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The Strategic Importance of Data ScienceJefferies, November 2018

Privileged and Confidential

“Difficulty lies not so much in developing new ideas as in escaping from old ones.” – John Maynard Keynes

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Observations in Data Science & Data Analytics

Market Challenges:

Underperformance of Active; Rotation into Passive/”Smart-Beta”

Crowding of Strategies and Positions

Search for Alpha using Alternative Data Sets

Consolidation of the Market Structure i.e. Buy-Side and Sell-Side

Goal:

Digesting Massive & Varied Data Amounts and Intelligently Deriving Insights, Trends, and Actionable Analytics

Why Now? : Unstructured Data/Big Data + Enhanced Semis + Analytical Software = Structured Data

Data Science Word Map

Data Science

Email Receipts

Advanced Text Search

Machine Learning Alpha

Data Scientist

Quantamental

Satellite Imagery

Predictive AnalyticsAlpha-Capture

Geo-Location

Data Revolution

Quants

NLP

Artificial Intelligence

Data Sets

Credit Card Panels

Data is Unique Content! : Unique Content is Always a Competitive Edge: Channel Checks, Proprietary Models, Experts

Social Media

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The Evolution of Investing: “Adapt or Die” – Moneyball

Today Future

Systematic

Systematic

Quantamental

Quantamental

Discretionary

Discretionary

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Relevant Market Themes

Quantitative Versus

Systematic

Fundamental Versus

Discretionary

Sell-Side & Buy-Side Evolution

Data Science Versus Data Analytics

Mutual Fund

Adoption

“We believe that if we can get insight through data, it will give us an advantage”

– Lawrence Fink, Blackrock, November 2015

“When we add in the use of data sets and “quantamental” techniques that are increasingly important to remain competitive while investing in single-

name equities, it is clear that our business is rapidly evolving.”– Dan Loeb, Third Point,3Q 2016 Investor Letter

Growth of In-House Data Science Teams: Dedicated Data Lead/Shared Data Lead

Arms Race to Data

Return & Growth of Alpha Capture

Focus on Data Analytics for Efficiencies and Cost Savings

Differentiator Across Buy-Side Among Allocators/Investors

Crowding of Market Positions

Rise of the “Quantamental” Investor via Convergence

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“Quantifying Process: Preparing for the Age of Data Ubiquity ”Published by the Jefferies Data Science Working Group (Q3’18)

While all forms of data will witness

considerable growth, we believe much of the mix will emerge

from IoT and sensors, creating

information sources valuable to investors

across nearly all sectors.

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*Please see Appendix 1 for citations 5-18

Data Supply Growth and Mix

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Widespread Industry Adoption of Data Science Used to Grow Top Line & Provide Efficiencies

Consumer Energy Financials

Information Technology Industrials Healthcare

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The Jefferies Data Science Working Group

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October 2017June 2017 September 2018

Quantifying Intuition:Mapping the Data Science Landscape in the

Hedge Fund Industry

Quantifying Strategy:Building Data Strategies for the Next Decade

Quantifying ProcessPreparing for the Age of Data Ubiquity

Quantifying Intuition broadly maps the data science landscape across

investment managers.

Quantifying Process examines the data, workflows, and infrastructure that help turn new information into actionable insights at investment

managers and corporations broadly.

Quantifying Strategy is a deep-dive into how nearly 50 funds actively

building these efforts are approaching them from a talent, infrastructure and

resourcing perspective.

Differentiated Tactical Content from Jefferies Equities Distribution – “Clients First”

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“Quantifying Strategy: Building Data Strategies for the Next Decade”Published by the Jefferies Data Science Working Group (Q3’17)

Alternative Data is leveraged as a source for idea generation ONLY

one-third of the time

It remains earliest days for discretionary managers to be broadly leveraging alternative

data or data efforts to source ideas

85% of investors surveyed cited enhancement of pre-existing

investment processes as a top objective

*In August 2017, Jefferies conducted ~50 in-depth interviews with investment managers

