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Foreseeing bankruptcy: A novel perspective based on the uncertainty of text-based communicative value of annual reports

Author

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  • Chen, Tsung-Kang
  • Hao, Yun
  • Chang, Ting-Ru
  • Lin, Yu-Chun

Abstract

We examine whether incorporating the uncertainty of text-based communicative value (U_TCV) in annual reports enhances bankruptcy prediction models under machine learning settings using U.S. firm data from 1996 to 2018. Our findings show that U_TCV variables significantly improve prediction effectiveness beyond Barboza et al.’s (2017) financial variables and Chen et al.’s (2023) text-based communicative value (TCV) variables. Notably, U_TCV variables have greater enhancement on medium- to long-term predictions, contribute to explaining bankruptcy risk term structure, significantly reducing Type I error (misjudging bankrupt firms as non-bankrupt) while maintaining a low Type II error, and complementing the predictive power of TCV variables.

Suggested Citation

  • Chen, Tsung-Kang & Hao, Yun & Chang, Ting-Ru & Lin, Yu-Chun, 2025. "Foreseeing bankruptcy: A novel perspective based on the uncertainty of text-based communicative value of annual reports," Finance Research Letters, Elsevier, vol. 84(C).
    • Handle: RePEc:eee:finlet:v:84:y:2025:i:c:s1544612325010293
    DOI: 10.1016/j.frl.2025.107771
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    References listed on IDEAS

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