Chapter 10
A Review of Two Decades
of Correlations, Hierarchies, Networks
and Clustering in Financial Markets
Gautier Marti, Frank Nielsen, Mikołaj Bi ´
nkowski, and Philippe Donnat
Abstract We review the state of the art of clustering financial time series and the
study of their correlations alongside other interaction networks. The aim of the review
is to gather in one place the relevant material from different fields, e.g. machine learning, information geometry, econophysics, statistical physics, econometrics, behavioral finance. We hope it will help researchers to use more effectively this alternative
modeling of the financial time series. Decision makers and quantitative researchers
may also be able to leverage its insights. Finally, we also hope that this review will
form the basis of an open toolbox to study correlations, hierarchies, networks and
clustering in financial markets.
10.1 Introduction
Since the seminal paper of Mantegna in 1999, many works have followed, and in
many directions (e.g. statistical methodology, fundamental understanding of markets,
risk, portfolio optimization, trading strategies, alphas), over the last two decades. We
felt the need to track the developments and organize them in this present review.
G. Marti
HKML Research Limited, 5/F., Bonham Trade Centre, 50 Bonham Strand, Sheung Wan, Hong
Kong
e-mail: gautier.marti@hkml-research.com
F. Nielsen (B)
Sony Computer Science Laboratories, Inc, Tokyo, Japan
e-mail: Frank.Nielsen@acm.org
M. Bi´ nkowski
Imperial College London, Exhibition Rd., South Kensington, London SW7 2BU, United Kingdom
e-mail: mikolaj.binkowski14@imperial.ac.uk
P. Donnat
Hellebore Capital Ltd., Michelin House, 81 Fulham Rd., London SW3 6RD, United Kingdom
e-mail: philippe.donnat@helleborecapital.com
© Springer Nature Switzerland AG 2021
F. Nielsen (ed.), Progress in Information Geometry,
Signals and Communication Technology,
https://doi.org/10.1007/978-3-030-65459-7_10
245
A Review of Two Decades
of Correlations, Hierarchies, Networks
and Clustering in Financial Markets
Gautier Marti, Frank Nielsen, Mikołaj Bi ´
nkowski, and Philippe Donnat
Abstract We review the state of the art of clustering financial time series and the
study of their correlations alongside other interaction networks. The aim of the review
is to gather in one place the relevant material from different fields, e.g. machine learning, information geometry, econophysics, statistical physics, econometrics, behavioral finance. We hope it will help researchers to use more effectively this alternative
modeling of the financial time series. Decision makers and quantitative researchers
may also be able to leverage its insights. Finally, we also hope that this review will
form the basis of an open toolbox to study correlations, hierarchies, networks and
clustering in financial markets.
10.1 Introduction
Since the seminal paper of Mantegna in 1999, many works have followed, and in
many directions (e.g. statistical methodology, fundamental understanding of markets,
risk, portfolio optimization, trading strategies, alphas), over the last two decades. We
felt the need to track the developments and organize them in this present review.
G. Marti
HKML Research Limited, 5/F., Bonham Trade Centre, 50 Bonham Strand, Sheung Wan, Hong
Kong
e-mail: gautier.marti@hkml-research.com
F. Nielsen (B)
Sony Computer Science Laboratories, Inc, Tokyo, Japan
e-mail: Frank.Nielsen@acm.org
M. Bi´ nkowski
Imperial College London, Exhibition Rd., South Kensington, London SW7 2BU, United Kingdom
e-mail: mikolaj.binkowski14@imperial.ac.uk
P. Donnat
Hellebore Capital Ltd., Michelin House, 81 Fulham Rd., London SW3 6RD, United Kingdom
e-mail: philippe.donnat@helleborecapital.com
© Springer Nature Switzerland AG 2021
F. Nielsen (ed.), Progress in Information Geometry,
Signals and Communication Technology,
https://doi.org/10.1007/978-3-030-65459-7_10
245