An Improved Neural Manufacturing Corporate Credit Rating Model Based on LSTM

Abstract

This paper proposes an improved neural manufacturing corporate credit rating model based on Multi-head Self-attention (MSA) mechanism and Long Short-Term Memory (LSTM) network. The proposed model leverages MSA to simulate the marketdynamics and generate dynamic weights for each indicator based on the financial dataof all manufacturing companies. Meanwhile, LSTM is utilized to extract sequential features from long-term financial and operational data to capture the long-term financial status and reduce the risk of deviation. The experimental results show that the proposedmodel provides more objective and reliable credit ratings for manufacturing companies.In the comparison experiment with the baseline model, it was proven that the model proposed in this paper outperforms other baseline models. In the comparison experiment with SMAGRU, it was proven that the proposed model has better predictionability than SMAGRU on both datasets, and it also demonstrates that the GRU simplifies the internal computation of LSTM. The ablation experiment verified the feasibility of the two modules of the proposed model separately, which further proved the effectiveness of the proposed model.