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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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 &quot;red flag&quot; 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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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 &quot;red flag&quot; 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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA16-19590S" target="_blank" >GA16-19590S: Analýza témat a sentimentu vícenásobných textových zdrojů pro finanční rozhodování podniků</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Enterprise Applications, Markets and Services in the Finance Industry

ISBN

978-3-319-52764-2

ISSN

1865-1348

e-ISSN

neuvedeno

Počet stran výsledku

15

Strana od-do

47-61

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Frankfurt

Datum konání akce

8. 12. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000416109500004