The application of structural and machine learning models to predict the default risk of listed companies in the Iranian capital market.

Inattention of economic policymakers to default risk and making inappropriate decisions related to this risk in the banking system and financial institutions can have many economic, political and social consequences. In this research, it has been tried to calculate the default risk of companies listed in the capital market of Iran. To achieve this goal, two structural models of Merton and Geske, two machine learning models of Random Forest and Gradient Boosted Decision Tree, as well as financial information of companies listed in the Iranian capital market during the years 2016 to 2021 have been used. Another goal of this research is to measure the predictive power of the four models presented in the calculation of default risk. The results obtained from the calculation of the default rate of the investigated companies show that 50 companies listed in the Iranian capital market (46 different companies) have defaulted during the 5-year research period and are subject to the Bankruptcy Article of the Iranian Trade Law. Also, the results obtained from the ROC curves for the predictive power of the presented models show that the structural models of Merton and Geske have almost equal power, but the predictive power of the Random Forest model is a little more than the Gradient Boosted Decision Tree model.

Investigating the monetary policy risk channel based on the dynamic stochastic general equilibrium model: Empirical evidence from Iran.

Investigating the credit channel and monetary policy risk channel in Iran's economy is the aim of this article. According to empirical studies, expansionary monetary policy increases the risk of banks, and on the other hand, the risk of banks affects economic activities and price levels. In order to investigate the mechanism of the credit channel and the risk channel (as a new channel), the effect of monetary policy on real variables and price levels in Iran's economy, the Dynamic Stochastic General Equilibrium (DSGE) model has been used by entering the information of the banking system and considering moral hazard and adverse choices. The obtained results show that there is a credit channel and a monetary policy risk channel for Iran's economy, and the expansionary monetary policy shock causes output, inflation, private sector consumption, investment, net worth in the economy and lending to increase. Also, when a credit shock occurs, with the increase in banks' lending power, production, private sector consumption, investment, net worth and total lending increase and the inflation level decreases. Also, by applying the risk shock caused by the increase in inflation and the decrease in consumption and investment, the volume of lending increases and the level of production does not change much.