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Introduction: windows on the world

Michael Lewis starts off by saying that the mental picture of stock market that most people away from Wall Street carry has changed dramatically over the last decade. He claims that his intent of writing this book is to draw a picture that is the new reality of U.S markets.

Hidden in plain sight

Only Michael Lewis can take a “laying a fiber optic cable” story and make it in to a page turner. This chapter talks about Dan Spivey and his efforts to connect Chicago and New York by a line that is as straightlinish as possible. The company formed by Dan Spivey, called “Spread Networks” began work in 2008 and was finally completed in 2010. To get the necessary approvals for constructing the network and selling this network to Wall Street people, Dan partnered with Jim Barksdale, David Barksdale and Larry Tabb. The whole point of the line was to create inside the public markets a private space, accessible only to those willing to pay the tens of millions of dollars in entry fees. Spread Networks first press release was titled,

Round-trip travel time from Chicago to New Jersey has been cut to 13 milliseconds.

Spread Networks set a goal of coming in at under 840 miles and beaten it; the line was 827 miles long. Spread Networks soon found that many Wall Street banks and hedge funds readily signed up for the line. This was an acknowledgement of the new reality of trading, “speed mattered” and it mattered a LOT.

Brad’s problem

clip_image002 Brad Katsuyama

This chapter introduces Brad Katsuyama, an RBC trader and the hero of the book. When Brad gets transferred to Wall Street office, he realizes that the culture on the street is very different from the conservative, team oriented culture he is used to, back in Canada. His real trouble began at the end of 2006, after RBC paid $100 million for a U.S. electronic stock market trading firm called Carlin Financial. There was a clash of cultures between RBC and Carlin. After the subprime crisis, he plans to leave Wall Street for good. However life had different plans for him. RBC severs the relationship with Carlin and Brad becomes the head for electronics trading. Initial thoughts of RBC team members who propose opening a dark pool makes no sense to him and he decides not to get in to dark pools game. As he tried to fix the “electronics trading biz”, he is perplexed with the whole system. He is clueless about certain aspects such as, Why was BATS going for inverse maker-taker model? Why was there a need for a maker-take model at all? The biggest problem that Brad faced when he starts using the “electronic trading systems” is that he never got fills at the prices that the screen showed. Every time he tried hitting the bid or lifting the offer, the market moved as soon as he sent the order. It looked like as though the market had read his mind and moved against him. At first he is not certain where the problem was. He thinks that there is a problem with Carlin’s systems, but soon figures out that traders at many other places were facing the same problem. He assembles a team of technologists and starts doing experiments with some very small size orders. This experimental reveals one surprising fact – When he sends an order to one exchange, the trade happens normally. However when the order is sent to multiple exchanges, only partial fills happen.

He becomes more certain that the stock market was no longer a market. It was a collection of small markets scattered across New Jersey and lower Manhattan. When bids and offers for shares sent to these places arrived at precisely the same moment, the markets acted as markets should. If they arrived even a millisecond apart, the market vanished, and all bets were off. Brad knew that he was being front-run—that some other trader was, in effect, noticing his demand for stock on one exchange and buying it on others in anticipation of selling it to him at a higher price. He’d identified a suspect: high-frequency traders.

 

Ronan’s problem

clip_image002[5] Ronan Ryan

This chapter is about Ronan Ryan who starts his career in a telecommunications company and soon lands up at Radianz where he is in charge of selling co-lo services. This is where Ronan learns about the importance of latency to every Wall Street firm. Radianz data center at Jersey city could bring down the latency from 43 milliseconds to 3.8 milliseconds, for a firm in Chicago. By early 2008 Ronan was spending a lot of his time abroad, helping high-frequency traders exploit the Americanization of foreign stock markets. In 2009 he is hired by Brad to work RBC as head of HFT trading. Brad educates Ronan about basic market concepts whereas Ronan imparts the tech stuff that he has learnt through his career. Ronan explains the reason for Brad’s successful order execution at BATS and failure at other exchanges. He also explains the reason for BATS strange policy of paying to take liquidity – BATS orders were leading indicators of what was about to happen at other exchanges. HFT players would quickly buy at other exchanges before everyone else and then sell it to the person who had no latency advantage. Brokers were also incentivized to send order flow to certain exchanges as they received payment and kickbacks

The team at RBC slowly realizes that the only way their orders would not get ripped off by HFT players is to send the orders at approximately same time to all the exchanges so that nobody could game it. For this to happen, they had to build their own network. By the end of 2010, Brad and Ronan met with roughly five hundred professional stock market investors who controlled, among them, many trillions of dollars in assets. They never created a PowerPoint; they never did anything more formal than sit down and tell people everything they knew in plain English. Then there was flash crash and Brad’s ideas were in demand.

