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The Research on Credit Risk of Manufacturing Listed Companies in China Based on Logistic Model

机译：基于逻辑模型的中国制造业上市公司信用风险研究

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摘要

Currently, three commonly used ways to research credit risk of listed company is KMV model, CreditMetrics model and CreditRisk+ model. But the three models are established based on data of capital market and credit rating. Consider China's special circumstance, the development of capital market and rating system is not mature. So the three models are not so applicable in China. But logistic model is based on financial data, so the result is more reliable. According to the characteristics of manufacturing industry, this paper choose 6 financial indicators to predict the default probability of manufacturing listed companies and construct a logistic model through principal component analysis. Empirical result shows that the predictive accuracy of logistic model is 81.3%, two principal components have significant effect on default probability. Compared with other models, logistic model has good predictive effect, can not only provide a standard for company's risk warning, but also provide a standard for commercial bank loans.
机译：目前，研究上市公司信用风险的三种常用方法是KMV模型，CreditMetrics模型和CreditRisk +模型。但是，这三个模型是根据资本市场和信用评级数据建立的。考虑到中国的特殊情况，资本市场的发展和评级体系还不成熟。因此，这三种模型在中国并不适用。但是逻辑模型基于财务数据，因此结果更加可靠。根据制造业的特点，本文选择了6个财务指标来预测制造业上市公司的违约概率，并通过主成分分析构建了物流模型。实证结果表明，逻辑模型的预测精度为81.3％，两个主成分对违约概率有显着影响。与其他模型相比，逻辑模型具有良好的预测效果，不仅可以为公司的风险预警提供标准，而且可以为商业银行贷款提供标准。

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