Bloomberg last week published an intriguing story about a new exchange traded fund (ETF) that uses artificial intelligence (AI) to outperform market indexes and active managers alike. The implication: a new era of AI-driven investing has dawned, putting the standard applications of indexing at a disadvantage. Yet a closer look at the so-called Robot ETF’s results via a factor-analysis lens tells a different story and one that can be explained with a mix of large-cap, small-cap and micro-cap equity betas. In turn, replicating the Robot ETF’s performance, which Bloomberg claims “leaves pros in the dust,” is a simple matter of holding a trio of plain-vanilla index funds.
To be fair, the Bloomberg article notes that the AI Powered Equity (AIEQ) has a healthy dose of small-cap bias. But we’re also told that the ETF uses IBM’s Watson platform, an AI system that’s deployed 24/7 to analyze “more than 6,000 US public companies each day before picking about 100 of them to own.” Impressive, but running a factor analysis on the fund suggests there’s an easier and more accessible methodology for achieving the same performance – with modestly lower risk.
As a test, let’s assume that the AIEQ’s returns can be largely explained by three equity betas, as proxied by a trio of the following conventional US stock ETFs:
The daily returns for all the ETFs are reduced by the risk-free rate, proxied by iShares Short Treasury Bond (SHV), to generate risk premia. One caveat: AIEQ was launched about a year ago and so the historical results are limited. But let’s ignore that drawback and see how the data stacks up by running a multiple regression on AIEQ against the three ETFs. Here are the results:
