Semiparametric Conditional Factor Models in Asset Pricing

Qihui Chen; Nikolai Roussanov; Xiaoliang Wang

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arXiv · 2112.07121

Semiparametric Conditional Factor Models in Asset Pricing

Qihui Chen·Nikolai Roussanov·Xiaoliang Wang

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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.

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BibTeX RIS

Qihui Chen, Nikolai Roussanov, Xiaoliang Wang. 2021-12-14. Semiparametric Conditional Factor Models in Asset Pricing. https://arxiv.org/abs/2112.07121

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