Quantitative Investors have long been charged with an exhilarating challenge - to derive insight from data. To support this ardor, a plethora of traditional data and technology vendors have entrenched themselves as critical partners in our pursuit of Alpha. Over the last decade, a new partner in the pursuit of “automated truth from data” has emerged. Billions of dollars in Venture Capital funding have created an ecosystem of “Big Data”, “Cognitive Intelligence”, “Cloud Technology”, etc. companies seeking to extract information from anything and everything (e.g. unstructured text, sensors, satellites, etc.). This “Data Revolution” began in California and is now blossoming globally. As “Silicon Alley” brings financial technology to the mainstream, what new opportunities await the ambitious? What disruptions threaten the complacent? And which historical analogs best illuminate the path forward for Quantitative Investors in the “Data Economy”?

1. T H E F U T U R E O F I N V E S T I N G I N T H E D ATA E C O N O M Y
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2. TABLE OF CONTEN TS
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 1
QuantCon 2016
Saturday, April 9, 2016
Michael M. Beal
Got API?
dcm@datacapitalmanagement.com
THE FUTURE OF INVESTING IN THE DATA ECONOMY

3. LESSONS FROM THE INDUSTRI AL REVOLUTI ON
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 2
Suggestions for Start-ups in the Data Economy
• Standardize industry taxonomies
• Be the best at depth of information for a given vertical
• Take responsibility for the veracity of information
• Focus on speed of delivery
• Focus on historical consistency
• Make adjustments visible to upstream users
• Focus on permissible data use rights
• Don’t be all things to all people
Got API?
dcm@datacapitalmanagement.com
Our goal is to help drive a standard for other players in our ecosystem to coalesce around
• This approach helps avoid the “tragedy of the commons” and maximize collective ROI
• We are set up to be “early customers” of new technologies / beta releases
• We drive a culture of focused and targeted feedback
For those with self-interests congruent to ours; let’s help each other

4. TABLE OF CONTEN TS
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 3
DATA CAPITAL MANAGEMENT OVERVIEW
WHAT IS THE DATA ECONOMY?
WHAT NEW CHALLENGES DOES THE DATA ECONOMY CREATE FOR INVESTORS?
THE FUTURE OF QUANTITATIVE INVESTING IN THE DATA ECONOMY
LESSONS FROM THE INDUSTRIAL REVOLUTION

5. Data Capital Management (“DCM”), is a news-aware systematic hedge
fund. DCM specializes in strategies that make use of novel sources of
information (news, images, social networks data, weather, etc.) as well
as state of the art machine learning analytics to generate differentiated
and uncorrelated investment returns.
We are a team of PhDs and MBAs, with degrees in computer science,
engineering, mathematics and business management, with expertise in
quantitative research and risk management at global investment banks,
fundamental analysis at leading investment funds and cutting edge
technologies in big data companies.
DCM’s proprietary trading system delivers uncorrelated, market-neutral
returns in liquid public markets through a portfolio of fully-systematic
advanced strategies.
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 4
INTRODUC TIO N TO DATA CAPITAL MANAGE ME N T

6. Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 5
DCM NEWS -AWAR E INVESTMEN T PHILOSOPHY
Fundamental Analysis
The analysis of company business
drivers and macroeconomic factors
-25
Analysis of
3,000+ listed
stocks & ETFs
in real time
Real Time Analysis at Large Scale and
Automatic Trade Execution
Quantitative Analysis
The analysis of security price and
trading volume statistics
Novel Data Sources Analysis
Cutting edge machine-learning
algorithms to analyze novel data
sources such as news, images, etc.
DCM combines the rules of fundamental investing, quantitative analysis, and trading
signals derived from novel data sources and machine learning algorithms to identify
alpha catalysts to changes in security prices
Parallel Cognitive Computing

