Tracing the temporal evolution of clusters in a financial stock market

TL;DR

This paper introduces a new methodology for clustering stock return time series and visualizing their evolution over time, aiding in understanding market dynamics and informing investment strategies.

Contribution

It presents a novel graphical set-up for tracking cluster evolution in financial data, enabling the application of classical graph algorithms for portfolio analysis.

Findings

Effective visualization of stock cluster evolution

Application to Madrid Stock Exchange data

Potential for improved portfolio strategies

Abstract

We propose a methodology for clustering financial time series of stocks' returns, and a graphical set-up to quantify and visualise the evolution of these clusters through time. The proposed graphical representation allows for the application of well known algorithms for solving classical combinatorial graph problems, which can be interpreted as problems relevant to portfolio design and investment strategies. We illustrate this graph representation of the evolution of clusters in time and its use on real data from the Madrid Stock Exchange market.