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The Role of Sentiment in Credit Rating: Natural Language Processing Machine Learning Approach

Author

Listed:
  • Gonul Colak

    (Sussex Business School, University of Sussex
    Prague University of Economics and Business, Department of Banking and Insurance)

  • Karel Janda

    (Prague University of Economics and Business, Department of Banking and Insurance
    Charles University, Institute of Economic Studies, Faulty of Social Sciences)

  • Natalie Toulova

    (Charles University, Institute of Economic Studies, Faulty of Social Sciences)

  • Binyi Zhang

    (Charles University, Institute of Economic Studies, Faulty of Social Sciences)

Abstract

We analyse 65,936 English news articles sourced from Factiva and apply the FinBERT model to evaluate how market-wide sentiment towards green bonds affects corporate bond credit rating dynamics. Using fixed-effects regressions that control for both bond-and firm-level characteristics, we find that negative sentiment has a significant adverse effect on corporate bond credit rating outcomes. The results remain robust and consistent across multiple model specifications and comprehensive sets of fundamental controls, highlighting the role of market sentiment in shaping credit ratings within the green bond market. Furthermore, we find that green and conventional bonds exhibit similar risk profiles under comparable corporate financial conditions. In addition, the issuer's ownership type, whether publicly listed or privately held, appears to be associated with credit risk. These findings underscore the importance of soft information, particularly public sentiment, in explaining variations in corporate bond credit risk and contribute to a broader understanding of how market perception influences credit rating assessment in green finance.

Suggested Citation

  • Gonul Colak & Karel Janda & Natalie Toulova & Binyi Zhang, 2026. "The Role of Sentiment in Credit Rating: Natural Language Processing Machine Learning Approach," Springer Proceedings in Business and Economics,, Springer.
    • Handle: RePEc:spr:prbchp:978-3-032-20143-0_2
    DOI: 10.1007/978-3-032-20143-0_2
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