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A.I.-Powered Smart Beta Could Be the Next Evolution in ETFs

This article was originally published on ETFTrends.com.

Smart beta ETFs that follow customized indexing methodologies have quickly gained in popularity, and as the ETF industry refines the indexing process, a new breed of artificial intelligence and machine learning based strategies could begin to take shape.

Robert Tull, President of ProcureAM, which offers the Procure Space ETF (UFO) , argued that A.I. and machine learning could be the next frontier for ETFs, CNBC reports.

“Active management has been out there for a long time, underperforming,” Tull told CNBC. “They haven’t found a solution yet, and I think the technology that I’ve run into is going to help the marketplace today.”

Tull explained that this new technology covers a new type of ensemble analytics, or a methodology that uses multiple learning algorithms to better predict performance.

“The technology’s been around for years,” Tull added. “It’s just never moved into the asset management space, so [it’s about] getting data collected, running permutations against it and then really focusing on the best of the best selection that’s diversified.”

This type of machine learning aims to make smart beta even smarter by taking data and refining it to build a better mouse trap.

For example, Dave Nadig, who runs ETF.com, underscored the AI Powered Equity ETF (NYSE Arca: AIEQ) , which is an active ETF built on EquBot’s proprietary algorithms, utilizing the cognitive and big data processing abilities of IBM Watson to analyze U.S.-listed investment opportunities.

The fund’s software ‘constantly’ analyzes information for roughly 6,000 U.S.-listed stocks, scanning through regulatory filings, news articles, social media posts, and traditional financial metrics - including factors pertaining to correlations and valuations - to find investments it perceives as undervalued.

The people behind AIEQ are programmers who understand machine learning and applied this background to finance.

“I think this is the next generation, frankly, of financial product development,” Nadig told CNBC. “Machine learning sounds big and scary, but all it is, is really just taking data and things you already know, how things perform, to generate rules – as opposed to hiring a bunch of CFAs to come up with those rules about what you’re going to buy and sell based on fundamentals.”

For more information on alternative index-based strategies, visit our Smart Beta Channel.

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