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Computational Reproducibility in Finance: Evidence from 1,000 Tests

Author

Listed:
  • Christophe Pérignon
  • Olivier Akmansoy
  • Christophe Hurlin
  • Anna Dreber
  • Felix Holzmeister
  • Jürgen Huber
  • Magnus Johannesson
  • Michael Kirchler
  • Albert J 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 J Menkveld & Michael Razen & Utz Weitzel, 2024. "Computational Reproducibility in Finance: Evidence from 1,000 Tests," The Review of Financial Studies, Society for Financial Studies, vol. 37(11), pages 3558-3593.
    • Handle: RePEc:oup:rfinst:v:37:y:2024:i:11:p:3558-3593.
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    File URL: http://hdl.handle.net/10.1093/rfs/hhae029
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    Keywords

    C80; C87;

    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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