This study provides the first large-scale study of the performance of expected-return proxies (ERPs) internationally. Analyst-forecast-based ICCs are sparsely populated and not robustly associated with future returns. Earnings-model-forecast-based ICCs are well-populated, but are unreliable outside the U.S. We adapt and extend the log-linear and present-value (LPV) framework—combining an accounting valuation anchor, its expected growth, and market prices—for estimating ERPs internationally, and implement a correction for the use of stale accounting data. An LPV ERP anchored on the book value of equity is positively associated with future returns in 26 of 29 equity markets, and largely subsumes the predictive ability of a broad set of firm characteristics previously shown to be associated with 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.