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.

中文摘要：

我们提出了一个关于未来收益增长的恒定收益学习的结构模型，其中包括对现金流时机的偏好。正如模型所暗示的，使用调查预测的横截面分解表明，高市盈率是由低预期回报和过高的预期收益增长造成的。该模型在数量上匹配了一些资产定价矩估计，通过理解对增长与现金流时机偏好的相互作用，并提供对股票横截面中风险溢价和错误定价的作用的见解。价格、收益增长之间波动的幅度和时机和异常的收益都与渐进的学习过程一致，而不像期望对最近价格实现的高度敏感。大额收益增长意外不会立即转化为大的单期回报，而是随着时间的推移，逐渐反映在未来收益率中。

嘉宾介绍：

韩笑是伦敦大学贝叶斯商学院的金融学助理教授。他在爱丁堡大学获得金融博士学位。他的研究涵盖了结构性和实证资产定价的主题，特别关注于机器学习、文本分析、机构投资者和主观期望。他的研究发表在Review of Financial Studies和European Financial Management。他的研究已在北大光华、哈佛和沃顿等学校以及NBER、AFA和EFA等会议上展示。