February 2019

Prosperously navigating

unexpected events

with great skill and agility

2

WHO WE ARE

3

BLACK SWAN

DEXTERITAS

(“BSD”)

TECHNOLOGY EXPERTISE

• Unique insight

from entrepreneurs leading

international tech development

based on needs creation

• Advisory Committee of tech leaders

who determine the global adoption

and success of new technologies

• Representation in all BSD-invested

tech sectors and sub-sectors, for

unrivalled expertise

• Portfolio Manager with 30 years of

portfolio management experience

across various asset classes at

asset management companies (LGT,

TAL, CIBC Asset Management), and

a pension (British Petroleum)

• Exceptional research team with a

wide breadth of knowledge in

research, finance, and engineering

• Intense due diligence process for our

stock selection process

• Unique risk management overlay

to minimize drawdowns and volatility

PORTFOLIO MANAGEMENT

EXPERIENCE

4

INVESTMENT METHODOLOGY

SECTOR ASSESSMENT

• Life Cycle: Sectors in introduction and growth stages with

high Total Addressable Market (TAM)

• Competition: High barrier of entry with differentiated products

and services within the sector

COMPANY ANALYSIS

• Business Model: Public ccompanies with high recurring revenue,

easily able to leverage network effects, strong negotiating power

with suppliers and customers, and strong corporate governance

• Size: Target small (500M+) to large cap public companies with

established track record of executing the business.

• Growth: Public companies with high and/or consistent revenue

growth

• Valuation: Determine if opportunities exist based on our fair

value expectation of stocks versus current stock prices

PORTFOLIO CONSTRUCTION

• Weightings: Determine % of portfolio allocated to holdings

based on risk-reward expectations

• Diversification: Well-diversified across 35 to 40 holdings to

maximize risk-adjusted returns

• Hedging: Utilize derivatives and fixed income products to

minimize drawdowns and generate alpha

IDEATION

• BSD Investment Advisory Committee: seek out global

growth themes and trends to overweight and underweight

various subsectors

• Experienced investment team sourcing trade ideas and

discussing vital macro economical forces in play

• Draw on sector experiences from members of the committee and

discuss emerging technology from the private and public space

• Deep dive into industry verticals to identify beneficiaries in other

primary, secondary, and tertiary markets

PORTFOLIO CONSTRUCTION

IDEATION

SECTOR

ASSESSMENT

COMPANYANALYSIS

PUBLIC COMPANIES

PERFORMANCE

PERFORMANCE METRICS* FUND RETURNS

FUND S&P 500

Return Since Inception

YTD Return

60 Day Return

20 Day Return

Daily Standard Dev.

Sharpe Ratio

Sortino Ratio

Correlation

49.49%

7.09%

-0.40%

7.17%

0.84%

0.56

0.78

-

60.81%

7.87%

-1.32%

7.73%

0.83%

0.67

0.94

0.96

* Management fees and expenses may be associated with investments. Investment funds are not guaranteed, their values change frequently and past performance may not be repeated. The indicated rate of return is the historical compounded total return including changes in share value and reinvestment of all dividends.

October 1, 2013 to January 31, 2019

5

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec YTD S&P 500 YTD

GLOBAL TECH FUND MONTHLY PERFORMANCE SINCE INCEPTION

BSD has outperformed our portfolio benchmark with lower risks through active diversification across various subsectors

2013 1.32% 0.35% 2.82% 4.55% 9.60%

2014 -2.08% 3.63% -2.07% -4.39% 2.38% 2.80% 2.21% 3.53% -1.64% 4.95% 2.89% -1.51% 10.69% 11.43%

2015 0.53% 5.39% -0.16% 2.98% 0.90% -0.91% 0.43% -6.67% -1.48% 9.68% 0.63% -0.76% 10.16% 0.47%

2016 7.41% -2.78% 5.31% -0.02% 2.33% -0.29% 3.66% 0.65% 1.63% 0.38% -3.75% -0.71% -1.63% 9.50%

2017 4.97% 2.14% 2.99% 2.08% 3.85% -2.55% 2.68% 2.12% 0.51% 3.41% 0.11% -0.20% 24.49% 19.42%

2018 2.89% 0.63% -0.62% -1.57% 4.11% -1.63% 0.63% 1.72% -2.18% -7.87% 2.70% -7.41% -10.01% -6.55%

