How AI Is Set to Reshape ETF Investing

How AI Is Set to Reshape ETF Investing

Innovation in ETFs Content Series: There are currently 30 exchange-traded funds with exposure to artificial intelligence.

Reviewed by: Lisa Barr
Edited by: Sean Allocca

This article is part of a new series from highlighting innovation in ETFs.

Artificial intelligence has the potential to reshape the financial landscape, and for exchange-traded fund investors, there are already ways to gain meaningful exposure. 

There are essentially two ways to take advantage of this technology: Invest in companies that are innovating and producing it, or use the technology itself to sift through data and develop portfolios. Teucrium, the agriculture-focused provider of ETFs, is bullish on the technology, and has thus far found success largely opting for the latter approach.  

“This technology is disruptive enough where it is going to be something that folks can profit from investing in, and I am bullish on the idea that you can use it to invest with,” said Jake Hanley, managing director and senior portfolio strategist at Teucrium, in the latest edition of’s podcast series Exchange Traded Fridays.  

Hanley and Editor-in-Chief Sean Allocca discussed how artificial intelligence and machine learning are revolutionizing the investment industry, including the role of the artificial intelligence chatbot ChatGPT.  

And with the current market having 30 ETFs with exposure to AI, with around $6.3 billion in assets under management, Hanley believes the market also has significant room for growth. “At a scale of 1 to 10, I’m at 100,” he said.  

AI, along with machine learning, can be used to enhance the performance and efficiency of ETFs by analyzing vast “neural networks” of data to make intelligent investment decisions, Hanley said.  

Identifying Patterns

Smart beta strategies are a prime example. Smart beta ETFs use a rules-based approach to construct an investment portfolio that aims to outperform traditional market-cap-weighted ETFs. The technologies can be used to identify factors that are likely to generate excess returns, such as low volatility, high quality and value. By using machine learning algorithms to analyze historical data, smart beta ETFs can also identify patterns and make predictions about future performance. 

Another way AI is being used in ETFs is through the development of automated investment platforms that use algorithms to make investment decisions on behalf of clients. By analyzing a client's financial goals, risk tolerance and investment horizon, these so-called robo advisors can construct a customized investment portfolio that is tailored to the client's needs.  

One potential concern regarding the use of AI and machine learning in ETFs is the risk of overreliance on algorithms and the potential for algorithmic bias. If they are not designed properly or are based on biased data, the algorithms may suggest making poor investment decisions.  

Therefore, ETF managers regularly test and audit their algorithms to verify that they are producing accurate and unbiased results that are useful to investors. In fact, regulators have already fined some firms over improperly used algorithms.  

While ChatGPT is making all the headlines, it is also providing investors with insights and analysis on market trends and investment opportunities. By analyzing vast amounts of data from various sources, the chatbot gives investors detailed information about possible investment choices based on their investment objectives and risk tolerance. It can also provide real-time updates on market conditions and investment performance. 

Hanley believes the future looks very bright: “This is as disruptive as the internet itself.” 

Nils Kuehn has been a writer for over 35 years. After graduating from the University of Michigan, he moved to the West Coast during the bubble of the late 1990s to further hone his craft, working in industries ranging from consulting to marketing to technology to finance to publishing. Now residing in Florida, Kuehn is a regular contributor to