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

Irmatova E.A.¹
¹ Российская академия народного хозяйства и государственной службы при Президенте РФ

Journal paper

Russian Journal of Entrepreneurship *
Volume 18, Number 10 (May 2017)
* Этот журнал не выпускается в Первом экономическом издательстве

Citation:

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.

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

References:

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