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Publication Bias and the Cross-Section of Stock Returns

Author

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  • Andrew Y. Chen
  • Thomas Zimmermann

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.

Suggested Citation

  • Andrew Y. Chen & Thomas Zimmermann, 2018. "Publication Bias and the Cross-Section of Stock Returns," Finance and Economics Discussion Series 2018-033, Board of Governors of the Federal Reserve System (US).
  • Handle: RePEc:fip:fedgfe:2018-33
    DOI: 10.17016/FEDS.2018.033
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    File URL: https://www.federalreserve.gov/econres/feds/files/2018033pap.pdf
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    References listed on IDEAS

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    Cited by:

    1. Yukun Liu & Aleh Tsyvinski, 2018. "Risks and Returns of Cryptocurrency," NBER Working Papers 24877, National Bureau of Economic Research, Inc.

    More about this item

    Keywords

    Data mining ; Mispricing ; Publication bias ; Stock return anomalies;

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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    1. Meta-Analysis in Economics

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