Forecasting The Financial Crisis in Iran According to The Slope of The Yield Curve and The Bank Credit Index: A Machine Learning Approach

This study examines the role of bank credit and macroeconomic variables in predicting financial crises in Iran. Given the importance of predicting and managing financial crises in the Iranian economy, this study aims to identify the key factors and build accurate models to predict these crises.Panel data for the period 2006 to 2022 was used for this study. Advanced machine learning techniques and neural networks were used to analyse the data and create predictive models. These approaches make it possible to examine complex relationships between variables and make more accurate predictions.The results of this study show that the bank debt service ratio, the slope of the yield curve and the investments made are the most important factors in predicting financial crises in Iran. Bank loans also play a minor role in these predictions. The models used, especially neural networks and random forests, have shown high accuracy in predicting financial crises. This study has important implications for economic and financial policies in Iran. The results emphasise the need to review debt management policies, improve the investment environment, and adjust monetary and credit policies more precisely. These findings can help policy makers and economic managers to make more informed decisions and prevent future financial crises.