Digitální knihovnaUPCE
 

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

Konferenční objektpeer-reviewedpostprint
Náhled

Datum 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

Trvalý odkaz na tento záznam

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

Přístup k tištěné verzi

Klíčová slova

Credit rating, Firms, Prediction, Concept extraction, Naive Bayesian network, úvěrový rating, firmy, predikce, extrakce konceptu, Naivní Bayesovská síť

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