A paper has been going around that describes a startling new world of high-velocity computerized trading that causes volume and volatility to soar and costs ordinary investors billions of dollars.
The paper, Toxic Equity Trading On Wall Street, appears to have been published late last year by Sal Arnuk and Joseph Saluzzi from a firm called Themis Trading. (One word of caution: We have not yet verified a single assertion made in the paper, and we had not heard of Themis Trading. We would be grateful if those of you with insight into this would help us understand the real facts here.)
The paper is embedded below (you can also download it at Themis’s web site). Here, in brief, is the world it describes:
Many trading orders these days are executed by computers. Like human traders, the computers break big orders into small chunks (say, 100 or 500 shares) and then match them with orders on electronic stock exchanges. The reason the orders are broken into chunks is so they won’t move the market too much. Stock trading is relatively illiquid, and big orders can drive the price of a stock sharply up or down. Since the dawn of Wall Street time, clever traders have tried to hide the amount of stock they ultimately want to buy or sell to avoid having their own orders move the market sharply against them.
In recent years, such “algorithmic” electronic trading execution has grown in popularity, and a number of electronic trading strategies have sprung up to exploit it.
In one of these strategies, called “liquidity rebate trading,” a program analyses the incoming order flow on an electronic exchange to try to spot a big institutional order that is just hitting the market (apparently this is relatively easy to do). The program then front-runs the order by modestly outbidding the institution for the stock and then turning around and selling it to the institution at a higher price than the institution would have otherwise paid.
Front-running is an age-old cheating technique: A trading firm gets a big order from a client and, before it executes it, buys some of the same stock for itself. Front-running is, in fact, what many Wall Street insiders thought Bernie Madoff was doing before they discovered he was running a Ponzi scheme. This new form of electronic front-running, however, is to traditional front-running as a Playstation first-person shooter 3D game is to a game of bridge.
The goal here, moreover, is not to make money by scalping ticks (buying a stock at $20.00 and selling it to the institution for $20.01). It is to get a “rebate” offered by the electronic exchange for order flow. To court order flow, exchanges offer 1/4-cent per share rebates to market-makers who actively increase trading volume. If you buy a stock for $20.00 and sell it for $20.00 and get a 1/4-cent per share rebate per share…and you buy and sell millions of shares…you can make a lot of money.
And that money drives up the cost of execution for traders who are buying the stock to hold it for more than a few milliseconds.
Here’s how the Themis paper describes this scheme:
To attract volume, all market centres (the exchanges and the ECNs) now offer rebates of about ¼ penny a share to broker dealers who post orders. It can be a buy or sell order, as long as it is offering to do something on the exchange or ECN in question. If the order is filled, the market centre pays the broker dealer a rebate and charges a larger amount to the broker dealer who took liquidity away from the market. This has led to trading strategies solely designed to obtain the liquidity rebate.
In this case, our institutional investor is willing to buy shares in a price range of $20.00 to $20.05. The algo [trading program] gets hit, and buys 100 shares at $20.00. Next, it shows it wants to buy 500 shares. It gets hit on that, and buys 500 more shares. Based on that information, a rebate trading computer program can spot the institution as having an algo order. Then, the rebate trading computer goes ahead of the algo by a penny, placing a bid to buy 100 shares at $20.01. Whoever had been selling to the institutional investor at $20.00 is likely to sell to the rebate trading computer at $20.01. That happens, and the rebate trading computer is now long 100 shares at $20.01 and has collected a rebate of ¼ penny a share. Then, the computer immediately turns around and offers to sell its 100 shares at $20.01. Chances are that the institutional algo will take them.
The rebate trading computer makes no money on the shares, but collects another ¼ penny for making the second offer. Net, net, the rebate trading computer makes a ½ penny per share, and has caused the institutional investor to pay a penny higher per share.
And here’s another electronic strategy that takes advantage of “Automated Market Making” (There’s nothing wrong with market-making. But this particular version isn’t market-making. It’s just making money for the “market maker”):
Automated market maker (AMM) firms run trading programs that ostensibly provide liquidity to the NYSE, NASDAQ and ECNs. AMMs are supposed to function like computerized specialists or market makers, stepping in to provide inside buy and sells, to make it easier for retail and institutional investors to trade.
AMMs, however, often work counter to real investors. AMMs have the ability to “ping” stocks to identify reserve book orders. In pinging, an AMM issues an order ultra fast, and if nothing happens, it cancels it. But if it is successful, the AMM learns a tremendous amount of hidden information that it can use to its advantage.
To show how this works, this time our institutional trader has input discretion into the algo to buy shares up to $20.03, but nobody in the outside world knows that. First, the AMM spots the institution as an algo order. Next, the AMM starts to ping the algo. The AMM offers 100 shares at $20.05. Nothing happens, and it immediately cancels. It offers $20.04. Nothing happens, and it immediately cancels.
Then it offers $20.03 – and the institutional algo buys. Now, the AMM knows it has found a reserve book buyer willing to pay up to $20.03. The AMM quickly goes back to a penny above the institution’s original $20.00 bid, buys more shares at $20.01 before the institutional algo can, and then sell those shares to the institution at $20.03.
The Themis paper lays out several other strategies like this, all of which are designed to exploit the speed with which electronic trading programs can place, cancel, and execute orders, as well as analyse the incoming order flow. One key element of some of these strategies is having one’s computers “co-located” at the NYSE to be closer to the action and thus reduce execution time. (In the time it takes for an electronic signal to travel from, say, Boston, to the floor, a server actually located near the floor can gain an edge). The NYSE allows firms to co-locate servers…and charges them an arm and a leg to do it.
(To give you an idea of how much time matters, one of Themis’s proposed “solutions” to this problem is for the SEC to require that electronic orders be good for at least one second. This will apparently stop the millisecond-then-cancelled orders used to fish out information above).
So what’s the problem?
The problem is that this sort of trading increases execution costs for traders who don’t have access to it–which includes most traders. Over time, with trillions of shares traded, even a penny a share adds up.
Here’s the Themis paper. Again, we would be grateful if those with insight into this would weigh in in the comments or email ([email protected]) and help us clarify the facts.
UPDATE 2: Joe Weisenthal thinks this is all much ado about nothing.
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