autonomous learning investment strategies...
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For investment professional use only. Not to be shown or distributed to the general public.
Autonomous Learning Investment Strategies (ALIS)
Adil Abdulali
The Next Investment Process Paradigm: The Third Wave
For investment professional use only. Not to be shown or distributed to the general public.
THE CONFLUENCE OF FIVE UNPRECEDENTED DEVELOPMENTS
HAS TRANSFORMED INTO THE NEW ALIS PARADIGM:
Enormous growth and
multiple structures of
data
New data science
and structuring
platforms to parse
and classify data
Machine Learning –
AI is finally working
Low cost on-demand and
scalable computing leads
to favorable investor fee
dynamics
Playing too close
to the information “Edge” is
a risk
Autonomous Learning Investment Strategies (ALIS): Why now?
1
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Structured Unstructured
Financial
Non-Financial
Source: IDC Digital Universe Study, 2010
Four quadrants—the multiple structures of data
News
SEC Filings
Credit Card Data
Purchase Scan Data
Search Keywords
Advertising Data
Municipal Traffic Data
Shipping Manifest
GPS Cell Phone Data
Blogs/Websites
Satellite Imaging
A L I S+C O M P U T A T I O N A L
F I N A N C E
2
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c
Exponential data growth—doubling at the rate of Moore’s Law1
0
5
10
15
20
25
30
35
40
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
Structured Data Unstructured Data
Ze
tta
byte
s
1 Kelly, Kevin. The Inevitable: Understanding the 12 Technological Forces that will Shape Our Future (p.257). Penguin Publishing Group. Kindle Edition2 McKenna, Brian “What does a petabyte look like?” ComputerWeekly.com. March 2013. According to Michael Chui, principal at McKinsey, the US Library of Congress “had collected 235 terabytes of data by April 2011 and a petabyte is more than four times that.”3 One zettabyte = one thousand exabytes = one million petabytes = one billion terabytes = one trillion gigabytes (1,000,000,000,000 gigabytes)
Source: IDC Digital Universe Study, 2010
1 ZETTABYTE:more than four million times the
size of the entire US Library of
Congress2,3
3
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Structured Unstructured
Financial
Non-Financial
DATA
Clean
How to interpret that data—new platforms
PLATFORM ORGANIZES DATA FOR
ANALYSIS
Data Science Platform
Parse
Classify Normalize
Categorize
Transform
4
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Traditional Programming
Hypothesis driven: Programmers develop
models, based on which they code rule-
based algorithms. To these they feed data
to produce the desired outcome
What is Machine Learning?
1Phil Simon (March 18, 2013). Too Big to Ignore: The Business Case for Big
Data. Wiley. p. 89. ISBN 978-1-118-63817-0
Source: Sebastian Raschka, 2016
Machine Learning
Data driven: Data is provided to a learning
algorithm from which models are
developed. These can be used to solve the
problem task.
Programmer
Inputs (observations)
Model Computer Outputs
Inputs
Outputs
ModelComputer
“Machine Learning is the field of study that gives computers the ability to
learn without being explicitly programmed .”
ARTHUR SAMUEL, 19591
5
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Human
Performance
Computer
Performance
Time
Pe
rfo
rma
nce
We are here.
Jeremy Howard TED talk
Now machines learn: AI is real, not artificial
6
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HOW MANY YEARS FOR A COMPUTER TO BECOME W ORLD MASTER?
