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with high-frequency trading have become a routine part of how the market operates. When a customer places a trade through a Charles Schwab account, for example, that order is likely to be handled by a high-speed algorithm. Institutional traders like Fidelity, which buy large blocks of shares for their mutual funds, use algorithmic trading to split their enormous orders into blocks of 100 to 300 shares so that other traders don’t recognize the true demand and take advantage of that knowledge for their own profit.

Hedge funds with high-frequency operations, like Tradeworx, work between and around the institutional traders and the market makers, and against each other, attempting to profit by anticipating the moves of others. Their reliance on statistical patterns and quantitative analysis has won them the name of “quant funds.” (A quant fund typically holds a portfolio derived from statistical analysis, but its trades may take place over months as well as microseconds. Though most high-frequency funds are quant funds, not all quant funds trade at high frequency.) The explosion in high-speed automated trading has engendered a massive buildup in technology; Renaissance Technologies, a hedge fund based in East Setauket, NY, boasts that its computing power is equal to that of the Lawrence Livermore National Laboratory.

Just one example of what speed can do explains a lot about how high-­frequency trading works and why it angers some observers, as Joseph Saluzzi and Sal Arnuk, the principals of the New Jersey-based Themis Trading, made clear in their 2008 white paper “Toxic Equity Trading Order Flow on Wall Street.” Imagine that a mutual fund enters a buy order, telling its computer to start by offering the current market price of $20.00 a share but to take any asked price up to $20.03. A high-speed trader, Saluzzi and Arnuk explained, can use a “predatory algo” to identify that limit by “pinging” the market with sell orders that are issued in fractions of a second and canceled just as fast. It might start at $20.05 and work its way down to $20.03, canceling and reordering until the mutual fund bites. The trader then buys closer to the current $20.00 price from another, slower investor, reselling to the fund at $20.03. Because the high-frequency trader has a speed advantage, he is able to do all this before the slower party can catch up and offer shares for $20.01. This speedy player has found the buyer’s limit, gathered up and sold an order, and snipped a few pennies off for himself.

Liquidity and Order

Picking up all those pennies can be risky, Narang says, but he makes what he considers an important distinction. “There is risk, definitely, but quant funds like us take it all,” he says. “If a quant meltdown happens, it won’t affect the retail investor.”

A monitor at Tradeworx’s offices keeps track of the net trading operations during the day. It generally ticks up.

Narang turns to his computer and brings up two graphs, superimposing one on the other. The first shows the erratic up-and-down crawl of the S&P 500, the value of the largest 500 companies in the United States, over the last six years. The second shows Tradeworx’s profit and loss over the same period. It is a steady march up; in Tradeworx’s worst year, it made 15 percent. “All [high-frequency] funds have a profit-and-loss line like this,” he says. Then he magnifies the graphs to show just the weeks around August 2007, when many quant funds self-destructed as they sold off their portfolios to meet increasing margin calls (see “The Blow-Up” November/December 2007). In those days, his P&L dropped by 7 percent, and many other funds saw similar losses. But the S&P 500, overall, was little affected.

“And here’s the second quant meltdown, in January of ‘08,” Narang says, zooming out and then in on another blip in the graph, showing the value of the S&P 500 when a second, albeit smaller, dislocation occurred. “It’s tiny. You can hardly see it. That’s because funds running quantitative strategies are mostly market neutral. When we take a position, we’re always

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Credits: Steve Moors
Video by JR Rost

Tagged: Computing

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