非上市企业信用风险预警研究——以福建省为例

Early Warning Research on Credit Risk of Unlisted Enterprises——Taking Fujian Province as an Example

经济下行背景下,防范化解信用风险已成为商业银行的重要挑战之一。这需要加快提升对信用风险的管控和计量能力。本文根据商业银行主要授信客户为非上市企业的特点,对经典KMV模型进行修正,并以福建样本实证检验了这一模型对非上市企业信用风险的识别能力。结果表明:利用修正后的KMV模型计算的五级分类企业的违约距离存在显著差异;修正后的KMV模型风险识别准确率达72.41%,根据样本数据分析可将违约距离(1.004)作为信用风险的早期预警线;企业的资产负债比、总资产收益率、速动比率与违约距离存在显著的正相关关系。

Under the background of economic downturn,preventing and resolving credit risk has become one of the important challenges for commercial banks,which requires them to accelerate the improvement of credit risk management and measurement capabilities.This paper selects the KMV model to measure the credit risk of commercial banks.Since the non-listed enterprises are the main credit customers of commercial banks,the classic KMV model is revised and empirical research is carried out with Fujian samples.The result shows that firstly,there are significant differences of the default distances among five-level classified enterprises using the revised KMV model.Secondly,the revised KMV model has a risk identification accuracy rate of 72.41%.According to the sample data analysis,the default distance=1.004 can be used as the early warning level for credit risk.Thirdly,there is a significant positive correlation between the company's assetliability ratio,total asset return rate,quick ratio and default distance.It is necessary to focus on these three indicators to prevent corporate credit risk.