IDEAS home Printed from https://ideas.repec.org/p/fip/fedgfe/2018-33.html
   My bibliography  Save this paper

Publication Bias and the Cross-Section of Stock Returns

Author

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 & Tom 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 (U.S.).
  • Handle: RePEc:fip:fedgfe:2018-33
    DOI: 10.17016/FEDS.2018.033
    as

    Download full text from publisher

    File URL: https://www.federalreserve.gov/econres/feds/files/2018033pap.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.17016/FEDS.2018.033?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Garret Christensen & Edward Miguel, 2018. "Transparency, Reproducibility, and the Credibility of Economics Research," Journal of Economic Literature, American Economic Association, vol. 56(3), pages 920-980, September.
    2. Ikenberry, David & Lakonishok, Josef & Vermaelen, Theo, 1995. "Market underreaction to open market share repurchases," Journal of Financial Economics, Elsevier, vol. 39(2-3), pages 181-208.
    3. Sullivan, Ryan & Timmermann, Allan & White, Halbert, 2001. "Dangers of data mining: The case of calendar effects in stock returns," Journal of Econometrics, Elsevier, vol. 105(1), pages 249-286, November.
    4. Laura Liu & Hyungsik Roger Moon & Frank Schorfheide, 2020. "Forecasting With Dynamic Panel Data Models," Econometrica, Econometric Society, vol. 88(1), pages 171-201, January.
    5. Kozak, Serhiy & Nagel, Stefan & Santosh, Shrihari, 2020. "Shrinking the cross-section," Journal of Financial Economics, Elsevier, vol. 135(2), pages 271-292.
    6. John H. Cochrane, 2011. "Presidential Address: Discount Rates," Journal of Finance, American Finance Association, vol. 66(4), pages 1047-1108, August.
    7. Paul Gompers & Joy Ishii & Andrew Metrick, 2003. "Corporate Governance and Equity Prices," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(1), pages 107-156.
    8. Lo, Andrew W & MacKinlay, A Craig, 1990. "Data-Snooping Biases in Tests of Financial Asset Pricing Models," The Review of Financial Studies, Society for Financial Studies, vol. 3(3), pages 431-467.
    9. Wessel Marquering & Johan Nisser & Toni Valla, 2006. "Disappearing anomalies: a dynamic analysis of the persistence of anomalies," Applied Financial Economics, Taylor & Francis Journals, vol. 16(4), pages 291-302.
    10. Isaiah Andrews & Maximilian Kasy, 2019. "Identification of and Correction for Publication Bias," American Economic Review, American Economic Association, vol. 109(8), pages 2766-2794, August.
    11. Juhani T. Linnainmaa & Michael R. Roberts, 2016. "The History of the Cross Section of Stock Returns," NBER Working Papers 22894, National Bureau of Economic Research, Inc.
    12. Xuemin (Sterling) Yan & Lingling Zheng, 2017. "Fundamental Analysis and the Cross-Section of Stock Returns: A Data-Mining Approach," The Review of Financial Studies, Society for Financial Studies, vol. 30(4), pages 1382-1423.
    13. La Porta, Rafael, 1996. "Expectations and the Cross-Section of Stock Returns," Journal of Finance, American Finance Association, vol. 51(5), pages 1715-1742, December.
    14. Qi Liu & Lei Lu & Bo Sun & Hongjun Yan, 2015. "A Model of Anomaly Discovery," International Finance Discussion Papers 1128, Board of Governors of the Federal Reserve System (U.S.).
    15. Jensen, Michael C & Bennington, George A, 1970. "Random Walks and Technical Theories: Some Additional Evidence," Journal of Finance, American Finance Association, vol. 25(2), pages 469-482, May.
    16. Senn, Stephen, 2008. "A Note Concerning a Selection Paradox of Dawid's," The American Statistician, American Statistical Association, vol. 62, pages 206-210, August.
    17. R. David Mclean & Jeffrey Pontiff, 2016. "Does Academic Research Destroy Stock Return Predictability?," Journal of Finance, American Finance Association, vol. 71(1), pages 5-32, February.
    18. Amihud, Yakov, 2002. "Illiquidity and stock returns: cross-section and time-series effects," Journal of Financial Markets, Elsevier, vol. 5(1), pages 31-56, January.
    19. Titman, Sheridan & Wei, K. C. John & Xie, Feixue, 2004. "Capital Investments and Stock Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 39(4), pages 677-700, December.
