Machine Learning in Finance programme

Workshop 2: Machine Learning in Finance

Machine Learning in Finance Programme 

08:30

Registration

09:00

Machine Learning Models for Risk Management

  • Supervised Learning
  • Unsupervised Learning
  • Semi-supervised Learning
  • Reinforcement Learning
  • Deep Learning
  • Advanced Machine Learning models
  • Case Study Examples

10:30

Morning coffee break

10:50

Machine Learning Applications in credit risk

  • Overview of common approaches
  • Approaches best capture the non-linear relationships common to credit risk
  • How to interpret model predictions in light of its “black box” nature
  • Integration of other data sources (e.g. transactional data, social media data, geographical information) to provide insights

12:15

Lunch

13:15

Machine Learning Applications in Non-financial Risk

  • Managing when data is not enough scenarios
  • Known “unknown” VS unknown “unknown”
  • Advanced fraud detection and neural networks
  • Managing machine learning black boxes
  • Case study examples 

14:45

Embracing Big Data and creating a comprehensive data strategy 

  • 3Vs of Big Data
  • Managing data from silo operations
  • Big data strategy and extracting value from limited sources
  • Gaining insights from complex data patterns
  • Learning from own ‘big data’ challenges
  • Case Study examples

15:45

Afternoon coffee break

16:00

Risk and Data Management in Machine Learning

  • 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

17:00

End of day 1

 

08:30

Registration

09:00

Machine Learning and Portfolio Management- an overview

  • Discovering Non-Linear Trends in Market Data
  • Churn Prediction & Prevention
  • Loan Default Calculation/Prediction
  • Quantitative Trading
  • Sentiment Analysis
  • Natural Language Processing of news sources/social media

10:15

Morning coffee break

10:30

Machine Learning and Trade strategies

  • Finding alpha
  • Value investing
  • Factor investment
  • Reinforcement Learning
  • Q learning
  • AI for ESG
  • Sentiment Analysis 

12:30

Lunch

13:00

Machine learning in portfolio construction – a focus on the techniques

  • Current status of the use of machine learning algorithms in portfolio construction
  • Portfolio construction:
    • Problem definition – what is the goal, what are the constraints
    • Optimisation techniques
    • Use of machine learning algorithms
  • Live example of building a portfolio

14:30

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

16:00

Afternoon coffee break

16:15

Conversational AI– Beyond the chatbot hype

  • Current state of the AI industry applied to digital assistants/chatbots
  • Source, real research and development behind it
  • Challenges
    • Data
    • Scalability and production issues
  • Practical possibilities ahead for organisations

17:00

End of Day 2