Semiparametric Conditional Factor Models in Asset Pricing

TL;DR

This paper presents a new semiparametric approach to estimate conditional factor models in asset pricing, effectively capturing factor betas and revealing persistent pricing errors with high Sharpe ratios.

Contribution

It introduces a tractable methodology for disentangling characteristics' roles in factor models and demonstrates its effectiveness on U.S. stock data, uncovering significant pricing errors.

Findings

Substantial nonzero pricing errors detected.

Factors outperform standard asset pricing tests.

High Sharpe ratios for unexplained portfolios.

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.