ABSTRACT
The relationships between accounting earnings and stock prices, as well as between unexpected earnings and returns, have received substantial attention in the empirical literature. Several theoretical models predict the shapes of these relationships. However, a comprehensive empirical description that could be used to evaluate these predictions is lacking. By integrating recent advances in statistics and machine learning with findings in the accounting literature, we develop an empirical method to identify the relationships, which is consistent with the firm-specific and nonlinear features of the theoretical models. Our approach provides a clear description of stylized and robust patterns in the relationships that are relevant to distinguish between existing models and to aid future theory development. The findings are consistent with recently proposed dynamic option models for both the earnings-price and unexpected earnings-returns relationships.
Data availability: Data are available from the public sources cited in the text. A summary of the R code used in the article is available in Starica and Marton (2024).
JEL Classifications: G10; G30; M41.
