Last year, the stock market suffered what's now called "the flash crash." The prices of shares in the US fell by 6 percent in 5 minutes. How could it happen? Algorithms. The algorithms that now control the stock market.
In the London Review of Books, Donald MacKenzie has a fascinating article that introduces us to the trading algorithms that control how trading takes place in global markets. The thing that's really interesting is that these algorithms are starting to feed on each other, sniffing out each others intentions and trying to trick each other into making buys or sells that are favorable to the companies that unleashed the algorithms in the first place. Are we heading to a point where our economy is in the hands of bots?
MacKenzie begins by introducing us to simple algorithms mostly designed to buy and sell in ways that perturb pricing the least. Algorithms like the VWAP allow companies to buy massive volumes of shares in little chunks so that other traders aren't tipped off to the buys, and can't horn in on the action with their own buys that drive up the price.
Far more controversial are algorithms that effectively prey on other algorithms. Some algorithms, for example, can detect the electronic signature of a big VWAP, a process called ‘algo-sniffing'. This can earn its owner substantial sums: if the VWAP is programmed to buy a particular corporation's shares, the algo-sniffing program will buy those shares faster than the VWAP, then sell them to it at a profit. Algo-sniffing often makes users of VWAPs and other execution algorithms furious: they condemn it as unfair, and there is a growing business in adding ‘anti-gaming' features to execution algorithms to make it harder to detect and exploit them . . .
Whatever view one takes on its ethics, algo-sniffing is indisputably legal. More dubious in that respect is a set of strategies that seek deliberately to fool other algorithms. An example is ‘layering' or ‘spoofing'. A spoofer might, for instance, buy a block of shares and then issue a large number of buy orders for the same shares at prices just fractions below the current market price. Other algorithms and human traders would then see far more orders to buy the shares in question than orders to sell them, and be likely to conclude that their price was going to rise. They might then buy the shares themselves, causing the price to rise. When it did so, the spoofer would cancel its buy orders and sell the shares it held at a profit. It's very hard to determine just how much of this kind of thing goes on, but it certainly happens. In October 2008, for example, the London Stock Exchange imposed a £35,000 penalty on a firm (its name has not been disclosed) for spoofing.
In a tip of the hat to science fiction scenarios of our algorithm overlords, he adds:
Tales of computers out of control are a well-worn fictional theme, so it's important to emphasise that it is not at all clear that automated trading is any more dangerous than the human trading it is replacing. If the danger had increased, one way it would manifest itself is in higher volatility of the prices of shares traded algorithmically. The evidence on that is not conclusive – like-for-like comparison is obviously hard, and the academic literature on automated trading is still small – but data we do have suggest, if anything, that automated trading reduces volatility. For example, statistical arbitrage algorithms that buy when prices fall and sell when they rise can normally be expected to dampen volatility.
MacKenzie goes on to explain exactly how algos caused the Flash Crash, but we're still left with a sense that in the end we're witnessing the beginning of a more rational and stable market (though a fairly complex one where trades happen at the subsecond level).
Basically we're entering the era of the Machines controlling everything. And it sounds like it's all to the good, just like in Isaac Asimov's vision of the world economy being stabilized by a A.I.s in the short story "The Evitable Conflict." As long as the algos don't go to war with each other and cause something even more difficult to diagnose than the Flash Crash.
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