improved gray wolf algorithm (gwo)

This study aimed to evaluate the performance of the gradient boosting decision tree model, the parameters of which were optimized with the improved Gray Wolf Algorithm (GWO) by adding financial network-related variables via the selected models of predicting financial distress. The proposed model of this study was implemented on the data of 123 manufacturing companies admitted to the Tehran Stock Exchange and Iran Fara Bourse Co. (IFB) from 2014 to 2021. Initially, the financial network was formed and then, the financial distress of companies was predicted by integrating the network-based variables with financial ratios and using a gradient boosting decision tree model. The model of the gradient boosting decision tree had better performance in terms of precision and Type I error by adding Financial Network Indicators (FNI) compared to the two models of K-Nearest Neighbor (KNN) and Logistic Regression (LR). Companies with betweenness centrality and high degree centrality were found to be less prone to financial distress and vice versa. This is the first study to predict financial distress by using financial network-related variables integrated with financial ratio variables through the novel gradient boosting decision tree method, the parameters of which were optimized with the improved GWO.