The Persistence of Miscalibration
57 Pages Posted: 28 Jul 2020 Last revised: 12 Aug 2020
Date Written: July 28, 2020
Using 14,800 forecasts of one-year S&P 500 returns made by Chief Financial Officers over a 12-year period, we track the individual executives who provide multiple forecasts to evaluate how they adapt and recalibrate in response to return realizations. We present a simple model of Bayesian learning which suggests that the evolution of beliefs should be impacted by return realizations, but that stronger priors yield a sluggish response. While CFOs' forecasts are unbiased, their confidence intervals are far too narrow, implying a very strong conviction in their beliefs. Consistent with Bayesian learning, we find that when return realizations fall outside of ex-ante confidence intervals, CFOs' subsequent confidence intervals become significantly wider. However, the magnitude of the updating is apparently dampened by the tightness of prior beliefs and, as a result, miscalibration persists.
Keywords: Learning, Information, Behavioral economics, Volatility forecasts, Market forecasts, Behavioral finance, Bayesian updating, Expectations
JEL Classification: G41, G30, D02, D22, D83, D84
Suggested Citation: Suggested Citation