Semiparametric Conditional Factor Models in Asset Pricing

Qihui Chen, Nikolai Roussanov and Xiaoliang Wang

Papers from arXiv.org

Abstract: We introduce a simple and tractable methodology for estimating semiparametric conditional latent factor models. Our approach disentangles the roles of characteristics in capturing factor betas of asset returns from ``alpha.'' We construct factors by extracting principal components from Fama-MacBeth managed portfolios. Applying this methodology to the cross-section of U.S. individual stock returns, we find compelling evidence of substantial nonzero pricing errors, even though our factors demonstrate superior performance in standard asset pricing tests. Unexplained ``arbitrage'' portfolios earn high Sharpe ratios, which decline over time. Combining factors with these orthogonal portfolios produces out-of-sample Sharpe ratios exceeding 4.

Date: 2021-12, Revised 2025-04
New Economics Papers: this item is included in nep-ecm
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