February 2019 Prosperously navigating unexpected events with great - - PowerPoint PPT Presentation

February 2019

Prosperously navigating unexpected events with great skill and agility

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WHO WE ARE

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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

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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 COMPANY ANALYSIS

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

October 1, 2013 to January 31, 2019

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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% Sep-13 Nov-13 Jan-14 Mar-14 May-14 Jul-14 Sep-14 Nov-14 Jan-15 Mar-15 May-15 Jul-15 Sep-15 Nov-15 Jan-16 Mar-16 May-16 Jul-16 Sep-16 Nov-16 Jan-17 Mar-17 May-17 Jul-17 Sep-17 Nov-17 Jan-18 Mar-18 May-18 Jul-18 Sep-18 Nov-18 Jan-19 BSD S&P 500

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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

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CURRENT OPPORTUNITIES AND INVESTMENT PIPLINE

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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

• verlap 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

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BIG DATA HARDWARE

BIG DATA HARDWARE

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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

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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.

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THE THREE Vs OF BIG DATA

Volume, Velocity, Variety

BIG DATA HARDWARE

THE THREE Vs OF BIG DATA

BIG DATA HARDWARE

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Source: 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

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

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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

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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

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BIG DATA COMMUNICATIONS AND PROCESSING ECOSYSTEM

Source: Gartner

In 2019, IT spending is projected to reach

$3.8T

Communications Processing

BIG DATA HARDWARE

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Source: 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 1,034 665 689 706 369 405 439 181 192 195 2017 2018 2019

Data Center Systems Enterprise Software Devices IT Services Communications Services

US $, in billions WORLDWIDE IT SPENDING FORECAST COMMUNICATIONS

BIG DATA HARDWARE

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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

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Source: Statista

11 14 16 19 21 24 26 27 29 31 32 33 9 10 12 14 15 16 17 19 20 22 23 24 8 11 14 17 20 24 27 31 34 38 42 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

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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

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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

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CPUs/GPUs TPU FPGA RAM

IBM Intel Nvidia AMD Amazon Intel Xilinx Samsung Micron SK hynix

PROCESSING BIG DATA PROCESSING

Google

BIG DATA HARDWARE

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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.