Detecting financial statements fraud: Evidence from listed companies in China

  • Yanmei Duan School of Management, Wenzhou Business College, Wenzhou 325035, China
  • Guangshun Qiao School of Finance and Trade, Wenzhou Business College, Wenzhou 325035, China
Keywords: financial statement fraud; listed companies in china; mixture hazard model
Ariticle ID: 301

Abstract

Financial statement fraud is the deliberate misrepresentation of a company’s financial statements. Financial statement fraud has been a global concern since it not only harms the investors and creditors but also undermine the public confidence of the capital market. Based on the fact that a common incentive for companies to manipulate financial statement is a decline in the company’s financial prospects, this paper applies the mixture hazard early warning model to identify the key impacting financial characteristics in detecting the financial statement fraud for listed companies in China. We find that in the construction industry the warning sign of suspcious business practices is a falling return on assets, while in the real estate industry the financial red flag is an increase in the inventory level. The estimation results indicate that the financial characteristics may have different implications in different industries in detecting financial statement fraud. This research has shed light on setting specific financial characteristics for fraud monitoring and detecting by the regulators.

References

1. Association of Certified Fraud Examiners (ACFE). Report to the nations on occupational fraud and abuse global fraud study 2020. ACFE; 2020.

2. Zhou W, & Kapoor G. Detecting evolutionary financial statement fraud. Decision support systems. 2011; 50(3): 570–575. doi:10.1016/j.dss.2010.08.007

3. Perols J. Financial statement fraud detection: An analysis of statistical and machine learning algorithms. Auditing: A Journal of Practice & Theory. 2011; 30(2): 19–50. doi:10.2308/ajpt-50009

4. Farewell VT. A model for a binary variable with time-censored observations. Biometrika. 1977; 64(1): 43–46. doi: 10.1093/biomet/64.1.43

5. Almanidis P, & Sickles RC. Banking crises, early warning models, and efficiency. In: Advances in Efficiency and Productivity. Springer, Cham. 2016; pp. 331–364. doi:10.1007/978-3-319-48461-7_14

6. Altman EI. Predicting financial distress of companies: revisiting the Z-score and ZETA® models. In Hand-book of research methods and applications in empirical finance. Edward Elgar Publishing; 2013.doi:10.4337/9780857936080.00027

7. Reurink A. Financial fraud: A literature review. Journal of Economic Surveys. 2018; 32(5): 1292–1325.doi:10.1111/joes.12294

8. Amiram D, Bozanic Z, Cox JD, et al. Financial reporting fraud and other forms of misconduct: a multidisciplinary review of the literature. Rev Account Stud. 2018; 23: 732-–783. doi:10.1007/s11142-017-9435-x

9. Rashid MA, Al-Mamun A, Roudaki H, & Yasser QR. An overview of corporate fraud and its prevention approach. Australasian Accounting, Business and Finance Journal. 2022; 16(1): 101–118.doi:10.14453/aabfj.v16i1.7

10. Ashtiani MN, & Raahemi B. Intelligent fraud detection in financial statements using machine learning and data mining: a systematic literature review. Ieee Access. 2021; 10: 72504–72525. doi:10.1109/ACCESS.2021.3096799.

11. Albashrawi M. Detecting financial fraud using data mining techniques: a decade review from 2004 to 2015. Journal of Data Science. 2016; 14(3): 553–569. doi:10.6339/JDS.201607_14(3).0010

12. Hajek P, & Henriques R. Mining corporate annual reports for intelligent detection of financial statement fraud–A comparative study of machine learning methods. Knowledge-Based Systems. 2017; 128: 139–152. doi:10.1016/j.knosys.2017.05.001

13. Kassem R. Elucidating corporate governance’s impact and role in countering fraud. Corporate Governance: The International Journal of Business in Society. 2022; 22(7): 1523–1546. doi:10.1108/CG-08-2021-0279

14. Mandal A. Preventing financial statement fraud in the corporate sector: insights from auditors. Journal of Financial Reporting and Accounting; 2023. doi:10.1108/JFRA-02-2023-0101

15. Chen ZY, & Han D. Detecting corporate financial fraud via two-stage mapping in joint temporal and financial feature domain. Expert Systems with Applications. 2023; 217: 119559. doi:10.1016/j.eswa.2023.119559

16. Gepp A, Linnenluecke MK, O’Neill TJ, & Smith T. Big data techniques in auditing research and practice: Current trends and future opportunities. Journal of Accounting Literature. 2018; 40(1): 102–115. doi:10.1016/j.acclit.2017.05.003

17. Hooi B, Song HA, Beute A, et al. Fraudar: Bounding graph fraud in the face of camouflage. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining; 13–17 August 2016; Francisco, CA, USA. pp. 895–904.doi:10.1145/2939672.2939747

18. Shin K, Hooi B, Kim J, & Faloutsos C. Densealert: Incremental dense-subtensor detection in tensor streams. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 13–17 Augus 2017; New York, NY USA. pp. 1057–1066.doi:10.1145/3097983.3098087

19. Craja P, Kim A, & Lessmann S. Deep learning for detecting financial statement fraud. Decision Support Systems. 2020; 139: 113421. doi:10.1016/j.dss.2020.113421

20. Jiang J, Li Y, He B, et al. Spade: A real-time fraud detection framework on evolving graphs. Proceedings of the VLDB Endowment. 2022; 16(3): 461–469. doi:10.14778/3570690.3570696

21. Jiang J, Chen Y, He B, et al. Spade+: A Generic Real-Time Fraud Detection Framework on Dynamic Graphs. IEEE Transactions on Knowledge and Data Engineering; 2024. doi:10.1109/TKDE.2024.3394155

22. Chen Y, Jiang J, Sun S, et al. RUSH: Real-Time Burst Subgraph Detection in Dynamic Graphs. Proceedings of the VLDB Endowment. 2024; 17(11): 3657–3665.

23. Kalbfleisch JD, & Prentice RL. The statistical analysis of failure time data. John Wiley & Sons; 2011.

24. Dempster AP, Laird NM, & Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological). 1977; 39(1): 1–22. doi:10.1111/j.2517-6161.1977.tb01600.x

25. Summers SL, & Sweeney JT. Fraudulently misstated financial statements and insider trading: An empirical analysis. Accounting Review. 1998; 131–146.

Published
2024-11-04
How to Cite
Duan, Y., & Qiao, G. (2024). Detecting financial statements fraud: Evidence from listed companies in China. Sustainable Economies, 2(4), 301. https://doi.org/10.62617/se.v2i4.301
Section
Article