Conference Day One: March 19 2019
8:00 am - 8:50 am Welcoming tea, coffee and registration
KEYNOTES & OPENING PLENARY SESSIONS
8:50 am - 9:00 am Chair's opening remarksJoseph 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, WorldQuant
9:20 am - 10:00 am Panel discussion: The asset allocator's viewAfsheen Afshar - Former Chief Artificial Intelligence Officer and Senior Managing Director Cerberus Capital Management
Joseph Simonian - Director of Quantitative Research, Natixis Investment Managers
Amit Soni - Portfolio Manager, New York Life Investments
- 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 AfsharFormer Chief Artificial Intelligence Officer and Senior Managing Director
Cerberus Capital Management
Amit SoniPortfolio Manager
New York Life Investments
10:00 am - 10:30 am Interview: Learn from this industry icon as they share their experience and insightsGregory Zuckerman - Special Writer The Wall Street Journal
Sandy Rattray - Chief Investment Officer, Man Group
10:30 am - 11:10 am Networking refreshment break in the exhibition area
11:10 am - 11:30 am Keynote: A new breed of investor: The development and disruption of asset management through machine learning techniquesMarcos López de Prado - Principal and Head of Machine Learning, AQR Capital Management
- 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 PradoPrincipal and Head of Machine Learning
AQR Capital Management
11:30 am - 12:10 pm Panel discussion: The talent war: How to attract and retain the top quant and data science mindsRob Sloan - Research Director WSJ Pro
Sheedsa Ali - Managing Director & Portfolio Manager, PineBridge Investments
Dan Furstenberg - Global Head of Hedge Fund Distribution & Data Strategy, Jefferies
Nitish Maini - General Manager, Virtual Research Centre & Vice President, Portfolio Manager, WorldQuant
Richard Pook - Partner, Dore Partnership
- 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 SloanResearch Director
Sheedsa AliManaging Director & Portfolio Manager
Dan FurstenbergGlobal Head of Hedge Fund Distribution & Data Strategy
Nitish MainiGeneral Manager, Virtual Research Centre & Vice President, Portfolio Manager
12:10 pm - 12:30 pm Keynote: "Big Data" is the new currencyArmando Gonzalez - CEO, RavenPack
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.
12:20 pm - 1:30 pm Networking lunch in the exhibition area
STREAM A1:30 pm - 1:35 pm ALT DATA Michael Marrale - Chief Executive Officer, M Science LLC
STREAM A1:35 pm - 2:15 pm Panel discussion: Understanding and navigating the ‘Wild Wild West’ of alt data vendors and suppliers Stewart Stimson - Head of Data Strategy Jump Trading
Olga Kokareva - Head of Data Sourcing and Strategy, Quantstellation
Robert Morse - Head of Data Strategy and Sourcing, PDT Partners
Lisa Schirf - Former COO Data Strategies Group and AI Research, Citadel
- 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 KokarevaHead of Data Sourcing and Strategy
Robert MorseHead of Data Strategy and Sourcing
Lisa SchirfFormer COO Data Strategies Group and AI Research
Stewart StimsonHead of Data Strategy
STREAM A2:15 pm - 2:25 pm Innovation in Alt Data Bill Dague - Head of Alternative Data Research, Quandl
STREAM A2:50 pm - 3:30 pm Panel discussion: What are the next hot data sources/ regions? What are the different business models that are emerging? Kristen Thiede - SVP Two Sigma
Bill Dague - Head of Alternative Data Research, Quandl
Rado Lipuš - CEO & Founder, Neudata
Hugh O'Connor - Director, Data Sourcing & Partnerships, Eagle Alpha
Greg Skibiski - Founder & CEO, Thasos Group
- 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?
Rado LipušCEO & Founder
Hugh O'ConnorDirector, Data Sourcing & Partnerships
STREAM B1:35 pm - 2:05 pm A specific framework and strategy for introducing quant methods and unique data into your fundamental approach to producing alpha Leigh Drogen - Founder and Executive Chairman, Estimize
Nick Jain - Chief Investment Officer, Citizen Asset Management
- 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 DrogenFounder and Executive Chairman
Nick JainChief Investment Officer
Citizen Asset Management
STREAM B2:05 pm - 2:50 pm Panel discussion: How best to integrate your portfolio management, research and data science teams to deliver alpha Paul Rowady - Director of Research Alphacution
Wesley Chan - Executive Vice President & Portfolio Manager, PIMCO
Ivailo Dimov - Quant and Data Science Research, Bloomberg
Tom Doris - Chief Data Scientist, Liquidnet
Poul Kristensen - Managing Director, Economist, and Portfolio Manager, New York Life Investment 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?
