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Readability of financial reports and stock price crash risk

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  • Zhang, Xiaoxu
  • Wang, Bo
  • Liu, Guangze

Abstract

The rapid evolution of machine learning makes text analysis more feasible. Utilizing a large language model, BERT, this article explores whether and how the readability of financial reports predicts firms’ stock price crash risk. Grounded in Chinese evidence, this research uncovers that the degree of readability is strongly negatively linked to firm stock price crash risk. This effect is stronger among firms led by more entrenched CEOs. These findings remain robust to various model specifications and to the instrumental variable method. Overall, this study, by shedding light on a novel driver of stock price crash risk, advances corporate finance literature.

Suggested Citation

  • Zhang, Xiaoxu & Wang, Bo & Liu, Guangze, 2025. "Readability of financial reports and stock price crash risk," Finance Research Letters, Elsevier, vol. 86(PC).
    • Handle: RePEc:eee:finlet:v:86:y:2025:i:pc:s154461232501743x
    DOI: 10.1016/j.frl.2025.108489
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