How machine learning is revolutionising market intelligence

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Some see the use of machine learning in finance as dystopian. But analogue methods are ripe for replacement

are housed in a funky-looking building overlooking the river. Two miles downstream, in a shared office space near Blackfriars Bridge, lives Arkera, a firm that uses machine-learning technology to sort intelligence from newspapers, websites and other public sources for emerging-market investors. Its location is happenstance. London has the right time zone, between the Americas and Asia. It is a nice place to live. The Thames happens to run through it.

Analysts have used text data to try to predict changes in asset prices for a century or more. In 1933 Alfred Cowles, an economist whose grandfather had founded the, published a pioneering paper in this vein. Cowles sorted stockmarket commentary by William Peter Hamilton, a long-ruling editor of the, into three buckets and attached an action to each .

It is tempting to focus on the black-box elements of all this: the language software that “reads” the source text and the algorithms that use the data to make predictions. But this is like judging a hi-fi system by its speakers. A lot of the important work comes earlier in the process. Arkera, for instance, spends a lot of effort finding all the relevant text and “cleaning” it—stripping it of extraneous junk, such as captions and disclaimers. “A good signal is crucial,” says Mr Gupta.

 

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