Identifying dynamical network markers of financial market instability

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 detection of market instability by applying the Dynamical Network Markers (DNM) theory to order placement and execution data from the Tokyo Stock Exchange. DNM theory identifies indicators associated with critical transitions as their precursors in high-dimensional financial market 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 obtained time series. The results indicate the presence of DNM-related patterns associated with large price movements on a daily time scale. These findings demonstrate the applicability of the DNM framework to market participant behavioral data and provide a basis for understanding mechanisms of instabilizing dynamics in financial market systems. The study could inform potential future developments of DNM-based early warning indicators through the identification of appropriate forecasting horizons and time scales as well as the incorporation of multiple aspects of trading behavior.