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

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.

Financial Statement Text Information Mining and Key Information Extraction Model Construction

“…In light of these issues, this study will investigate the approaches, strategies, and applications of financial statement text information mining and key information extraction model development [7]. They hope to develop unique solutions for automating the extraction of critical financial insights from textual data by conducting a thorough examination of advanced NLP approaches, machine learning algorithms, and transdisciplinary ideas [8] [9]. By bridging the gap between computational linguistics and financial analysis, they want to uncover textual data's transformative potential in defining the future of finance and accounting [10].…”