Conference Day Two: March 20 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 remarksTim Baker - Global Head of Applied Innovation, Refinitiv
9:00 am - 9:20 am Keynote: Creating an international top tier data science and AI teamMatthew Granade - Chief Market Intelligence Officer & Managing Director, Point72
- Building a vision: understanding and defining the data requirements
- Selling the business case: do you need quick wins to sustain the long-term vision?
- Growing and developing the functionality: refining, improving and scaling up
Matthew GranadeChief Market Intelligence Officer & Managing Director
9:20 am - 10:00 am Panel discussion: How are investment banks leveraging their quant and data science teams to build new business models and add value to clients?Tim Baker - Global Head of Applied Innovation Refinitiv
Lindsey Burik - Managing Director, Head of Electronic Trading Americas, Mizuho
Brice Rosenzweig - Global Head of Data & Innovation Group, Bank of America Merrill Lynch
Ingrid Tierens - Managing Director, Goldman Sachs
- How to add value in the age of access to alt data?
- Identifying trade ideas your client hasn’t considered yet
- Leveraging the vast reams of historical data that banks have stored
- Implementing AI and ML to help manage the regulatory burden
Lindsey BurikManaging Director, Head of Electronic Trading Americas
Brice RosenzweigGlobal Head of Data & Innovation Group
Bank of America Merrill Lynch
Ingrid TierensManaging Director
10:00 am - 10:30 am Keynote: Risk models for quant tradingZura Kakushadze - President & CEO, Quantigic Solutions
- How to build risk models for short-horizon & ephemeral ML-based data-mined alphas?
- How to make the covariance matrix out-of-sample stable & invertible for short lookbacks?
- Can using ML-based methods (e.g., clustering) for building such risk models add value?
- Should PMs allocate resources for building custom risk models or use commercial ones?
Zura KakushadzePresident & CEO
10:30 am - 11:10 am Networking refreshment break in the exhibition area
11:10 am - 12:00 pm Panel discussion: The outlook for emerging quantitative concepts as a tool to find edgeTim Baker - Global Head of Applied Innovation Refinitiv
Mark Antonio Awada - Chief Risk & Data Analytics Officer, Alpha Innovations
Alberto Cozzini - Head of Research and Senior Portfolio Manager, Devet Capital Management
Joel Nathaniel Bloch - Founding Partner & Chief Risk Officer, Trinnacle Capital Management
Daniel Sandberg - Director, Quantamental Research, S&P Global Market Intelligence
- Evaluating new and emerging strategies to maximise alpha from a blended man and machine approach
- Measuring effectiveness: understanding the ROI on your strategies – are they always practical and cost-effective to execute?
- When and how will quant funds consistently outperform a traditional approach?
Mark Antonio AwadaChief Risk & Data Analytics Officer
Alberto CozziniHead of Research and Senior Portfolio Manager
Devet Capital Management
Joel Nathaniel BlochFounding Partner & Chief Risk Officer
Trinnacle Capital Management
Daniel SandbergDirector, Quantamental Research
S&P Global Market Intelligence
12:00 pm - 12:50 pm Panel discussion: Understanding the future contributions of AI, ML & data science in the end to end investment processAnthony Ledford - Chief Scientist Man AHL
Sameer Gupta - Head of Data Sourcing, Point72
Sarah Hoffman - VP, AI & Machine Learning Research, Fidelity Investments
Claudia Perlich - Senior Data Scientist, Two Sigma
- Hear these leading data scientists discuss the latest techniques, concepts and trends
- Evaluate where technology can take potential achievements in data science
- Ask your questions to the esteemed panel for unique insights and takeaways
Anthony LedfordChief Scientist
Sameer GuptaHead of Data Sourcing
Sarah HoffmanVP, AI & Machine Learning Research
Claudia PerlichSenior Data Scientist
12:50 pm - 2:00 pm Networking lunch in the exhibition area
STREAM A1:55 pm - 2:15 pm What do good data management processes look like? Olga Kokareva - Head of Data Sourcing and Strategy, Quantstellation
- Significant work is required before a data set is fit for purpose, with months of analysis, tidying, infrastructure set up, fitting into algorithms and strategies without overfitting. What are the biggest stumbling blocks and how can they be avoided?
- Internal comms between engineers and data scientists
- Building internally vs vendor selection
- Using ML in data processing and cleanup
Olga KokarevaHead of Data Sourcing and Strategy
STREAM A2:35 pm - 2:45 pm Spotlight on data management L.D Salmanson - Co-Founder, Cherre
- From raw data to actionable signal - programmatic use of data requires a clean and streamlined data pipeline, otherwise it's garbage in garbage out.
