به‌کارگیری پدیده تونلینگ جهت افزایش توانایی پیش‌بینی مدیریت سود در مدل بنیش بر مبنای تکنیک شبکه‌های عصبی مصنوعی و الگوریتم بهینه‌سازی حرکت تجمعی ذرات

نوع مقاله : علمی - پژوهشی

نویسندگان

1 دانشجوی دکتری حسابداری، واحد کرمانشاه، دانشگاه آزاد اسلامی، کرمانشاه، ایران.

2 استادیار، گروه حسابداری، واحد کرمانشاه، دانشگاه آزاد اسلامی، کرمانشاه، ایران.

چکیده

هدف از انجام این پژوهش بهینه‌سازی مدل پیش‌بینی مدیریت سود بنیش با پدیده تونلینگ و الگوریتم بهینه‌سازی حرکت تجمعی ذرات است. جامعه آماری پژوهش شرکت‌های پذیرفته‌شده در بورس اوراق بهادار تهران و تعداد شرکت موردمطالعه، شامل 196 شرکت پذیرفته‌شده طی سال‌های 1393 تا 1398 است. روش پژوهش توصیفی- پیمایشی و ازنظر ارتباط بین متغیرها علی- همبستگی است و ازنظر هدف کاربردی و ازلحاظ رخداد، پس‌رویدادی است. به‌منظور تجزیه ‌و تحلیل داده‌ها از روش رگرسیونی و شبکه عصبی مصنوعی و الگوریتم بهینه‌سازی حرکت تجمعی ذرات استفاده‌شده است. نتایج حاصل از تحلیل مدل نشان داد که کلیه نسبت‌های مالی بر پیش‌بینی مدیریت سود بنیش تأثیر معنادار داشته و بیشترین تأثیر در پیش‌بینی مدیریت سود بنیش را شاخص پدیده تونلینگ INE و کمترین تأثیر را شاخص اهرم مالی داشته است. نتایج حاصل از برآورد شبکه‌های عصبی طراحی‌شده نشان می‌دهد که استفاده از الگوریتم بهینه‌سازی تجمعی ذرات جهت پیش‌بینی مدیریت سود برای شرکت‌های پذیرفته‌شده در بورس اوراق بهادار تهران، از عملکرد قابل قبولی برخوردار است.

کلیدواژه‌ها


عنوان مقاله [English]

Presenting the developed model of Benish by using tunneling phenomena based on artificial neural network technique and particle swarm optimization algorithm to identifying profit manipulating companies

نویسندگان [English]

  • Farhad Azadi 1
  • Mehrdad Ghanbari 2
  • Babak Jamshidi Navid 2
  • Javad Masodi 2
1 Ph.D. Student, Department of Accounting, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
2 Assistant Prof, Department of Accounting, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
چکیده [English]

The purpose of this study is to optimize the Bayesian profit management model with tunneling phenomenon and cumulative particle motion optimization algorithm. The statistical population of the study included companies listed in the Tehran Stock Exchange and the number of companies under study, including 196 companies listed during the years 2015 to 2020. The research method is descriptive-correlational and in terms of causal-correlational variables and in terms of purpose and event, it is post-event. In order to analyze the data, regression and artificial neural network and cumulative particle motion optimization algorithm were used. The results of the model analysis showed that all financial ratios had a significant effect on the earnings management prediction of insight and the greatest impact on the prediction of earnings management was on the INE tunneling phenomenon and the least on financial leverage. The results of the estimation of the designed neural networks show that the use of cumulative particle optimization algorithm to predict the Profit management for companies listed in Tehran Stock Exchange is acceptable.

کلیدواژه‌ها [English]