Another incident happened in September, 2010; a sleepy stock exchange called the CBSX switched to inverted maker-taker model and its trading volume skyrocketed. Ronan and Brad put their heads together and figured out that this was another classic case of ripping off money from investors.Spread Networks had flipped its switch and turned itself on just two weeks earlier. CBSX then inverted its pricing. By inverting its pricing—by paying brokers to execute customers’ trades for which they would normally be charged a fee—the exchange enticed the brokers to send their customers’ orders to the CBSX so that they might be front-run back to New Jersey by high-frequency traders using Spread Networks. The information that high-frequency traders gleaned from trading with investors in Chicago they could use back in the markets in New Jersey. It was now very much worth it to them to pay the CBSX to “make” liquidity. It was exactly the game they had played on BATS, of enticing brokers to reveal their customers’ intentions so that they might exploit them elsewhere. But racing a customer order from Weehawken to other points in New Jersey was hard compared to racing it from Chicago on Spread’s new line.

Tracking the predator

clip_image003 John Schwall

Brad’s next hire was John Schwall and this chapter talks briefly about his background. Schwall started out his work at Bank of America. After Bank Am took over Merrill Lynch during financial crisis, Schwall decided to move to RBC. Schwall knew a lot about RegNMS and could clearly understand the way HFT players played the SIP game. NBBO calculation at the centralized server was slower than that of HFT players and this gave to massive arb opportunities for all those who had faster connectivity. Schwall goes through a ton of LinkedIn profiles and figures out that all the dark pools have HFT players as their clientele. By diverting the client’s order flow in to their dark pools, the brokers were ripping of their client. In return HFT players paid a ton of money for doing this.

Putting a face on HFT

clip_image005 Sergey Aleynikov

This chapter narrates the story of Sergey Aleynikov, who is given 8 years of imprisonment for stealing HFT strategy code from Goldman. Much before this book went to the press, there was an article in Vanity Fair, titled, “Did Goldman Sachs Overstep in Criminally Charging Its Ex-Programmer ?”, that has most of the stuff from this chapter. Michael Lewis pieces together Sergey’s childhood, his career at a telecommunications firm , his programming job Goldman and says

Thus the only Goldman Sachs employee arrested by the FBI in the aftermath of a financial crisis Goldman had done so much to fuel was the employee Goldman asked the FBI to arrest.

How to take billions from Wall Street

This chapter talks about how Brad quits his job at RBC, assembles a team to build an exchange, IEX(Investors Exchange), whose philosophy was to save the investor from getting ripped off by financial intermediaries. Ironically, in his fund raising efforts, he had to feign that he was greedy and only then he could manage to hold potential investors attention. By mid-December he’d sewn up $9.4 million from nine different big money managers. Six months later he’d raise $15 million from four new investors. The money Brad needed that he didn’t get he kicked in himself: By January 1, 2013, he’d put his life savings on the line. At the same time, he went looking for people: software developers and hardware engineers. Brad hired Don Bollerman who had spent 7 years at NASDAQ and had seen it all – a sleepy exchange that turned in to HFT player’s favorite ground.

IEX goal was not to exterminate the hyenas and the vultures but, more subtly, to eliminate the opportunity for the kill. To do that, they needed to figure out the ways that the financial ecosystem favored predators over their prey. Brad hired Dan Aisen and Francis Chung who were ace puzzle crackers in the literal sense of it. Brad also hired Constantine Sokoloff(Matching engine specialist from NASDAQ) to mentor the puzzle masters. The team at IEX got to work and started by analyzing various order types. The more analyzed the order types, they found that almost every fancy order type was meant to rip off investors. The team created a taxonomy of predatory behavior in the stock market. The first they called “electronic front-running”—seeing an investor trying to do something in one place and racing him to the next. (What had happened to Brad, when he traded at RBC.) The second they called “rebate arbitrage”—using the new complexity to game the seizing of whatever kickbacks the exchange offered without actually providing the liquidity that the kickback was presumably meant to entice. The third, and probably by far the most widespread, they called “slow market arbitrage.” This occurred when a high-frequency trader was able to see the price of a stock change on one exchange, and pick off orders sitting on other exchanges, before the exchanges were able to react.

The team at IEX wanted to create an exchange where all the predatory behavior could be attacked. They came up with a brilliant idea. Have their matching engine very far away from the place the broker’s point of presence. They zeroed on making a 350 millisecond delay between the point of presence and matching engine and this they achieved by merely coiling the wire innumerable times (another simple yet damn effective way to INCREASE latency). At the same time, IEX laid its infrastructure in such that it was the fastest to reach other exchanges.

Despite this wonderful idea and infrastructure, they faced one big problem – How should they generate order flow for the exchange by playing a fair game?

An army of one

The last section of the book goes through the struggles that IEX faces in building order flow. One big success comes their way when they manage to sign up Goldman Sachs and in fact on one of trading days, their volume exceeds that of AMEX.

 

imageWhat has happened to IEX since Flash Boys success?

IEX has grown rapidly in 2014. Its daily trading volume has tripled since the first quarter and participant volume has exceeded 100 million shares per day. It is seeking regulatory approval to become a full-fledged stock exchange. If IEX manages to grow its trading volume and its business, it can be a great transformation to the U.S markets. 

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