Publication Bias and the Cross-Section of Stock Returns

Andrew Chen and Tom Zimmermann

No 2018-033, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

Abstract: We develop an estimator for publication bias and apply it to 156 hedge portfolios based on published cross-sectional return predictors. Publication bias adjusted returns are only 12% smaller than in-sample returns. The small bias comes from the dispersion of returns across predictors, which is too large to be accounted for by data-mined noise. Among predictors that can survive journal review, a low t-stat hurdle of 1.8 controls for multiple testing using statistics recommended by Harvey, Liu, and Zhu (2015). The estimated bias is too small to account for the deterioration in returns after publication, suggesting an important role for mispricing.

Keywords: Data mining; Mispricing; Publication bias; Stock return anomalies (search for similar items in EconPapers)
JEL-codes: G10 G12 (search for similar items in EconPapers)
Pages: 71 pages
Date: 2018-05-11
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
https://www.federalreserve.gov/econres/feds/files/2018033pap.pdf (application/pdf)

Related works:
Journal Article: Publication Bias and the Cross-Section of Stock Returns (2020)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgfe:2018-33

DOI: 10.17016/FEDS.2018.033

Access Statistics for this paper

More papers in Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.) Contact information at EDIRC.
Bibliographic data for series maintained by Ryan Wolfslayer ; Keisha Fournillier ().