[Paper Review] RELARM: A rating model based on relative PCA attributes and k-means clustering

Elnura Irmatova|arXiv (Cornell University)|Aug 23, 2016

Face and Expression Recognition4 citations

TL;DR

This paper proposes RELARM, a novel credit rating model that leverages relative PCA attributes and k-means clustering to improve rating accuracy. By projecting cluster centers onto a rating vector derived from relative principal component attributes, the model achieves high alignment with S&P, Moody’s, and Fitch ratings, demonstrating strong empirical approximation performance.

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.

Motivation & Objective

• To develop a data-driven credit rating model that enhances accuracy by incorporating relative attribute relationships among financial objects.
• To address limitations in traditional rating models by introducing relative PCA attributes that capture comparative financial characteristics.
• To improve clustering-based rating assignment through projection of cluster centers onto a specially selected rating vector.
• To empirically validate the model’s performance against established ratings from S&P, Moody’s, and Fitch.

Proposed method

• The model defines relative PCA attributes as pairwise comparisons of principal component scores across objects, capturing relative financial strength or risk.
• It constructs a rating vector based on these relative PCA attributes to represent the ordinal structure of credit quality.
• K-means clustering is applied to group rating objects based on their parameter vectors, identifying natural clusters of creditworthiness.
• Cluster centers are projected onto the rating vector to assign each object to a discrete rating category.
• The rating assignment process ensures that relative rankings within clusters are preserved through the projection mechanism.
• The method uses principal component analysis (PCA) to reduce dimensionality and extract dominant financial features before computing relative attributes.

Experimental results

Research questions

• RQ1 Can relative PCA attributes effectively represent the comparative financial characteristics of credit-rated entities?
• RQ2 How well can k-means clustering, combined with projection onto a rating vector, reproduce existing credit rating categories?
• RQ3 To what extent does the RELARM model approximate the ratings issued by S&P, Moody’s, and Fitch?
• RQ4 Does the integration of relative attributes with clustering improve rating accuracy over standard clustering or PCA-based models?

Key findings

• The RELARM model achieves a high level of approximation to the existing S&P, Moody’s, and Fitch ratings, indicating strong empirical validity.
• The use of relative PCA attributes enables the model to capture nuanced, comparative financial relationships that are often lost in absolute scoring.
• Projection of cluster centers onto the rating vector effectively maps data-driven clusters to discrete rating categories.
• The model demonstrates robustness in preserving relative rankings within clusters, enhancing interpretability and consistency.
• Empirical results confirm that the integration of relative attributes and clustering yields a more accurate and stable rating system.
• The approach shows potential for application in automated credit rating systems due to its data-driven and scalable nature.