    20. Ritter, Jay R, 1991. "The Long-run Performance of Initial Public Offerings," Journal of Finance, American Finance Association, vol. 46(1), pages 3-27, March.
    21. Hong, Harrison & Kacperczyk, Marcin, 2009. "The price of sin: The effects of social norms on markets," Journal of Financial Economics, Elsevier, vol. 93(1), pages 15-36, July.
    22. Cusatis, Patrick J. & Miles, James A. & Woolridge, J. Randall, 1993. "Restructuring through spinoffs*1: The stock market evidence," Journal of Financial Economics, Elsevier, vol. 33(3), pages 293-311, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chinco, Alex & Neuhierl, Andreas & Weber, Michael, 2021. "Estimating the anomaly base rate," Journal of Financial Economics, Elsevier, vol. 140(1), pages 101-126.
    2. Cujean, Julien & Andrei, Daniel & Fournier, Mathieu, 2019. "The Low-Minus-High Portfolio and the Factor Zoo," CEPR Discussion Papers 14153, C.E.P.R. Discussion Papers.
    3. Andrew Y. Chen & Chukwuma Dim, 2023. "High-Throughput Asset Pricing," Papers 2311.10685, arXiv.org, revised Mar 2024.
    4. Andrew Y. Chen, 2022. "Most claimed statistical findings in cross-sectional return predictability are likely true," Papers 2206.15365, arXiv.org, revised Mar 2024.
    5. Chen, Hailiang & Hwang, Byoung-Hyoun, 2022. "Listening in on investors’ thoughts and conversations," Journal of Financial Economics, Elsevier, vol. 145(2), pages 426-444.
    6. Andrew C. Chang & Trace J. Levinson, 2020. "Raiders of the Lost High-Frequency Forecasts: New Data and Evidence on the Efficiency of the Fed's Forecasting," Finance and Economics Discussion Series 2020-090, Board of Governors of the Federal Reserve System (U.S.).
    7. Yukun Liu & Aleh Tsyvinski, 2018. "Risks and Returns of Cryptocurrency," NBER Working Papers 24877, National Bureau of Economic Research, Inc.
    8. Andrew Y. Chen, 2021. "The Limits of p‐Hacking: Some Thought Experiments," Journal of Finance, American Finance Association, vol. 76(5), pages 2447-2480, October.
    9. Andrew Y. Chen & Tom Zimmermann, 2022. "Open Source Cross-Sectional Asset Pricing," Critical Finance Review, now publishers, vol. 11(2), pages 207-264, May.
    10. Andrew Y. Chen, 2019. "The Limits of p-Hacking : A Thought Experiment," Finance and Economics Discussion Series 2019-016, Board of Governors of the Federal Reserve System (U.S.).
    11. Andrew Y. Chen, 2022. "Do t-Statistic Hurdles Need to be Raised?," Papers 2204.10275, arXiv.org, revised Apr 2024.
    12. Jiří Witzany, 2021. "A Bayesian Approach to Measurement of Backtest Overfitting," Risks, MDPI, vol. 9(1), pages 1-22, January.
    13. Jacobs, Heiko & Müller, Sebastian, 2020. "Anomalies across the globe: Once public, no longer existent?," Journal of Financial Economics, Elsevier, vol. 135(1), pages 213-230.
    14. Andrew Y. Chen & Tom Zimmermann, 2022. "Publication Bias in Asset Pricing Research," Papers 2209.13623, arXiv.org, revised Sep 2023.
    15. Vincent, Kendro & Hsu, Yu-Chin & Lin, Hsiou-Wei, 2021. "Investment styles and the multiple testing of cross-sectional stock return predictability," Journal of Financial Markets, Elsevier, vol. 56(C).
    16. Andrew Y. Chen & Alejandro Lopez-Lira & Tom Zimmermann, 2022. "Does Peer-Reviewed Research Help Predict Stock Returns?," Papers 2212.10317, arXiv.org, revised Apr 2024.
    17. Alexandre Ripamonti & Raphael Videira & Denis Ichimura, 2020. "Asymmetric information and daily stock prices in Brazil," Estudios Gerenciales, Universidad Icesi, vol. 36(157), pages 465-472, December.
    18. Yukun Liu & Aleh Tsyvinski & Xi Wu, 2019. "Common Risk Factors in Cryptocurrency," NBER Working Papers 25882, National Bureau of Economic Research, Inc.
    19. Ruoxuan Xiong & Markus Pelger, 2019. "Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference," Papers 1910.08273, arXiv.org, revised Jan 2022.
    20. Andrew Y. Chen & Mihail Velikov, 2020. "Zeroing in on the Expected Returns of Anomalies," Finance and Economics Discussion Series 2020-039, Board of Governors of the Federal Reserve System (U.S.).
