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RELARM: A rating model based on relative PCA attributes and k-means clustering


Irmatova E.A.
(about the author)

Irmatova Elnura Anvarovna –

Published in:
Russian Journal of Entrepreneurship
– Volume 18, Number 10 (May 2017)



Keywords: credit rating, k-means clustering, Principal Component Analysis, rating model, relative PCA attribute


Citation:
Irmatova E.A. (2017). RELARM: A rating model based on relative PCA attributes and k-means clustering. Russian Journal of Entrepreneurship, 18(10), 1597-1614. doi: 10.18334/rp.18.10.37967


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Abstract:

Following the concept, which is widely used in visual recognition of relative attributes, the article defines the relative PCA attributes for a class of objects defined by their parameter vectors. We built a new rating model (RELARM) using ranking functions of the relative PCA attributes for the description of rating object and k-means clustering algorithm. Assignment of each rating object to a rating category occurs as a result of the projection of cluster centers on a specially selected rating vector. Using the test model of sovereign states solvency we showed a high approximation level of the ratings assigned by rating agencies, such as S & P, Moody's and Fitch RELARM rating.







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