One way to prevent financial distortion is to detect it early so that precautionary measures can be taken. The aim of this study was to investigate the ability of two models of forecasting distortions based on financial statements including M-SCORE Banish and F-SCORE Decho models and to measure their accuracy. The financial statements of 164 companies listed on the Tehran Stock Exchange between 2015 Will be studied until 2019. According to the results, the accuracy of Dechu F-SCORE criterion in identifying companies with the possibility of tampering and otherwise, is more than 70, which indicates the average ability of the model in detecting fraud. Also, the ability of the above-mentioned criterion with 73.17% in detecting fraud is higher than Banish model with 69.51%. In terms of diagnostic error, type I and II errors, which indicate the error of efficiency and effectiveness of the model, respectively, in the model of Decho et al. (2011) is much less than the model of Banish. Therefore, it can be concluded that Dechu F-SCORE criterion has performed better in cases of detecting the possibility of tampering with the financial statements of companies listed on the Tehran Stock Exchange between 2015 and 2019.
پیوندی. سعیده (1393)، به کارگیری مدل بنیش جهت پیش بینی تقلب و ارتباط آن با بازده سهام و کیفیت سود با رویکرد حسابداری جنا، پایان نامه کارشناسی ارشد، دانشگاه سمنان
جمشیدی نوید. بابک، محمد موسا. جابر، قنبری. مهرداد، خیراللهی. فرشید، (1398)، تدوین مدل کشف تقلب با استفاده از رویکرد ترکیبی برپایه مدل تحلیل عاملی و روش شبکه عصبی مصنوعی در شرکت های پذیرفته شده در بورس اوراق بهادار تهران، فصلنامه حسابداری مدیریت، 12، 42. 75-87
حاجی حیدری. راضیه، رحیمیان. نظام الدین، (1398)، کشف تقلب با استفاده از مدل تعدیل شده بنیش و نسبتهای مالی، پژوهش های تجربی حسابداری، 8، 31، 47-70
عسگری آلوج. حسین، کرمی. غلامرضا، نیک بخت. محمدرضا، مومنی. منصور، (1398)، توسعه مدل بنیش با ترکیب شبکههای عصبی مصنوعی و الگوریتم بهینهسازی حرکت تجمعی ذرات برای پیشبینی دستکاری سود، بررسی های حسابداری و حسابرسی، 26، 4، 615-638
کردستانی. غلامرضا، تاتلی. رشید، (1395)، پیش بینی دستکاری سود: توسعة یک مدل، بررسی های حسابداری و حسابرسی، 33، 1، 73-96
شعری،صابر،خراسانی، ابوطالب،(1396)، واکاوی مفهوم تقلب و بررسی آثار بکارگیری استانداردهای حسابرسی در افشای اطلاعات گزارشگری مالی متقلبانه،اولین همایش بین المللی و سومین همایش ملی پژوهش های مدیریت و علوم انسانی.
مونا سادات کابلی علی رحمانی هاشم نیکومرام فریدون رهنمای رودپشتی، اثربخشی ارزش های انگیزشی شوآرتز و اخلاق حرفه ای حسابداری بر گزارشگری مالی متقلبانه، مطالعات تجربی حسابداری مالی سال شانزدهم تابستان ۱۳۹۹ شماره ۶۶ 27 - 50
Bai, Jushan, and Serena Ng. 2005. Tests for skewness, kurtosis, and normality for time series data. Journal of Business & Economic Statistics 23: 49–60.
Beneish, M. (1997). Detecting GAAP violation: Implications for assessing earnings manage - ment among firms with extreme financial performance. Journal of Accounting and Public Policy, 16(3), 271–309.
Beneish, M. D. (1999). Incentives and penalties related to earnings overstatements that violate GAAP. The Accounting Review, 74(4), 425-457.
