The stock market is an example of a complex system, i.e. it consists of a number of traders, interacting in such a way that their collective behaviour, the behaviour of the market, is not a simple combination of their individual behaviour. One of the most important tasks in modern finance is finding efficient ways of summarizing and visualizing the stock market data to obtain useful information about the behavior of the market.
In this thesis we investigate the possibility of finding a way to summarize and cluster share ownership data from the Swedish stock market. This is done by using a network approach to analyze the structure of the share ownership in order to find significant patterns in the data. The analysis of the network is performed with the community detection algorithm InfoMap, which turns the problem of finding clusters into the problem of optimally compressing the flow of information on the structure of the network.
The results of the analysis indicate that it is possible to find significant patterns in the ownership data when looking at the holdings of individuals using a binary approach. By using the clusters with the largest information flow, a majority of the analyzed individuals are categorized into clusters that accommodates for different properties regarding the ownership of the included individuals. The clustering results are visualized using alluvial diagrams which also are used to display changes that occur in the ownership structure between two dates.