IDEAS home Printed from https://ideas.repec.org/a/bla/acctfi/v63y2023i5p5093-5142.html
   My bibliography  Save this article

Bagging or boosting? Empirical evidence from financial statement fraud detection

Author

Listed:
  • Xiaowei Chen
  • Cong Zhai

Abstract

Ensemble learning, specifically bagging and boosting, has been widely used in the financial field for detecting financial fraud, but their relative performance still lacks consensus. This study compares the performance of five ensemble learning models based on bagging and boosting, using data from Chinese A‐share listed companies from 2012 to 2022, including the COVID‐19 pandemic period. Results show that bagging outperforms boosting in various evaluation indicators, with profitability and asset quality positively affecting financial fraud. This study reveals the mechanism by which ensemble learning affects financial fraud detection and expands related research in the financial field.

Suggested Citation

  • Xiaowei Chen & Cong Zhai, 2023. "Bagging or boosting? Empirical evidence from financial statement fraud detection," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(5), pages 5093-5142, December.
    • Handle: RePEc:bla:acctfi:v:63:y:2023:i:5:p:5093-5142
    DOI: 10.1111/acfi.13159
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/acfi.13159
    Download Restriction: no
    File URL: https://libkey.io/10.1111/acfi.13159?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Leippold, Markus & Wang, Qian & Zhou, Wenyu, 2022. "Machine learning in the Chinese stock market," Journal of Financial Economics, Elsevier, vol. 145(2), pages 64-82.
    3. Jeremy Bertomeu & Edwige Cheynel & Eric Floyd & Wenqiang Pan, 2021. "Using machine learning to detect misstatements," Review of Accounting Studies, Springer, vol. 26(2), pages 468-519, June.
    4. Geng, Ruibin & Bose, Indranil & Chen, Xi, 2015. "Prediction of financial distress: An empirical study of listed Chinese companies using data mining," European Journal of Operational Research, Elsevier, vol. 241(1), pages 236-247.
    5. Beneish, Messod D., 1997. "Detecting GAAP violation: implications for assessing earnings management among firms with extreme financial performance," Journal of Accounting and Public Policy, Elsevier, vol. 16(3), pages 271-309.
    6. Fich, Eliezer M. & Shivdasani, Anil, 2007. "Financial fraud, director reputation, and shareholder wealth," Journal of Financial Economics, Elsevier, vol. 86(2), pages 306-336, November.
    7. Patricia M. Dechow & Weili Ge & Chad R. Larson & Richard G. Sloan, 2011. "Predicting Material Accounting Misstatements," Contemporary Accounting Research, John Wiley & Sons, vol. 28(1), pages 17-82, March.
    8. Xin Xu & Feng Xiong & Zhe An, 2023. "Using Machine Learning to Predict Corporate Fraud: Evidence Based on the GONE Framework," Journal of Business Ethics, Springer, vol. 186(1), pages 137-158, August.
    9. Adrian Gepp & Kuldeep Kumar & Sukanto Bhattacharya, 2021. "Lifting the numbers game: identifying key input variables and a best‐performing model to detect financial statement fraud," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(3), pages 4601-4638, September.
    10. Mark Cecchini & Haldun Aytug & Gary J. Koehler & Praveen Pathak, 2010. "Detecting Management Fraud in Public Companies," Management Science, INFORMS, vol. 56(7), pages 1146-1160, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Chanyuan (Abigail) & Cho, Soohyun & Vasarhelyi, Miklos, 2022. "Explainable Artificial Intelligence (XAI) in auditing," International Journal of Accounting Information Systems, Elsevier, vol. 46(C).
    2. Yunchuan Sun & Xiaoping Zeng & Ying Xu & Hong Yue & Xipu Yu, 2024. "An intelligent detecting model for financial frauds in Chinese A‐share market," Economics and Politics, Wiley Blackwell, vol. 36(2), pages 1110-1136, July.
