An Accruals Quality Model: A Group Method of Data Handling Approa

Saqafi, Ali; Blue, Ghasem; Sohrabi Varzaneh, HosseinAli

doi:10.22054/qjma.2021.58242.2219

An Accruals Quality Model: A Group Method of Data Handling Approa

Document Type : Research Paper

Authors

Ali Saqafi ¹

Ghasem Blue ²

HosseinAli Sohrabi Varzaneh ³

¹ Professor of Accounting Department, Allameh Tabataba'i University, Tehran, Iran

² Associate Professor of Accounting Department, Allameh Tabataba'i University, Tehran, Iran

³ PhD student in Accounting, Allameh Tabataba'i University, Tehran, Iran

https://doi.org/10.22054/qjma.2021.58242.2219

Abstract

Development of Earnings quality measures, especially Accruals quality measures, has been a critical line of research over more than three decades. Literature indicates that linear-regression-based measures are subject to (suffer from) significant estimation error in non-discretionary accruals estimation. Therefore, recent research used machine learning algorithms including multilayer perceptron and radial basis neural networks, in order to address the issue. However, being founded on Blackbox approach limits future development and applicability of these methods. So, to address the limitations, we have used Group Method of Handling Data (GMDH) approach, as a Whitebox approach, in order to estimate the accruals. Findings using data from 299 Tehran Securities Exchange listed companies during 1385 to 1397 suggests that GMDH-based models perform superior to regression models and multilayer perceptron neural networks in terms of estimation error measured by mean squared error. Moreover, Cash flow approach in total accruals calculation leads to less estimation error compared to balance sheet approach. As a result, the model developed in this article can be used by market participants such as regulators, analyst and auditors in order to detect probable financial reporting misstatements.

Keywords

Group Method of Handling Data (GMDH)

