Flash Boys: A Wall Street Revolt (27 page)

On IEX’s opening day—when it had traded just half a million shares—the flow of orders through its computers had been too rapid for the human eye to make sense of it. Brad had spent the first week or so glued to his terminal, trying to see whatever he could see. Even that first week, he was trying to make sense of lines scrolling down his computer screen at a rate of fifty per second. It felt like speed-reading
War and Peace
in under a minute. All he could see was that a shocking number of the orders being sent by the Wall Street banks to IEX came in small 100-share lots. The HFT guys used 100-share lots as bait on the exchanges, to tease information out of the market while taking as little risk as possible. But these weren’t HFT orders; these were from the big banks. At the end of one day, he asked for a count of one bank’s orders: 87 percent of them were in these tiny 100-share lots. Why?

The week after Brad had quit his job at the Royal Bank of Canada, his doctor noted that his blood pressure had collapsed to virtually normal levels, and he’d cut his medication in half. Now, in response to this new situation he couldn’t make sense of, Brad had migraines, and his blood pressure was again spiking. “I’m straining to see patterns,” he said. “The patterns are being shown to me, but my eyes can’t pick them up.”

One afternoon, an IEX employee named Josh Blackburn overheard Brad mention his problem. Josh was quiet—not just reserved, but intensely so—and didn’t say anything at first. But he thought he knew how to solve the problem. With pictures.

Josh, like Zoran, traced his career back to September 11, 2001. He’d just started college when a friend messaged him to turn on the TV, and he’d watched the Twin Towers collapse. “When that happened it was kind of a
what can I do
moment?” A couple of months later, he’d gone to the local air force recruiting center and attempted to enlist. They’d told him to wait until the end of his freshman year. At the end of the school year he’d returned. The air force sent him to Qatar, where a colonel figured out that he had a special talent for writing computer code; one thing led to another, and two years later he was in Baghdad. There he created a system for getting messages to all remote units, and another system for creating a Google-like map, before the existence of Google maps. From Baghdad he’d gone to Afghanistan, where he wound up being in charge of taking the data from all the branches of the U.S. military across all battlefields and turning it into a single picture the generals could use to make decisions. “It told them everything that was going on, real-time, on a twenty-foot wall map,” Josh said. “You could see trends. You could see origins of rocket attacks. You could see patterns in when they occurred—the attacks on [U.S. Army base] Camp Victory would come after afternoon prayer. You could see what the projections were [of where and when the attacks might occur] and how they compared to where attacks actually happened.” The trick was not simply to write the code that turned information into pictures but to find the best pictures to draw—shapes and colors that led the mind to meaning. “Once you got all that stuff together and showed it in the best way possible, you could find patterns,” Josh said.

The job was hard to do, but, as it turned out, harder to stop doing. When his first tour of duty was up, Josh reenlisted, and when that tour ended, he re-upped again. When his third tour was over, he saw the war winding down and his usefulness diminish. “You find it very difficult to come home from,” said Josh. “Because you see the impact of your work. After that, I couldn’t find any passion in anything I did, any meaning.” Coming home, he looked for a place to deploy his skill—and a friend in finance told him about an opening in a new high-frequency trading firm. “In the war, you’re trying to use the picture you create to take advantage of the enemy,” said Josh. “In this case, you’re trying to take advantage of the market.” He worked for the HFT firm for six weeks before it failed, but he found the job unsatisfying.

He’d come to IEX in the usual way: John Schwall had found him while trolling on LinkedIn and asked him to come for an interview. At that point, Josh was being inundated with offers from other high-frequency trading firms. “There was a lot of ‘we are elite,’ ” he said. “They kept hitting the elite thing.” He didn’t care all that much about being elite; he just wanted his work to mean something. “I came in for an interview on Friday. Saturday they made me an offer. Brad said, we’re going to change the way things work. But I didn’t really know what Brad was talking about.” Since joining, he’d been quiet and had put himself where he liked to be, in the background. “I just try to take in what people are saying, and listen to what everyone is complaining about,” he said. “
I wish this
or
I wish that
, and then bring it together and find the solution.”

