Using Meta Learning Methods to Forecast Sub-Sovereign Credit Ratings

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Using Meta Learning Methods to Forecast Sub-Sovereign Credit Ratings

Original language description

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.

Czech name

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Czech description

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Type

J_ost - Miscellaneous article in a specialist periodical

CEP classification

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OECD FORD branch

50206 - Finance

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2018

Confidentiality

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

Name of the periodical

Journal of Eastern Europe Research in Business and Economics

ISSN

2169-0367

e-ISSN

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Volume of the periodical

2018

Issue of the periodical within the volume

2018

Country of publishing house

US - UNITED STATES

Number of pages

12

Pages from-to

1-12

UT code for WoS article

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EID of the result in the Scopus database

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