  • Insights Earnings Management
  • Tunneling Phenomenon
  • Artificial Neural Network
  • Particle Cumulative Motion Optimization Algorithm
  • Tehran Stock Exchange
1.Akhgar, M.O., & Samireh, D. (2019). Investigating the ability of the company's accounting system to provide comparable information and profit management, Accounting Knowledge Quarterly. (In Persian)
2.Alhadab, M. (2018). The impact of executive compensation and audit quality on accrual-based and real-based earnings management: Evidence from Jordan, Corporate Ownership and Control, 15(2-1), 209-219. https://doi.org/10.22495/cocv15i2c1p7. (In Persian)
3.Alikhani Dehghi, H., Izadinia, N., & Kiani, G.H. (2020). The role of earnings management in identifying fraudulent financial statements in companies listed on the Tehran Stock Exchange, Asset Management and Finance, 8 (4), 21-28. (In Persian)
4.Almahrog, Y.E., & Lasyoud, A.A. (2021). An Overview of Earnings Management Detection Approaches, JOURNAL OF CRITICAL REVIEWS, 08(02), 92-101.
5.Artur, H. (2020). Using the Beneish M-score model: Evidence from non-financial companies listed on the Warsaw Stock Exchange. Investment Management and Financial Innovations, 17(4), 389-401. doi:10.21511/imfi.17(4).2020.33
6.Asgari Alouj, H., Nikobakht, M.R., Karami, Gh., & Momeni, M. (2019). Development of Benish model by combining artificial neural networks and particle aggregation optimization algorithm to predict profit manipulation, Accounting and Auditing Reviews, 26 (4), 615-638. (In Persian)
7.Atanasov, V., Black, B., Ciccotello, C., & Gyoshev, S. (2009). The anatomy of financial tunneling in emerging market, Finance Working Paper No 123/2006, European Corporate Governance Institute, Brussels, Belgium.
8.Atanasov, V., Bernard B., & Conrad, C. (2009). Unbundling and Measuring Tunneling’, Working Paper, available at http://ssrn/com/abstract1030529 (last accessed October 2011).
9.Barton, J., Hansen, B., & Pownall, G. (2010). Which performance measures do investors around the world value the most—and why? Accounting Review, 85 (3),753–789.
10.Beneish, M. D. (1999). The detection of earnings manipulation. Financial Analysts Journal, 55(5), 24-36. https://doi.org/10.2469/faj.v55.n5.2296.
11.Chang, C. S., Yu, S. W., & Hung, C. H. (2015). Firm risk and performance: The role of Corporate Governance. Review of Management Science, 9, 141-173. https://doi.org/10.1007/s11846-014-0132-x.
12.Fathi, S., & Abdi, R. (2018). The Relationship between Capital Structure and Real Profit Management (Case Study of Companies Listed on the Tehran Stock Exchange), International Conference on Management, Accounting, Banking and Economics on Iranian Horizon 1404, Mashhad, Kamravash Danesh Bonyan Cooperative Institute. (In Persian)
13.Ghaderi, I., Amini, P., & Mohammadi Molqarni, A. (2020). Applying the combined model of artificial neural networks with meta-exploration algorithms (ICA, PSO) in profit management prediction, Empirical Accounting Research, 10 (2), 213-248. (In Persian)
14.Gordon, E.A., Henry, E. & Palia, D. (2004). RELATED PARTY TRANSACTIONS AND CORPORATE GOVERNANCE, Hirschey, M., and, K.J. and Makhija, A.K. (Ed.) Corporate Governance (Advances in Financial Economics, Emerald Group Publishing Limited, Bingley, 9, 1-27. https://doi.org/10.1016.
15.Hapsoro, T., & Rani, S. (2018). Does Audit Quality Mediate the Effect of Auditor Tenure, Abnormal Audit Fee and Auditor's Reputation on Giving Going Concern Opinion?, International Journal of Economics and Financial Issues, Econjournals, 8(1), 143-152.
16.Heidarzadeh Hanzaei, A., & Barati, L. (2019). Information Environment and Profit Management in Dual-Interest Companies, Investment Knowledge, 8 (29), 215-332. (In Persian)
17.Homsian Kashani, Z., & Gholami Jamkarani, R. (2017). Management Entrenchment and Profit Management, 2nd International Conference on Management, Industrial Engineering, Economics and Accounting, Tbilisi-Georgia, Permanent Secretariat in cooperation with Imam Sadegh University. (In Persian)
18.Javaheri, M.R., & Zanjirdar, M. (2017). The relationship between earnings management and the performance of the studied companies in Tehran Stock Exchange, Productivity Management, 11, 3 (42), 197-218. (In Persian)
19.Johnson, S., R. La Porta, F. Lopez-de-Silanes, & A. Shleifer. (2000). Tunneling, American Economic Review Papers and Proceedings, XC, 22-27.
20.Johnson, S., Rafael L.P., Florencio, L-de-S. & Andrei, Sh. (2000). ‘Tunneling’, American Economic Review, 90, 22–7. 5.
