Research on Financial Difficulties Prediction and Optimization Strategies of Small and Medium-sized Enterprises Based on Support Vector Machine

In order to improve the accuracy of financial distress prediction for small and medium-sized enterprises (SMEs) and enhance the prediction performance, this paper adopts principal component analysis (PCA) to extract the input variables required for financial distress prediction, and applies Gray Wolf Optimization Algorithm (GWO) to the optimization of penalty coefficients and kernel function parameters of the Support Vector Machine model (SVM) to propose a financial distress prediction model based on GWO-SVM. The performance of this paper’s GWO-SVM model is evaluated in terms of fitness and prediction classification accuracy. On the same dataset, the CPU time of this paper’s model is 48.44s, the classification accuracy can reach 87.01%, and the classification accuracy in the confusion matrix results can reach up to 93.48%, which outperforms other grid search, GA, PSO, and GWO comparison models. In addition, the prediction accuracy of this paper’s model is always higher than that of the SVM and ICA-SVM models as comparisons, whether in the balanced sample dataset or the unbalanced sample dataset, and maintains the prediction accuracy level of more than 80% and 70% in different datasets, respectively.