Conference Day One: March 17 2020
8:00 am - 8:40 am Welcoming tea, coffee and registration
KEYNOTES & OPENING PLENARY SESSIONS
8:40 am - 8:50 am Chair's opening remarks
Chair’s welcome and start of conference
8:50 am - 9:20 am AI and capital markets – where are we now?
After the excitement of alt data, new ML techniques and cost-effective GPU availability, what’s actually been achieved in using AI in asset management? A global consultancy shares current research that’s mapped out what’s worked, what hasn’t and who’s now leading the pack
9:20 am - 10:00 am Panel: The reality of implementing AI within financial markets
Moderator:
Afsheen Afshar Former Chief Artificial Intelligence Officer & Senior Managing Director Cerberus Capital Management
Speakers:
Richard Craib Founder & CEO Numerai
Rajesh Krishnamachari Head of Data Science, Data and Innovation Group Bank of America
Mike Lock Vice President AQR Capital
Ingrid Tierens Managing Director Goldman Sachs
Afsheen Afshar Former Chief Artificial Intelligence Officer & Senior Managing Director Cerberus Capital Management
Speakers:
Richard Craib Founder & CEO Numerai
Rajesh Krishnamachari Head of Data Science, Data and Innovation Group Bank of America
Mike Lock Vice President AQR Capital
Ingrid Tierens Managing Director Goldman Sachs
Panel discussion on the realistic use cases of ML processes within financial markets – how to separate fad from reality and utilise Machine Learning effectively.
Afsheen Afshar
Former Chief Artificial Intelligence Officer & Senior Managing DirectorCerberus Capital Management
10:00 am - 10:30 am AI based macro models
AI’s approach to macroeconomic modelling
10:30 am - 11:00 am Networking refreshment break in the exhibition area
MORNING SESSIONS
11:00 am - 11:30 am Keynote interview
A fireside chat with a leading asset manager on how they are revolutionizing finance and using data to shape their investment philosophy and decision-making
11:30 am - 11:50 am Application of machine learning processes from other industries to financial markets
Learning how ML processes are applied in another industry and key elements that can be translated within trading to achieve a competitive edge
11:50 am - 12:10 pm Keynote: Quantum computing and future of finance
- The journey to achieving ‘Quantum readiness’ within financial market
- Use cases from within the industry and the obstacles needed to overcome to be fully integrated
- Potential impact of quantum computing on risk management and asset management
12:10 pm - 12:30 pm Natural language processing and application within finance
Understand NLP’s place within capital markets and its practical application
12:30 pm - 1:30 pm Networking lunch in the exhibition area
AFTERNOON STREAMS
STREAM A
1:30 pm - 1:35 pm Stream A: Quant for fundamentalSTREAM A
1:35 pm - 2:15 pm Panel: Fundamental vs quantitative – comparing and blending strategies within the shifting market
STREAM A
2:15 pm - 2:35 pm The future for ‘quantamental’ funds – remaining competitive in a saturated market- How to stay ahead with the proliferation of quant funds
- Overcoming human biases and cognitive blockers
- Recreating your quant teams within an AI and data-driven and increasingly regulated risk market
STREAM A
2:35 pm - 3:25 pm Panel: Journey of embedding quant methods within your fundamental approach effectively- Instigating the integration of new technology into a traditional structure
- Building internal capabilities through investment in people and technology
- Remaining competitive in a saturated market
STREAM B
1:30 pm - 1:35 pm Stream B: Data engineering and infrastructureSTREAM B
1:35 pm - 2:15 pm Data-informed insights: In-house vs outsourced- Identifying when to use in-house AI services vs outsourced options
-Determining how to get better insights, reduce costs and improve security and transparency
- Considerations necessary when deciding on a provider
STREAM B
2:15 pm - 2:55 pm Panel: Building an infrastructural data strategy- How to reliably build a data strategy from scratch
- Defining your data strategy and investing in the infrastructural elements to support it
- Building models to optimize and predict investment outcomes
STREAM B
2:55 pm - 3:25 pm Commoditization of data engineering- The future for the commoditization of data engineering
- Utilising cloud providers and open source tools to streamline your optimise your investment workflow
