Why do banks fail? An investigation via text mining

Hanh Hong Le, Jean-Laurent Viviani () and Fitriya Fauzi
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Hanh Hong Le: RMIT University Vietnam
Jean-Laurent Viviani: CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique
Fitriya Fauzi: RMIT University Vietnam

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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.

Keywords: text mining; US failed bank; BoW; k-means; topic modeling; hierarchies clustering; G00; G21 (search for similar items in EconPapers)
Date: 2023
Note: View the original document on HAL open archive server: https://hal.science/hal-04223185v1
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Citations: View citations in EconPapers (1)

Published in Cogent Economics & Finance, 2023, 11 (2), pp.2251272. ⟨10.1080/23322039.2023.2251272⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04223185

DOI: 10.1080/23322039.2023.2251272

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