A Novel Methodology for Credit Spread Prediction: Depth-Gated Recurrent Neural Network with Self-Attention Mechanism

This paper develops a depth-gated recurrent neural network (DGRNN) with self-attention mechanism (SAM) based on long-short-term memory (LSTM)\gated recurrent unit (GRU) \Just Another NETwork (JANET) neural network to improve the accuracy of credit spread prediction. The empirical results of the U.S. bond market indicate that the DGRNN model is more effective than traditional machine learning methods. Besides, we discovered that the Depth-JANET model with one gated unit performs better than Depth-GRU and Depth-LSTM models with more gated units. Furthermore, comparative analyses reveal that SAM significantly improves DGRNN's prediction performance. The results show that Depth-JANET neural network with SAM outperforms most other methods in credit spread prediction.