RELARM: A rating model based on relative PCA attributes and k-means clustering
Computational Finance
2016-08-24 v1 Risk Management
Applications
Abstract
Following widely used in visual recognition concept of relative attributes, the article establishes definition of the relative PCA attributes for a class of objects defined by vectors of their parameters. A new rating model (RELARM) is built using relative PCA attribute ranking functions for rating object description and k-means clustering algorithm. Rating assignment of each rating object to a rating category is derived as a result of cluster centers projection on the specially selected rating vector. Empirical study has shown a high level of approximation to the existing S & P, Moody's and Fitch ratings.
Keywords
Cite
@article{arxiv.1608.06416,
title = {RELARM: A rating model based on relative PCA attributes and k-means clustering},
author = {Elnura Irmatova},
journal= {arXiv preprint arXiv:1608.06416},
year = {2016}
}