March 16 - 18, 2020
Metropolitan Pavilion NYC
Metropolitan Pavilion NYC
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 remarks
9:00 am - 9:20 am Keynote: Creating an international top tier data science and AI team
- 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
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?
- 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
10:00 am - 10:30 am Keynote: Risk models for quant trading
- 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?
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 edge
Moderator:
Tim Baker Global Head of Applied Innovation Refinitiv
Speakers:
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
Tim Baker Global Head of Applied Innovation Refinitiv
Speakers:
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?
12:00 pm - 12:50 pm Panel discussion: Understanding the future contributions of AI, ML & data science in the end to end investment process
- 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
12:50 pm - 2:00 pm Networking lunch in the exhibition area
AFTERNOON STREAMS
STREAM A
1:50 pm - 1:55 pm DEALING WITH DATA
STREAM A
1:55 pm - 2:15 pm What do good data management processes look like?- 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
STREAM A
2:15 pm - 2:25 pm Spotlight on data management
STREAM A
2:25 pm - 2:35 pm Spotlight on data management- 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 A
2:45 pm - 3:25 pm Panel discussion: Effectively navigating the potential legal and regulatory risk associated with alt data sets- 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?
STREAM B
1:50 pm - 1:55 pm QUANT & RISK METHODS
STREAM B
1:55 pm - 2:15 pm Practical Applications of Reinforcement Learning in the Investment Optimization Process- 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?
STREAM B
2:15 pm - 3:05 pm Panel discussion: Uncovering and assessing risk factors of a pure quant strategy- 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?
STREAM B
3:05 pm - 3:25 pm Bridging the gap: Leveraging academic theory and quantitative techniques in models for broad audiencesHow 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
STREAM C
1:50 pm - 1:55 pm UTILIZING CLOUD & OPEN SOURCE TECHNOLOGIES
STREAM C
1:55 pm - 2:15 pm Technology: The journey to the cloudAs 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
STREAM C
2:15 pm - 2:35 pm The open source culture: why is the industry and this fund embracing a new age of openness?- 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?
STREAM C
2:35 pm - 3:25 pm Panel discussion: Quantum computing: Closer than ever – understanding the landscape, risks and opportunities- 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?
3:25 pm - 3:50 pm Networking refreshment break in the exhibition area
STREAM A
3:50 pm - 3:55 pm AI & DATA
STREAM A
3:55 pm - 4:15 pm Cutting edge applications of AI
STREAM A
4:15 pm - 4:55 pm Panel discussion: Understanding, managing and effectively mitigating the hidden risks associated with AI
Moderator:
Pavan Arora Former Chief Data Officer & Director IBM Watson
Speakers:
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
Pavan Arora Former Chief Data Officer & Director IBM Watson
Speakers:
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?
STREAM A
4:55 pm - 5:15 pm The critical issues of interpretability: understanding what it really means?- 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
STREAM B
3:50 pm - 3:55 pm QUANT & RISK METHODS
STREAM B
3:55 pm - 4:15 pm Portfolio model risk for systematic/ quant trading
STREAM B
4:15 pm - 4:35 pm Developing macro predictions using alternative data
STREAM B
4:35 pm - 4:55 pm Putting AI to work for long-term sustainable investing- 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
STREAM B
4:55 pm - 5:15 pm How your asset class will influence your quantitative methods- 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
STREAM C
3:50 pm - 3:55 pm NEW APPLICATIONS FOR ML/DEEP LEARNING
STREAM C
3:55 pm - 4:15 pm Generating Alpha from Crypto with systematic strategies- 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
STREAM C
4:15 pm - 4:35 pm How can blockchain enable certain asset classes to become tradeable?- 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