Earnings quality

Neural Networks

Accruals

Accruals Quality

Subjects

Accounting report

Abdou, H. A., Ellelly, N. N., Elamer, A. A., Hussainey, K., & Yazdifar, H. (2020). Corporate governance and earnings management nexus: Evidence from the UK and Egypt using neural networks. International Journal of Finance & Economics, ijfe.2120. https://doi.org/10.1002/ijfe.2120
Al-Dmour, A. H., & Al-Dmour, R. H. (2018). Applying Multiple Linear Regression and Neural Network to Predict Business Performance Using the Reliability of Accounting Information System. International Journal of Corporate Finance and Accounting, 5(2), 12–26. https://doi.org/10.4018/IJCFA.2018070102
BALL, R., & SHIVAKUMAR, L. (2006). The Role of Accruals in Asymmetrically Timely Gain and Loss Recognition. Journal of Accounting Research, 44(2), 207–242. https://doi.org/10.1111/j.1475-679X.2006.00198.x
Calderon, T. G., & Cheh, J. J. (2002). A roadmap for future neural networks research in auditing and risk assessment. International Journal of Accounting Information Systems. https://doi.org/10.1016/S1467-0895(02)00068-4
Dag, O., Karabulut, E., & Alpar, R. (2019). GMDH2: Binary Classification via GMDH-Type Neural Network Algorithms—R Package and Web-Based Tool. International Journal of Computational Intelligence Systems, 12(2), 649. https://doi.org/10.2991/ijcis.d.190618.001
Dameri, R. P., Garelli, R., & Resta, M. (2020). Neural Networks in Accounting: Clustering Firm Performance Using Financial Reporting Data. Journal of Information Systems, 34(2), 149–166. https://doi.org/10.2308/isys-18-002
Dechow, P. M., & Dichev, I. D. (2002). Quality Earnings : The The Accruals Accrual Estimation Errors. The Accounting Review, 77(2002), 35–59.
Dechow, P. M., & Skinner, D. J. (2000). Earnings management: Reconciling the views of accounting academics, practitioners, and regulators. Accounting Horizons, 14(2), 235-250. https://doi.org/10.2308/acch.2000.14.2.235
Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1995). Detecting Earnings Management Author(s): Detecting Earnings Management. The Accounting Review, 70(2), 193-225.
DeFond, M. L. (2010). Earnings quality research: Advances, challenges and future research. Journal of Accounting and Economics, 50(2–3), 402–409. https://doi.org/10.1016/j.jacceco.2010.10.004
DeTienne, K. B., DeTienne, D. H., & Joshi, S. A. (2003). Neural Networks as Statistical Tools for Business Researchers. Organizational Research Methods, 6(2), 236-265. https://doi.org/10.1177/1094428103251907
Dong, G. (2019). Exploiting the Power of Group Differences: Using Patterns to Solve Data Analysis Problems. Synthesis Lectures on Data Mining and Knowledge Discovery, 11(1), 1–146. https://doi.org/10.2200/S00897ED1V01Y201901DMK016
Duan, Y., Yeh, C.-H., & Dowe, D. L. (2018). Accounting Results Modelling with Neural Networks: The Case of an International Oil and Gas Company. International Conference on Neural Information Processing 2018, 275–285. https://doi.org/10.1007/978-3-030-04179-3_24
Farlow, S. J. (1981). The GMDH Algorithm of Ivakhnenko. The American Statistician, 35(4), 210–215. https://doi.org/10.1080/00031305.1981.10479358
Francis, J., LaFond, R., Olsson, P., & Schipper, K. (2005). The market pricing of accruals quality. Journal of Accounting and Economics, 39(2), 295–327. https://doi.org/10.1016/j.jacceco.2004.06.003
Guo, J., Huang, W., Mao, Q., Wang, X., Wang, X., & Song, T. (2018). Modified GMDH networks for oilfield production prediction. Geosystem Engineering, 21(4), 217–225. https://doi.org/10.1080 /12269328.2017.1398110
Hassan, M. N., Sulaiman, M. N., Ibrahim, N. A., & Lukman, I. (2010). Group Method of Data Handling (GMDH) for Economic Evaluation of Air Pollution. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1557193
Healy, P. M. (1985). The effect of bonus schemes on accounting decisions. Journal of Accounting and Economics, 7(1–3), 85–107. https://econpapers.repec.org/RePEc:eee:jaecon:v:7:y:1985:i:1-3:p:85-107
Höglund, H. (2010). Detecting Earnings Management Using Neural Networks. Hanken School of Economics.
Höglund, H. (2012). Detecting earnings management with neural networks. Expert Systems with Applications, 39(10), 9564–9570. https://doi.org/10.1016/j.eswa.2012.02.096
Jones, J. J. (1991). Earnings Management During Import Relief Investigations. Journal of Accounting Research, 29(2), 193. https://doi.org/10.2307/2491047
Kothari, S. P., Leone, A. J., & Wasley, C. E. (2005). Performance matched discretionary accrual measures. Journal of Accounting and Economics, 39(1), 163-197. https://doi.org/10.1016/j.jacceco.2004.11.002
Loyola-Gonzalez, O. (2019). Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View. IEEE Access, 7, 154096–154113. https://doi.org/10.1109/ACCESS.2019.2949286
McNichols, M. F. (2002). Discussion of the quality of accruals and earnings: The role of accrual estimation errors. Accounting Review, 77(1), 61-69. https://doi.org/10.2308/accr.2002.77.s-1.61
Nazemi, B., & Rafiean, M. (2020). Forecasting house prices in Iran using GMDH. International Journal of Housing Markets and Analysis, 14(3), 555-568. https://doi.org/10.1108/IJHMA-05-2020-0067
Parfet, W. U. (2000). Accounting Subjectivity and Earnings Management: A Preparer Perspective. Accounting Horizons, 14(4), 481–488. https://doi.org/10.2308/acch.2000.14.4.481
Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-019-0048-x
Sloan, R. G. (1996). Do stock prices fully reflect information in accruals and cash flows about future earnings? Accounting Review, 71(3), 289–315.
Teoh, S. H., Welch, I., & Wong, T. J. (1998). Earnings management and the underperformance of seasoned equity offerings. Journal of Financial Economics, 50(1), 63–99. https://doi.org/10.1016/s0304-405x(98)00032-4
Abbaszadeh, Mohmmad Reza., Javad Rajabalizadeh., & Mostafa Ghannad. (1398). Political Connections, Related Party Transactions and Earnings Management In Listed Companies in Tehran Stock Exchange. Empirical Studies in Financial Accounting, 16(63), 129-155. [In Persian]
Anvari Rostami, Ali Asghar., Adel Azar., & Mohammad Norozi. (1392). EPS Modeling and Prediction of Listed Companies in Tehran Stock Exchange with GMDH Neural Network Approach. Accounting and Auditing Reviews, 20(1), 1-18. [In Persian]
Ghaderi, Eghbal., Peiman Amini., Iraj Noravesh., & Ata Mohammadi Moqrni. (1397). Explaining the model of earning management measurement using an intelligent hybrid method of neural networks and meta heuristic algorithms (Genetic and particle swarm optimization). Financial Engineering and Securities Management (Portfolio Management), 36. 99-127. [In Persian]
Hejazi, Rezvan., Shapoor Mohamadi., Zahra Aslani., & Majid Aghajani. (1391). Earnings Management Prediction Using Neural Networks and Decision Tree in TSE. Accounting and Auditing Reviews. 19(2). 31-46. [In Persian]
Kashanipour, Mohammad., Gholamreza Karami., & Mohsen Moradi Joz. (1398). A Model for Ranking of Earnings Quality Measures in Iran. Empirical Studies of Financial Accounting. 16(62). 127-150. [In Persian]
Kordestani, Gholamreza., Javad Masomi., & Vahid Baghaee. (1392). Predicting Earnings Management level By Using Artificial Neural Networks. Journal of Accounting Advances, 5(1), 169-190. [In Persian]
Mashayekhi, Bita., Haniyeh Beyrami., Hani Beyrami., & Sara Sadat Akhlaghi. (1391). Detecting Earnings Management Using Neural Networks. Financial Engineering and Securities Management (Portfolio Management), 3(11), 63-79. [In Persian]