Forecasting Sub-Sovereign Credit Ratings using Machine Learning Methods

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F17%3A39911542" target="_blank" >RIV/00216275:25410/17:39911542 - isvavai.cz</a>

Výsledek na webu

<a href="http://ibima.org/accepted-paper/forecasting-sub-sovereign-credit-ratings-using-machine-learning-methods/" target="_blank" >http://ibima.org/accepted-paper/forecasting-sub-sovereign-credit-ratings-using-machine-learning-methods/</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Forecasting Sub-Sovereign Credit Ratings using Machine Learning Methods

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. We examine its forecasting accuracy 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 minimize the cost of credit rating classification for sub-sovereign entities across countries and world regions. We looked at each side of the economic, financial and debt and budget, revenues and expenditures, to provide sufficient inputs for the machine learning models. The analyses is to consider the ordinal character of the rating classes, classification cost (cost-sensitive) which is used as objective function, in assessing credit ratings and evaluating of bonds i.e. regional credit rating modelling.

Název v anglickém jazyce

Forecasting Sub-Sovereign Credit Ratings using Machine Learning Methods

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. We examine its forecasting accuracy 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 minimize the cost of credit rating classification for sub-sovereign entities across countries and world regions. We looked at each side of the economic, financial and debt and budget, revenues and expenditures, to provide sufficient inputs for the machine learning models. The analyses is to consider the ordinal character of the rating classes, classification cost (cost-sensitive) which is used as objective function, in assessing credit ratings and evaluating of bonds i.e. regional credit rating modelling.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 30th International Business Information Management Association Conference

ISBN

978-0-9860419-9-0

ISSN

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e-ISSN

neuvedeno

Počet stran výsledku

9

Strana od-do

1271-1279

Název nakladatele

International Business Information Management Association-IBIMA

Místo vydání

Norristown

Místo konání akce

Madrid

Datum konání akce

8. 11. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000443640500127