Abstract
In China, small enterprises have a direct role in economic growth, but they have difficulty in financing development. To address this problem, this paper creates a small business credit evaluation index using a two-stage Bayesian discriminant model. In the first stage, customers are distinguished by whether they are in default, and in the second stage, customers with continuing default are divided into those with a high default loss rate and those with a low default loss rate. The literature to date has identified a credit index only for the first stage; the credit evaluation index proposed here is based on two stages, which is more sensitive. Then, we conduct an empirical analysis using credit data on 3,111 small enterprises in China with a two-stage nonparametric Bayesian discriminant model and a parametric discriminant model, and then, we test the two indicator systems with discriminant accuracy and an ROC curve; the discriminant accuracy of the established index system is 77.95% and 70.95%, respectively, and their prediction accuracy is 0.902 and 0.866, respectively; they show that the constructed indicator system is robust and effective. Finally, we conduct a comparative analysis of discriminant accuracy in three models, finding that the two-stage nonparametric model is optimal, the two-stage logistic regression model is suboptimal, and the two-stage parametric model is poor.
Highlights
Small business is one of the most active economic parts of the Chinese economy, but its development has been limited to some extent by its difficulty in obtaining financing
The issue facing banks is how to determine which factors explain the credit status of small enterprises. e number of such credit indicators for small enterprises are so numerous that a problem of information duplication arises. ey need a way to evaluate enterprise credit that is both more sensitive and less quantitatively intensive. erefore, this paper proposes a two-stage discriminant method for the selection of credit evaluation indicators to construct a scientific and complete indicator system that can distinguish the state of default of small enterprises
We can see that the two-stage nonparametric Bayesian discriminant model has a stronger sensitivity and higher ability of default discrimination, which can be applied in practice, and opens up a new way of two-stage discriminant in the construction of credit evaluation index system in the future
Summary
Small business is one of the most active economic parts of the Chinese economy, but its development has been limited to some extent by its difficulty in obtaining financing. Small enterprises themselves financial system is not standardized, their financial information is not perfect, and it is difficult for them to provide mortgage guarantee. For these and other reasons, they face problems in obtaining bank loans. Erefore, this paper proposes a two-stage discriminant method for the selection of credit evaluation indicators to construct a scientific and complete indicator system that can distinguish the state of default of small enterprises. We believe that the two-stage credit evaluation index screening model can build a more sensitive credit evaluation index system, which can provide a reference for banks to conduct a scientific evaluation and credit evaluation of small enterprises
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