We propose a structural model of constant gain learning about future earnings growth that incorporates preferences for the timing of cash flows. As implied by the model, a cross-sectional decomposition using survey forecasts shows that high price-earnings ratios are accounted for by both low expected returns and overly high expected earnings growth. The model quantitatively matches a number of asset pricing moments, as learning about growth interacts strongly with the preference for the timing of cash flows, and provides insights on the roles of risk premia and mispricing in the cross-section of stocks. The magnitudes and timing of the comovement between prices, earnings growth surprises, and anomaly returns are all consistent with a gradual learning process rather than expectations being highly sensitive to the most recent realization. Large earnings growth surprises do not immediately translate into large one-period returns, but instead are gradually reflected in future returns over time. 

Xiao Han is an assistant professor of finance at Bayes Business school (formerly known as Cass), City, University of London. He obtained his PhD in Finance from University of Edinburgh. His research covers topics in structural and empirical asset pricing with specific focus on machine learning, textual analysis, institutional investors, and subjective expectations. His research has been published on Review of Financial Studies and European Financial Management. His research has been presented at schools such as Guanghua， Harvard, and Wharton, and conferences such as NBER, AFA and EFA.