March 19 - 20, 2019
Metropolitan Pavilion NYC
Metropolitan Pavilion NYC
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 view
Joseph Simonian - Director of Quantitative Research, Natixis Investment ManagersAmit 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
10:00 am - 10:30 am Interview: Learn from this industry icon as they share their experience and insights
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
Marcos 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?
11:30 am - 12:10 pm Panel discussion: The talent war: How to attract and retain the top quant and data science minds
Afsheen Afshar - Former Chief Artificial Intelligence Officer and Senior Managing Director, Cerberus Capital ManagementDan 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 Afshar
Former Chief Artificial Intelligence Officer and Senior Managing DirectorCerberus Capital Management
Nitish Maini
General Manager, Virtual Research Centre & Vice President, Portfolio ManagerWorldQuant
12:10 pm - 12:30 pm Keynote: "Big Data" is the new currency
Armando Gonzalez - CEO, RavenPackThe 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
AFTERNOON STREAMS
STREAM A
1:35 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 TradingOlga 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
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? Tammer Kamel - CEO & Founder, QuandlRado 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?
STREAM B
1:35 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 B
1:55 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, LiquidnetPoul 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?
Poul Kristensen
Managing Director, Economist, and Portfolio ManagerNew York Life Investment Management
STREAM B
2:40 pm - 3:30 pm Fireside Debate – When hedge funds ate their own Paul Rowady - Director of Research, AlphacutionOne 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 C
1:35 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”
STREAM C
2:00 pm - 2:20 pm Cutting edge applications of AI Mohsen Chitsaz - Founder & Chief Investment Officer, Alpha Beta Investments
STREAM C
2:20 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 C
2:40 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
STREAM C
3:05 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 A
3:55 pm - 4:00 pm NATURAL LANGUAGE PROCESSING 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 David Pope - Managing Director of Quantitative Research, S&P Global Market IntelligenceHow 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?
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? 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
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? Peter Hafez - Chief Data Scientist, RavenPackAlejandra 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)
STREAM B
4:00 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.
STREAM B
4:20 pm - 4:40 pm Explainable AI: A fundamentally systematic approach Aric Whitewood - Founding Partner, WilmotMLCombining 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
STREAM B
4:40 pm - 5:00 pm Utilizing a blended man & machine approach to generating Alpha Manoj Narang - CEO, MANA Partners LLCIntegrating 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
STREAM B
5:00 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?
STREAM C
3:55 pm - 4:00 pm PRACTICAL APPLICATIONS OF AI/ML Benoit Mondoloni - Director, Bank of America Merrill Lynch
STREAM C
4:00 pm - 4:20 pm Practical applications of machine learning in investment banking Charles Elkan - Managing Director, Goldman Sachs
STREAM C
4:20 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
STREAM C
4:40 pm - 5:00 pm WALLACE: The self-taught fully functional investment system Adrian de Valois-Franklin - CEO, Castle RidgeWALLACE 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
