Cohen and Wang (2013) (CW2013) provide evidence consistent with market participants perceiving staggered boards to be value reducing. Amihud and Stoyanov (2016) (AS2016) contests these findings, reporting some specifications under which the results are not statistically significant. We show that the results retain their significance under a wide array of robustness tests that address the concerns expressed by AS2016. Our empirical findings reinforce the conclusions of CW2013.

We provide a tractable model of firm-level expected holding period returns using two firm fundamentals—book-to-market ratio and return on equity—and study the cross-sectional properties of the model-implied expected returns. We find that firm-level expected returns and expected profitability are time-varying but highly persistent and that forecasts of holding period returns strongly predict the cross section of future returns up to three years ahead. We show a highly significant predictive pooled regression slope for future quarterly returns of 0.86. The popular factor-based expected return models have either an insignificant or a significantly negative association with future returns. In supplemental analyses, we show that these forecasts are also informative of the time series variation in aggregate conditions. For a representative firm, the slope of the conditional expected return curve is more positive in good times, when expected short-run returns are relatively low, and the model-implied forecaster of aggregate returns exhibits modest predictive ability. Collectively, we provide a simple, theoretically motivated, and practically useful approach to estimating multi-period-ahead expected returns.

We explain when and how staggered difference-in-differences regression estimators, commonly applied to assess the impact of policy changes, are biased. These biases are likely to be relevant for a large portion of research settings in finance, accounting, and law that rely on staggered treatment timing, and can result in Type-I and Type-II errors. We summarize three alternative estimators developed in the econometrics and applied literature for addressing these biases, including their differences and tradeoffs. We apply these estimators to re-examine prior published results and show, in many cases, the alternative causal estimates or inferences differ substantially from prior papers.