7. PRICES REAC T QUICKER THAN PEOPLE
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 6
DCM’s proprietary technology enables a faster response to changing prices and unexpected news announcements.
Source: Interactive Brokers price data.
For illustrative purposes only. Back test results are not indicative of future returns. Strategies are
preliminary and are not necessarily those that will be deployed in the market.
Source: Bloomberg news, Interactive Brokers price data.
For illustrative purposes only. Back test results are not indicative of future returns. Strategies are
preliminary and are not necessarily those that will be deployed in the market.
10.3%
-12.3%
-13.8%
-20.0%
-15.0%
-10.0%
-5.0%
0.0%
5.0%
10.0%
15.0%
Oil (WTI) Chipotle
(CMG)
Staples
(SPLS)
For Illustrative Purposes Only. Back test results are not indicative of future returns. Strategies are preliminary and are not necessarily those that will be deployed in the market.
• 8.27.15: Venezuela
seeks OPEC emergency
meeting on oil prices
• 11.20.15: Oregon
agency probes E. coli
cases linked to Chipotle
• 12.7.15: Staples-Office
Depot Deal in Doubt as
FTC Moves to Block
A
B
C
A B C
1-day price returns following unexpected news events

8. INVESTMEN T PHILOSOPHY
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 7
DCM combines the rules of fundamental investing, quantitative analysis, and trading signals derived from novel data sources and
machine learning algorithms to identify short-term-alpha catalysts to changes in security prices
• Temporal
• Cross-sectional
• Short-term stat-arb
• Rebalancing
• Momentum
• Momentum Reversals
• Machine Learning Predictive
• Corporate Earnings
• Central Bank
• Cross-asset arbitrage
• M&A arbitrage
• News based
News-aware
investing enables
greater adaptive
diversification to
varying market
conditions through
an ability to switch
between investment
models based on
“market psychology”
Mean Reversion
Trend Following (Momentum)
Event Based
StrategyCategories
Traditional portfolio theory
Price Volume
Technical
Execution
Flow
Consumer
Net Exports
Government News Satellite Sensors
SpeechesWeather
Earnings & Margins
Valuations & Growth
Data Categories
Price / Volume Macro Economics Fundamental Novel Data
Etc.
News-aware investing
Commodities

9. TABLE OF CONTEN TS
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DATA CAPITAL MANAGEMENT OVERVIEW
WHAT IS THE DATA ECONOMY?
WHAT NEW CHALLENGES DOES THE DATA ECONOMY CREATE FOR INVESTORS?
THE FUTURE OF QUANTITATIVE INVESTING IN THE DATA ECONOMY
LESSONS FROM THE INDUSTRIAL REVOLUTION

10. WHAT IS THE DATA ECONOMY?
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 9
The Data Economy is the economy in
which individuals, institutions and
corporations commercialize their
Intellectual Property assets and
services in the form of web-based APIs
to third parties with the goal of
monetizing Positive Data Externalities
through the creation of new information-
based assets
Source: google images
For illustrative purposes only. Back test results are not indicative of future returns.

11. WHAT IS THE DATA ECONOMY?
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 10
The Next Industrial
Revolution:
An Economy Based on
the Creation and
Exchange of Data

12. TABLE OF CONTEN TS
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 11
DATA CAPITAL MANAGEMENT OVERVIEW
WHAT IS THE DATA ECONOMY?
WHAT NEW CHALLENGES DOES THE DATA ECONOMY CREATE FOR INVESTORS?
THE FUTURE OF QUANTITATIVE INVESTING IN THE DATA ECONOMY
LESSONS FROM THE INDUSTRIAL REVOLUTION

13. NEW DATA ECONOMY CHALLEN GES FOR TRADI TI ON AL INVESTORS
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 12
The new investment world requires an increased ability to handle breadth depth and
complication of relationships….
(1)Breadth: Identification of most relevant data feeds and information sources (news, images,
social networks data, macroeconomic data, etc.) and linking them to all the Entities
(companies, currencies, commodities) they impact. Real-time analysis of massive amounts
of heterogeneous data and predicting their impact on related Entities.
(2)Depth: 360° Entity Relationships Graph modeling relationships between vast number of
related entities (e.g. customer-supplier relationships) and their dependencies. Real-time
monitoring of changes on an entity level and predicting the impact on related entities.
(3)Speed: Identify, Analyze and execute long/short strategies based on new information (e.g.
change in relevant data, or change on an entity level) in under 5 seconds.
(4) Experience: Ability to understand macro and micro regimes and their likely impact on
company cash flows and investment returns

14. While data has increased
in value; the relative
importance of any given
data point on its own has
decreased
NEW DATA ECONOMY CHALLEN GES FOR TRADI TI ON AL INVESTORS
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 13
Breadth Depth Speed of Data has exploded
Source: DCM Analytics
For illustrative purposes only. Back test results are not indicative of future returns.

15. Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 14
NEW DATA ECONOMY CHALLEN GES FOR TRADI TI ON AL INVESTORS
Company networks have gotten more complex; making defined SQL queries
increasingly painful…
Source: google images
For illustrative purposes only. Back test results are not indicative of future returns.

16. Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 15
NEW DATA ECONOMY CHALLEN GES FOR TRADI TI ON AL INVESTORS
… A machine learning approach is required to understand complex entity relationships
Source: google images
For illustrative purposes only. Back test results are not indicative of future returns.

17. NEW DATA ECONOMY CHALLEN GES FOR TRADI TI ON AL INVESTORS
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 16
… Yet as humans gain experience, processing power decreases
Source: google images
For illustrative purposes only. Back test results are not indicative of future returns.

18. NEW DATA ECONOMY CHALLEN GES FOR TRADI TI ON AL INVESTORS
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 17
In a world driven by trend seeking algorithms, are fundamental long/short investors prepared for
the data economy?
We have come regretfully to the
conclusion that the current
algorithmically driven market
environment is one which is
increasingly incompatible with
our fundamental, research
orientated, investment process
– Nevsky Capital
Source: google images
For illustrative purposes only. Back test results are not indicative of future returns.

19. TABLE OF CONTEN TS
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 18
DATA CAPITAL MANAGEMENT OVERVIEW
WHAT IS THE DATA ECONOMY?
WHAT NEW CHALLENGES DOES THE DATA ECONOMY CREATE FOR INVESTORS?
THE FUTURE OF QUANTITATIVE INVESTING IN THE DATA ECONOMY
LESSONS FROM THE INDUSTRIAL REVOLUTION

20. | DATA CAPITAL MANAGEMENT | 19
In essence all professional investment managers share the same process to make decisions
DATA ACQUISITION ANALYSIS DECISION
Breadth: Data is data; novel
data sources including news,
images, social networks,
macroeconomic feeds, etc. are
linked to price movements in
securities, currencies,
commodities, etc.
Depth: Analyzes, prioritizes
and monitors big-data input
and its impact using systematic
and quantitative metrics
Speed: Real-time, rules-based
extraction and interpretation of
information based on event
triggers from over 20,000
leading global newswires,
online newspapers,
aggregators,
and blogs in under 5 seconds
VOLUME &
VARIETY
VERACITY VELOCITY
FUTURE OF QUANTI TATIVE INVESTING IN THE DATA ECONOMY

21. Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 20
• Parallel Processing
• In-memory compute
• Elastic Resource Management
• On-Demand load provisioning
Advances from the west coast have enabled machines to scale horizontally and linearly
Source: DCM Analytics; Google images
For illustrative purposes only. Back test results are not indicative of future returns.
FUTURE OF QUANTI TATIVE INVESTING IN THE DATA ECONOMY

22. Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 21
Cognitive Computing is in the early stages; the capital markets afford a fertile ground for
rapid learning
Source: IBM, DCM and google images
For illustrative purposes only. Back test results are not indicative of future returns.
FUTURE OF QUANTI TATIVE INVESTING IN THE DATA ECONOMY
-25
Analysis of
3,000+ listed
stocks & ETFs
in real time

23. Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 22
Machine learning is impacting all industries; in the process algorythms are learning
generalized and industry-specific knowledge
FUTURE OF QUANTI TATIVE INVESTING IN THE DATA ECONOMY

24. FUTURE OF QUANTI TATIVE INVESTING IN THE DATA ECONOMY
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 23
Consistent approach to analyzing company value through its use of predictive modeling and rules-based execution. Real-time
adjustments to portfolio risks and strategy weightings are made in response to specific trading environments.
MACHINE-LEARNING ALGORITHMS
Dynamically adapt portfolio allocations
among mean reversion, momentum, and
event-driven strategies to capitalize on specific
market conditions
Combine advanced self-learning models and
proven risk management to diversify the
portfolio and produce superior uncorrelated
risk-adjusted returns
Model market context, company-specific inputs,
and risk factor analysis for rules-based portfolio
allocation among strategies
NEW DATA SOURCES
Breadth: Ability to identify non-obvious
correlation factors from novel data sources
including news, images, social networks,
macroeconomic feeds, etc. and link to price
movements in securities, currencies,
commodities, etc.
Depth: Analyzes, prioritizes and monitors big-
data input and its impact using systematic and
quantitative metrics
Speed: Real-time, rules-based extraction and
interpretation of information based on event
triggers from over 20,000 leading global
newswires, online newspapers, aggregators,
and blogs in under 5 seconds
CUTTING-EDGE TECHNOLOGY
Accommodate big data feeds and multi-factor
risk systems with fast, scalable infrastructure
and code
Employ sophisticated systems to codify
fundamental statistics for 3,000 US exchange-
traded companies and ETFs
Utilize advanced artificial intelligence algorithms
to predict idiosyncratic shocks, market
sentiment, and cycle duration

