Making Money in Microseconds

Via Ritholtz, Donald McKenzie looks at computerized trading and high frequency trading algorithms:

Little of this has to do directly with human action. None of us can react to an event in a millisecond: the fastest we can achieve is around 140 milliseconds, and that’s only for the simplest stimulus, a sudden sound. The periodicities and spasms found by Hasbrouck and Saar are the traces of an epochal shift. As recently as 20 years ago, the heart of most financial markets was a trading floor on which human beings did deals with each other face to face. The ‘open outcry’ trading pits at the Chicago Mercantile Exchange, for example, were often a mêlée of hundreds of sweating, shouting, gesticulating bodies. Now, the heart of many markets (at least in standard products such as shares) is an air-conditioned warehouse full of computers supervised by only a handful of maintenance staff.
The deals that used to be struck on trading floors now take place via ‘matching engines’, computer systems that process buy and sell orders and execute a trade if they find a buy order and a sell order that match. The matching engines of the New York Stock Exchange, for example, aren’t in the exchange’s century-old Broad Street headquarters with its Corinthian columns and sculptures, but in a giant new 400,000-square-foot plain-brick data centre in Mahwah, New Jersey, 30 miles from downtown Manhattan. Nobody minds you taking photos of the Broad Street building’s striking neoclassical façade, but try photographing the Mahwah data centre and you’ll find the police quickly taking an interest: it’s classed as part of the critical infrastructure of the United States.
Human beings can, and still do, send orders from their computers to the matching engines, but this accounts for less than half of all US share trading. The remainder is algorithmic: it results from share-trading computer programs. Some of these programs are used by big institutions such as mutual funds, pension funds and insurance companies, or by brokers acting on their behalf. The drawback of being big is that when you try to buy or sell a large block of shares, the order typically can’t be executed straightaway (if it’s a large order to buy, for example, it will usually exceed the number of sell orders in the matching engine that are close to the current market price), and if traders spot a large order that has been only partly executed they will change their own orders and their price quotes in order to exploit the knowledge. The result is what market participants call ‘slippage’: prices rise as you try to buy, and fall as you try to sell.

It is legalized looting to allow market makers to take advantage of the data to take little amounts of money on vast numbers of shares, but try getting that practice banned.  It seems detrimental to this country that our brightest minds are being used to come up with trading algorithms to shave tiny amounts of money from retail traders and pension funds and deposit that in investment banker bonuses.