Wesley ChanExecutive Vice President & Portfolio Manager
Ivailo DimovQuant and Data Science Research
Tom DorisChief Data Scientist
Poul KristensenManaging Director, Economist, and Portfolio Manager
New York Life Investment Management
STREAM B2:50 pm - 3:30 pm Fireside Debate – When hedge funds ate their own Michael Recce - Chief Data Scientist, Neuberger Berman
Paul Rowady - Director of Research, Alphacution
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 RecceChief Data Scientist
STREAM C1:35 pm - 2:00 pm Utilizing AI/ML to lend predictive/adaptive capability in continuously changing market environments Yoshinori Nomura - Director, Fund Manager, Simplex Asset Management
- 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 NomuraDirector, Fund Manager
Simplex Asset Management
STREAM C2:00 pm - 2:20 pm The very latest AI developments machine learning for security selection and the dangers of overfitting Keywan Rasekhschaffe - Senior Quantitative Strategist and Portfolio Manager, Gresham Investment Management
- 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 RasekhschaffeSenior Quantitative Strategist and Portfolio Manager
Gresham Investment Management
STREAM C2:20 pm - 2:40 pm Practical applications of AI/ML Jamie Wise - President, Periscope Capital
- 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
STREAM C2:40 pm - 3:05 pm Big Data's Dirty Secret Harvey Stein - Head of the Quantitative Risk Analytics Group, Bloomberg
"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 SteinHead of the Quantitative Risk Analytics Group
STREAM C3:05 pm - 3:30 pm FED AI: Applying AI techniques and macro economics George Lentzas - Manager & Chief Data Scientist, Springfield Capital Management
3:30 pm - 3:55 pm Networking refreshment break in the exhibition area
STREAM A3:55 pm - 4:00 pm NATURAL LANGUAGE PROCESSING Gary Kazantsev - Head of the Machine Learning Engineering Team, Bloomberg
STREAM A4:00 pm - 4:20 pm Hanging on every word: Natural Language Processing unlocks new frontiers in corporate earnings sentiment analysis David Pope - Managing Director of Quantitative Research, S&P Global Market Intelligence
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 PopeManaging Director of Quantitative Research
S&P Global Market Intelligence
STREAM A4:20 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? Mike Chen - Director and Lead ML Researcher, Dynamic Equity; Lead Portfolio Manager, ESG Equity, PanAgora Asset Management
- 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 ChenDirector and Lead ML Researcher, Dynamic Equity; Lead Portfolio Manager, ESG Equity
PanAgora Asset Management
STREAM A4:40 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? Peter Hafez - Chief Data Scientist RavenPack
Javed Jussa - QES Team, Wolfe Research
Alejandra Litterio - Co-founder & Chief Research Officer, Eye Capital
Gurraj Singh Sangha - Global Head of Risk, State Street Verus
- 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 HafezChief Data Scientist
Javed JussaQES Team
Alejandra LitterioCo-founder & Chief Research Officer
Gurraj Singh SanghaGlobal Head of Risk
State Street Verus
STREAM B4:00 pm - 4:20 pm Building a unique, fully quant-driven strategy on fundamental principles: Lessons learned, pitfalls to avoid Mikhail Samonov - Founder, Two Centuries Investments
- 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.
Two Centuries Investments
STREAM B4:20 pm - 4:40 pm Explainable AI for asset management Aric Whitewood - Founding Partner, WilmotML
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:
- Risk Management
- Dealing with new and uncertain regimes
Aric WhitewoodFounding Partner
STREAM B4:40 pm - 5:00 pm Utilizing a blended man & machine approach to generating Alpha Manoj Narang - CEO, MANA Partners LLC
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
MANA Partners LLC
STREAM B5:00 pm - 5:20 pm Model risk management for alpha strategies created with deep learning Ben Steiner - Global Fixed Income, BNP Paribas Asset Management
- 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 SteinerGlobal Fixed Income
BNP Paribas Asset Management
STREAM C3:55 pm - 4:00 pm PRACTICAL APPLICATIONS OF AI/ML Roland Fejfar - Head TechBD EMEA/ APAC, Morgan Stanley
STREAM C4:20 pm - 4:40 pm Applications of deep learning in portfolio management Calvin Yu - Managing Director and Head of Multi-Asset Solutions, Qplum
- 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 YuManaging Director and Head of Multi-Asset Solutions
STREAM C4:40 pm - 5:00 pm WALLACE: The self-taught fully functional investment system Alex Bogdan - Chief Scientific Officer, Castle Ridge Asset Management, Castle Ridge Asset Management
Adrian de Valois-Franklin - CEO, Castle Ridge
Edwin Li - Managing Partner, Castle Ridge Asset Management
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 BogdanChief Scientific Officer, Castle Ridge Asset Management
Castle Ridge Asset Management
Adrian de Valois-FranklinCEO
Edwin LiManaging Partner
Castle Ridge Asset Management
STREAM C5:00 pm - 5:20 pm Merchant mapping, ticker tagging, and panel stabilization: Cracking the dirty jobs in alternative data Gene Ekster - CEO, Alternative Data Group
- 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?
Alternative Data Group