Publication Bias and the Cross-Section of Stock Returns

Citations

Cited by:

1. 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.
2. Azevedo, Vitor & Müller, Sebastian, 2024. "Analyst recommendations and mispricing across the globe," Journal of Banking & Finance, Elsevier, vol. 169(C).
3. Chinco, Alex & Neuhierl, Andreas & Weber, Michael, 2021. "Estimating the anomaly base rate," Journal of Financial Economics, Elsevier, vol. 140(1), pages 101-126.
   • Alexander M. Chinco & Andreas Neuhierl & Michael Weber, 2019. "Estimating The Anomaly Base Rate," NBER Working Papers 26493, National Bureau of Economic Research, Inc.
4. Chen, Hailiang & Hwang, Byoung-Hyoun, 2022. "Listening in on investors’ thoughts and conversations," Journal of Financial Economics, Elsevier, vol. 145(2), pages 426-444.
5. 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.).
6. 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.).
7. Chen, Andrew Y. & McCoy, Jack, 2024. "Missing values handling for machine learning portfolios," Journal of Financial Economics, Elsevier, vol. 155(C).
8. 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.
9. Andrew Y. Chen & Alejandro Lopez-Lira & Tom Zimmermann, 2022. "Does Peer-Reviewed Research Help Predict Stock Returns?," Papers 2212.10317, arXiv.org, revised Mar 2025.
   • Chen, Andrew Y. & Lopez-Lira, Alejandro & Zimmermann, Tom, 2024. "Does peer-reviewed research help predict stock returns?," CFR Working Papers 24-02, University of Cologne, Centre for Financial Research (CFR).
10. Yukun Liu & Aleh Tsyvinski & Xi Wu, 2019. "Common Risk Factors in Cryptocurrency," NBER Working Papers 25882, National Bureau of Economic Research, Inc.
11. 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.
    • 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.
12. 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).
13. Andrew Y. Chen & Chukwuma Dim, 2023. "High-Throughput Asset Pricing," Papers 2311.10685, arXiv.org, revised Jun 2025.
14. 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).
15. Andrew Y. Chen, 2022. "Most claimed statistical findings in cross-sectional return predictability are likely true," Papers 2206.15365, arXiv.org, revised Jun 2025.
16. Yukun Liu & Aleh Tsyvinski, 2018. "Risks and Returns of Cryptocurrency," NBER Working Papers 24877, National Bureau of Economic Research, Inc.
    • Yukun Liu & Aleh Tsyvinski, 2019. "Risks and Returns of Cryptocurrency," 2019 Meeting Papers 160, Society for Economic Dynamics.
17. Shynkevich, Andrei, 2025. "Wine market efficiency: Is glass half full or half empty?," International Review of Economics & Finance, Elsevier, vol. 98(C).
18. Han, Yufeng & Lu, Yueliang (Jacques) & Xu, Weike & Zhou, Guofu, 2024. "Mispricing and Anomalies: An Exogenous Shock to Short Selling from JGTRRA," Journal of Empirical Finance, Elsevier, vol. 78(C).
19. 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.
20. Andrew Y. Chen & Tom Zimmermann, 2022. "Open Source Cross-Sectional Asset Pricing," Critical Finance Review, now publishers, vol. 11(2), pages 207-264, May.
    • Chen, Andrew Y. & Zimmermann, Tom, 2020. "Open source cross-sectional asset pricing," CFR Working Papers 20-04, University of Cologne, Centre for Financial Research (CFR).
    • Andrew Y. Chen & Tom Zimmermann, 2021. "Open Source Cross-Sectional Asset Pricing," Finance and Economics Discussion Series 2021-037, Board of Governors of the Federal Reserve System (U.S.).
21. Andrew Y. Chen, 2022. "Do t-Statistic Hurdles Need to be Raised?," Papers 2204.10275, arXiv.org, revised Apr 2024.
22. Jiří Witzany, 2021. "A Bayesian Approach to Measurement of Backtest Overfitting," Risks, MDPI, vol. 9(1), pages 1-22, January.
23. Andrew Y. Chen & Tom Zimmermann, 2022. "Publication Bias in Asset Pricing Research," Papers 2209.13623, arXiv.org, revised Sep 2023.
24. 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).
25. 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.
26. 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.).