Monitoring the Dynamic Networks of Stock Returns
This work addresses financial market monitoring for investors or regulators, but it is incremental as it applies existing methods to a specific dataset.
The paper tackled the problem of monitoring dynamic relationships among companies in the Swedish stock market by constructing networks from stock returns and using hierarchical clustering to track changes over time, applying Shewhart control charts to detect abnormal market changes.
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.