Machine learning for actively-managed ETFs

It’s been one year since my landscape post on AI in Digital Wealth mgt: Sniffing out investment opportunities and the conversation continues on the Fintech Genome with the addition of new ventures and with other insightful conversations around whether Could fund strategies be better managed by Intelligent ML/Ai based platforms?

This is all happening while the rise of passive investing continues and the push for non-active ETFs continues. Statistics show that human stock picking is in decline, with Blackrock showing 64% of its assets under management in passive-focused index and exchange-traded funds and only 29% in funds that follow active strategies.

AI in digital wealth management clearly isn’t about passive and indexing. It is about discovering new sources of alpha, managing risk in a better way or about democratizing access to certain kinds of alpha. AI in wealth management is always about extracting value from data, whether it is natural language data, sentiment analysis, or numerical data processing.

Today I’ll zoom into a partnership between two AI&ML analytics providers and two Fintechs, that aims to capture alpha and offer it to all of us, in the most popular wrapper, the ETF.

Watson is not new to financial services. It is behind various IBM initiatives (e.g. IBM Cloud for financial services) and it is powering already some Fintechs, like Alpha Modus, focused on asset management services using cognitive analytics. None of these ventures have packaged financial instruments ready to be added to our portfolio.

The AIEQ ETF to be listed on NYSE

The AIEQ ETF pending its approval will be listed on the NYSE. It is the brainchild of a triangular collaboration between Watson + TensorFlow, Equbot, and the ETF Managers Group.

Watson shares its ability to parse text (news, articles etc) to uncover catalysts that can affect (positively or negatively) the individual stocks and real estate trusts under consideration.

Equbot, is the US based Fintech that sprung out of the IBM Global Entrepreneur roaster. It leverages the Watson AI abilities, to build predictive models that combine fundamental analysis and Machine Learning. It uses the open source Machine Learning libray TensorFlow to build the Equbot model. This model identifies 30-70 US stocks on a daily basis that have the greatest appreciation potential.

The ETF Managers Group (ETFMG), is the largest private ETF issuer/manager in the US, and is managing the process of filling for an ETF to be listed on the NYSE. The ETF is named the Equbot with Watson AI Total US ETF (AIEQ). Equbot is the subadvisor to this fund. Through its partnership with Watson and TensorFlow, it will develop the Equbot model which is driving the actively managed fund decisions.

Actual risk-adjusted performance can only tell whether stock picking based on the combination of ML software and cognitive analytics, is generating alpha after the taking into account the costs of active trading and managing the ETF wrapper.

Efi Pylarinou is a Fintech thought-leader, consultant, and investor. 

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3 thoughts on “Machine learning for actively-managed ETFs

  1. Interesting read! The use of AI is at its preliminary stages but it alreadly looks promising. Of course, programming a software to be profitable is definitely diffucult especially since the software may not have access to all information (such as insider info etc). Furthermore, putting a quantitative value to investors confidence has not been successful and is definitely worth exploring! Excited to see the prospects of machine learning!

    Liked by 1 person

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