Brad knew little of Josh’s past—only that whatever Josh had done for the U.S. military sounded like the sort of thing he couldn’t talk about. “All I knew was that he was in a trailer in Afghanistan, working with generals,” said Brad. “When I tell him my problem—that I couldn’t
see
the data—he just says, ‘Hit Refresh.’ ”

Quietly, Josh had gone off and created for Brad pictures of the activity on IEX. Brad hit Refresh; the screen was now organized in different shapes and colors. The strange 100-lot trades were suddenly bunched together and highlighted in useful ways: He could see patterns. And in the patterns he could see predatory activity neither he nor the investors had yet imagined.

These new pictures showed him how the big Wall Street banks typically handled investors’ stock market orders. Here’s how it worked: Say you are a big investor—a mutual fund or a pension fund—and you have decided to make a big investment in Procter & Gamble. You are acting on behalf of a lot of ordinary Americans who have given you their savings to manage. You call some broker—Bank of America, say—and tell them you’d like to buy 100,000 shares of Procter & Gamble. P&G’s shares are trading at, say, 82.95–82.97, with 1,000 shares listed on each side. You tell the big Wall Street bank you are willing to pay up to, say, $82.97 a share. From that point on, you basically have no clue how your order—and the information it contains—is treated. Now Brad saw: The first thing the broker did was to ping IEX with an order to buy 100 shares, to see if IEX had a seller. This made total sense: You didn’t want to reveal you had a big buyer until you found a seller. What made a lot less sense was what many of the brokers did after they discovered the seller. They avoided him.

Say, for example, that IEX actually had a seller waiting on it—a seller of 100,000 shares at $82.96. Instead of coming in and trying to buy a much bigger chunk of P&G, the big bank just kept pinging IEX with tiny 100-share orders—or the bank vanished entirely. If the bank had simply sent IEX an order to buy 100,000 shares of P&G at $82.97, the investor would have purchased all the shares he wanted without driving up the price. Instead, the bank had pinged away and—by revealing its insistent, noisy demand—goosed up the price of P&G’s stock, at the expense of the investor whose interests the bank was meant to represent. Adding to the injury, the bank typically wound up with only a fraction of the stock its customer wanted to buy. “It opened up this whole new realm of activity that was crazy to me,” Brad told his audience. It was as if the big Wall Street banks were looking to see if IEX had a big seller to avoid trading with him. “I thought, Why the hell would anyone do this? All you do is increase the chances that an HFT will pick up your signal.”

They didn’t all behave this way: A couple of the big banks followed up their 100-share orders by forking over the meat of the buy order, and executed the trade their customer had asked them to execute. (The Royal Bank of Canada was by far the best behaved.) But, in general, the big Wall Street banks who had connected to IEX—a group that in the first week of trading excluded Bank of America and Goldman Sachs—connected disingenuously. It was as if they wished to appear to be interacting with the entire stock market, while actually they were trying to prevent any trades from happening outside their own dark pools.

Brad now explained to the investors, who were of course paying the price for this behavior, the reasons that the banks behaved as they did. The most obvious was to maximize the chance of executing the stock market orders given to them by investors in their own dark pools. The less honestly a bank looked for P&G stock outside of its own dark pool, the less likely it was to find it. This evasiveness explained the banks’ incredible ability to find, eventually, the other side of any trade inside their own dark pools. A bank that controlled less than 10 percent of all U.S. stock market orders was somehow able to satisfy more than half of its customers’ orders without ever leaving its own dark pool. Collectively, the banks had managed to move 38 percent of the entire U.S. stock market now traded inside their dark pools—and this is how they had done it. “It’s a façade that the market is interconnected,” said Brad.

