Corporate rating forecasting using Artificial Intelligence statistical techniques

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F19%3A10242771" target="_blank" >RIV/61989100:27510/19:10242771 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.21511/imfi.16(2).2019.25" target="_blank" >http://dx.doi.org/10.21511/imfi.16(2).2019.25</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.21511/imfi.16(2).2019.25" target="_blank" >10.21511/imfi.16(2).2019.25</a>

Result language
angličtina
Original language name
Corporate rating forecasting using Artificial Intelligence statistical techniques
Original language description
Forecasting companies long-term financial health is provided by Credit Rating Agencies (CRA) such as S&P, Moody's, Fitch and others. Estimates of rates are based on publicly available data, and on the so-called 'qualitative information'. Nowadays, it is possible to produce quite precise forecasts for these ratings using economic and financial information that is available in financial databases, utilizing statistical models or, alternatively, Artificial Intelligence techniques. Several approaches, both cross section and dynamic are proposed, using different methods. Artificial Neural Networks (ANN) provide better results than multivariate statistical methods and are used to estimate ratings within all the range provided by the CRAs, obtaining more desegregated results than several proposed models available for intervals of ratings. Two large samples of companies 'public data' obtained from Bloomberg are used to obtain forecasts of S&P and Moody's ratings directly from these data with high level of accuracy. This also permits to check the published rating's reliability provided by different CRAs.
Czech name
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Czech description
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Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
50202 - Applied Economics, Econometrics

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical
International Research Journal: Investment Management and Financial Innovations
ISSN
1810-4967
e-ISSN
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Volume of the periodical
16
Issue of the periodical within the volume
2
Country of publishing house
UA - UKRAINE
Number of pages
18
Pages from-to
295-312
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-85068121642

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