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The primary purpose of this research is to investigate the impacts of corporate governance on credit risk in the Iranian banking industry. The sample consists of 20 banks listed on the Tehran Stock Exchange during 2011-2016, using panel data. In this research, credit risk and corporate governance are the dependent and independent variables, respectively. The meta-synthesis method was used for compiling a checklist of corporate governance indicators. Then, the content analysis method was applied for measuring the corporate governance index; i.e., the number of dimensions disclosed on the total number of disclosable dimensions. The results indicate that after adjusting the control variables namely the size, the financial leverage, the ratio of capital adequacy, the GDP and inflation, there is a significant negative relationship between corporate governance quality and the credit risk, which means more effective corporate governance will reduce information asymmetry, increases the clarity and stakeholder confidence, and finally reduces banks’ credit risk. Accordingly, the final recommendation is to reduce credit risk by improving the mechanisms of corporate governance in the Iranian banking industry.

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One of the significant problems of banks and investors in Iran is the lack of precise awareness about the financial performance of each bank and the roadmap for improving the conditions. Besides, the undesirable status of the financial performance of banks becomes evident only when the improvement of conditions is complicated. In this paper, a data mining-based early warning system (EWS) model has been presented to capture the financial performance of banks. To design this model, the CHAID decision tree has been used. Using this model, the banks have been classified as poor, medium, and good regarding financial performance, and the roadmap to achieving the desirable status has been determined. For this purpose, 13 Iranian banks have been investigated within the years 2003-2017. Eventually, the results obtained from the decision tree have been compared with the findings achieved from the CAMELS model. Based on the designed decision tree, 8 profiles have been extracted; 2 representing good, 3 medium, and 3 poor financial performance. Based on these profiles, according to the latest reports published by the studied banks, eight banks have a mediocre financial performance while five banks suffer poor financial performance. According to these profiles, four variables of the asset to shareholders’ equity ratio, the shareholders’ equity to loans ratio, the long-term debt to equity ratio, and liquidity coverage ratio were identified as the most relevant variables associated with the financial performance of banks.

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This study investigates the effect of Exchange Rate Pass-Through (ERPT) on the price of imported medical goods in Iran by utilizing the nonlinear autoregressive distributed lag (NARDL) method. The obtained results showed that the coefficient of positive and negative exchange rate fluctuations, in the long term, is less than one unit. In the long term, the price of imported medical goods is also affected by the positive fluctuations of domestic production. With positive fluctuations in domestic production and rising incomes, the demand for imported medical goods tends to increase, leading to higher prices for imported medical goods. Negative fluctuations in the degree of economic openness have negative effects on the price of imported medical goods. Eventually, the effect of the positive and negative fluctuations of trading partners' final cost variables on the price index of imported medical goods is positive. Given the increase in the exchange rate and the subsequent devaluation of the national currency, the increase in the price of imports, and, consequently, the cost of imported inputs, the increase in import prices can be one of the causes of domestic inflation.