|
|
Modern Management 2023
基于机器学习的康美药业财务舞弊甄别研究
|
Abstract:
如何有效甄别上市公司财务舞弊行为，成为业界和学界持续关注的重要议题。本研究将最近五年受到中国证监会处罚的医药生物行业A股上市公司作为样本，以康美药业为例，基于舞弊三角理论选取24个特征，采用结合SMOTE过采样技术的随机森林分类算法模型进行测试与分析。结果表明，相较于将公司简单归类为舞弊与非舞弊两类，使用多个不同的特征集建立模型或构建多个不同算法的模型进行财务舞弊甄别研究的效果更好。
How to effectively identify financial fraud behavior of listed companies has become an important issue of continuous concern in the industry and academia. In this study, the A-share listed companies in the pharmaceutical and biological industry that have been punished by the China Securities Regulatory Commission in the past five years are taken as samples. Taking Kangmei Pharmaceutical as an example, 24 features are selected based on the fraud triangle theory, and Random forest classification algorithm model combined with SMOTE Oversampling technology is used for testing and analysis. The results indicate that compared to simply categorizing companies into fraudulent and non fraudulent categories, using multiple different feature sets to establish models or constructing models with multiple different algorithms for financial fraud screening research is more effective.
| [1] | 刘秀兰, 吕灿, 付强. 近年来我国企业财务信息失真又趋严重的原因及对策探讨[J]. 西南民族大学学报(人文社会科学版), 2012, 33(12): 150-155. |
| [2] | Khan, A.M.A. and Peng, J. (2022) Using Machine Learning Meta-Classifiers to Detect Financial Frauds. Finance Research Letters, 48, Article 102915. https://doi.org/10.1016/j.frl.2022.102915 |
| [3] | 伍彬, 刘云菁, 张敏. 基于机器学习的分析师识别公司财务舞弊风险的研究[J]. 管理学报, 2022, 19(7): 1082-1091. |
| [4] | Marco, S.A., Luis, U.A. and José, E.J. (2022) Predictive Fraud Analysis Applying the Fraud Triangle Theory through Data Mining Techniques. Applied Sciences, 12, 3382. https://doi.org/10.3390/app12073382 |
| [5] | Gozman, D. and Currie, W. (2014) The Role of Investment Management Systems in Regulatory Compliance: A Post-Financial Crisis Study of Displacement Mechanisms. Journal of Information Technology, 29, 44-58.
https://doi.org/10.1057/jit.2013.16 |
| [6] | Cressey, D.R. (1953) Other People’s Money; a Study of the Social Psy-chology of Embezzlement. Patterson Smith Publishing Corporation, Montclair. |
| [7] | Call, A.C., Kedia, S. and Rajgopal, S. (2016) Rank and File Employees and the Discovery of Misreporting: The Role of Stock Options. Journal of Account-ing and Economics, 62, 277-300. https://doi.org/10.1016/j.jacceco.2016.06.003 |
| [8] | 崔东颖, 胡明霞. “雅百特”财务舞弊案例研究——基于舞弊三角理论的视角[J]. 财会通讯, 2019(4): 6-9. |
| [9] | Etemadi, H. and Zolghi, H. (2013) Application of Logistic Regression to Identify Fraudulent Financial Reporting. Journal of Audit Science, 13, 5-23. |
| [10] | Persons, O.S. (2011) Using Financial Statement Data to Identify Factors Associated with Fraudulent Finan-cial Reporting. Journal of Applied Business Research, 11, 38-46. https://doi.org/10.19030/jabr.v11i3.5858 |
| [11] | Cecchini, M., Aytug, H., Koehler, G.J. and Pathak, P. (2010) Mak-ing Words Work: Using Financial Text as a Predictor of Financial Events. Decision Support Systems, 50, 164-175. https://doi.org/10.1016/j.dss.2010.07.012 |
| [12] | Bao, Y., Ke, B., Li, B., Yu, Y.J. and Zhang, J. (2020) Detecting Accounting Fraud in Publicly Traded US Firms Using a Machine Learning Approach. Journal of Accounting Research, 58, 199-235.
https://doi.org/10.1111/1475-679X.12292 |
| [13] | 赵浩, 李盼盼. 基于邻近梯度的机器学习特征选择优化方法[J]. 计算机仿真, 2020, 37(11): 289-293. |
| [14] | An, B. and Suh, Y. (2020) Identifying Financial Statement Fraud with Deci-sion Rules Obtained from Modified Random Forest. Data Technologies and Applications, 54, 235-255. https://doi.org/10.1108/DTA-11-2019-0208 |
| [15] | 陶世银, 贺敬安. 基于XGBoost与特征重要性筛选的闪电预报模型构建研究[J]. 国外电子测量技术, 2022, 41(1): 99-105. |
