A mathematical model to predict corporate bankruptcy using financial, managerial and economic variables And compare it with other models
محورهای موضوعی : Financial Accounting
Jafar Zarin
1
(
Department of Accounting, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
)
Babak Jamshidinavid
2
(
Department of Accounting, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
)
Mehrdad Ghanbari
3
(
Department of Accounting, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
)
Afshin Baghfalaki
4
(
Department of Accounting, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
)
کلید واژه: Financial ratios, Bankruptcy forecasts, Intellectual Capital, corporate governance and currency fluctuations,
چکیده مقاله :
Many studies have been conducted in the field of bankruptcy prediction; But in most of them only financial ratios are used. However, in Iran, many non-financial factors affect bankruptcy. The main purpose of this study is to develop a mathematical model in which financial and non-financial indicators such as management and economics factors are used to predict bankruptcy. In this study, 44 variables that had the greatest impact on bankruptcy forecast were selected and with confirmatory factor analysis, a questionnaire was developed and sent to experts in the fields of management, accounting and economics to rank the impact of these variables. The statistical sample of the study includes 200 bankrupt and non-bankrupt companies listed in the period 2009-2018. After collecting the questionnaires using the OLS regression estimation method, the variables that had a factor load of less than 0.5 were eliminated and in the final model 9 main variables. The research model identified 95% of bankrupt companies and 93% of non-bankrupt companies with 95.4% confidence. Then, for verification, two hypotheses were developed and the model of this research was compared with two existing models. The ability to distinguish bankrupt companies from non-bankrupt ones by our proposed model was 6% more accurate than the Pourheidari et al. model, and 9.4% more accurate than Altman’s model.
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