- "Real-time" vs Streaming data - overhead and practicality considerations.
- Programmatic source management and evaluation - data sources continue to expand, and discovery and testing for signal needs to be programmatic.
- Data fusion as a service - manual processes are not scalable, and automated approaches have to be utilized.
- Red flags - signs that you have a bad process at scale.
STREAM A2:45 pm - 3:25 pm Panel discussion: Effectively navigating the potential legal and regulatory risk associated with alt data sets Emmett Kilduff - Founder, Eagle Alpha
Evan Reich - Head of Data Strategy and Sourcing, BlueMountain Capital Management
Stan Yakoff - Quant Compliance, Marshall Wace
- What are the ethics of good data practice and who is responsible for ensuring these are followed as data hungry algorithms process larger and larger quantities from more and more diverse sources?
- Best data practices: where does the responsibility for the data you are using lie?
- Evaluating the insider risk: has your purchased data set offered jigsaw pieces or the whole picture?
- GDPR and regulatory risk: Has your data been anonymised? Does it meet data privacy requirements?
Evan ReichHead of Data Strategy and Sourcing
BlueMountain Capital Management
Stan YakoffQuant Compliance
STREAM B1:50 pm - 1:55 pm QUANT & RISK METHODS Stuart Kozola - Global Head of FinTech Products, MathWorks
STREAM B1:55 pm - 2:15 pm Practical Applications of Reinforcement Learning in the Investment Optimization Process Edward Mitby - Senior AI Engineer, Vanguard
- A major challenge for quantitative/systematic investing is identifying regime change.
- Reinforcement learning solves for an optimal policy based on dynamically updated states.
- How can reinforcement learning be used to tactically allocate asset classes based on changing macro investment regimes?
Edward MitbySenior AI Engineer
STREAM B2:15 pm - 3:05 pm Panel discussion: Uncovering and assessing risk factors of a pure quant strategy Alexander Fleiss - Founder & CEO Rebellion Research
Javed Ashraf - Partner, Blackbox Alpha Management
George Mylnikov - VP, Head of Quantitative Research, Windhaven Investment Management
Milind Sharma - CEO, QuantZ Capital Management
- How can you accurately predict in an undefined universe without overfitting?
- Interpretability: Can you ever really trust a black box to make the right decisions without human understanding, and will the regulators agree?
- Examining the success of your algorithm in different market conditions: have you correctly identified the relevant factors?
- Coping with tail/extreme events: are you prepared for the unexpected?
Alexander FleissFounder & CEO
Blackbox Alpha Management
George MylnikovVP, Head of Quantitative Research
Windhaven Investment Management
QuantZ Capital Management
STREAM B3:05 pm - 3:25 pm Bridging the gap: Leveraging academic theory and quantitative techniques in models for broad audiences Stephanie Lo - Securities Finance QDR, State Street Associates
Jian Wu - Trading and Algorithmic Strategies, State Street Global Markets, Securities Finance
How do you develop research for a wide audience, from the fundamentals to the quants?
- Alt Data + Quant Methods + PhDs/Quants/Academic Partners => Partnerships: academic and business
- Research approach: Academic/financial theory, alt data, and quant techniques
- Applied examples of our approach
Stephanie LoSecurities Finance QDR
State Street Associates
Jian WuTrading and Algorithmic Strategies
State Street Global Markets, Securities Finance
STREAM C1:55 pm - 2:15 pm Technology: The journey to the cloud Andrew Janian - Head of Data Engineering, Two Sigma Investments
As acceptance for cloud increases, the questions become how to use and implement it.
- Understanding the benefits of a cloud based infrastructure: scalability with flexibility
- Data and storage improvements and forecasts
- The disruptor’s take on cloud for AI: enabling David to compete with Goliath
- Implementing cloud in your company and systems: pitfalls to be avoided
Andrew JanianHead of Data Engineering
Two Sigma Investments
STREAM C2:15 pm - 2:35 pm The open source culture: why is the industry and this fund embracing a new age of openness? Michael Beal - CEO, Data Capital Management
- The merits of open source: culture: attracting talent, efficiencies and the hive mind
- Considering the impact on your internal procedures
- Is there a downside and how can this be mitigated?
Data Capital Management
STREAM C2:35 pm - 3:25 pm Panel discussion: Quantum computing: Closer than ever – understanding the landscape, risks and opportunities Michael Brett - CEO, QxBranch
Paul Burchard - Managing Director, Technology Division, Goldman Sachs
Yianni Gamvros - Head of Business Development, QC Ware
- How and why quantum computing will change the world of finance as we know it: can you trust the liquidity of your system should its very foundations change?