2019 7.09% 7.09% 7.87%

-10%

0%

10%

20%

30%

40%

50%

60%

70%

80%

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BSD S&P 500

6

HOW BSD COMPARES TO OTHER HEDGE FUNDS

HEDGE FUND STRATEGIES* 2014 RETURN 2015 RETURN 2016 RETURN 2017 RETURN 2018 RETURN

PERFORMANCE

BSD Global Technology

Absolute Return

Multi-Region

Equal Weighted Strategies

Relative Value Arbitrage

Macro/CTA

Fixed Income - Credit

Global Hedge Fund

Equity Hedge

North America

Emerging Markets Composite

Market Directional

Event Driven

10.69%

0.67%

1.71%

-0.56%

-3.06%

5.09%

-1.86%

-0.60%

1.37%

-4.13%

-8.03%

5.13%

-4.06%

Our outperformance relative to other funds are indicative of our core competency in

generating outsized returns and navigating a challenging market environment

* Hedge fund index data is provided by Hedge Fund Research Index (HFRI) as of January 2018.

10.16%

2.86%

-1.19%

-1.54%

-3.10%

-1.96%

-4.38%

-3.64%

-2.33%

-9.35%

-5.26%

-8.58%

-6.94%

-1.63%

0.31%

1.95%

3.78%

1.03%

-2.93%

4.97%

2.50%

5.49%

4.14%

6.77%

9.86%

10.50%

24.99%

3.91%

6.58%

6.10%

4.28%

7.43%

4.55%

8.04%

12.78%

6.25%

8.99%

4.68%

7.22%

-10.02%

-0.49%

-5.90%

-5.35%

-1.17%

-3.25%

-2.55%

-6.72%

-9.42%

-7.62%

-7.55%

-12.54%

11.68%

TOP 10 STRATEGIC TECH TRENDS FOR 2019-2021

7

CURRENT OPPORTUNITIES AND INVESTMENT PIPLINE

8

BIG DATA HARDWARE

• Due to the increasing popularity of the Internet and the growing

demand for data transfer infrastructure, the telecommunications

equipment sector and the IT equipment sector have started to

overlap more and more in the last few years.

• Worldwide IT spending is projected to total $3.8 trillion in 2019, an

increase of 3.2 percent from expected spending of $3.7 trillion in

2018

• The number of cellular IoT connections is expected to reach 4.1

billion in 2024, increasing with a CAGR of 27%.

• Big data is a key driver of overall growth in stored data. Big data will

reach 403EB by 2021, up almost 8-fold from 51EB in 2016. Big data

alone will represent 30% of data stored in data centers by 2021, up

from 18% in 2016

9

BIG DATA HARDWARE

BIG DATA HARDWARE

10

AGENDA

BIG DATA HARDWARE ECOSYSTEM

THE THREE Vs OF BIG DATA

BIG DATA SOURCES

BIG DATA COMMUNICATIONS AND PROCESSING ECOSYSTEM

COMMUNICATIONS

PROCESSING

FUTURE TRENDS

BIG DATA HARDWARE

11

BIG DATA HARDWARE ECOSYSTEM

Sensing Hardware

The Cloud

Data Analytics

Output Hardware

Sensing Hardware: Equipment that

collects consumer inputs: smartphones

(as personal location and activity

sensors), security cameras (collect

timestamp data and gender and age

bracket), sensors (motion and

temperature), POS terminals

(collecting consumer purchasing

behaviors), etc.

The Cloud: Where all the data

collected from the sensing hardware is

stored.

Data Analytics: Where all the data

gets analyzed and interaction

decisions get made (can be housed in

the cloud).

Output Hardware: How the customer

gets the desired experience.

12

THE THREE Vs OF BIG DATA

Volume, Velocity, Variety

BIG DATA HARDWARE THE THREE Vs OF BIG DATA

BIG DATA HARDWARE

13Source: Ericsson

Internet of Things on the rise – the number of cellular IoT

connections is expected to reach 4.1 billion in 2024,

increasing with a CAGR of 27%.

IOT – CONNECTED DEVICES FORECAST

0

5000

10000

15000

20000

25000

30000

35000

40000

2018 2019 2020 2021 2022 2023 2024

Fixed phones Mobile phones PC/Laptop/Tablet Short-Range IoT Wide-Area IoT

BIG DATA SOURCES

BIG DATA HARDWARE

14

Autonomous vehicle technology, or "self-driving“, refers to

vehicles that use sensory data of the surrounding

environment to navigate without the use of human drivers.

