00

3.00

4.00

5.00

6.00

Airtel BSNL Mobile Idea Reliance TATA DoCoMo Vodafone


10.50

4.46

12.07

8.79

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

Airtel Vodafone



Network quality statistics – Regional- Indonesia

Indonesia: Banten 3G

Indonesia: Jakarta Raya: 3G


Indonesia: Jakarta Raya: 4G

Indonesia: Jawa Barat 3G Indonesia: Jawa Barat 4G

Indonesia : Banten 4G

Appendix

60

8.055.69

25.99

8.74

3.09

6.31

16.64

6.94

0.00

5.00

10.00

15.00

20.00

25.00

30.00

Indosat Smartfren Telkomsel XL


2.26

3.87

0.67

6.61

3.24

1.84 2.15

0.56

6.98

3.58

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00



10.19

7.43

18.35

9.42

3.30

6.65

13.02

7.24

0.002.004.006.008.00

10.0012.0014.0016.0018.0020.00



2.46

3.83

0.68

6.70

3.51

2.52 2.27

0.68

7.37

3.82

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00



7.94

4.51

17.83

9.87

3.29 3.85

10.75

6.77

0.002.004.006.008.00

10.0012.0014.0016.0018.0020.00



2.20

3.17

0.60

5.92

2.89

2.03 1.85

0.54

5.93

3.08

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00




Network quality statistics – Regional- Indonesia, Malaysia

Indonesia: Jawa Tengah 3G

Indonesia: Jawa Timur 3G


Indonesia: Jawa Timur 4G

Malaysia: Central region 3G Malaysia: Central region 4G

Indonesia: Jawa Tengah 4G

Appendix

61

7.69

3.44

15.1813.16

4.004.87

17.17

11.76

0.002.004.006.008.00

10.0012.0014.0016.0018.0020.00



2.48 2.63

0.54

4.93

2.572.08

1.68

0.57

5.72

2.99

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00



9.26

4.28

16.86

12.71

4.285.79

13.98

11.27

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00



2.67 2.70

0.61

4.84

3.50

2.311.81

0.68

5.79

3.47

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00



15.80 15.85

19.89

14.44

19.10

14.96

20.36

11.26

0.00

5.00

10.00

15.00

20.00

25.00



4.13

2.84

3.432.96

3.82

3.083.52

2.52

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50




Network quality statistics – Regional- Malaysia, Pakistan, Philippines

Malaysia: Southern region 3G

Pakistan: Punjab 3G


Pakistan: Sind 3G

Philippines: NCR 3G Philippines: NCR 4G

Malaysia: Southern region 4G

Appendix

62

7.50

11.04

7.88

10.77

0.00

2.00

4.00

6.00

8.00

10.00

12.00

Globe Smart


2.73

2.94

2.80

3.07

2.30

2.40

2.50

2.60

2.70

2.80

2.90

3.00

3.10

Globe Smart


13.4014.75

20.37

15.2115.48 15.52

24.10

11.12

0.00

5.00

10.00

15.00

20.00

25.00

30.00



3.14

2.572.97

3.223.242.93 3.03

3.21

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00



3.05

1.54

2.87

3.443.17

2.36

3.323.57

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

Mobilink Telenor Ufone Zong


3.72

2.642.37

3.67

4.98

3.21

2.26

3.53

0.00

1.00

2.00

3.00

4.00

5.00

6.00

Mobilink Telenor Ufone Zong



Network quality statistics – Regional- Philippines, South Korea

Philippines: South Luzon 3G

Philippines: Visayas 3G


Philippines: Visayas 4G

South Korea: Gyeonggi-do 4G South Korea: Seoul 4G

Philippines: South Luzon 4G

Appendix

63

8.93

5.99

7.89 7.52

0.001.002.003.004.005.006.007.008.009.00

10.00

Globe Smart


2.00

1.37

2.21

1.87

0.00

0.50

1.00

1.50

2.00

2.50

Globe Smart


3.84

7.70

6.46

8.37

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

Globe Smart


1.95

1.46

2.02

1.58

0.00

0.50

1.00

1.50

2.00

2.50

Globe Smart


42.7746.99 49.37

42.75 44.7340.58

0.00

10.00

20.00

30.00

40.00

50.00

60.00



50.95

42.0845.6748.02

39.6636.00

0.00

10.00

20.00

30.00

40.00

50.00

60.00


May-June 2016 June-August 2016 Oct-Dec 16 Nov'16-Jan'17


Network quality statistics – Regional- Taiwan

Taiwan: Kaohsiung 3G

Taiwan: New Taipei 3G


Taiwan: New Taipei 4G

Taiwan: Taichung 3G Taiwan: Taichung 4G

Taiwan: Kaohsiung 4G

Appendix

64

25.6521.86

28.6524.78

26.56

34.55

23.83

30.40

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00



7.17 7.24

3.72

7.837.37

8.62

4.42

6.85

0.001.002.003.004.005.006.007.008.009.00

10.00



29.15

23.3120.21

26.4927.55

31.90

20.92

29.99