Is the investment game too complicated? It’s been said before…
CHECKERS
“It’s (the computer’s) guidelines
for judgement are not nearly as
good as a human’s”
MARION TINSLEY, 1994 1
1994
Computer becomes Checkers World Champion
10^20 Permutations
”It may be 100 years before a computer
beats humans at Go – maybe even
longer”
DR PIET HUT FOR PRINCETON'S INSTITUTE
FOR ADVANCED STUDIES, 19973
2016
AlphaGo Computer beats human
World Champion (Lee Sedol)
GO
10^700 Permutations
”No computer will ever beat me”
GARRY KASPAROV, 1987 2
1997
Computer wins a series against a reigning World Champion
(Kasparov)
10^120 Permutations
CHESS
1Corcoran, E. “Squaring off in a game of Checkers”. The Washington Post, August 15,
1994 / 2Hsu, F.H. Behind Deep Blue: Building the Computer that Defeated the World
Chess Champion Princeton University Press (2002) / 3Johnson, G. “To Test a Powerful
Computer, Play an Ancient Game. The New York Times. July 29 1997
0 5 10 15 20 25 30 35 40
Go
Chess
Checkers 38 years
12 years
6 years
1950 2016201020001990198019701960
7
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Making decisions under conditions of uncertainty with incomplete information
2011
IBM’s Watson machine defeats the highest ranking
Jeopardy champions in history
JEOPARDY
Uncertain information: the question is
unknown, and must be solved with a conviction
level bet faster than the opponent
“Defeating a chess champion is a
piece of cake compared to parsing
puns and analyzing language”
WSJ, 20111
1990 201720152010200520001995
Checkers Chess Jeopardy Go Poker
1Baker, S. “Can a Computer Win on ‘Jeopardy’?”. The Wall Street Journal, February, 5, 20112Wilson, C. “Jeopardy, Scheopardy”, Slate, February 11, 2011
POKER
Incomplete information: the state of the game
is unknown (cards are hidden), and the
opponent’s strategy is unknown (they bluff)
2017
Libratus computer beats four professionals at no-
limit Texas Hold ‘em poker by $1.7million
“Watson can win at Jeopardy, but how
would it do at poker? …This is outside
the realm of traditional game theory”
Slate, 20112
8
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This isn’t a false dawn
0%
5%
10%
15%
20%
25%
2011 2012 2013 2014 2015
Computer error rates on ImageNet Visual Recognition Challenge
Sources: The Economist, Google, ImageNet, Stanford Vision Lab
RAPID MACHINE-LEARNING IMPROVEMENTS MEAN COMPUTERS HAVE SURPASSED
HUMANS AT PREVIOUSLY UNTHINKABLE TASKS
HUMAN LEVEL
9
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GARY KASPAROV1
“Weak human + machine + better process was superior to a strong computer
alone and, more remarkably, superior to a strong human + machine + inferior
process.”
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
Man and MachineMachine onlyMan only
Man and Machine dominates Machine–only and Man–only
Data from Kenneth W. Regan; Ranking by raw error as of 2010 using chess program Rybka 3;
http://www.cse.buffalo.edu/~regan/chess/fidelity/FreestyleStudy.html1Garry Kasparov “The Chess Master and the Computer”. The New York Review of Books, February
11 2010; http://www.nybooks.com/articles/2010/02/11/the-chess-master-and-the-computer/
% OF TOP 206 ALL-TIME HIGHEST RATED CHESS PERFORMANCES BY ‘CYBORGS’
(COMBINATIONS OF MAN AND MACHINE), MACHINES ONLY AND MAN ONLY
10
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Machine learning (ML) frameworks and methodologies
SUPERVISED LEARNING
• Predicting Value
• Decision Tree Regression
• Nearest Neighbors
• Predicting Classification
• Neural Networks/Deep Learning
• Support Vector Machines
• Baysian Classifiers
• Genetic Algorithms
• Markovian Decision Processes • Gaming AI
REINFORCEMENT LEARNING OTHER
UNSUPERVISED LEARNING
• Clustering
• K-means clustering
• Association Rule Learning
• Dimensionality Reduction
• Principal Components
Analysis
Note: Many of these techniques can be used across multiple frameworks – The
above is just illustrative of potential types of ML. 11
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c
Plummeting processing costs make ML methodologies achievable
0.000001
0.00001
0.0001
0.001
0.01
0.1
1
10
100
1000
1980 1985 1990 1995 2000 2005 2010
$ c
ost p
er
1b
n c
alc
ula
tio
ns (
log
sca
le)
MACHINES DON’T OWN HOUSES IN THE HAMPTONS!
