Impact of Internal Control Weaknesses on Financial Reporting Risk

Subject Areas : Financial Accounting

mohsen azhdar ¹ , mohsen dastgir ² , saeid aliahmadi ³

1 - Department of Accounting, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
2 - Department of Accounting, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
3 - Department of Accounting, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran

Received: 2021-12-03 Accepted : 2022-02-04 Published : 2023-01-01

Keywords: Internal control weaknesses, Keywords: Financial reporting risk, Quality of accruals,

Abstract :

The main role of financial reporting in capital markets is to provide the necessary conditions for the optimal allocation of resources and making correct and timely decisions. Achieving this goal is possible if the financial statements are consistent with economic realities or even have the slightest deviation from economic performance. However, over the past few decades, fraud detected in corporate financial reporting has increased the risk of financial reporting. Therefore, the current paper aims at investigating the impact of internal control weaknesses on financial reporting risk in companies listed on the Tehran Stock Exchange. In order to achieve the purpose of the research, using the method of systematic elimination of information related to 143 companies among the companies listed on the Tehran Stock Exchange in the period from 2009 to 2019 was collected. A multivariate regression model based on composite data was used to test the research hypothesis. The research findings show the significant positive impact of internal control weaknesses on financial reporting risk. The results indicate that reducing the weaknesses of internal control can reduce the risk of financial reporting and reduce information asymmetry and consequently improve accountability processes.

References:

[1] Sufian, F., Determinants of bank efficiency during unstable macroeconomic environment: Empirical evidence from Malaysia, Res. Int. Bus. Financ, 2009, 23, P.54–77, Doi:10.1016/j.ribaf.2008.07.002.

[2] Manandhar, R., Tang, J.C.S.,The evaluation of bank branch performance using data envelopment analysis, A framework. J. High Technol. Manag. Res, 2002, 13, P. 1–17, Doi:10.1016/S1047-8310(01)00045-1.

[3] Frei, F.X., Harker, P.T., Measuring aggregate process performance using AHP. Eur. J. Oper. Res, 1999, 116, P.436–442, Doi:10.1016/S0377-2217(98)00134-9.

[4] Lotto, J., Papavassiliou, V., Evaluation of factors influencing bank operating efficiency in Tanzanian banking sector, Cogent Econ. Financ, 2019, 7, 1664192, Doi:10.1080/23322039.2019.1664192.

[5] Wang, K., Huang, W., Wu, J., Liu, Y.N., Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA, Omega (United Kingdom) 2014, 44, P.5–20, Doi:10.1016/j.omega.2013.09.005.

[6] Ahadzadeh Namin, M., Khamseh, E., Mohamadi, F., Evaluate the performance of bank branches using the control approach in analyzing the data cover weight, 2019, 10, P.1–28.

[7] Henriques, I.C., Sobreiro, V.A., Kimura, H., Mariano, E.B., Efficiency in the Brazilian banking system using data envelopment analysis, Futur. Bus. J. 2018, 4, P.157–178, Doi: 10.1016/j.fbj.2018.05.001.

[8] Jiang, H., He, Y., Applying data envelopment analysis in measuring the efficiency of Chinese listed banks in the context of macroprudential framework. Mathematics 2018, 6, P.184, Doi:10.3390/math6100184.

[9] Razipour-GhalehJough, S., Hosseinzadeh Lotfi, F., Jahanshahloo, G., Rostamy-malkhalifeh, M., Sharafi, H. Finding closest target for bank branches in the presence of weight restrictions using data envelopment analysis, Ann. Oper. Res. 2020, 288, P.755–787, Doi:10.1007/s10479-019-03166-6.

[10] Ihaddaden, M.E.F., Investigating Eurosystem Central Banking Efficiency: A Data Envelopment Analysis Approach, 2019, Doi:10.2139/SSRN.3326536.

[11] Yu, M.M., Lin, C.I., Chen, K.C., Chen, L.H. Measuring Taiwanese bank performance: A two-system dynamic network data envelopment analysis approach. Omega (United Kingdom) 2021, 98, 102145,

Doi: 10.1016/j.omega.2019.102145.

[12] abidin, z., prabantarikso, r.m., wardhani, r.a., endri, e. Analysis of Bank Efficiency Between Conventional Banks and Regional Development Banks in Indonesia. J. Asian Financ. Econ. Bus. 2021, 8, P.741–750.

 Doi: 10.13106/jafeb.2021.vol8.no1.741.

[13] Shair, F., Shaorong, S., Kamran, H.W., Hussain, M.S., Nawaz, M.A., Nguyen, V.C. Assessing the efficiency and total factor productivity growth of the banking industry: do environmental concerns matters? Environ. Sci. Pollut. Res. 2021, 28, P.20822–20838, Doi:10.1007/s11356-020-11938-y.

[14] An, Q., Wu, Q., Zhou, X., Chen, X., Closest target setting for two-stage network system: An application to the commercial banks in China, Expert Syst. Appl. 2021, 175,  P.114799, Doi: 10.1016/j.eswa.2021.114799.

[15] Asmild, M., Kronborg, D., Mahbub, T., Matthews, K., Inefficiency patterns in family-owned banks in Bangladesh. J. Econ. Stud. 2021, Doi:10.1108/JES-06-2020-0286.

[16] Azad, M.A.K., Talib, M.B.A., Kwek, K.T., Saona, P., Conventional versus Islamic bank efficiency: A dynamic network Data-Envelopment-Analysis approach. J. Intell. Fuzzy Syst. 2021, 40, P.1921–1933, Doi:10.3233/JIFS-189196.

