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Factor Model Comparisons with Conditioning Information

Published online by Cambridge University Press:  29 January 2024

Wayne E. Ferson Open the ORCID record for Wayne E. Ferson [Opens in a new window] ,
Andrew F. Siegel Open the ORCID record for Andrew F. Siegel [Opens in a new window]  and
Junbo L. Wang Open the ORCID record for Junbo L. Wang [Opens in a new window]
Wayne E. Ferson*
Affiliation:
Finance at the Marshall School, University of Southern California, and a Research Associate at the National Bureau of Economic Research
Andrew F. Siegel
Affiliation:
University of Washington Foster School of Business Finance and Business Economics and Information Systems and Operations Management aasiegel@u.washington.edu
Junbo L. Wang
Affiliation:
Ourso College of Business, Louisiana State University junbowang@lsu.edu
*
ferson@usc.edu (corresponding author)
Rights & Permissions [Opens in a new window]

Abstract

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We develop methods for testing factor models when the weights in portfolios of factors and test assets can vary with lagged information. We derive and evaluate consistent standard errors and finite sample bias adjustments for unconditional maximum squared Sharpe ratios and their differences. Bias adjustment using a second-order approximation performs well. We derive optimal zero-beta rates for models with dynamically trading portfolios. Factor models’ Sharpe ratios are larger but standard test asset portfolios’ maximum Sharpe ratios are larger still when there is dynamic trading. As a result, most of the popular factor models are rejected.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , First View , pp. 1 - 26
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

Footnotes

We are grateful to an anonymous referee, Andrew Detzel, Thierry Foucault (the editor), Raymond Kan, Xiaolu Wang, Baozhong Yang, Paolo Zaffaroni, Guofu Zhou, the editor for comments, and to seminar participants at the 2019 Financial Management Conference, the 2021 China International Finance Conference, the 2022 Southwestern Finance Association, the 2022 Southern Finance Association, the 2022 World Symposium on Investment Research, the University of Texas at Austin, the University of New Orleans, the Louisiana State University, and the University of Washington.

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