“Stock Return Variability, Forecast Revisions, and Investors’ Learning ” by Ruojun Rosalin WU
Ruojun Rosalin WU
University of California, San Diego
This paper studies the impact of parameter uncertainty and investors' learning on the variance components of stock returns. The full information assumption in Campbell and Shiller (1988) and other papers is relaxed. Learning under imperfect information adds extra variability to investors' forecast revisions, and thus increases the level of the variance components as well. Two learning schemes are examined: a naive learning scheme where investors merely rely on historical data in real-time; and a sophisticated learning scheme where they incorporate their prior beliefs with observed data by Bayes rule. Empirical results under full information and under different learning schemes are reported. As expected, I find learning adds variability to both the risk premium component and the cash flow component. Moreover, as opposed to conventional wisdom, cash flow news contributes more than risk premium news under learning. Potential explanations are proposed.