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Analyzing Short Term Corporate Credit Risk Indicators Based on User Generated Content During the Corona-Pandemic

  • During the Corona-Pandemic, information (e.g. from the analysis of balance sheets and payment behavior) traditionally used for corporate credit risk analysis became less valuable because it represents only past circumstances. Therefore, the use of currently published data from social media platforms, which have shown to contain valuable information regarding the financial stability of companies, should be evaluated. In this data e. g. additional information from disappointed employees or customers can be present. In order to analyze in how far this data can improve the information base for corporate credit risk assessment, Twitter data regarding the ten greatest insolvencies of German companies in 2020 and solvent counterparts is analyzed in this paper. The results from t-tests show, that sentiment before the insolvencies is significantly worse than in the comparison group which is in alignment with previously conducted research endeavors. Furthermore, companies can be classified as prospectively solvent or insolvent with up to 70% accuracy by applying the k-nearest-neighbor algorithm to monthly aggregated sentiment scores. No significant differences in the number of Tweets for both groups can be proven, which is in contrast to findings from studies which were conducted before the Corona-Pandemic. The results can be utilized by practitioners and scientists in order to improve decision support systems in the domain of corporate credit risk analysis. From a scientific point of view, the results show, that the information asymmetry between lenders and borrowers in credit relationships, which are principals and agents according to the principal-agent-theory, can be reduced based on user generated content from social media platforms. In future studies, it should be evaluated in how far the data can be integrated in established processes for credit decision making. Furthermore, additional social media platforms as well as samples of companies should be analyzed. Lastly, the authenticity of user generated contend should be taken into account in order to ensure, that credit decisions rely on truthful information only.

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Metadaten
Author:Aaron Mengelkamp, Frederik Marinski, Amy Oevermann, Maximilian Vogelsang
URN:urn:nbn:de:bsz:960-opus4-29095
DOI:https://doi.org/10.25968/opus-2909
DOI original:https://doi.org/10.34190/ecsm.10.1.1022
ISBN:978-1-914587-66-5
ISSN:2055-7221
Parent Title (English):Proceedings of the 10th European Conference on Social Media
Document Type:Conference Proceeding
Language:English
Year of Completion:2023
Publishing Institution:Hochschule Hannover
Release Date:2023/07/17
Tag:Corporate Credit Risk
GND Keyword:Kreditrisiko; Unternehmen; Twitter <Softwareplattform>; User Generated Content
Volume:10
Issue:1
First Page:181
Last Page:190
Link to catalogue:1858966337
Institutes:Fakult├Ąt IV - Wirtschaft und Informatik
DDC classes:330 Wirtschaft
Licence (German):License LogoCreative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International