Source: Nordhaus (2007); updated data through 2010 from Nordhaus, personal website,
http://www.econ.yale.edu/Nordhaus/homepage/”Two Centuries of Productivity Growth in
Computing”, authors calculations. Adjusted for year 2006 purchasing power
EXPONENTIAL
COST REDUCTION:
A million dollars of 1980
computing power costs
less than 4 cents today
12
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The Cloud: on-demand and scalable computing costs plummet
0
0.05
0.1
0.15
0.2
0.25
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
$ p
er
GB
pe
r M
on
th
Amazon Microsoft Google IBM
The lowest price has
fallen 84% since 2010
March 2006: The first ‘big player’
to enter cloud computing
November 2010:
Enters the market with
Microsoft Azure
May 2011: Launches Google Cloud May 2013: Acquires Softlayer
Early entrant
August 2008Early Entrant October 2015:
Dell to acquire
early entrant EMC
June 2009 January 2011 June 2012November 2011
Source: Alex Teu, “Cloud Storage is Esting the World Alive” TechCrunch. 20 August 2014;
Trefis Team. “IBM Cloud Services Part-II” Forbes. 9 March 2015; Company Websites.
13
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c
When costs plummet, investors gain
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
0.00% 5.00% 10.00% 15.00% 20.00% 25.00%
Old 2&20 New 1/10/20
OPPORTUNITIES FOR FAVORABLE FEE RESTRUCTURING
Gross Returns of Underlying Portfolio
% o
f G
ross R
etu
rn R
eta
ine
d B
y I
nve
sto
rs
% Change
125%
50.0%
26.6%
7.7%
Gross Return
2.5%
5.0%
10.0%
15.0%
14
Source: Hedge Fund Fees – A Perfect Solution by Jeffrey Tarrant, Adil Abdulali and Michael
Weinberg http://www.pionline.com/article/20170306/ONLINE/170309918/hedge-fund-fees-
8211-a-perfect-solution
For investment professional use only. Not to be shown or distributed to the general public.
Man + Machine + Data Science + Four Quadrants + Low Costs = ALIS
Model
Inputs Outputs
15
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c MOV37 ALIS
Sweet Spot
Portfolio Holding Period
Sca
lab
ility
of S
tra
teg
y
HIGH
LOW
SHORT-TERM MEDIUM-TERM LONG-TERM
a
No High
Frequency
Trading
This is not high frequency trading
16
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“Repeatable process”
Information source
Discretionary
Unique analytical processGood Judgement of
Portfolio Manager
Structured financial data
and “edge” through
relationships within
sectors, regions, and other
informational sources,
expert networks etc.
Top Tier MBAs &
Finance Professionals
Human intellectual
and intuitive pattern
recognition: 10,000
hours1
Massive, finite
quantities of expensive
structured financial
data
PhDs, algorithms and
expensive processing
power
Many PhD’s
assisted by machineQuant
ALIS
The Third Wave: The Darwinian Evolution of the “Repeatable Process”
Source: Malcolm Gladwell. “Outliers” 17
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Pay a Fine
Meanwhile, the information “edge” game has changed
Back off the “Edge” Go to Jail
Regulation FD (Fair Disclosure)
–Company information to
everyone at the same time
2000 2013
“SAC Capital to pay
$1.8 billion, the largest insider
trading fine ever”1
2014
“SAC’s Martoma gets
nine years prison for insider
trading”2
1 Sheelah Kolhatkar, “SAC Capital to pay $1.8 billion, the largest insider trading fin ever”
Bloomberg News, November 4, 2013 / 2 Nate Raymond “SAC’s Martoma gets nine years
prison for insider trading” Reuters, September 8, 2014 / Source: Vintage Monopoy Mr
Pennybags Chance Card Vectors by robdevenney on DeviantArt.com
Investment players have three choices
18
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The genealogy of existing quant managers
1982 RenTech
DE Shaw1988
2 Sigma
The Voleon
Group
Winton
AHL
(now Man AHL)
2001
1987
1997
Today
2007
Aspect
Capital
$36bn Closed $6.6bn $19.2bn $39bn $35bn $34bn $1.5bn
Medallion
Fund
$6.7bn
1993 PDT
1986APT (Morgan
Stanley)
2011PDT
Partners
Jim Simons establishes
Renaissance Technologies
David Shaw joins APT group
at Morgan Stanley
Mike Adam, Martin Lueck,
and David Harding form AHL
(now Man AHL)
David Shaw leaves APT
to form own fund
Pete Muller’s PDT Group spins
Out of Morgan Stanley
Pete Muller works for and is offered
a job at RenTech, but instead forms
PDT out of APT at Morgan Stanley
Lueck & Harding spin off to form
own companies
Michael Kharitonov & Jon
McAuliffe, spin off DE Shaw to form
Voleon
David Siegel & John Overdeck
spin off DE Shaw to form 2 Sigma
19
Flagships Close As Assets Grow
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“I have one guy who has a PhD. in finance. We don’t hire people from
business schools. We don’t hire people from Wall Street. We hire people
who have done good science”
Existing Quant Managers Industry Connections Universities
Where’s the talent?
