Conference Day One: March 19 2019

8:00 am - 8:50 am Welcoming tea, coffee and registration

KEYNOTES & OPENING PLENARY SESSIONS

Joseph Simonian, Director of Quantitative Research at Natixis Investment Managers

Joseph Simonian

Director of Quantitative Research
Natixis Investment Managers

9:00 am - 9:20 am Opening keynote: Outlook for the hedge fund industry: survival of the leanest or the most tech savvy?


David Rukshin, Chief Technology Officer at WorldQuant

David Rukshin

Chief Technology Officer
WorldQuant

  • Evaluation: What does a buy side investor look for in quantitative strategies
  • Analysis: Finding risk, uniqueness and maintaining a diverse yet unbiased holding: the challenges of asset allocation 
  • Risk: Meeting your mandate while incorporating technologically forward investment strategies
Afsheen Afshar, Former Chief Artificial Intelligence Officer and Senior Managing Director at Cerberus Capital Management

Afsheen Afshar

Former Chief Artificial Intelligence Officer and Senior Managing Director
Cerberus Capital Management

Joseph Simonian, Director of Quantitative Research at Natixis Investment Managers

Joseph Simonian

Director of Quantitative Research
Natixis Investment Managers

Amit Soni, Portfolio Manager at New York Life Investments

Amit Soni

Portfolio Manager
New York Life Investments

10:00 am - 10:30 am Interview: Learn from this industry icon as they share their experience and insights


Gregory Zuckerman, Special Writer at The Wall Street Journal

Gregory Zuckerman

Special Writer
The Wall Street Journal

Sandy Rattray, Chief Investment Officer at Man Group

Sandy Rattray

Chief Investment Officer
Man Group

10:30 am - 11:10 am Networking refreshment break in the exhibition area

MORNING SESSIONS

11:10 am - 11:30 am Keynote: A new breed of investor: The development and disruption of asset management through machine learning techniques

  • The new model: What do you really need to launch a new age hedge fund?
  • Practical recent examples and insights into what it takes to succeed and common pitfalls to avoid
  • The best and worst attributes of AI: Where do funds go wrong?
Marcos López de Prado, Principal and Head of Machine Learning at AQR Capital Management

Marcos López de Prado

Principal and Head of Machine Learning
AQR Capital Management

  • How to reach the best people and the top minds
  • Understanding that all data scientists and quants are not created equal: what looks like a data scientist may not be!
  • How to create a culture that will entice them to join and stay with you: understand your value proposition as an employer
  • Motivating your workforce to share their best ideas, and building a culture of collaboration in a typically combative and siloed industry
  • Considering the factors that will affect retaining and motivating top talent
Rob Sloan, Research Director at WSJ Pro

Rob Sloan

Research Director
WSJ Pro

Sheedsa Ali, Managing Director & Portfolio Manager at PineBridge Investments

Sheedsa Ali

Managing Director & Portfolio Manager
PineBridge Investments

Dan Furstenberg, Global Head of Hedge Fund Distribution & Data Strategy at Jefferies

Dan Furstenberg

Global Head of Hedge Fund Distribution & Data Strategy
Jefferies

Nitish Maini, General Manager, Virtual Research Centre & Vice President, Portfolio Manager at WorldQuant

Nitish Maini

General Manager, Virtual Research Centre & Vice President, Portfolio Manager
WorldQuant

Richard Pook, Partner at Dore Partnership

Richard Pook

Partner
Dore Partnership

12:10 pm - 12:30 pm Keynote: "Big Data" is the new currency

The way data is collected, anonymized and monetized largely without the owner’s permission is ready to be disrupted providing many benefits to hedge fund data buyers.  This presentation provides a pathway for the individual to control and share in the value their data creates, and for data users to gain access to richer more specific data sets.
Armando Gonzalez, CEO at RavenPack

