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Market structure explained by pairwise interactions

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  • Bury, Thomas

Abstract

Financial markets are a typical example of complex systems where interactions between constituents lead to many remarkable features. Here we give empirical evidence, by making as few assumptions as possible, that the market microstructure capturing almost all of the available information in the data of stock markets does not involve higher order than pairwise interactions. We give an economic interpretation of this pairwise model. We show that it accurately recovers the empirical correlation coefficients; thus the collective behaviors are quantitatively described by models that capture the observed pairwise correlations but no higher-order interactions. Furthermore, we show that an order–disorder transition occurs, as predicted by the pairwise model. Last, we make the link with the graph-theoretic description of stock markets recovering the non-random and scale-free topology, shrinking length during crashes and meaningful clustering features, as expected.

Suggested Citation

  • Bury, Thomas, 2013. "Market structure explained by pairwise interactions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1375-1385.
    • Handle: RePEc:eee:phsmap:v:392:y:2013:i:6:p:1375-1385
    DOI: 10.1016/j.physa.2012.10.046
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    Cited by:

    1. Bing Li, 2017. "Network Evolution of the Chinese Stock Market: A Study based on the CSI 300 Index," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 7(3), pages 1-5.
    2. Mario González & María del Mar Alonso-Almeida & David Dominguez, 2018. "Mapping global sustainability report scoring: a detailed analysis of Europe and Asia," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(3), pages 1041-1055, May.
    3. Valle, Mauricio A. & Ruz, Gonzalo A. & Rica, Sergio, 2019. "Market basket analysis by solving the inverse Ising problem: Discovering pairwise interaction strengths among products," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 36-44.
    4. Ditian Zhang & Yangyang Zhuang & Pan Tang & Hongjuan Peng & Qingying Han, 2023. "Financial price dynamics and phase transitions in the stock markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(3), pages 1-21, March.
    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. 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.
    7. Thomas Bury, 2013. "Predicting trend reversals using market instantaneous state," Papers 1310.8169, arXiv.org, revised Mar 2014.

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