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

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  • Elnura Irmatova

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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  • Elnura Irmatova, 2016. "RELARM: A rating model based on relative PCA attributes and k-means clustering," Papers 1608.06416, arXiv.org.
    • Handle: RePEc:arx:papers:1608.06416
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    Cited by:

    1. Parisa Golbayani & Ionuc{t} Florescu & Rupak Chatterjee, 2020. "A comparative study of forecasting Corporate Credit Ratings using Neural Networks, Support Vector Machines, and Decision Trees," Papers 2007.06617, arXiv.org.
    2. Ilya Solntsev & Anatoly Vorobyev & Elnura Irmatova & Nikita Osokin, 2016. "Rating evaluation of sports development efficiency using statistical analysis: evidence from Russian football," Papers 1612.07543, arXiv.org.
    3. Golbayani, Parisa & Florescu, IonuĊ£ & Chatterjee, Rupak, 2020. "A comparative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).

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