In late March, the initial public offering on trading platform BATS (which stands for “Better Alternative Trading System”) was torpedoed by “systems issues” that slammed share prices down from $15.25 to a few cents in less than 900 milliseconds and caused a temporary halt in trading on Apple.
Eric Hunsader looked at the trade-flow data and smelled an algorithm — something he’s gotten quite adept at as president of market data-feed firm Nanex.
“It’s not like somebody threw in a bad market order and it just worked its way to zero,” he says. “This was an algorithm that was waiting, and it waited and waited until the bid price posted lower, and then it hit that price — and hit it and hit it — 567 times.”
He says it’s not that difficult to see if you know where to look, and he assumes scores of traders have seen it, too.
“Whoever did this is a seasoned veteran,” he says. “The algorithm was designed with intent, and it used ISOs (intermarket sweep orders), which only people with sponsored access can use.”
In January, his company uncovered evidence of widespread spoofing (entering and then cancelling orders to trick the market) designed to manipulate the National Best Bid or Offer (NBBO), which is the reference price for securities across the United States. He says also that bid/offer spreads in equities actually widened after the 2007 introduction of Reg NMS (Regulation National Market System), which is the legislation that sparked the high-frequency trading (HFT) boom by requiring brokers to execute trades at the lowest price being offered on any platform.
All of this directly contradicts the generally held perception that HFT has made bid/offer spreads tighter and commissions lower than they’ve ever been — an achievement Hunsader attributes to the advent of automated trading in general and not to HFT in particular.
Still, HFT now accounts for 60% of U.S. stock volume and 40% of volume on U.S. futures, according to Rosenblatt Securities, which says the figures are lower on options in the United States and on all markets around the world, with high-flying Brazil and most of Asia hovering around 10% on equities.
The entire world is retooling for HFT, however, and established players are salivating over the fat spreads on developing exchanges as their own per-trade profits drop in the United States.
Regulators, for their part, still are wrestling with a definition for HFT. In the United States, the Commodity Futures Trading Commission (CFTC) has set up a working group to come up with one, while the European Union is kicking around a working definition as part of its makeover of the Markets in Financial Instruments Directive (MiFID II). European Parliamentarian Marcus Ferber wants to define HFT as any trading that meets four of the following criteria: It uses co-location facilities; has a daily portfolio turnover of at least 50%; the ratio of orders to trades exceeds 4:1; 20% or more of orders are cancelled; the majority of positions are intraday and more than 50% of orders are placed on trading venues offering discounts or rebates for providing liquidity.
Beyond seeking to define HFT, regulators on both sides of the Atlantic are asking the same question we examined one year ago in “High-frequency trading: Good, bad or just different?” — namely, whether HFT enhances liquidity in ways that longer-term algorithmic strategies don’t, or whether it disrupts trade.
The answer, increasingly, seems to be “both of the above,” and the question now is how to maximize liquidity and minimize disruption as engineers work to bring latency times down from five microseconds to three to less than two. For some, the answer appears to be: Just don’t go there. The Securities and Exchange Board of India (SEBI), for example, is considering a speed limit of, say, five microseconds on transactions.