Data Analytics Capstone Project: A data-driven approach to building and running themed ETFs

How can data analytics methods drive the creation of themed ETFs?

This 2-day workshop explores the issues and approaches.


This course is for data analysts with some background in Python. It’s also suitable for asset managers, trading desks, structurers and others.

Learning Outcomes

Participants will be able to:

  • Describe and use data-driven methods for determining ‘sticky’ themes
  • Understand issues in benchmarking, especially for ground-breaking themes
  • Describe and choose fund types
  • Explain regulatory and operational issues around managing an ETF

Course Content

  • Orientation: what current themed ETFs exist, who are the players, what are the issues
  • Case studies from: robotics, silver economy, autonomous vehicle, mobility, space, diversity and inclusion, women leaders, water, climate, ESG, traditional Big AM firms, index providers from established to Fintech disruptors, sell sides, regulators
  • Regulatory concerns and how to find information on getting regulators comfortable
  • Where will we then list them? world exchanges, pros and cons
  • Sales and marketing plan pre-launch, launch, post-launch
  • Capital markets function; transaction costs, spreads, impact of reg like MiFID II on passive
  • Choosing a theme: data-gathering and NLP techniques
  • Sources of data – open and alt sources
  • Try it: query google/twitter search API
  • First steps – create corpus, eliminate stop words
  • Finding most frequent words and building uni, bi, tri groupings around them
  • More advanced topic modelling (LDA)
  • Other analyses: sentiment analysis (using a simple tool like Text Blob)
  • Stock/ security selection
  • Return a list of 20 stocks/ securities based on our theme criteria
  • What screening processes are used? Fundamentals/ pricing/ vol criteria, thematic relevance
  • Once stocks are selected, what fund type? market cap weight, same weight, smart beta, factor based, multi factor, low vol, etc. and why people pick different types
  • Benchmark selection
  • Pricing an ETF; market comparison, if it’s a true leader then how do we benchmark? examples through bitcoin ETNs on OMX bonds, equity, commodities – across the products, dynamic, non-transparent ETFs AUM game for beta builders while selective exposure more important for more esoteric
  • Backtesting: based on historical data and simulation, does the fund fulfil its mandate?
  • Building a backtester: what are the essential components?
  • Implementing random walks and bootstrap simulations
  • Capturing key stats: AUM, Sharpe Ratio, Beta, Vol
  • Handling portfolio rebalancing, redemptions and inflows


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