Course Agenda

Agenda

Course Agenda

Day One

09:00

Registration and refreshments

09:30

Recent Trends in ML Application to Quant Finance and Risk 

  • Understanding the drivers of opportunity 
    • Changing nature of data
    • Computing power and quantum computing – current application and future uses
  • Regulation - data privacy and RegTech
  • Application to risk management – fraud detection, credit score, early warning system
  • Application to investment – stock selection, portfolio optimisation, trade execution strategies
  • Data quality and analytics – outlier identification, data filling/interpolation, sentiment analysis
  • Knowledge Graphs 

Speaker: Johannes van de Wetering, Head of Quantitative Risk, Capital Markets Risk Management, CIBC

11:00

Morning break

11:30

Machine Learning Models

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

Speaker: Jesús Calderón, Managing Director, Gravito

1:00

Lunch

2:00

Machine Learning and Risk 

  • Machine learning in banking, risk management & modelling
  • Analysis of rare events
  • Labelled, unbalanced data  
  • Anomaly detection
  • Network analysis
  • Time series spikes and breakouts

Speaker: Jesús Calderón, Managing Director, Gravito

3:30

Afternoon Break

4:00

Machine Learning in Finance: Putting it into Practice

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

Speaker: Joseph Simonian, Director of Quantitative Research, Natixis Investment Management 

5:30

End of day one

Day Two

09:00

Refreshments

09:30

Utilizing Machine Learning for Model Validation and Monitoring of Valuation Models

  • Why banks need larger validation throughput and how to use AI to speed up
  • Measuring data quality with ML
  • Building AI challenger models for model risk uncertainty measurement
  • Generating test scenarios with ML 
  • Solving PDE's with deep reinforcement learning
  • Validation with AI of market data generation algorithms (IR curve building, volatility surface construction)
  • Monitoring valuation models with ML (PnL & XVA) 

Speaker: Jos Gheerardyn, CEO, Yields.io

11:00

Morning break

11:30

Location Intelligence in Machine Learning 

  • Location intelligence and its applications 
  • The intersection of machine learning and the extensions location intelligence permits 
  • Futures in the combination of the discipline and the tool (ML)

Speaker: Arthur Berrill, Head of Content and Location Services, DNA, RBC 

1:00

Lunch

2:00

Machine Learning and Trading

  • Machine learning for trading
  • Practical Considerations

  • Reinforcement Learning
  • Determining the dynamic trading strategy that optimizes expected utility of final wealth
  • Appropriate choice of the reward function

Speaker: Armando Benitez, Director, Data and AI Lead, BMO Capital Markets 

3:30

Afternoon Break

4:00

Risk and Regulatory Framework Around AI Models 

  • Making the case for AI & current applications 
  • Understanding where the risks lie - systemic risk and risks from deployment 
  • Current state of laws / regulations around AI 
  • Regulatory expectations and evolving landscape  

Speaker: Amit Srivastav, Executive Director, Quantitative Analytics Group, Morgan Stanley 

5:30

End of course