Paper: arXiv 2508.02738

Authors: Yumeng Shi, Zhongliang Yang, DiYang Lu, Yisi Wang, Yiting Zhou, Linna Zhou

Abstract

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.

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 8.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper uses standard deep learning architectures (GRU, FinBERT) without complex mathematical derivations, but demonstrates strong empirical rigor with a purpose-built dataset (CCRD), specific accuracy improvements (8-12%), and clear implementation details for NLP feature extraction.

Research Flowchart

  flowchart TD
  A["Research Goal: <br> Improve Credit Rating Accuracy <br> by Integrating Annual Report Insights"] --> B

  subgraph B["Methodology & Data"]
      direction LR
      B1["Dataset: CCRD<br>Financial + Annual Reports"] --> B2["Feature Engineering<br>FinBERT for Text Features"]
  end

  B --> C["Computational Process<br>Deep Learning Model<br>Integrated Financial & Text Features"]

  C --> D["Key Findings & Outcomes"]
  D --> D1["8-12% Accuracy Improvement"]
  D --> D2["Enhanced Reliability of Ratings"]
  D --> D3["Unlocks Value of Unstructured Data"]

Cited References (28)

  1. Mining Emotions of the Public from Social Media for Enhancing Corporate Credit Rating (2018) — link
  2. Social Media Analytic-Based Corporate Credit Rating Forecasting (2024)
  3. Investment Value Evaluation of Listed Companies Based on Machine Learning (2023)
  4. Predicting distresses using deep learning of text segments in annual reports (2019)
  5. Every corporation owns its image: Corporate credit ratings via convolutional neural networks (2020)
  6. A novel end-to-end corporate credit rating model based on self-attention mechanism (2020)
  7. Every corporation owns its structure: corporate credit rating via graph neural networks (2022)
  8. Adversarial semi-supervised learning for corporate credit ratings (2021) — link
and 20 more
  1. Contrastive pre-training for imbalanced corporate credit ratings (2022)
  2. Multi-task Envisioning Transformer-based Autoencoder for Corporate Credit Rating Migration Early Prediction (2022)
  3. SparseGraphSage: A Graph Neural Network Approach for Corporate Credit Rating (2024)
  4. Credit rating analysis with support vector machines and neural networks: a market comparative study (2004)
  5. Application of deep neural networks to assess corporate credit rating (2020) — link
  6. Predicting corporate credit rating based on qualitative information of MD&A transformed using document vectorization techniques (2020)
  7. Multi-Modal Deep Learning for Credit Rating Prediction Using Text and Numerical Data Streams — → 2304.10740
  8. A hybrid KMV model, random forests and rough set theory approach for credit rating (2012)
  9. A comparative study on base classifiers in ensemble methods for credit scoring (2017)
  10. The impact of news articles and corporate disclosure on credit risk valuation (2016)
  11. The information value of credit rating action reports: A textual analysis (2016)
  12. An empirical analysis of changes in credit rating properties: Timeliness, accuracy and volatility (2009)
  13. Feature selection in corporate credit rating prediction (2013)
  14. Credit ratings and credit risk: Is one measure enough? (2017)
  15. ZETATM analysis A new model to identify bankruptcy risk of corporations (1977)
  16. Multivariate adaptive regression splines (1991)
  17. On the predictive ability of narrative disclosures in annual reports (2010)
  18. FinLlama: Financial Sentiment Classification for Algorithmic Trading Applications — → 2403.12285
  19. Finbert: A pre-trained financial language representation model for financial text mining (2021)
  20. Bert: Pre-training of deep bidirectional transformers for language understanding (2018) — link

2 of 28 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.