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Chaos / Volume 22 / Issue 1 / REGULAR ARTICLES

Chaos 22, 013117 (2012); http://dx.doi.org/10.1063/1.3683467 (6 pages)

The structure and resilience of financial market networks

Thomas Kauê Dal’Maso Peron1, Luciano da Fontoura Costa1, and Francisco A. Rodrigues2

1Instituto de Física de São Carlos, Universidade de São Paulo, Av. Trabalhador São Carlense 400, Caixa Postal 369, CEP 13560-970, São Carlos, São Paulo, Brazil
2Departamento de Matemática Aplicada e Estatística, Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, Caixa Postal 668, 13560-970 São Carlos, São Paulo, Brazil

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(Received 22 November 2011; accepted 13 January 2012; published online 14 February 2012)

Financial markets can be viewed as a highly complex evolving system that is very sensitive to economic instabilities. The complex organization of the market can be represented in a suitable fashion in terms of complex networks, which can be constructed from stock prices such that each pair of stocks is connected by a weighted edge that encodes the distance between them. In this work, we propose an approach to analyze the topological and dynamic evolution of financial networks based on the stock correlation matrices. An entropy-related measurement is adopted to quantify the robustness of the evolving financial market organization. It is verified that the network topological organization suffers strong variation during financial instabilities and the networks in such periods become less robust. A statistical robust regression model is proposed to quantity the relationship between the network structure and resilience. The obtained coefficients of such model indicate that the average shortest path length is the measurement most related to network resilience coefficient. This result indicates that a collective behavior is observed between stocks during financial crisis. More specifically, stocks tend to synchronize their price evolution, leading to a high correlation between pair of stock prices, which contributes to the increase in distance between them and, consequently, decrease the network resilience.

© 2012 American Institute of Physics

Lead Paragraph

The complex organization of the financial market can represented as network. Each pair of stocks is connected according to their price evolution. In the current work, we quantified the robustness of the New York Stock Exchange against financial instabilities. We verified that during crashes, the network organization suffers strong variation becoming less robust. In addition, we proposed a regression model that suggests the emergence of a collective behavior during financial crisis. Thus, during this time, stocks tend to synchronize their price evolution and evolve similarly.

Article Outline

  1. INTRODUCTION
  2. CONCEPTS AND METHODS
    1. Stock market database
    2. Network construction
    3. Network characterization
    4. Network resilience
    5. Regression analysis
  3. RESULTS
  4. DISCUSSION

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KEYWORDS and PACS

Keywords

complex networks, econophysics, regression analysis, stock markets, topology

PACS

  • 89.65.Gh

    Economics; econophysics, financial markets, business and management

  • 02.50.-r

    Probability theory, stochastic processes, and statistics

  • 89.75.Hc

    Networks and genealogical trees

ARTICLE DATA

Digital Object Identifier
http://dx.doi.org/10.1063/1.3683467

PUBLICATION DATA

ISSN

1054-1500 (print)  
1089-7682 (online)

Publisher

$abstract.society.value is a Member of CrossRef American Institute of Physics

For access to fully linked references, you need to log in.
    P. Gopikrishnan, B. Rosenow, V. Plerou, and H. Stanley, Phys. Rev. E 64, 035106 (2001).

    J. Onnela, A. Chakraborti, K. Kaski, J. Kertész, and A. Kanto, Phys. Rev. E 68, 056110 (2003).

    J. Onnela, J. Saramaki, J. Kertész, and K. Kaski, Phys. Rev. E 71, 065103 (2005).

    J. Saramäki, M. Kivelä, J. Onnela, K. Kaski, and J. Kertész, Phys. Rev. E 75, 027105 (2007).


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