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

TL;DR

RELARM is a novel rating model that combines relative PCA attributes with k-means clustering to classify objects, demonstrating high accuracy in approximating major credit ratings.

Contribution

The paper introduces RELARM, a new rating model that integrates relative PCA attributes and clustering for improved object rating classification.

Findings

High approximation accuracy to S&P, Moody's, and Fitch ratings.

Effective use of relative PCA attributes for rating tasks.

Successful application of k-means clustering in rating classification.

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.