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Spectrum, intensity and coherence in weighted networks of a financial market

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  • Tibély, Gergely
  • Onnela, Jukka-Pekka
  • Saramäki, Jari
  • Kaski, Kimmo
  • Kertész, János

Abstract

We construct a correlation matrix based financial network for a set of New York Stock Exchange (NYSE) traded stocks with stocks corresponding to nodes and the links between them added one after the other, according to the strength of the correlation between the nodes. The eigenvalue spectrum of the correlation matrix reflects the structure of the market, which also shows in the cluster structure of the emergent network. The stronger and more compact a cluster is, the earlier the eigenvalue representing the corresponding business sector occurs in the spectrum. On the other hand, if groups of stocks belonging to a given business sector are considered as a fully connected subgraph of the final network, their intensity and coherence can be monitored as a function of time. This approach indicates to what extent the business sector classifications are visible in market prices, which in turn enables us to gauge the extent of group-behaviour exhibited by stocks belonging to a given business sector.

Suggested Citation

  • Tibély, Gergely & Onnela, Jukka-Pekka & Saramäki, Jari & Kaski, Kimmo & Kertész, János, 2006. "Spectrum, intensity and coherence in weighted networks of a financial market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(1), pages 145-150.
    • Handle: RePEc:eee:phsmap:v:370:y:2006:i:1:p:145-150
    DOI: 10.1016/j.physa.2006.04.042
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    Cited by:

    1. Coletti, Paolo, 2016. "Comparing minimum spanning trees of the Italian stock market using returns and volumes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 246-261.
    2. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    3. Neil Johnson & Guannan Zhao & Eric Hunsader & Jing Meng & Amith Ravindar & Spencer Carran & Brian Tivnan, 2012. "Financial black swans driven by ultrafast machine ecology," Papers 1202.1448, arXiv.org.

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