Using Meta Learning Methods to Forecast Sub-Sovereign Credit Ratings

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F18%3A39913447" target="_blank" >RIV/00216275:25410/18:39913447 - isvavai.cz</a>

Výsledek na webu

<a href="https://ibimapublishing.com/articles/JEERBE/2018/870203/870203.pdf" target="_blank" >https://ibimapublishing.com/articles/JEERBE/2018/870203/870203.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5171/2018.870203" target="_blank" >10.5171/2018.870203</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Using Meta Learning Methods to Forecast Sub-Sovereign Credit Ratings

Popis výsledku v původním jazyce

This paper mainly analyses the forecasting of sub-sovereign credit ratings using machine learning methods in the non-US, Europe and other regional and sub-sovereign ratings. Specific focus is based on developing an accurate forecasting model based on machine learning. The forecasting accuracy was examined on two forecasting horizons, one and two years ahead. The study was designed to determine the cost sensitivity of various machine learning methods and to develop an accurate decision-support system that minimizes the cost of credit rating classification for sub-sovereign entities across countries and world regions. Each side of the economic, financial and debt and budget, revenues and expenditures were considered to provide sufficient inputs for the machine learning models. The analyses is to consider the ordinal character of the rating classes, classification cost (cost-sensitivity) which is used as objective function, in assessing credit ratings and evaluating of bonds i.e. regional credit rating modeling. This paper has been able to demonstrate that machine learning models based on current available financial and economic data present accurate classifications of credit ratings. Also the sub-sovereign credit rating forecast signified that the Random Forest and SMO algorithm performed significantly better than the statistical methods. Some practical implications were also provided.

Název v anglickém jazyce

Using Meta Learning Methods to Forecast Sub-Sovereign Credit Ratings

Popis výsledku anglicky

This paper mainly analyses the forecasting of sub-sovereign credit ratings using machine learning methods in the non-US, Europe and other regional and sub-sovereign ratings. Specific focus is based on developing an accurate forecasting model based on machine learning. The forecasting accuracy was examined on two forecasting horizons, one and two years ahead. The study was designed to determine the cost sensitivity of various machine learning methods and to develop an accurate decision-support system that minimizes the cost of credit rating classification for sub-sovereign entities across countries and world regions. Each side of the economic, financial and debt and budget, revenues and expenditures were considered to provide sufficient inputs for the machine learning models. The analyses is to consider the ordinal character of the rating classes, classification cost (cost-sensitivity) which is used as objective function, in assessing credit ratings and evaluating of bonds i.e. regional credit rating modeling. This paper has been able to demonstrate that machine learning models based on current available financial and economic data present accurate classifications of credit ratings. Also the sub-sovereign credit rating forecast signified that the Random Forest and SMO algorithm performed significantly better than the statistical methods. Some practical implications were also provided.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

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OECD FORD obor

50206 - Finance

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

Journal of Eastern Europe Research in Business and Economics

ISSN

2169-0367

e-ISSN

—

Svazek periodika

2018

Číslo periodika v rámci svazku

2018

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

1-12

Kód UT WoS článku

—

EID výsledku v databázi Scopus

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