1Lux, Hal ‘The Secret World of Jim Simons’ Institutional Investor, 11/1/2000
JIM SIMONS1
INVESTORS
20
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WE ARE
Pivotal Moves of Man and Machine
“Kasparov had concluded that the counterintuitive
play must be a sign of superior intelligence”1
“AlphaGo’s move was simply beyond the current
human understanding of the game...”2
1Murray Campbell, one of the IBM scientists who designed Deep Blue, speaking to Nate Silver for his
book The Signal and the Noise. Finley, Klint. ‘Did a computer bug help deep blue beat Kasparov?’
Wired.com, September 2012 / 2Esteban, Cristobal, “Move 37”, Medium. March 2016,
https://medium.com/@cristobal_esteban/move-37-a3b500aa75c2#.1d9d7vwcm 3MOV in assembly language means to replace the old with the new.
…YOUR MOVE3
KASPAROV RESIGNED ON HIS 37TH MOVE AGAINST DEEP BLUE
ALPHAGO’S 37TH MOVE FLUMMOXED GO MASTER LEE SEDOL
21
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Autonomous Technical2,3,4,5
Growth of Unit Investment Performance in MSCI World Down Months
ANNUALIZED RETURN
BETA
MSCI WORLD
R
MSCI WORLD
R-SQ
MSCI WORLD
31.6% 0.24 0.29 0.09
1.0%
-2.4%
-8%
-6%
-4%
-2%
0%
2%
4%
Jan-13 Jan-14 Jan-15 Jan-16 Jan-17
Autonomous Technical MSCI World Index
Autonomous Technical Average MSCI World Index Average
$289
$145
$90
$110
$130
$150
$170
$190
$210
$230
$250
$270
$290
$310
Jan-13 Jan-14 Jan-15 Jan-16 Jan-17
Autonomous Technical MSCI World Index
Data estimated of February 28, 2017
Please refer to Disclaimer and Notes beginning on Page 44.
Underlying Manager Profile
STRATEGY OVERVIEW CLASSIFICATION REGION
FUND
LAUNCH
DATE
Autonomous technical
Inspired by video gaming AI, this strategy utilizes 1000’s of independent Jesse Livermore like autonomous computer
agents trading, adapting and learning from each trade in a simple portfolio with clear risk management limits and
protocols but implemented with no human intervention at all. The non-traditional background of the founder as well as
the unusual structure of the organization makes this the ultimate financial market hack.
Autonomous
TechnicalUS 2013
22
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Flow Detection US Equities2,3,4,5
STRATEGY OVERVIEW CLASSIFICATION REGION
FUND
LAUNCH
DATE
Capital Flow detection by ML
algorithms
A two state statistical arbitrage strategy driven by prevailing liquidity conditions in the market based on measuring
investment flows in the equity markets using sophisticated mathematical techniques. These techniques allow the
machine to process large amounts of data across a large number of securities simultaneously to uncover patterns
that would not be possible for a human. One of the states is active in stress and driven by liquidation flows and the
other state is active in calm markets and is driven by sentiment indicators processed in a unique way. ML is used in
the construction of each strategy as well as the switching algorithm between the two
Flow Detection
US EquitiesUS 2013
ANNUALIZED RETURN
BETA
MSCI WORLD
R
MSCI WORLD
R-SQ
MSCI WORLD
4.8% 0.20 0.17 0.03
$123
$137
$80
$90
$100
$110
$120
$130
$140
$150
Jun-13 Jun-14 Jun-15 Jun-16
Flow Detection US Equities MSCI World
0.9%
-2.4%
-8%
-4%
0%
4%
8%
Jun-13 Jun-14 Jun-15 Jun-16
Flow Detection MSCI World Index
Flow Detection Average MSCI World Index Average
Underlying Manager Profile
Growth of Unit Investment Performance in MSCI World Down Months
23Data estimated of February 28, 2017
Please refer to Disclaimer and Notes beginning on Page 44.
For investment professional use only. Not to be shown or distributed to the general public.
