Quantitative hedge funds, also known as “quant funds,” have been around since the 1990s. These algorithm-powered funds follow factors set by humans, and they’re taking over more of the U.S. stock market.
The algorithms powering quant funds, like the humans who develop them, are vulnerable to inherent biases.
In 2017, quant funds became the dominant method of institutional trading in the U.S. Quant funds use algorithms to buy and sell stocks based on factors, such as quality and value, that help forecast performance over a given period of time. Some use machines to mimic previous human strategies, while others use generative adversarial networks and advanced deep learning techniques to create new strategies on their own.
The world’s largest algorithmic hedge fund managers—AQR Capital, Bridgewater, Two Sigma—execute trades quickly and efficiently, and sometimes at lower costs than non-algorithmic funds. The world’s best funds used to run primarily on human brain power, but today A.I. is increasingly taking on a greater role in their data-driven trading strategies.
As A.I. systems become more sophisticated and powerful, quant fund investors are now asking computers not only to crunch numbers and execute trades, but to identify the decision-making factors, too. While machines might surface entirely new criteria for trades, it’s important to note that the human programmers who originally built those systems made choices about which data to train and which algorithms to run.
Those human decisions have downstream implications. Bias is a well-known problem in the A.I. ecosystem, which means that fund architects will need to redouble their efforts to ensure their systems aren’t missing critical information or factors that might have been excluded due to human error or ignorance.
As quant funds advance to include more machine-derived factors, there will be new strategic advantages—and risks—for investors and fund managers.
ARQ Capital, Bridgewater, Kensho, Man Group, Renaissance, Two Sigma.
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