Beneish, M.D. (1999), ``Incentives and penalties related to earnings overstatements that violate GAAP’’, The Accounting Review, Vol. 74 No. 4, pp. 425-57.
Chen, K.Y., Lin, K.L., & Zhou, J. (2005). Audit Quality and Earnings Management for Taiwan IPO firms. Managerial Auditing Journal, 20(1), 86–104.
Dechow, P, Sloan, R. & Sweeney, A. (2011). Detecting Earnings Management. The Accounting Review: 70 (2) , 193-225.
Dechow, P. M., Ge, W., Larson, C. R., & Sloan, R. G. (2011). Predicting material accounting misstatements. Contemporary Accounting Research, 27(1), 17-82.
P, Valaskova. K, Chlebikova. K, Krastev.V, Atanasova. I, (2020), Heads and Tails of Earnings Management: Quantitative Analysis in Emerging Countries, Risks 2020, 8, 57; doi:10.3390/risks8020057
D , Ha. H, Binh. D (2017), Application of شاخص اف in Predicting Fraud, Errors: Experimental Research in Vietnam, International Journal of Accounting and Financial Reporting , Vol. 7, No. 2
Kwiatkowski, Denis, Peter C. B. Phillips, Peter Schmidt, and Yongcheol Shin. 1992. Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics 54: 159–78.
Lei, J. Z., & Ghorbani, A. A. (2012). Improved competitive learning neural networks for network intrusion and fraud detection. Neurocomputing, 75(1), 135-145
Omar N. , Kunji K. R. , Mohd S. Z. , And Shafie, N. A. (2014). Financial Statement Fraud: A Case Examination Using Beneish Model and Ratio Analysis, International Journal of Trade, Economics and Finance: 5 (2) ,184-186.
Richardson, S. A., Sloan, R. G., Soliman, M. T., & Tuna, I. (2005). Accrual reliability, earnings persistence, and stock prices. Journal of Accounting and Economics, 39(3), 437-485.
Susanto, Yulius Kurnia, Kashan Pirzada, and Adrianne Sheryl. 2019. Is tax aggressiveness an indicator of earnings management? Polish Journal of Management Studies 20: 516–27
Valaskova, Katarina, Tomas Kliestik, and Maria Kovacova. 2019. Assessment of Selected Models of Earnings Management in Economic Conditions of Slovakia. Paper presented at 33rd International-BusinessInformation-Management-Association (IBIMA) Conference Education Excellence and Innovation Management Through Vision 2020, Granada, Spain, April 10–11; pp. 3922–31.
White, Gary E. 1970. Discretionary accounting decisions and income normalization. Journal of Accounting Research 8: 260–73.
amirmoezzi,H. , poraghajan,A. and jafari,A. (2024). Detecting Financial Statement Fraud: Comparing the Ability of Models Based on Accounting Variables. Journal of Management Accounting and Auditing Knowledge, 13(52), 173-188.
MLA
amirmoezzi,H. , , poraghajan,A. , and jafari,A. . "Detecting Financial Statement Fraud: Comparing the Ability of Models Based on Accounting Variables", Journal of Management Accounting and Auditing Knowledge, 13, 52, 2024, 173-188.
HARVARD
amirmoezzi H., poraghajan A., jafari A. (2024). 'Detecting Financial Statement Fraud: Comparing the Ability of Models Based on Accounting Variables', Journal of Management Accounting and Auditing Knowledge, 13(52), pp. 173-188.
CHICAGO
H. amirmoezzi, A. poraghajan and A. jafari, "Detecting Financial Statement Fraud: Comparing the Ability of Models Based on Accounting Variables," Journal of Management Accounting and Auditing Knowledge, 13 52 (2024): 173-188,
VANCOUVER
amirmoezzi H., poraghajan A., jafari A. Detecting Financial Statement Fraud: Comparing the Ability of Models Based on Accounting Variables. Journal of Management Accounting and Auditing Knowledge, 2024; 13(52): 173-188.