Overreaction in Expectations: Evidence and Theory
通过大规模随机实验，研究人们在预测稳定随机过程时对最新观测值的过度反应，发现过程越不持久、预测期越长，过度反应越强，并构建了考虑过去信息处理成本的模型来解释这些现象。
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 report three facts. First, forecasts display significant overreaction to the most recent observation. Second, overreaction is stronger for less persistent processes. Third, overreaction is also stronger for longer forecast horizons. We develop a tractable model of expectations formation with costly processing of past information, which closely fits the empirical facts. We also perform additional experiments to test the mechanism of the model.