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Aggregation of individual credit assessments using methods of expert information analysis

Priority areas of development: economics
2017
The project has been carried out as part of the HSE Program of Fundamental Studies.

Goal of research

A comprehensive analysis of approaches to aggregation of individual credit assessments using methods of expert information analysis and computational social choice.

Methodology

Computational social choice theory, convex analysis, memetic algorithms and data visualization based on cutting-edge research papers. Numerical computations were carried out using the Matlab software.

Empirical base of research

Developed approach to aggregation is applied to long-term credit ratings assigned by seven credit rating agencies (CRAs) to Russian banks in national scales over the period from 2010 to 2015. Data on defaults of Russian banks is used in order to measure the discriminatory power of aggregated rating

Results of research

We characterize credit assessment of entities as a weak partial order of objects and determine aggregated ratings as a ranking which is most consistent with all individual credit assessments, i.e. aggregated rating is Kemeny median. As Kemeny median is generally multiple for weak partial orders we introduce a supplementary criterion and set lexicographic ordering optimization problem in order to obtain a unique solution. Solving this problem we obtain a Kemeny median which is optimal according to the supplementary criterion.  As this problem is computationally hard we reformulate it into regularization problem and adopt memetic algorithm to find solution approximately with practical precision over reasonable time. Prior to applying our approach to real data, we carry out a simulation study in order to make sure that numerical solution is robust. The result of an aggregation of ratings assigned by seven credit rating agencies (CRAs) to Russian banks is highly consistent with individual ratings and demonstrates practical discriminatory power, therefore aggregated rating can be used for the purposes of credit analysis, mapping of credit ratings and benchmarking.

Publications:


Заночкин А. Ю., Буздалин А. В., Курбангалеев М. З., Смирнов С. Н. Агрегация кредитных рейтингов как задача построения консенсуса в системе экспертных оценок // Глобальные рынки и финансовый инжиниринг. 2017. Т. 4. № 3
Lapshin V. A. Inconsistencies in bond market quotes: is it the wrong model or the wrong data? // Journal of Computational Science. 2018. Vol. 24. P. 255-265. doi