Predicting financial distress of Chinese listed companies using machine learning: To what extent does textual disclosure matter?

Qi Zhao, Weijun Xu and Yucheng Ji

International Review of Financial Analysis, 2023, vol. 89, issue C

Abstract: Using machine learning to predict the financial distress of Chinese listed companies, this study shows that the incremental value of textual disclosure in financial distress prediction diminishes in the presence of detailed financial data. Detailed financial data itself has the capacity to accurately predict financial distress, and its prediction performance is not improved when combined with predictors extracted from textual disclosure. The model using combined predictors attaches more importance to financial-data-based predictors than textual-data-based ones. Our results provide evidence about the overstated value of textual disclosure and the understated information value of detailed financial data in financial distress prediction.

Keywords: Financial distress prediction; Machine learning; Textual disclosure (search for similar items in EconPapers)
JEL-codes: C53 G17 M41 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1057521923002867
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:89:y:2023:i:c:s1057521923002867

DOI: 10.1016/j.irfa.2023.102770

Access Statistics for this article

International Review of Financial Analysis is currently edited by B.M. Lucey

More articles in International Review of Financial Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().