In the New York Times’ Upshot yesterday, Nathanial Popper points out that if you analyze markets and talk to investors and academics, high frequency trading is not the problem that headlines would lead you to believe. “Most of the investors who are actually facing off against the high-frequency traders — often on behalf of retirement savers — don’t see this as anything like the most costly problem they are facing, even in the arcane realm of trading mechanics,” he writes.
That’s because automated trading lowers costs, tightens spreads, and helps make markets more efficient for market participants. We recognize that there are issues with market structure today, and we’ve put out proposals to address complexity, fragmentation, and opacity. But as Popper notes, in markets with less automation, costs are far higher: “The cost of buying and selling municipal bonds, for instance, a popular investment for ordinary savers, is at least five times the cost of trading a stock, and usually much more than that.”
So why is there so much focus on high frequency trading? Terrence Hendershott of UC Berkeley thinks it’s because people don’t understand automated trading. He tells Popper that automated traders, “make money in ways that people don’t understand.”
It’s true that the algorithms behind many trades are complex, as is the computerized technology that has allowed for electronic trading. That’s the nature of technology today. But the concept of what we do is simple: we act as liquidity providers for market participants so they can manage their risks in a timely manner and invest effectively. As Popper wrote, “most high-frequency trading firms were small start-ups that set out to offer better prices than what the banks and Wall Street firms were previously charging as middlemen in the stock market.”
Instead of focusing on solving problems that don’t exist, let’s continue to engage in productive, data-driven discussions on how to improve market structure for all participants.