Credit Claim Appeals: Strategies And Insights From Boston Attorneys

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Credit Claim Appeals: Strategies And Insights From Boston Attorneys

Feature papers represent the most advanced research with significant impact in the field. A Feature Paper should be a significant original article that incorporates multiple techniques or approaches, provides insights for future research directions, and describes potential research applications.

Evidence Based Communication Strategies Exposed

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Tobias Nießner Tobias Nießner Scilit Preprints.org Google Scholar 1, Daniel H. Gross Daniel H. Gross Scilit Preprints.org Google Scholar 2 and Matthias Schumann Matthias Schumann Scilit Preprints.org Google Scholar 1, *

Received: 12/09/2022 / Revised: 4/10/2022 / Approved: 11/10/2022 / Published: 13/10/2022

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Qualitative information from company financial statements provides useful information that can increase the accuracy of bankruptcy prediction models. In this study, 924,903 financial statement materials from 355,704 German companies, classified as solvent, financially distressed and bankrupt using Bureau van Dijk’s Amadeus database, were examined. The results provide empirical evidence that a corpus-linguistic approach that implements a strategy analysis of evidence in financial statements helps to differentiate the financial situations of companies. They show that companies use different approaches and confidence judgments when evaluating their financial statements based on solvency and vary their use of evidence strategies accordingly. This leads to the proposal of a procedure to quantify and generate features based on the analysis of evidence strategies that can be used to improve corporate bankruptcy prediction. The results presented here derive from an interdisciplinary adaptation of linguistic findings and provide another means of analysis in the field of text mining for future research.

Recently, the use of artificial intelligence in connection with financial statement analysis to predict corporate bankruptcy has received more and more attention in research (Roumani et al. 2020; Tanaka et al. 2019). Considering the information provided by the financial statements, a distinction must be made between artificial intelligence approaches that traditionally use quantitative balance sheet data to develop artificial intelligence (Smith and Alvarez 2021) and those that, on the contrary, evaluate financial statement text to analyze the financial situation of companies (Myšková and Hájek 2020). The crucial issue in predicting corporate insolvency with the help of artificial intelligence is therefore the integration of information from both components of the financial statements. The data base of quantitative economic parameters concerning the bankruptcy forecast of companies has already been extensively studied (Altman 1968; Altman et al. 1977). A simple keyword search revealed approximately 9591 articles published in Elsevier Scopus (Elsevier 2022) prior to 2020 related to predicting corporate bankruptcy. The analysis of qualitative text data offers a new way of optimizing existing models, which seems quite promising (Chou et al. 2018; Loughran and McDonald 2016; Luo and Zhou 2020). The fact that German companies, according to §289 of the German Commercial Code (HGB 2021), must present the current and future situation of opportunities, risks, research and development further strengthens the interest in research in this field. Although various established text mining methods, such as sentiment analysis and dictionary-based approaches, have already been studied in English financial statements (Caserio et al. 2020; Loughran and McDonald 2011; Myšková and Hájek 2020), but corpus linguistic analysis. factors concerning argument structures are missing. In order to use text data, it is also necessary to develop appropriate transparent and reproducible text mining methods (Loughran and McDonald 2016). However, in connection with the analysis of annual financial statement data in German, there are preliminary text segmentation methods that show how information extracted from the risk report of the financial statement can be combined with financial indicators to predict corporate bankruptcies (Lohmann and Ohliger 2020).

This paper examines the concept of evidence strategies in financial statements based on the study of word collocations (Kloptchenko et al. 2004a). In language research, evidence strategies have received more attention relatively recently. The results showed that certain evidential expressions are associated with certain discourse domains (Marín Arrese 2017), and different instantiation strategies were detectable in scientific discourse and proved to be quantitative (Hidalgo-Downing 2017). The motivated use of these strategies is related to the speaker’s commitment to evaluation (Besnard 2017). It is reasonable to argue that these characteristics could make the use of evidence strategies an important metric in examining a firm’s financial performance. The goal is therefore to investigate the applicability of evidence strategies as such a measurement and to develop an approach to their quantification in relation to their use in the development of artificial intelligence for bankruptcy prediction. Based on this, the following research question is formulated: RQ: How can financial statement evidence strategies be used to predict corporate bankruptcy?

When developing an approach based on the examination of evidence strategies, it is necessary to make use of previous literature on the use of textual information in financial statements to predict corporate bankruptcy, as well as literature on the linguistic category of evidence to form an understanding. from a corpus analytic approach originating in linguistics. After this, the German financial statement material on which the paper is based must be presented. The language of the material was chosen after the promising results of the German language evidence analysis. Moving on to the qualitative analysis and results, three categories of companies are examined: solvent, financially distressed and those undergoing insolvency proceedings. At the end of the article, an overview and discussion of the effects of the results on theory and practice is presented. In addition, research limitations and future research opportunities are discussed.

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The following is a summary of the literature flow on the use of textual information in predicting the insolvency of companies using computational methods. This particular reflection was chosen to situate the approach appropriately in related research, as there are already reviews related to corporate bankruptcy prediction in general (Kirkos 2015; Veganzones and Severin 2021). In addition, a basis for understanding the linguistic structure of evidence strategies is provided.

In order to place the approach presented in this study in existing research, already existing research methods on the use of textual information in predicting corporate bankruptcies should be reviewed. The review is limited to the literature on the usefulness of qualitative information in corporate financial statements and does not examine external data sources. First of all, the goal of using text data can be defined as improving the ability of artificial intelligence-based models in predicting corporate bankruptcies, as a result of which the prediction probability should be increased (Hájek et al. 2014). Since the literature often assumes that better information can be extracted from the analysis of the text of the financial statements rather than the statements themselves (Kirkos 2015; Luo and Zhou 2020; Nießner et al. 2021), there are studies using text mining methods (Shirata et al. 2011) or combinations of quantitative and qualitative data from the financial statements to predict the future financial situation of companies (Balasubramanian et al. 2019; Chou et al. 2018). These studies examine the extent to which text features are suitable for predicting bankruptcies, but also the company’s financial situation in general (Kloptchenko et al. 2004b). Therefore, approaches can be identified that investigate e.g. readability (Bushee et al. 2018; Luo and Zhou 2020), the use of hedging terms (Humpherys 2009) and also the opinion of financial statement text data (Caserio et al. 2020). Mayew et al. 2015). In addition, there are text mining methods created exclusively for qualitative data from financial statements, as well as studies that analyze corporate environmental variables such as company size and industry to predict corporate bankruptcy (Jones 2017; Pamuk et al. 2021; Pasternak-Malicka et al. 2021). A study of annual financial statements in German found that the analysis of the risk report in terms of linguistic complexity, length and emotional presentation provides suitable information for optimizing company bankruptcy prediction (Lohmann and Ohliger 2020). ). This is also shown by previous results using collocation networks in English financial statements

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