Research on Early Warning Model of Financial Crisis Based on SCAD-SVM

Abstract

After more than half a century of development, there are numerous empirical analyses on financial early warning models, most of which take listed companies as the research object. The financial crisis early warning of listed companies is a small sample, and the application of neural network and logistic regression model has the problem of over-fitting, which leads to the effect of the model is not obvious. In recent years, the support vector machine method given risk minimization criteria has been widely used in financial crisis early warning, but few scholars combine SVM and penalty letter SCAD methods for financial early warning analysis. Therefore, this paper proposes an improved variable selection method Scad and support vector machine (SVM) combined model algorithm to select the best indicators and used for financial crisis early warning, and then based on the Shanghai and Shenzhen A-share listed companies as the research sample comparison Empirical effects of SVM, Lasso-SVM and Dantzig-SVM, the research results show that the selection of variables can greatly improve the accuracy of the financial crisis early warning model, and the SCAD method in the variable selection method has more advantages than Lasso and Dantzig. The SCAD-SVM model proposed has an accuracy rate of over 96% on both the training set and the test set in this paper. The model has a good classification effect and a strong economic interpretation ability. The research results of this paper not only enrich the financial early warning model, but also greatly improve the accuracy of financial crisis early warning, and provide a theoretical basis for the scientific decision-making of enterprises and shareholders.