high-speed algorithmic trading will lead to a smaller-scale version of the crash of 1987, when the market dropped 22 percent in one day. Many now blame that crash on simple automated “portfolio insurance” systems, which were meant to keep a fund’s holdings from losing more than a preset amount of value by automatically selling shares when the price dropped by a certain amount. They had their roots in a practice used by floor traders: the “stop loss” order, which initiates the sale of a given share if it falls below a given price. But the herd of computers issuing stop-loss orders created a stampede that pushed the then-dominant floor traders to sell as well. Donefer worries that if such a sell-off happened now, it would happen many times faster.
While such “forced selling” can be the result of forethought (misguided or not), it can also start with a mistake: pressing an extra button (what traders call “fat-finger syndrome”) or botching the code that drives an automated algorithm. In 2003, shares of Corinthian Colleges, a company that manages for-profit educational institutions, plummeted when faulty code or human error caused a computer to begin selling shares its user did not have. The system had been programmed to sell if the security returned to the price at which it had been bought. When that time came, the computer sold the shares the customer held and just kept going. In 12 minutes, it sold short nearly three million shares at prices from $57.50 all the way down to $39.50. In a market dominated by high-frequency trading, such glitches could mushroom within seconds.
Even some high-frequency traders worry about what Donefer calls “algos gone wild.” John Jacobs, the COO of the New York City-based Lime Brokerage, wrote the SEC in 2009 to voice concerns over the proliferation of brokers who allow major clients to engage in high-frequency trading without validating their margins–that is to say, without making sure they actually have enough money to back a trade. Lime provides high-speed market access and order validation to hedge funds and other traders, some of whom cannot, or don’t want to, place their own servers on an exchange floor. In his position, Jacobs regularly sees algorithms executing more than 1,000 orders a second. At that rate, one algorithm trading the wrong way could execute 120,000 orders in two minutes. At 1,000 shares per order and an average price of, say, $20 a share, that’s $2.4 billion inunintended trades. In his letter, Jacobs warned of “the potential for trading-induced multiple domino bankruptcies.” He cautioned that “unrestrained computer-generated trading has the potential to create catastrophic economic damage to the U.S. national market system.”
The players in high-frequency trading are many and varied. Some are institutional investors like pension funds, endowments, and mutual funds; others are brokerages or trading desks at banks, using the banks’ own money. Enormous hedge funds like the Citadel Investment Group in Chicago use these techniques, and so do startups like PhaseCapital in Boston, which began trading with just the partners’ money in the spring. Designated “market makers”–traders licensed by an exchange to create a stable market in a security by making it available to both buyers and sellers in an orderly fashion–use high-frequency strategies to fill orders and to hedge positions, constantly rebalancing inventory so as not to get caught with too many or too few shares. And the field will only grow. Companies now offer high-frequency packages that include software, brokerage hookups, and as much consulting as you can afford.
Speed means profits as the company races against other firms using similar high-frequency tactics to take advantage of small movements in the markets.
Indeed, in many ways, practices associated