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Collective behavior in financial market

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  • Thomas Kau^e Dal'Maso Peron
  • Francisco Aparecido Rodrigues

Abstract

Financial market is an example of complex system, which is characterized by a highly intricate organization and the emergence of collective behavior. In this paper, we quantify this emergent dynamics in the financial market by using concepts of network synchronization. We consider networks constructed by the correlation matrix of asset returns and study the time evolution of the phase coherence among stock prices. It is verified that during financial crisis a synchronous state emerges in the system, defining the market's direction. Furthermore, the paper proposes a statistical regression model able to identify the topological features that mostly influence such an emergence. The coefficients of the proposed model indicate that the average shortest path length is the measurement most related to network synchronization. Therefore, during economic crisis, the stock prices present a similar evolution, which tends to shorten the distances between stocks, indication a collective dynamics.

Suggested Citation

  • Thomas Kau^e Dal'Maso Peron & Francisco Aparecido Rodrigues, 2011. "Collective behavior in financial market," Papers 1109.1167, arXiv.org.
  • Handle: RePEc:arx:papers:1109.1167
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    Cited by:

    1. Durante, Daniele & Dunson, David B., 2014. "Bayesian dynamic financial networks with time-varying predictors," Statistics & Probability Letters, Elsevier, vol. 93(C), pages 19-26.
    2. Jiang, Xiong-Fei & Zheng, Bo & Ren, Fei & Qiu, Tian, 2017. "Localized motion in random matrix decomposition of complex financial systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 154-161.
    3. de Resende, Charlene C. & Pereira, Adriano C.M. & Cardoso, Rodrigo T.N. & de Magalhães, A.R. Bosco, 2017. "Investigating market efficiency through a forecasting model based on differential equations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 199-212.
    4. 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 Sep 2017.
    5. Bury, Thomas, 2014. "Predicting trend reversals using market instantaneous state," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 404(C), pages 79-91.
    6. Liu, Yi-Fang & Zhang, Wei & Xu, Hai-Chuan, 2014. "Collective behavior and options volatility smile: An agent-based explanation," Economic Modelling, Elsevier, vol. 39(C), pages 232-239.

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