Dispelling Conventional Premises

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Since the publication of this content in 2017, we believe

the number of Dedicated Data Leads has eclipsed 70% amongst our sample

set

Shared vs. Dedicated Data Lead Today

For those with a Shared Data Lead, intent to hire a Dedicated Data Lead in the next 12 months

Intent to Hire


• Background of hires is situationally dependent on the fund, based on factors including firm strategy, size, maturity, organizational structure and investment style

• These initiatives can be strongly accretive to long term talent attraction and retention

• Responsibilities associated with data efforts are fairly fungible and amorphous (of ~50 interviews “Data Leads” held 18 different organization titles)

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Despite concerns that new entrants to data science may be “late to the game” or sourcing data sets that have “already been arb’d”, data

sets are often used for entirely different purposes.

Layering multiple data sets into a mosaic creates an infinite number of combinations and often entirely new signals for the investment process

A number of investors commented that they wished there was better alignment between the tenor of the data subscriptions and the length of

use the investor needs it for


Number of Alternative Data Sets Leveraged

Perceived Commoditization:

Holy Grail:

Subscription vs. Transitory Use:

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Key Takeaways20% --- More than 100 Data Sets41% --- Fewer than 30 Data Sets56% --- Fewer than 55 Data Sets

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A streamlined process including distinct responsibilities for data

Acquisition, Ingestion, Application & Distribution

A practitioner, “bridge-role”, positioned between data science

team and investment team is crucial in making data a profit center

rather than a cost center

Buy-in from the C-Suite and cultural integration within investment

team are key to success


Top Data Science Goals for Next 12 – 18 Months

Jefferies Prescription

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Integrated vs. Push/Pull Approaches

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Integrated vs. Push/Pull Approaches

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Jefferies Prescription for Data Evolution: Issues and Considerations

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1

2

StructureFormal Head of Data Science/Data Acquisition/Practitioners’ Bridge

CultureSenior/C-Suite Mandate with Incentives

ProcessAccountability, Re-occurring meetings, Integrated vs. Push/Pull Approaches

Data

▪ Multi-Dataset Analysis

▪ Periodic Review/New Use Cases

▪ Vendor Relationships

▪ Opportunities for Exclusivity

▪ Enhancement vs. Decay

Process

▪ Integrated vs. Push/Pull Approaches

▪ Broad Enterprise Use Cases

▪ Profit Center vs. Cost Center

▪ Defined Goals

▪ Quarterly Presentations to Stakeholders

▪ Raw vs. “Wrapped” Data Sets

Structure

▪ Bridge between Data Science/Traditional Investment Team(s)

▪ Centralization of Data and Resources

▪ Reporting Lines

▪ Budget/Cost

Culture

▪ Mandate Buy-In from C-Suite

▪ Education/Adoption

▪ Incentives

▪ Demographics

▪ Socialization

▪ Compliance

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“Powered by Data” – Jefferies Equity Research

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Exploring Data Science and Analytics Processes within Corporates

Alternative Data Powered Stock & Sector AnalysisBiotech

“The Exciting Potential and Challenge of Big

Data in Biotech”5/31/2018

Health Care Facilities“Heath Care Provider

Data Strategy Handbook”

6/5/2018

Pharmaceutical Services

“How Data Is Powering Trials”

4/11/2018

Health Care

IoT“Sensors & Analytics: Trends,

Opportunities & Threats”3/26/2018

Industrials

Software “The Alchemy of Extracting

Value from Big Data”3/26/2018

TMT

Financials

Self-Storage REITs“Self-Storage Performance

Increasingly Driven by Data Analytics”

6/4/2018

Banks“Powered By

Data: Driven By Digital”6/5/2018

Consumer Finance“How Tech-Based

Alternative Data Apps Benefit Consumer

Finance”5/21/2018

Consumer

HPC and Beverages“Initial Assessment of HPC and Beverage Companies in Data

Analytics” 3/29/2018

Food Products “The Makings of a Brand

Resurgence”6/6/2018

Specialty Retail“Assessing Who's Winning/Losing

The Data Wars in Retail” 3/19/2018

Beauty “Beauty Tech Is Next Natural

Evolution for Growing Industry”3/22/2018

*Web Traffic – SimilarWeb*Credit Card – Technomic

*Sentiment – Merchant Centric*EMV – Tribe Dynamics

Vendor Usage

Brokers, Asset Managers & Exchanges (SCHW, AMTD, ETFC)“Re-Positioning the Robo & Web

