How machine learning is revolutionising market intelligence
THE THAMES seems to draw people who work on intelligence-gathering. The spooks of MI6 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.
Arkera’s founders, Nav Gupta and Vinit Sahni, both have a background in “macro” hedge funds, the sort that like to bet on big moves in currencies and bond and stock prices ahead of predicted changes in the political climate. The firm’s clients might want a steer on the political risks affecting public finances in Brazil, or to gauge the social pressures that could arise as a consequence of an austerity programme in Egypt. It applies machine learning to find market intelligence and make it usable.
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For many people, the use of such technologies in finance is the stuff of dystopian science fiction, of machines running amok. But once you look at market intelligence through the eyes of computer science, it provokes disquieting thoughts of a different kind. It gives a sense of just how creaky and haphazard the old-school, analogue business of intelligence-gathering has been.
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 Chicago Tribune, published a pioneering paper in this vein. Cowles sorted stockmarket commentary by William Peter Hamilton, a long-ruling editor of the Wall Street Journal, into three buckets (bullish, bearish or doubtful) and attached an action to each (buy, sell or avoid). He concluded that investors would have done better simply to buy and hold the leading st