Modelling Credit Risk for Chinese SMEs - Fan, Wenye (2020)
Considering the key role of small and medium-sized enterprises (SMEs) in the economy of many countries, this study focuses on the Chinese market and explores the credit risk by specifically developing one-year default prediction models for SMEs. Logistic regression is applied to unbalanced panel data of 144 listed SMEs in the manufacturing industry over the period 2013-2018. Model 1 only contains financial information, while Model 2 includes both financial predictors and qualitative predictors. The findings are as follows. First, SMEs with higher leverage and lower profitability are more likely to default. Second, SMEs with larger boards are prone to default due to the inefficiency of decision-making resulting from more problems of communication and coordination among directors. Third, SMEs in the middle and western China have higher probabilities of default compared to those in the east which has more geographical advantages. More notably, the goodness of fit of the model and overall prediction accuracy is improved after adding qualitative variables. In particular, the prediction accuracy of defaulted SMEs increases by 25% when taking corporate governance and geographical location into account. It helps mitigate the adverse consequences of misclassifying default into non-default to a large extent.