Credit Risk Evaluation of Chinese Listed Companies Based on Sparse Principal Components

• 1、School of Economics and Trade, Hunan University,410000

Abstract：Credit is the cornerstone of the normal operation of the economy, and it is also the prerequisite and foundation for the continuous development of the economy. Strengthening the evaluation and management of credit risks of listed companies in China is of great significance to listed companies, investors, and financial institutions. Existing studies have problems such as imperfect index systems, repeated redundant input variable information, and unreasonable sample pairing ratios. There is room for improvement. In this paper, 51 ST companies in the Shanghai and Shenzhen A-share listed companies in China in 2018 are selected as credit default samples, and 153 non-ST listed companies are selected as non-default samples for empirical research using a 1: 3 sample matching ratio. Select financial indicators including operating ability, debt paying ability, profitability, and growth ability, and select 25 financial indicators based on statistical inspection methods. Combine the sparse principal component analysis method to extract the principal component factors, and add the company size, equity structure, and Three non-financial indicators of equity pledge. A sparse principal component-based Logistic model was constructed for credit risk evaluation and prediction, and the performance of the Logistic regression model in prediction accuracy and stability was verified.

Keywords： credit risk sparse principal component logistic regression model