Predicting Corporate Credit Ratings Using Content Analysis of Annual Reports - A Naive Bayesian Network Approach
Konferenční objektpeer-reviewedpostprintDatum publikování
2017
Vedoucí práce
Oponent
Název časopisu
Název svazku
Vydavatel
SPRINGER INTERNATIONAL PUBLISHING AG
Abstrakt
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 Naive 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 Naive 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.
Rozsah stran
p. 47-61
ISSN
1865-1348
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Projekt
Zdrojový dokument
ENTERPRISE APPLICATIONS, MARKETS AND SERVICES IN THE FINANCE INDUSTRY, FINANCECOM 2016
Vydavatelská verze
https://link.springer.com/chapter/10.1007/978-3-319-52764-2_4
Přístup k e-verzi
open access
Název akce
8th International Workshop on Enterprise Applications, Markets and Services in the Finance Industry (FinanceCom)
ISBN
978-3-319-52764-2
Studijní obor
Studijní program
Signatura tištěné verze
Umístění tištěné verze
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Klíčová slova
Credit rating, Firms, Prediction, Concept extraction, Naive Bayesian network, úvěrový rating, firmy, predikce, extrakce konceptu, Naivní Bayesovská síť