Predicting Corporate Credit Ratings Using Content Analysis of Annual Reports - A Naive Bayesian Network Approach

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F17%3A39911516" target="_blank" >RIV/00216275:25410/17:39911516 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-319-52764-2_4" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-319-52764-2_4</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-52764-2_4" target="_blank" >10.1007/978-3-319-52764-2_4</a>

Result language
angličtina
Original language name
Predicting Corporate Credit Ratings Using Content Analysis of Annual Reports - A Naive Bayesian Network Approach
Original language description
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.
Czech name
—
Czech description
—

Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/GA16-19590S" target="_blank" >GA16-19590S: Topic and sentiment analysis of multiple textual sources for corporate financial decision-making</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Enterprise Applications, Markets and Services in the Finance Industry
ISBN
978-3-319-52764-2
ISSN
1865-1348
e-ISSN
neuvedeno
Number of pages
15
Pages from-to
47-61
Publisher name
Springer
Place of publication
Heidelberg
Event location
Frankfurt
Event date
Dec 8, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000416109500004

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