Global Market Perspectives - Investment Revie€¦ · Global Market Perspectives Robert Almeida...

FOR INVESTMENT PROFESSIONAL AND INSTITUTIONAL USE ONLY. FOR INVESTMENT PROFESSIONAL AND INSTITUTIONAL USE ONLY.

The collapse of intellectual property

Global Market Perspectives

Robert AlmeidaGlobal Investment Strategist / Portfolio Manager

MFS Investment Management

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The views expressed in this presentation are those of the speaker and are subject to change at any time. These views should not be relied upon as investment advice, as securities recommendations, or as an indication of trading intent o behalf of any other MFS investment product. No forecasts can be guaranteed. MFS Investment Management, 111 Huntington Ave., Boston, MA 02199

FOR INVESTMENT PROFESSIONAL AND INSTITUTIONAL USE ONLY.

Overview

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• A different beta landscape ahead

•Decelerating returns

• Less diversification

• A different alpha landscape ahead

•Retailing

•Media

•Consumer (Discretionary & Staples)

•Automotive Transportation (TaaS)

•Health Care: admin & pharma

•The IT Stack: compute, storage, service

• Conclusions


A different beta landscape ahead

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Decelerating returns

Source: MFS Long Term Market Expectations (Canada Edition) as of August 2018. Global balanced portfolio is comprised of 60% global equity (represented by MSCI ACWI Index Unhedged in CAD) and 40% global fixed income (represented by Bloomberg Barclays Global Aggregate Index Hedged in CAD). Risk-Volatility is represented by standard deviation. Capital Markets View is for informational purposes only and any general commentary on market activity, industry or sector trends, or other broad based economic or political conditions does not constitute a recommendation or investment advice. References to future expected returns and performance are not promises or estimates of actual performance that may be realized by an investor, and should not be relied upon. The forecasts are for illustrative purposes only and are not to be relied upon as advice, interpreted as a recommendation, or be guarantees of performance. The forecasts are based upon subjective estimates and assumptions that have yet to take place or may occur. The projections have limitations because they are not based on actual transactions, but are based on the models and data compiled by MFS. The results do not represent nor are indicative of actual results that may be achieved in the future. Individual investor performance may vary significantly.

CAD Cash

US High YieldUS IG Bonds

CAD 7-10 Yr Bonds

CAD Universe Bonds

CAD Long Bonds

Emerging Markets Debt

Global Agg Bonds

US Equities

Asia Pac ex Japan Equities

EAFE Equities

EM equities

Europe ex UK EquitiesGlobal Equities Canadian Equities

UK Equities

Diversified hedge funds

Global REITs

Global balanced

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

0% 2% 4% 6% 8% 10% 12% 14% 16% 18%

Exp

ecte

d R

etu

rn

Risk-Volatility


A different alpha landscape ahead

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Barriers-to-entry to Index creation have collapsed

For the number of indices the first 5 data points are based on Wurgler (2011) “On the Economics Consequences of Index-Linked Investing”The last two data points refers to the cumulative number of factor indices 4274 from ERI Scientific Beta and 673 ETF’s identified by MorningstarWe have fitted an exponential curve though we have left the scale on the x axis non linear on purpose as in fact the recent rate of index creation exceeds that fitted by an exponential curve.S&P's claim of a million indices spans all asset classes, of that they say 850,000 are equity indicesSource: Bernstein Analysis. "Fund Management Strategy: A million indices, the death of benchmarking and the passive singularity", Wurgler (2011), FT, ERI Scientific Beta, Worldbank

0

10,000

20,000

30,000

40,000

50,000

0

1,000

2,000

3,000

4,000

5,000

1920 1929 1960 1985 2010 2012 2016

There are more Benchmarks/ETFs than StocksNumber of indices (LHS) Number of stocks (RHS)

3.2 million Benchmarks/ETFs



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Retailing

Source: LHS - US Everrcore ISI, Initiating Coverage of Internet: Appetite for Disruption, December 5, 2017RHS - Sanford Bernstein, Weekend Consumer Blast: Time to go shopping with Alexa?, March 9, 2018

Retail Sales

E-commerce Brick & Mortar

~$17tn

89%

76%70%

63%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Convenience Time Saving Lower Prices Better Selection

“What were the top 3 reasons you decided to make your purchase via e-commerce?”



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Retailing

Source: Bernstein, US Broadlines & Hardlines Retail December 15, 2017. The companies referenced are used for informational purposes only and do not necessarily represent holdings in any MFS portfolio.