21.Jones, J., (1995). Earnings Management during Import Relief Investigations, Journal of Accounting Research, 29, (2), 193-228.
22.Kang, M., Lee, H. Y., Lee, M. G., & Park, J. C. (2014). The association between relat-ed-party transactions and control–ownership wedge: Evidence from Ko-rea. Pacific-Basin Finance Journal, 29, 272-296.
23.Kothari, S.P., Andrew L. Leone, & Charles E. Wasley. (2005). Performance matched discretionary accrual measures. Journal of Accounting and Economics, 39, 1 (February), 163–197.
24.Kurdistani, Gh., Tanli, R. (2016). Predicting Profit Manipulation: Developing a Model, Accounting and Auditing Reviews, 23 (1), 73-96. (In Persian)
25.Laksmana, I., & Yang, Y. W. (2014). Product market competition and earnings management: Evidence from discretionary accruals and real activity manipulation. Advances in Accounting, 30(2), 263-275.
26.Li, T., & Zaiats, N. (2017). Information environment and earnings management of dual class firms around the world. Journal of Banking & Finance, 74, 1-23.
27.Mardani, M., Fazeli, N., & Faghani Makrani, Kh. (2020). Assessing the role of company life cycle in optimizing accrual quality prediction models, management accounting and auditing knowledge, 9 (33), 157-178. (In Persian)
28.Marefat, B., & Parsafard, B. (2017). Tunneling: Major Shareholder Collaboration and Management, Second Annual Conference on Economics, Management and Accounting, Ahvaz, https://civilica.com/doc/671628. (In Persian)
29.Mohammadi, M., Yazdani, Sh., & Khanmohammadi, M.H. (2021). Presenting a Model for Financial Reporting Fraud Detection using Genetic Algorithm, Advances in Mathematical Finance & Applications, 6(2), 377-392. DOI: 10.22034/amfa.2019.1872783.1252
30.Najafizadeh, B., & Kayhan, M. (2015). Investigating the relationship between earnings management and information asymmetry in the context of environmental uncertainty in companies listed on the Tehran Stock Exchange, Fourth National Conference on Management, Economics and Accounting, Tabriz, East Azerbaijan Industrial Management Organization, University of Tabriz. (In Persian)
31.Poor Ali, M.R., & Kouchaki Tajani, M. (2020). Comparing the accuracy of companies' profit manipulation prediction using colonial competition algorithm and genetic algorithm, the first international conference on new challenges and solutions in industrial engineering and management and accounting, Sari, Mazandaran, Iran. (In Persian)
32.Rostami, W., Ghorbani, B., & Mehtari, Z. (2015). The Effect of Competition in the Product Market on the Real Profit Management of Companies Listed on the Stock Exchange, The First International Conference on Management and Accounting with Value Creation Approach, Islamic Azad University, Fars Branch, Shiraz. (In Persian)
33.Rotemberg, J. & Scharfstein, D. (1990). Shareholder value maximization and produc market competition. Review of Financial Studies, 3(3), 367–391.
34.Salehi, M., & Farrokhi Pilehroud, L. (2018). Predicting profit management using neural network and decision tree. Quarterly Journal of Financial Accounting and Auditing Research, 10 (37), 1-24. (In Persian)
35.Scott, M. (2000). Financial accounting theory. (3 rd ed.). New Jersey: Prentice Hall.
36.Shahzad, A. (2016). Detecting Earning Management and Earning Manipulation in BRIC Countries; a Panel Data Analysis for Post Global Financial Crisis Period. Int J Account Res, 4, 134. doi:10.4172/ijar.1000134.
37.Sheri Anaghiz, S., Rahimian, N., Salehi Sedghiani, J., & Khorasani, A. (2017). Investigating and applying the accuracy of the results obtained from hedging and modified hedging models based on Iran's economic environment in detecting and exposing fraudulent financial reporting, Financial Management Perspective Quarterly, 7 (18), 105-123. (In Persian)
38.Sheri, S., & Hamidi, E. (2012). Identifying Motives for Dealing with Affiliates, Empirical Accounting Research, 2 (4), 49-64. (In Persian)
39.Stayesh, M.H., Nejad Mostafa, K., & Shafiei, Mohammad, J. (2013). Application of genetic algorithm in determining the optimal capital structure of companies listed on the Tehran Stock Exchange. Accounting and Auditing Reviews, 16 (56), 39-58. (In Persian)
40.Yuriy, B., & Veronika, J. (2021). Detection of earnings management by different Models, SHS Web of Conferences 02005(2021), Globalization and its Socio-Economic Consequences 2020, https://doi.org/10.1051/shsconf/20219202005.