Yueran Ma, University of Chicago
ABSTRACT:We investigate biases in expectations across different settings through a large-scale randomized experiment where participants forecast stable stochastic processes. The experiment allows us to control forecasters’ information sets as well as the data generating process, so we can cleanly measure biases in beliefs. We find that forecasts display significant overreaction to the most recent observation. Moreover, overreaction is especially pronounced for less persistent processes and longer forecast horizons. We also find that commonly-used expectations models do not easily account for these variations in the degree of overreaction across different settings. To explain the observed patterns of overreaction, we develop a tractable model of expectations formation with costly information processing. Our model closely fits the empirical findings and generates additional predictions that we confirm in the data.