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To most humans, the following sentence makes little sense: “Symbol ticker = ‘MAN’ country = ‘US’ cusip = ‘56418H100’ isin = ‘US56418H1005’ / symbol changesign = ‘+’ caltype = ‘percent’ 2.5 / change to price value = ‘44.52’.”
But to a new breed of computers specially programmed to trade automatically on the latest news stories, it could be enough to make a huge sum of money.
Hedge funds and bank trading desks are pouring unprecedented sums into such computers to find faster and more inventive ways to outsmart their rivals.
News has always affected market prices. And there are already programs that track news headlines and alert traders if certain market-sensitive terms or words appear frequently. “Hurricane” could signal a shift to sell insurance stocks. “Drought” could affect wheat prices.
But a recent breakthrough is the ability to use computers to analyse years’ worth of news stories to see how certain headlines affected market movements and use those patterns to program computers to trade on the latest news developments.
Computers are now being used to generate news stories about company earnings results or economic statistics as they are released. And this almost instantaneous information forms a direct feed into other computers, which trade on the news.
The result is a boom in demand from news and information providers such as Reuters, Bloomberg and Thomson Financial for “machine readable news”, which is written in a computer-friendly language of strings of words and numbers without sentences. Computers can trade on such news within milliseconds of receiving it – much faster than a human trader.
“One of the big consumers of news now is a computer,” says Matthew Burkley, senior vice-president of strategy at Thomson Financial. “This area has turned out to be broader than we thought. Instead of being limited to a marginal number of our clients, the demand for news which is readable by a computer is very widely spread.”
Reuters reports a similar demand. “There is real interest in moving the process of interpreting news from the humans to the machines,” says Kirsti Suutari, global business manager of algorithmic trading at Reuters. “More of our customers are finding ways to use news content to make money. This is where news is exciting.”
The human eye is far from redundant, however. “News events are extremely subjective,” says Will Sterling, head of institutional electronic trading at UBS. “Our general approach has been to blend the automation . . . with a degree of human oversight. It’s better to take an extra few seconds to be sure.”
Additional reporting by Michael Mackenzie in New York