A proposal of a methodological framework with experimental guidelines to investigate clustering stability on financial time series

TL;DR

This paper introduces an empirical framework with experimental guidelines to evaluate clustering stability in financial time series, aiding in validity assessment and market insights through data perturbations.

Contribution

It proposes a novel methodological framework with specific perturbation strategies for assessing clustering stability in financial data.

Findings

Framework applied to credit default swap data

Provides multi-view analysis of asset clustering behavior

Enhances understanding of clustering robustness in finance

Abstract

We present in this paper an empirical framework motivated by the practitioner point of view on stability. The goal is to both assess clustering validity and yield market insights by providing through the data perturbations we propose a multi-view of the assets' clustering behaviour. The perturbation framework is illustrated on an extensive credit default swap time series database available online at www.datagrapple.com.