Corporate rating forecasting using Artificial Intelligence statistical techniques
Identifikátory výsledku
Kód výsledku v 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>
Výsledek na webu
<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>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Corporate rating forecasting using Artificial Intelligence statistical techniques
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
Corporate rating forecasting using Artificial Intelligence statistical techniques
Popis výsledku anglicky
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.
Klasifikace
Druh
J<sub>SC</sub> - Článek v periodiku v databázi SCOPUS
CEP obor
—
OECD FORD obor
50202 - Applied Economics, Econometrics
Návaznosti výsledku
Projekt
—
Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Ostatní
Rok uplatnění
2019
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
International Research Journal: Investment Management and Financial Innovations
ISSN
1810-4967
e-ISSN
—
Svazek periodika
16
Číslo periodika v rámci svazku
2
Stát vydavatele periodika
UA - Ukrajina
Počet stran výsledku
18
Strana od-do
295-312
Kód UT WoS článku
—
EID výsledku v databázi Scopus
2-s2.0-85068121642