Retailer Store closings

1Q17

Family Christian 240RadioShack 1000Crocs 160Vanity 140HH Gregg 88Michigan Sporting Goods 68Abercrombie & Fitch 60Staples 70JCP 138BCBG 120Bebe 312

2Q17

Payless ~640Rue 21 396Michael Kors 100-125Lululemon (ivivva) 40J Crew 20Gymboree 350Ascena Retail 250-650Teavana 379

3Q17

Kmart 28DineEquity (Applebees/IHOP) 160Gap Inc. (Gap Brand & Banana Republic) ~200Neiman Marcus (Last Call) 10Sears Canada 195

4Q17Sears 45Kmart 18

Retail Margins: 20 Year View

Time

EBIT Margins

Pre Amazon Era

Margin pressures:Wages

Price CompetitionStore Remodels

Ecommerce Expenses

We are here?

1

2

3a

3b

4 5?

SG&A reduction offsets margin

pressures

Retailers unwilling/unable to reduce SG&A go out of business

High levels of consolidation;

margins improve

Retail “War” ends, next generation

of commerce



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Retailing

Source Bloomberg Barclays POINT & Credit Suisse as of 31 August 2018. Dispersion of spreads is the differential between the 80th percentile spread and the 20th percentile investment grade corporate spread within the retail industry.

0

100

200

300

400

500

600

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Spre

ad d

isp

ersi

on

(b

p)

Dispersions of investment grade retailing corporate bond spreads

2001 stores closed in

2003


2006


2009


2013


2017


121 143 165

160

192

215

100

200

300

400

500

2016 2017 2018

Hardcore fans (mn) Casual Viewers (mn)


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Media

Sources: LHS - Everrcore ISI, Initiating Coverage of Internet: Appetite for Disruption, December 5, 2017. RHS - Bank of America Merill Lynch as of 21 May 2018.

75

80

85

90

95

100

20

14

20

15

20

16

20

17

E

20

18

E

20

19

E

20

20

E

20

21

E

20

22

E

Traditional Pay-TV Subscribers (mn)

75

80

85

90

95

100

2014 2015 2016 2017E 2018E 2019E 2020E 2021E 2022E

Traditional Pay-TV Subscribers (mn)

Global eSports Viewer Base (millions)



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Consumer (Discretionary & Staples)

Sources: LHS - Evercore ISI, Initiating Coverage of Internet: Appetite for Disruption, December 5, 2017. RHS - Sanford Bernstein, Weekend Consumer Blast: Bragging Rights – How to create the most Instagrammable Hotel, December 1, 2017

35%

45%

55%

65%

95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15

Spend on things Spend on experiences

35%

45%

55%

65%

95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16


35%

45%

55%

65%

93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16


Germany: Split of Discretionary Expenditure

UK: Split of Discretionary Expenditure

US: Split of Discretionary Expenditure

AuthenticCommodity

Consumers have more choices today



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Automotive Transportation (TaaS)

Sources: LHS - Everrcore ISI, Initiating Coverage of Internet: Appetite for Disruption, December 5, 2017; Monitoring the Future survey, University of Michigan.RHS - ISI, Waymo: The First Billion and Beyond; August 22, 2018

$515

$1,677 $1,915

$2,700

$6,328$6,697

$8,950

$0

$2,000

$4,000

$6,000

$8,000

$10,000

Global Mkt Value in Billions (2017)

1,000

5,000

8,000

20,000

0

5,000

10,000

15,000

20,000

25,000

Jan

-09

Jan

-10

Jan

-11

Jan

-12

Jan

-13

Jan

-14

Jan

-15

Jan

-16

Jan

-17

Jan

-18

Waymo Autonomous Miles Traveled (000s)


Active opportunities

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Health Care: admin & pharma

Sources: LHS - Factset; Everrcore ISI, AMZN, BRK, & JPM Attempt to ‘De-Worm’ US Healthcare, January 30, 2017.RHS - Biosimilarpipeline.com and PharmaCompass.com as of 31 May 2018. The companies referenced are used for informational purposes only, and do not necessarily represent holdings in any MFS portfolio. Individual securities mentioned are for illustrative purposes only and may not be relied upon as investment advice or as an indication of trading intent on behalf of any MFS product. They do not represent holdings in any MFS portfolio.

Biologic DrugPercent of2017 sales

# of biosimilars

Humira 18.9% 34

Enbrel 8.3% 32

Remicade 7.8% 17

Herceptin 7.4% 42

Avastin 7.1% 30

Lantus 5.7% 10

Neulasta 4.6% 250

50

100

150

200

250

300

350

400

450

$0

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000

19

60

19

65

19

70

19

75

19

80

19

85

19

90

19

95

20

00

20

05

20

10

20

15

20

20

20

25

US

Pop

ula

tio

n (

MM

)

USD

Bill

ion

s

Healthcare Cost InflationNational Health Expenditures ($Bn) (LHS) U.S. Population (MM) (RHS)



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The IT Stack: compute, storage, service (IaaS, PaaS, SaaS)