- What is the current state of quantum computing and how is finance adopting the technology?
- What will be the area of biggest impact within capital markets once quantum computing becomes a reality?
- As a highly automated sector, how can Finance prepare for the changes?
Paul BurchardManaging Director, Technology Division
Yianni GamvrosHead of Business Development
3:25 pm - 3:50 pm Networking refreshment break in the exhibition area
STREAM A3:50 pm - 3:55 pm AI & DATA Pavan Arora - Former Chief Data Officer & Director, IBM Watson
STREAM A3:55 pm - 4:15 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 A4:15 pm - 4:55 pm Panel discussion: Understanding, managing and effectively mitigating the hidden risks associated with AI Gary Kazantsev - Head of the Machine Learning Engineering Team, Bloomberg
Zach Lipton - Assistant Professor, Machine Learning Department, Carnegie Mellon University
Stefano Pasquali - Managing Director & Head of Liquidity Research Group, BlackRock
- The risks of the person behind the AI/model: how to remove bias?
- Coping with tail risks
- Can you ever model all circumstances in an undefinable and constantly moving market?
- How can AI measure a risk it hasn’t seen yet?
- Balancing the compliance and risk demands of creating human understanding of complex models
- Overfitting: Have you applied diversification in your quant strategies? A variety of asset classes? Appropriate algorithm choices?
- Would a failure of model or liquidity mean you have inappropriate risk management in place?
Gary KazantsevHead of the Machine Learning Engineering Team
Zach LiptonAssistant Professor, Machine Learning Department
Carnegie Mellon University
Stefano PasqualiManaging Director & Head of Liquidity Research Group
STREAM A4:55 pm - 5:15 pm The critical issues of interpretability: understanding what it really means? Zach Lipton - Assistant Professor, Machine Learning Department, Carnegie Mellon University
- Underspecified and unclear: have you appropriately defined your model and the properties it satisfies?
- Understanding causal structure: validating the decisions your machine made
- Resilience and robustness: The importance of factoring in domain adaptation and distribution shift
- Anomaly detection: quantifying and actively compensating by modifying your beliefs and constraints
Zach LiptonAssistant Professor, Machine Learning Department
Carnegie Mellon University
STREAM B3:50 pm - 3:55 pm QUANT & RISK METHODS Bruno Dupire - Head of Quantitative Research, Bloomberg
STREAM B3:55 pm - 4:15 pm Portfolio model risk for systematic/ quant trading Gordon Ritter - Former Senior Portfolio Manager, GSA Capital
STREAM B4:15 pm - 4:35 pm Developing macro predictions using alternative data Apurv Jain - Visiting Researcher, Harvard Business School
STREAM B4:35 pm - 4:55 pm Putting AI to work for long-term sustainable investing Maarten Smit - Senior Portfolio Manager, Quantitative Equities, APG Asset Management
- Innovation to maximize pension value: returns, risk, costs, and ESG
- Building and integrating ESG factors and data into your models and investment approach
- How to set up and organize an effective data science capability
Maarten SmitSenior Portfolio Manager, Quantitative Equities
APG Asset Management
STREAM B4:55 pm - 5:15 pm How your asset class will influence your quantitative methods Dmitry Green - Chief Risk Officer, Mariner Investment Group
- Evaluate the different approaches required based on the underlying asset class of your investment strategy
- Understanding the different approaches to fixed income, equity, commodities and more
Dmitry GreenChief Risk Officer
Mariner Investment Group
STREAM C3:50 pm - 3:55 pm NEW APPLICATIONS FOR ML/DEEP LEARNING Roland Fejfar - Executive Director, IBD Fintech,, Morgan Stanley
STREAM C3:55 pm - 4:15 pm Generating Alpha from Crypto with systematic strategies Amit Kaushik - Head of Quantitative Research and Portfolio Manager, SciFeCap
- Systematic strategies as a hedge against a long-only strategy
- A Crypto strategy with low correlation against BTC and CCi30
- Small BTC exposure leads to a jump in the Sharpe ratio of a stock portfolio
- Limits to arbitrage in a crypto strategy
Amit KaushikHead of Quantitative Research and Portfolio Manager
STREAM C4:15 pm - 4:35 pm How can blockchain enable certain asset classes to become tradeable? Ksenia Semenova - Chief Marketing and Business Development Officer, Cindicator
- Understanding the benefits of a natively digital trading technology to maximise data and analytical capabilities
- Practical applications in new asset classes such as syndicated loans
- Its application within algorithms: removing the interpretability issue
Ksenia SemenovaChief Marketing and Business Development Officer