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Using Stocks or Portfolios in Tests of Factor Models

Published online by Cambridge University Press:  01 April 2019

Andrew Ang ,
Jun Liu  and
Krista Schwarz Open the ORCID record for Krista Schwarz [Opens in a new window]
Andrew Ang
Affiliation:
Ang, [email protected], Blackrock Incorporated
Jun Liu
Affiliation:
Liu, [email protected], University of California San Diego Rady School of Management
Krista Schwarz*
Affiliation:
Schwarz, [email protected], University of Pennsylvania Wharton School
*
Schwarz (corresponding author), [email protected]
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Abstract

We examine the efficiency of using individual stocks or portfolios as base assets to test asset pricing models using cross-sectional data. The literature has argued that creating portfolios reduces idiosyncratic volatility and allows more precise estimates of factor loadings, and consequently risk premia. We show analytically and empirically that smaller standard errors of portfolio beta estimates do not lead to smaller standard errors of cross-sectional coefficient estimates. Factor risk premia standard errors are determined by the cross-sectional distributions of factor loadings and residual risk. Portfolios destroy information by shrinking the dispersion of betas, leading to larger standard errors.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 55 , Issue 3 , May 2020 , pp. 709 - 750
Copyright
Copyright © Michael G. Foster School of Business, University of Washington 2019

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Footnotes

We thank Hendrik Bessembinder (the editor), Wayne Ferson (the referee), Rob Grauer, Cam Harvey, Bob Hodrick, Raymond Kan, Bob Kimmel, Georgios Skoulakis, Yuhang Xing, and Xiaoyan Zhang for helpful discussions, and seminar participants at the American Finance Association, Columbia University, CRSP forum, Texas A&M University, and the Western Finance Association for comments. The usual disclaimer applies.

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