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

79 Pages Posted: 6 Aug 2010 Last revised: 20 Apr 2020

See all articles by Charles M.C. Lee

Charles M.C. Lee

Stanford University - Graduate School of Business

Eric C. So

Massachusetts Institute of Technology (MIT) - Sloan School of Management

Charles C. Y. Wang

Harvard Business School

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

Lee, Charles M.C. and So, Eric C. and Wang, Charles C. Y., Evaluating Firm-Level Expected-Return Proxies: Implications for Estimating Treatment Effects (April 16, 2020). 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, Available at SSRN: https://ssrn.com/abstract=1653940 or http://dx.doi.org/10.2139/ssrn.1653940

Charles M.C. Lee (Contact Author)

Stanford University - Graduate School of Business ( email )

Stanford Graduate School of Business
655 Knight Way
Stanford, CA 94305-5015
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650-721-1295 (Phone)

Eric C. So

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

100 Main Street
E62-416
Cambridge, MA 02142
United States

Charles C. Y. Wang

Harvard Business School ( email )

Soldiers Field Road
Morgan 270C
Boston, MA 02163
United States

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