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Computational Reproducibility in Finance: Evidence from 1,000 Tests

Author

Listed:
  • Christophe Pérignon
  • Olivier Akmansoy
  • Christophe Hurlin

    (LEO - Laboratoire d'Économie d'Orleans [2022-...] - UO - Université d'Orléans - UT - Université de Tours - UCA - Université Clermont Auvergne)

  • Anna Dreber
  • Felix Holzmeister
  • Jürgen Huber
  • Magnus Johannesson
  • Michael Kirchler
  • Albert Menkveld
  • Michael Razen
  • Utz Weitzel

Abstract

We analyze the computational reproducibility of more than 1,000 empirical answers to 6 research questions in finance provided by 168 research teams. Running the researchers' code on the same raw data regenerates exactly the same results only 52% of the time. Reproducibility is higher for researchers with better coding skills and those exerting more effort. It is lower for more technical research questions, more complex code, and results lying in the tails of the distribution. Researchers exhibit overconfidence when assessing the reproducibility of their own research. We provide guidelines for finance researchers and discuss implementable reproducibility policies for academic journals.

Suggested Citation

  • Christophe Pérignon & Olivier Akmansoy & Christophe Hurlin & Anna Dreber & Felix Holzmeister & Jürgen Huber & Magnus Johannesson & Michael Kirchler & Albert Menkveld & Michael Razen & Utz Weitzel, 2024. "Computational Reproducibility in Finance: Evidence from 1,000 Tests," Post-Print hal-04797779, HAL.
    • Handle: RePEc:hal:journl:hal-04797779
    DOI: 10.1093/rfs/hhae029
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    Cited by:

    1. Julian Junyan Wang & Victor Xiaoqi Wang, 2025. "Assessing Consistency and Reproducibility in the Outputs of Large Language Models: Evidence Across Diverse Finance and Accounting Tasks," Papers 2503.16974, arXiv.org, revised Mar 2025.
    2. Tom L. Dudda & Lars Hornuf, 2025. "The Perks and Perils of Machine Learning in Business and Economic Research," CESifo Working Paper Series 11721, CESifo.

    More about this item

    JEL classification:

    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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