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Predicting Corporate Credit Ratings Using Content Analysis of Annual Reports – A Naïve Bayesian Network Approach

  • Petr HajekEmail author
  • Vladimir Olej
  • Ondrej Prochazka
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 276)

Abstract

Corporate credit ratings are based on a variety of information, including financial statements, annual reports, management interviews, etc. Financial indicators are critical to evaluate corporate creditworthiness. However, little is known about how qualitative information hidden in firm-related documents manifests in credit rating process. To address this issue, this study aims to develop a methodology for extracting topical content from firm-related documents using latent semantic analysis. This information is integrated with traditional financial indicators into a multi-class corporate credit rating prediction model. Informative indicators are obtained using a correlation-based filter in the process of feature selection. We demonstrate that Naïve Bayesian networks perform statistically equivalent to other machine learning methods in terms of classification performance. We further show that the “red flag” values obtained using Naïve Bayesian networks may indicate a low credit quality (non-investment rating classes) of firms. These findings can be particularly important for investors, banks and market regulators.

Keywords

Credit rating Firms Prediction Concept extraction Naïve Bayesian network 

Notes

Acknowledgments

This work was supported by the scientific research project of the Czech Sciences Foundation Grant No: GA16-19590S and by the grant No. SGS_2016_023 of the Student Grant Competition.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of System Engineering and Informatics, Faculty of Economics and AdministrationUniversity of PardubicePardubiceCzech Republic

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