Programme

Programme

Programme

Day 1 - Risk

08:30

Registration 

09:00

Machine Learning Models

  • Supervised Vs Unsupervised models
    • Classification
    • Clustering
    • Regression
    • Dimensionality Reduction
  • Reinforcement Learning models
  • Deep Learning models
  • Selection of model
  • Explainability of model
  • Challenges, Ethics and Regulations of ML models

Harsh Prasad, Vice President, MORGAN STANLEY 

11:00

Morning coffee break

11:30

Understanding and developing an effective big data strategy

  • Extracting value from limited information
  • Three key components of big data analytics
  • Big data management: infrastructure and technology
  • Data availability, accessibility and integrity
  • Batch and stream processing
  • Advanced analytics: insights generation and algorithms development
  • Modern data analysis: structured/unstructured data modelling
  • Insights consumption: application and visualization

Johnson Poh, Head Data Science for Big Data Analytics Center of Excellence, DBS BANK

13:30

Lunch 

14:30

Introduction on AI drivers and applications

  • Machine Learning in Finance 
  • Development of trading strategy leveraging data analysis and visualisation with Python libraries
  • Development of predictive model for a continuous time series data set
    • Machine Learning Algorithms
    • Recurrent Neural Network Learning Architecture
  • Boosting your performance with Ensemble Learning including Random Forest and Gradient Boost

B. Carolina Hoffmann-Becking, Senior Consultant, Artificial Intelligence Customer Strategy, EY

16:00

Afternoon coffee break 

16:30

Q&A session

A panel of expert speakers will summarise key takeaways from their presentations and will open up for the Q&A and final thoughts on the industry’s adoption of ML tools.

17:00

End of Day 1

Day 2 - Investments

08:30

Registration 

09:00

Machine learning and trade strategies 

  • Finding alpha - value investing 
  • Factor investment 
  • Reinforcement learning 
  • Q learning 
  • AI for ESG 
  • Sentiment analysis 

Harsh Prasad, Vice President, MORGAN STANLEY 

10:30

Morning coffee break

11:00

Machine Learning and Portfolio Construction in Fixed Income - Part I 

  • Current status of the use of machine learning algorithms in FI portfolios 
    • how ML is used in the equity world
    • the challenges of using ML in FI
  • Current evolution of ML algos 
  • Building fixed income portfolios: 
    • Problem definition – what is the goal, what are the constraints
    • Optimisation techniques
    • Use of machine learning algorithms: 
      • data – predict, clean, fix
      • dimension reduction
      • attribute and behaviour prediction (i.e. default)
      • allocation of assets in portfolio 
        • covariance prediction
        • index construction
  • Live example of building a portfolio (e.g. to replicate an ETF) 

Hillel Raz, Chief Scientist, BondIT

12:00

Lunch

13:00

Machine Learning and Portfolio Construction in Fixed Income - Part II 

  • Building fixed income portfolios: 
    • Problem definition – what is the goal, what are the constraints
    • Optimisation techniques
    • Use of machine learning algorithms: 
      • data – predict, clean, fix
      • dimension reduction
      • attribute and behaviour prediction (i.e. default)
      • allocation of assets in portfolio 
        • covariance prediction
        • index construction
  • Live example of building a portfolio (e.g. to replicate an ETF) 

Hillel Raz, Chief Scientist, BondIT

14:00

Afternoon coffee break 

14:30

Machine learning in finance: future opportunities

  • Drivers of opportunity 
    • Changing nature of data
    • Computing power
  • Nature of opportunity 
    • Data gathering 
    • Problem solving 

Harsh Prasad, Vice President, MORGAN STANLEY 

15:30

End of Day 2