The Persistence of Miscalibration
57 Pages Posted: 28 Jul 2020 Last revised: 28 Oct 2020
Date Written: October 20, 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 study how their beliefs evolve dynamically. While CFOs' return forecasts are systematically unbiased, their confidence intervals are far too narrow, implying significant miscalibration. We find that when return realizations fall outside of ex-ante confidence intervals, CFOs' subsequent confidence intervals widen considerably. These results are consistent with a model of Bayesian learning which suggests that the evolution of beliefs should be impacted by return realizations. However, the magnitude of the updating is dampened by the strong conviction in beliefs inherent in the initial miscalibration 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