Switch on any business TV channel and you’ll be bombarded with scrolling quotes and jagged graphs. It’s all very well if you like to watch that kind of thing, but the flow of financial data can lull us into an illusion: that we understand what is happening in the economy at the moment that it happens. We don’t.
Take those share prices: each one is a miniature forecast of future profits for the company in question. They may or may not be good forecasts, but what they are not is a measure of real economic activity today: the price of shares in BP reflects today’s supply and demand for shares in BP, not today’s supply and demand for petrol.
Away from the financial markets things are even more obscure. We have indices of house prices, measuring how they have changed over the past month, quarter or year. But the houses bought and sold last month are not the same as the houses bought and sold the month before: are they more expensive because housing itself is more expensive, or because fewer studio flats and more penthouses were sold?
As for measures of retail sales or unemployment – or, above all, gross domestic product, the summary measure of economic activity – this all arrives with a delay, sometimes of years. We must judge conditions today by looking in the rear-view mirror. Regardless of what Bloomberg TV may tell us, much of today’s economic conditions will only be understood in 2012 or 2013.
All this may now be changing, because it is now possible to gather, in real time, information flowing through computers. For instance, a curious piece of research was published last year by three computer scientists, Johan Bollen, Huina Mao and Xiao-Jun Zeng. The paper, “Twitter mood predicts the stock market”, has a self-explanatory title. The researchers searched Twitter for expressions of mood such as “I’m feeling happy”, and discovered that expressions of calm predicted movements in the Dow a few days in advance.
If this effect is real, it will almost certainly be exploited by automated trading strategies. But other computer-based indicators may prove more useful and more lasting, because they are tied not to mood but to actual activity.
The most obvious, perhaps, is the information that we give Google every time we make a search. Much of what Google knows about us as individuals – based on our use of Gmail, or the physical locations from which we access the search engine – is information that it will keep to itself. But the search engine giant does release data on search volumes: how many people in the UK are searching for “estate agents”, for instance, or for “jobs”, “JSA” (Jobseeker’s Allowance) or “unemployed”? Two analysts at the Bank of England, Nick McLaren and Rachana Shanbhogue, have been examining whether the volume of these search terms tells us anything useful about economic activity. It seems that it does. For example, searches for “JSA” are correlated with the official unemployment data gathered by the Labour Force Survey – but are available a month earlier. For the Bank’s Monetary Policy Committee this is a useful peek at the state of the economy at the moment that it makes its decisions.
The most striking research of all, for my money, was published by Nikolaos Askitas and Klaus Zimmermann as an IZA working paper in February. Askitas and Zimmermann used data from a new system designed to collect road tolls: most heavy vehicles in Germany have GPS-based technology that tracks their movements and levies the right toll. Unsurprisingly, their “toll index” provides a very effective “nowcast” of German industrial production. Perhaps we can look forward to the day when real economic data are collected and disseminated almost as easily as the charts on business television.
Tim Harford’s new book is ‘Adapt: Why Success Always Starts With Failure’ (Little, Brown)