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

11:00

Morning break

11:30

Machine Learning Models

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

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

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?

5:30

End of day one

Day Two

09:00

Refreshments

09:30

Big Data and Machine Learning

  • Big data in the finance landscape:
    • Financial modelling, data governance, integration, NoSQL, batch and real-time computing and storage
  • Challenges presented by data analysis and how to overcome them
  • Assessing data for Alpha content
  • Big data and AI strategies: Machine learning and alternative data approach to investing

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

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

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 deloyment 
  • Current state of laws / regulations around AI 
  • Regulatory expectations and evolving landscape  

5:30

End of course