Deep Learning Market Neutral2,3,4,5
STRATEGY OVERVIEW CLASSIFICATION REGION
FUND
LAUNCH
DATE
Deep Learning Market Neutral
Uses Deep Learning algorithms, Natural Language Understanding and large scale parallel computing on historical
data to predict prices. The founder is a Protégé of one of the more successful global stat arb funds and has used
machine learning to take it to the next level. R&D makes up a significant part of the investment process.
Deep Learning US 2016
ANNUALIZED RETURN
BETA
MSCI WORLD
R
MSCI WORLD
R-SQ
MSCI WORLD
5.4% -0.33 -0.46 0.22
$104
$111
98
100
102
104
106
108
110
112
Jun-16 Aug-16 Oct-16 Dec-16 Feb-17
Deep Learning Market Neutral MSCI World Index
2.1%
-1.5%
-3%
-2%
-2%
-1%
-1%
0%
1%
1%
2%
2%
3%
3%
Jun-16 Aug-16 Oct-16 Dec-16 Feb-17
Deep Learning Market Neutral MSCI World Index
Deep Learning Market Neutral Average MSCI World Index average
Underlying Manager Profile
Growth of Unit Investment Performance in MSCI World Down Months
24Data estimated of February 28, 2017
Please refer to Disclaimer and Notes beginning on Page 44.
For investment professional use only. Not to be shown or distributed to the general public.
Genetic Algorithmic2,3,4,5
$155
$178
$80
$100
$120
$140
$160
$180
$200
Jan-12 Jan-13 Jan-14 Jan-15 Jan-16 Jan-17
Genetic Algorithm MSCI World Index
2.1%
-2.6%
-10%
-8%
-6%
-4%
-2%
0%
2%
4%
Jan-12 Jan-13 Jan-14 Jan-15 Jan-16 Jan-17
Genetic Algorithm MSCI World Index
Genetic Algorithm Average MSCI World Index Average
STRATEGY OVERVIEW CLASSIFICATION REGION
FUND
LAUNCH
DATE
Genetic Algorithmic
Seeks to identify an optimal, diverse collection of trading models to achieve investment targets using genetic
algorithms. These algorithms provide a framework to identify best solutions from a massive pool of potential solutions
and to evolve them over time. The managers background is from the data science technology industry
Genetic
AlgorithmicUS 2012
ANNUALIZED RETURN
BETA
MSCI WORLD
R
MSCI WORLD
R-SQ
MSCI WORLD
8.8% 0.10 0.16 0.02
Underlying Manager Profile
Growth of Unit Investment Performance in MSCI World Down Months
25Data estimated of February 28, 2017
Please refer to Disclaimer and Notes beginning on Page 44.
For investment professional use only. Not to be shown or distributed to the general public.
ML Fundamental2,3,4,5
STRATEGY OVERVIEW CLASSIFICATION REGION
FUND
LAUNCH
DATE
ML Fundamental
This strategy uses the latest deep learning techniques from the world of artificial intelligence and applies it to stock
picking. It is a completely systematic strategy anchored in fundamental analysis based on processing a much larger
volume and breadth of information than a human ever could. The systematic pattern matching algorithm uses
fundamentals, technicals, news, governance and 3rd party research estimates simultaneously to make its stock picks.
The portfolio is liquid, has low turnover, does not use balance sheet leverage and targets 100 to 240 stocks in total.
ML Fundamental Global 2015
ANNUALIZED RETURN
BETA
MSCI WORLD
R
MSCI WORLD
R-SQ
MSCI WORLD
7.6% -0.14 -0.28 0.08
$113
$108
80
85
90
95
100
105
110
115
120
Aug-15 Nov-15 Feb-16 May-16 Aug-16 Nov-16 Feb-17
ML Fundamental MSCI World Index
1.0%
-2.7%
-9%
-7%
-5%
-3%
-1%
1%
3%
5%
Aug-15 Nov-15 Feb-16 May-16 Aug-16 Nov-16 Feb-17
ML Fundamental MSCI World Index
ML Fundamental Average MSCI World Index Average
Underlying Manager Profile
Growth of Unit Investment Performance in MSCI World Down Months
26Data estimated of February 28, 2017
Please refer to Disclaimer and Notes beginning on Page 44.
For investment professional use only. Not to be shown or distributed to the general public.
