Identifying dynamical network markers of financial market instability

TL;DR

This paper applies Dynamical Network Marker theory to high-frequency trading data from the Tokyo Stock Exchange to identify early warning signals of market instability, potentially enabling proactive risk management.

Contribution

It demonstrates the feasibility of using DNM theory on multivariate trading activity data to detect precursors of large price movements in financial markets.

Findings

Early warning signals can be detected on a daily time scale.

The framework treats each trading participant as an interacting element.

Potential for developing practical early-warning systems for market crashes.

Abstract

Market instability has been extensively studied using mathematical approaches to characterize complex trading dynamics and detect structural change points. This study explores the potential for early warning of market instability by applying the Dynamical Network Marker (DNM) theory to order placement and execution data from the Tokyo Stock Exchange. DNM theory identifies indicators associated with critical slowing down -- a precursor to critical transitions -- in high-dimensional systems of many interacting elements. In this study, market participants are identified using virtual server IDs from the trading system, and multivariate time series representing their trading activities are constructed. This framework treats each participant as an interacting element, thereby enabling the application of DNM theory to the resulting time series. The results suggest that early warning signals of large price movements can be detected on a daily time scale. These findings highlight the potential to develop practical DNM-based early-warning systems for large price movements by further refining forecasting horizons and integrating multiple time series capturing different aspects of trading behavior.