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00



6.917.45

4.18

7.997.16

7.73

3.86

7.48

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00



29.97 30.57

22.0126.17

31.12

36.27

22.74

30.19

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00



7.216.64

3.35

7.97

5.946.39

2.96

7.89

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00




Network quality statistics – Regional- Thailand

Thailand: Central 3G

Thailand: North 3G


Thailand: North 4G

Thailand: Northeast 3G Thailand: Northeast 4G

Thailand: Central 4G

Appendix

65

20.24

14.72 14.1014.6313.33

16.24

0.00

5.00

10.00

15.00

20.00

25.00

AIS DTAC TrueMove


3.393.07

2.152.50

2.782.58

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

AIS DTAC TrueMove


23.53

16.9015.82

18.27

13.8712.64

0.00

5.00

10.00

15.00

20.00

25.00

AIS DTAC TrueMove


3.13

3.74

2.252.57

2.792.36

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

AIS DTAC TrueMove


20.74

14.61 15.0212.52 12.00 11.16

0.00

5.00

10.00

15.00

20.00

25.00

AIS DTAC TrueMove


2.37

2.72

1.651.82

2.32 2.21

0.00

0.50

1.00

1.50

2.00

2.50

3.00

AIS DTAC TrueMove



Disclosures

Companies Discussed in This Report (all prices in this report as of market close on 16 March 2017)China Telecom (0728.HK/HK$3.66/Overweight), KT Corp. (030200.KS/W32650/Overweight), LG Uplus (032640.KS/W14750/Overweight)

Analyst Certification: The research analyst(s) denoted by an “AC” on the cover of this report certifies (or, where multiple research analysts are primarily responsible for this report, the research analyst denoted by an “AC” on the cover or within the document individually certifies, with respect to each security or issuer that the research analyst covers in this research) that: (1) all of the views expressed in this report accurately reflect his or her personal views about any and all of the subject securities or issuers; and (2) no part of any of the research analyst's compensation was, is, or will be directly or indirectly related to the specific recommendations or views expressed by the research analyst(s) in this report. For all Korea-based research analysts listed on the front cover, they also certify, as per KOFIA requirements, that their analysis was made in good faith and that the views reflect their own opinion, without undue influence or intervention.

Research excerpts: This note includes excerpts from previously published research. For access to the full reports, including analyst certification and important disclosures, investment thesis, valuation methodology, and risks to rating and price targets, please contact your salesperson or the covering analyst’s team or visit www.jpmorganmarkets.com.

Important Disclosures

Market Maker/ Liquidity Provider: J.P. Morgan Securities plc and/or an affiliate is a market maker and/or liquidity provider in securities issued by LG Uplus, KT Corp.. Market Maker/ Liquidity Provider (Hong Kong): J.P. Morgan Securities (Asia Pacific) Limited and/or J.P. Morgan Broking (Hong Kong) Limited and/or an affiliate is a market maker and/or liquidity provider in the securities of China Telecom and/or warrants or options thereon, which are listed or traded on The Stock Exchange of Hong Kong Limited. Beneficial Ownership (1% or more): J.P. Morgan beneficially owns 1% or more of a class of common equity securities of China Telecom. Client: J.P. Morgan currently has, or had within the past 12 months, the following entity(ies) as clients: China Telecom, KT Corp.. Client/Non-Investment Banking, Securities-Related: J.P. Morgan currently has, or had within the past 12 months, the following entity(ies) as clients, and the services provided were non-investment-banking, securities-related: KT Corp.. Non-Investment Banking Compensation: J.P. Morgan has received compensation in the past 12 months for products or services other than investment banking from KT Corp.. Other Significant Financial Interests: J.P. Morgan owns a position of 1 million USD or more in the debt securities of LG Uplus, China Telecom, KT Corp..

