The Strategic Importance of Data Sciencecom.estimize.public.s3.amazonaws.com/l2q presentations/5_l2q_zur… · –Dan Loeb, Third Point 3Q 2016 Investor Letter Rise of the “Quantamental”

Jefferies LLC

The Strategic Importance of Data ScienceJefferies, March 2018

Privileged and Confidential

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

Jefferies LLC

Initial Views on 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

Why Not? : Superior Data is Always a Competitive Edge: Channel Checks, Proprietary Models, Research, 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 Point3Q 2016 Investor Letter

Rise of the “Quantamental” Investor via Convergence

Arms Race to Data

Differentiator Across Buy-Side Among Allocators/Investors

Focus on Data Analytics for Efficiencies and Cost Savings

Crowding of Market Positions

Return & Growth of Alpha Capture

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

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In July and August of 2017, the Jefferies Data Science Working Group conducted

nearly fifty in-depth interviews with buy-side “Data Leads” to obtain a more

granular view of how investment managers are developing data efforts in practice.

The next five slides highlight our observations…

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

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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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A streamlined process including distinct responsibilities for data

Acquisition, Ingestion and 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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The percentage of firms who have a Dedicated Data Lead

could double by 2019

Dedicated vs. Shared 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 investors may be “late to the game” or sourcing data sets that have

“already been arb’d”, data sets are 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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Where Data Costs Are Allocated

Firms surveyed estimated individual data set spend ranging

from $20,000-$75,000 with considerable variability between

duration of subscriptions

Most firms are splitting the cost between

the management company and the fund

A small, but growing, number of firms report having created “R&D”

budgets specifically focused on building their data strategies

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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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Jefferies Prescription for Data Evolution: Issues and Considerations

3

1

2

StructureFormal Head of Data Science/Data Acquisition

CultureVertical and Horizontal Education

ProcessAccountability, Re-occurring meetings, Push vs. Pull

Data

▪ Multi-Dataset Analysis

▪ Periodic Review/New Use Cases

▪ Vendor Relationships

▪ Opportunities for Exclusivity

▪ Enhancement vs. Decay

Process

▪ Survey/AAR

▪ Cost Center vs. Profit Center

▪ Use Cases

▪ Defined Goals

▪ Quarterly Presentations to Stakeholders

▪ Raw vs. “Wrapped”

Structure

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

▪ Reporting Lines

▪ Centralization of Data and Resources

▪ Budget/Cost

Culture

▪ Education/Adoption

▪ Mandate Buy-In

▪ Incentives

▪ Demographics

▪ Socialization

▪ Compliance

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Case Study #1: M Science – Data Driven Research

September 11, 2017 October 4, 2017

October 10, 2017

A combination of data science and

fundamental analyst’s written work

incorporating estimates and

insights for companies and

industries to generate alpha by

unifying data from disparate

sources.

Daily updates to key metrics across

430+ companies and sub brands of

both public and private companies.

Our infrastructure enables portfolio

mangers, analysts, and data

scientists to seamlessly access and

integrate data directly into their

models and workflow.

M Science

▪ Non-traditional alternative data-driven research

▪ Alternative data aggregation and implementation via proprietary infrastructure

© 2018 M Science LLC. All rights reserved. Not to be reproduced or retransmitted without permission. It should not be assumed that recommendations made in the future will be profitable or will equal the performance of those contained herein. Comparisons of M Science published estimates to reported results are available by emailing [email protected]. More information about M Science is available at http://www.adviserinfo.sec.gov/

(Domino’s Pizza Group PLC – DOM.LN)

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mailto:[email protected]

http://www.adviserinfo.sec.gov/

Jefferies LLC

Case Study #2: Visible Alpha – Sellside Deep Consensus Analytics

Visible Alpha

▪ Live feeds of raw sellside models▪ Systematically creates deep

Consensus estimates in real-time▪ Current consensus, revisions

history, dispersion metrics, etc.▪ Web platform, Excel add-in, API▪ Conduit to raw working models Estimate Heatmap – Visualize Dispersion Across the StreetGranular Segment, Product and Operating Metric Views

Broker Broker Broker Broker Broker Broker Broker Broker

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Case Study #3: ListenFirst – Social Engagement

ListenFirst

▪ Social engagement ratings via FB, TWTR, Instagram, YouTube, Tumblr

▪ Clicks, Conversation, Search, Views▪ Engagement, NOT sentiment

ListenFirst’s proprietary interest score surrounding multiple important 2015 shoe launches helped lead

investors towards Adidas’ footwear market share traction

New Fashion Sneaker Product Launches

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Case Study #4: Estimize – Crowdsourced Company Estimates

Estimize

▪ Crowdsourced estimate platform▪ Gather a sense of buyside estimates▪ Add to your investment mosaic▪ Complement sellside estimates▪ EPS, Revenue, KPIs (SSS, Bookings,

MAUs, etc.)

Capture pre-earnings drift unappreciated by sellside estimates

F2Q’15 Report

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Case Study #5: Second Measure – Transaction Analytics

Second Measure

▪ Billions of purchases from millions of U.S. consumers

▪ 3,000+ brands covered, both public + private

▪ Web-based analytics and data feeds for: Daily sales, retention, lifetime value, etc.

▪ View by location, customer income, etc.

Blue Apron monthly customer retention data revealed

weakness two years prior to IPO despite revenue growth

Second Measure provides a layer of idea-generating

analytics beyond the data, allowing the consumer to skip the tagging and mapping stage

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THIS MESSAGE CONTAINS INSUFFICIENT INFORMATION TO MAKE AN INVESTMENT DECISION.

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

Equity Sales and Trading department. Unless otherwise specifically stated, any views or opinions expressed herein are solely those of the individual author

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.

Jefferies may trade or make markets for its own account on a principal basis in the securities referenced in this communication. Jefferies may engage in

securities transactions that are inconsistent with this communication and may have long or short positions in such securities.

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

market prices, data and other information are not warranted as to the completeness or accuracy and are subject to change without notice. In preparing this

material, the Firm has relied on information provided by third parties and has not independently verified such information. Past performance is not indicative

of future results, and no representation or warranty, express or implied, is made regarding future performance. The Firm is not a registered investment adviser

and is not providing investment advice through this material. This material does not take into account individual client circumstances, objectives, or needs

and is not intended as a recommendation to particular clients. Securities, financial instruments, products or strategies mentioned in this material may not be

suitable for all investors. Jefferies does not provide tax advice. As such, any information contained in Equity Sales and Trading department communications

relating to tax matters were neither written nor intended by Jefferies to be used for tax reporting purposes. Recipients should seek tax advice based on their

particular circumstances from an independent tax advisor. In reaching a determination as to the appropriateness of any proposed transaction or strategy,

clients should undertake a thorough independent review of the legal, regulatory, credit, accounting and economic consequences of such transaction in relation

to their particular circumstances and make their own independent decisions.

© 2018 Jefferies LLC

Disclaimer

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The Strategic Importance of Data Sciencecom.estimize.public.s3.amazonaws.com/l2q presentations/5_l2q_zur… · –Dan Loeb, Third Point 3Q 2016 Investor Letter Rise of the “Quantamental”

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