An attention-based Logistic-CNN-BiLSTM hybrid neural network for credit risk prediction of listed real estate enterprises
Abstract:
Enterprise credit risk prediction is to predict whether enterprises will default in the future, according to a variety of historical data by establishing a corresponding relationship between historical operating conditions and default status. To improve the accuracy of credit risk prediction of listed real estate enterprises and effectively reduce difficulty of government management, we propose an attention-based CNN-BiLSTM hybrid neural network enhanced with features of results of logistic regression, and constructs the credit risk prediction index system of listed real estate enterprises from five characteristic dimensions: profitability, debt-paying ability, growth ability, operating ability and enterprise basic information. This study uses data from the 2017-2020 annual reports of listed real estate enterprises on China's Shanghai and Shenzhen stock exchanges. A five different verifications yields average sensitivity, specificity, and quality index of 99.28%, 94.57% and 97.15%, respectively. The results show that our approach achieves better experimental results than previous works, by comparing PSO-SVM model, RS-PSO-SVR model and PSO-BP model. We conclude that the Logistic-CNN-BiLSTM-att model is more effective for the credit risk prediction of listed real estate enterprises.
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