Evaluating the Model and Formula of Continuity of Activities and Use of the Health Diagnosis Division in Iranian Firms

Document Type : Original Article

Authors

1 Department of Accounting، College of humanities، Khomein Branch، Islamic Azad university، Khomein ،Iran

2 Associate Professor، Department of Accounting, lorestan University, Khoramabad, Iran

3 Associate Professor, Department of management، College of management، Arak Branch، Islamic Azad university ،Arak، Iran

10.30495/ijfma.2023.69443.1921

Abstract

The present research concerns the bankrupt manufacturing companies from the perspectives of the independent and court-assigned auditors, as well as creditors and banks, as the rising number of bankruptcies in the country has left negative impacts on unemployment and undesirable social harms, in addition to incurring heavy financial burdens for the three powers of the government, the parliament, the judiciary and the police. In the previous article, the intended formula and model were first elicited from the classic Grounded Theory (Emergent - estimated, graded). The present study, however, compares the logistic regression with Sugeno’s adaptive neuro-fuzzy inference system (ANFIS) to describe the Kolmogorov-Smirnov, Mann–Whitney U, and Hosmer-Lemeshow tests, etc. Here, the study forms five ANFIS methods and three sets of training, testing and checking (validation) to investigate and confirm the formula of the health diagnosis division and its continuity of use. Evaluation of the best and worst manufacturing companies using the devised model reveals a range of numbers, including + 2.113 to - 0.189, which indicate the normal and abnormal situations of the companies. As for the continuity of the activities (standard 570 of the auditing) and the clause on specific content, the numerical indicator of 0.595 and the contingent clause with the numerical indicator of 0.595 before 0.189, and the rejected clause with the numerical indicator of -0.189 can be used for the statement of the independent auditors. Also, for the court auditors, the time of the beginning of the bankruptcy with the numerical indicator of 0595

Keywords


Botsheken, M. H., Salimi, M. J., Falahatgar Athadjo, S. (2017). Presenting a hybrid method to predict the financial distress of companies listed on the Tehran Stock Exchange. Financial Research, (2), 173-192.
Buachoom, W., Kasemsan, M.L.K. (2011). Business Failure Prediction by Using the Hybrid Technique of GA and ANFIS Based on Financial Ratio: Evident from Listed Companies in the Stock Exchange of Thailand. Journal of Financial Studies and Research, 2011, 499279.
Fakhrhosseini, S. F., Aghaei Meibodi, O. (2018). Prediction and determination of companies with a high probability of bankruptcy listed in Tehran Stock Exchange (different analysis of models). Journal of decision making and research in operations, (1), 3-14.
Hu, Y. C., Tseng, F. M. (2005). Applying backpropagation Neural Networks to Bankruptcy Prediction. Journal of Electronic Business Management, 3(2), 97-103.
Mirbaqarihir, M., Nahidi Amirkhaiz, M., Shekohi Fard, S. (2015). Evaluating the financial stability and explaining the factors affecting the financial stability of the country's banks. Financial and Economic Policy Quarterly, 4(15), 23-42.
Nowruzi Seyed Hosseini, R. (2019). Qualitative research method using data application and theory. Bonyad-e-Tehran: Tarbiat Modares University.
Parker, S., Castillo, N., Garon, T., Levy, R. (2018). Eight Ways to  Measure Financial Health. Chicago: Center for Financial Services Innovation.
Portborstani, A., Dadashi, I., Zare Behnmiri, M. J. (2018). Quantification of independent auditors' report using a fuzzy approach and investigating the capability of predicting bankruptcy from quantified reports in comparison to the type of audit report. Knowledge of Accounting and Management Audit, 31, 169-186.
Purvinis, O., Virbickaite, R., Sukys, P. (2008). Interpretable Nonlinear Model for Enterprise Bankruptcy Prediction, Nonlinear Analysis: Modeling and Control, 13(1), 61-70.
Vakilifar, H. R., Pilevari, N., Zeidi, S. (2013). Providing bankruptcy model using ANFIS. Financial Engineering and Securities Management, 18, 17-30