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Dissecting financial markets: Sectors and states

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  • Matteo Marsili

Abstract

By analyzing a large data set of daily returns with data clustering technique, we identify economic sectors as clusters of assets with a similar economic dynamics. The sector size distribution follows Zipf's law. Secondly, we find that patterns of daily market-wide economic activity cluster into classes that can be identified with market states. The distribution of frequencies of market states shows scale-free properties and the memory of the market state process extends to long times ($\sim 50$ days). Assets in the same sector behave similarly across states. We characterize market efficiency by analyzing market's predictability and find that indeed the market is close to being efficient. We find evidence of the existence of a dynamic pattern after market's crashes.

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  • Matteo Marsili, 2002. "Dissecting financial markets: Sectors and states," Papers cond-mat/0207156, arXiv.org.
    • Handle: RePEc:arx:papers:cond-mat/0207156
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    Cited by:

    1. Damien Challet & Tobias Galla, 2005. "Price return autocorrelation and predictability in agent-based models of financial markets," Quantitative Finance, Taylor & Francis Journals, vol. 5(6), pages 569-576.
    2. Bolgorian, Meysam & Raei, Reza, 2010. "Convergence of fundamentalists and chartists’ expectations: An alarm for stock market crash," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(18), pages 3822-3827.
    3. Nobi, Ashadun & Maeng, Seong Eun & Ha, Gyeong Gyun & Lee, Jae Woo, 2014. "Effects of global financial crisis on network structure in a local stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 135-143.
    4. Joel Bun & Jean-Philippe Bouchaud & Marc Potters, 2016. "Cleaning large correlation matrices: tools from random matrix theory," Papers 1610.08104, arXiv.org.
    5. Desislava Chetalova & Rudi Schafer & Thomas Guhr, 2014. "Zooming into market states," Papers 1406.5386, arXiv.org.
    6. Teh, Boon Kin & Goo, Yik Wen & Lian, Tong Wei & Ong, Wei Guang & Choi, Wen Ting & Damodaran, Mridula & Cheong, Siew Ann, 2015. "The Chinese Correction of February 2007: How financial hierarchies change in a market crash," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 225-241.
    7. George Barnes & Sanjaye Ramgoolam & Michael Stephanou, 2023. "Permutation invariant Gaussian matrix models for financial correlation matrices," Papers 2306.04569, arXiv.org.
    8. Heckens, Anton J. & Guhr, Thomas, 2022. "New collectivity measures for financial covariances and correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    9. Dieter Hendricks & Tim Gebbie & Diane Wilcox, 2015. "Detecting intraday financial market states using temporal clustering," Papers 1508.04900, arXiv.org, revised Feb 2017.
    10. Torsten Heinrich & Jangho Yang & Shuanping Dai, 2022. "Levels of structural change," Journal of Evolutionary Economics, Springer, vol. 32(1), pages 35-86, January.
    11. 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.
    12. Tanya Araujo & Francisco Louca, 2007. "The geometry of crashes. A measure of the dynamics of stock market crises," Quantitative Finance, Taylor & Francis Journals, vol. 7(1), pages 63-74.
    13. Christian Bongiorno & Damien Challet, 2020. "Nonparametric sign prediction of high-dimensional correlation matrix coefficients," Papers 2001.11214, arXiv.org.
    14. Pharasi, Hirdesh K. & Seligman, Eduard & Sadhukhan, Suchetana & Majari, Parisa & Seligman, Thomas H., 2024. "Dynamics of market states and risk assessment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    15. Challet, Damien, 2008. "Inter-pattern speculation: Beyond minority, majority and $-games," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 85-100, January.
    16. Esmalifalak, Hamidreza, 2022. "Euclidean (dis)similarity in financial network analysis," Global Finance Journal, Elsevier, vol. 53(C).
    17. Mario L'opez P'erez & Ricardo Mansilla, 2021. "Ordinal Synchronization and Typical States in High-Frequency Digital Markets," Papers 2110.07047, arXiv.org, revised Mar 2022.
    18. Yelibi, Lionel & Gebbie, Tim, 2020. "Fast Super-Paramagnetic Clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    19. Tanya Ara'ujo & Francisco Louc{c}~a, 2005. "The Geometry of Crashes - A Measure of the Dynamics of Stock Market Crises," Papers physics/0506137, arXiv.org, revised Jul 2005.
    20. López Pérez, Mario & Mansilla Corona, Ricardo, 2022. "Ordinal synchronization and typical states in high-frequency digital markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).

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