Factor Model Comparisons with Conditioning Information

Document Type

Article

Publication Date

5-1-2025

Abstract

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.

Publication Source (Journal or Book title)

Journal of Financial and Quantitative Analysis

First Page

1401

Last Page

1426

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