An attention‐based Logistic‐CNN‐BiLSTM hybrid neural network for credit risk prediction of listed real estate enterprises

Xinsheng Zhang · Yulong Ma · Minghu Wang

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Financial Distress and Bankruptcy Prediction Stock Market Forecasting Methods Evaluation and Optimization Models

Abstract

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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References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1980Financial Ratios and the Probabilistic Prediction of Bankruptcy 2000A comparative analysis of current credit risk models 2018A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees 2008Support vector machines for credit scoring and discovery of significant features 2020A novel graph convolutional feature based convolutional neural network for stock trend prediction 2011Mixture cure models in credit scoring: If and when borrowers default 2019Financial system modeling using deep neural networks (DNNs) for effective risk assessment and prediction

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