We develop a framework for screening and validating empirical designs in natural experiments. As an illustration of the framework, we establish a common testing ground for three popular research designs – instrumental variables (IV), fuzzy regression discontinuity (FRD), and difference-in-differences (DiD) – that exploit the annual Russell 1000/2000 Indexes reconstitution. Of the three designs, we find that only the IV approach spuriously detects large and statistically significant “effects” that by construction should be immaterial and statistically indistinguishable from zero. We advocate the use of simulation evidence to support key identifying assumptions for future research designs based on natural experiments.

We examine abnormal stock returns surrounding contemporaneous earnings and dividend announcements in order to determine whether investors evaluate the two announcements in relation to each other. We find that there is a statistically significant interaction effect. The abnormal return corresponding to any earnings or dividend announcement depends upon the value of the other announcement. This evidence suggests the existence of a corroborative relationship between the two announcements. Investors give more credence to unanticipated dividend increases or decreases when earnings are also above or below expectations, and vice versa.

We examine abnormal stock returns surrounding contemporaneous earnings and dividend announcements in order to determine whether investors evaluate the two announcements in relation to each other. We find that there is a statistically significant interaction effect. The abnormal return corresponding to any earnings or dividend announcement depends upon the value of the other announcement. This evidence suggests the existence of a corroborative relationship between the two announcements. Investors give more credence to unanticipated dividend increases or decreases when earnings are also above or below expectations, and vice versa.