Sources: LHS – Bernstein “Weekend Tech Byte: Software vs Services – who wins and loses in the Cloud” as of 15 September 2018. RHS - CM Research. "Global Investment Themes: technology, media and telecoms." February 21, 2017; Everrcore ISI, The Salad Days of Software: A View Towards 2018 & Beyond, December 19, 2017

AI TECHNOLOGY S CURVE

2010 TodayTime

Ad

op

tio

n/P

erf

orm

ance

Early AdoptersTechnology

vendors begin to utilize AI

BreakthroughMass adoption

occurring in software, technology is improving & application vendors starting to embed AI within applications

“Table Stakes”By 2020 application vendors will need AI embedded in their applications to

compete effectively

Middle Tier

Servers

Web Servers

Operating System

Data Base(s)

Data

Application Layer

Operating System Layer

Compute Resource

Virtualization Layer

Hardware Layer (servers, switches, etc.)

IT Stack

Co

mp

any O

wn

s and

Man

ages

Cu

stom

erIaaS V

end

or

Cu

stom

erPaaS V

end

or

Cu

stom

erSaaS V

end

or

On-Premise

IaaS PaaS SaaS

Ownership and Management Structure Cloud & On-Premise


Conclusions

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▪Starting valuations may anchor market returns

▪Lower diversification may impact distribution of returns

▪Alpha may offer solutions to market return and diversification shortfalls


Thank You

The information included in this presentation as well as individual companies and/or securities mentioned should not be construed as investment advice, a recommendation to buy or sell or an indication of trading intent on behalf of any MFS product.

Unless otherwise indicated, logos and product and service names are trademarks of MFS® and its affiliates and may be registered in certain countries.

Distributed by: U.S. – MFS Institutional Advisors, Inc. ("MFSI"), MFS Investment Management and MFS Fund Distributors, Inc.; Latin America – MFS International Ltd.; Canada – MFS Investment Management Canada Limited. No securities commission or similar regulatory authority in Canada has reviewed this communication; U.K. - MFS International (U.K.) Limited (“MIL UK”), a private limited company registered in England and Wales with the company number 03062718, and authorized and regulated in the conduct of investment business by the U.K. Financial Conduct Authority. MIL UK, an indirect subsidiary of MFS, has its registered office at One Carter Lane, London, EC4V 5ER UK and provides products and investment services to institutional investors globally. This material shall not be circulated or distributed to any person other than to professional investors (as permitted by local regulations) and should not be relied upon or distributed to persons where such reliance or distribution would be contrary to local regulation; Singapore – MFS International Singapore Pte. Ltd. (CRN 201228809M); Australia/New Zealand - MFSI and MIL UK are exempt from the requirement to hold an Australian financial services licence under the Corporations Act 2001 in respect of the financial services they provide to Australian wholesale investors. MFS International Australia Pty Ltd (" MFS Australia") holds an Australian financial services licence number 485343. In Australia and New Zealand: MFSI is regulated by the US Securities & Exchange Commission under US laws and MIL UK is regulated by the UK Financial Conduct Authority under UK laws, which differ from Australian and New Zealand laws. MFS Australia is regulated by the Australian Securities and Investments Commission.; Hong Kong - MFS International (Hong Kong) Limited ("MIL HK"), a private limited company licensed and regulated by the Hong Kong Securities and Futures Commission (the "SFC"). MIL HK is approved to engage in dealing in securities and asset management regulated activities and may provide certain investment services to "professional investors" as defined in the Securities and Futures Ordinance ("SFO").


MFS® Long Term Capital Market Expectations

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Source: August 2018 MFS Long Term Capital Market Expectations – Canada Edition

The MFS Long-Term Capital Markets Expectations (LTCME) for 2018 includes return and risk expectations for equity, fixed income and alternative asset classes across country, regional and global markets. The focus of these expectations is to provide a strategic, long-term, forward-looking view of various global markets. We use a proprietary top-down approach by employing quantitative, country based models as the foundation for our expectations and then integrating bottom-up fundamental views from our global equity and fixed income investment teams to inform our final expectations.

Our expectations are developed across 26 countries comprising 18 developed countries and 8 emerging market countries.

Equity expectations MFS equity market expectations are displayed in unhedged, nominal total return and are developed using a building-blocks approach. Elements of market history and mean reversion are incorporated into our models. Reversion speed and target levels are calibrated based on our analysis of historical data and forward looking expectations. Any return figure should be viewed as the mid-point in that range of outcomes.

Fixed income expectations MFS fixed income market expectations are displayed in nominal total return, hedged to the investor’s home currency. As with our equity model, our fixed income model employs a building-blocks approach. And, again like the equity model, the fixed income model derives its reversion speed and target level parameters from careful historical research as well as forward looking expectations. In our forecast, we focus on the returns from carry, yield change, roll-down and credit loss (where appropriate). Using this framework, we develop expectations across a range of sovereign, global credit and regional credit markets, while being careful to tune our models in accordance with the unique attributes of the various fixed income markets.

