IDEAS home Printed from https://ideas.repec.org/p/arx/papers/physics-0509014.html
   My bibliography  Save this paper

Transfer Entropy Analysis of the Stock Market

Author

Listed:
  • Seung Ki Baek
  • Woo-Sung Jung
  • Okyu Kwon
  • Hie-Tae Moon

Abstract

In terms of transfer entropy, we investigated the strength and the direction of information transfer in the US stock market. Through the directionality of the information transfer, the more influential company between the correlated ones can be found and also the market leading companies are selected. Our entropy analysis shows that the companies related with energy industries such as oil, gas, and electricity influence the whole market.

Suggested Citation

  • Seung Ki Baek & Woo-Sung Jung & Okyu Kwon & Hie-Tae Moon, 2005. "Transfer Entropy Analysis of the Stock Market," Papers physics/0509014, arXiv.org, revised Sep 2005.
    • Handle: RePEc:arx:papers:physics/0509014
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/physics/0509014
    File Function: Latest version
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Lim, Kyuseong & Kim, Sehyun & Kim, Soo Yong, 2017. "Information transfer across intra/inter-structure of CDS and stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 118-126.
    2. Scaramozzino, Roberta & Cerchiello, Paola & Aste, Tomaso, 2021. "Information theoretic causality detection between financial and sentiment data," LSE Research Online Documents on Economics 110903, London School of Economics and Political Science, LSE Library.
    3. Leonidas Sandoval Junior & Asher Mullokandov & Dror Y. Kenett, 2015. "Dependency Relations among International Stock Market Indices," JRFM, MDPI, vol. 8(2), pages 1-39, May.
    4. Toan Luu Duc Huynh & Muhammad Shahbaz & Muhammad Ali Nasir & Subhan Ullah, 2022. "Financial modelling, risk management of energy instruments and the role of cryptocurrencies," Annals of Operations Research, Springer, vol. 313(1), pages 47-75, June.
    5. Xiurong Chen & Yixiang Tian & Rubo Zhao, 2017. "Study of the cross-market effects of Brexit based on the improved symbolic transfer entropy GARCH model—An empirical analysis of stock–bond correlations," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-14, August.
    6. Xiurong Chen & Aimin Hao & Yali Li, 2020. "The impact of financial contagion on real economy-An empirical research based on combination of complex network technology and spatial econometrics model," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-20, March.
    7. Banerjee, Ameet Kumar & Akhtaruzzaman, Md & Dionisio, Andreia & Almeida, Dora & Sensoy, Ahmet, 2022. "Nonlinear nexus between cryptocurrency returns and COVID-19 news sentiment," Journal of Behavioral and Experimental Finance, Elsevier, vol. 36(C).
    8. Bekiros, Stelios & Jlassi, Mouna & Lucey, Brian & Naoui, Kamel & Uddin, Gazi Salah, 2017. "Herding behavior, market sentiment and volatility: Will the bubble resume?," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 107-131.
    9. Dimpfl Thomas & Peter Franziska Julia, 2013. "Using transfer entropy to measure information flows between financial markets," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(1), pages 85-102, February.
    10. Nicoló Andrea Caserini & Paolo Pagnottoni, 2022. "Effective transfer entropy to measure information flows in credit markets," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(4), pages 729-757, October.
    11. Caferra, Rocco, 2022. "Sentiment spillover and price dynamics: Information flow in the cryptocurrency and stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    12. Gu, Danlei & Lin, Aijing & Lin, Guancen, 2022. "Sleep and cardiac signal processing using improved multivariate partial compensated transfer entropy based on non-uniform embedding," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    13. Xie, Wen-Jie & Yong, Yang & Wei, Na & Yue, Peng & Zhou, Wei-Xing, 2021. "Identifying states of global financial market based on information flow network motifs," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    14. Chen, Yu & Ling, Guang & Song, Xiangxiang & Tu, Wenhui, 2023. "Characterizing the statistical complexity of nonlinear time series via ordinal pattern transition networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    15. Roberta Scaramozzino & Paola Cerchiello & Tomaso Aste, 2021. "Information theoretic causality detection between financial and sentiment data," DEM Working Papers Series 202, University of Pavia, Department of Economics and Management.

    More about this item

    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:physics/0509014. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.