ABSTRACT Staggered boards (SBs) are one of the most potent common entrenchment devices, and their value effects are considerably debated. We study SBs' effects on firm value, managerial behavior, and investor composition using a quasi‐experimental setting: a 1990 law that imposed SBs on all Massachusetts‐incorporated firms. We find that relative to a matched control group of companies, for treated companies the law led to an increase in Tobin's Q, investment in capital expenditures and R&D, patents, and higher‐quality patented innovations, resulting in higher profitability. These effects are concentrated in innovating firms, especially those facing greater Wall Street scrutiny. An increase in institutional and dedicated investors also accompanied the imposition of SBs, facilitating a longer‐term orientation. The evidence suggests that SBs can benefit early‐life‐cycle firms facing high information asymmetries by allowing their managers to focus on long‐term investments and innovations.

Using a novel dataset, we show that components of firms’ GAAP earnings stemming from ancillary business activities or transitory shocks are significant in frequency and magnitude. These components have grown over time and are dispersed across various sections of the 10-K. Excluding them from GAAP earnings yields a core earnings measure that distinguishes between the recurring and non-recurring components of net income and forecasts future performance. Analysts and market participants are slow to impound these earnings components’ implications, particularly the amounts disclosed in the footnotes. Trading strategies that exploit non-core earnings produce abnormal returns of 8% per year.

Abstract We introduce a parsimonious framework for choosing among alternative expected-return proxies (ERPs) when estimating treatment effects. By comparing ERPs’ measurement error variances in the cross-section and in the time series, we provide new evidence on the relative performance of firm-level ERPs nominated by recent studies. Generally, “implied-costs-of-capital” metrics perform best in the time series, whereas “characteristic-based” proxies perform best in the cross-section. Factor-based ERPs, even the latest renditions, perform poorly. We revisit four prior studies that use ex ante ERPs and illustrate how this framework can potentially alter either the sign or the magnitude of prior inferences.