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

2016/08/23 by Irmatova, Elnura
#Applications (stat.AP) #Computational Finance (q-fin.CP) #FOS: Computer and information sciences #FOS: Economics and business #Risk Management (q-fin.RM)

paper · doi:10.48550/arxiv.1608.06416

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.

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