Neural Network VS Genetic and Particle Swarm Optimization Algorithms in Bankruptcy

The goal of the study is to estimate an artificial neural network (ANN) model for bankruptcy prediction and optimize processes using the Particle Swarm (PSO) and Genetic (GA) algorithms. 21 variables that were related to the likelihood of bankruptcy were chosen for the study. Neural networks (NNs) choose the optimal network with the least error in training and evaluating patterns in the second phase. The neural network's weights and biases were optimized in the final stage by combining GA and PSO with the neural network. The results showed that the ability to explain the initial pattern has risen using GA and PSO. The evaluation of ANN performance demonstrates the superiority of the models over linear regression. Finally, four variables—current ratio, sales to current assets ratio, economic value added, and gross profit margin ratio—that may reliably predict bankruptcy were found using the ANNs-PSO and ANNs-GA hybrid approach. The evidence reveals the effectiveness of the metaheuristic algorithms compared to linear ones in predicting bankruptcy. This further highlights the new breed of computational tools available to techno-savvy financial analysts and investors.