Evaluating Firm-Level Expected-Return Proxies: Implications for Estimating Treatment Effects
Harvard Business School Accounting & Management Unit Working Paper No. 15-022
Rock Center for Corporate Governance at Stanford University Working Paper No. 197
Stanford University Graduate School of Business Research Paper No. 15-57
The Review of Financial Studies (RFS), Forthcoming
79 Pages Posted: 6 Aug 2010 Last revised: 3 Mar 2021
Date Written: April 16, 2020
Abstract
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 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 time series; while “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.
Keywords: Expected returns, expected return proxies, measurement errors, treatment effects
JEL Classification: G10, G11, G12, G14, M41
Suggested Citation: Suggested Citation
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