25. TABLE OF CONTEN TS
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 24
DATA CAPITAL MANAGEMENT OVERVIEW
WHAT IS THE DATA ECONOMY?
WHAT NEW CHALLENGES DOES THE DATA ECONOMY CREATE FOR INVESTORS?
THE FUTURE OF QUANTITATIVE INVESTING IN THE DATA ECONOMY
LESSONS FROM THE INDUSTRIAL REVOLUTION

26. LESSONS FROM THE INDUSTRI AL REVOLUTI ON
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 25
James Pierpont Morgan
Suggestions for FinTech start-ups in the Data Economy
• Standardize industry taxonomies
• Be the best at depth of information for a given vertical
• Take responsibility for the veracity of information
• Focus on speed of delivery
• Focus on historical consistency
• Make adjustments visible to upstream users
• Focus on permissible data use rights
• Don’t be all things to all people
Source: google images

27. LESSONS FROM THE INDUSTRI AL REVOLUTI ON
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 26
Standardize Industry Taxonomies
Source: google images; Wikipedia
For illustrative purposes only. Back test results are not indicative of future returns.
An International Securities Identification Number
(ISIN) uniquely identifies a security. Its structure is
defined in ISO 6166. Securities for which ISINs are
issued include bonds, commercial paper, stocks
and warrants.
U.S. Railway System 1830 - 1850 Ford plant Dearborn Michigan; 1928

28. Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 27
0
20
40
60
80
100
120
140
160
$95
$100
$105
$110
$115
$120
$125
Millions
AAPL
Volume Close
AAPL Price and Volume 1/12/15 – 2/5/15
AAPL News Volume and Sentiment 1/26/15 – 1/29/15
1/26/15 1/28/15 1/29/151/27/15
Sentiment Score Alone is Not Sufficient
LESSONS FROM THE INDUSTRI AL REVOLUTI ON
Be the Best at Depth of information for a given vertical
Source: DCM Analytics’ Google Images
For illustrative purposes only. Back test results are not indicative of future returns.
• Sentiment score alone is insufficient for
unsupervised systematic execution:
• ~200 identified articles in universe
• ~40% neutral
• ~40% positive
• ~20% negative
James Pierpont Morgan

29. LESSONS FROM THE INDUSTRI AL REVOLUTI ON
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 28
Take Responsibility for your Veracity
Source: Google Images
For illustrative purposes only. Back test results are not indicative of future returns.
Eads Bridge June 14th 1874Does your product do what you say it does?
Andrew Carnegie first used steel because he was in the bridge building business. Up until
the “elephant parade” of June 14th 1874, bridges were made of Iron and could not
successfully cross the Mississippi River and thus connect the East Coast to the West
Coast. Following this success, he transformed America and powered the Industrial
Revolution…. Not by bridges; but through steel

30. LESSONS FROM THE INDUSTRI AL REVOLUTI ON
Extremely Confidential. Not for Public Distribution. | DATA CAPITAL MANAGEMENT | 29
Suggestions for Start-ups in the Data Economy
• Standardize industry taxonomies
• Be the best at depth of information for a given vertical
• Take responsibility for the veracity of information
• Focus on speed of delivery
• Focus on historical consistency
• Make adjustments visible to upstream users
• Focus on permissible data use rights
• Don’t be all things to all people
Got API?
dcm@datacapitalmanagement.com
Our goal is to help drive a standard for other players in our ecosystem to coalesce around
• This approach helps avoid the “tragedy of the commons” and maximize collective ROI
• We are set up to be “early customers” of new technologies / beta releases
• We drive a culture of focused and targeted feedback
For those with self-interests congruent to ours; let’s help each other