Conference Day One: March 19 2019
7:45 am - 8:50 am Welcoming tea, coffee and registration
KEYNOTES & OPENING PLENARY SESSIONS
8:50 am - 9:00 am Chair's opening remarks
9:00 am - 9:20 am Opening keynote: Outlook for the hedge fund industry: survival of the leanest or the most tech savvy?
9:20 am - 10:00 am Panel discussion: The asset allocator's viewJoseph Simonian - Director of Quantitative Research, Natixis Investment Managers
Amit Soni - Director, 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
Joseph SimonianDirector of Quantitative Research
Natixis Investment Managers
New York Life Investments
10:00 am - 10:30 am Interview: Learn from this industry icon as they share their experience and insightsSandy 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 mindsAfsheen Afshar - Former Chief Artificial Intelligence Officer and Senior Managing Director, Cerberus Capital Management
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
Afsheen AfsharFormer Chief Artificial Intelligence Officer and Senior Managing Director
Cerberus Capital Management
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, Large Global Hedgefund
- 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
Large Global Hedgefund
Stewart StimsonHead of Data Strategy
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? Tammer Kamel - CEO & Founder, Quandl
Rado Lipuš - CEO & Founder, Neudata
Greg Skibiski - Founder & CEO, Thasos Group
Kristen Thiede - SVP, Two Sigma
- 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
STREAM B1:35 pm - 1:55 pm Introducing quant methods into your fundamental approach to enhance alpha capture
- How this fundamental house took a considered approach to introducing systematic methods
- Presenting the business case, investment required & time frame for returns
- A culture change: embracing the value that technology can add
- Using algorithms to optimise your position sizing: can you eliminate the human bias?
STREAM B1:55 pm - 2:40 pm Panel discussion: How best to integrate your portfolio management, research and data science teams to deliver alpha Tom Doris - Chief Data Scientist, Liquidnet
Poul Kristensen - Managing Director, Economist, and Portfolio Manager, New York Life Investment Management
Arun Verma - Head of Quant Research Solutions, Bloomberg
- 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?
Tom DorisChief Data Scientist
Poul KristensenManaging Director, Economist, and Portfolio Manager
New York Life Investment Management
Arun VermaHead of Quant Research Solutions
STREAM B2:40 pm - 3:30 pm Fireside Debate – When hedge funds ate their own 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.
STREAM C1:35 pm - 2:00 pm Utilizing AI/ML to lend predictive/adaptive capability in continuously changing market environments Yoshinori Nomura - Director, Simplex Asset Management
- Understanding correlation between momentum & mean reversion
- Overcoming lack of volatility without DOP
- Applicability to other markets: the “walk forward test”
Simplex Asset Management
STREAM C2:00 pm - 2:20 pm Cutting edge applications of AI Mohsen Chitsaz - Founder & Chief Investment Officer, Alpha Beta Investments
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 Neo-cybernetics: New and developing AI techniques Paul Bilokon - Founder, Thalesians & Senior Quantitative Consultant, BNP Paribas
- Extending the ideas and principles of classical finance to change the foundations of the use of AI in modern finance
- Practical examples: How to recognize and avert catastrophic phenomena like market crashes
- Understanding the applications to finance and beyond
Paul BilokonFounder, Thalesians & Senior Quantitative Consultant
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 - Portfolio Manager, 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 rap, slang and unusual language structure
- Other practical NLP hints and tricks to set you on the path to alpha generation
Mike ChenPortfolio Manager
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
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
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: A fundamentally systematic approach 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 investment strategies with deep learning Ben Steiner - Global Fixed Income, BNP Paribas Asset Management
- Understanding the challenges of using machine learning to build your alpha generation strategies
- Ongoing monitoring for model risk when using machine learning strategies to make your decisions
- Can you ever model all circumstances in an undefinable and constantly moving market? How can AI make a decision on data it hasn’t seen yet?
Ben SteinerGlobal Fixed Income
BNP Paribas Asset Management
STREAM C3:55 pm - 4:00 pm PRACTICAL APPLICATIONS OF AI/ML Benoit Mondoloni - Director, Bank of America Merrill Lynch
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 Adrian de Valois-Franklin - CEO, Castle Ridge
WALLACE is a genetic algorithm based on the concept of survival of the fittest. Constantly growing and developing, it is currently analysing about 10,000 securities across 42 dimension space features. As a self-learning system, the team at Castle Ridge offer it new features and data sources, and WALLACE decides what will add value from these, adopting and learning itself to build a highly liquid market neutral strategy.
- 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 to overcome interpretability issues
Adrian de Valois-FranklinCEO