The Research on Credit Rating of Information Technology Enterprises in China:Based on the Perspective of Default Identification and Feature Selection
In view of default identification and feature selection,this paper uses machine learning algorithms to study the credit rating problem of information technology enterprises in China.The results shows that using SMOTE method to process imbalanced sample data solves the problem of class preference in prediction models caused by imbalanced sample data in different categories;that we use the Logistic Lasso method to select indicators and calculate the default probability of enterprises,and then to calculate credit rating,ensures the simplification of the credit rating model and the reliability of default prediction,and improves the mismatch between credit rating and default probability.The credit rating model includes 39 indicators and the identification accuracy is over 98％.The reliability and practicality of the model are superior to other common machine learning models.In addition,according to the credit level of enterprises,from the perspective of enterprises themselves,investors and regulatory authorities,the corresponding countermeasures to control risks are put forward.
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