Evidential strategies in financial statement analysis: a corpus linguistic text mining approach to bankruptcy prediction

  • The qualitative information of companies’ financial statements provides useful information that can increase the accuracy of bankruptcy prediction models. In this research, a dataset of 924,903 financial statements from 355,704 German companies classified into solvent, financially distressed, and bankrupt companies using the Amadeus database from Bureau van Dijk was examined. The results provide empirical evidence that a corpus linguistic approach implementing evidential strategy analysis towards financial statements helps to distinguish between companies’ financial situations. They show that companies use different approaches and confidence assessments when evaluating their financial statements based on solvency and vary their use of evidential strategies accordingly. This leads to the proposition of a procedure to quantify and generate features based on the analysis of evidential strategies that can be used to improve corporate bankruptcy prediction. The results presented here stemThe qualitative information of companies’ financial statements provides useful information that can increase the accuracy of bankruptcy prediction models. In this research, a dataset of 924,903 financial statements from 355,704 German companies classified into solvent, financially distressed, and bankrupt companies using the Amadeus database from Bureau van Dijk was examined. The results provide empirical evidence that a corpus linguistic approach implementing evidential strategy analysis towards financial statements helps to distinguish between companies’ financial situations. They show that companies use different approaches and confidence assessments when evaluating their financial statements based on solvency and vary their use of evidential strategies accordingly. This leads to the proposition of a procedure to quantify and generate features based on the analysis of evidential strategies that can be used to improve corporate bankruptcy prediction. The results presented here stem from an interdisciplinary adaptation of linguistic findings and provide future research with another means of analysis in the area of text mining.show moreshow less

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Metadaten
Author:Tobias Nießner, Daniel H. GrossORCiD, Matthias Schumann
URN:urn:nbn:de:bvb:384-opus4-990921
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/99092
ISSN:1911-8074OPAC
Parent Title (English):Journal of Risk and Financial Management
Publisher:MDPI
Place of publication:Basel
Type:Article
Language:English
Date of first Publication:2022/10/13
Publishing Institution:Universität Augsburg
Release Date:2022/11/15
Tag:text mining; evidential strategies; bankruptcy prediction; financial statement analysis
Volume:15
Issue:10
First Page:459
DOI:https://doi.org/10.3390/jrfm15100459
Institutes:Philologisch-Historische Fakultät
Philologisch-Historische Fakultät / Anglistik / Amerikanistik
Philologisch-Historische Fakultät / Anglistik / Amerikanistik / Lehrstuhl für Angewandte Sprachwissenschaft Anglistik
Dewey Decimal Classification:4 Sprache / 42 Englisch, Altenglisch / 420 Englisch, Altenglisch
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)