IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2312.10084.html
   My bibliography  Save this paper

A Decadal Analysis of the Lead-Lag Effect in the NYSE

Author

Listed:
  • Aarush Pratik Sheth
  • Jonah Riley Weinbaum
  • Kevin Javier Zvonarek

Abstract

As is widely known, the stock market is a complex system in which a multitude of factors influence the performance of individual stocks and the market as a whole. One method for comprehending -- and potentially predicting -- stock market behavior is through network analysis, which can offer insights into the relationships between stocks and the overall market structure. In this paper, we seek to address the question: Can network analysis of the stock market, specifically in observation of the lead-lag effect, provide valuable insights for investors and market analysts?

Suggested Citation

  • Aarush Pratik Sheth & Jonah Riley Weinbaum & Kevin Javier Zvonarek, 2023. "A Decadal Analysis of the Lead-Lag Effect in the NYSE," Papers 2312.10084, arXiv.org.
  • Handle: RePEc:arx:papers:2312.10084
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2312.10084
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Reginald D. Smith, 2009. "The Spread of the Credit Crisis: View from a Stock Correlation Network," Papers 0901.1392, arXiv.org, revised Jun 2009.
    2. Namaki, A. & Shirazi, A.H. & Raei, R. & Jafari, G.R., 2011. "Network analysis of a financial market based on genuine correlation and threshold method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(21), pages 3835-3841.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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.
    3. Chen, Wei & Hou, Xiaoli & Jiang, Manrui & Jiang, Cheng, 2022. "Identifying systemically important financial institutions in complex network: A case study of Chinese stock market," Emerging Markets Review, Elsevier, vol. 50(C).
    4. 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.
    5. 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.
    6. Neto, José de Paula Neves & Figueiredo, Daniel Ratton, 2023. "Ranking influential and influenced stocks over time using transfer entropy networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    7. Uddin, Ajim & Tao, Xinyuan & Yu, Dantong, 2023. "Attention based dynamic graph neural network for asset pricing," Global Finance Journal, Elsevier, vol. 58(C).
    8. Song, Jae Wook & Ko, Bonggyun & Cho, Poongjin & Chang, Woojin, 2016. "Time-varying causal network of the Korean financial system based on firm-specific risk premiums," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 287-302.
    9. Busu, Cristian & Busu, Mihail, 2019. "Modeling the predictive power of the singular value decomposition-based entropy. Empirical evidence from the Dow Jones Global Titans 50 Index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    10. Sayantan Banerjee & Kousik Guhathakurta, 2019. "Change-point Analysis in Financial Networks," Papers 1911.05952, arXiv.org.
    11. Huang, Wei-Qiang & Zhuang, Xin-Tian & Yao, Shuang & Uryasev, Stan, 2016. "A financial network perspective of financial institutions’ systemic risk contributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 183-196.
    12. Nie, Chun-Xiao, 2017. "Correlation dimension of financial market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 632-639.
    13. Huang, Wei-Qiang & Wang, Dan, 2018. "A return spillover network perspective analysis of Chinese financial institutions’ systemic importance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 405-421.
    14. Hosseiny, Ali, 2017. "A geometrical imaging of the real gap between economies of China and the United States," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 151-161.
    15. Seyed Soheil Hosseini & Nick Wormald & Tianhai Tian, 2019. "A Weight-based Information Filtration Algorithm for Stock-Correlation Networks," Papers 1904.06007, arXiv.org.
    16. Xi, Xian & An, Haizhong, 2018. "Research on energy stock market associated network structure based on financial indicators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1309-1323.
    17. Ausloos, Marcel & Saeedian, Meghdad & Jamali, Tayeb & Farahani, S. Vasheghani & Jafari, G. Reza, 2017. "How visas shape and make visible the geopolitical architecture of the planet," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 267-275.
    18. Bentian Li & Dechang Pi, 2018. "Analysis of global stock index data during crisis period via complex network approach," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-16, July.
    19. Chao Yang & Heyang Sun & Tong Li & Hengji Xie & Zhenjiang Lei & Jinliang Song & He Cai & Jiaxuan Yang & Gangjun Gong & Shuai Ren, 2022. "Coupled Model and Node Importance Evaluation of Electric Power Cyber-Physical Systems Considering Carbon Power Flow," Energies, MDPI, vol. 15(21), pages 1-21, November.
    20. Dion Harmon & Marco Lagi & Marcus A M de Aguiar & David D Chinellato & Dan Braha & Irving R Epstein & Yaneer Bar-Yam, 2015. "Anticipating Economic Market Crises Using Measures of Collective Panic," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-27, July.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:arx:papers:2312.10084. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.