Autonomous Learning Investment Strategies (ALIS)
Adil Abdulali
The Next Investment Process Paradigm: The Third Wave
For investment professional use only. Not to be shown or distributed to the general public.
Disclaimer & Notes
D I S C L A I M E R
The information contained in this document is confidential and is intended only for
the use of the person to whom it is given and is not to be reproduced or
redistributed. It is not a solicitation to invest in any investment product, nor is it
intended to provide investment advice. It is intended for information purposes only
and should be used only by sophisticated investors who are knowledgeable of the
risks involved. This document does not constitute an offer to sell or the solicitation
of an offer to buy any securities, nor will any sale of a security occur in any
jurisdiction where such an offer, solicitation or sale would be unlawful.
This document may only be distributed to persons who are “accredited investors”
and “qualified purchasers” within the meaning of U.S. Securities laws and to non-
U.S. persons. It is intended solely for the use of the person to whom it is given and
may not be reproduced or distributed to any other person. The MOV37™ Funds of
Funds have not been, and will not be, registered under the Investment Company
Act of 1940, as amended, and interests of the MOV37 Funds of
Funds have not been, and will not be, registered under the Securities Act of 1933,
as amended, and may only be offered in private placement transactions.
An investment in a MOV37™ Funds of Funds may be made only pursuant to the
applicable offering documents. Past performance is not a guarantee of future
results. Inherent in any investment is the potential for loss. This summary is for
discussion purposes only. It is not intended to supplement or replace a
confidential offering memorandum or related offering materials, which should be
the sole basis for making an investment decision.
Important information: Hedge funds are speculative investments and are not
suitable for all investors, nor do they represent a complete investment program.
Hedge funds are not subject to the same regulatory requirements as mutual funds.
MOV37™ Fund of funds are only opened to qualified investors who are
comfortable with the substantial risks associated with investing in hedge funds.
MOV37™ Fund of Funds’ investment programs are speculative and entail
substantial risks. An investment in the MOV37™ Fund of Funds includes the risks
inherent in an investment in securities, as well as specific risks associated with
limited liquidity, the use of leverage, arbitrage, short sales, options, futures,
derivative instruments, investment in non-US securities, “junk” bonds and illiquid
investments. In particular, the MOV37™ Fund of Funds may use leverage in
making investments in underlying funds, and underlying fund managers also may
employ leverage through a number of measures, either of which could increase
any loss incurred. The more leverage employed, the more likely a substantial
change will occur, either up or down, in the value of the investment. There can be
no assurances that the strategy pursued by MOV37™ (hedging or otherwise), or
the strategy of any underlying fund manager with whom the MOV37™ Fund of
Funds invest, will be successful or that MOV37™ (or any underlying fund
manager) will employ such strategies with respect to all or any portion of a
portfolio. Past performance is not indicative of future results.
This message contains information that is confidential and privileged. Unless you
receive prior authorization from MOV37™, you may not use, copy, print, forward or
disclose to anyone the message or any information contained in the
message. Thank you for your cooperation.
Although this summary has been prepared using sources, models and data that
MOV37™ believes to be reasonably reliable, its accuracy, completeness or
suitability cannot be guaranteed. Therefore, the information is supplied on an “AS
IS” basis, and no warranty is made as to its accuracy, completeness, non-
infringement of
third-party rights, merchantability or fitness for a particular purpose. Furthermore,
the information used to generate this summary includes third party unverified data
as well as MOV37™’s assumptions, made on a best efforts basis, with regard to
incomplete information, and independent of market conditions. The recipients of
this report assume all risks in relying on the information set forth herein.
NOTES:
[1] The annualized return since inception for each fund and strategy is reported net
of all underlying manager fees and gross of MOV37™ expenses, management
fees and incentive fees. Returns will vary based on the timing of investment.
Returns, allocations and other data are estimated through February 28, 2017.
[2] The data provided herein has been calculated based upon the most recent data
that MOV37™ has received from each hedge fund manager. MOV37™ cannot
confirm or guarantee the accuracy of the data provided.
[3] The index information is included for discussion purposes only to show the
general trends in certain markets in the periods indicated and is not intended to
imply that the MOV37™ funds will be similar to these indices in performance,
composition, or element of risk. While MOV37™ does not use benchmarks for its
funds, it tracks various indices for comparative purposes only. Index comparisons
for the underlying funds were chosen for comparative purposes only in MOV37™’s
good faith judgment and may not reflect actual fund benchmarks, if any.
