Monitoring the Dynamic Networks of Stock Returns

TL;DR

This study analyzes the evolving relationships among Swedish OMX30 companies by constructing dynamic networks using clustering and control charts to detect market anomalies over time.

Contribution

It introduces a method combining hierarchical clustering and Shewhart control charts to monitor and detect abnormal changes in stock market networks.

Findings

Identified periods of abnormal market activity

Mapped dynamic changes in company relationships

Provided a framework for real-time market monitoring

Abstract

In this paper, we study the connection between the companies in the Swedish capital market. We consider 28 companies included in the determination of the market index OMX30. The network structure of the market is constructed using different methods to determine the distance between the companies. We use hierarchical clustering methods to find the relation among the companies in each window. Next, we obtain one-dimensional time series of the distances between the clustering trees that reflect the changes in the relationship between the companies in the market over time. The method of statistical process control, namely the Shewhart control chart, is applied to those time series to detect abnormal changes in the financial market.