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Publication Bias and the Cross-Section of Stock Returns

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  • Andrew Y Chen
  • Tom Zimmermann
  • Jeffrey Pontiff

Abstract

We develop an estimator for publication bias-adjusted returns and apply it to 156 published long-short portfolios. Our adjustment uses only in-sample data and provides sharper inferences than out-of-sample tests. Bias-adjusted returns are only 12.3% smaller than in-sample returns with a standard error of 1.7 percentage points. The small bias comes from the dispersion of returns across predictors, which is too large to be explained by data-mined noise. The bias is much smaller than post-publication decay (p-value ¡.0001), suggesting mispricing is important. Our results offer a different perspective about recent papers that find most published predictors are likely false. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Suggested Citation

  • Andrew Y Chen & Tom Zimmermann & Jeffrey Pontiff, 2020. "Publication Bias and the Cross-Section of Stock Returns," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 10(2), pages 249-289.
  • Handle: RePEc:oup:rasset:v:10:y:2020:i:2:p:249-289.
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    File URL: http://hdl.handle.net/10.1093/rapstu/raz011
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    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.
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    Cited by:

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    2. Chinco, Alex & Neuhierl, Andreas & Weber, Michael, 2021. "Estimating the anomaly base rate," Journal of Financial Economics, Elsevier, vol. 140(1), pages 101-126.
    3. Chen, Hailiang & Hwang, Byoung-Hyoun, 2022. "Listening in on investors’ thoughts and conversations," Journal of Financial Economics, Elsevier, vol. 145(2), pages 426-444.
    4. 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.).
    5. 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.).
    6. Chen, Andrew Y. & McCoy, Jack, 2024. "Missing values handling for machine learning portfolios," Journal of Financial Economics, Elsevier, vol. 155(C).
    7. 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.
    8. Andrew Y. Chen & Alejandro Lopez-Lira & Tom Zimmermann, 2022. "Does Peer-Reviewed Research Help Predict Stock Returns?," Papers 2212.10317, arXiv.org, revised Jun 2024.
    9. Yukun Liu & Aleh Tsyvinski & Xi Wu, 2019. "Common Risk Factors in Cryptocurrency," NBER Working Papers 25882, National Bureau of Economic Research, Inc.
    10. Xiong, Ruoxuan & Pelger, Markus, 2023. "Large dimensional latent factor modeling with missing observations and applications to causal inference," Journal of Econometrics, Elsevier, vol. 233(1), pages 271-301.
    11. 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).
    12. Andrew Y. Chen & Chukwuma Dim, 2023. "High-Throughput Asset Pricing," Papers 2311.10685, arXiv.org, revised Mar 2024.
    13. Berggrun, Luis & Cardona, Emilio & Lizarzaburu, Edmundo, 2024. "Evaluating asset pricing anomalies: Evidence from Latin America," Research in International Business and Finance, Elsevier, vol. 70(PB).
    14. Andrew Y. Chen, 2022. "Most claimed statistical findings in cross-sectional return predictability are likely true," Papers 2206.15365, arXiv.org, revised Sep 2024.
    15. Yukun Liu & Aleh Tsyvinski, 2018. "Risks and Returns of Cryptocurrency," NBER Working Papers 24877, National Bureau of Economic Research, Inc.
    16. 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.
    17. Andrew Y. Chen & Tom Zimmermann, 2022. "Open Source Cross-Sectional Asset Pricing," Critical Finance Review, now publishers, vol. 11(2), pages 207-264, May.
    18. Andrew Y. Chen, 2022. "Do t-Statistic Hurdles Need to be Raised?," Papers 2204.10275, arXiv.org, revised Apr 2024.
    19. Jiří Witzany, 2021. "A Bayesian Approach to Measurement of Backtest Overfitting," Risks, MDPI, vol. 9(1), pages 1-22, January.
    20. Andrew Y. Chen & Tom Zimmermann, 2022. "Publication Bias in Asset Pricing Research," Papers 2209.13623, arXiv.org, revised Sep 2023.
    21. 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).
    22. 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.
    23. 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.).

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    More about this item

    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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