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Rejoinder to the Critique of an Article on Machine Learning in the Detection of Accounting Fraud

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  • Stephen Walker

Abstract

This is a rejoinder to the reply by Yang Bao, Bin Ke, Bin Li, Y. Julia Yu, and Jie Zhang to my article “Critique of an Article on Machine Learning in the Detection of Accounting Fraud,” published in Econ Journal Watch in March 2021. Here I explain why the authors’ reply did not address the fundamental issue raised in my critique, which asked for a reasonable justification as to why fraud identifiers were changed for select observations in the sample—a choice that was undisclosed in the original publication, and one that contradicted the logic presented in their paper. That change was critical. Without it, their publication failed to improve upon prior literature in the detection of accounting fraud.

Suggested Citation

  • 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.
    • Handle: RePEc:ejw:journl:v:18:y:2021:i:2:p:230-234
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    Citations

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    Cited by:

    1. Md Jahidur Rahman & Hongtao Zhu, 2023. "Predicting accounting fraud using imbalanced ensemble learning classifiers – evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(3), pages 3455-3486, September.
    2. Stephen Walker, 2022. "Erroneous Erratum to Accounting Fraud Article," Econ Journal Watch, Econ Journal Watch, vol. 19(2), pages 190–203-1, September.

    More about this item

    Keywords

    training set; machine learning; serial fraud;
    All these keywords.

    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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