BIG DATA SOURCES

SENSOR FUSION FOR AUTONOMUS DRIVING

BIG DATA HARDWARE

15

BIG DATA SOURCES

THESE COMPANIES ARE TESTING SELF-DRIVING CARS IN CALIFORNIA

88

5

5

6

8

11

11

12

14

39

51

55

104

Others

BIG DATA HARDWARE

16

BIG DATA COMMUNICATIONS AND PROCESSING ECOSYSTEM

Source: Gartner

In 2019,

IT spending is

projected to reach

$3.8T

Communications Processing

BIG DATA HARDWARE

17Source: Gartner

Due to the increasing popularity of the Internet and the

growing demand for data transfer infrastructure, the

telecommunications equipment sector and the IT equipment

sector have started to overlap more and more in the past

few years.

1,392 1,425 1,442

931 987 1,034

665689 706

369405

439181192

195

2017 2018 2019

Data Center Systems

Enterprise Software

Devices

IT Services

Communications Services

US $, in billions

WORLDWIDE IT SPENDING FORECAST

COMMUNICATIONS

BIG DATA HARDWARE

18

TELECOMMUNICATIONS EQUIPMENT COMPANIES

Source: Statista

2.8

5.03

6.38

10.12

16.71

22.29

23.95

24.16

27.73

38.57

48

92.55

Ciena

Juniper

Motorola Solutions

Corning

ZTE

Qualcomm

Nec Corporation

Ericson

Nokia

Fujitsu

Cisco

Huawei

Huawei was the largest

telecommunications

equipment company (revenue

across all business

segments) in the world in

2017 with revenues of more

than 90 billion U.S. dollars.

US $, in billions

COMMUNICATIONS

BIG DATA HARDWARE

19Source: Statista

11 14 16 19 21 24 26 27 29 31 32 33910

1214

1516

1719

2022 23 24

8

11

14

17

20

24

27

31

34

3842

46

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027

Services Hardware Sofware

The hardware segment is projected to increase from $12B

in 2018 to $24B in 2027.

US $, in billions

BIG DATA REVENUE FORECAST BY MAJOR SEGMENTS

PROCESSING

BIG DATA HARDWARE

20

PROCESSING

DATA STORED IN DATA CENTERS

286

397

547

721

985

1327

2016 2017 2018 2019 2020 2021

Globally, the data stored in data centers will grow 4.6-fold

by 2021 to reach 1.3 ZB, up from 286 EB in 2016

36% CAGR

2016-2021

A zettabyte is a measure of storage capacity and is 2 to the 70th power bytes, also expressed

as 10^21 (1,000,000,000,000,000,000,000 bytes) or 1 sextillion bytes.

One Zettabyte is approximately equal to a thousand Exabytes, a billion Terabytes, or atrillion

Gigabytes.

in Exabytes

Source: Cisco

BIG DATA HARDWARE

21

PROCESSING

BIG DATA VOLUMES

Big data will reach 403EB by 2021, up almost 8-fold from

51EB in 2016. Big data alone will represent 30% of data

stored in data centers by 2021, up from 18% in 2016

51

81

124

179

272

405

2016 2017 2018 2019 2020 2021

in Exabytes

Source: Cisco

51% CAGR

2016-2021

BIG DATA HARDWARE

22

CPUs/GPUs TPU FPGA RAM

IBM

Intel

Nvidia

AMD

Amazon

Intel

Xilinx

Samsung

Micron

SK hynix

PROCESSING

BIG DATA PROCESSING

Google

BIG DATA HARDWARE

23

PROCESSING

BIG DATA PROCESSING – FUTURE TRENDS

Naturally, there are different opinions on the best way to

implement machine learning at the hardware level. Several

major players have each opted for a different approach:

NVIDIA’s going for GPUs, Microsoft’s all for FPGAs, and

Google’s trying TPUs.

•CPU: central processing unit. Avery general-purpose processor. You have at least one of these in your computer right now.

•GPU: graphics processing unit. A processor specially designed for the types of calculations needed for computer graphics.

•DNN: deep neural network. Neural networks are a common approach to machine learning, and the deep essentially refers to the level of complexity (specifically, DNNs include a lot of hidden layers).

•DPU: deep neural network (DNN) processing unit.

•FPGA: field programmable gate array. This is a general-purpose device that can be reprogrammed at the logic gate level.

•Hard DPU: “hard” refers to the fact that the DPU cannot be reprogrammed, unlike the “soft” FPGA.

•ASIC: application-specific integrated circuit, designed to be very effective for one application only.

•TPU: tensor processing unit. The name of Google’s architecture for machine learning.