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.

The well-established negative correlation between staggered boards (SBs) and firm value could be due to SBs leading to lower value or a reflection of low-value firms' greater propensity to maintain SBs. We analyze the causal question using a natural experiment involving two Delaware court rulings—separated by several weeks and going in opposite directions—that affected the antitakeover force of SBs. We contribute to the long-standing debate on staggered boards by presenting empirical evidence consistent with the market viewing SBs as leading to lower firm value for the affected firms.

The correlation between governance indices and abnormal returns documented for 1990–1999 subsequently disappeared. The correlation and its disappearance are both due to market participants' gradually learning to appreciate the difference between good-governance and poor-governance firms. Consistent with learning, the correlation's disappearance was associated with increases in market participants' attention to governance; market participants and security analysts were, until the beginning of the 2000s but not subsequently, more positively surprised by the earning announcements of good-governance firms; and, although governance indices no longer generated abnormal returns during the 2000s, their negative association with firm value and operating performance persisted.

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.

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.

After decades of de-prioritizing shareholders’ economic interests and low corporate profitability, Japan introduced the JPX-Nikkei400 in 2014. The index highlighted the country’s “best-run” companies by annually selecting the 400 most profitable of its large and liquid firms. We find that managers competed for inclusion in the index by significantly increasing return on equity (ROE), and they did so at least in part due to their reputational or status concerns. The ROE increase was predominantly driven by improvements in margins, which were in turn partially driven by cutting research and development (R&D) intensity. Our findings suggest that indexes can affect managerial behavior through reputational or status incentives.

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.

Applying a “co-search” algorithm to Internet traffic at the SEC׳s EDGAR website, we develop a novel method for identifying economically related peer firms and for measuring their relative importance. Our results show that firms appearing in chronologically adjacent searches by the same individual (Search-Based Peers or SBPs) are fundamentally similar on multiple dimensions. In direct tests, SBPs dominate GICS6 industry peers in explaining cross-sectional variations in base firms׳ out-of-sample: (a) stock returns, (b) valuation multiples, (c) growth rates, (d) R&D expenditures, (e) leverage, and (f) profitability ratios. We show that SBPs are not constrained by standard industry classification, and are more dynamic, pliable, and concentrated. We also show that co-search intensity captures the degree of similarity between firms. Our results highlight the potential of the collective wisdom of investors — extracted from co-search patterns — in addressing long-standing benchmarking problems in finance.