Modeling the Causes of Business Failure Using Audit Variables: an Interpretive Structural Approach (a case study of agricultural firms in the Tehran Stock Exchange)

Subject Areas : Agriculture Marketing and Commercialization

Vahid  Farham ¹ (Department of Accounting, Sirjan Branch, Islamic Azad University, Sirjan, Iran)
Hossein  Shafiee ² (Department of Accounting, Sirjan Branch, Islamic Azad University, Sirjan, Iran,)
Abas  Sheybani Tazaroji ³ (Department of Accounting, Sirjan Branch, Islamic Azad University, Sirjan, Iran)

Received: 2020-08-18 Accepted : 2021-01-25 Published : 2021-06-01

Keywords:

References:

• Mehrani S. & Mehrani K. & Monsefi Y. & Karami G. (2005), Implication Zmijewski & Shirata bankruptcy predictioan models in Tehran Stock Exchange, Accounting and Auditing Review, 41(12): 105-131.


• Amini P. (2006), Investigating the feasibility of using the Fulmer model to estimate the bankruptcy of companies listed on the Tehran Stock Exchange, Master Thesis, Tehran: Accounting, Tarbiat Modares University, 9(1): 66-78.


• Agarwal A. & Shankar R. & Tiwari M. (2006), Modeling Agility of supply chain, Industrial Marketing Manage Ment, 36(1): 443- 457.


• Makian N. & Karimi Takloo S. (2010), Predicting the bankruptcy of manufacturing companies using artificial neural networks, Quantitative Economics Quarterly, 6(1): 129-144.


• Abzari M. & Mirshams Shahshahani M. & Rasoulzadeh M. (2001), Application of Altman model to determine the bankruptcy status of companies listed on the Tehran Stock Exchange (Textile and Basic Metals Industries), Master Thesis, Faculty of Management, Isfahan: University of Isfahan, 16(2): 118-130.


• Br´edart X. (2014), Bankruptcy prediction model using neural networks, Accounting and Finance Research, 3(2): 124-128


• Agarwal V. & Taffler R. (2007), Twenty-five years of the Taffler Z-score model: Does it really have predictive ability?  Accounting and Business Research, 37(4): 285-300.


• Rama krishnan S. & Mirzaei M. & Bekri M. (2015), Corporate default prediction with adaboost and bagging classifiers, Journal Teknologi (Sciences & Engineering), 73(2): 45-50.


• Yarifard R. (2019), Investigation of Bankruptcy Prediction Models (Fulmer and Spring Models) in Tehran Stock Exchange, Master Thesis in Accounting, Mazandaran: University of Mazandaran, 8(6): 147-160.


• Azar A. (2013), Soft Operations Research, Tehran: Industrial Management Organization Publications, 15(2): 29-40.


• Takata Y. & Hosaka T. & Ohnuma H. (2017), Boosting approach to early bankruptcy prediction from multiple-year financial statements, Asia Pacific Journal of Advanced Business and Social Studies, 3(2): 66-76.


• Barboza F. & Kimura H. & Altman E. (2017), “Machine learning models and bankruptcy prediction”, Expert Systems with Applications, 8(3): 405-417.


• Salehi N. & Azimi Yancheshmeh M. (2016), Comparative investigate of Hazard model and traditional models for bankruptcy predication, Quarterly Financial Accounting Journal, 2(1): 33-46.


• Campbell J. & Hilscher Y. & Szilagyi J. (2008), In search of distress risk, Journal of Finance, 63(6): 2899-2939.


• Ra'ei R. & Fallahpour S. (2008), Application of support vector machine in predicting financial distress of companies using financial ratios, Quarterly Journal of Accounting and Auditing Reviews, 53(15): 17-34.


• Shumway T. (2001), Forecasting bankruptcy more accurately: A simple hazard model, The Journal of Business, 74(1): 101-124.


• Talibnia A. (2016), Predicting the financial distress of companies listed on the Tehran Stock Exchange using Bayesian networks, Quarterly Journal of Accounting and Auditing Reviews, 26(16): 59-78.


• Farajzadeh Dehkordi H. (2007), Application of Genetic Algorithm in Bankruptcy Prediction. Master Thesis, Tehran: Tarbiat Modares University, 17(2): 88-96.


• Iturriaga F. & Sanz I. (2015), Bankruptcy visualization and prediction using neural networks: A study of US commercial banks, Expert Systems with applications, 42(6): 2857-2869.


• Back B. & Laitinen T. & Sere K. (2019), Neural networks and genetic algorithms for bankruptcy predictions, Expert Systems with Applications, 11(4): 407- 413.


• Fallahpour S. & Reza R. (2004), Predicting the financial distress of companies using artificial neural networks, Financial Research, 17(1): 39-69.


• Porzamani Z. (2009), Predicting the economic and financial status of companies through financial ratios based on profitability, cash flows and growth, The Financial Accounting and Auditing Researches, 5(1): 93-115.


• Sharma H. & Gupta A. (2017), The Objetives of Waste Management in India: a Future Inquiry, Technological Forecasting and Social Change, 48(5): 285-309.


• Huang J. & Tzeng G. (2005), Ong Ch.; Multidimensional Data in Multidimensional Scaling Using the Analytic Network Process; Pattern Recognition Letters, 26(8): 115-129.


• Faisal M. & Banwat D. & Shankar R. (2007), InforMation risks Management in Supply Chain: An assessment and mitigation framework, Journal of Enterprise Information Management, 20(6), 677-699.


• Warfield J. (1974), Toward Interpretation of Complex Structural Modeling; IEEE Trans, Systems Man Cybernet, 5(4): 11-29.


• Ravi T. & Shankar R. & Tiwari M. (2005), Productiity Improvement of a Computer Hardware Supply Chain, International Journal of Production and Performance Measurement, 54(4): 239-255.


• Watson R. (1978), Interpretive Structural Modeling- A useful tool for Worth Assessment? Technological Forcasting and Social Change, 11(6): 165-185.