“Their funds are much cheaper than the competition’s,” said Balchunas.
QIS’s big-data strategies manage about $25 billion in a series of U.S. mutual funds and other products. Quants use machine-learning algorithms to sift through data, including credit-card receipts, to glean clues that might translate into wagers. Chropuvka said they have found 150 different trading signals, increasing the chances his team’s bets won’t mimic those of other quant funds.
Signals sometimes stop working. Tracking changes in analyst recommendations on companies used to be an indicator, but over time it became too widely followed and lost its predicative power. Now Goldman’s proprietary natural-language processing algorithms examine the tone in analyst reports, a more subtle signal that still has value, Chropuvka said.
Quant Overcrowding
The big-data mutual funds generally have performed well. The nine U.S. equity Insights funds on average beat 74 percent of rivals over the past five years, according to data compiled by Bloomberg. The unit manages an additional $8 billion of quantitative investments that attempt to duplicate hedge fund strategies at a lower price.
Quants are more worried about overcrowding and a decline in performance than a repeat of the panic of 2007. Rob Arnott, the founder of Research Affiliates and one of pioneers of smart-beta investing, says the rush of money pouring into the category could be its undoing.
“I am urging caution for a simple reason. It is human nature to engage in performance chasing,” Arnott told Bloomberg in June. “People think I am saying smart beta is a bad idea. I am not saying that all, but look before you leap.”
Jonathon Jacobson, founder of Highfields Capital Management, questioned the sustainability of quant performance in a second-quarter letter to investors. “Hundreds of billions of dollars can’t exploit the same inefficiency for long,” the hedge fund manager wrote. “My sense is we are a lot closer to the end than the beginning of these strategies producing excess returns.”
Goldman in a December white paper pointed out that not all smart-beta funds emphasize the same factors, which means the danger of crowding may be not as severe as some fear.
Chropuvka stresses that quant investing doesn’t imply that all decisions are made by machines on autopilot. Managers still set the strategies, make the wagers and monitor risk to try to prevent another quant blowup.