Event Agenda

Agenda

Course Agenda

Day One

09:00

Registration and refreshments

09.30

Big Data in the Finance Landscape

  • Big data in the finance landscape: Financial modelling, data governance, integration, NoSQL, batch and real-time computing and storage
  • Infrastructure and technology
  • New data sources
  • Modern data analysis - Structured/unstructured data and new models

Speaker: Ksenia Shnyra, Managing Partner, Exeter Consulting and Capital Management 

11:00

Morning break

11:30

Machine Learning Models

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
  • Deep learning
  • Advanced machine learning models

Speaker: Jésus Caldéron, Co-Founder and Director, Gravito  

1:00

Lunch

2:00

Machine Learning in Finance: Practice

  • Pros and cons of applying ML to investing 
  • Importance of features selection
  • Subtleties of applying ML to investing 
  • Where to start? 

Speaker: George Lentzas, Manager & Chief Data Scientist and Adjunct Associate Professor, Springfield Capital Management, Columbia Business School & New York University 

3:30

Afternoon break

4:00

AI Interpretability

  • Deep Learning models often perform remarkable tasks and are highly accurate
  • But they are also highly opaque: they work, but we often don’t understand how
  • What are the risks of deploying uninterpretable models?
  • Discuss the concepts of AI Security and AI Safety
  • Introduce Adversarial Examples to illustrate one risk
  • The thrust of the talk will be to teach techniques to better interpret Deep Learning models

Speaker: Ken Perry, Consultant in Risk and Quantamental Investing, CRO, formerly of Och Ziff

5:30

End of day one

Day Two

09:00

Refreshments

09:30

Quantum Machine Learning

  • This session will analyse the emerging techniques applicable to quantum computing and its applications.

Speaker: Steve Yalovitser, Co-Founder, New York Quantum Computing Meet-up and Director, XVA Quant Core Lead, Wells Fargo

11:00

Morning break

11:30

Machine Learning and Portfolio Construction

  • Data science & machine learning in quantitative finance
  • New tools and techniques in large-scale machine learning and analytics
  • An overview of developments in data science and machine learning through the context of the needs of the quantitative finance industry
  • Multi-period portfolio optimization

Speaker: Petter Kolm, Director of the Mathematics in Finance Program and Clinical Professor, Courant Institute of Mathematical Sciences, New York University

1:00

Lunch

2:00

Machine Learning and Trading

  • Machine learning for trading
  • Determining the dynamic trading strategy that optimizes expected utility of final wealth
  • Appropriate choice of the reward function
  • Research in portfolio transitions

3:30

Afternoon break

4:00

Machine Learning and Model Risk

  • Model Risk Management: what is it?
    • Managing the risk inherent in machine learning models.
  • The 3 challenges of using machine learning for investment strategies
    • Non-stationarity
    • Interpretation
    • Rediscovering existing factors
  • Practical solutions when using machine learning for investment strategies
    • Evaluating investment strategies
    • Ongoing monitoring

Speaker: Ben Steiner, Global Fixed Income, BNP Paribas Asset Management 

5:30

End of course