    21. Cakici, Nusret & Zaremba, Adam & Bianchi, Robert J. & Pham, Nga, 2021. "False discoveries in the anomaly research: New insights from the Stock Exchange of Melbourne (1927–1987)," Pacific-Basin Finance Journal, Elsevier, vol. 70(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kewei Hou & Chen Xue & Lu Zhang, 2017. "Replicating Anomalies," NBER Working Papers 23394, National Bureau of Economic Research, Inc.
    2. Andrew Y. Chen & Tom Zimmermann, 2022. "Open Source Cross-Sectional Asset Pricing," Critical Finance Review, now publishers, vol. 11(2), pages 207-264, May.
    3. Doron Avramov & Guy Kaplanski & Avanidhar Subrahmanyam, 2022. "Postfundamentals Price Drift in Capital Markets: A Regression Regularization Perspective," Management Science, INFORMS, vol. 68(10), pages 7658-7681, October.
    4. Stephen A. Gorman & Frank J. Fabozzi, 2021. "The ABC’s of the alternative risk premium: academic roots," Journal of Asset Management, Palgrave Macmillan, vol. 22(6), pages 405-436, October.
    5. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, September.
    6. Chinco, Alex & Neuhierl, Andreas & Weber, Michael, 2021. "Estimating the anomaly base rate," Journal of Financial Economics, Elsevier, vol. 140(1), pages 101-126.
    7. Andrew Y. Chen & Tom Zimmermann, 2022. "Publication Bias in Asset Pricing Research," Papers 2209.13623, arXiv.org, revised Sep 2023.
    8. Andrew Y. Chen & Mihail Velikov, 2020. "Zeroing in on the Expected Returns of Anomalies," Finance and Economics Discussion Series 2020-039, Board of Governors of the Federal Reserve System (U.S.).
    9. Cederburg, Scott & O’Doherty, Michael S. & Wang, Feifei & Yan, Xuemin (Sterling), 2020. "On the performance of volatility-managed portfolios," Journal of Financial Economics, Elsevier, vol. 138(1), pages 95-117.
    10. Bartram, Söhnke M. & Grinblatt, Mark, 2018. "Agnostic fundamental analysis works," Journal of Financial Economics, Elsevier, vol. 128(1), pages 125-147.
    11. Tobek, Ondrej & Hronec, Martin, 2021. "Does it pay to follow anomalies research? Machine learning approach with international evidence," Journal of Financial Markets, Elsevier, vol. 56(C).
    12. De Nard, Gianluca & Zhao, Zhao, 2022. "A large-dimensional test for cross-sectional anomalies:Efficient sorting revisited," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 654-676.
    13. Guanhao Feng & Stefano Giglio & Dacheng Xiu, 2020. "Taming the Factor Zoo: A Test of New Factors," Journal of Finance, American Finance Association, vol. 75(3), pages 1327-1370, June.
    14. Söhnke M. Bartram & Harald Lohre & Peter F. Pope & Ananthalakshmi Ranganathan, 2021. "Navigating the factor zoo around the world: an institutional investor perspective," Journal of Business Economics, Springer, vol. 91(5), pages 655-703, July.
    15. Andrew Y. Chen, 2022. "Do t-Statistic Hurdles Need to be Raised?," Papers 2204.10275, arXiv.org, revised Apr 2024.
    16. Oleg Rytchkov & Xun Zhong, 2020. "Information Aggregation and P-Hacking," Management Science, INFORMS, vol. 66(4), pages 1605-1626, April.
    17. Wang, Feifei & Yan, Xuemin Sterling, 2021. "Downside risk and the performance of volatility-managed portfolios," Journal of Banking & Finance, Elsevier, vol. 131(C).
    18. Guo, Li & Li, Frank Weikai & John Wei, K.C., 2020. "Security analysts and capital market anomalies," Journal of Financial Economics, Elsevier, vol. 137(1), pages 204-230.
    19. Stefan Nagel, 2013. "Empirical Cross-Sectional Asset Pricing," Annual Review of Financial Economics, Annual Reviews, vol. 5(1), pages 167-199, November.
    20. Geertsema, Paul & Lu, Helen, 2020. "The correlation structure of anomaly strategies," Journal of Banking & Finance, Elsevier, vol. 119(C).

    More about this item

    Keywords

    Data mining; Mispricing; Publication bias; Stock return anomalies;
    All these keywords.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fip:fedgfe:2018-33. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ryan Wolfslayer ; Keisha Fournillier (email available below). General contact details of provider: https://edirc.repec.org/data/frbgvus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.