Fuzzy Math in Quant ETFs? A $1 Trillion Boom Draws Naysayers
Bloomberg
October 30, 2018
Justina Lee and Lu Wang
Today’s rules-based investing models were supposed to take the guesswork out of Ben Graham’s age-old principle of using “established standards of value” in deciding what to own. In practice, some argue, they’ve made things more confusing. Take three well-known products that aim to replicate that tried-and-true strategy of buying low. Depending on the one you look at, U.S. value stocks are either up 1 percent in 2018, down 6 percent, or down 8 percent. That’s great, if you happen to own the one that’s rising, an index from Deutsche Bank. But if you owned an ETF tracking the Russell 1000 Value Index? Not so much. For investors less concerned with a metaphysical quant debate and more with returns, the problem is various constructions of the same strategy can often show lucrative results in backtests. Believing in Buffett is not enough. “The fact that quant managers show a lot of data doesn’t mean they have greater ability to guarantee a particular investment outcome,” said Jason Hsu, chief investment officer at Rayliant Global Advisors and co-founder of Research Affiliates, pioneer of smart beta. “More accurately, you should think of it as scientific marketing: using a lot of data to help sell products.”
