Ordinal Synchronization and Typical States in High-Frequency Digital Markets

TL;DR

This study models high-frequency digital markets as dynamical networks using ordinal pattern analysis, revealing collective states and their dynamics through synchronization measures and clustering, with implications for understanding market behavior.

Contribution

Introduces an information-theoretic synchronization measure and applies clustering to identify collective market states in high-frequency trading data.

Findings

Identified two coherent seasons with distinct synchronization patterns.

Reproduced and explained anomalous behaviors at the individual stock level.

Modeled state dynamics with a simple Markov process.

Abstract

In this paper we study Algorithmic High-Frequency Financial Markets as dynamical networks. After an individual analysis of 24 stocks of the US market during a trading year of fully automated transactions by means of ordinal pattern series, we define an information-theoretic measure of pairwise synchronization for time series which allows us to study this subset of the US market as a dynamical network. We apply to the resulting network a couple of clustering algorithms in order to detect collective market states, characterized by their degree of centralized or descentralized synchronicity. This collective analysis has shown to reproduce, classify and explain the anomalous behavior previously observed at the individual level. We also find two whole coherent seasons of highly centralized and descentralized synchronicity, respectively. Finally, we model these states dynamics through a simple Markov model.