It turns out that the drop in the Dow caused by the fake AP tweet was probably not carried out by human traders, but rather robot ones, specifically high-frequency trading (HFT) algorithms, which parse and automatically act on key words contained in raw news feeds (in this case, “explosions” and “White House” in the same headline). Matthew Phillips explains more:
Though it’ll be months before we know exactly what happened, the consensus is that a handful of trading algorithms responded to the fake tweet by selling a broad range of stocks, bonds, and commodities. As message traffic spiked and prices started declining, HFT firms started backing out of the market, just as they did during the May 2010 Flash Crash [which was caused by a rogue algorithm]. As a result, liquidity dried up, as you can see here in this chart from Nanex. Since there were suddenly relatively few buy orders to match against all those sell orders flooding the market, the dip picked up speed. “When the amount of bids and offers thins out like that, it takes very little volume to move the market in a big way,” says Manoj Narang, chief executive officer and founder of Tradeworx, a Red Bank (N.J.)-based HFT firm.
Phillips goes on to detail how many firms employ aggregation firms to collect and analyze potentially important news for their trading models. But it now seems clear that some firms are also “mainlining Twitter’s ‘firehose’ feed directly into [their] trading algorithm, allowing the model to place trades instantly off the information it’s gleaning through some sort of text-analysis program.” That’s probably not a good idea:
As Narang points out, the vast amount of trading done by computers is statistical in nature, meaning algorithms look for historical patterns in the volumes of market data they sort through every day. The problem with tweets is two-fold. One, they’re super noisy, so gleaning a decent signal out of them takes a lot of analysis and is still pretty hairy. But also, even though some 400 million tweets get sent every day, there’s still not enough historical data out there. They’re still too new.
However, Steven Gandel reports that while the tweet-reading algorithms surely played their part in Tuesday’s flash-crash, it’s unlikely they actually started it:
The fake tweet went out 1:07 PM and 50 seconds. According to Eric Hunsader, who runs market research firm Nanex, the first high-frequency trading firms didn’t react to the fake tweet until 15 seconds after that. Most algorithms are written to respond to data within milliseconds. What’s more, most only respond to specific market-moving news events, like the monthly jobs number.
So Hunsader thinks humans made the first trades based on the fake tweet. Nonetheless, computer trading algorithms, trained to follow the market, likely piled into the selling once it got started. Hunsader says that computer trading systems may have incorporated the potentially bad news into their algorithms. When actual selling began to occur, those systems were primed and headed for the exits faster than usual.
Zooming out, Nick Baumann recently explored the rise and potential dangers of high frequency trading:
This rapid churn has reduced the average holding period of a stock: Half a century ago it was eight years; today it is around five days. Most experts agree that high-speed trading algorithms are now responsible for more than half of US trading. Computer programs send and cancel orders tirelessly in a never-ending campaign to deceive and outrace each other, or sometimes just to slow each other down. They might also flood the market with bogus trade orders to throw off competitors, or stealthily liquidate a large stock position in a manner that doesn’t provoke a price swing. It’s a world where investing—if that’s what you call buying and selling a company’s stock within a matter of seconds—often comes down to how fast you can purchase or offload it, not how much the company is actually worth.
As technology has ushered in a brave new world on Wall Street, the nation’s watchdogs remain behind the curve, unable to effectively monitor, much less regulate, today’s markets. As in 2008, when regulators only seemed to realize after the fact the threat posed by the toxic stew of securitization, the financial whiz kids are again one step—or leap—ahead.