Credit Risk Prediction Model for Listed Companies Based on CNN-LSTM and Attention Mechanism

The financial market has been developing rapidly in recent years, and the issue of credit risk concerning listed companies become increasingly prominent. Therefore, predicting is an urgent concern for banks, regulators investors. commonly used models are Z-score, Logit (logistic regression model), kernel-based virtual machine (KVM) neural network models. However, results achieved could be more satisfactory. This paper proposes a credit-risk-prediction model based on CNN-LSTM attention mechanism, Our approach benefits long short-term memory (LSTM) long-term time-series prediction combined with convolutional (CNN) model. Furthermore, advantages being integrated into include reducing complexity data, improving calculation speed training solving possible lack historical data sequence LSTM model, resulting accuracy. To reduce problems, we introduced mechanism to assign weights independently optimize show that our distinct compared other CNNs, LSTMs, CNN-LSTMs research credit-risk listing formula significant meaning.