Armando Gonzalez

CEO
RavenPack

12:20 pm - 1:30 pm Networking lunch in the exhibition area


AFTERNOON STREAMS

STREAM A

1:30 pm - 1:35 pm ALT DATA

Michael Marrale, Chief Executive Officer at M Science LLC

Michael Marrale

Chief Executive Officer
M Science LLC

  • Usefulness of data – evaluating the landscape and the trends
  • Filtering and working with vendors: 
  • Excessive demand for newer/more interesting data sources places the onus of data prep firmly on the purchaser whilst low quality/inconsistent data gives a low conversion rate to prospective sources. How can you work with your supplier to provide the product you need?
  • Evaluating the pros and cons of searching for the foundations of specific trade ideas vs ‘playing’ with new and interesting concepts
  • Best data sourcing practices

Olga Kokareva, Head of Data Sourcing and Strategy at Quantstellation

Olga Kokareva

Head of Data Sourcing and Strategy
Quantstellation

Robert Morse, Head of Data Strategy and Sourcing at PDT Partners

Robert Morse

Head of Data Strategy and Sourcing
PDT Partners

Lisa Schirf, Former COO Data Strategies Group and AI Research at Citadel

Lisa Schirf

Former COO Data Strategies Group and AI Research
Citadel

Stewart Stimson, Head of Data Strategy at Jump Trading

Stewart Stimson

Head of Data Strategy
Jump Trading

STREAM A

2:15 pm - 2:25 pm Innovation in Alt Data

Bill Dague, Head of Alternative Data Research at Quandl

Bill Dague

Head of Alternative Data Research
Quandl

STREAM A

2:25 pm - 2:40 pm Innovation in Alt Data

Daryl Smith, Head of Research at Neudata

Daryl Smith

Head of Research
Neudata

STREAM A

2:40 pm - 2:50 pm Innovation in Alt Data

Greg Skibiski, Founder & CEO at Thasos Group

Greg Skibiski

Founder & CEO
Thasos Group

STREAM A

2:50 pm - 3:30 pm Panel discussion: What are the next hot data sources/ regions? What are the different business models that are emerging?
  • We’ve had sentiment analysis on social media, car park image processing, mobile positioning, video, credit card transactions… Where or what is the next big alt data source?
  • Evaluating the global demand for data
  • Data, data everywhere. But how can you find the signals?

Kristen Thiede, SVP at Two Sigma

Kristen Thiede

SVP
Two Sigma

Bill Dague, Head of Alternative Data Research at Quandl

Bill Dague

Head of Alternative Data Research
Quandl

Rado Lipuš, CEO & Founder at Neudata

Rado Lipuš

CEO & Founder
Neudata

Hugh O'Connor, Director, Data Sourcing & Partnerships at Eagle Alpha

Hugh O'Connor

Director, Data Sourcing & Partnerships
Eagle Alpha

Greg Skibiski, Founder & CEO at Thasos Group

Greg Skibiski

Founder & CEO
Thasos Group

STREAM B

1:30 pm - 1:35 pm QUANT FUNDAMENTAL

Paul Rowady, Director of Research at Alphacution

Paul Rowady

Director of Research
Alphacution

STREAM B

1:35 pm - 2:05 pm A specific framework and strategy for introducing quant methods and unique data into your fundamental approach to producing alpha
  • A step by step approach to building viable investment signals from unique data sources 
  • How to build a stock selection, risk management and portfolio construction practice using quantamental methods
  • How fundamental firms can overcome institutional paralysis towards building better decision-making processes 
  • Why most investment firms don't do R&D, and why they should

Leigh Drogen, Founder and Executive Chairman at Estimize

Leigh Drogen

Founder and Executive Chairman
Estimize

Nick Jain, Chief Investment Officer at Citizen Asset Management

Nick Jain

Chief Investment Officer
Citizen Asset Management

  • Embracing the culture: What is needed to make the changes
  • Practical insights into effective integration
  • Evaluating the success: How can you measure the benefits of a successful integration
  • Understanding the risks of a market saturated with the systematic approach: when will the models fail?


