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False Financial Statements: Characteristics Of China'S Listed Companies And Cart Detecting Approach

Author

Listed:
  • BELINNA BAI

    (Credit Department, Agriculture Bank of China (HK Branch), 23/F, Tower 1, Admiralty Centre, Hong Kong)

  • JEROME YEN

    (Department of Finance, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong)

  • XIAOGUANG YANG

    (Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100080, China)

Abstract

False Financial Statements (FFS) have long been a serious problem in China and other Asian countries, which significantly dampen the confidence of the investors. Regardless of listed companies or non-listed companies, the percentage of financial statements that contained false information is quite high, which is one of the major reasons why China stock markets moved in the opposite direction towards its wonderful economic growth over the past few years. The objective of this research is to introduce one statistical technique — Classification and Regression Tree (CART), to identify and predict the impacts of FFS. We survey financial statements manipulation tricks, FFS indicators and FFS detection techniques from both China and international perspective, and further look into ten listed companies with known FFS history in China; combining these findings, we propose key indicators to work with CART.Our analysis includes 24 false financial reports, and 124 non-false financial reports. We use CART to develop two FFS detecting models: CART without industry benchmark and CART with industry benchmark. For supporting comparison, we also build a Logit regression which is a commonly used technique in FFS detecting. We find that CART is effective in distinguishing FFS from non-FFS. Both CART models achieve better accuracy in identifying fraud cases and making predictions than Logit regression does, and CART with industry benchmark is slightly better than CART without benchmark, but it does not always have superior performance. Our CART model also tries to capture the indicators and their combinations that could reflect firms with high possibility of FFS in China.

Suggested Citation

  • Belinna Bai & Jerome Yen & Xiaoguang Yang, 2008. "False Financial Statements: Characteristics Of China'S Listed Companies And Cart Detecting Approach," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 7(02), pages 339-359.
    • Handle: RePEc:wsi:ijitdm:v:07:y:2008:i:02:n:s0219622008002958
    DOI: 10.1142/S0219622008002958
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    Citations

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    Cited by:

    1. Shen, Chung-Hua & Luo, Fuyan & Huang, Dengshi, 2015. "Analysis of earnings management influence on the investment efficiency of listed Chinese companies," Journal of Empirical Finance, Elsevier, vol. 34(C), pages 60-78.
    2. Damian Przekop, 2020. "Feature Engineering for Anti-Fraud Models Based on Anomaly Detection," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 12(3), pages 301-316, September.
    3. Nan Zhou & Andrew Delios, 2012. "Diversification and diffusion: A social networks and institutional perspective," Asia Pacific Journal of Management, Springer, vol. 29(3), pages 773-798, September.
    4. 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.
    5. Nan Zhou, 2018. "Hybrid State-Owned Enterprises and Internationalization: Evidence from Emerging Market Multinationals," Management International Review, Springer, vol. 58(4), pages 605-631, August.
    6. Joanna Wyrobek & Lukasz Poplawski & Marcin Surowka, 2020. "Identification of a Fraudulent Organizational Culture in Enterprises Listed in Warsaw Stock Exchange," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 2), pages 622-637.
    7. Elias Zavitsanos & Dimitris Mavroeidis & Konstantinos Bougiatiotis & Eirini Spyropoulou & Lefteris Loukas & Georgios Paliouras, 2023. "Financial misstatement detection: a realistic evaluation," Papers 2305.17457, arXiv.org.

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