Dissecting anomalies in conditional asset pricing
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Author(s)
Zaffaroni, Paolo
Type
Journal Article
Abstract
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 R-squared 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.
version robust to global misspecification. We introduce a novel R-squared 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.
Date Acceptance
2026-03-24
Citation
Management science
ISSN
0025-1909
Publisher
Institute for Operations Research and Management Sciences
Journal / Book Title
Management science
Copyright Statement
Copyright This paper is embargoed until publication. Once published the author’s accepted manuscript will be made available under a CC-BY License in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy).
License URL
Publication Status
Accepted