The reasons experts are bad at forecasting
I recently pointed out an errant recession forecast made a year ago by a person who has been more or less been predicting an economic slump since 2011. I was hoping to spark a discussion of why forecasts are so counterproductive, and why no one should make investments based on them. Instead, a discussion of passive bulls mocking active bears broke out. This was not what I intended. I made a few assumptions, perhaps erroneously: I thought that the idea I have been harping on for more than a decade had been thoroughly beaten to death, and perhaps it was time to give it a rest. By now, I figured, everyone surely understands that market forecasts — indeed almost all forecasts — are folly.
Alas, my assumption was proven wrong. Thus, we go once more unto the breach, to remind readers what we know about forecasts and predictions, and why they are so rarely right:
Not everything is a forecast: This is especially true in terms of markets and the economy, and so a reasonable definition of a forecast is as follows: It pertains to a specific asset or asset class and/or economic data series, at a given price or level and a specific time. It also must be disprovable. Making a statement that can’t be proven or disproven is not a forecast; it’s a theoretical academic debate.
Consider the following statements: “Stocks tend to go higher” or “recessions are cyclical.” These are not forecasts because they lack specifics. The statement, “The Dow will hit 25,000 by the second quarter of 2018,” on the other hand, will either be proven right or wrong.
We are very bad at forecasting: Examples are everywhere: Economic forecasts, earnings estimates, market forecasts, expectations of future technologies, not to mention election predictions. The data overwhelmingly show that as a species, we are simply awful at this.
We are even worse at predicting our own behavior: Whenever you see someone forecasting their own behavior, what you are getting is a read of their emotional state. Whether it’s holiday shopping, company hiring plans, voting intentions — people say what they are feeling at the time the question is posed, and it is not predictive of what they are actually going to do.
Technology is tricky: We are particularly bad at making predictions about technology. This goes back a long ways, to claims in the late 19th century that no one would ever need or want a telephone, or that cars would never replace horses and buggies. And folks are still at it, despite their inability to get it right. Check Microsoft Inc. chief Steve Ballmer’s 2007 prediction that Apple Inc.’s iPhone would never catch on.
Random luck: People tend to overfocus on the outcome rather than paying closer attention to the process. This leads to an overemphasis on guesses that were merely lucky and therefore cannot be replicated.
Asymmetric risk: Bad forecasts are quickly forgotten, while those who make accurate predictions that are nothing more than the result of luck or random chance get elevated to stardom. This is the entire underpinning of why people make forecasts — and especially radical scary ones — in the first place. The potential rewards for being right can be significant, while being wrong has little downside.