درخت حوزه‌های تخصصی

Climate change and infectious diseases have emerged as two of the most significant challenges facing the world and its economic systems today. Recent studies have increasingly focused on understanding how phenomena such as the COVID-19 pandemic and the climate change crisis—including rising greenhouse gas emissions and uncertainties in climate policy—affect macroeconomic variables and capital markets. Many experts assert that climate change poses a serious threat to human civilization. In light of these pressing concerns, this paper examines the impact of Climate Policy Uncertainty (CPU) and Infectious Disease Risk (IDR) on the Islamic Dow Jones Index (IDJ). To achieve this, the study utilizes monthly data spanning from January 2016 to March 2021 and employs a Structural Vector Autoregressive (SVAR) model. Additionally, Contour Plot graphs are applied to estimate the interaction effects on the IDJ. The findings reveal contrasting impacts of the CPU and IDR indices on the IDJ. Specifically, an increase in the CPU index leads to higher utilization of the Earth's resources for producing goods and services by companies included in the index, resulting in a positive effect. Conversely, a rise in the IDR index exacerbates economic recession, thereby causing a decline in the IDJ.

Stakeholders of an organization avoid undesirable outcomes caused by ignoring the risks. Various models and tools can be used to predict future outcomes, aiming to avoid the undesirable ones. Early warning models are one of the approaches that could help them in doing so. This study focuses on developing an early warning system using machine learning algorithms for predicting solvency in the insurance industry. This study analyses 23 financial ratios from Iranian general insurance companies listed on the Tehran Stock Exchange between 2015 and 2020. The model uses Decision Tree, Random Forest, Artificial Neural Networks, Gradient Boosting Machine and XGBoost algorithms, with Boruta as a feature selection method. The dependent variable is the solvency margin ratio, and the other 22 ratios are the independent variables, which Boruta reduces to 7 variables. Firstly, the performance of the machine learning models on two datasets, one with 22 independent variables and one with 7, is compared based on RMSE values. The XGBoost algorithm performs the best on both data sets. Additionally, the study predicts the 2020 values for 19 insurance companies, performs stage classifications, and compares actual stages to predicted stages. In this analysis, Random Forest has the best estimate accuracy on both data sets, while Gradient Boosting Machine has the best estimate accuracy on the Boruta data set. Finally, the study compares the machine learning models' results in terms of capital adequacy classification, where Random Forest performs the best on both data sets, and Gradient Boosting Machine on the Boruta data set.