Clustering and information in correlation based financial networks

By Jukka-Pekka Onnela, Kimmo K. Kaski, János Kertész. 2003

373 citations recorded by the source corpus.

Abstract.Networks of companies can be constructed by using return correlations. A crucial issue in this approach is to select the relevant correlations from the correlation matrix. In order to study this problem, we start from an empty graph with no edges where the vertices correspond to stocks. Then, one by one, we insert edges between the vertices according to the rank of their correlation strength, resulting in a network called asset graph. We study its properties, such as topologically different growth types, number and size of clusters and clustering coefficient. These properties, calculated from empirical data, are compared against those of a random graph. The growth of the graph can be classified according to the topological role of the newly inserted edge. We find that the type of growth which is responsible for creating cycles in the graph sets in much earlier for the empirical asset graph than for the random graph, and thus reflects the high degree of networking present in the market. We also find the number of clusters in the random graph to be one order of magnitude higher than for the asset graph. At a critical threshold, the random graph undergoes a radical change in topology related to percolation transition and forms a single giant cluster, a phenomenon which is not observed for the asset graph. Differences in mean clustering coefficient lead us to conclude that most information is contained roughly within 10% of the edges.

Subfields: Condensed Matter Physics, Economics and Econometrics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/3a4e9d39bd2df177b3af3ebed608042cd4691e5a

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Citations in the corpus

Analysis of a large-scale weighted network of one-to-one human communication (2007, background): “We now turn to an examination of the implications of link removal on the global properties of networks, which has many precedents in the complex network literature [16, 37, 39, 40, 41, 42, 43, 44, 45].”
Correlation structure and dynamics of international real estate securities markets: A network perspective (2015, methodology): “2 The other correlation network-based methods contain the correlation threshold method [30], the market graph [27], the winner-take-all method [5], and the partial correlation PMFG [31].”
Extreme risk spillover network: application to financial institutions (2017, background): “Onnela et al. 2003, Kenett et al. 2010). In our case, the 84 publicly listed financial institutions fall into four subsectors, banks, diversified financials, insurance and real estate. To investigate how sectors influence each other, we introduce a measure of sector degree (SD) from one sector m to ”
Analyzing and modeling real-world phenomena with complex networks: a survey of applications (2007, background): “[155] focused on the details of the construction of the asset graph, and measured how the clustering coefficient cha ged in this process.”