Dynamics of market correlations: taxonomy and portfolio analysis.

By Jukka-Pekka Onnela, Anirban Chakraborti, Kimmo K. Kaski, János Kertész, A. Kanto. Physical review. E, Statistical, nonlinear, and soft matter physics, 2003

730 citations recorded by the source corpus.

The time dependence of the recently introduced minimum spanning tree description of correlations between stocks, called the "asset tree" has been studied in order to reflect the financial market taxonomy. The nodes of the tree are identified with stocks and the distance between them is a unique function of the corresponding element of the correlation matrix. By using the concept of a central vertex, chosen as the most strongly connected node of the tree, an important characteristic is defined by the mean occupation layer. During crashes, due to the strong global correlation in the market, the tree shrinks topologically, and this is shown by a low value of the mean occupation layer. The tree seems to have a scale-free structure where the scaling exponent of the degree distribution is different for "business as usual" and "crash" periods. The basic structure of the tree topology is very robust with respect to time. We also point out that the diversification aspect of portfolio optimization results in the fact that the assets of the classic Markowitz portfolio are always located on the outer leaves of the tree. Technical aspects such as the window size dependence of the investigated quantities are also discussed.

Subfields: Economics and Econometrics, Finance, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/3b2cdbbb0ae52ab1f89dd7a909e9b07440bf422a

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Citations in the corpus

Networks of equities in financial markets (2004, methodology): “er time of the MST characteristics has been investigated in a series of studies [36,37,38,39,24]. The ﬁltering approach based on the MST can also be used to consider aspects of portfolio optimization [40] and to perform a correlation based classiﬁcation of relevant economic entities such as banks [4”
Stock network stability in times of crisis (2014, methodology): “Several filteringmethods have been successfully used to achieve such a reduction, for instance minimal spanning trees (MST) [2–5], planar graphs [6], average linkage MST [7], dynamic MST and asset graphs [8–10] or the winner-take-all approach [11] which is, for instance, applied in Ref.”
Topological properties of stock market networks: The case of Brazil (2010, background): “Furthermore, there is ample evidence that it is possible to study the topological properties of financial networks, which have an associated meaningful economic taxonomy [2,3,9–17,6,7,5,4,18,19].”
Analyzing and modeling real-world phenomena with complex networks: a survey of applications (2007, methodology): “[92] obtained a similar exponent (γ = 2.”
Uncovering the overlapping community structure of complex networks in nature and society (2005, background): “Identifying these a priori unknown building blocks (functionally related proteins [5, 6], industrial sectors [7], groups of people [8, 9], etc .) is crucial to the understanding of the structural and functional properties of networks.”
Community detection in graphs (2009, background): “In early studies, scholars found clusters of correlated stocks by computing the maximum spanning tree of the network (Bonanno et al., 2003, 2000; Onnela et al., 2003, 2002) (Section A.1), and realized that such clusters match quite well the economic sectors of the stocks.”
Econophysics: Empirical facts and agent-based models (2009, neutral): “The time dependent properties of the minimum spanning tree (introduced by Mantegna), called a ‘dynamic asset tree’, were studied by Onnela et al. (2003b).”
Synchronization in complex networks (2008, background): “Recently, the authors in [258] have taken a similar approach to analyze the dynamics of markets.”