The Power of Words for Credit Risk Classification: Sentiment Analysis As An Alternative Credit Rating Tool
DOI:
https://doi.org/10.37241/jatss.2026.149Keywords:
credit rating reports, credit rating prediction, transformer-based classification, financial natural language processingAbstract
Introduction: This study examines whether qualitative narratives in credit rating reports can be systematically structured using machine learning and to what extent indicators derived from these narratives carry information for predicting national credit ratings. The research is significant because it operationalizes narrative-based assessments, central to rating processes yet difficult to integrate into quantitative models, making them measurable and analytically tractable.
Method: This study analyzes publicly available institutional credit rating reports. Reports statements are first classified as strengths or constraints using a transformer-based model and then transformed into narrative indicators. Using only these features, national credit ratings are predicted via multi-class classification, with firm-level train–test splitting to avoid data leakage. Performance is assessed using accuracy, precision, recall, and F1-score.
Results or Findings: The strength–constraint classifier achieved high performance (accuracy = 0.977; F1 = 0.981). For rating prediction, accuracy was 0.517, with macro-F1 = 0.403 and weighted-F1 = 0.500. Class-level results were more stable for AA and BBB, but limited for AAA and BB.
Discussion or Conclusion: The findings indicate that indicators derived from report narratives carry a meaningful signal about credit ratings, yet this signal is not sufficient on its own. The proposed approach can be used for rapid summarization of newly released reports, tracking changes in the Strengths–Constraints balance, and supporting scalable credit surveillance and early-warning systems. Future research is encouraged to adopt approaches that integrate narrative indicators with financial and macroeconomic variables.
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