Scholarlink

<p>Precise corporate credit risk (CCR) prediction empowers investors, banks and other financial institutions to build risk prevention and crisis evasion mechanisms. Currently, CCR prediction gradually trends towards integrating multi-source data for more accurate prediction, but the effective fusion of these data is still insufficient. For this purpose, this paper presents a novel method titled the multimodal extended deep belief network (MEDBN) for CCR prediction. First, a multimodal dataset comprising numerical, categorical, and textual data is constructed based on two public corporate credit rating datasets, with textual data sourced from 10-K/Q filings. Each modality is processed using specialized preprocessing techniques. Then, to effectively extract features from each modality while addressing the challenges of multimodal fusion and joint representation learning, the DBN is extended by incorporating advanced deep learning techniques, including residual networks (ResNet), embedding layers, and bidirectional encoders (BERT). Finally, MEDBN undergoes two-stage training consisting of unsupervised pretraining followed by supervised fine-tuning, enabling sufficient learning of joint representations to enhance model performance. Extensive experimental results indicate that MEDBN consistently outperforms benchmark models across most key metrics, with particularly notable robustness on smaller datasets. These findings imply that MEDBN can effectively fuse and utilize multimodal data to achieve more reliable predictions. Additionally, this study demonstrates how the textual content influences performance, providing interpretable insights to support the decision-making process.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Efficient multimodal learning for corporate credit risk prediction with an extended deep belief network

  • 期刊论文

  • 发表日期：

    2025年5月5日

查看全文
  • Sichong Lu,
  • Xiaoming Zhang,
  • Yi Su,
  • Xiaojun Liu,
  • Lean Yu

摘要

Precise corporate credit risk (CCR) prediction empowers investors, banks and other financial institutions to build risk prevention and crisis evasion mechanisms. Currently, CCR prediction gradually trends towards integrating multi-source data for more accurate prediction, but the effective fusion of these data is still insufficient. For this purpose, this paper presents a novel method titled the multimodal extended deep belief network (MEDBN) for CCR prediction. First, a multimodal dataset comprising numerical, categorical, and textual data is constructed based on two public corporate credit rating datasets, with textual data sourced from 10-K/Q filings. Each modality is processed using specialized preprocessing techniques. Then, to effectively extract features from each modality while addressing the challenges of multimodal fusion and joint representation learning, the DBN is extended by incorporating advanced deep learning techniques, including residual networks (ResNet), embedding layers, and bidirectional encoders (BERT). Finally, MEDBN undergoes two-stage training consisting of unsupervised pretraining followed by supervised fine-tuning, enabling sufficient learning of joint representations to enhance model performance. Extensive experimental results indicate that MEDBN consistently outperforms benchmark models across most key metrics, with particularly notable robustness on smaller datasets. These findings imply that MEDBN can effectively fuse and utilize multimodal data to achieve more reliable predictions. Additionally, this study demonstrates how the textual content influences performance, providing interpretable insights to support the decision-making process.