CreditARF: A Framework for Corporate Credit Rating with Annual Report and Financial Feature Integration
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Abstract (arXiv)
Corporate credit rating serves as a crucial intermediary service in the market economy, playing a key role in maintaining economic order. Existing credit rating models rely on financial metrics and deep learning. However, they often overlook insights from non-financial data, such as corporate annual reports. To address this, this paper introduces a corporate credit rating framework that integrates financial data with features extracted from annual reports using FinBERT, aiming to fully leverage the potential value of unstructured text data. In addition, we have developed a large-scale dataset, the Comprehensive Corporate Rating Dataset (CCRD), which combines both traditional financial data and textual data from annual reports. The experimental results show that the proposed method improves the accuracy of the rating predictions by 8-12%, significantly improving the effectiveness and reliability of corporate credit ratings.
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References · 6
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2023Multi-Modal Deep Learning for Credit Rating Prediction Using Text and Numerical Data Streamscited by 13
- 2020Application of Deep Neural Networks to assess corporate Credit Ratingcited by 9
- 2021Contrastive Pre-training for Imbalanced Corporate Credit Ratingscited by 4
- 2022Multi-task Envisioning Transformer-based Autoencoder for Corporate Credit Rating Migration Early Predictioncited by 3
- 2021Adversarial Semi-supervised Learning for Corporate Credit Ratingscited by 2
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