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Factor Model Comparisons with Conditioning Information

Published online by Cambridge University Press:  29 January 2024

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)
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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.

Information

Type
Research Article
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington
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