How to Make Money in Microseconds

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When two American financial economists, Joel Hasbrouck and Gideon Saar, did this a couple of years ago, they found strange periodicities and spasms. The most striking periodicity involves large peaks of activity separated by almost exactly 1000 milliseconds: they occur 10-30 milliseconds after the ‘tick’ of each second. The spasms, in contrast, seem to be governed not directly by clock time but by an event: the execution of a buy or sell order, the cancellation of an order, or the arrival of a new order. Average activity levels in the first millisecond after such an event are around 300 times higher than normal. There are lengthy periods – lengthy, that’s to say, on a scale measured in milliseconds – in which little or nothing happens, punctuated by spasms of thousands of orders for a corporation’s shares and cancellations of orders. These spasms seem to begin abruptly, last a minute or two, then end just as abruptly.

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.

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The goal of execution algorithms is to avoid losing money while trading. The other major classes of algorithm are designed to make money by trading, and it is their operation that gives rise to the spasms found by Hasbrouck and Saar. ‘Electronic market-making’ algorithms replicate what human market makers have always tried to do – continuously post a price at which they will sell a corporation’s shares and a lower price at which they will buy them, in the hope of earning the ‘spread’ between the two prices – but they revise prices as market conditions change far faster than any human being can. Their doing so is almost certainly the main component of the flood of orders and cancellations that follows even minor changes in supply and demand.

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The goal of execution algorithms is to avoid losing money while trading. The other major classes of algorithm are designed to make money by trading, and it is their operation that gives rise to the spasms found by Hasbrouck and Saar. ‘Electronic market-making’ algorithms replicate what human market makers have always tried to do – continuously post a price at which they will sell a corporation’s shares and a lower price at which they will buy them, in the hope of earning the ‘spread’ between the two prices – but they revise prices as market conditions change far faster than any human being can. Their doing so is almost certainly the main component of the flood of orders and cancellations that follows even minor changes in supply and demand.