Design and Validation of a Financial Distress Risk Assessment Model
Keywords:
Financial distress, bank credit risk, bank liquidity riskAbstract
The objective of this study was to design and validate a model for assessing financial distress risk. The research method employed was based on an econometric model using the GARCH framework, implemented through EViews software version 10. The statistical population consisted of listed banks during the period from 2012 to 2023, and the research sample included data relevant to the financial distress risk assessment model. Among the key findings of the study, it was observed that based on the regression coefficients of the criteria, bank credit risk and bank liquidity risk—with coefficients of 99% and 95%, respectively—contributed significantly to the reduction of financial distress risk in listed banks. Additionally, changes in net income to total bank assets and net working capital to total bank assets demonstrated highly significant impacts on the reduction of financial distress risk, with calculated coefficients of 99% and 94%, respectively. Furthermore, bank market risk, market value to book value of the bank, and total liabilities to total assets of the bank, each with a 91% coefficient, showed similarly significant effects in reducing financial distress risk in listed banks. In contrast, operational risk of the bank, interest rate risk, net income to shareholders' equity, and the total asset size of the bank ranked next with approximate coefficients of 80%. Moreover, according to the power-dependence chart, bank liquidity risk exhibited the highest influence power at 100% and the lowest dependence level at 17%, placing it in the “independent” zone (low dependence, high influence). In fact, bank credit risk and operational risk showed high influence power—83% and 67%, respectively—and moderate dependence levels of 50%, positioning them in the “linkage” zone (high influence, moderate dependence). Conversely, net working capital to total assets, net income to total assets, and market risk of the bank exhibited the highest dependence level at 100% and the lowest influence power at 50%.
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