Wall Street Wises Up to the Folly of Forecasting
One economist says what should be obvious: Making predictions only means you will be either wrong or lucky.
Bloomberg, December 15, 2017
It is that time of year, when the financial industry engages in its annual ritual of making forecasts, which is usually little more than the prelude to looking foolish. Titles like “Outlook for 2018, “What to expect in the new year,” or some variation thereof litter the landscape. Over the years, it has been my distinct privilege (and truth be told, pleasure) to point out how silly this process is.
On this topic, I am thrilled to welcome some new company. Some of the bigger firms and mainstream economists have recognized that this sort of game is no longer worth the effort. Perhaps no one is better placed to recognize the absurdity of the annual forecasting binge than UBS global chief economist, Paul Donovan. In a note to clients this week, he exhorts his economic brethren to stop the ridiculous prognostications, and instead, provide meaningful value to clients. In a note titled “What can we forecast about next year?,” Donovan bluntly states:
Economists should not make forecasts.
December always has a rash of economic forecasts for the year ahead. The reception areas at CNBC and Bloomberg TV are crowded with mobs of economists fighting to get their forecasts on air. But there are big problems with making precise economic forecasts.
Economic models are not precise. Models use lots of assumptions. Those assumptions may not turn out to be true. Models give a range of possibilities rather than a single, certain number. Economists know and understand these issues. However, the world of hashtag economics does not allow for all of this to be explained. It is difficult to warn about possibility ranges and underlying assumptions in 280 characters. This is why economists should not use Twitter (follow me @PDonovan_Econ). Economic views often give a false sense of precision, because the reporting of economics is simplified and shortened. That precision simply is not there, in our view.
There is a lot of wisdom in those three paragraphs; let’s unpack them to see if we can identify what there is to learn.
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