# A review of two decades of correlations, hierarchies, networks and clustering in financial markets

**Authors:** Gautier Marti, Frank Nielsen, Mikołaj Bińkowski, Philippe Donnat  
**arXiv:** [1703.00485](https://arxiv.org/abs/1703.00485)  
**Date:** 2017-03-01  
**Summary page:** https://www.summarizepaper.com/en/paper/1703.00485v7/

## Key points

- Authors Gautier Marti, Frank Nielsen, Mikołaj Bińkowski, and Philippe Donnat review clustering financial time series and analyzing correlations in interaction networks
- Review consolidates material from machine learning, information geometry, econophysics, statistical physics, econometrics, and behavioral finance
- Provides valuable insights for researchers seeking alternative modeling approaches for financial time series
- Highlights potential impact on decision makers and quantitative researchers in financial markets
- Envisions review as foundational tool for studying correlations, hierarchies, networks, and clustering within financial markets
- Published as a chapter in "Progress in Information Geometry: Theory and Applications"
- Contributes significantly to discourse on complex financial systems and offers roadmap for future research

## Summary

In their review titled "A Review of Two Decades of Correlations, Hierarchies, Networks and Clustering in Financial Markets," authors Gautier Marti, Frank Nielsen, Mikołaj Bińkowski, and Philippe Donnat delve into the state-of-the-art techniques for clustering financial time series and analyzing their correlations within various interaction networks. The comprehensive review aims to consolidate relevant material from diverse fields such as machine learning, information geometry, econophysics, statistical physics, econometrics, and behavioral finance. By synthesizing insights from these disciplines, the authors provide a valuable resource for researchers looking to enhance their understanding of alternative modeling approaches for financial time series. This review also highlights the potential impact on decision makers and quantitative researchers who can leverage these insights to make informed decisions in the realm of financial markets. The authors envision that this review will serve as a foundational tool for studying correlations, hierarchies, networks, and clustering within financial markets. Published as a chapter in "Progress in Information Geometry: Theory and Applications," this work contributes significantly to the ongoing discourse surrounding complex financial systems and offers a roadmap for future research endeavors.