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Dynamics of Stock Market Correlations

Author

Listed:
  • Dror Y. Kenett

    (Tel-Aviv University, School of Physics and Astronomy, The Raymond and Beverly Sackler Faculty of Exact Sciences, Tel-Aviv, Israel)

  • Yoash Shapira

    (Tel-Aviv University, School of Physics and Astronomy, The Raymond and Beverly Sackler Faculty of Exact Sciences, Tel-Aviv, Israel)

  • Asaf Madi

    (Tel Aviv University, Faculty of Medicine, Tel Aviv, Israel)

  • Sharron Bransburg-Zabary

    (Tel Aviv University, Faculty of Medicine, Tel Aviv, Israel)

  • Gitit Gur-Gershgoren

    (Israel Securities Authority, Jerusalem, Israel
    Ben Gurion University, School of Business and Management, Beer Sheva, Israel)

  • Eshel Ben-Jacob

    (Tel-Aviv University, School of Physics and Astronomy, The Raymond and Beverly Sackler Faculty of Exact Sciences, Tel-Aviv, Israel)

Abstract

We present a novel approach to the study the dynamics of stock market correlations. This is achieved through an innovative visualization tool that allows an investigation of the structure and dynamics of the market, through the study of correlations. This is based on the Stock Market Holography (SMH) method recently introduced. This qualitative measure is complemented by the use of the eigenvalue entropy measure, to quantify how the information in the market changes in time. Using this innovative approach, we analyzed data from the New York Stock Exchange (NYSE), and the Tel Aviv Stock Exchange (TASE), for daily trading data for the time period of 2000–2009. This paper covers these new concepts for the study of financial markets in terms of structure and information as reflected by the changes in correlations over time.

Suggested Citation

  • Dror Y. Kenett & Yoash Shapira & Asaf Madi & Sharron Bransburg-Zabary & Gitit Gur-Gershgoren & Eshel Ben-Jacob, 2010. "Dynamics of Stock Market Correlations," Czech Economic Review, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, vol. 4(3), pages 330-340, November.
    • Handle: RePEc:fau:aucocz:au2010_330
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    Citations

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    Cited by:

    1. Leonidas Sandoval Junior, 2011. "Cluster formation and evolution in networks of financial market indices," Papers 1111.5069, arXiv.org.
    2. Caraiani, Petre, 2014. "The predictive power of singular value decomposition entropy for stock market dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 571-578.
    3. Champagne, Claudia, 2014. "The international syndicated loan market network: An “unholy trinity”?," Global Finance Journal, Elsevier, vol. 25(2), pages 148-168.
    4. Jiang, Jiaqi & Gu, Rongbao, 2016. "Using Rényi parameter to improve the predictive power of singular value decomposition entropy on stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 448(C), pages 254-264.
    5. 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.
    6. Dror Y Kenett & Matthias Raddant & Thomas Lux & Eshel Ben-Jacob, 2012. "Evolvement of Uniformity and Volatility in the Stressed Global Financial Village," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-8, February.
    7. Irena Vodenska & Alexander P. Becker & Di Zhou & Dror Y. Kenett & H. Eugene Stanley & Shlomo Havlin, 2016. "Community Analysis of Global Financial Markets," Risks, MDPI, vol. 4(2), pages 1-15, May.
    8. Jianrong Wei & Jiping Huang, 2012. "An Exotic Long-Term Pattern in Stock Price Dynamics," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-5, December.
    9. Vishwas Kukreti & Hirdesh K. Pharasi & Priya Gupta & Sunil Kumar, 2020. "A perspective on correlation-based financial networks and entropy measures," Papers 2004.09448, arXiv.org.
    10. Dror Y Kenett & Yoash Shapira & Asaf Madi & Sharron Bransburg-Zabary & Gitit Gur-Gershgoren & Eshel Ben-Jacob, 2011. "Index Cohesive Force Analysis Reveals That the US Market Became Prone to Systemic Collapses Since 2002," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-8, April.
    11. Sandoval, Leonidas, 2012. "Pruning a minimum spanning tree," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(8), pages 2678-2711.
    12. Lei Tan & Bo Zheng & Jun-Jie Chen & Xiong-Fei Jiang, 2015. "How Volatilities Nonlocal in Time Affect the Price Dynamics in Complex Financial Systems," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-16, February.
    13. Andrey Kudryavtsev & Gil Cohen & Julia Pavlodsky, 2012. "Incorporating Weekend Information in Stock Prices: Evidence from Israeli Stock Market," Journal of Finance and Investment Analysis, SCIENPRESS Ltd, vol. 1(4), pages 1-1.
    14. Kocheturov, A. & Batsyn, M. & Pardalos, P., 2015. "Dynamics of Cluster Structures in Stock Market Networks," Journal of the New Economic Association, New Economic Association, vol. 28(4), pages 12-30.
    15. Leonidas Sandoval Junior, 2011. "Pruning a Minimum Spanning Tree," Papers 1109.0642, arXiv.org.
    16. Stosic, Darko & Stosic, Dusan & Ludermir, Teresa & Stosic, Tatijana, 2016. "Correlations of multiscale entropy in the FX market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 52-61.

    More about this item

    Keywords

    Correlation; Stock Market Holography; eigenvalue entropy; sliding window;
    All these keywords.

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools

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