It’s easy to be impressed by the way AIEQ’s seems to adapt over time, adding large caps to its portfolio and dialing down smaller companies right as they fell out of favor. In truth, though, other things may be going on. Sure, the machine might’ve sussed out an unwinding of the small-cap trade in the wake of rising rates and less trade bluster. Less amazingly, the ETF may have just needed to buy larger stocks as it got bigger and swelled toward $200 million.
Either way, it helped. Since the start of August, AIEQ’s greater allocation to larger companies has juiced its performance, a Bloomberg analysis shows.
“That’s the benefit of our strategy, right? It is very flexible and dynamic,’’ said Art Amador, COO and co-founder of Equbot, the company behind the ETF’s software. “The idea of, ‘Hey, it’s small caps today,’ doesn’t mean tomorrow we’re going to keep playing in the small-cap universe. In fact, it’s quite the opposite.’’
Fine, but the question of whether the artificial intelligence ETF knows what it’s doing or is simply getting lucky is a long way from solved -- maybe a decade away. To make any sort of judgment, you need so many picks to go right over so long a period that it becomes impossible to credit the gains to mathematical happenstance. That could be eight to 10 years of data spanning a full cycle, according to Sameer Samana, a global quantitative and technical strategist for Wells Fargo Investment Institute.
“In a world where there are so many managers, about the only place where there might be any skill is if you literally occupy the the third percentile and higher,” Samana said by phone, referring to a paper written by Eugene Fama and Kenneth French. “Your top three percent of managers is literally the starting point for anybody who might even have any skill. You can pretty much ignore anybody else.”
This article was provided by Bloomberg News.