The 2-and-20 (or 1-and-10) crowd was already having a hard enough time justifying their generous fees to irate limited partners as they struggled to explain why they avoided the FAANG stocks and subsequently underperformed the S&P 500 so far this year as the index broke through 2,900 and touched new record highs earlier this month. But the handful of rich suckers who haven’t already bought into the “hedge funds control for volatility” explanation will probably be even more difficult to persuade after reading this Bloomberg story about AIEQ’s benchmark-beating performance during its first year. As we noted around the time of its launch, AIEQ is a wholly AI-powered fund that is powered by IBM’s Watson supercomputer. According to the press release, the fund’s processing capabilities can replace an army of analysts.
Here’s a brief description of AIEQ from the press release announcing its launch back in late 2017:
Business Wire explained how EquBot makes investment decisions “EquBot’s approach ranks investment opportunities based on their probability of benefiting from current economic conditions, trends, and world- and company-specific events, and identifies those equities with the greatest potential for appreciation. EquBot and ETFMG expect the fund’s portfolio to typically consist of 30 to 70 of U.S. equities only and volatility comparable to the broader U.S. equity market…the fund’s underlying technology is constantly analyzing information for approximately 6,000 U.S.-listed equities, including company management and market sentiment, and processes more than one million regulatory filings, quarterly results releases, news articles, and social media posts every day.”
The fund generates most of its alpha via investments in small companies.
Running 24/7 on IBM Corp.’s Watson platform, the fund culls data on more than 6,000 U.S. public companies each day before picking about 100 of them to own. Of the top 15 holdings that contributed most to AIEQ’s gain, 10 of them are too small to be in the in the S&P 500 Index, a Bloomberg portfolio analysis shows.
After launching in October 2017, AIEQ has racked up an 11.81% annualized return from its debut a year ago through Wednesday, just edging out the S&P 500. That’s a better showing than 87% of active managers over the same stretch.
To be sure, active managers still have some support to help them justify their existence (at least until the next downturn, which, depending upon whom you believe, may have already begun).
“You can’t really conclude that these guys have figured something out until you see their performance through something other than the rather good market conditions that we’ve been enjoying,” said Tammer Kamel, CEO of Quandl Inc., an alternative data platform. “If the AI that these guys are using can navigate a correction and still outperform the market, then they’ve really found nirvana.”
Looking back, AIEQ’s biggest scores came from bets on tiny stocks that posted outsize returns that also, incidentally, allowed AIEQ to outperform the Russell 2000 by 6%. The fund struck gold with a massive bet on Penn Virginia Corp., a $1.3 billion oil and gas driller based in Houston with fewer than 100 employees, which was the fund’s largest holding as of June. Penn is up over 100% this year and has added the most to the fund’s returns over its lifespan.
Boyd Gaming Corp., a Las Vegas-based gaming company, was one of the ETF’s top five holdings when it was launched in October of last year. The stock climbed roughly 50% before plunging, but amazingly, AIEQ had dumped most of its shares before the selloff. But perhaps the most stunning example of the fund’s uncanny knack for market timing was its rotation out of small caps and into large caps over the summer, right before the bottom fell out of the Russell.
Doubters say it will take another 10 years – with data spanning a full business cycle that includes a downturn – for AIEQ to prove its mettle. But its adherents insist that its performance isn’t a stroke of luck.
“It’s not serendipity or luck. Rather, it is grunt work analysis of computational analysis of data and looking at your blogs and social media and press releases, and a conglomerate of all that,” Rick Roche, managing director at Little Harbor Advisors, a boutique investment firm focused on quantitative strategies, said in an interview at Bloomberg’s New York headquarters. “Machine learning’s power in terms of getting data and uncovering potential alpha is much better in the small cap and the mid cap space than it is large cap.”
And with active management increasingly losing out to passive funds (which recently reached a record 44% of the market’s total AUM), the robot takeover of Wall Street is already well underway.
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