Company-Specific Disclosures: Important disclosures, including price charts and credit opinion history tables, are available for compendium reports and all J.P. Morgan–covered companies by visiting https://jpmm.com/research/disclosures, calling 1-800-477-0406, or e-mailing [email protected] your request. J.P. Morgan’s Strategy, Technical, and Quantitative Research teams may screen companies not covered by J.P. Morgan. For important disclosures for these companies, please call 1-800-477-0406 or e-mail [email protected].



www.jpmorganmarkets.com

https://jpmm.com/research/disclosures

mailto:[email protected]


Disclosures

67

0

3,562

7,124

10,686

14,248

17,810

21,372

24,934

Price(W)

Aug07

Feb09

Aug10

Feb12

Aug13

Feb15

Aug16

LG Uplus (032640.KS, 032640 KS) Price Chart

N W9,500 N W6,500 OW W13,500OW W14,000OW W14,800OW W14,000OW W19,000

OW W10,500 N W8,000UW W5,300 OW W13,000OW W13,800OW W11,200OW W17,000OW W13,500OW W15,000

OW W12,000OW W10,000N W10,000N W9,400 N W8,800 N W5,300N W6,500N W8,000N W9,000NR OW W13,000OW W16,000OW W16,000OW W12,000OW W14,500

Source: Bloomberg and J.P. Morgan; price data adjusted for stock splits and dividends.

Break in coverage Sep 19, 2013 - Nov 15, 2013.

Date Rating Share Price (W)

Price Target (W)

10-Oct-07 OW 9450 12000

31-Jan-08 OW 8430 10000

29-Apr-08 OW 8230 10500

30-Jul-08 N 8450 9500

01-Oct-08 N 9910 10000

02-Aug-09 N 8480 9400

12-Aug-10 N 7680 8800

05-Nov-10 N 7250 8000

01-Feb-11 N 6670 6500

31-Jul-11 N 5390 5300

28-Oct-11 UW 7110 5300

29-Mar-12 N 6460 6500

27-Sep-12 N 7220 8000

31-Jan-13 N 8070 9000

01-May-13 OW 9830 13000

19-Sep-13 NR 12000 --

15-Nov-13 OW 11150 13800

29-Jan-14 OW 10500 13500

28-Apr-14 OW 10050 13000

01-Aug-14 OW 9520 11200

27-Oct-14 OW 10850 14000

14-Jan-15 OW 12350 16000

23-Jan-15 OW 12750 17000

28-Apr-15 OW 10600 14800

31-Jul-15 OW 10850 16000

02-Feb-16 OW 9570 12000

27-Apr-16 OW 10950 13500

01-Aug-16 OW 11400 14000

31-Oct-16 OW 11800 14500

03-Feb-17 OW 11550 15000

08-Mar-17 OW 13850 19000


Disclosures

68

0

1

2

3

4

5

6

7

8

9

10

11

12

Price(HK$)

Oct06

Apr08

Oct09

Apr11

Oct12

Apr14

Oct15

Apr17

China Telecom (0728.HK, 728 HK) Price Chart

N HK$4 N HK$4 OW HK$5.3

N HK$3.6N HK$6N HK$6.5N HK$4.5N HK$4N HK$4.1OW HK$4.7OW HK$5.6 N HK$3.6OW HK$5.4OW HK$5.6

N HK$3N HK$5.7N HK$5.8N HK$5N HK$3.5N HK$4.1N HK$4OW HK$4.6OW HK$5.2N HK$4.6OW HK$5.4N HK$4.5N HK$4.2OW HK$5.1OW HK$6.5OW HK$6.1OW HK$5.1OW HK$4.9


Initiated coverage Nov 08, 2006.

Date Rating Share Price (HK$)

Price Target (HK$)

08-Nov-06 N 3.29 3.00

15-Jan-07 N 3.80 3.60

18-Jan-07 N 3.90 4.00

03-Oct-07 N 6.50 5.70

24-Oct-07 N 6.65 6.00

22-Feb-08 N 5.97 5.80

28-Feb-08 N 6.05 6.50

29-Jun-08 N 4.21 5.00

28-Aug-08 N 4.09 4.50

21-Oct-08 N 2.86 4.00

25-Mar-09 N 3.08 3.50

20-Apr-09 N 3.58 4.00

27-Aug-09 N 3.87 4.10

21-Oct-09 N 3.82 4.10

22-Mar-10 N 3.56 4.00

02-Aug-10 OW 3.95 4.60

24-Sep-10 OW 4.19 4.70

20-Feb-11 OW 4.51 5.20

24-Mar-11 OW 4.63 5.60

21-Mar-12 N 4.50 4.60

23-Aug-12 OW 4.17 5.40

21-Mar-13 N 3.90 4.50

10-Jan-14 N 3.66 4.20

20-Mar-14 N 3.15 3.60

28-Aug-14 OW 4.34 5.10

28-Oct-14 OW 4.91 5.40

28-Apr-15 OW 5.91 6.50

10-Aug-15 OW 4.33 6.10

12-Oct-15 OW 4.00 5.60

24-Nov-15 OW 3.97 5.30

08-Mar-16 OW 3.86 5.10

24-Aug-16 OW 4.05 4.90


Disclosures

The chart(s) show J.P. Morgan's continuing coverage of the stocks; the current analysts may or may not have covered it over the entire period. J.P. Morgan ratings or designations: OW = Overweight, N= Neutral, UW = Underweight, NR = Not Rated