Analysis Takes” 5/17/2018

Teladoc Inc (TDOC)“App/Website Traffic Data Points

to Better-Than-Expected 2Q18 Utilization” 7/31/2018

Facebook, Inc. (FB)“Web Traffic Data Shows Friends

Don't Let Friends Leave Facebook” 4/9/2018

Under Armour (UAA)“Data Checkup – Credit Card

Trends Better +HOVR Sell-Through Still Strong” 4/27/2018

Activision Blizzard, Inc (ATVI) “Call of Duty is a Juggernaut.

Positive Beta Test Suggests EPS Upside” 9/11/2018

McDonalds (MCD)“Some N-T EOTF SSS Headwinds, but Alt Data Points to Traction in

Other Key Areas” 8/28/2018

PayPal (PYPL)“Mobile More than Just Venmo –

Core Biz Trend Focus; eBay Update” 8/29/2018

Restaurants“The Data Dish! Jefferies Analysis of Technomic Transaction Insights

Data” 11/12/2018

Cooper Companies (COO)“Lens Web-Scrape Data Points to

Better US Trends” 6/4/2018

Chemicals“Calibrating Intuition Around the Yield Curve & US Chemicals…A Survival Regression” 1/29/2018

Integrating Alternative Data Into Fundamental Stock Research

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Sell-Side Leader in Alternative Data Events & Access

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Morning Differentiated Content – Investing in ALL sectors using Alt DataPanels, Presentations and Fireside Chats

Afternoon 15-minute 1x1 meetings with >200 data vendorsOver 2,000 1x1 Meetings Facilitated at Battlefin NYC 2018

Over 1,000 1x1 Meetings Facilitated at Battlefin London 2018

An introduction to quantitative investing for the fundamental investor

Estimize & Jefferies Learn2Quant Conferences

Americas - New York, Toronto, Boston

Europe – London, Zurich, Frankfurt

Asia – Hong Kong

Learn2Quant Conference & Event Locations

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5 “A Guide to the Internet of Things: How Billions of Online Objects are Making the Web Wiser,” Intel 6 comScore: The Future of Voice From Smartphones to Smart Speakers to Smart Homes. December 15, 2017 7 “How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read,” Forbes, May 21, 2018 8 “Here’s How Many Digital Photos Will Be Taken in 2017,” Mylio, December 2, 2016 9 Cisco Visual Networking Index: Forecast and Methodology, 2016-2021, Cisco, September 15, 2017 10 https://www.data.gov/; Jefferies 11 Worldwide internet user penetration from 2014 to 2021, Statista, https://www.statista.com/statistics/325706/global-internet-userpenetration/ 12 Cisco Visual Networking Index: Forecast and Methodology, 2016 – 2021 https://www.cisco.com/c/en/us/solutions/collateral/serviceprovider/visual-networking-index-vni/complete-white-paper-c11-481360.html 13 “How Many Satellites are Orbiting the Earth in 2017?” Pixalytics, November 15, 2017 14 “Total Global Satellite Plans Could Have around 20,000 Satellites in Low and Mid Earth Orbits in the 2020s,” Next Big Future, March 4, 2017 15 Current Employment Statistics (CES) homepage, BLS 16 “Five Takeaways from the 2017 Contingent Worker Supplement,” Aspen Institute, June 8, 2018 17 “Shipping Smarter: IoT Opportunities in Transport and Logistics,” Deloitte, September 15, 2015 18 Air Cargo Summary Data, Bureau of Transportation Statistics, May 2018

Appendix - Citations

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This is not a product of Jefferies' Research Department, and it should not be regarded as research or a research report. This material is a product of Jefferies

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and may differ from the views and opinions expressed by the Firm's Research Department or other departments or divisions of the Firm and its affiliates.

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The information and any opinions contained herein are as of the date of this material and the Firm does not undertake any obligation to update them. All

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© 2018 Jefferies LLC

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