Knowing which factors are significant in credit rating assignment leads to
better decision-making. However, the focus of the literature thus far has been
mostly on structured data, and fewer studies have addressed unstructured or
multi-modal datasets. In this paper, we present an analysis of the most
effective architectures for the fusion of deep learning models for the
prediction of company credit rating classes, by using structured and
unstructured datasets of different types. In these models, we tested different
combinations of fusion strategies with different deep learning models,
including CNN, LSTM, GRU, and BERT. We studied data fusion strategies in terms
of level (including early and intermediate fusion) and techniques (including
concatenation and cross-attention). Our results show that a CNN-based
multi-modal model with two fusion strategies outperformed other multi-modal
techniques. In addition, by comparing simple architectures with more complex
ones, we found that more sophisticated deep learning models do not necessarily
produce the highest performance; however, if attention-based models are
producing the best results, cross-attention is necessary as a fusion strategy.
Finally, our comparison of rating agencies on short-, medium-, and long-term
performance shows that Moody's credit ratings outperform those of other
agencies like Standard & Poor's and Fitch Ratings.

本研究对于公司信用评级的深度学习模型进行结构化数据和非结构化数据的多模态融合，其中以卷积神经网络为基础的模型与两种融合策略的效果最佳；此外，我们还发现更复杂的深度学习模型未必能够带来最好的性能，然而，如果注意力机制为性能最佳，那么交叉注意力机制为必要的融合策略；最后，我们比较了短期、中期和长期表现，并得出 Moody's 信用评级超过标普和惠誉等其他机构的结论。