عنوان مقاله [English]
Purpose: Considering the importance of corporate default prediction for various stakeholders, the study aims to identify potential drivers of this event and to present a model for predicting corporate default in automotive and auto parts manufacturing industry, basic metals industry, and chemicals industry in Tehran stock exchange.
Method: In this study, first, by conducting library research and through the fuzzy Delphi method, the factors affecting corporate default were identified. Then, using partial least squares structural equation modeling (PLS-SEM) technique, corporate default drivers were introduced, and the model for predicting this event in each industry was extracted and presented. Finally, the accuracy of the extracted model was tested.
Results: the following variables could be introduced as corporate default drivers in the abovementioned industries: the ratios of net income to total assets, earnings before interest and tax to total assets, earnings before interest and tax to total liabilities, sales to total assets, sales growth rate, retained earnings to total assets, net working capital to total assets, cash to current liabilities, current liabilities to total assets, total liabilities to total assets, short-term and long-term loans to total equity, short-term and long-term loans to total assets, cash flow from operating activities to sales, cash flow from operating activities to earnings before interest and tax, cash flow from operating activities to total liabilities, cash flow from operating activities to current liabilities, and market value of equity to total liabilities.
Conclusion: It was found that in automotive and auto parts manufacturing industry and basic metals industry, only accounting ratios, and in chemicals industry, both accounting ratios and market variables were introduced as corporate default drivers, and other potential drivers (according to previous research findings and experts’ opinions) including macroeconomic indicators, nonfinancial factors, and earning quality measures did not play a role in predicting corporate default.