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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. 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.
    2. Nicolás Magner & Jaime F Lavin & Mauricio Valle & Nicolás Hardy, 2021. "The predictive power of stock market’s expectations volatility: A financial synchronization phenomenon," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-21, May.
    3. Kashyap, G. & Ambika, G., 2019. "Link deletion in directed complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 631-643.
    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 Nov 2020.
    5. Hanie. Vahabi & Ali Namaki & Reza Raei, 2020. "Comparing the collective behavior of banking industry," Papers 2011.02026, arXiv.org.
    6. 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.
    7. Garcia, M.M. & Machado Pereira, A.C. & Acebal, J.L. & Bosco de Magalhães, A.R., 2020. "Forecast model for financial time series: An approach based on harmonic oscillators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    8. Yang, Ming-Yuan & Wu, Zhen-Guo & Wu, Xin, 2022. "An empirical study of risk diffusion in the cryptocurrency market based on the network analysis," Finance Research Letters, Elsevier, vol. 50(C).
    9. 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.
    10. Durante, Daniele & Dunson, David B., 2014. "Bayesian dynamic financial networks with time-varying predictors," Statistics & Probability Letters, Elsevier, vol. 93(C), pages 19-26.
    11. Bury, Thomas, 2014. "Predicting trend reversals using market instantaneous state," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 404(C), pages 79-91.

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