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Erroneous Erratum to Accounting Fraud Article

Author

Listed:
  • Stephen Walker

Abstract

This paper treats an erratum published in Journal of Accounting Research (JAR) in August 2022. The erratum was prompted by two critical comments authored by me and published in Econ Journal Watch. The erratum mischaracterizes its authors’ previous research related to the preferred test sample period. More importantly, the authors say that I identified an error in their program code. This is false. Rather, I identified a misidentification within the dataset, a misidentification that was disclosed neither in their original JAR article, nor in the program code appended to that article, nor in their Econ Journal Watch reply to my first comment. Finally, the erratum never addresses why the misidentification occurred, nor why they did not acknowledge the misidentification on the two prior opportunities to do so. I have asked for an investigation at the Journal of Accounting Research into academic research misconduct.

Suggested Citation

  • Stephen Walker, 2022. "Erroneous Erratum to Accounting Fraud Article," Econ Journal Watch, Econ Journal Watch, vol. 19(2), pages 190–203-1, September.
    • Handle: RePEc:ejw:journl:v:19:y:2022:i:2:p:190-203
    as

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    File URL: https://econjwatch.org/File+download/1245/WalkerSept2022.pdf?mimetype=pdf
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    File URL: https://econjwatch.org/1286
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    References listed on IDEAS

    as
    1. Stephen Walker, 2021. "Critique of an Article on Machine Learning in the Detection of Accounting Fraud," Econ Journal Watch, Econ Journal Watch, vol. 18(1), pages 1-61–70, March.
    2. Yang Bao & Bin Ke & Bin Li & Y. Julia Yu & Jie Zhang, 2021. "A Response to "Critique of an Article on Machine Learning in the Detection of Accounting Fraud"," Econ Journal Watch, Econ Journal Watch, vol. 18(1), pages 1-71–78, March.
    3. Stephen Walker, 2021. "Rejoinder to the Critique of an Article on Machine Learning in the Detection of Accounting Fraud," Econ Journal Watch, Econ Journal Watch, vol. 18(2), pages 230–234-2, September.
    4. Yang Bao & Bin Ke & Bin Li & Y. Julia Yu & Jie Zhang, 2020. "Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach," Journal of Accounting Research, Wiley Blackwell, vol. 58(1), pages 199-235, March.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Machine learning; serial fraud;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting

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