Paul Rowady, Director of Research at Alphacution

Paul Rowady

Director of Research
Alphacution

Wesley Chan, Executive Vice President & Portfolio Manager at PIMCO

Wesley Chan

Executive Vice President & Portfolio Manager
PIMCO

Ivailo Dimov, Quant and Data Science Research at Bloomberg

Ivailo Dimov

Quant and Data Science Research
Bloomberg

Tom Doris, Chief Data Scientist at Liquidnet

Tom Doris

Chief Data Scientist
Liquidnet

Poul Kristensen, Managing Director, Economist, and Portfolio Manager at New York Life Investment Management

Poul Kristensen

Managing Director, Economist, and Portfolio Manager
New York Life Investment Management

STREAM B

2:50 pm - 3:30 pm Fireside Debate – When hedge funds ate their own
One side believes a blended, “quantamental” approach will consistently outperform a purely systematic approach. The other side favors an increasing level of automation. Recent empirical evidence supports both arguments. Come and find out what happens when two heavyweights battle it out.

Michael Recce, Chief Data Scientist at Neuberger Berman

Michael Recce

Chief Data Scientist
Neuberger Berman

Paul Rowady, Director of Research at Alphacution

Paul Rowady

Director of Research
Alphacution

STREAM C

1:30 pm - 1:35 pm AI/ML CUTTING EDGE ACADEMIC RESEARCH

Vasant Dhar, Professor at NYU

Vasant Dhar

Professor
NYU

STREAM C

1:35 pm - 2:00 pm Utilizing AI/ML to lend predictive/adaptive capability in continuously changing market environments
  • Common problems around utilizing AI/ML in quant analysis
  • Universality, Causality and Machine Learning
  • Ad hoc demonstration: The walk forward test in randomly created market environment
  • Understanding the market as an unstable dynamical system


Yoshinori Nomura, Director, Fund Manager at Simplex Asset Management

Yoshinori Nomura

Director, Fund Manager
Simplex Asset Management

STREAM C

2:00 pm - 2:20 pm The very latest AI developments machine learning for security selection and the dangers of overfitting
  • Data prep and feature engineering: Is the AI built over the data based on solid foundations?
  • Issues of overfitting and maximizing the signal to noise ratio
  • Evaluating your algorithm choice: what do you want to achieve?
  • Understanding fake signals: when machine learning fails

Keywan Rasekhschaffe, Senior Quantitative Strategist and Portfolio Manager at Gresham Investment Management

Keywan Rasekhschaffe

Senior Quantitative Strategist and Portfolio Manager
Gresham Investment Management

STREAM C

2:20 pm - 2:40 pm Practical applications of AI/ML
  • Once a qualitative “factor,” sentiment around individual stocks with scale levels of online discussion can now be measured 
  • Applications of ‘task-specific’ vs ‘general’ artificial intelligence
  • Is sentiment predictive or contrarian? 
  • Implications for portfolio construction
Jamie Wise, President at Periscope Capital

Jamie Wise

President
Periscope Capital

STREAM C

2:40 pm - 3:05 pm Big Data's Dirty Secret
Abstract:
 
"Let the data speak for themselves."
 
"We apply machine learning to the problem of..."
 
These are two commonly heard phrases these days. But what data exactly are we speaking about, and what do we intend to do with it? What is ignored all too often is the quality of the data being used and how it impacts the analyses being done. Are there holes in the data? Are there anomalies? Given how dirty data can be, a more apt phrase might be "Garbage in, garbage out".
 
In this talk we will discuss some of the data problems we've encountered in financial data, and approaches that can be used to address them. Our particular focus will be on techniques we've employed to address missing data and bad data in credit default swap (CDS) spread histories.

Harvey Stein, Head of the Quantitative Risk Analytics Group at Bloomberg

Harvey Stein

Head of the Quantitative Risk Analytics Group
Bloomberg

STREAM C

3:05 pm - 3:30 pm FED AI: Applying AI techniques and macro economics



George Lentzas, Manager & Chief Data Scientist at Springfield Capital Management

George Lentzas

Manager & Chief Data Scientist
Springfield Capital Management

3:30 pm - 3:55 pm Networking refreshment break in the exhibition area

STREAM A

3:55 pm - 4:00 pm NATURAL LANGUAGE PROCESSING
Gary Kazantsev, Head of the Machine Learning Engineering Team at Bloomberg

