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.

Suitability

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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