IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v388y2009i18p3851-3858.html
   My bibliography  Save this article

Structure of a financial cross-correlation matrix under attack

Author

Listed:
  • Lim, Gyuchang
  • Kim, SooYong
  • Kim, Junghwan
  • Kim, Pyungsoo
  • Kang, Yoonjong
  • Park, Sanghoon
  • Park, Inho
  • Park, Sang-Bum
  • Kim, Kyungsik

Abstract

We investigate the structure of a perturbed stock market in terms of correlation matrices. For the purpose of perturbing a stock market, two distinct methods are used, namely local and global perturbation. The former involves replacing a correlation coefficient of the cross-correlation matrix with one calculated from two Gaussian-distributed time series while the latter reconstructs the cross-correlation matrix just after replacing the original return series with Gaussian-distributed time series. Concerning the local case, it is a technical study only and there is no attempt to model reality. The term ‘global’ means the overall effect of the replacement on other untouched returns. Through statistical analyses such as random matrix theory (RMT), network theory, and the correlation coefficient distributions, we show that the global structure of a stock market is vulnerable to perturbation. However, apart from in the analysis of inverse participation ratios (IPRs), the vulnerability becomes dull under a small-scale perturbation. This means that these analysis tools are inappropriate for monitoring the whole stock market due to the low sensitivity of a stock market to a small-scale perturbation. In contrast, when going down to the structure of business sectors, we confirm that correlation-based business sectors are regrouped in terms of IPRs. This result gives a clue about monitoring the effect of hidden intentions, which are revealed via portfolios taken mostly by large investors.

Suggested Citation

  • Lim, Gyuchang & Kim, SooYong & Kim, Junghwan & Kim, Pyungsoo & Kang, Yoonjong & Park, Sanghoon & Park, Inho & Park, Sang-Bum & Kim, Kyungsik, 2009. "Structure of a financial cross-correlation matrix under attack," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(18), pages 3851-3858.
    • Handle: RePEc:eee:phsmap:v:388:y:2009:i:18:p:3851-3858
    DOI: 10.1016/j.physa.2009.05.018
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437109003811
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2009.05.018?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Johnson, Neil F. & Jefferies, Paul & Hui, Pak Ming, 2003. "Financial Market Complexity," OUP Catalogue, Oxford University Press, number 9780198526650.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. MohammadReza Zahedian & Mahsa Bagherikalhor & Andrey Trufanov & G. Reza Jafari, 2022. "Financial Crisis in the Framework of Non-zero Temperature Balance Theory," Papers 2202.03198, arXiv.org.
    2. Hanie. Vahabi & Ali Namaki & Reza Raei, 2020. "Comparing the collective behavior of banking industry," Papers 2011.02026, arXiv.org.
    3. Ali Namaki & Jamshid Ardalankia & Reza Raei & Leila Hedayatifar & Ali Hosseiny & Emmanuel Haven & G. Reza Jafari, 2020. "Analysis of the Global Banking Network by Random Matrix Theory," Papers 2007.14447, arXiv.org.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tobias Galla & David Sherrington, 2005. "Stationary states of a spherical Minority Game with ergodicity breaking," Papers cond-mat/0508413, arXiv.org, revised Aug 2005.
    2. V. Alfi & L. Pietronero & A. Zaccaria, 2008. "Minimal Agent Based Model For The Origin And Self-Organization Of Stylized Facts In Financial Markets," Papers 0807.1888, arXiv.org.
    3. Campos, Daniel & Llebot, Josep E. & Méndez, Vicenç, 2008. "Limited resources and evolutionary learning may help to understand the mistimed reproduction in birds caused by climate change," Theoretical Population Biology, Elsevier, vol. 74(1), pages 16-21.
    4. Gou, Chengling & Guo, Xiaoqian & Chen, Fang, 2008. "Study on system dynamics of evolutionary mix-game models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(25), pages 6353-6359.
    5. Mello, Bernardo A. & Cajueiro, Daniel O., 2008. "Minority games, diversity, cooperativity and the concept of intelligence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(2), pages 557-566.
    6. Pištěk, Miroslav & Slanina, František, 2011. "Diversity of scales makes an advantage: The case of the Minority Game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(13), pages 2549-2561.
    7. Li, Da-Ye & Nishimura, Yusaku & Men, Ming, 2014. "Fractal markets: Liquidity and investors on different time horizons," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 144-151.
    8. Karpio, Krzysztof & Załuska–Kotur, Magdalena A. & Orłowski, Arkadiusz, 2007. "Gain–loss asymmetry for emerging stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 375(2), pages 599-604.
    9. Moews, Ben & Ibikunle, Gbenga, 2020. "Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    10. Ferreira, Fernando F. & de Oliveira, Viviane M. & Crepaldi, Antônio F. & Campos, Paulo R.A., 2005. "Agent-based model with heterogeneous fundamental prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 357(3), pages 534-542.
    11. Oldham, Matthew, 2020. "Quantifying the concerns of Dimon and Buffett with data and computation," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    12. Ted Theodosopoulos, 2004. "Uncertainty relations in models of market microstructure," Papers math/0409076, arXiv.org, revised Feb 2005.
    13. Jørgen Vitting Andersen, 2014. "From Minority Games to $-Games," Working Papers halshs-00971373, HAL.
    14. A. Garcia-Bernabeu & J. V. Salcedo & A. Hilario & D. Pla-Santamaria & Juan M. Herrero, 2019. "Computing the Mean-Variance-Sustainability Nondominated Surface by ev-MOGA," Complexity, Hindawi, vol. 2019, pages 1-12, December.
    15. Kostanjcar, Zvonko & Jeren, Branko & Juretic, Zeljan, 2012. "Impact of uncertainty in expected return estimation on stock price volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5563-5571.
    16. Ni, Y.C. & Xu, C. & Hui, P.M. & Johnson, N.F., 2009. "Cooperative behavior in evolutionary snowdrift game with bounded rationality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(23), pages 4856-4862.
    17. Niu, Hongli & Wang, Jun, 2017. "Return volatility duration analysis of NYMEX energy futures and spot," Energy, Elsevier, vol. 140(P1), pages 837-849.
    18. Strozzi, Fernanda & Zaldívar, José-Manuel & Zbilut, Joseph P., 2007. "Recurrence quantification analysis and state space divergence reconstruction for financial time series analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 376(C), pages 487-499.
    19. Campos, Daniel & Méndez, Vicenç & Llebot, Josep E. & Hernández, Germán A., 2010. "Analytical model for minority games with evolutionary learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(12), pages 2397-2407.
    20. Gao, Tingting & Chen, Yu, 2017. "A quantum anharmonic oscillator model for the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 307-314.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:388:y:2009:i:18:p:3851-3858. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.