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Identifying long-term precursors of financial market crashes using correlation patterns

Author

Listed:
  • Hirdesh K. Pharasi
  • Kiran Sharma
  • Rakesh Chatterjee
  • Anirban Chakraborti
  • Francois Leyvraz
  • Thomas H. Seligman

Abstract

The study of the critical dynamics in complex systems is always interesting yet challenging. Here, we choose financial market as an example of a complex system, and do a comparative analyses of two stock markets - the S&P 500 (USA) and Nikkei 225 (JPN). Our analyses are based on the evolution of crosscorrelation structure patterns of short time-epochs for a 32-year period (1985-2016). We identify "market states" as clusters of similar correlation structures, which occur more frequently than by pure chance (randomness). The dynamical transitions between the correlation structures reflect the evolution of the market states. Power mapping method from the random matrix theory is used to suppress the noise on correlation patterns, and an adaptation of the intra-cluster distance method is used to obtain the "optimum" number of market states. We find that the USA is characterized by four market states and JPN by five. We further analyze the co-occurrence of paired market states; the probability of remaining in the same state is much higher than the transition to a different state. The transitions to other states mainly occur among the immediately adjacent states, with a few rare intermittent transitions to the remote states. The state adjacent to the critical state (market crash) may serve as an indicator or a "precursor" for the critical state and this novel method of identifying the long-term precursors may be very helpful for constructing the early warning system in financial markets, as well as in other complex systems.

Suggested Citation

  • Hirdesh K. Pharasi & Kiran Sharma & Rakesh Chatterjee & Anirban Chakraborti & Francois Leyvraz & Thomas H. Seligman, 2018. "Identifying long-term precursors of financial market crashes using correlation patterns," Papers 1809.00885, arXiv.org, revised Sep 2018.
    • Handle: RePEc:arx:papers:1809.00885
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    Citations

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    Cited by:

    1. Xinyu Wang & Liang Zhao & Ning Zhang & Liu Feng & Haibo Lin, 2022. "Stability of China's Stock Market: Measure and Forecast by Ricci Curvature on Network," Papers 2204.06692, arXiv.org.
    2. Hirdesh K. Pharasi & Eduard Seligman & Suchetana Sadhukhan & Parisa Majari & Thomas H. Seligman, 2020. "Dynamics of market states and risk assessment," Papers 2011.05984, arXiv.org, revised Sep 2023.
    3. 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).
    4. Anwesha Sengupta & Shashankaditya Upadhyay & Indranil Mukherjee & Prasanta K. Panigrahi, 2022. "Describing the effect of influential spreaders on the different sectors of Indian market: a complex networks perspective," Papers 2303.05432, arXiv.org.
    5. Hirdesh K. Pharasi & Suchetana Sadhukhan & Parisa Majari & Anirban Chakraborti & Thomas H. Seligman, 2021. "Dynamics of the market states in the space of correlation matrices with applications to financial markets," Papers 2107.05663, arXiv.org.
    6. M. Mija'il Mart'inez-Ramos & Parisa Majari & Andres R. Cruz-Hern'andez & Hirdesh K. Pharasi & Manan Vyas, 2024. "Coarse graining correlation matrices according to macrostructures: Financial markets as a paradigm," Papers 2402.05364, arXiv.org.
    7. 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.
    8. Upadhyay, Shashankaditya & Banerjee, Anirban & Panigrahi, Prasanta K., 2020. "Causal evolution of global crisis in financial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    9. Vishwas Kukreti, 2022. "Early Warning Signals for Cryptocurrency Market States," Papers 2211.12356, arXiv.org.
    10. 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.
    11. Kiran Sharma & Parul Khurana, 2021. "Growth and dynamics of Econophysics: a bibliometric and network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4417-4436, May.
    12. Hirdesh K. Pharasi & Kiran Sharma & Anirban Chakraborti & Thomas H. Seligman, 2018. "Complex market dynamics in the light of random matrix theory," Papers 1809.07100, arXiv.org, revised Sep 2018.
    13. Upadhyay, Shashankaditya & Mukherjee, Indranil & Panigrahi, Prasanta K., 2023. "Inner composition alignment networks reveal financial impacts of COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    14. 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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