Evidential Strategies in Financial Statement Analysis: A Corpus Linguistic Text Mining Approach to Bankruptcy Prediction

Tobias Nießner, Daniel H. Gross and Matthias Schumann ()
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Tobias Nießner: Faculty of Business and Economics, University of Goettingen, 37073 Goettingen, Germany
Daniel H. Gross: Faculty of Philology and History, University of Augsburg, 86135 Augsburg, Germany
Matthias Schumann: Faculty of Business and Economics, University of Goettingen, 37073 Goettingen, Germany

JRFM, 2022, vol. 15, issue 10, 1-15

Abstract: 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 stem from an interdisciplinary adaptation of linguistic findings and provide future research with another means of analysis in the area of text mining.

Keywords: text mining; evidential strategies; bankruptcy prediction; financial statement analysis (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2022
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