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 in the time series, we provide new evidence on the relative performance of firm-level ERPs no...

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Bibliographic Details
Published in:The Review of financial studies 2021-04, Vol.34 (4), p.1907-1951
Main Authors: Lee, Charles M C, So, Eric C, Wang, Charles C Y
Format: Article
Language:English
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Summary: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.
ISSN:0893-9454
1465-7368
DOI:10.1093/rfs/hhaa066