    3. Xin Xu & Feng Xiong & Zhe An, 2023. "Using Machine Learning to Predict Corporate Fraud: Evidence Based on the GONE Framework," Journal of Business Ethics, Springer, vol. 186(1), pages 137-158, August.
    4. Dan Amiram & Zahn Bozanic & James D. Cox & Quentin Dupont & Jonathan M. Karpoff & Richard Sloan, 2018. "Financial reporting fraud and other forms of misconduct: a multidisciplinary review of the literature," Review of Accounting Studies, Springer, vol. 23(2), pages 732-783, June.
    5. Abdullah Albizri & Deniz Appelbaum & Nicholas Rizzotto, 2019. "Evaluation of financial statements fraud detection research: a multi-disciplinary analysis," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 16(4), pages 206-241, December.
    6. Zhao, Qi & Xu, Weijun & Ji, Yucheng, 2023. "Predicting financial distress of Chinese listed companies using machine learning: To what extent does textual disclosure matter?," International Review of Financial Analysis, Elsevier, vol. 89(C).
    7. Achakzai, Muhammad Atif Khan & Peng, Juan, 2023. "Detecting financial statement fraud using dynamic ensemble machine learning," International Review of Financial Analysis, Elsevier, vol. 89(C).
    8. Lukui Huang & Alan Abrahams & Peter Ractham, 2022. "Enhanced financial fraud detection using cost‐sensitive cascade forest with missing value imputation," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(3), pages 133-155, July.
    9. Maria Tragouda & Michalis Doumpos & Constantin Zopounidis, 2024. "Identification of fraudulent financial statements through a multi‐label classification approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(2), June.
    10. Zhou, Ying & Li, Haoran & Xiao, Zhi & Qiu, Jing, 2023. "A user-centered explainable artificial intelligence approach for financial fraud detection," Finance Research Letters, Elsevier, vol. 58(PA).
    11. Rahman, Md Jahidur & Zhu, Hongtao, 2024. "Detecting accounting fraud in family firms: Evidence from machine learning approaches," Advances in accounting, Elsevier, vol. 64(C).
    12. 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.
    13. Bolin Liao & Zhendai Huang & Xinwei Cao & Jianfeng Li, 2022. "Adopting Nonlinear Activated Beetle Antennae Search Algorithm for Fraud Detection of Public Trading Companies: A Computational Finance Approach," Mathematics, MDPI, vol. 10(13), pages 1-14, June.
    14. Rind, Asad Ali & Abbassi, Wajih & Allaya, Manel & Hammouda, Amira, 2022. "Local peers and firm misconduct: The role of sustainability and competition," Economic Modelling, Elsevier, vol. 116(C).
    15. Slavko ?odan, 0000. "Can Accrual-based Metrics Indicate Material Accounting Misstatements? Evidence on Audit Adjustments," Proceedings of Economics and Finance Conferences 14416287, International Institute of Social and Economic Sciences.
    16. Irena Hutton & Danling Jiang & Alok Kumar, 2015. "Political Values, Culture, and Corporate Litigation," Management Science, INFORMS, vol. 61(12), pages 2905-2925, December.
    17. Achakzai, Muhammad Atif Khan & Juan, Peng, 2022. "Using machine learning Meta-Classifiers to detect financial frauds," Finance Research Letters, Elsevier, vol. 48(C).
    18. Elias Zavitsanos & Dimitris Mavroeidis & Konstantinos Bougiatiotis & Eirini Spyropoulou & Lefteris Loukas & Georgios Paliouras, 2023. "Financial misstatement detection: a realistic evaluation," Papers 2305.17457, arXiv.org.
    19. 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.
    20. So-Jin Yu & Jin-Sung Rha, 2021. "Research Trends in Accounting Fraud Using Network Analysis," Sustainability, MDPI, vol. 13(10), pages 1-26, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:acctfi:v:63:y:2023:i:5:p:5093-5142. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/aaanzea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.