Solvency

Changes in credit risk may arise when either the value or the risk of corporate assets changes. Changes in the equity value associated with the changes in the asset value and changes in asset risk can be characterized into potentially countervailing direct and indirect effects. The indirect effect of risk on equity value is a function of factors that affect the debt value of including leverage, asset value, and asset risk. This study examines whether the equity value reflects the profits and losses associated with the changes in the debt value consistent with the predictions of Merton [21]. The insurance companies listed in the Stock Exchange during 2010-2015 were selected to test the desired hypotheses. It has been found that the stock returns are negatively related to the increase in credit risk as reflected in the changes of estimated bond ratings. More importantly for the research question, it has been realized that the relationship between risk changes and equity returns is negative when the leverage is higher.

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.