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.
翻译:了解信用评级分配中的重要因素有助于更好的决策。然而,目前文献的焦点大多集中在结构化数据上,较少研究涉及非结构化或多模态数据集。在本文中,我们针对不同类型的结构化和非结构化数据集,提出了融合深度学习模型进行公司信用评级类别预测的最有效架构分析。在这些模型中,我们测试了不同深度学习模型的融合策略组合,包括CNN,LSTM,GRU和BERT。我们通过级别(包括早期和中期融合)和技术(包括串联和交叉注意力)研究了数据融合策略。我们的结果表明,基于CNN的多模态模型采用两种融合策略优于其他多模态技术。此外,通过比较简单架构和更复杂的架构,我们发现,更复杂的深度学习模型不一定产生最高性能;但是,如果基于注意力的模型产生最佳结果,则交叉注意力作为融合策略是必要的。最后,我们对短期、中期和长期绩效进行的评级机构比较显示,穆迪的信用评级优于其他机构,如标准普尔和惠誉。