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Multisource evidence theory‐based fraud risk assessment of China's listed companies

Author

Listed:
  • Shi Qiu
  • Yuansheng Luo
  • Hongwei Guo
Abstract
Financial fraud has great negative impact on capital markets. It misleads investors and the government and will hurt the development of the capital market in China. To detect corporate fraud, an artificial intelligence‐based method is proposed to evaluate the fraud risk. A new model for assessing the fraud risk of listed companies in China is put forward. The proposed approach collects multisource evidence from inside and outside listed companies. The internal evidence for fraud risk assessment is gathered by a machine‐learning method. The external evidence for fraud risk assessment is obtained by a web crawler. An evidence theory‐based is employed for integrating the multisource evidence. The Fraud Risk Assessment Model based on Multisource Evidence Theory is established. The proposed model is applied to assess the fraud risk of the listed companies in China. The results indicate that the proposed model is effective at evaluating the fraud risk of listed companies, and it also has a higher precision, recall, and F‐1 measure than the traditional probability model.

Suggested Citation

  • Shi Qiu & Yuansheng Luo & Hongwei Guo, 2021. "Multisource evidence theory‐based fraud risk assessment of China's listed companies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1524-1539, December.
    • Handle: RePEc:wly:jforec:v:40:y:2021:i:8:p:1524-1539
    DOI: 10.1002/for.2782
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    References listed on IDEAS

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    1. Lynnette Purda & David Skillicorn, 2015. "Accounting Variables, Deception, and a Bag of Words: Assessing the Tools of Fraud Detection," Contemporary Accounting Research, John Wiley & Sons, vol. 32(3), pages 1193-1223, September.
    2. Alexander Dyck & Adair Morse & Luigi Zingales, 2010. "Who Blows the Whistle on Corporate Fraud?," Journal of Finance, American Finance Association, vol. 65(6), pages 2213-2253, December.
    3. Gregory S. Miller, 2006. "The Press as a Watchdog for Accounting Fraud," Journal of Accounting Research, Wiley Blackwell, vol. 44(5), pages 1001-1033, December.
    4. Jian Zhang, 2018. "Public Governance and Corporate Fraud: Evidence from the Recent Anti-corruption Campaign in China," Journal of Business Ethics, Springer, vol. 148(2), pages 375-396, March.
    5. 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.
    6. Omaima Hassan & Gianluigi Giorgioni, 2018. "The impact of corruption on analyst coverage," Managerial Auditing Journal, Emerald Group Publishing Limited, vol. 34(3), pages 305-323, October.
    7. Jiandong Chen & Douglas Cumming & Wenxuan Hou & Edward Lee, 2016. "Does the External Monitoring Effect of Financial Analysts Deter Corporate Fraud in China?," Journal of Business Ethics, Springer, vol. 134(4), pages 727-742, April.
    8. Srivastava, Rajendra P., 2011. "An introduction to evidential reasoning for decision making under uncertainty: Bayesian and belief function perspectives," International Journal of Accounting Information Systems, Elsevier, vol. 12(2), pages 126-135.
    9. Power, Michael, 2013. "The apparatus of fraud risk," Accounting, Organizations and Society, Elsevier, vol. 38(6), pages 525-543.
    10. Sheng, Jie & Lan, Hao, 2019. "Business failure and mass media: An analysis of media exposure in the context of delisting event," Journal of Business Research, Elsevier, vol. 97(C), pages 316-323.
    11. Fen-May Liou, 2008. "Fraudulent financial reporting detection and business failure prediction models: a comparison," Managerial Auditing Journal, Emerald Group Publishing, vol. 23(7), pages 650-662, July.
    12. Skaife, Hollis A. & Veenman, David & Wangerin, Daniel, 2013. "Internal control over financial reporting and managerial rent extraction: Evidence from the profitability of insider trading," Journal of Accounting and Economics, Elsevier, vol. 55(1), pages 91-110.
    13. Rajendra P. Srivastava & Theodore J. Mock & Lei Gao, 2011. "The Dempster-Shafer Theory: An Introduction and Fraud Risk Assessment Illustration," Australian Accounting Review, CPA Australia, vol. 21(3), pages 282-291, September.
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