The big Wall Street banks wanted to trade in their own dark pools not only because they made more money—on top of their commissions—by selling the right to HFT to exploit orders inside their dark pools. They wanted to trade their orders inside their dark pools to boost the volumes in those pools, for appearances’ sake. The statistics used to measure the performance of the dark pools, as well as the performance of the public stock exchanges, were more than a little screwy. A stock market was judged by the volume of trading that occurred on it, and the nature of that volume. It was widely believed, for example, that the bigger the average trade size on an exchange, the better the market was for an investor. (By requiring fewer trades to complete his purchase or sale, the exchange reduced the likelihood of revealing an investor’s intentions to high-frequency traders.) Every dark pool and every stock exchange found ways to cook its own flattering statistics; the art of torturing data may never have been so finely practiced. For example, to show that they were capable of hosting big trades, the exchanges published the number of “block” trades of more than 10,000 shares they facilitated. The New York Stock Exchange sent IEX a record of 26 small trades it had made after IEX had routed an order to it—and then published the result on the ticker tape as a
single
15,000-share block. The dark pools were even worse, as no one but the banks that ran them had a clear view of what happened inside them. The banks all published their own self-generated stats on their own dark pools: Every bank ranked itself #1. “It’s an entire industry that overglorifies data, because data is so easy to game, and the true data is so hard to obtain,” said Brad.

The banks did not merely manipulate the relevant statistics in their own dark pools; they often sought to undermine the stats of their competitors. That was another reason the banks were sending IEX orders in tiny 100-share lots: to lower the average trade size in a market that competed with the banks’ dark pools. A lower average trade size made IEX’s stats look bad—as if IEX were heavily populated by high-frequency traders. “When the customer goes to his broker and says, ‘What the hell happened? Why am I getting all these hundred-share fills?,’ his broker could easily say, ‘Well, I put the order on IEX,’ ” said Brad. The strategy cost their customers money, and the opportunity to buy and sell shares, but the customers wouldn’t know about it: All they would see was IEX’s average trade size falling.

Soon after it opened for trading, IEX published its own statistics—to describe, in a general way, what was happening in its market. “Since everyone is behaving in a particular way, you can’t see if anyone is behaving particularly badly,” said Brad. Now you could see. Despite the best efforts of Wall Street banks, the average size of IEX’s trades was by far the biggest of any stock exchange, public or private. More importantly, the trading that occurred was more random, unlinked to activity elsewhere in the stock market: For instance, the percentage of trades on IEX that followed the change in the price of some stock was half that of the other exchanges. (Investors were being picked off—as West Chester, Pennsylvania, money manager Rich Gates had been picked off—on exchanges that failed to move their standing orders quickly enough to keep up when stock prices changed.) Trades on IEX were also four times more likely than those elsewhere to trade at the midpoint between the current market bid and offer—which is to say, the price that most would agree was fair. Despite the reluctance of the big Wall Street banks to send them orders, the new exchange was already making the dark pools and public exchanges look bad, even by their own screwed-up standards.
‡‡

Brad’s biggest weakness, as a strategist, was his inability to imagine just how badly others might behave. He had expected that the big banks would resist sending orders to IEX. He hadn’t imagined they would use their customers’ stock market orders to actively try
at their customers’ expense
to sabotage an exchange created to help their customers. “You want to create a system where behaving correctly would be rewarded,” he concluded. “And the system has been doing the opposite. It’s rational for a broker to behave badly.”

The bad behavior played right into the hands of high-frequency traders in the most extraordinary ways. One day while watching the pictures Josh Blackburn had created for him, Brad saw a bank machine-gun IEX with 100-share lots and drive up a stock price 5 cents inside of 232 milliseconds. IEX’s delay—one-third of a millisecond—was of little use in disguising an investor’s stock market order if a broker insisted on broadcasting a big order he controlled over a far longer period: HFT picked up the signal and was getting out in front of it. Wondering if the broker was spreading news of his buy order elsewhere, Brad turned his attention to the consolidated tape of all the trades that occurred in the U.S. stock market. “I just wondered: Is this broker peppering the whole Street, or is it just us?” he told the room full of investors. “What we found blew our minds.”

For each trade on IEX, he’d spotted a nearly identical trade that had occurred at nearly the same time in some other market. “I noticed the odd trade sizes,” he said. He’d see a trade on IEX for 131 shares of, say, Procter & Gamble, and then he’d see, in some other market, exactly the same trade—131 shares of Procter & Gamble—within a few milliseconds, but at a slightly different price. It happened over and over again. He also noticed that, in each case, on one side of the trade was a broker who had rented out his pipes to a high-frequency trader.