Forecasting Sub-Sovereign Credit Ratings using Machine Learning Methods

Result code in 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>

Result on the web

<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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Result language

angličtina

Original language name

Forecasting Sub-Sovereign Credit Ratings using Machine Learning Methods

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. 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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

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

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2017

Confidentiality

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

Article name in the collection

Proceedings of the 30th International Business Information Management Association Conference

ISBN

978-0-9860419-9-0

ISSN

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e-ISSN

neuvedeno

Number of pages

9

Pages from-to

1271-1279

Publisher name

International Business Information Management Association-IBIMA

Place of publication

Norristown

Event location

Madrid

Event date

Nov 8, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000443640500127