Alternative ExpectationsDue to the unique characteristics and varying drivers of return in alternatives, we vary our approach for each category. Our equity and fixed income capital market expectations serve as key variables in our alternatives models.


Appendix

16



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Source: FactSet Market Aggregates as of 31 July 2018. Forward price to earnings (P/E) is next-twelve-months (NTM). Historical average is longest time period available (29 October 1999)

14.9

12.1 11.5

14.312.9

16.5

13.314.5 12.2

9.9

14.0

18.6

15.6

13.2

7x

11x

15x

19x

23x

27x

MSCI AC World MSCI AC Asia exJP

MSCI EM MSCI Europe exUK

MSCI Japan MSCI NorthAmerica

MSCI UnitedKingdom

Forw

rad

P/E

Rat

io

Equity Valuations

Current P/E (Forward) Average +/- One Standard Deviation around Historical Average



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Source: Global Financial Data and Bernstein analysis. “Trust in Asset Management” March 2018. 10-year forward total return is based on Shiller constructed data based on the S&P 500 methodology.

0

5

10

15

20

25

30

35

40

45

50-10

-5

0

5

10

15

20

25

31

/01

/18

81

31

/07

/18

87

31

/01

/18

94

31

/07

/19

00

31

/01

/19

07

31

/07

/19

13

31

/01

/19

20

31

/07

/19

26

31

/01

/19

33

31

/07

/19

39

31

/01

/19

46

31

/07

/19

52

31

/01

/19

59

31

/07

/19

65

31

/01

/19

72

31

/07

/19

78

31

/01

/19

85

31

/07

/19

91

31

/01

/19

98

31

/07

/20

04

31

/01

/20

11

7/3

1/2

01

7

US

Shill

er P

/E (

Inve

rted

)

10

-yea

r Fo

rwar

d R

etu

rn

10-year forward total return (annualized) US Shiller PE (RHS, Inverted)

Shiller PE and 10-year forward returns



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Source: Bloomberg as of 31 July 2018. US IG Corporates = Bloomberg Barclays US Aggregate Corporate Index; US HY Corporates = Bloomberg Barclays US High Yield Index; Euro IG Corporates = Bloomberg Barclays Euro Aggregate Corporate Index; Euro HY Corporates = Bloomberg Barclays Pan Euro High Yield Index; Sovereign EMD = J.P. Morgan EMBI Global Index; Corporate EMD = J.P. Morgan CEMBI Index.

107

326

115

353 362307

132

510

136

598

377328

0

100

200

300

400

500

600

700

800

900

1000

US IG Corporates(1989)

US HY Corporates(1994)

Euro IG Corporates(1999)

Euro HY Corporates(1999)

Sovereign EMD(2002)

Corporate EMD(2001)

Ssp

read

(b

ps)

Credit Spreads

Current Average +/- One Standard Deviation around Historical Average



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Source: Global Financial Data and Bernstein analysis. “Trust in Asset Management” March 2018. Based on US 10-year treasury bond.

0

2

4

6

8

10

12

14

16

18

31

/01

/18

00

31

/05

/18

10

30

/09

/18

20

31

/01

/18

31

31

/05

/18

41

30

/09

/18

51

31

/01

/18

62

31

/05

/18

72

30

/09

/18

82

31

/01

/18

93

31

/05

/19

03

30

/09

/19

13

31

/01

/19

24

31

/05

/19

34

30

/09

/19

44

31

/01

/19

55

31

/05

/19

65

30

/09

/19

75

31

/01

/19

86

31

/05

/19

96

30

/09

/20

06

31

/01

/20

17

%

Bond yields and 10-year forward bond returns10-year forward bond total return (annualized) USA 10-year bond constant maturity yield



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Less diversification

Source: Datastream, as of 3/31/18, Global Financial data, Thomas and Dimsdale (2016) and Bank of England calculations. Line shows ten-year trailing correlation of monthly returns on equities and bonds. See also Roberts-Sklar, M. (2016), “250 years of the bond-equity correlation”, Bank Underground, 20 October 2016.

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

31

/01

/17

63

31

/05

/17

76

30

/09

/17

89

31

/01

/18

03

31

/05

/18

16

30

/09

/18

29

31

/01

/18

43

31

/05

/18

56

30

/09

/18

69

31

/01

/18

83

31

/05

/18

96

30

/09

/19

09

31

/01

/19

23

31

/05

/19

36

30

/09

/19

49

31

/01

/19

63

31

/05

/19

76

30

/09

/19

89

31

/01

/20

03

31

/05

/20

16

10

-Yea

r R

olli

ng

Co

rrel

atio

n

Over 250 years of stock-bond correlation in the UK & US

UK US

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