- Defining key aspects from whole investment workflow
STREAM C
1:30 pm - 1:35 pm Stream C: AI/ML case studies
STREAM C
1:35 pm - 2:05 pm Transfer learning for machine learning and NLP: adapting models to changing markets- Challenges of transfer learning within financial markets
- Identifying how to use transfer learning to your advantage
- Techniques and case studies to adapt models across new instruments and markets
STREAM C
2:05 pm - 2:50 pm How are financial decisions made?- Outlining the process of making impactful decisions
- Using attention-tracking hardware and intuition-capturing techniques to quantify and model the financial decision-making process
- Assessing the dependency of decisions and their corresponding previous sequential activity
STREAM C
2:25 pm - 3:05 pm Panel: Building the optimal data/ML teams- Hiring and retaining the perfect data team
- The war for talent and where to find the best data professionals
- Identifying the skills needed for the best data professional in the industry and where to find them
STREAM C
3:05 pm - 3:25 pm Issues and solution building an AI-driven fund from scratch- Bumps in the road in terms of biases, methodology, technology and data
- Diversified team, specialized methodology and AI design to avoid common pitfalls
- Alpha using an orchestra of agenda and a conductor based on deep reinforcement learning
3:25 pm - 3:55 pm Networking refreshment break in the exhibition area
STREAM A
3:55 pm - 4:00 pm Stream A: Quant and risk methods
STREAM A
4:00 pm - 4:20 pm Extracting insights from earnings transcripts
STREAM A
4:20 pm - 5:00 pm Panel: ML applications in risk managements and portfolio optimization
Speakers:
Irina Bogacheva Head of Multi-Asset Research QS Investors
Jonathan Chin Co-Founder & SVP, Product Management ARM Insight
Robert Gingrich Manager, Derivatives, Alternative Risk, Quantiative Investment Solutions Group Western Asset Management Group
Colm O'Cinneide Head of Portolio Construction QS Investors
Irina Bogacheva Head of Multi-Asset Research QS Investors
Jonathan Chin Co-Founder & SVP, Product Management ARM Insight
Robert Gingrich Manager, Derivatives, Alternative Risk, Quantiative Investment Solutions Group Western Asset Management Group
Colm O'Cinneide Head of Portolio Construction QS Investors
- Identifying when to intervene with AI model, when to leave it alone and when to start again
- Overcoming difficulty in explaining ML in risk models to clients (little economic interpretability)
- Validation and testing of ML models to mitigate risk
Robert Gingrich
Manager, Derivatives, Alternative Risk, Quantiative Investment Solutions GroupWestern Asset Management Group
STREAM A
5:00 pm - 5:20 pm Managing and modelling risk using ML for portfolio optimization- Allocating a risk budget based on model’s prediction of equity market
- Using ML to predict a stable portfolio
STREAM B
3:55 pm - 4:00 pm Stream B: Data quality and linkageSTREAM B
4:00 pm - 4:20 pm Ending overfitting systematically through Numerai’s data science tournament- Obfuscating data to prevent bias
- Learning from adversarial users
- Blind hold out test data with no feedback
STREAM B
4:20 pm - 5:00 pm Panel: Mitigation of cognitive bias from market dataSTREAM B
5:00 pm - 5:20 pm Combining Quantamental with alt data in the context of LBO predictions- How to use data to predict Leveraged Buyouts
- Ascertaining benefits of trading with a Quantamental signal
- Preserving investment banker/PE intuition in guiding variable selection
STREAM C
3:55 pm - 4:00 pm Stream C: AI/ML case studiesSTREAM C
4:00 pm - 4:20 pm Factor forecasting and rotation: Comparing Econometric and Machine Learning approaches- Highlighting the difference between econometric and machine learning approaches in developing factor forecasting and rotation using a high macroeconomic dataset
- Exploring techniques which are key in preventing overfitting
- Identifying machine learning approaches that outperform on a risk-adjusted basis
STREAM C
4:20 pm - 5:00 pm Panel: How to recover when machine learning goes wrong- Identifying at what point human intervention is necessary
- Avoiding building models on spurious relationships
- Overcoming structural challenges and training models differently
- Driving value through cost reduction
- How to organize institutional data to scale