The Effect of Data Breach Disclosure Laws on Analysts' Forecasts

Abstract

This study investigates the impact of Data Breach Disclosure Laws (DBDLs) on analyst forecast characteristics. Exploiting the staggered adoption of state-level DBDLs in the United States, we employ a difference-in-differences (DiD) design to identify the effects. We measure analyst forecast characteristics using forecast error and forecast dispersion, which capture the forecast accuracy and disagreement, respectively. Our results show that the implementation of DBDLs is associated with a significant reduction in both forecast error and forecast dispersion, suggesting that enhanced disclosure requirements improve the information environment and reduce uncertainty faced by analysts. The findings are robust to a range of alternative model specifications, different definitions of the treatment variable, and additional sensitivity analyses. Overall, this study provides evidence that regulatory disclosure aimed at improving information transparency can enhance the quality of disclosed information, thereby improving analysts' forecasting characteristics.