Gary Kazantsev

Head of the Machine Learning Engineering Team
Bloomberg

STREAM A

4:00 pm - 4:20 pm Hanging on every word: Natural Language Processing unlocks new frontiers in corporate earnings sentiment analysis
How can NLP be applied to corporate earnings call transcripts? Can you dissect the tone, complexity, and overall level of engagement with analysts as indicators of earnings sentiment?
David Pope, Managing Director of Quantitative Research at S&P Global Market Intelligence

David Pope

Managing Director of Quantitative Research
S&P Global Market Intelligence

STREAM A

4:20 pm - 4:40 pm Practical uses of cutting edge NLP: what is the current state of play, and how can you benefit from incorporating it into your investment strategy?
  • Defining the value you can add from overcoming language barriers and maximizing your translation services to processing unstructured data from around the world in a variety of formats
  • Enhancing your systems abilities: overcoming the translation barriers: an insight into unsupervised learning of cyberslang and unusual language structure
  • Other practical NLP hints and tricks to set you on the path to alpha generation

Mike Chen, Director and Lead ML Researcher, Dynamic Equity; Lead Portfolio Manager, ESG Equity at PanAgora Asset Management

Mike Chen

Director and Lead ML Researcher, Dynamic Equity; Lead Portfolio Manager, ESG Equity
PanAgora Asset Management

STREAM A

4:40 pm - 5:20 pm Panel discussion: Understanding the very latest developments in NLP: what is the current state of play, and how can you benefit from incorporating it into your investment strategy?
  • Rule based NLP vs deep learning, i.e. relying on human intervention and ingesting touches of augmented AI rules to better detect key elements of languages in a proper NLP system
  • Using proprietary deep tech and being early adopters of new technologies as opposed to relying on open source
  • The challenges of adapting an English NLP engine to different languages, public sources (such as Twitter that almost has its own language), or thousands of different types of file formats (proprietary textual content from clients)

Peter Hafez, Chief Data Scientist at RavenPack

Peter Hafez

Chief Data Scientist
RavenPack

Javed Jussa, QES Team at Wolfe Research

Javed Jussa

QES Team
Wolfe Research

Alejandra Litterio, Co-founder & Chief Research Officer at Eye Capital

Alejandra Litterio

Co-founder & Chief Research Officer
Eye Capital

Gurraj Singh Sangha, Global Head of Risk at State Street Verus

Gurraj Singh Sangha

Global Head of Risk
State Street Verus

STREAM B

3:55 pm - 4:00 pm QUANTAMENTAL & RISK
Yin Luo, Vice Chairman - QES at Wolfe Research

Yin Luo

Vice Chairman - QES
Wolfe Research

STREAM B

4:00 pm - 4:20 pm Building a unique, fully quant-driven strategy on fundamental principles: Lessons learned, pitfalls to avoid
  • Great quant models are built based on a deep, refined and experience-driven understanding of some processes in asset pricing. 
  • There are many such in-depth fundamental investment philosophies that can provide robust frameworks for great quant models. 
  • Most important benefits of a fundamental framework are the insightful questions it poses. Quants with data and computing tools are great at answering questions, but not at articulating them.  
  • Example: Dynamic Contextual Alpha Model based on a Fundamental Investment Philosophy. 
  • Other potential examples: Industry Models, Stock Specific Models, Macro Sensitivities on Stocks. 
  • Things that don't work: Making fundamental analysts do quant things like rank stocks. Making quant models do fundamental things, like screen a small group of stocks for fundamental analysts to pick from; Quants using 'of the shelf' 'academically tested' 'fundamental' approaches. Fundamentals using 'textbook' security analysis. Alpha comes from innovation, uniqueness in style, refined and customized competitive edge in the investing process. 

