Clustering stock market companies via chaotic map synchronization

TL;DR

This paper applies chaotic map synchronization to cluster Dow Jones companies based on their stock price behavior, revealing industry groupings that can inform portfolio strategies.

Contribution

It introduces a novel application of chaotic map clustering to financial data for identifying company groups based on temporal stock behavior.

Findings

Companies in the same industry are often grouped together.

The method effectively identifies clusters that reflect industrial sectors.

Clustering results can be used to improve portfolio optimization.

Abstract

A pairwise clustering approach is applied to the analysis of the Dow Jones index companies, in order to identify similar temporal behavior of the traded stock prices. To this end, the chaotic map clustering algorithm is used, where a map is associated to each company and the correlation coefficients of the financial time series are associated to the coupling strengths between maps. The simulation of a chaotic map dynamics gives rise to a natural partition of the data, as companies belonging to the same industrial branch are often grouped together. The identification of clusters of companies of a given stock market index can be exploited in the portfolio optimization strategies.