Bankruptcy Prediction Model Based on Business Risk Reports: Use of Natural Language Processing Techniques

Onjaniaina Mianin'Harizo Rasolomanana

No 358, Discussion paper series. A from Graduate School of Economics and Business Administration, Hokkaido University

Abstract: The purpose of this study is to assess how useful risk information is in bankruptcy prediction, by performing a sentiment analysis of the texts. The proposed method involves the use of Natural Language Processing (NLP) and machine learning techniques. The results show that neural networks performed better than other classifiers, with a classification accuracy of 96.15% for this particular text classification problem. This work demonstrates that business risks reports carry information that helps predict the likelihood of bankruptcy.

Keywords: Bankruptcy prediction; Business risk; Natural language processing; NLP; Sentiment analysis; Neural Networks (search for similar items in EconPapers)
Pages: 14 pages
Date: 2021-04
New Economics Papers: this item is included in nep-big, nep-cmp and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/2115/81088 (text/html)
https://eprints.lib.hokudai.ac.jp/dspace/bitstream/2115/81088/1/DPA358.pdf (application/pdf)

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:hok:dpaper:358

Access Statistics for this paper

More papers in Discussion paper series. A from Graduate School of Economics and Business Administration, Hokkaido University Contact information at EDIRC.
Bibliographic data for series maintained by Hokkaido University Library ().