Mikhail Samonov, Founder at Two Centuries Investments

Mikhail Samonov

Founder
Two Centuries Investments

STREAM B

4:20 pm - 4:40 pm Explainable AI for asset management
Combining fundamental knowledge and quantitative techniques into an explainable AI framework for investment decisions:
  • We use a fundamental macro framework to guide data selection and curation, in combination with AI
  • Combining fundamental and quant, how to balance the two

How we approach the prediction problem: 
  • Definition of market regimes
  • Accounting for investor behavior
  • This includes human and machine, as well as machine-only modes of operation

Interrogation versus explanation:
  • The former is used by our team, the latter to provide information to investors
  • What is the best way of surfacing explanations to investment professionals?
  • Investor mental models

Use of the system for:
  • Investing
  • Risk Management
  • Indicators
  • Dealing with new and uncertain regimes

Aric Whitewood, Founding Partner at WilmotML

Aric Whitewood

Founding Partner
WilmotML

STREAM B

4:40 pm - 5:00 pm Utilizing a blended man & machine approach to generating Alpha
Integrating data, machine learning and fundamental insights to drive alpha
  • Finding patterns in data to aid your strategy
  • Automatic feed: immediately processing news and live market updates to your portfolio to accurately maintain risk

Manoj Narang, CEO at MANA Partners LLC

Manoj Narang

CEO
MANA Partners LLC

STREAM B

5:00 pm - 5:20 pm Model risk management for alpha strategies created with deep learning
  • Understanding the challenges of using deep learning to build alpha generation strategies
  • Model risk management to detect when machine learning strategies are not performing as intended.
  • Concept drift: Can you model an undefinable and constantly moving market? When DL should (and should not) be used.

Ben Steiner, Global Fixed Income at BNP Paribas Asset Management

Ben Steiner

Global Fixed Income
BNP Paribas Asset Management

STREAM C

3:55 pm - 4:00 pm PRACTICAL APPLICATIONS OF AI/ML
Roland Fejfar, Head TechBD EMEA/ APAC at Morgan Stanley

Roland Fejfar

Head TechBD EMEA/ APAC
Morgan Stanley

STREAM C

4:00 pm - 4:20 pm Practical applications of machine learning in investment banking

Charles Elkan, Managing Director at Goldman Sachs

Charles Elkan

Managing Director
Goldman Sachs

STREAM C

4:20 pm - 4:40 pm Applications of deep learning in portfolio management
  • How deep learning can help CIOs with common portfolio management challenges
  • Business drivers when considering an AI-driven approach to asset management
  • An end-to-end deep learning model for portfolio allocation
  • Deployment and scalability related practical challenges for a deep learning strategy

Calvin Yu, Managing Director and Head of Multi-Asset Solutions at Qplum

Calvin Yu

Managing Director and Head of Multi-Asset Solutions
Qplum

WALLACE is a self-evolving AI based on the concept of genetic algorithms. Constantly learning, WALLACE analyses over 10,000 securities across 42 dimensions each day. WALLACE’s most powerful features is its ability to anticipate market events. Over a 48- month period, WALLACE successfully predicted 40 public company acquisitions.  Similarly, WALLACE avoided major market selloffs.  As a result, WALLACE outperformed benchmarks and designed numerous hedge fund strategies.
  • Survival of the fittest: An insight into the process and journey of creating WALLACE 
  • The burden of unsupervised learning: enabling the machine to decide what inputs are more or less useful 
  • Becoming social: How WALLACE explains its decisions and exposes its thought process

Alex Bogdan, Chief Scientific Officer, Castle Ridge Asset Management at Castle Ridge Asset Management

Alex Bogdan

Chief Scientific Officer, Castle Ridge Asset Management
Castle Ridge Asset Management

Adrian de Valois-Franklin, CEO at Castle Ridge

Adrian de Valois-Franklin

CEO
Castle Ridge

Edwin Li, Managing Partner at Castle Ridge Asset Management

Edwin Li

Managing Partner
Castle Ridge Asset Management

STREAM C

5:00 pm - 5:20 pm Merchant mapping, ticker tagging, and panel stabilization: Cracking the dirty jobs in alternative data
  • Application of natural language processing technology for ticker tagging.
  • Using deep neural nets to clean credit card, email receipt, URLs and other alternative datasets.
  • In a world where raw data validation and structuring are handled by AI, what would be the role of today's R&D teams across the data supply chain?


Gene Ekster, CEO at Alternative Data Group

Gene Ekster

CEO
Alternative Data Group

5:20 pm - 5:30 pm Closing remarks

5:30 pm - 7:00 pm Drinks reception and networking in the exhibition area