Dissecting Anomalies in Conditional Asset Pricing

This paper introduces a novel methodology for analyzing anomalies in conditional asset pricing models with time-varying risk exposures and premia. Our approach extends the conventional two-pass methodology to include both ordinary and weighted least-squares estimation in a conditional setting. We establish closed-form standard errors to statistically dissect anomalies, including a version robust to global misspecification. We introduce a novel R² criterion to quantify the joint contribution of large anomaly sets in explaining cross-sectional stock return variations. Our analysis highlights the significant impact of anomalies during economic and financial crises, linking them closely with market conditions.

This paper was accepted by Kay Giesecke, finance.

Funding: This project has received funding from the postdoctoral fellowships programme Beatriu de Pinos, funded by the Secretary of Universities and Research (Government of Catalonia) and by the Horizon 2020 programme of research and innovation of the European Union under the [Marie Sktodowska-Curie Grant Agreement 801370].

Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.06968.