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Betting on AI

Observations
February 6, 2018

New ground was broken last year when EquBot LLC and ETF Managers Group launched the AI Powered Equity ETF (AIEQ), the first exchange-traded fund powered by artificial intelligence. This fits neatly into the fintech trend, which is guided by the belief that AI is the future of investing. Will the same technology that drives search engines, facilitates communication, and promotes sales lead to better portfolios?

ETFs are ubiquitous. The largest and most successful cost a few basis points and track diversified benchmarks or factor tilts. AIEQ is different. It carries an expense ratio of 0.75% and tracks a portfolio of typically 40 to 70 names handpicked by IBM’s renowned Jeopardy! champion Watson.*

Despite AIEQ’s high-tech pedigree, its marketing material hits some familiar notes. The messaging is confident and guardedly optimistic, and it “explains” AIEQ’s investment thesis without actually saying anything.

EquBot positions investment solutions based on in-depth analysis of investor demands. We strive to create products that deliver positive long-term results on a risk-adjusted basis through optimizing proprietary investment research and trading models.*

To get a better understanding of AIEQ, I turned to the untrendy yet reliable method of factor profiling, which is Aperio’s go-to method for answering our clients’ questions about the parts of their portfolios we do not manage. Against the S&P 500, AEIQ’s dominant factor exposures were beta (high), size (small), and financial services (overweight); these factors collectively explained roughly two-thirds of the tracking error of 5.1%. Its beta was 1.2.

AIEQ percent contribution to tracking error


With the spectacular exception of the current volatility spike, equity markets have been calm since the inception of AIEQ on October 18, 2017, with CBOE Volatility Index (VIX) typically in single digits. Historically, calm markets have been favorable for risky investments. Nevertheless, AIEQ’s total return of 2.58% has fallen short of the 3.97% total return delivered by the S&P 500, which is more diversified and less risky.

AIEQ vs S&P 500

What is Watson thinking? Is AIEQ’s strategy too complex for humans to comprehend? Or is it taking advantage of low market volatility to bet on high-beta stocks? Does Watson have a view on whether the recent volatility spike will persist? Will AIEQ eventually outperform a diversified benchmark?

The real question is whether AI will transform financial markets, as it has transformed the way we search for information, communicate, and sell products. Are we at the beginning of the AI revolution in finance? Please join me for a discussion at Stanford Engineering’s AI in Fintech Forum on February 8.


Send questions or comments to blog@aperiogroup.com.


*Source: AIEQ website: www.equbotetf.com.

This article is provided for informational purposes only. The information contained within this article was carefully compiled from sources Aperio believes to be reliable, and it is accurate to the best of our knowledge and belief. However, Aperio cannot guarantee its accuracy, completeness, and validity, and cannot be held liable for any errors or omissions. All information contained herein should be independently verified and confirmed. Aperio does not accept any liability for any loss or damage whatsoever caused in reliance upon such information. Aperio provides this information with the understanding that it is not engaged in rendering legal, accounting, or tax services. In particular, none of the examples should be considered advice tailored to the needs of any specific investor. Aperio recommends that all investors seek out the services of competent professionals in any of the aforementioned areas. With respect to the description of any investment strategies, simulations, or investment recommendations, Aperio cannot provide any assurances that they will perform as expected and as described in this article. Past performance is not indicative of future results. Every investment program has the potential for loss as well as gain.