检验Altman和Beneish模型在财务欺诈和财务操纵检测中的有效性:以科威特股票为例

IF 0.6 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Biometrics Pub Date : 2020-09-16 DOI:10.5539/IJBM.V15N10P70
R. Akra, Jamil Chaya
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引用次数: 5

摘要

本研究采用了两种概率财务分析方法,Altman模型和Beneish模型,这两种方法分别在早期发现可能的财务困境和利润操纵方面被证明是有效的。由于模型的有效性,本文将该方法应用于科威特股票市场,不包括银行和保险公司。结果表明,Altman在工业和房地产公司的背景下具有较低的预测能力,而Beneish在发现被测试公司可能存在的盈利操纵或欺诈性报告方面具有很强的预测能力,这一点通过对公司和新闻来源的事后审查得到了证实。除了建议金融分析师和相关方同时使用这两种模型外,我们建议根据行业对Altman模型进行重新校准。
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Testing the Effectiveness of Altman and Beneish Models in Detecting Financial Fraud and Financial Manipulation: Case Study Kuwaiti Stock
This study is an adoption of two probabilistic financial analysis methods, Altman and Beneish Models that have proven effective in early detection of possible financial distress and profit manipulation respectively. Motivated by the effectiveness of the models, this paper applies the methodology on the Kuwaiti Stock Market excluding banking and insurance companies. Results demonstrated that Altman has less predictive power in the context of industrial and real estate companies while Beneish has a strong predictive power to uncover possible manipulation in earnings or fraudulent reporting in the tested companies as confirmed with an ex-post review of the companies and news sources. We recommend a recalibration of the Altman model according to industry in addition to recommending that financial analysts and interested parties use both models.
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来源期刊
International Journal of Biometrics
International Journal of Biometrics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
1.50
自引率
0.00%
发文量
46
期刊介绍: Biometrics and human biometric characteristics form the basis of research in biological measuring techniques for the purpose of people identification and recognition. IJBM addresses the fundamental areas in computer science that deal with biological measurements. It covers both the theoretical and practical aspects of human identification and verification.
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