关键词:

违约预测, 指标组合遴选, 决策树参数

Abstract:

Accurate prediction of default risk of listed companies is essential to credit risk evaluation and an important basis for financial institutions to make credit decisions. This paper, by selecting the optimal feature subset with a strong default discriminative ability using the linear regression model based on the Akaike information criterion (AIC) measure, and utilizing particle swarm optimization (PSO) algorithm, builds an extreme gradient boosting (XGBoost) default prediction model based on selected feature subset. Based on the dataset covering 3 425 A-share listed companies in China for different time windows, it empirically compares the proposed model (PSO-XGBoost)  with thirteen well-known benchmark models, including logistic regression and support vector machine, to check the effectiveness of the model. Moreover, it uses Friedman test to further examine the significant difference between the proposed model and the benchmark models using three credit datasets from UCI machine learning repository. The empirical results on listed companies dataset show that the proposed model has a good prediction performance and outperforms other benchmark models in terms of geometric mean （G-mean）. The majority of performance measures on three credit datasets show that the average prediction performance of the proposed model surpasses that of other benchmark models. This paper obtains the feature importance measured by the relative contribution of each feature to the prediction results and increases the interpretability of the model. The findings reveal that financial indicators containing asset liability ratio, current ratio, and long-term debt to asset ratio have the greatest effects on default prediction. Macro factors including industry prosperity index, gross retail sales growth rate of consumer goods, and growth rate of cash in circulation （M0）supply, are important features affecting default prediction. This paper provides effective methods and empirical evidence for improving the prediction accuracy of default risk, which helps strengthen the early warning and prevention of default risk for listed companies, reduces regulatory costs for default risk, and provides decision-making support for enterprise managers, creditors, and investors.

Key words:

default prediction, feature subset selection, parameters for decision tree