Market Data Firm Spots the Tracks of Bizarre Robot Traders

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Pigeon
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Market Data Firm Spots the Tracks of Bizarre Robot Traders

Post by Pigeon » Sun Apr 03, 2011 10:57 pm

Some interest data arises from the investigation of the May 6, 2010 flash crash.

Mysterious and possibly nefarious trading algorithms are operating every minute of every day in the nation's stock exchanges.
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In fact, it's hard to figure out exactly what they're up to or gauge their impact. Are they doing something illicit? If so, what? Or do the patterns emerge spontaneously, a kind of mechanical accident? If so, why? No matter what the answers to these questions turn out to be, we're witnessing a market phenomenon that is not easily explained. And it's really bizarre.

It's thanks to Nanex, the data services firm, that we know what their handiwork looks like at all. In the aftermath of the May 6 "flash crash," which saw the Dow plunge nearly 1,000 points in just a few minutes, the company spent weeks digging into their market recordings, replaying the day's trades and trying to understand what happened. Most stock charts show, at best, detail down to the one-minute scale, but Nanex's data shows much finer slices of time. The company's software engineer Jeffrey Donovan stared and stared at the data. He began to think that he could see odd patterns emerge from the numbers. He had a hunch that if he plotted the action around a stock sequentially at the millisecond range, he'd find something. When he tried it, he was blown away by the pattern. He called it "The Knife."

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"We probably get 10 stocks in any 10 minutes where we see something like this," Donovan said. "It's happening all the time."

These odd bots don't really make sense within the normal parameters of the high-frequency trading business. High-frequency traders do employ algorithms to look for patterns in the market and exploit them, but their goal is making winning trades, not simply sending quotes into the financial ether.

Here's the way a stock trade is supposed to work: a buyer says they'll pay some amount for 100 shares of a company, a seller makes an ask for slightly more money, and the two of them usually meet in the middle. Perhaps a middle man (no joke intended) helps match buyer and seller and takes a cut. That's the role that a lot of high-frequency traders play: they help make markets work. Regulatory changes over the past several years have extended their usefulness and provided a nice business model for those that can move quickly to provide options for buyers and sellers.

"Under the maker-taker model, market participants that offer to provide, or make, liquidity by posting an order to buy or sell a certain number of shares at a particular price receive a rebate," explained Michael Peltz in a June feature for Institutional Investor. "Those that execute against that order -- that is, take the liquidity -- have to pay a fee. Exchanges earn the difference between the rebate they pay and the fee they charge. The SEC limits taker fees to 0.30 cents a share; rebates tend to be lower for economic reasons, but for high frequency firms trading millions of shares a day, they can make for a pretty good living."
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But the algorithms we see at work here are different. They don't serve any function in the market. University of Pennsylvania finance professor, Michael Kearns, a specialist in algorithmic trading, called the patterns "curious," and noted that it wasn't immediately apparent what such order placement strategies might do.

Donovan thinks that the odd algorithms are just a way of introducing noise into the works. Other firms have to deal with that noise, but the originating entity can easily filter it out because they know what they did. Perhaps that gives them an advantage of some milliseconds. In the highly competitive and fast HFT world, where even one's physical proximity to a stock exchange matters, market players could be looking for any advantage.

"They are moving the high-frequency services as close to the exchanges as possible because even the speed of light matters," in such a competitive market, said Stanford finance professor Peter Hansen.

Given Nanex's data, let's say that these algorithms are being run each and every day, just about every minute. Are they really a big deal? Donovan said that quote stuffing or market spoofing played a role in the Flash Crash, but that event appears to have had so many causes and failures that it's nearly impossible to apportion blame. (It is worth noting that European markets are largely protected from a similar event by volatility interruption auctions.)

But already since the May event, Nanex's monitoring turned up another potentially disastrous situation. On July 16 in a quiet hour before the market opened, suddenly they saw a huge spike in bandwidth. When they looked at the data, they found that 84,000 quotes for each of 300 stocks had been made in under 20 seconds.
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