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.

ABSTRACT This study examines how an increase in tick size affects algorithmic trading (AT), fundamental information acquisition (FIA), and the price discovery process around earnings announcements (EAs). Leveraging the SEC's randomized Tick Size Pilot experiment, we show that a tick size increase results in a decline in AT and a sharp drop in absolute cumulative abnormal returns and volume around EAs. More importantly, we find increased FIA in the preannouncement period. Specifically, we show: (1) treatment firms' pre-announcement returns better anticipate next quarter's standardized unexpected earnings; (2) these firms experience an increase in EDGAR web traffic prior to EAs; and (3) they exhibit a drop in price synchronicity with index returns. Taken together, our evidence suggests that while an increase in tick size reduces AT and abnormal market reaction after EAs, it also increases FIA activities prior to EAs. JEL Classifications: M40; M41; G12; G14.