february 2019 - wordpress.com · • the number of cellular iot connections is expected to reach...
TRANSCRIPT
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
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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
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
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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%
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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%
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
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
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
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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.
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
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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
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
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.