Evaluating Firm-Level Expected-Return Proxies: Implications for Estimating Treatment Effects

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 time series, we provide new evidence on the relative performance of firm-level ERPs nominated by recent studies. In general, “implied-costs-of-capital” metrics perform best in time series; while “characteristic-based” proxies perform best in the cross-section. Factor-based ERPs, even the latest renditions, perform poorly. We further show the main finding from two seminal studies linking expected returns to information quality fails to survive when estimated with the ERPs deemed most reliable by our framework.