Abstract

Credit Rating Agencies rate firms and countries by internal experts but with a final qualitative judgment by their management acting as decision makers. These ratings on their turn influence the countries credit rating and ipso facto of their enterprises. The work of the CRA is in fact double: credit rating of firms and other organizations at one side and countries on the other. Considering the credit rating of firms, the CRA made significant mistakes during the Recession 2007−2009 and their judgment is too much American oriented, in any way from a European point of view. Consequently, in Europe many efforts were made to come to a new agency, but all efforts failed. It could be different for the rating of countries. Is a more scientific approach, eventually on a quantitative and structural basis, not possible? Therefore, MULTIMOORA, a quantitative method, is suggested. The study was made for all countries of the European Continent. Based on data available in 2013 and on their extrapolation, the results are quite comparable to the results of Standard & Poor’s Credit Rating System of the moment. As the classifications of Moody’s and Fitch are very similar to those of Standard & Poor’s the outcome would be similar for these other Credit Rating Agencies.

Highlights

  • The Multi-objective optimization based on Ratio Analysis Method (MOORA) is one of the known MCDM methods introduced by Brauers and Zavadskas (2006)

  • Multi-Objective Optimization by Ratio Analysis (MOORA) prefer a ratio system in which the response of each alternative on an objective is compared to a denominator, which is representative for all alternatives concerning that objective

  • Three Rating Agencies of American origin have a quasi-monopoly for credit rating of companies and governments: Standard & Poor’s

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Summary

Introduction

The Multi-objective optimization based on Ratio Analysis Method (MOORA) is one of the known MCDM methods introduced by Brauers and Zavadskas (2006). Hafezalkotob, 2017), selection of residential house construction materials and elements (Zavadskas, Bausys, Juodagalviene & Garnyte-Sapranaviciene, 2017), evaluation and selection of optimal robot for an industrial application (Zhou, You, Zhao & Liu, 2018), selection of appropriate performance appraisal method in organizations (Maghsoodi, Abouhamzeh, Khalilzadeh, & Zavadskas, 2018), selection of the proper technological forecasting method (Dahooie et al, 2019), sustainability assessment for implementation of EU energy policy priorities in the Baltic Sea Region countries (Siksnelyte, Zavadskas, Bausys, & Streimikiene, 2019) and so forth. Brauers, Zavadskas, and Lepkova (2017) made a forecast of facilities management sector in Lithuania and for the analysis applied multi-objective optimization method MULMOORA which helped to obtain a ranking of effectiveness of the firms offering facilities management.

Some information on Credit Rating offices
ERA of Roland Berger
INCRA of the Bertelsmann Stiftung
The European Commission
A Choice of a method for Multi-Objective Optimization
The importance given to an objective in MOORA
MULTIMOORA
Final classification of the European Countries by MULTIMOORA
Missing Countries
Luxemburg: another exception
European and World classification
Ireland
United Kingdom
Conclusions
Findings
Better Legislation in Supervision MAX
Full Text
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