The S&P 500 Index is compiled by Standard & Poor’s and includes 500 leading
companies in leading industries of the U.S. economy. The S&P 500 DRI stands for
dividend reinvestment index. This description has been obtained from
www.standardandpoors.com. The S&P 500 Index is included for informational
purposes only and is not representative of the type of securities invested in by the
MOV37™ funds.
The MSCI World Local Index is a free float-adjusted market capitalization weighted
index that is designed to measure the equity market performance of developed
markets. The MSCI World Index consists of the following 23 developed market
country indexes: Australia, Austria, Belgium, Canada, Denmark, Finland, France,
Germany, Hong Kong, Ireland, Israel, Italy, Japan, Netherlands, New Zealand,
Norway, Portugal, Singapore, Spain, Sweden, Switzerland, the United Kingdom,
and the United States. This description has been obtained from www.msci.com.
The MSCI World Index is included for informational purposes only and is not
representative of the type of securities invested in by the MOV37™ Funds or any
underlying hedge fund.
This description has been obtained from www.msci.com. The MSCI World Index is
included for informational purposes only and is not representative of the type of
securities invested in by the MOV37™ funds.
The HFOF Composite return is the average of the following five fund of funds
indices: EDHC, HFRI, INVESTHEDGE, Eurekahedge and Barclay FOF Indices.
[4] Growth of Unit Investment represents the growth of $100 had it been invested
in the fund or hypothetical portfolio for the time period indicated and has been
calculated net of the underlying managers’ fees and expenses and gross of
MOV37™’s expenses, management fee and incentive fee. Growth of $100 had it
achieved the return of the indicated index is shown for comparative purposes only.
[5] Annualized return has been reported by the manager and represents the
annualized return since the fund’s inception estimated through February 28, 2017.
[6] The proposed portfolio is for illustrative purposes only and is intended to show
how the hypothetical portfolio would have performed if it had been invested in
particular portfolio funds during the period commencing January 1, 2014. The
returns are reported net of all underlying manager fees and gross of MOV37™’s
expenses, management fees and incentive fees. Returns, allocations and other
data are estimated through February 28, 2017
The data is based on a hypothetical portfolio that has not be created and includes
7 underlying hedge funds, which are comprised of underlying hedge funds that
MOV37™ funds are not currently invested in. The data has been estimated by
MOV37™ for a hypothetical portfolio with $200 million in assets under
management based on information provided by the underlying hedge fund
managers, conversations with the underlying hedge fund managers and SEC
filings, among other sources of information, and MOV37™’s good faith
assumptions with respect to incomplete information. MOV37™ cannot confirm or
guarantee the accuracy of the information provided that was used to calculate the
portfolio breakdown and exposures. The underlying hedge funds and allocations
thereto for the proposed portfolio are hypothetical and may not be available at the
time of investment due to capacity constraints. Furthermore, any hedge funds that
are not currently in MOV37™’s portfolio will be subject to MOV37™’s operational
due diligence process. More parameters, other than investment strategy, are
considered when constructing a portfolio, such as risk levels, desired
transparency, liquidity requirements and asset classes, for example. Any future
portfolio created for the investor may not have the characteristics outlined herein.
THE PROPOSED MOV37™ PORTFOLIO IS HYPOTHETICAL AND DOES NOT
REPRESENT THE ACTUAL CHARACTERISTICS EXPERIENCED BY MOV37™.
Hypothetical performance is inherently limited and does not reflect actual trading
or decisions that may be made in response to market or other conditions. Even
though the hypothetical information does not represent past performance, any past
performance is not a guide to future results.
The proposed portfolio is equally weighted across all funds in the portfolio, and
rebalanced quarterly. For periods when the returns of funds are not available, the
allocation that would have been applied to that fund is distributed equally across
the remaining funds.
[7] MOV37™ has characterized the techniques employed by the ALIS managers,
as reflected in this slide, based upon information which was provided to it or made
available by the ALIS manager through extensive process-focused meetings and
due diligence. MOV37™ has not conducted an examination of the ALIS
managers’ codes. There can be no assurance that the characterizations are
correct or will accurately reflect the techniques in the future.
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