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Identifying contextual content-based risk drivers for advanced risk management strategies

Author

Listed:
  • Huang, Shirley Hsueh-Li
  • Hu, Guo-Hsin
  • Hsu, Ming-Fu

Abstract

This research proposes a profound contextual topic identifier that incorporates topic modelling and a word embedding technique to discover and quantify corporate risks from its self-identified risk disclosures and examines the association between each risk type and operating performance via artificial intelligence (AI) technique. Via topic modelling adoption, we are able to discover the most essential risks confronted by corporates in the near future and evaluate how they respond to these risks. To gain deeper insight, the study performs a bidirectional encoder representation from transformers (BERT) (one type of word embedding approach) to extract and quantify the semantic features embedded into each risk disclosure. The results show that operating performance significantly and positively relates to corporate-specific risks. This study offers solid and direct support for authorities that set accounting principles to encourage firm managers to add a new section on risk factors in annual reports.

Suggested Citation

  • Huang, Shirley Hsueh-Li & Hu, Guo-Hsin & Hsu, Ming-Fu, 2025. "Identifying contextual content-based risk drivers for advanced risk management strategies," Research in International Business and Finance, Elsevier, vol. 73(PB).
    • Handle: RePEc:eee:riibaf:v:73:y:2025:i:pb:s0275531924004367
    DOI: 10.1016/j.ribaf.2024.102643
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