Changes in how groups of traders place orders may offer early indications of stock market instability, according to a study by researchers from the University of Tokyo and Rikkyo University. The team found that fluctuations in the trading activity of certain market participants increased in the days preceding several periods of market turmoil.
Published in EPJ Data Science, the research applies a mathematical framework called Dynamical Network Markers (DNM) theory to trading data from the Tokyo Stock Exchange. The approach could eventually help financial authorities and stock exchanges identify emerging instability before it becomes visible through sharp price movements.
Mathematical theory originally developed for disease detection
Dynamical Network Markers theory was developed by Kazuyuki Aihara, a senior author of the study at the University of Tokyo, and his colleagues to identify changes associated with pre-disease states.
The theory examines interactions within complex systems and looks for changes in key elements that may indicate an approaching transition or instability. Researchers explored whether the same approach could be applied to financial markets, where traders, orders and transactions form interconnected systems.
To investigate this possibility, the team analysed order and execution data from the Tokyo Stock Exchange. They grouped anonymised virtual server IDs displaying similar trading activities to identify market participants and collected measurements of their activity at one-minute intervals.
Lead author Mariko Ito of the University of Tokyo explained, “Our insight into the Tokyo Stock Exchange was taken from market participants identified by grouping anonymized virtual server IDs that displayed similar trading activities. To assess a snapshot of participant activities, we gathered one-minute measures of data.”
Collective trading behaviour showed signs before market turmoil
The researchers examined trading activity from 186 anonymised market participants using data collected between late 2019 and 2020.
They focused on five days when the market experienced clear instability. In several instances, fluctuations in indicators representing the trading activity of certain participants increased in the days leading up to these events.
However, when researchers examined participants individually, they found no statistically clear early warning signs. The pattern emerged more clearly when the activities of groups of participants were considered together.
This suggests that potential signs of instability may be embedded in collective trading behaviour rather than the actions of individual market participants.
Order activity sometimes changed before conventional volatility measures
Previous research into financial market instability has often relied on aggregate indicators, including historical price movements. The University of Tokyo study instead examined the behaviour of market participants through their trading activity.
In some cases, fluctuations in group trading indicators increased before a market turmoil day, while lagged volatility, a conventional measure based on past price changes, rose only afterwards.
The findings suggest that order activity may contain information about emerging instability that is not immediately apparent from price movements alone.
However, the researchers cautioned that the approach is not yet capable of predicting sharp price changes with practical accuracy. External events can also influence markets and complicate the detection of warning signs. For example, during February and March 2020, repeated external shocks affected market conditions, but the researchers did not observe a clear increase in group-level fluctuations.
Researchers explore potential early warning applications
Despite these limitations, the team considers the findings an initial step towards developing tools that could help financial authorities and stock exchanges monitor market conditions more closely.
Corresponding author Yudai Honma of the University of Tokyo explained the motivation behind the research:
“My field is using mathematics to design systems that keep the flows in society moving smoothly. A stock market is also such a flow, with orders moving all the time,” he said. “Instead of looking for the cause after prices change sharply, we want to look for signs of instability in the flow of orders before that happens.”
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