# Monitoring the Dynamic Networks of Stock Returns

arXiv: https://arxiv.org/abs/2210.16679 · HTML: https://mlsanity.com/paper/2210.16679/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 39 references that are in the map.
- First posted on arXiv 29 Oct 2022.

## Builds on (most cited first, 5 of 5)

- [A cost function for similarity-based hierarchical clustering](https://mlsanity.com/paper/1510.05043/) (2015)
- [An Improved Cost Function for Hierarchical Cluster Trees](https://mlsanity.com/paper/1812.02715/) (2018)
- [Hierarchical Clustering of Asymmetric Networks](https://mlsanity.com/paper/1607.06294/) (2016)
- [FPT-algorithms for computing Gromov-Hausdorff and interleaving distances between trees](https://mlsanity.com/paper/1811.02425/) (2018)
- [Relative Clustering Coefficient](https://mlsanity.com/paper/2106.05145/) (2021)

## Abstract (arXiv)

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.