Forecasting Sub-Sovereign Credit Ratings using Machine Learning Methods
Konferenční objektStatus neznámýpeer-reviewedpostprintDatum publikování
2017
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Název časopisu
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Vydavatel
International Business Information Management Association-IBIMA
Abstrakt
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.
Rozsah stran
p. 1-11
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Projekt
SGS_2017_017/Podpora rozvoje chytrých měst a regionů
Zdrojový dokument
Proceedings of the 30th International Business Information Management Association Conference
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Přístup k e-verzi
pouze v rámci univerzity
Název akce
30th IBIMA conference on Vision 2020 : Sustainable Economic development, Innovation Management, and Global Growth (08.11.2017 - 09.11.2017, Madrid)
ISBN
978-0-9860419-9-0
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Klíčová slova
rating model, sub-sovereign, credit risk, credit rating, machine learning, ratingový model, úvěrové riziko, úvěrový rating, strojové učení