Programme

Programme

Programme

Day 1 - Risk

08:30

Registration 

09:30

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 visualisation
  • Case studies

11:00

Morning break

11:30

Machine learning models

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

13:00

Lunch 

14:00

Risk Management via Alternate Data Sources

  • Identifying entities, relations and other metadata from unstructured data
  • Hands on python exercises for tagging unstructured data
  • Understanding graph data - rdf
    • Converting relational data to graph data
  • Tagging news items and getting real time sentiment
    • Use case demo and discussion
  • Impact of news on portfolio of securities
  • Supply chain risk management
    • Use case demo and discussion

15:30

Afternoon coffee break 

16:00

Machine learning in banking, risk management & modelling

  • ML applications in banking and risk management
  • Analyze large amounts of data while maintaining granularity of analysis
  • Tools to optimize and accelerate model risk management
  • Reporting requirements within financial services 
  • Money laundering detection and credit risk modeling

17:00

Q&A session 

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

17:30

End of Day 1

Day 2 - Investment 

08:30

Registration 

09:30

Trading Strategies based on news and sentiment data

  • Machine readable news - format and metadata description
  • Analyzing news data
  • Understand real time sentiment data
  • Using kalman filter on sentiment data and identifying sentiment regimes
    • Use case demo and discussion
  • Trading strategies based on sentiment data
  • Understanding multidimensional real time sentiment data
  • Cross rotation strategy formulation and back-testing
    • Use case demo and discussion
  • Deep learning based trading strategy
    • Use case demo and discussion

11:00

Morning break

11:30

Machine Learning and Portfolio Construction in Fixed Income

  • 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) 

13:00

Lunch

14:00

Applying artificial intelligence and data analytics in financial market (Part 1)

  • Data as the New Oil incl. Data Assertions and Its Value Map
  • Algorithmic “Google” on streaming-data
  • Searching for the most appropriate investment & trading opportunities from a risk-return perspective (e.g., Genetic Algorithm & Digital “Signatures”)  
  • Multi-strategy back- & forward-testing (e.g., Scenario Analysis & Fuzzy Logic)
  • Concluding Remarks – Session 1

15:30

Afternoon break 

16:00

Applying artificial intelligence and data analytics in financial market (Part 2)

  • Back to basics after sifting through the wreckage of long-held theories and developing new ideas, with case studies via an evidence-based approach
    • Efficient Frontier Analysis – Dealing with Deficient Frontier Scenarios  
    • Passive (vs Active) Investing – An Evidence based Approach (Knowledge Discovery)
    • Multi-Dimensional Diversification and Market Regime Shift, Factor investing, Smartbeta design, Multi-factor investing (e.g., Combinatorial and NN optimization) 
  • Case studies from global markets and further discussion incl.
    • Sector studies, Size migration, Risk regime perspectives, etc.
    • Diversification, counter-cyclical and regime switches
  • External manager selection, due diligence and in-house management 

Concluding Remarks – Session 2

17:00

Q&A session 

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

17:30

End of Day 2