Why do banks fail? An investigation via text mining

Hanh Hong Le, Jean- Laurent Viviani and Fitriya Fauzi

Cogent Economics & Finance, 2023, vol. 11, issue 2, 2251272

Abstract: This study aims to investigate the material loss review published by the Federal Deposit Insurance Corporation (FDIC) on 98 failed banks from 2008 to 2015. The text mining techniques via machine learning, i.e. bag of words, document clustering, and topic modeling, are employed for the investigation. The pre-processing step of text cleaning is first performed prior to the analysis. In comparison with traditional methods using financial ratios, our study generates actionable insights extracted from semi-structured textual data, i.e. the FDIC’s reports. Our text analytics suggests that to prevent from being a failure; banks should beware of loans, board management, supervisory process, the concentration of acquisition, development, and construction (ADC), and commercial real estate (CRE). In addition, the primary reasons that US banks went failure from 2008 to 2015 are explained by two primary topics, i.e. loan and management.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/23322039.2023.2251272 (text/html)
Access to full text is restricted to subscribers.

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:taf:oaefxx:v:11:y:2023:i:2:p:2251272

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/OAEF20

DOI: 10.1080/23322039.2023.2251272

Access Statistics for this article

Cogent Economics & Finance is currently edited by Steve Cook, Caroline Elliott, David McMillan, Duncan Watson and Xibin Zhang

More articles in Cogent Economics & Finance from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().