[17] Paulet, E., Mavoori, H. Cross-regional analysis of banking efficiency drivers. Appl. Econ. 2021, 53, P.2042–2065, Doi:10.1080/00036846.2020.1855312.

[18] Sáez-Fernández, F.J., Picazo-Tadeo, A.J., Jiménez-Hernández, I. Performance and risk in the Brazilian banking industry. Heliyon 2021, 7, e06524, Doi: 10.1016/j.heliyon.2021.e06524.

[19] Lartey, T., James, G.A., Danso, A., Interbank funding, bank risk exposure and performance in the UK: A three-stage network DEA approach. Int. Rev. Financ. Anal. 2021, 75, 101753, Doi: 10.1016/j.irfa.2021.101753.

[20] Kazemi, S., Tavana, M., Toloo, M., Zenkevich, N.A. A common weights model for investigating efficiency-based leadership in the russian banking industry. RAIRO-Oper. Res. 2021, 55, P.213–229, Doi:10.1051/ro/2020143.

[21] Bou-Hamad, I., Anouze, A.L., Osman, I.H., A cognitive analytics management framework to select input and output variables for data envelopment analysis modeling of performance efficiency of banks using random forest and entropy of information. Ann. Oper. Res. 2021, P.1–30, Doi:10.1007/s10479-021-04024-0.

[22] Lotfi, F.H., Lotfi, F.H., Navabakhs, M., Tehranian, A., Rostamy-malkhalifeh, M., Shahverdi, R. Ranking Bank Branches with Interval Data the Application of DEA. Int. Math. Forum 2007, 9, P.429–440.

[23] Shafiee, M., Bank branches efficiency assessment using dynamic data envelopment analysis approach to SBM. J. New Res. Math. 2017, 3, P.5–18.

[24] Charles, V., Aparicio, J., Zhu, J., The curse of dimensionality of decision-making units: A simple approach to increase the discriminatory power of data envelopment analysis. Eur. J. Oper. Res. 2019, 279, P.929–940,

 Doi: 10.1016/j.ejor.2019.06.025.

[25] Dar, Q.F., Ahn, Y.-H., Dar, G.F. The Impact of International Trade on Central Bank Efficiency: An Application of DEA and Tobit Regression Analysis. Stat. Optim. Inf. Comput. 2021, 9, P.223–240, Doi:10.19139/soic-2310-5070-1077.

[26] Eskelinen, J., Comparison of variable selection techniques for data envelopment analysis in a retail bank. Eur. J. Oper. Res. 2017, 259, P.778–788, Doi: 10.1016/j.ejor.2016.11.009.

[27] Niknafs, J., Keramati, M.A., Haghighatmonfared, J., Estimating Efficiency of Bank Branches by Dynamic Network Data Envelopment Analysis and Artificial Neural Network. Adv. Math. Financ. Appl. 2020, 5, P. 377–390, Doi:10.22034/AMFA.2019.1585957.1192.

[28] Akbari, S., Heydari, J., Keramati, M., Keramati, A., Designing A Mixed System of Network DEA for Evaluating the Efficiency of Branches of Commercial Banks in Iran, Adv. Math. Financ. Appl. 2019, 4, P.1–13, Doi:10.22034/AMFA.2019.582260.1165.

[29] Esfandiar, M., Saremi, M., Jahangiri Nia, H., Assessment of the efficiency of banks accepted in Tehran Stock Exchange using the data envelopment analysis technique, Adv. Math. Financ. Appl. 2018, 3, P.1–11, Doi:10.22034/AMFA.2018.540815.

[30] Fanchon, P., Variable selection for dynamic measures of efficiency in the computer industry, Int. Adv. Econ. Res. 2003, 9, P.175–188, Doi:10.1007/BF02295441.

[31] Wagner, J.M., Shimshak, D.G., Stepwise selection of variables in data envelopment analysis: Procedures and managerial perspectives, Eur. J. Oper. Res. 2007, 180, P.57–67, Doi: 10.1016/j.ejor.2006.02.048.

[32] Madhanagopal, R., Chandrasekaran, R., Selecting Appropriate Variables for DEA Using Genetic Algorithm (GA) Search Procedure. Int. J. Data Envel. Anal. Operations Res. 2014, 1, P.28–33, Doi:10.12691/IJDEAOR-1-2-3.

[33] Fernandez-Palacin, F., Lopez-Sanchez, M.A., Muñoz-Márquez, M., Stepwise selection of variables in dea using contribution loads, Pesqui. Operacional 2018, 38, P.31–52, Doi:10.1590/0101-7438.2018.038.01.0031.

[34] Adler, N., Yazhemsky, E., Improving discrimination in data envelopment analysis: PCA-DEA or variable reduction. Eur. J. Oper. Res. 2010, 202, P.273–284, Doi: 10.1016/j.ejor.2009.03.050.

[35] Pastor, J.T., Ruiz, J.L., Sirvent, I., Enhanced DEA Russell graph efficiency measure, Eur. J. Oper. Res. 1999, 115, P.596–607, Doi:10.1016/S0377-2217(98)00098-8.

[36] Lozano, S., Villa, G., Centralized resource allocation using data envelopment analysis, J. Product. Anal. 2004, 22, P.143–161, Doi:10.1023/b:prod.0000034748.22820.33.

[37] Charnes, A., Cooper, W.W. Programming with linear fractional functionals, Nav. Res. Logist. Q. 1963, 10, P.273–274, Doi:10.1002/nav.3800100123.

 [38] Moazzami Gudarzi, M., Jaberansari, M., Moallem, A., Shakiba, M., Applying Data Envelopment Analysis (DEA) for Measuring Relative Efficiency and Ranking Branches of Refah Kargaran Bank in Lorestan Province. QJER 2014, 14(1), P.115-126.