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Is There A Replication Crisis In Finance?

Author

Listed:
  • Theis Ingerslev Jensen
  • Bryan T. Kelly
  • Lasse Heje Pedersen

Abstract

Several papers argue that financial economics faces a replication crisis because the majority of studies cannot be replicated or are the result of multiple testing of too many factors. We develop and estimate a Bayesian model of factor replication, which leads to different conclusions. The majority of asset pricing factors: (1) can be replicated, (2) can be clustered into 13 themes, the majority of which are significant parts of the tangency portfolio, (3) work out-of-sample in a new large data set covering 93 countries, and (4) have evidence that is strengthened (not weakened) by the large number of observed factors.

Suggested Citation

  • Theis Ingerslev Jensen & Bryan T. Kelly & Lasse Heje Pedersen, 2021. "Is There A Replication Crisis In Finance?," NBER Working Papers 28432, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:28432
    Note: AP
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    Citations

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    Cited by:

    1. Hollstein, Fabian, 2022. "The world of anomalies: Smaller than we think?," Journal of International Money and Finance, Elsevier, vol. 129(C).
    2. Chiah, Mardy & Long, Huaigang & Zaremba, Adam & Umar, Zaghum, 2023. "Trade competitiveness and the aggregate returns in global stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 148(C).
    3. Guillaume Coqueret, 2023. "Forking paths in financial economics," Papers 2401.08606, arXiv.org.
    4. Rolf Uwe Fülbier & Thorsten Sellhorn, 2023. "Understanding and improving the language of business: How accounting and corporate reporting research can better serve business and society," Journal of Business Economics, Springer, vol. 93(6), pages 1089-1124, August.
    5. Bollerslev, Tim & Patton, Andrew J. & Zhang, Haozhe, 2022. "Equity clusters through the lens of realized semicorrelations," Economics Letters, Elsevier, vol. 211(C).
    6. Antoine Falck & Adam Rej & David Thesmar, 2021. "Why and how systematic strategies decay," Papers 2105.01380, arXiv.org.
    7. Mercik, Aleksander & Słoński, Tomasz & Karaś, Marta, 2024. "Understanding crypto-asset exposure: An investigation of its impact on performance and stock sensitivity among listed companies," International Review of Financial Analysis, Elsevier, vol. 92(C).
    8. Long, Huaigang & Demir, Ender & Będowska-Sójka, Barbara & Zaremba, Adam & Shahzad, Syed Jawad Hussain, 2022. "Is geopolitical risk priced in the cross-section of cryptocurrency returns?," Finance Research Letters, Elsevier, vol. 49(C).
    9. Christophe Pérignon & Olivier Akmansoy & Christophe Hurlin & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johanneson & Michael Kirchler & Albert Menkveld & Michael Razen & Utz Weitzel, 2022. "Reproducibility of Empirical Results: Evidence from 1,000 Tests in Finance," Working Papers hal-03810013, HAL.
    10. Ghazi, Soroush & Schneider, Mark & Dorobiala, Zachary, 2024. "Speculative and non-speculative equity premia," Economics Letters, Elsevier, vol. 236(C).
    11. Calice, Giovanni & Lin, Ming-Tsung, 2021. "Exploring risk premium factors for country equity returns," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 294-322.
    12. Simon, Frederik & Weibels, Sebastian & Zimmermann, Tom, 2023. "Deep parametric portfolio policies," CFR Working Papers 23-01, University of Cologne, Centre for Financial Research (CFR).
    13. Guillaume Chevalier & Guillaume Coqueret & Thomas Raffinot, 2022. "Supervised portfolios," Post-Print hal-04144588, HAL.
    14. Tian Ma & Cunfei Liao & Fuwei Jiang, 2023. "Timing the factor zoo via deep learning: Evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(1), pages 485-505, March.
    15. Sara Ali & Ihsan Badshah & Riza Demirer & Prasad Hegde, 2023. "Economic policy uncertainty and fund flow performance sensitivity: Evidence from New Zealand," International Review of Finance, International Review of Finance Ltd., vol. 23(3), pages 666-679, September.
    16. Andrew Y. Chen, 2022. "Most claimed statistical findings in cross-sectional return predictability are likely true," Papers 2206.15365, arXiv.org, revised Sep 2024.
    17. 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.
    18. Beckmeyer, Heiner & Wiedemann, Timo, 2022. "Recovering Missing Firm Characteristics with Attention-Based Machine Learning," VfS Annual Conference 2022 (Basel): Big Data in Economics 264135, Verein für Socialpolitik / German Economic Association.
    19. Bali, Turan G. & Beckmeyer, Heiner & Moerke, Mathis & Weigert, Florian, 2021. "Option return predictability with machine learning and big data," CFR Working Papers 21-08, University of Cologne, Centre for Financial Research (CFR).
    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.
    21. Kilic, Mete & Yang, Louis & Zhang, Miao Ben, 2022. "The cross-section of investment and profitability: Implications for asset pricing," Journal of Financial Economics, Elsevier, vol. 145(3), pages 706-724.
    22. Xin Chen & Wei He & Libin Tao & Jianfeng Yu, 2023. "Attention and Underreaction-Related Anomalies," Management Science, INFORMS, vol. 69(1), pages 636-659, January.
    23. Simkus, Matthew & Truong, Helen & Hoang, Khoa & Huang, Ronghong, 2022. "Economic uncertainty and cross section of stock returns: Australian evidence," Pacific-Basin Finance Journal, Elsevier, vol. 74(C).
    24. Lioui, Abraham & Tarelli, Andrea, 2022. "Chasing the ESG factor," Journal of Banking & Finance, Elsevier, vol. 139(C).

    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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