Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/304193 
Year of Publication: 
2023
Citation: 
[Journal:] Cogent Economics & Finance [ISSN:] 2332-2039 [Volume:] 11 [Issue:] 2 [Article No.:] 2251272 [Year:] 2023 [Pages:] 1-19
Publisher: 
Taylor & Francis, Abingdon
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.
Subjects: 
BoW
hierarchies clustering
k-means
text mining
topic modeling
US failed bank
JEL: 
G00
G21
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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