Explanation of Equity Research Ratings, Designations and Analyst(s) Coverage Universe: J.P. Morgan uses the following rating system: Overweight [Over the next six to twelve months, we expect this stock will outperform the average total return of the stocks in the analyst’s (or the analyst’s team’s) coverage universe.] Neutral [Over the next six to twelve months, we expect this stock will perform in line with the average total return of the stocks in the analyst’s (or the analyst’s team’s) coverage universe.] Underweight [Over the next six to twelve months, we expect this stock will underperform the average total return of the stocks in the analyst’s (or the analyst’s team’s) coverage universe.] Not Rated (NR): J.P. Morgan has removed the rating and, if applicable, the price target, for this stock because of either a lack of a sufficient fundamental basis or for legal, regulatory or policy reasons. The previous rating and, if applicable, the price target, no longer should be relied upon. An NR designation is not a recommendation or a rating. In our Asia (ex-Australia) and U.K. small- and mid-cap equity research, each stock’s expected total return is compared to the expected total return of a benchmark

69

0

11,564

23,128

34,692

46,256

57,820

69,384

80,948

92,512

Price(W)

Jun07

Dec08

Jun10

Dec11

Jun13

Dec14

Jun16

KT Corp. (030200.KS, 030200 KS) Price Chart

N W35,000 OW W57,000 UW W26,000 OW W42,000

N W46,000 OW W53,000OW W55,000 N W29,000UW W28,000N W33,000 OW W40,000

N W44,000 N W38,000N W39,000N W44,000N W43,500N W39,000N W35,000N W39,000N W42,000NR UW W30,000N W34,000N W34,000OW W35,000OW W40,000


Break in coverage Sep 19, 2013 - Nov 15, 2013.

Date Rating Share Price (W)

Price Target (W)

26-Apr-08 N 46300 44000

27-Jul-08 N 42200 46000

02-Nov-08 N 32000 35000

20-Jun-09 N 37300 38000

03-Nov-09 N 38700 39000

21-Jan-10 OW 48700 53000

29-Jan-10 OW 49800 57000

27-Jul-10 N 42100 44000

06-Oct-10 OW 46500 55000

31-Jan-11 N 42150 43500

05-Aug-11 N 40200 39000

10-Jan-12 N 33150 35000

27-Sep-12 N 35450 39000

04-May-13 N 38750 42000

19-Sep-13 NR 36250 --

15-Nov-13 N 33000 29000

24-Jan-14 UW 30400 26000

01-May-14 UW 32800 30000

30-Jul-14 UW 31300 28000

13-Nov-14 N 32450 34000

14-Jan-15 N 29950 33000

01-Aug-15 N 30600 34000

31-Jan-16 OW 27750 35000

29-Apr-16 OW 30700 40000

29-Jul-16 OW 31500 42000

02-Feb-17 OW 29450 40000


Disclosures

country market index, not to those analysts’ coverage universe. If it does not appear in the Important Disclosures section of this report, the certifying analyst’s coverage universe can be found on J.P. Morgan’s research website, www.jpmorganmarkets.com.

Coverage Universe: Sullivan, James: Astro Malaysia Holdings Bhd (ASTR.KL), Cheetah Mobile Inc (CMCM), Kingsoft Corporation Limited (3888.HK), NetEase (NTES), PT Media Nusantara Citra Tbk (MNCN.JK)

J.P. Morgan Equity Research Ratings Distribution, as of January 02, 2017

*Percentage of investment banking clients in each rating category.For purposes only of FINRA/NYSE ratings distribution rules, our Overweight rating falls into a buy rating category; our Neutral rating falls into a hold rating category; and our Underweight rating falls into a sell rating category. Please note that stocks with an NR designation are not included in the table above.

Equity Valuation and Risks: For valuation methodology and risks associated with covered companies or price targets for covered companies, please see the most recent company-specific research report at http://www.jpmorganmarkets.com, contact the primary analyst or your J.P. Morgan representative, or email [email protected].

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70

Overweight(buy)

Neutral(hold)

Underweight(sell)

J.P. Morgan Global Equity Research Coverage 43% 45% 12% IB clients* 52% 48% 34%JPMS Equity Research Coverage 43% 50% 7% IB clients* 67% 61% 43%


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http://www.optionsclearing.com/publications/risks/riskstoc.pdf

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