Given the name, investors considering the Goldman Sachs Human Evolution ETF (GDNA) can be forgiven for wondering if the portfolio includes Cyberdyne Systems, Incite Inc., and Delos. (Dystopian pop-culture references to fictional companies in the Terminator movies and HBO’s Westworld series.) It doesn’t. GNDA invests largely in health and biotechnology stocks, tracking a bespoke index that tries to provide “exposure to the beneficiaries of technological innovation, regardless of sector, geography or market capitalization.” The investments are selected and weighted “by a function of ‘thematic’ beta.’” Top holdings include Intuitive Surgical Inc., Abbott Laboratories, Medtronic, and Johnson & Johnson.
GDNA’s management fee is high compared to ultra-low-cost plain-vanilla index funds, but it isn’t outrageous for smart-beta funds or niche products. One plain-vanilla index option is the iShares Genomics Immunology and Healthcare ETF (IDNA), which isn’t much cheaper than GDNA. For investors who want access to the sector and don’t mind paying a bit extra, there’s also actively-managed ETFs such as the ARK Genomic Revolution ETF (ARKG). Run by the stock-picking team at ARK Invest, ARKG absolutely trounced GDNA’s performance in the first five months of 2020, a tumultuous period marked by pandemic shutdowns and social unrest.
GDNA is one of five ETFs that Goldman launched in 2019 with indexes designed by Motif Investing Inc. The indexes selected stocks using artificial intelligence and machine learning. Goldman worked with Motif to design indexes that aim to identify companies that fit with broad themes that Goldman’s money managers thought would drive growth. (The “Motif” moniker was removed from the fund name in May 2020 when Motif dropped out as the index provider.) The funds were yet another example of Wall Street’s increased use of sophisticated automation. Computers read faster than human analysts, quickly scouring regulatory filings, company reports and news stories for information on a company’s prospects. Using AI and machine learning isn’t that much of a divergence — after all, most factor funds and smart-beta ETFs are really just attempts to codify investing strategies employed by human stock pickers. Whether it’s a plain-vanilla index or something more sophisticated, index methodology is basically just programmed code that tells the index what to buy and sell.