PSO-CNN-BiLSTM-MQA for Credit Risk Prediction of Listed Enterprises Based on Hierarchical Feature Modeling

Aiming at the problems of insufficient feature discrimination, difficulty in parameter adjustment, and lack of time dependence capture ability in the existing credit risk prediction models of listed companies, this paper proposes a PSOCNN-BiLSTM-MQA credit risk prediction method PCBM based on hierarchical feature modeling. Based on the financial data of listed companies in Guotai’an database, the method divides the data into four types of first-level characteristics: debt paying ability, operating ability, profitability and development ability. Each class of first-level features is further subdivided into several second-level features. In the feature extraction stage, a twochannel Convolutional Neural Network (CNN) was used to extract the local features of each class of level-1 features. One channel processed the original data, and the other channel processed the features converted to grayscale images. After being processed by different CNNS, the multi-query attention mechanism (MQA) was used to fuse the features from different channels to enhance the attention to features at different levels. At the same time, the bidirectional long short-term memory network is used to capture the long-term dependencies in the time series data, and the fused features are integrated through the MQA mechanism. In order to further improve the performance of the model, the particle swarm optimization algorithm is used to optimize the model parameters. Experimental results show that compared with the existing prediction models, the PCBM model shows significant improvement in accuracy, precision, recall rate and F1-score, which proves the effectiveness of this method in the credit risk prediction of listed companies.