" The Bayesian Invisible Hand
… free-market capitalism and Bayes’ theorem come out of something of the same intellectual tradition. Adam Smith and Thomas Bayes were contemporaries, and both were educated in Scotland and were heavily influenced by the philosopher David Hume. Smith’s 'Invisible hand' might be thought of as a Bayesian process, in which prices are gradually updated in response to changes in supply and demand, eventually reaching some equilibrium. Or, Bayesian reasoning might be thought of as an 'invisible hand' wherein we gradually update and improve our beliefs as we debate our ideas, sometimes placing bets on them when we can’t agree. Both are consensus-seeking processes that take advantage of the wisdom of crowds.
It might follow, then, that markets are an especially good way to make predictions. That’s really what the stock market is: a series of predictions about the future earnings and dividends of a company. My view is that this notion is 'mostly' right 'most' of the time. I advocate the use of betting markets for forecasting economic variables like GDP, for instance. One might expect these markets to improve predictions for the simple reason that they force us to put our money where our mouth is, and create an incentive for our forecasts to be accurate.
Another viewpoint, the efficient-market hypothesis, makes this point much more forcefully: it holds that it is 'impossible' under certain conditions to outpredict markets. This view, which was the orthodoxy in economics departments for several decades, has become unpopular given the recent bubbles and busts in the market, some of which seemed predictable after the fact. But, the theory is more robust than you might think.
And yet, a central premise of this book is that we must accept the fallibility of our judgment if we want to come to more accurate predictions. To the extent that markets are reflections of our collective judgment, they are fallible too. In fact, a market that makes perfect predictions is a logical impossibility. "
― Nate Silver , The Signal and the Noise: Why So Many Predictions Fail—But Some Don't